<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yun, Taedong</style></author><author><style face="normal" font="default" size="100%">Li, Helen</style></author><author><style face="normal" font="default" size="100%">Chang, Pi-Chuan</style></author><author><style face="normal" font="default" size="100%">Lin, Michael F</style></author><author><style face="normal" font="default" size="100%">Carroll, Andrew</style></author><author><style face="normal" font="default" size="100%">McLean, Cory Y</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accurate, scalable cohort variant calls using DeepVariant and GLnexus.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Bioinformatics</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Jan 05</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;MOTIVATION: &lt;/b&gt;Population-scale sequenced cohorts are foundational resources for genetic analyses, but processing raw reads into analysis-ready cohort-level variants remains challenging.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We introduce an open-source cohort-calling method that uses the highly-accurate caller DeepVariant and scalable merging tool GLnexus. Using callset quality metrics based on variant recall and precision in benchmark samples and Mendelian consistency in father-mother-child trios, we optimized the method across a range of cohort sizes, sequencing methods, and sequencing depths. The resulting callsets show consistent quality improvements over those generated using existing best practices with reduced cost. We further evaluate our pipeline in the deeply sequenced 1000 Genomes Project (1KGP) samples and show superior callset quality metrics and imputation reference panel performance compared to an independently-generated GATK Best Practices pipeline.&lt;/p&gt;&lt;p&gt;&lt;b&gt;AVAILABILITY AND IMPLEMENTATION: &lt;/b&gt;We publicly release the 1KGP individual-level variant calls and cohort callset (https://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP) to foster additional development and evaluation of cohort merging methods as well as broad studies of genetic variation. Both DeepVariant (https://github.com/google/deepvariant) and GLnexus (https://github.com/dnanexus-rnd/GLnexus) are open-sourced, and the optimized GLnexus setup discovered in this study is also integrated into GLnexus public releases v1.2.2 and later.&lt;/p&gt;&lt;p&gt;&lt;b&gt;SUPPLEMENTARY INFORMATION: &lt;/b&gt;Supplementary data are available at Bioinformatics online.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33399819?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garg, Shilpa</style></author><author><style face="normal" font="default" size="100%">Fungtammasan, Arkarachai</style></author><author><style face="normal" font="default" size="100%">Carroll, Andrew</style></author><author><style face="normal" font="default" size="100%">Chou, Mike</style></author><author><style face="normal" font="default" size="100%">Schmitt, Anthony</style></author><author><style face="normal" font="default" size="100%">Zhou, Xiang</style></author><author><style face="normal" font="default" size="100%">Mac, Stephen</style></author><author><style face="normal" font="default" size="100%">Peluso, Paul</style></author><author><style face="normal" font="default" size="100%">Hatas, Emily</style></author><author><style face="normal" font="default" size="100%">Ghurye, Jay</style></author><author><style face="normal" font="default" size="100%">Maguire, Jared</style></author><author><style face="normal" font="default" size="100%">Mahmoud, Medhat</style></author><author><style face="normal" font="default" size="100%">Cheng, Haoyu</style></author><author><style face="normal" font="default" size="100%">Heller, David</style></author><author><style face="normal" font="default" size="100%">Zook, Justin M</style></author><author><style face="normal" font="default" size="100%">Moemke, Tobias</style></author><author><style face="normal" font="default" size="100%">Marschall, Tobias</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author><author><style face="normal" font="default" size="100%">Aach, John</style></author><author><style face="normal" font="default" size="100%">Chin, Chen-Shan</style></author><author><style face="normal" font="default" size="100%">Church, George M</style></author><author><style face="normal" font="default" size="100%">Li, Heng</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chromosome-scale, haplotype-resolved assembly of human genomes.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Biotechnol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Biotechnol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosomes, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Haplotypes</style></keyword><keyword><style  face="normal" font="default" size="100%">Heterozygote</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">309-312</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Haplotype-resolved or phased genome assembly provides a complete picture of genomes and their complex genetic variations. However, current algorithms for phased assembly either do not generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method named diploid assembly (DipAsm) that uses long, accurate reads and long-range conformation data for single individuals to generate a chromosome-scale phased assembly within 1 day. Applied to four public human genomes, PGP1, HG002, NA12878 and HG00733, DipAsm produced haplotype-resolved assemblies with minimum contig length needed to cover 50% of the known genome (NG50) up to 25 Mb and phased ~99.5% of heterozygous sites at 98-99% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies for the discovery of structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as the human leukocyte antigen (HLA) and killer cell immunoglobulin-like receptor (KIR) regions. DipAsm will facilitate high-quality precision medicine and studies of individual haplotype variation and population diversity.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33288905?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kasela, Silva</style></author><author><style face="normal" font="default" size="100%">Ortega, Victor E</style></author><author><style face="normal" font="default" size="100%">Martorella, Molly</style></author><author><style face="normal" font="default" size="100%">Garudadri, Suresh</style></author><author><style face="normal" font="default" size="100%">Nguyen, Jenna</style></author><author><style face="normal" font="default" size="100%">Ampleford, Elizabeth</style></author><author><style face="normal" font="default" size="100%">Pasanen, Anu</style></author><author><style face="normal" font="default" size="100%">Nerella, Srilaxmi</style></author><author><style face="normal" font="default" size="100%">Buschur, Kristina L</style></author><author><style face="normal" font="default" size="100%">Barjaktarevic, Igor Z</style></author><author><style face="normal" font="default" size="100%">Barr, R Graham</style></author><author><style face="normal" font="default" size="100%">Bleecker, Eugene R</style></author><author><style face="normal" font="default" size="100%">Bowler, Russell P</style></author><author><style face="normal" font="default" size="100%">Comellas, Alejandro P</style></author><author><style face="normal" font="default" size="100%">Cooper, Christopher B</style></author><author><style face="normal" font="default" size="100%">Couper, David J</style></author><author><style face="normal" font="default" size="100%">Criner, Gerard J</style></author><author><style face="normal" font="default" size="100%">Curtis, Jeffrey L</style></author><author><style face="normal" font="default" size="100%">Han, MeiLan K</style></author><author><style face="normal" font="default" size="100%">Hansel, Nadia N</style></author><author><style face="normal" font="default" size="100%">Hoffman, Eric A</style></author><author><style face="normal" font="default" size="100%">Kaner, Robert J</style></author><author><style face="normal" font="default" size="100%">Krishnan, Jerry A</style></author><author><style face="normal" font="default" size="100%">Martinez, Fernando J</style></author><author><style face="normal" font="default" size="100%">McDonald, Merry-Lynn N</style></author><author><style face="normal" font="default" size="100%">Meyers, Deborah A</style></author><author><style face="normal" font="default" size="100%">Paine, Robert</style></author><author><style face="normal" font="default" size="100%">Peters, Stephen P</style></author><author><style face="normal" font="default" size="100%">Castro, Mario</style></author><author><style face="normal" font="default" size="100%">Denlinger, Loren C</style></author><author><style face="normal" font="default" size="100%">Erzurum, Serpil C</style></author><author><style face="normal" font="default" size="100%">Fahy, John V</style></author><author><style face="normal" font="default" size="100%">Israel, Elliot</style></author><author><style face="normal" font="default" size="100%">Jarjour, Nizar N</style></author><author><style face="normal" font="default" size="100%">Levy, Bruce D</style></author><author><style face="normal" font="default" size="100%">Li, Xingnan</style></author><author><style face="normal" font="default" size="100%">Moore, Wendy C</style></author><author><style face="normal" font="default" size="100%">Wenzel, Sally E</style></author><author><style face="normal" font="default" size="100%">Zein, Joe</style></author><author><style face="normal" font="default" size="100%">Langelier, Charles</style></author><author><style face="normal" font="default" size="100%">Woodruff, Prescott G</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author><author><style face="normal" font="default" size="100%">Christenson, Stephanie A</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">NHLBI SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS)</style></author><author><style face="normal" font="default" size="100%">NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic and non-genetic factors affecting the expression of COVID-19-relevant genes in the large airway epithelium.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Angiotensin-Converting Enzyme 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Asthma</style></keyword><keyword><style  face="normal" font="default" size="100%">Bronchi</style></keyword><keyword><style  face="normal" font="default" size="100%">Cardiovascular Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Obesity</style></keyword><keyword><style  face="normal" font="default" size="100%">Pulmonary Disease, Chronic Obstructive</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Respiratory Mucosa</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Smoking</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 04 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">66</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33883027?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hansen, Adam W</style></author><author><style face="normal" font="default" size="100%">Arora, Payal</style></author><author><style face="normal" font="default" size="100%">Khayat, Michael M</style></author><author><style face="normal" font="default" size="100%">Smith, Leah J</style></author><author><style face="normal" font="default" size="100%">Lewis, Andrea M</style></author><author><style face="normal" font="default" size="100%">Rossetti, Linda Z</style></author><author><style face="normal" font="default" size="100%">Jayaseelan, Joy</style></author><author><style face="normal" font="default" size="100%">Cristian, Ingrid</style></author><author><style face="normal" font="default" size="100%">Haynes, Devon</style></author><author><style face="normal" font="default" size="100%">DiTroia, Stephanie</style></author><author><style face="normal" font="default" size="100%">Meeks, Naomi</style></author><author><style face="normal" font="default" size="100%">Delgado, Mauricio R</style></author><author><style face="normal" font="default" size="100%">Rosenfeld, Jill A</style></author><author><style face="normal" font="default" size="100%">Pais, Lynn</style></author><author><style face="normal" font="default" size="100%">White, Susan M</style></author><author><style face="normal" font="default" size="100%">Meng, Qingchang</style></author><author><style face="normal" font="default" size="100%">Pehlivan, Davut</style></author><author><style face="normal" font="default" size="100%">Liu, Pengfei</style></author><author><style face="normal" font="default" size="100%">Gingras, Marie-Claude</style></author><author><style face="normal" font="default" size="100%">Wangler, Michael F</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna M</style></author><author><style face="normal" font="default" size="100%">Lupski, James R</style></author><author><style face="normal" font="default" size="100%">Kaplan, Craig D</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Germline mutation in : a heterogeneous, multi-systemic developmental disorder characterized by transcriptional dysregulation.</style></title><secondary-title><style face="normal" font="default" size="100%">HGG Adv</style></secondary-title><alt-title><style face="normal" font="default" size="100%">HGG Adv</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Jan 14</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">2</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt; germline variation in  was recently reported to associate with a neurodevelopmental disorder. We report twelve individuals harboring putatively pathogenic  or inherited variants in , detail their phenotypes, and map all known variants to the domain structure of  and crystal structure of RNA polymerase II. Affected individuals were ascertained from a local data lake, pediatric genetics clinic, and an online community of families of affected individuals. These include six affected by  missense variants (including one previously reported individual), four clinical laboratory samples affected by missense variation with unknown inheritance-with yeast functional assays further supporting altered function-one affected by a  in-frame deletion, and one affected by a C-terminal frameshift variant inherited from a largely asymptomatic mother. Recurrently observed phenotypes include ataxia, joint hypermobility, short stature, skin abnormalities, congenital cardiac abnormalities, immune system abnormalities, hip dysplasia, and short Achilles tendons. We report a significantly higher occurrence of epilepsy (8/12, 66.7%) than previously reported (3/15, 20%) (p value = 0.014196; chi-square test) and a lower occurrence of hypotonia (8/12, 66.7%) than previously reported (14/15, 93.3%) (p value = 0.076309). -related developmental disorders likely represent a spectrum of related, multi-systemic developmental disorders, driven by distinct mechanisms, converging at a single locus.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33665635?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ebert, Peter</style></author><author><style face="normal" font="default" size="100%">Audano, Peter A</style></author><author><style face="normal" font="default" size="100%">Zhu, Qihui</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Martin, Bernardo</style></author><author><style face="normal" font="default" size="100%">Porubsky, David</style></author><author><style face="normal" font="default" size="100%">Bonder, Marc Jan</style></author><author><style face="normal" font="default" size="100%">Sulovari, Arvis</style></author><author><style face="normal" font="default" size="100%">Ebler, Jana</style></author><author><style face="normal" font="default" size="100%">Zhou, Weichen</style></author><author><style face="normal" font="default" size="100%">Serra Mari, Rebecca</style></author><author><style face="normal" font="default" size="100%">Yilmaz, Feyza</style></author><author><style face="normal" font="default" size="100%">Zhao, Xuefang</style></author><author><style face="normal" font="default" size="100%">Hsieh, PingHsun</style></author><author><style face="normal" font="default" size="100%">Lee, Joyce</style></author><author><style face="normal" font="default" size="100%">Kumar, Sushant</style></author><author><style face="normal" font="default" size="100%">Lin, Jiadong</style></author><author><style face="normal" font="default" size="100%">Rausch, Tobias</style></author><author><style face="normal" font="default" size="100%">Chen, Yu</style></author><author><style face="normal" font="default" size="100%">Ren, Jingwen</style></author><author><style face="normal" font="default" size="100%">Santamarina, Martin</style></author><author><style face="normal" font="default" size="100%">Höps, Wolfram</style></author><author><style face="normal" font="default" size="100%">Ashraf, Hufsah</style></author><author><style face="normal" font="default" size="100%">Chuang, Nelson T</style></author><author><style face="normal" font="default" size="100%">Yang, Xiaofei</style></author><author><style face="normal" font="default" size="100%">Munson, Katherine M</style></author><author><style face="normal" font="default" size="100%">Lewis, Alexandra P</style></author><author><style face="normal" font="default" size="100%">Fairley, Susan</style></author><author><style face="normal" font="default" size="100%">Tallon, Luke J</style></author><author><style face="normal" font="default" size="100%">Clarke, Wayne E</style></author><author><style face="normal" font="default" size="100%">Basile, Anna O</style></author><author><style face="normal" font="default" size="100%">Byrska-Bishop, Marta</style></author><author><style face="normal" font="default" size="100%">Corvelo, André</style></author><author><style face="normal" font="default" size="100%">Evani, Uday S</style></author><author><style face="normal" font="default" size="100%">Lu, Tsung-Yu</style></author><author><style face="normal" font="default" size="100%">Chaisson, Mark J P</style></author><author><style face="normal" font="default" size="100%">Chen, Junjie</style></author><author><style face="normal" font="default" size="100%">Li, Chong</style></author><author><style face="normal" font="default" size="100%">Brand, Harrison</style></author><author><style face="normal" font="default" size="100%">Wenger, Aaron M</style></author><author><style face="normal" font="default" size="100%">Ghareghani, Maryam</style></author><author><style face="normal" font="default" size="100%">Harvey, William T</style></author><author><style face="normal" font="default" size="100%">Raeder, Benjamin</style></author><author><style face="normal" font="default" size="100%">Hasenfeld, Patrick</style></author><author><style face="normal" font="default" size="100%">Regier, Allison A</style></author><author><style face="normal" font="default" size="100%">Abel, Haley J</style></author><author><style face="normal" font="default" size="100%">Hall, Ira M</style></author><author><style face="normal" font="default" size="100%">Flicek, Paul</style></author><author><style face="normal" font="default" size="100%">Stegle, Oliver</style></author><author><style face="normal" font="default" size="100%">Gerstein, Mark B</style></author><author><style face="normal" font="default" size="100%">Tubio, Jose M C</style></author><author><style face="normal" font="default" size="100%">Mu, Zepeng</style></author><author><style face="normal" font="default" size="100%">Li, Yang I</style></author><author><style face="normal" font="default" size="100%">Shi, Xinghua</style></author><author><style face="normal" font="default" size="100%">Hastie, Alex R</style></author><author><style face="normal" font="default" size="100%">Ye, Kai</style></author><author><style face="normal" font="default" size="100%">Chong, Zechen</style></author><author><style face="normal" font="default" size="100%">Sanders, Ashley D</style></author><author><style face="normal" font="default" size="100%">Zody, Michael C</style></author><author><style face="normal" font="default" size="100%">Talkowski, Michael E</style></author><author><style face="normal" font="default" size="100%">Mills, Ryan E</style></author><author><style face="normal" font="default" size="100%">Devine, Scott E</style></author><author><style face="normal" font="default" size="100%">Lee, Charles</style></author><author><style face="normal" font="default" size="100%">Korbel, Jan O</style></author><author><style face="normal" font="default" size="100%">Marschall, Tobias</style></author><author><style face="normal" font="default" size="100%">Eichler, Evan E</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Haplotype-resolved diverse human genomes and integrated analysis of structural variation.</style></title><secondary-title><style face="normal" font="default" size="100%">Science</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Science</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Haplotypes</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">INDEL Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Interspersed Repetitive Sequences</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Population Groups</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Retroelements</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Inversion</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 04 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">372</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6537</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33632895?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniloski, Zharko</style></author><author><style face="normal" font="default" size="100%">Jordan, Tristan X</style></author><author><style face="normal" font="default" size="100%">Wessels, Hans-Hermann</style></author><author><style face="normal" font="default" size="100%">Hoagland, Daisy A</style></author><author><style face="normal" font="default" size="100%">Kasela, Silva</style></author><author><style face="normal" font="default" size="100%">Legut, Mateusz</style></author><author><style face="normal" font="default" size="100%">Maniatis, Silas</style></author><author><style face="normal" font="default" size="100%">Mimitou, Eleni P</style></author><author><style face="normal" font="default" size="100%">Lu, Lu</style></author><author><style face="normal" font="default" size="100%">Geller, Evan</style></author><author><style face="normal" font="default" size="100%">Danziger, Oded</style></author><author><style face="normal" font="default" size="100%">Rosenberg, Brad R</style></author><author><style face="normal" font="default" size="100%">Phatnani, Hemali</style></author><author><style face="normal" font="default" size="100%">Smibert, Peter</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author><author><style face="normal" font="default" size="100%">tenOever, Benjamin R</style></author><author><style face="normal" font="default" size="100%">Sanjana, Neville E</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification of Required Host Factors for SARS-CoV-2 Infection in Human Cells.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">A549 Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Alveolar Epithelial Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Angiotensin-Converting Enzyme 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Biosynthetic Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol</style></keyword><keyword><style  face="normal" font="default" size="100%">Clustered Regularly Interspaced Short Palindromic Repeats</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Endosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockdown Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockout Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Host-Pathogen Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">rab GTP-Binding Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">rab7 GTP-Binding Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Interference</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Single-Cell Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Viral Load</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 01 07</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">184</style></volume><pages><style face="normal" font="default" size="100%">92-105.e16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;To better understand host-virus genetic dependencies and find potential therapeutic targets for COVID-19, we performed a genome-scale CRISPR loss-of-function screen to identify host factors required for SARS-CoV-2 viral infection of human alveolar epithelial cells. Top-ranked genes cluster into distinct pathways, including the vacuolar ATPase proton pump, Retromer, and Commander complexes. We validate these gene targets using several orthogonal methods such as CRISPR knockout, RNA interference knockdown, and small-molecule inhibitors. Using single-cell RNA-sequencing, we identify shared transcriptional changes in cholesterol biosynthesis upon loss of top-ranked genes. In addition, given the key role of the ACE2 receptor in the early stages of viral entry, we show that loss of RAB7A reduces viral entry by sequestering the ACE2 receptor inside cells. Overall, this work provides a genome-scale, quantitative resource of the impact of the loss of each host gene on fitness/response to viral infection.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33147445?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ranallo-Benavidez, T Rhyker</style></author><author><style face="normal" font="default" size="100%">Lemmon, Zachary</style></author><author><style face="normal" font="default" size="100%">Soyk, Sebastian</style></author><author><style face="normal" font="default" size="100%">Aganezov, Sergey</style></author><author><style face="normal" font="default" size="100%">Salerno, William J</style></author><author><style face="normal" font="default" size="100%">McCoy, Rajiv C</style></author><author><style face="normal" font="default" size="100%">Lippman, Zachary B</style></author><author><style face="normal" font="default" size="100%">Schatz, Michael C</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimized sample selection for cost-efficient long-read population sequencing.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Res</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 May</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">910-918</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;An increasingly important scenario in population genetics is when a large cohort has been genotyped using a low-resolution approach (e.g., microarrays, exome capture, short-read WGS), from which a few individuals are resequenced using a more comprehensive approach, especially long-read sequencing. The subset of individuals selected should ensure that the captured genetic diversity is fully representative and includes variants across all subpopulations. For example, human variation has historically focused on individuals with European ancestry, but this represents a small fraction of the overall diversity. Addressing this, SVCollector identifies the optimal subset of individuals for resequencing by analyzing population-level VCF files from low-resolution genotyping studies. It then computes a ranked list of samples that maximizes the total number of variants present within a subset of a given size. To solve this optimization problem, SVCollector implements a fast, greedy heuristic and an exact algorithm using integer linear programming. We apply SVCollector on simulated data, 2504 human genomes from the 1000 Genomes Project, and 3024 genomes from the 3000 Rice Genomes Project and show the rankings it computes are more representative than alternative naive strategies. When selecting an optimal subset of 100 samples in these cohorts, SVCollector identifies individuals from every subpopulation, whereas naive methods yield an unbalanced selection. Finally, we show the number of variants present in cohorts selected using this approach follows a power-law distribution that is naturally related to the population genetic concept of the allele frequency spectrum, allowing us to estimate the diversity present with increasing numbers of samples.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33811084?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Khayat, Michael M</style></author><author><style face="normal" font="default" size="100%">Li, He</style></author><author><style face="normal" font="default" size="100%">Chander, Varuna</style></author><author><style face="normal" font="default" size="100%">Hu, Jianhong</style></author><author><style face="normal" font="default" size="100%">Hansen, Adam W</style></author><author><style face="normal" font="default" size="100%">Li, Shoudong</style></author><author><style face="normal" font="default" size="100%">Traynelis, Josh</style></author><author><style face="normal" font="default" size="100%">Shen, Hua</style></author><author><style face="normal" font="default" size="100%">Weissenberger, George</style></author><author><style face="normal" font="default" size="100%">Stossi, Fabio</style></author><author><style face="normal" font="default" size="100%">Johnson, Hannah L</style></author><author><style face="normal" font="default" size="100%">Lupski, James R</style></author><author><style face="normal" font="default" size="100%">Posey, Jennifer E</style></author><author><style face="normal" font="default" size="100%">Sabo, Aniko</style></author><author><style face="normal" font="default" size="100%">Meng, Qingchang</style></author><author><style face="normal" font="default" size="100%">Murdock, David R</style></author><author><style face="normal" font="default" size="100%">Wangler, Michael</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phenotypic and protein localization heterogeneity associated with AHDC1 pathogenic protein-truncating alleles in Xia-Gibbs syndrome.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mutat</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mutat</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 May</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">577-591</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Xia-Gibbs syndrome (XGS) is a rare Mendelian disease typically caused by de novo stop-gain or frameshift mutations in the AT-hook DNA binding motif containing 1 (AHDC1) gene. Patients usually present in early infancy with hypotonia and developmental delay and later exhibit intellectual disability (ID). The overall presentation is variable, however, and the emerging clinical picture is still evolving. A detailed phenotypic analysis of 34 XGS individuals revealed five core phenotypes (delayed motor milestones, speech delay, low muscle tone, ID, and hypotonia) in more than 80% of individuals and an additional 12 features that occurred more variably. Seizures and scoliosis were more frequently associated with truncations that arise before the midpoint of the protein although the occurrence of most features could not be predicted by the mutation position. Transient expression of wild type and different patient truncated AHDC1 protein forms in human cell lines revealed abnormal patterns of nuclear localization including a diffuse distribution of a short truncated form and nucleolar aggregation in mid-protein truncated forms. Overall, both the occurrence of variable phenotypes and the different distribution of the expressed protein reflect the heterogeneity of this syndrome.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33644933?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ilumäe, Anne-Mai</style></author><author><style face="normal" font="default" size="100%">Post, Helen</style></author><author><style face="normal" font="default" size="100%">Flores, Rodrigo</style></author><author><style face="normal" font="default" size="100%">Karmin, Monika</style></author><author><style face="normal" font="default" size="100%">Sahakyan, Hovhannes</style></author><author><style face="normal" font="default" size="100%">Mondal, Mayukh</style></author><author><style face="normal" font="default" size="100%">Montinaro, Francesco</style></author><author><style face="normal" font="default" size="100%">Saag, Lauri</style></author><author><style face="normal" font="default" size="100%">Bormans, Concetta</style></author><author><style face="normal" font="default" size="100%">Sanchez, Luisa Fernanda</style></author><author><style face="normal" font="default" size="100%">Ameur, Adam</style></author><author><style face="normal" font="default" size="100%">Gyllensten, Ulf</style></author><author><style face="normal" font="default" size="100%">Kals, Mart</style></author><author><style face="normal" font="default" size="100%">Mägi, Reedik</style></author><author><style face="normal" font="default" size="100%">Pagani, Luca</style></author><author><style face="normal" font="default" size="100%">Behar, Doron M</style></author><author><style face="normal" font="default" size="100%">Rootsi, Siiri</style></author><author><style face="normal" font="default" size="100%">Villems, Richard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phylogenetic history of patrilineages rare in northern and eastern Europe from large-scale re-sequencing of human Y-chromosomes.</style></title><secondary-title><style face="normal" font="default" size="100%">Eur J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Eur J Hum Genet</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 May 07</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The most frequent Y-chromosomal (chrY) haplogroups in northern and eastern Europe (NEE) are well-known and thoroughly characterised. Yet a considerable number of men in every population carry rare paternal lineages with estimated frequencies around 5%. So far, limited sample-sizes and insufficient resolution of genotyping have obstructed a truly comprehensive look into the variety of rare paternal lineages segregating within populations and potential signals of population history that such lineages might convey. Here we harness the power of massive re-sequencing of human Y chromosomes to identify previously unknown population-specific clusters among rare paternal lineages in NEE. We construct dated phylogenies for haplogroups E2-M215, J2-M172, G-M201 and Q-M242 on the basis of 421 (of them 282 novel) high-coverage chrY sequences collected from large-scale databases focusing on populations of NEE. Within these otherwise rare haplogroups we disclose lineages that began to radiate ~1-3 thousand years ago in Estonia and Sweden and reveal male phylogenetic patterns testifying of comparatively recent local demographic expansions. Conversely, haplogroup Q lineages bear evidence of ancient Siberian influence lingering in the modern paternal gene pool of northern Europe. We assess the possible direction of influx of ancestral carriers for some of these male lineages. In addition, we demonstrate the congruency of paternal haplogroup composition of our dataset with two independent population-based cohorts from Estonia and Sweden.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33958743?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jung, In-Hyuk</style></author><author><style face="normal" font="default" size="100%">Elenbaas, Jared S</style></author><author><style face="normal" font="default" size="100%">Alisio, Arturo</style></author><author><style face="normal" font="default" size="100%">Santana, Katherine</style></author><author><style face="normal" font="default" size="100%">Young, Erica P</style></author><author><style face="normal" font="default" size="100%">Kang, Chul Joo</style></author><author><style face="normal" font="default" size="100%">Kachroo, Puja</style></author><author><style face="normal" font="default" size="100%">Lavine, Kory J</style></author><author><style face="normal" font="default" size="100%">Razani, Babak</style></author><author><style face="normal" font="default" size="100%">Mecham, Robert P</style></author><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SVEP1 is a human coronary artery disease locus that promotes atherosclerosis.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Transl Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Transl Med</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Mar 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A low-frequency variant of sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SVEP1), an extracellular matrix protein, is associated with risk of coronary disease in humans independent of plasma lipids. Despite a robust statistical association, if and how SVEP1 might contribute to atherosclerosis remained unclear. Here, using Mendelian randomization and complementary mouse models, we provide evidence that SVEP1 promotes atherosclerosis in humans and mice and is expressed by vascular smooth muscle cells (VSMCs) within the atherosclerotic plaque. VSMCs also interact with SVEP1, causing proliferation and dysregulation of key differentiation pathways, including integrin and Notch signaling. Fibroblast growth factor receptor transcription increases in VSMCs interacting with SVEP1 and is further increased by the coronary disease-associated  variant p.D2702G. These effects ultimately drive inflammation and promote atherosclerosis. Together, our results suggest that VSMC-derived SVEP1 is a proatherogenic factor and support the concept that pharmacological inhibition of SVEP1 should protect against atherosclerosis in humans.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">586</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33762433?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Somineni, Hari K</style></author><author><style face="normal" font="default" size="100%">Nagpal, Sini</style></author><author><style face="normal" font="default" size="100%">Venkateswaran, Suresh</style></author><author><style face="normal" font="default" size="100%">Cutler, David J</style></author><author><style face="normal" font="default" size="100%">Okou, David T</style></author><author><style face="normal" font="default" size="100%">Haritunians, Talin</style></author><author><style face="normal" font="default" size="100%">Simpson, Claire L</style></author><author><style face="normal" font="default" size="100%">Begum, Ferdouse</style></author><author><style face="normal" font="default" size="100%">Datta, Lisa W</style></author><author><style face="normal" font="default" size="100%">Quiros, Antonio J</style></author><author><style face="normal" font="default" size="100%">Seminerio, Jenifer</style></author><author><style face="normal" font="default" size="100%">Mengesha, Emebet</style></author><author><style face="normal" font="default" size="100%">Alexander, Jonathan S</style></author><author><style face="normal" font="default" size="100%">Baldassano, Robert N</style></author><author><style face="normal" font="default" size="100%">Dudley-Brown, Sharon</style></author><author><style face="normal" font="default" size="100%">Cross, Raymond K</style></author><author><style face="normal" font="default" size="100%">Dassopoulos, Themistocles</style></author><author><style face="normal" font="default" size="100%">Denson, Lee A</style></author><author><style face="normal" font="default" size="100%">Dhere, Tanvi A</style></author><author><style face="normal" font="default" size="100%">Iskandar, Heba</style></author><author><style face="normal" font="default" size="100%">Dryden, Gerald W</style></author><author><style face="normal" font="default" size="100%">Hou, Jason K</style></author><author><style face="normal" font="default" size="100%">Hussain, Sunny Z</style></author><author><style face="normal" font="default" size="100%">Hyams, Jeffrey S</style></author><author><style face="normal" font="default" size="100%">Isaacs, Kim L</style></author><author><style face="normal" font="default" size="100%">Kader, Howard</style></author><author><style face="normal" font="default" size="100%">Kappelman, Michael D</style></author><author><style face="normal" font="default" size="100%">Katz, Jeffry</style></author><author><style face="normal" font="default" size="100%">Kellermayer, Richard</style></author><author><style face="normal" font="default" size="100%">Kuemmerle, John F</style></author><author><style face="normal" font="default" size="100%">Lazarev, Mark</style></author><author><style face="normal" font="default" size="100%">Li, Ellen</style></author><author><style face="normal" font="default" size="100%">Mannon, Peter</style></author><author><style face="normal" font="default" size="100%">Moulton, Dedrick E</style></author><author><style face="normal" font="default" size="100%">Newberry, Rodney D</style></author><author><style face="normal" font="default" size="100%">Patel, Ashish S</style></author><author><style face="normal" font="default" size="100%">Pekow, Joel</style></author><author><style face="normal" font="default" size="100%">Saeed, Shehzad A</style></author><author><style face="normal" font="default" size="100%">Valentine, John F</style></author><author><style face="normal" font="default" size="100%">Wang, Ming-Hsi</style></author><author><style face="normal" font="default" size="100%">McCauley, Jacob L</style></author><author><style face="normal" font="default" size="100%">Abreu, Maria T</style></author><author><style face="normal" font="default" size="100%">Jester, Traci</style></author><author><style face="normal" font="default" size="100%">Molle-Rios, Zarela</style></author><author><style face="normal" font="default" size="100%">Palle, Sirish</style></author><author><style face="normal" font="default" size="100%">Scherl, Ellen J</style></author><author><style face="normal" font="default" size="100%">Kwon, John</style></author><author><style face="normal" font="default" size="100%">Rioux, John D</style></author><author><style face="normal" font="default" size="100%">Duerr, Richard H</style></author><author><style face="normal" font="default" size="100%">Silverberg, Mark S</style></author><author><style face="normal" font="default" size="100%">Zwick, Michael E</style></author><author><style face="normal" font="default" size="100%">Stevens, Christine</style></author><author><style face="normal" font="default" size="100%">Daly, Mark J</style></author><author><style face="normal" font="default" size="100%">Cho, Judy H</style></author><author><style face="normal" font="default" size="100%">Gibson, Greg</style></author><author><style face="normal" font="default" size="100%">McGovern, Dermot P B</style></author><author><style face="normal" font="default" size="100%">Brant, Steven R</style></author><author><style face="normal" font="default" size="100%">Kugathasan, Subra</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Whole-genome sequencing of African Americans implicates differential genetic architecture in inflammatory bowel disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">African Americans</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Calbindin 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Colitis, Ulcerative</style></keyword><keyword><style  face="normal" font="default" size="100%">Crohn Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">European Continental Ancestry Group</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Inflammatory Bowel Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Multifactorial Inheritance</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptors, Prostaglandin E, EP4 Subtype</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 03 04</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">108</style></volume><pages><style face="normal" font="default" size="100%">431-445</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Whether or not populations diverge with respect to the genetic contribution to risk of specific complex diseases is relevant to understanding the evolution of susceptibility and origins of health disparities. Here, we describe a large-scale whole-genome sequencing study of inflammatory bowel disease encompassing 1,774 affected individuals and 1,644 healthy control Americans with African ancestry (African Americans). Although no new loci for inflammatory bowel disease are discovered at genome-wide significance levels, we identify numerous instances of differential effect sizes in combination with divergent allele frequencies. For example, the major effect at PTGER4 fine maps to a single credible interval of 22 SNPs corresponding to one of four independent associations at the locus in European ancestry individuals but with an elevated odds ratio for Crohn disease in African Americans. A rare variant aggregate analysis implicates Ca-binding neuro-immunomodulator CALB2 in ulcerative colitis. Highly significant overall overlap of common variant risk for inflammatory bowel disease susceptibility between individuals with African and European ancestries was observed, with 41 of 241 previously known lead variants replicated and overall correlations in effect sizes of 0.68 for combined inflammatory bowel disease. Nevertheless, subtle differences influence the performance of polygenic risk scores, and we show that ancestry-appropriate weights significantly improve polygenic prediction in the highest percentiles of risk. The median amount of variance explained per locus remains the same in African and European cohorts, providing evidence for compensation of effect sizes as allele frequencies diverge, as expected under a highly polygenic model of disease.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33600772?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patel, Aniruddh P</style></author><author><style face="normal" font="default" size="100%">Wang, Minxian</style></author><author><style face="normal" font="default" size="100%">Fahed, Akl C</style></author><author><style face="normal" font="default" size="100%">Mason-Suares, Heather</style></author><author><style face="normal" font="default" size="100%">Brockman, Deanna</style></author><author><style face="normal" font="default" size="100%">Pelletier, Renee</style></author><author><style face="normal" font="default" size="100%">Amr, Sami</style></author><author><style face="normal" font="default" size="100%">Machini, Kalotina</style></author><author><style face="normal" font="default" size="100%">Hawley, Megan</style></author><author><style face="normal" font="default" size="100%">Witkowski, Leora</style></author><author><style face="normal" font="default" size="100%">Koch, Christopher</style></author><author><style face="normal" font="default" size="100%">Philippakis, Anthony</style></author><author><style face="normal" font="default" size="100%">Cassa, Christopher A</style></author><author><style face="normal" font="default" size="100%">Ellinor, Patrick T</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author><author><style face="normal" font="default" size="100%">Ng, Kenney</style></author><author><style face="normal" font="default" size="100%">Lebo, Matthew</style></author><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Association of Rare Pathogenic DNA Variants for Familial Hypercholesterolemia, Hereditary Breast and Ovarian Cancer Syndrome, and Lynch Syndrome With Disease Risk in Adults According to Family History.</style></title><secondary-title><style face="normal" font="default" size="100%">JAMA Netw Open</style></secondary-title><alt-title><style face="normal" font="default" size="100%">JAMA Netw Open</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Colorectal Neoplasms, Hereditary Nonpolyposis</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Hereditary Breast and Ovarian Cancer Syndrome</style></keyword><keyword><style  face="normal" font="default" size="100%">Heterozygote</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Hyperlipoproteinemia Type II</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Pedigree</style></keyword><keyword><style  face="normal" font="default" size="100%">Proportional Hazards Models</style></keyword><keyword><style  face="normal" font="default" size="100%">United Kingdom</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Exome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 04 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">e203959</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;Importance: &lt;/b&gt;Pathogenic DNA variants associated with familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and Lynch syndrome are widely recognized as clinically important and actionable when identified, leading some clinicians to recommend population-wide genomic screening.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Objectives: &lt;/b&gt;To assess the prevalence and clinical importance of pathogenic or likely pathogenic variants associated with each of 3 genomic conditions (familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and Lynch syndrome) within the context of contemporary clinical care.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Design, Setting, and Participants: &lt;/b&gt;This cohort study used gene-sequencing data from 49 738 participants in the UK Biobank who were recruited from 22 sites across the UK between March 21, 2006, and October 1, 2010. Inpatient hospital data date back to 1977; cancer registry data, to 1957; and death registry data, to 2006. Statistical analysis was performed from July 22, 2019, to November 15, 2019.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Exposures: &lt;/b&gt;Pathogenic or likely pathogenic DNA variants classified by a clinical laboratory geneticist.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Main Outcomes and Measures: &lt;/b&gt;Composite end point specific to each genomic condition based on atherosclerotic cardiovascular disease events for familial hypercholesterolemia, breast or ovarian cancer for hereditary breast and ovarian cancer syndrome, and colorectal or uterine cancer for Lynch syndrome.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Results: &lt;/b&gt;Among 49 738 participants (mean [SD] age, 57 [8] years; 27 144 female [55%]), 441 (0.9%) harbored a pathogenic or likely pathogenic variant associated with any of 3 genomic conditions, including 131 (0.3%) for familial hypercholesterolemia, 235 (0.5%) for hereditary breast and ovarian cancer syndrome, and 76 (0.2%) for Lynch syndrome. Presence of these variants was associated with increased risk of disease: for familial hypercholesterolemia, 28 of 131 carriers (21.4%) vs 4663 of 49 607 noncarriers (9.4%) developed atherosclerotic cardiovascular disease; for hereditary breast and ovarian cancer syndrome, 32 of 116 female carriers (27.6%) vs 2080 of 27 028 female noncarriers (7.7%) developed associated cancers; and for Lynch syndrome, 17 of 76 carriers (22.4%) vs 929 of 49 662 noncarriers (1.9%) developed colorectal or uterine cancer. The predicted probability of disease at age 75 years despite contemporary clinical care was 45.3% for carriers of familial hypercholesterolemia, 41.1% for hereditary breast and ovarian cancer syndrome, and 38.3% for Lynch syndrome. Across the 3 conditions, 39.7% (175 of 441) of the carriers reported a family history of disease vs 23.2% (34 517 of 148 772) of noncarriers.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Conclusions and Relevance: &lt;/b&gt;The findings suggest that approximately 1% of the middle-aged adult population in the UK Biobank harbored a pathogenic variant associated with any of 3 genomic conditions. These variants were associated with an increased risk of disease despite contemporary clinical care and were not reliably detected by family history.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32347951?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kim-Hellmuth, Sarah</style></author><author><style face="normal" font="default" size="100%">Aguet, François</style></author><author><style face="normal" font="default" size="100%">Oliva, Meritxell</style></author><author><style face="normal" font="default" size="100%">Muñoz-Aguirre, Manuel</style></author><author><style face="normal" font="default" size="100%">Kasela, Silva</style></author><author><style face="normal" font="default" size="100%">Wucher, Valentin</style></author><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Hamel, Andrew R</style></author><author><style face="normal" font="default" size="100%">Viñuela, Ana</style></author><author><style face="normal" font="default" size="100%">Roberts, Amy L</style></author><author><style face="normal" font="default" size="100%">Mangul, Serghei</style></author><author><style face="normal" font="default" size="100%">Wen, Xiaoquan</style></author><author><style face="normal" font="default" size="100%">Wang, Gao</style></author><author><style face="normal" font="default" size="100%">Barbeira, Alvaro N</style></author><author><style face="normal" font="default" size="100%">Garrido-Martín, Diego</style></author><author><style face="normal" font="default" size="100%">Nadel, Brian B</style></author><author><style face="normal" font="default" size="100%">Zou, Yuxin</style></author><author><style face="normal" font="default" size="100%">Bonazzola, Rodrigo</style></author><author><style face="normal" font="default" size="100%">Quan, Jie</style></author><author><style face="normal" font="default" size="100%">Brown, Andrew</style></author><author><style face="normal" font="default" size="100%">Martinez-Perez, Angel</style></author><author><style face="normal" font="default" size="100%">Soria, José Manuel</style></author><author><style face="normal" font="default" size="100%">Getz, Gad</style></author><author><style face="normal" font="default" size="100%">Dermitzakis, Emmanouil T</style></author><author><style face="normal" font="default" size="100%">Small, Kerrin S</style></author><author><style face="normal" font="default" size="100%">Stephens, Matthew</style></author><author><style face="normal" font="default" size="100%">Xi, Hualin S</style></author><author><style face="normal" font="default" size="100%">Im, Hae Kyung</style></author><author><style face="normal" font="default" size="100%">Guigo, Roderic</style></author><author><style face="normal" font="default" size="100%">Segrè, Ayellet V</style></author><author><style face="normal" font="default" size="100%">Stranger, Barbara E</style></author><author><style face="normal" font="default" size="100%">Ardlie, Kristin G</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">GTEx Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Cell type-specific genetic regulation of gene expression across human tissues.</style></title><secondary-title><style face="normal" font="default" size="100%">Science</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Science</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Organ Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Long Noncoding</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 09 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">369</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6509</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32913075?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Biffi, Alessandro</style></author><author><style face="normal" font="default" size="100%">Urday, Sebastian</style></author><author><style face="normal" font="default" size="100%">Kubiszewski, Patryk</style></author><author><style face="normal" font="default" size="100%">Gilkerson, Lee</style></author><author><style face="normal" font="default" size="100%">Sekar, Padmini</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Torres, Axana</style></author><author><style face="normal" font="default" size="100%">Bettin, Margaret</style></author><author><style face="normal" font="default" size="100%">Charidimou, Andreas</style></author><author><style face="normal" font="default" size="100%">Pasi, Marco</style></author><author><style face="normal" font="default" size="100%">Kourkoulis, Christina</style></author><author><style face="normal" font="default" size="100%">Schwab, Kristin</style></author><author><style face="normal" font="default" size="100%">DiPucchio, Zora</style></author><author><style face="normal" font="default" size="100%">Behymer, Tyler</style></author><author><style face="normal" font="default" size="100%">Osborne, Jennifer</style></author><author><style face="normal" font="default" size="100%">Morgan, Misty</style></author><author><style face="normal" font="default" size="100%">Moomaw, Charles J</style></author><author><style face="normal" font="default" size="100%">James, Michael L</style></author><author><style face="normal" font="default" size="100%">Greenberg, Steven M</style></author><author><style face="normal" font="default" size="100%">Viswanathan, Anand</style></author><author><style face="normal" font="default" size="100%">Gurol, M Edip</style></author><author><style face="normal" font="default" size="100%">Worrall, Bradford B</style></author><author><style face="normal" font="default" size="100%">Testai, Fernando D</style></author><author><style face="normal" font="default" size="100%">McCauley, Jacob L</style></author><author><style face="normal" font="default" size="100%">Falcone, Guido J</style></author><author><style face="normal" font="default" size="100%">Langefeld, Carl D</style></author><author><style face="normal" font="default" size="100%">Anderson, Christopher D</style></author><author><style face="normal" font="default" size="100%">Kamel, Hooman</style></author><author><style face="normal" font="default" size="100%">Woo, Daniel</style></author><author><style face="normal" font="default" size="100%">Sheth, Kevin N</style></author><author><style face="normal" font="default" size="100%">Rosand, Jonathan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining Imaging and Genetics to Predict Recurrence of Anticoagulation-Associated Intracerebral Hemorrhage.</style></title><secondary-title><style face="normal" font="default" size="100%">Stroke</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stroke</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Anticoagulants</style></keyword><keyword><style  face="normal" font="default" size="100%">Apolipoprotein E4</style></keyword><keyword><style  face="normal" font="default" size="100%">Cerebral Hemorrhage</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnetic Resonance Imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuroimaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Recurrence</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">51</style></volume><pages><style face="normal" font="default" size="100%">2153-2160</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND AND PURPOSE: &lt;/b&gt;For survivors of oral anticoagulation therapy (OAT)-associated intracerebral hemorrhage (OAT-ICH) who are at high risk for thromboembolism, the benefits of OAT resumption must be weighed against increased risk of recurrent hemorrhagic stroke. The ε2/ε4 alleles of the  () gene, MRI-defined cortical superficial siderosis, and cerebral microbleeds are the most potent risk factors for recurrent ICH. We sought to determine whether combining MRI markers and  genotype could have clinical impact by identifying ICH survivors in whom the risks of OAT resumption are highest.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Joint analysis of data from 2 longitudinal cohort studies of OAT-ICH survivors: (1) MGH-ICH study (Massachusetts General Hospital ICH) and (2) longitudinal component of the ERICH study (Ethnic/Racial Variations of Intracerebral Hemorrhage). We evaluated whether MRI markers and  genotype predict ICH recurrence. We then developed and validated a combined -MRI classification scheme to predict ICH recurrence, using Classification and Regression Tree analysis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Cortical superficial siderosis, cerebral microbleed, and  ε2/ε4 variants were independently associated with ICH recurrence after OAT-ICH (all &lt;0.05). Combining  genotype and MRI data resulted in improved prediction of ICH recurrence (Harrell C: 0.79 versus 0.55 for clinical data alone, =0.033). In the MGH (training) data set, CSS, cerebral microbleed, and  ε2/ε4 stratified likelihood of ICH recurrence into high-, medium-, and low-risk categories. In the ERICH (validation) data set, yearly ICH recurrence rates for high-, medium-, and low-risk individuals were 6.6%, 2.5%, and 0.9%, respectively, with overall area under the curve of 0.91 for prediction of recurrent ICH.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Combining MRI and  genotype stratifies likelihood of ICH recurrence into high, medium, and low risk. If confirmed in prospective studies, this combined -MRI classification scheme may prove useful for selecting individuals for OAT resumption after ICH.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32517581?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Walker, Ryan W</style></author><author><style face="normal" font="default" size="100%">Belbin, Gillian M</style></author><author><style face="normal" font="default" size="100%">Sorokin, Elena P</style></author><author><style face="normal" font="default" size="100%">Van Vleck, Tielman</style></author><author><style face="normal" font="default" size="100%">Wojcik, Genevieve L</style></author><author><style face="normal" font="default" size="100%">Moscati, Arden</style></author><author><style face="normal" font="default" size="100%">Gignoux, Christopher R</style></author><author><style face="normal" font="default" size="100%">Cho, Judy</style></author><author><style face="normal" font="default" size="100%">Abul-Husn, Noura S</style></author><author><style face="normal" font="default" size="100%">Nadkarni, Girish</style></author><author><style face="normal" font="default" size="100%">Kenny, Eimear E</style></author><author><style face="normal" font="default" size="100%">Loos, Ruth J F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A common variant in PNPLA3 is associated with age at diagnosis of NAFLD in patients from a multi-ethnic biobank.</style></title><secondary-title><style face="normal" font="default" size="100%">J Hepatol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Hepatol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 06</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">72</style></volume><pages><style face="normal" font="default" size="100%">1070-1081</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND &amp; AIMS: &lt;/b&gt;The Ile138Met variant (rs738409) in the PNPLA3 gene has the largest effect on non-alcoholic fatty liver disease (NAFLD), increasing the risk of progression to severe forms of liver disease. It remains unknown if the variant plays a role in age of NAFLD onset. We aimed to determine if rs738409 impacts on the age of NAFLD diagnosis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We applied a novel natural language processing (NLP) algorithm to a longitudinal electronic health records (EHR) dataset of &gt;27,000 individuals with genetic data from a multi-ethnic biobank, defining NAFLD cases (n = 1,703) and confirming controls (n = 8,119). We conducted i) a survival analysis to determine if age at diagnosis differed by rs738409 genotype, ii) a receiver operating characteristics analysis to assess the utility of the rs738409 genotype in discriminating NAFLD cases from controls, and iii) a phenome-wide association study (PheWAS) between rs738409 and 10,095 EHR-derived disease diagnoses.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The PNPLA3 G risk allele was associated with: i) earlier age of NAFLD diagnosis, with the strongest effect in Hispanics (hazard ratio 1.33; 95% CI 1.15-1.53; p &lt;0.0001) among whom a NAFLD diagnosis was 15% more likely in risk allele carriers vs. non-carriers; ii) increased NAFLD risk (odds ratio 1.61; 95% CI 1.349-1.73; p &lt;0.0001), with the strongest effect among Hispanics (odds ratio 1.43; 95% CI 1.28-1.59; p &lt;0.0001); iii) additional liver diseases in a PheWAS (p &lt;4.95 × 10) where the risk variant also associated with earlier age of diagnosis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Given the role of the rs738409 in NAFLD diagnosis age, our results suggest that stratifying risk within populations known to have an enhanced risk of liver disease, such as Hispanic carriers of the rs738409 variant, would be effective in earlier identification of those who would benefit most from early NAFLD prevention and treatment strategies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;LAY SUMMARY: &lt;/b&gt;Despite clear associations between the PNPLA3 rs738409 variant and elevated risk of progression from non-alcoholic fatty liver disease (NAFLD) to more severe forms of liver disease, it remains unknown if PNPLA3 rs738409 plays a role in the age of NAFLD onset. Herein, we found that this risk variant is associated with an earlier age of NAFLD and other liver disease diagnoses; an observation most pronounced in Hispanic Americans. We conclude that PNPLA3 rs738409 could be used to better understand liver disease risk within vulnerable populations and identify patients that may benefit from early prevention strategies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32145261?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sabo, Aniko</style></author><author><style face="normal" font="default" size="100%">Murdock, David</style></author><author><style face="normal" font="default" size="100%">Dugan, Shannon</style></author><author><style face="normal" font="default" size="100%">Meng, Qingchang</style></author><author><style face="normal" font="default" size="100%">Gingras, Marie-Claude</style></author><author><style face="normal" font="default" size="100%">Hu, Jianhong</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Community-based recruitment and exome sequencing indicates high diagnostic yield in adults with intellectual disability.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Genet Genomic Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Genet Genomic Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Testing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Independent Living</style></keyword><keyword><style  face="normal" font="default" size="100%">Intellectual Disability</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mediator Complex</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Nuclear Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Patient Selection</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Tumor Suppressor Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Exome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e1439</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Establishing a genetic diagnosis for individuals with intellectual disability (ID) benefits patients and their families as it may inform the prognosis, lead to appropriate therapy, and facilitate access to medical and supportive services. Exome sequencing has been successfully applied in a diagnostic setting, but most clinical exome referrals are pediatric patients, with many adults with ID lacking a comprehensive genetic evaluation.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Our unique recruitment strategy involved partnering with service and education providers for individuals with ID. We performed exome sequencing and analysis, and clinical variant interpretation for each recruited family.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;All five families enrolled in the study opted-in for the return of genetic results. In three out of five families exome sequencing analysis identified pathogenic or likely pathogenic variants in KANSL1, TUSC3, and MED13L genes. Families discussed the results and any potential medical follow-up in an appointment with a board certified clinical geneticist.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Our study suggests high yield of exome sequencing as a diagnostic tool in adult patients with ID who have not undergone comprehensive sequencing-based genetic testing. Research studies including an option of return of results through a genetic clinic could help minimize the disparity in exome diagnostic testing between pediatric and adult patients with ID.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32767738?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sekar, Shobana</style></author><author><style face="normal" font="default" size="100%">Tomasini, Livia</style></author><author><style face="normal" font="default" size="100%">Proukakis, Christos</style></author><author><style face="normal" font="default" size="100%">Bae, Taejeong</style></author><author><style face="normal" font="default" size="100%">Manlove, Logan</style></author><author><style face="normal" font="default" size="100%">Jang, Yeongjun</style></author><author><style face="normal" font="default" size="100%">Scuderi, Soraya</style></author><author><style face="normal" font="default" size="100%">Zhou, Bo</style></author><author><style face="normal" font="default" size="100%">Kalyva, Maria</style></author><author><style face="normal" font="default" size="100%">Amiri, Anahita</style></author><author><style face="normal" font="default" size="100%">Mariani, Jessica</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author><author><style face="normal" font="default" size="100%">Urban, Alexander E</style></author><author><style face="normal" font="default" size="100%">Vaccarino, Flora M</style></author><author><style face="normal" font="default" size="100%">Abyzov, Alexej</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Complex mosaic structural variations in human fetal brains.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Res</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">1695-1704</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Somatic mosaicism, manifesting as single nucleotide variants (SNVs), mobile element insertions, and structural changes in the DNA, is a common phenomenon in human brain cells, with potential functional consequences. Using a clonal approach, we previously detected 200-400 mosaic SNVs per cell in three human fetal brains (15-21 wk postconception). However, structural variation in the human fetal brain has not yet been investigated. Here, we discover and validate four mosaic structural variants (SVs) in the same brains and resolve their precise breakpoints. The SVs were of kilobase scale and complex, consisting of deletion(s) and rearranged genomic fragments, which sometimes originated from different chromosomes. Sequences at the breakpoints of these rearrangements had microhomologies, suggesting their origin from replication errors. One SV was found in two clones, and we timed its origin to ∼14 wk postconception. No large scale mosaic copy number variants (CNVs) were detectable in normal fetal human brains, suggesting that previously reported megabase-scale CNVs in neurons arise at later stages of development. By reanalysis of public single nuclei data from adult brain neurons, we detected an extrachromosomal circular DNA event. Our study reveals the existence of mosaic SVs in the developing human brain, likely arising from cell proliferation during mid-neurogenesis. Although relatively rare compared to SNVs and present in ∼10% of neurons, SVs in developing human brain affect a comparable number of bases in the genome (∼6200 vs. ∼4000 bp), implying that they may have similar functional consequences.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33122304?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brasó-Vives, Marina</style></author><author><style face="normal" font="default" size="100%">Povolotskaya, Inna S</style></author><author><style face="normal" font="default" size="100%">Hartasánchez, Diego A</style></author><author><style face="normal" font="default" size="100%">Farré, Xavier</style></author><author><style face="normal" font="default" size="100%">Fernandez-Callejo, Marcos</style></author><author><style face="normal" font="default" size="100%">Raveendran, Muthuswamy</style></author><author><style face="normal" font="default" size="100%">Harris, R Alan</style></author><author><style face="normal" font="default" size="100%">Rosene, Douglas L</style></author><author><style face="normal" font="default" size="100%">Lorente-Galdos, Belen</style></author><author><style face="normal" font="default" size="100%">Navarro, Arcadi</style></author><author><style face="normal" font="default" size="100%">Marques-Bonet, Tomas</style></author><author><style face="normal" font="default" size="100%">Rogers, Jeffrey</style></author><author><style face="normal" font="default" size="100%">Juan, David</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Copy number variants and fixed duplications among 198 rhesus macaques (Macaca mulatta).</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Copy Number Variations</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Duplication</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Population</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Macaca mulatta</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Open Reading Frames</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Species Specificity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 05</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">e1008742</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The rhesus macaque is an abundant species of Old World monkeys and a valuable model organism for biomedical research due to its close phylogenetic relationship to humans. Copy number variation is one of the main sources of genomic diversity within and between species and a widely recognized cause of inter-individual differences in disease risk. However, copy number differences among rhesus macaques and between the human and macaque genomes, as well as the relevance of this diversity to research involving this nonhuman primate, remain understudied. Here we present a high-resolution map of sequence copy number for the rhesus macaque genome constructed from a dataset of 198 individuals. Our results show that about one-eighth of the rhesus macaque reference genome is composed of recently duplicated regions, either copy number variable regions or fixed duplications. Comparison with human genomic copy number maps based on previously published data shows that, despite overall similarities in the genome-wide distribution of these regions, there are specific differences at the chromosome level. Some of these create differences in the copy number profile between human disease genes and their rhesus macaque orthologs. Our results highlight the importance of addressing the number of copies of target genes in the design of experiments and cautions against human-centered assumptions in research conducted with model organisms. Overall, we present a genome-wide copy number map from a large sample of rhesus macaque individuals representing an important novel contribution concerning the evolution of copy number in primate genomes.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32392208?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Li, Chang</style></author><author><style face="normal" font="default" size="100%">Mou, Chengcheng</style></author><author><style face="normal" font="default" size="100%">Swartz, Michael D</style></author><author><style face="normal" font="default" size="100%">Yu, Bing</style></author><author><style face="normal" font="default" size="100%">Bai, Yongsheng</style></author><author><style face="normal" font="default" size="100%">Tu, Yicheng</style></author><author><style face="normal" font="default" size="100%">Liu, Xiaoming</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">dbMTS: A comprehensive database of putative human microRNA target site SNVs and their functional predictions.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mutat</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mutat</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 06</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">1123-1130</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;MicroRNAs (miRNA) are short noncoding RNAs that can repress the expression of protein-coding messenger RNAs (mRNAs) by binding to the 3'-untranslated region (UTR) of the target. Genetic mutations such as single nucleotide variants (SNVs) in the 3'-UTR of the mRNAs can disrupt miRNA regulation. In this study, we presented dbMTS, a database for miRNA target site (MTS) SNVs and their functional annotations. This database can help studies easily identify putative SNVs that affect miRNA targeting and facilitate the prioritization of their functional importance. dbMTS is freely available for academic use at http://database.liulab.science/dbMTS as a web service or a downloadable attached database of dbNSFP.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32227657?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xavier, Catarina</style></author><author><style face="normal" font="default" size="100%">de la Puente, María</style></author><author><style face="normal" font="default" size="100%">Mosquera-Miguel, Ana</style></author><author><style face="normal" font="default" size="100%">Freire-Aradas, Ana</style></author><author><style face="normal" font="default" size="100%">Kalamara, Vivian</style></author><author><style face="normal" font="default" size="100%">Vidaki, Athina</style></author><author><style face="normal" font="default" size="100%">E Gross, Theresa</style></author><author><style face="normal" font="default" size="100%">Revoir, Andrew</style></author><author><style face="normal" font="default" size="100%">Pośpiech, Ewelina</style></author><author><style face="normal" font="default" size="100%">Kartasińska, Ewa</style></author><author><style face="normal" font="default" size="100%">Spólnicka, Magdalena</style></author><author><style face="normal" font="default" size="100%">Branicki, Wojciech</style></author><author><style face="normal" font="default" size="100%">E Ames, Carole</style></author><author><style face="normal" font="default" size="100%">M Schneider, Peter</style></author><author><style face="normal" font="default" size="100%">Hohoff, Carsten</style></author><author><style face="normal" font="default" size="100%">Kayser, Manfred</style></author><author><style face="normal" font="default" size="100%">Phillips, Christopher</style></author><author><style face="normal" font="default" size="100%">Parson, Walther</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">VISAGE Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Development and validation of the VISAGE AmpliSeq basic tool to predict appearance and ancestry from DNA.</style></title><secondary-title><style face="normal" font="default" size="100%">Forensic Sci Int Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Forensic Sci Int Genet</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 09</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">102336</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Forensic DNA phenotyping is gaining interest as the number of applications increases within the forensic genetics community. The possibility of providing investigative leads in addition to conventional DNA profiling for human identification provides new insights into otherwise &quot;cold&quot; police investigations. The ability of reporting on the bio-geographical ancestry (BGA), appearance characteristics and age based on DNA obtained from a crime scene sample of an unknown donor makes the exploration of such markers and the development of new methods meaningful for criminal investigations. The VISible Attributes through GEnomics (VISAGE) Consortium aims to disseminate and broaden the use of predictive markers and develop fully optimized and validated prototypes for forensic casework implementation. Here, the first VISAGE appearance and ancestry tool development, performance and validation is reported. A total of 153 SNPs (96.84 % assay conversion rate) were successfully incorporated into a single multiplex reaction using the AmpliSeq™ design pipeline, and applied for massively parallel sequencing with the Ion S5 platform. A collaborative effort involving six VISAGE laboratory partners was devised to perform all validation tests. An extensive validation plan was carefully organized to explore the assay's overall performance with optimum and low-input samples, as well as with challenging and casework mock samples. In addition, forensic validation studies such as concordance and mixture tests recurring to the Coriell sample set with known genotypes were performed. Finally, inhibitor tolerance and specificity were also evaluated. Results showed a robust, highly sensitive assay with good overall concordance between laboratories.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32619960?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xavier, C</style></author><author><style face="normal" font="default" size="100%">de la Puente, M</style></author><author><style face="normal" font="default" size="100%">Phillips, C</style></author><author><style face="normal" font="default" size="100%">Eduardoff, M</style></author><author><style face="normal" font="default" size="100%">Heidegger, A</style></author><author><style face="normal" font="default" size="100%">Mosquera-Miguel, A</style></author><author><style face="normal" font="default" size="100%">Freire-Aradas, A</style></author><author><style face="normal" font="default" size="100%">Lagace, R</style></author><author><style face="normal" font="default" size="100%">Wootton, S</style></author><author><style face="normal" font="default" size="100%">Power, D</style></author><author><style face="normal" font="default" size="100%">Parson, W</style></author><author><style face="normal" font="default" size="100%">Lareu, M V</style></author><author><style face="normal" font="default" size="100%">Daniel, R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Forensic evaluation of the Asia Pacific ancestry-informative MAPlex assay.</style></title><secondary-title><style face="normal" font="default" size="100%">Forensic Sci Int Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Forensic Sci Int Genet</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 09</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">102344</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;DNA intelligence, and particularly the inference of biogeographical ancestry (BGA) is increasing in interest, and relevance within the forensic genetics community. The majority of current MPS-based forensic ancestry-informative assays focus on the differentiation of major global populations. The recently published MAPlex (Multiplex for the Asia Pacific) panel contains 144 SNPs and 20 microhaplotypes and aims to improve the differentiation of populations in the Asia Pacific region. This study reports the first forensic evaluation of the MAPlex panel using AmpliSeq technology and Ion S5 sequencing. This study reports on the overall performance of MAPlex including the assay's sequence coverage distribution and stability, baseline noise and description of problematic SNPs. Dilution series, artificially degraded and mixed DNA samples were also analysed to evaluate the sensitivity of the panel with challenging or compromised forensic samples. As the first panel to combine biallelic SNPs, multiple-allele SNPs and microhaplotypes, the MAPlex assay demonstrated an enhanced capacity for mixture detection, not easily performed with common binary SNPs. This performance evaluation indicates that MAPlex is a robust, stable and highly sensitive assay that is applicable to forensic casework for the prediction of BGA.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32615397?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rochtus, Anne</style></author><author><style face="normal" font="default" size="100%">Olson, Heather E</style></author><author><style face="normal" font="default" size="100%">Smith, Lacey</style></author><author><style face="normal" font="default" size="100%">Keith, Louisa G</style></author><author><style face="normal" font="default" size="100%">El Achkar, Christelle</style></author><author><style face="normal" font="default" size="100%">Taylor, Alan</style></author><author><style face="normal" font="default" size="100%">Mahida, Sonal</style></author><author><style face="normal" font="default" size="100%">Park, Meredith</style></author><author><style face="normal" font="default" size="100%">Kelly, McKenna</style></author><author><style face="normal" font="default" size="100%">Shain, Catherine</style></author><author><style face="normal" font="default" size="100%">Rockowitz, Shira</style></author><author><style face="normal" font="default" size="100%">Rosen Sheidley, Beth</style></author><author><style face="normal" font="default" size="100%">Poduri, Annapurna</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort.</style></title><secondary-title><style face="normal" font="default" size="100%">Epilepsia</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Epilepsia</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Age of Onset</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosomes, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Epilepsy</style></keyword><keyword><style  face="normal" font="default" size="100%">Epilepsy, Generalized</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Testing</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Microarray Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Exome Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">61</style></volume><pages><style face="normal" font="default" size="100%">249-258</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;We evaluated the yield of systematic analysis and/or reanalysis of whole exome sequencing (WES) data from a cohort of well-phenotyped pediatric patients with epilepsy and suspected but previously undetermined genetic etiology.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We identified and phenotyped 125 participants with pediatric epilepsy. Etiology was unexplained at the time of enrollment despite clinical testing, which included chromosomal microarray (57 patients), epilepsy gene panel (n = 48), both (n = 28), or WES (n = 8). Clinical epilepsy diagnoses included developmental and epileptic encephalopathy (DEE), febrile infection-related epilepsy syndrome, Rasmussen encephalitis, and other focal and generalized epilepsies. We analyzed WES data and compared the yield in participants with and without prior clinical genetic testing.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Overall, we identified pathogenic or likely pathogenic variants in 40% (50/125) of our study participants. Nine patients with DEE had genetic variants in recently published genes that had not been recognized as epilepsy-related at the time of clinical testing (FGF12, GABBR1, GABBR2, ITPA, KAT6A, PTPN23, RHOBTB2, SATB2), and eight patients had genetic variants in candidate epilepsy genes (CAMTA1, FAT3, GABRA6, HUWE1, PTCHD1). Ninety participants had concomitant or subsequent clinical genetic testing, which was ultimately explanatory for 26% (23/90). Of the 67 participants whose molecular diagnoses were &quot;unsolved&quot; through clinical genetic testing, we identified pathogenic or likely pathogenic variants in 17 (25%).&lt;/p&gt;&lt;p&gt;&lt;b&gt;SIGNIFICANCE: &lt;/b&gt;Our data argue for early consideration of WES with iterative reanalysis for patients with epilepsy, particularly those with DEE or epilepsy with intellectual disability. Rigorous analysis of WES data of well-phenotyped patients with epilepsy leads to a broader understanding of gene-specific phenotypic spectra as well as candidate disease gene identification. We illustrate the dynamic nature of genetic diagnosis over time, with analysis and in some cases reanalysis of exome data leading to the identification of disease-associated variants among participants with previously nondiagnostic results from a variety of clinical testing strategies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31957018?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gulsuner, S</style></author><author><style face="normal" font="default" size="100%">Stein, D J</style></author><author><style face="normal" font="default" size="100%">Susser, E S</style></author><author><style face="normal" font="default" size="100%">Sibeko, G</style></author><author><style face="normal" font="default" size="100%">Pretorius, A</style></author><author><style face="normal" font="default" size="100%">Walsh, T</style></author><author><style face="normal" font="default" size="100%">Majara, L</style></author><author><style face="normal" font="default" size="100%">Mndini, M M</style></author><author><style face="normal" font="default" size="100%">Mqulwana, S G</style></author><author><style face="normal" font="default" size="100%">Ntola, O A</style></author><author><style face="normal" font="default" size="100%">Casadei, S</style></author><author><style face="normal" font="default" size="100%">Ngqengelele, L L</style></author><author><style face="normal" font="default" size="100%">Korchina, V</style></author><author><style face="normal" font="default" size="100%">van der Merwe, C</style></author><author><style face="normal" font="default" size="100%">Malan, M</style></author><author><style face="normal" font="default" size="100%">Fader, K M</style></author><author><style face="normal" font="default" size="100%">Feng, M</style></author><author><style face="normal" font="default" size="100%">Willoughby, E</style></author><author><style face="normal" font="default" size="100%">Muzny, D</style></author><author><style face="normal" font="default" size="100%">Baldinger, A</style></author><author><style face="normal" font="default" size="100%">Andrews, H F</style></author><author><style face="normal" font="default" size="100%">Gur, R C</style></author><author><style face="normal" font="default" size="100%">Gibbs, R A</style></author><author><style face="normal" font="default" size="100%">Zingela, Z</style></author><author><style face="normal" font="default" size="100%">Nagdee, M</style></author><author><style face="normal" font="default" size="100%">Ramesar, R S</style></author><author><style face="normal" font="default" size="100%">King, M-C</style></author><author><style face="normal" font="default" size="100%">McClellan, J M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetics of schizophrenia in the South African Xhosa.</style></title><secondary-title><style face="normal" font="default" size="100%">Science</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Science</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Age Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Autistic Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">Bipolar Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">Dopamine</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">gamma-Aminobutyric Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Glutamine</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Schizophrenia</style></keyword><keyword><style  face="normal" font="default" size="100%">Sex Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">South Africa</style></keyword><keyword><style  face="normal" font="default" size="100%">Synapses</style></keyword><keyword><style  face="normal" font="default" size="100%">Synaptic Transmission</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 01 31</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">367</style></volume><pages><style face="normal" font="default" size="100%">569-573</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Africa, the ancestral home of all modern humans, is the most informative continent for understanding the human genome and its contribution to complex disease. To better understand the genetics of schizophrenia, we studied the illness in the Xhosa population of South Africa, recruiting 909 cases and 917 age-, gender-, and residence-matched controls. Individuals with schizophrenia were significantly more likely than controls to harbor private, severely damaging mutations in genes that are critical to synaptic function, including neural circuitry mediated by the neurotransmitters glutamine, γ-aminobutyric acid, and dopamine. Schizophrenia is genetically highly heterogeneous, involving severe ultrarare mutations in genes that are critical to synaptic plasticity. The depth of genetic variation in Africa revealed this relationship with a moderate sample size and informed our understanding of the genetics of schizophrenia worldwide.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6477</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32001654?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Winkler, Thomas W</style></author><author><style face="normal" font="default" size="100%">Grassmann, Felix</style></author><author><style face="normal" font="default" size="100%">Brandl, Caroline</style></author><author><style face="normal" font="default" size="100%">Kiel, Christina</style></author><author><style face="normal" font="default" size="100%">Günther, Felix</style></author><author><style face="normal" font="default" size="100%">Strunz, Tobias</style></author><author><style face="normal" font="default" size="100%">Weidner, Lorraine</style></author><author><style face="normal" font="default" size="100%">Zimmermann, Martina E</style></author><author><style face="normal" font="default" size="100%">Korb, Christina A</style></author><author><style face="normal" font="default" size="100%">Poplawski, Alicia</style></author><author><style face="normal" font="default" size="100%">Schuster, Alexander K</style></author><author><style face="normal" font="default" size="100%">Müller-Nurasyid, Martina</style></author><author><style face="normal" font="default" size="100%">Peters, Annette</style></author><author><style face="normal" font="default" size="100%">Rauscher, Franziska G</style></author><author><style face="normal" font="default" size="100%">Elze, Tobias</style></author><author><style face="normal" font="default" size="100%">Horn, Katrin</style></author><author><style face="normal" font="default" size="100%">Scholz, Markus</style></author><author><style face="normal" font="default" size="100%">Cañadas-Garre, Marisa</style></author><author><style face="normal" font="default" size="100%">McKnight, Amy Jayne</style></author><author><style face="normal" font="default" size="100%">Quinn, Nicola</style></author><author><style face="normal" font="default" size="100%">Hogg, Ruth E</style></author><author><style face="normal" font="default" size="100%">Küchenhoff, Helmut</style></author><author><style face="normal" font="default" size="100%">Heid, Iris M</style></author><author><style face="normal" font="default" size="100%">Stark, Klaus J</style></author><author><style face="normal" font="default" size="100%">Weber, Bernhard H F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Med Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Med Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Markers</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Macular Degeneration</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 08 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">120</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Advanced age-related macular degeneration (AMD) is a leading cause of blindness. While around half of the genetic contribution to advanced AMD has been uncovered, little is known about the genetic architecture of early AMD.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;To identify genetic factors for early AMD, we conducted a genome-wide association study (GWAS) meta-analysis (14,034 cases, 91,214 controls, 11 sources of data including the International AMD Genomics Consortium, IAMDGC, and UK Biobank, UKBB). We ascertained early AMD via color fundus photographs by manual grading for 10 sources and via an automated machine learning approach for &gt; 170,000 photographs from UKBB. We searched for early AMD loci via GWAS and via a candidate approach based on 14 previously suggested early AMD variants.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Altogether, we identified 10 independent loci with statistical significance for early AMD: (i) 8 from our GWAS with genome-wide significance (P &lt; 5 × 10), (ii) one previously suggested locus with experiment-wise significance (P &lt; 0.05/14) in our non-overlapping data and with genome-wide significance when combining the reported and our non-overlapping data (together 17,539 cases, 105,395 controls), and (iii) one further previously suggested locus with experiment-wise significance in our non-overlapping data. Of these 10 identified loci, 8 were novel and 2 known for early AMD. Most of the 10 loci overlapped with known advanced AMD loci (near ARMS2/HTRA1, CFH, C2, C3, CETP, TNFRSF10A, VEGFA, APOE), except two that have not yet been identified with statistical significance for any AMD. Among the 17 genes within these two loci, in-silico functional annotation suggested CD46 and TYR as the most likely responsible genes. Presence or absence of an early AMD effect distinguished the known pathways of advanced AMD genetics (complement/lipid pathways versus extracellular matrix metabolism).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Our GWAS on early AMD identified novel loci, highlighted shared and distinct genetics between early and advanced AMD and provides insights into AMD etiology. Our data provide a resource comparable in size to the existing IAMDGC data on advanced AMD genetics enabling a joint view. The biological relevance of this joint view is underscored by the ability of early AMD effects to differentiate the major pathways for advanced AMD.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32843070?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hindy, George</style></author><author><style face="normal" font="default" size="100%">Aragam, Krishna G</style></author><author><style face="normal" font="default" size="100%">Ng, Kenney</style></author><author><style face="normal" font="default" size="100%">Chaffin, Mark</style></author><author><style face="normal" font="default" size="100%">Lotta, Luca A</style></author><author><style face="normal" font="default" size="100%">Baras, Aris</style></author><author><style face="normal" font="default" size="100%">Drake, Isabel</style></author><author><style face="normal" font="default" size="100%">Orho-Melander, Marju</style></author><author><style face="normal" font="default" size="100%">Melander, Olle</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Regeneron Genetics Center</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome-Wide Polygenic Score, Clinical Risk Factors, and Long-Term Trajectories of Coronary Artery Disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Arterioscler Thromb Vasc Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Arterioscler Thromb Vasc Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Coronary Artery Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Heart Disease Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Heredity</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Incidence</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Multifactorial Inheritance</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Assessment</style></keyword><keyword><style  face="normal" font="default" size="100%">Sweden</style></keyword><keyword><style  face="normal" font="default" size="100%">Time Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">United Kingdom</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">2738-2746</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;To determine the relationship of a genome-wide polygenic score for coronary artery disease (GPS) with lifetime trajectories of CAD risk, directly compare its predictive capacity to traditional risk factors, and assess its interplay with the Pooled Cohort Equations (PCE) clinical risk estimator. Approach and Results: We studied GPS in 28 556 middle-aged participants of the Malmö Diet and Cancer Study, of whom 4122 (14.4%) developed CAD over a median follow-up of 21.3 years. A pronounced gradient in lifetime risk of CAD was observed-16% for those in the lowest GPS decile to 48% in the highest. We evaluated the discriminative capacity of the GPS-as assessed by change in the C-statistic from a baseline model including age and sex-among 5685 individuals with PCE risk estimates available. The increment for the GPS (+0.045, &lt;0.001) was higher than for any of 11 traditional risk factors (range +0.007 to +0.032). Minimal correlation was observed between GPS and 10-year risk defined by the PCE (=0.03), and addition of GPS improved the C-statistic of the PCE model by 0.026. A significant gradient in lifetime risk was observed for the GPS, even among individuals within a given PCE clinical risk stratum. We replicated key findings-noting strikingly consistent results-in 325 003 participants of the UK Biobank.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;GPS-a risk estimator available from birth-stratifies individuals into varying trajectories of clinical risk for CAD. Implementation of GPS may enable identification of high-risk individuals early in life, decades in advance of manifest risk factors or disease.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32957805?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nomura, Akihiro</style></author><author><style face="normal" font="default" size="100%">Emdin, Connor A</style></author><author><style face="normal" font="default" size="100%">Won, Hong Hee</style></author><author><style face="normal" font="default" size="100%">Peloso, Gina M</style></author><author><style face="normal" font="default" size="100%">Natarajan, Pradeep</style></author><author><style face="normal" font="default" size="100%">Ardissino, Diego</style></author><author><style face="normal" font="default" size="100%">Danesh, John</style></author><author><style face="normal" font="default" size="100%">Schunkert, Heribert</style></author><author><style face="normal" font="default" size="100%">Correa, Adolfo</style></author><author><style face="normal" font="default" size="100%">Bown, Matthew J</style></author><author><style face="normal" font="default" size="100%">Samani, Nilesh J</style></author><author><style face="normal" font="default" size="100%">Erdmann, Jeanette</style></author><author><style face="normal" font="default" size="100%">McPherson, Ruth</style></author><author><style face="normal" font="default" size="100%">Watkins, Hugh</style></author><author><style face="normal" font="default" size="100%">Saleheen, Danish</style></author><author><style face="normal" font="default" size="100%">Elosua, Roberto</style></author><author><style face="normal" font="default" size="100%">Kawashiri, Masa-Aki</style></author><author><style face="normal" font="default" size="100%">Tada, Hayato</style></author><author><style face="normal" font="default" size="100%">Gupta, Namrata</style></author><author><style face="normal" font="default" size="100%">Shah, Svati H</style></author><author><style face="normal" font="default" size="100%">Rader, Daniel J</style></author><author><style face="normal" font="default" size="100%">Gabriel, Stacey</style></author><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Heterozygous  Gene Deficiency and Risk of Coronary Artery Disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Circ Genom Precis Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Circ Genom Precis Med</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">417-423</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Familial sitosterolemia is a rare Mendelian disorder characterized by hyperabsorption and decreased biliary excretion of dietary sterols. Affected individuals typically have complete genetic deficiency-homozygous loss-of-function (LoF) variants-in the  or  genes and have substantially elevated plasma sitosterol and LDL (low-density lipoprotein) cholesterol (LDL-C) levels. The impact of partial genetic deficiency of  or -as occurs in heterozygous carriers of LoF variants-on LDL-C and risk of coronary artery disease (CAD) has remained uncertain.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We first recruited 9 sitosterolemia families, identified causative LoF variants in  or , and evaluated the associations of these  or  LoF variants with plasma phytosterols and lipid levels. We next assessed for LoF variants in  or  in CAD cases (n=29 321) versus controls (n=357 326). We tested the association of rare LoF variants in  or  with blood lipids and risk for CAD. Rare LoF variants were defined as protein-truncating variants with minor allele frequency &lt;0.1% in  or .&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;In sitosterolemia families, 7 pedigrees harbored causative LoF variants in  and 2 pedigrees in . Homozygous LoF variants in either  or  led to marked elevations in sitosterol and LDL-C. Of those sitosterolemia families, heterozygous carriers of  LoF variants exhibited increased sitosterol and LDL-C levels compared with noncarriers. Within large-scale CAD case-control cohorts, prevalence of rare LoF variants in  and in  was ≈0.1% each.  heterozygous LoF variant carriers had significantly elevated LDL-C levels (25 mg/dL [95% CI, 14-35]; =1.1×10) and were at 2-fold increased risk of CAD (odds ratio, 2.06 [95% CI, 1.27-3.35]; =0.004). By contrast,  heterozygous LoF carrier status was not associated with increased LDL-C or risk of CAD.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Although familial sitosterolemia is traditionally considered as a recessive disorder, we observed that heterozygous carriers of an LoF variant in  had significantly increased sitosterol and LDL-C levels and a 2-fold increase in risk of CAD.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32862661?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Oliva, Meritxell</style></author><author><style face="normal" font="default" size="100%">Muñoz-Aguirre, Manuel</style></author><author><style face="normal" font="default" size="100%">Kim-Hellmuth, Sarah</style></author><author><style face="normal" font="default" size="100%">Wucher, Valentin</style></author><author><style face="normal" font="default" size="100%">Gewirtz, Ariel D H</style></author><author><style face="normal" font="default" size="100%">Cotter, Daniel J</style></author><author><style face="normal" font="default" size="100%">Parsana, Princy</style></author><author><style face="normal" font="default" size="100%">Kasela, Silva</style></author><author><style face="normal" font="default" size="100%">Balliu, Brunilda</style></author><author><style face="normal" font="default" size="100%">Viñuela, Ana</style></author><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Mohammadi, Pejman</style></author><author><style face="normal" font="default" size="100%">Aguet, François</style></author><author><style face="normal" font="default" size="100%">Zou, Yuxin</style></author><author><style face="normal" font="default" size="100%">Khramtsova, Ekaterina A</style></author><author><style face="normal" font="default" size="100%">Skol, Andrew D</style></author><author><style face="normal" font="default" size="100%">Garrido-Martín, Diego</style></author><author><style face="normal" font="default" size="100%">Reverter, Ferran</style></author><author><style face="normal" font="default" size="100%">Brown, Andrew</style></author><author><style face="normal" font="default" size="100%">Evans, Patrick</style></author><author><style face="normal" font="default" size="100%">Gamazon, Eric R</style></author><author><style face="normal" font="default" size="100%">Payne, Anthony</style></author><author><style face="normal" font="default" size="100%">Bonazzola, Rodrigo</style></author><author><style face="normal" font="default" size="100%">Barbeira, Alvaro N</style></author><author><style face="normal" font="default" size="100%">Hamel, Andrew R</style></author><author><style face="normal" font="default" size="100%">Martinez-Perez, Angel</style></author><author><style face="normal" font="default" size="100%">Soria, José Manuel</style></author><author><style face="normal" font="default" size="100%">Pierce, Brandon L</style></author><author><style face="normal" font="default" size="100%">Stephens, Matthew</style></author><author><style face="normal" font="default" size="100%">Eskin, Eleazar</style></author><author><style face="normal" font="default" size="100%">Dermitzakis, Emmanouil T</style></author><author><style face="normal" font="default" size="100%">Segrè, Ayellet V</style></author><author><style face="normal" font="default" size="100%">Im, Hae Kyung</style></author><author><style face="normal" font="default" size="100%">Engelhardt, Barbara E</style></author><author><style face="normal" font="default" size="100%">Ardlie, Kristin G</style></author><author><style face="normal" font="default" size="100%">Montgomery, Stephen B</style></author><author><style face="normal" font="default" size="100%">Battle, Alexis J</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author><author><style face="normal" font="default" size="100%">Guigo, Roderic</style></author><author><style face="normal" font="default" size="100%">Stranger, Barbara E</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">GTEx Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">The impact of sex on gene expression across human tissues.</style></title><secondary-title><style face="normal" font="default" size="100%">Science</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Science</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chromosomes, Human, X</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Epigenesis, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Organ Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Sex Characteristics</style></keyword><keyword><style  face="normal" font="default" size="100%">Sex Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 09 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">369</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6509</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32913072?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bick, Alexander G</style></author><author><style face="normal" font="default" size="100%">Weinstock, Joshua S</style></author><author><style face="normal" font="default" size="100%">Nandakumar, Satish K</style></author><author><style face="normal" font="default" size="100%">Fulco, Charles P</style></author><author><style face="normal" font="default" size="100%">Bao, Erik L</style></author><author><style face="normal" font="default" size="100%">Zekavat, Seyedeh M</style></author><author><style face="normal" font="default" size="100%">Szeto, Mindy D</style></author><author><style face="normal" font="default" size="100%">Liao, Xiaotian</style></author><author><style face="normal" font="default" size="100%">Leventhal, Matthew J</style></author><author><style face="normal" font="default" size="100%">Nasser, Joseph</style></author><author><style face="normal" font="default" size="100%">Chang, Kyle</style></author><author><style face="normal" font="default" size="100%">Laurie, Cecelia</style></author><author><style face="normal" font="default" size="100%">Burugula, Bala Bharathi</style></author><author><style face="normal" font="default" size="100%">Gibson, Christopher J</style></author><author><style face="normal" font="default" size="100%">Lin, Amy E</style></author><author><style face="normal" font="default" size="100%">Taub, Margaret A</style></author><author><style face="normal" font="default" size="100%">Aguet, François</style></author><author><style face="normal" font="default" size="100%">Ardlie, Kristin</style></author><author><style face="normal" font="default" size="100%">Mitchell, Braxton D</style></author><author><style face="normal" font="default" size="100%">Barnes, Kathleen C</style></author><author><style face="normal" font="default" size="100%">Moscati, Arden</style></author><author><style face="normal" font="default" size="100%">Fornage, Myriam</style></author><author><style face="normal" font="default" size="100%">Redline, Susan</style></author><author><style face="normal" font="default" size="100%">Psaty, Bruce M</style></author><author><style face="normal" font="default" size="100%">Silverman, Edwin K</style></author><author><style face="normal" font="default" size="100%">Weiss, Scott T</style></author><author><style face="normal" font="default" size="100%">Palmer, Nicholette D</style></author><author><style face="normal" font="default" size="100%">Vasan, Ramachandran S</style></author><author><style face="normal" font="default" size="100%">Burchard, Esteban G</style></author><author><style face="normal" font="default" size="100%">Kardia, Sharon L R</style></author><author><style face="normal" font="default" size="100%">He, Jiang</style></author><author><style face="normal" font="default" size="100%">Kaplan, Robert C</style></author><author><style face="normal" font="default" size="100%">Smith, Nicholas L</style></author><author><style face="normal" font="default" size="100%">Arnett, Donna K</style></author><author><style face="normal" font="default" size="100%">Schwartz, David A</style></author><author><style face="normal" font="default" size="100%">Correa, Adolfo</style></author><author><style face="normal" font="default" size="100%">de Andrade, Mariza</style></author><author><style face="normal" font="default" size="100%">Guo, Xiuqing</style></author><author><style face="normal" font="default" size="100%">Konkle, Barbara A</style></author><author><style face="normal" font="default" size="100%">Custer, Brian</style></author><author><style face="normal" font="default" size="100%">Peralta, Juan M</style></author><author><style face="normal" font="default" size="100%">Gui, Hongsheng</style></author><author><style face="normal" font="default" size="100%">Meyers, Deborah A</style></author><author><style face="normal" font="default" size="100%">McGarvey, Stephen T</style></author><author><style face="normal" font="default" size="100%">Chen, Ida Yii-Der</style></author><author><style face="normal" font="default" size="100%">Shoemaker, M Benjamin</style></author><author><style face="normal" font="default" size="100%">Peyser, Patricia A</style></author><author><style face="normal" font="default" size="100%">Broome, Jai G</style></author><author><style face="normal" font="default" size="100%">Gogarten, Stephanie M</style></author><author><style face="normal" font="default" size="100%">Wang, Fei Fei</style></author><author><style face="normal" font="default" size="100%">Wong, Quenna</style></author><author><style face="normal" font="default" size="100%">Montasser, May E</style></author><author><style face="normal" font="default" size="100%">Daya, Michelle</style></author><author><style face="normal" font="default" size="100%">Kenny, Eimear E</style></author><author><style face="normal" font="default" size="100%">North, Kari E</style></author><author><style face="normal" font="default" size="100%">Launer, Lenore J</style></author><author><style face="normal" font="default" size="100%">Cade, Brian E</style></author><author><style face="normal" font="default" size="100%">Bis, Joshua C</style></author><author><style face="normal" font="default" size="100%">Cho, Michael H</style></author><author><style face="normal" font="default" size="100%">Lasky-Su, Jessica</style></author><author><style face="normal" font="default" size="100%">Bowden, Donald W</style></author><author><style face="normal" font="default" size="100%">Cupples, L Adrienne</style></author><author><style face="normal" font="default" size="100%">Mak, Angel C Y</style></author><author><style face="normal" font="default" size="100%">Becker, Lewis C</style></author><author><style face="normal" font="default" size="100%">Smith, Jennifer A</style></author><author><style face="normal" font="default" size="100%">Kelly, Tanika N</style></author><author><style face="normal" font="default" size="100%">Aslibekyan, Stella</style></author><author><style face="normal" font="default" size="100%">Heckbert, Susan R</style></author><author><style face="normal" font="default" size="100%">Tiwari, Hemant K</style></author><author><style face="normal" font="default" size="100%">Yang, Ivana V</style></author><author><style face="normal" font="default" size="100%">Heit, John A</style></author><author><style face="normal" font="default" size="100%">Lubitz, Steven A</style></author><author><style face="normal" font="default" size="100%">Johnsen, Jill M</style></author><author><style face="normal" font="default" size="100%">Curran, Joanne E</style></author><author><style face="normal" font="default" size="100%">Wenzel, Sally E</style></author><author><style face="normal" font="default" size="100%">Weeks, Daniel E</style></author><author><style face="normal" font="default" size="100%">Rao, Dabeeru C</style></author><author><style face="normal" font="default" size="100%">Darbar, Dawood</style></author><author><style face="normal" font="default" size="100%">Moon, Jee-Young</style></author><author><style face="normal" font="default" size="100%">Tracy, Russell P</style></author><author><style face="normal" font="default" size="100%">Buth, Erin J</style></author><author><style face="normal" font="default" size="100%">Rafaels, Nicholas</style></author><author><style face="normal" font="default" size="100%">Loos, Ruth J F</style></author><author><style face="normal" font="default" size="100%">Durda, Peter</style></author><author><style face="normal" font="default" size="100%">Liu, Yongmei</style></author><author><style face="normal" font="default" size="100%">Hou, Lifang</style></author><author><style face="normal" font="default" size="100%">Lee, Jiwon</style></author><author><style face="normal" font="default" size="100%">Kachroo, Priyadarshini</style></author><author><style face="normal" font="default" size="100%">Freedman, Barry I</style></author><author><style face="normal" font="default" size="100%">Levy, Daniel</style></author><author><style face="normal" font="default" size="100%">Bielak, Lawrence F</style></author><author><style face="normal" font="default" size="100%">Hixson, James E</style></author><author><style face="normal" font="default" size="100%">Floyd, James S</style></author><author><style face="normal" font="default" size="100%">Whitsel, Eric A</style></author><author><style face="normal" font="default" size="100%">Ellinor, Patrick T</style></author><author><style face="normal" font="default" size="100%">Irvin, Marguerite R</style></author><author><style face="normal" font="default" size="100%">Fingerlin, Tasha E</style></author><author><style face="normal" font="default" size="100%">Raffield, Laura M</style></author><author><style face="normal" font="default" size="100%">Armasu, Sebastian M</style></author><author><style face="normal" font="default" size="100%">Wheeler, Marsha M</style></author><author><style face="normal" font="default" size="100%">Sabino, Ester C</style></author><author><style face="normal" font="default" size="100%">Blangero, John</style></author><author><style face="normal" font="default" size="100%">Williams, L Keoki</style></author><author><style face="normal" font="default" size="100%">Levy, Bruce D</style></author><author><style face="normal" font="default" size="100%">Sheu, Wayne Huey-Herng</style></author><author><style face="normal" font="default" size="100%">Roden, Dan M</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Manson, JoAnn E</style></author><author><style face="normal" font="default" size="100%">Mathias, Rasika A</style></author><author><style face="normal" font="default" size="100%">Desai, Pinkal</style></author><author><style face="normal" font="default" size="100%">Taylor, Kent D</style></author><author><style face="normal" font="default" size="100%">Johnson, Andrew D</style></author><author><style face="normal" font="default" size="100%">Auer, Paul L</style></author><author><style face="normal" font="default" size="100%">Kooperberg, Charles</style></author><author><style face="normal" font="default" size="100%">Laurie, Cathy C</style></author><author><style face="normal" font="default" size="100%">Blackwell, Thomas W</style></author><author><style face="normal" font="default" size="100%">Smith, Albert V</style></author><author><style face="normal" font="default" size="100%">Zhao, Hongyu</style></author><author><style face="normal" font="default" size="100%">Lange, Ethan</style></author><author><style face="normal" font="default" size="100%">Lange, Leslie</style></author><author><style face="normal" font="default" size="100%">Rich, Stephen S</style></author><author><style face="normal" font="default" size="100%">Rotter, Jerome I</style></author><author><style face="normal" font="default" size="100%">Wilson, James G</style></author><author><style face="normal" font="default" size="100%">Scheet, Paul</style></author><author><style face="normal" font="default" size="100%">Kitzman, Jacob O</style></author><author><style face="normal" font="default" size="100%">Lander, Eric S</style></author><author><style face="normal" font="default" size="100%">Engreitz, Jesse M</style></author><author><style face="normal" font="default" size="100%">Ebert, Benjamin L</style></author><author><style face="normal" font="default" size="100%">Reiner, Alexander P</style></author><author><style face="normal" font="default" size="100%">Jaiswal, Siddhartha</style></author><author><style face="normal" font="default" size="100%">Abecasis, Gonçalo</style></author><author><style face="normal" font="default" size="100%">Sankaran, Vijay G</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author><author><style face="normal" font="default" size="100%">Natarajan, Pradeep</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">NHLBI Trans-Omics for Precision Medicine Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Inherited causes of clonal haematopoiesis in 97,691 whole genomes.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nature</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Africa</style></keyword><keyword><style  face="normal" font="default" size="100%">African Continental Ancestry Group</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">alpha Karyopherins</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Self Renewal</style></keyword><keyword><style  face="normal" font="default" size="100%">Clonal Hematopoiesis</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA-Binding Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Germ-Line Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Hematopoietic Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Intracellular Signaling Peptides and Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">National Heart, Lung, and Blood Institute (U.S.)</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword><keyword><style  face="normal" font="default" size="100%">Proto-Oncogene Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Tripartite Motif Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">United States</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">586</style></volume><pages><style face="normal" font="default" size="100%">763-768</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer and coronary heart disease-this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP). Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7831</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33057201?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Satterstrom, F Kyle</style></author><author><style face="normal" font="default" size="100%">Kosmicki, Jack A</style></author><author><style face="normal" font="default" size="100%">Wang, Jiebiao</style></author><author><style face="normal" font="default" size="100%">Breen, Michael S</style></author><author><style face="normal" font="default" size="100%">De Rubeis, Silvia</style></author><author><style face="normal" font="default" size="100%">An, Joon-Yong</style></author><author><style face="normal" font="default" size="100%">Peng, Minshi</style></author><author><style face="normal" font="default" size="100%">Collins, Ryan</style></author><author><style face="normal" font="default" size="100%">Grove, Jakob</style></author><author><style face="normal" font="default" size="100%">Klei, Lambertus</style></author><author><style face="normal" font="default" size="100%">Stevens, Christine</style></author><author><style face="normal" font="default" size="100%">Reichert, Jennifer</style></author><author><style face="normal" font="default" size="100%">Mulhern, Maureen S</style></author><author><style face="normal" font="default" size="100%">Artomov, Mykyta</style></author><author><style face="normal" font="default" size="100%">Gerges, Sherif</style></author><author><style face="normal" font="default" size="100%">Sheppard, Brooke</style></author><author><style face="normal" font="default" size="100%">Xu, Xinyi</style></author><author><style face="normal" font="default" size="100%">Bhaduri, Aparna</style></author><author><style face="normal" font="default" size="100%">Norman, Utku</style></author><author><style face="normal" font="default" size="100%">Brand, Harrison</style></author><author><style face="normal" font="default" size="100%">Schwartz, Grace</style></author><author><style face="normal" font="default" size="100%">Nguyen, Rachel</style></author><author><style face="normal" font="default" size="100%">Guerrero, Elizabeth E</style></author><author><style face="normal" font="default" size="100%">Dias, Caroline</style></author><author><style face="normal" font="default" size="100%">Betancur, Catalina</style></author><author><style face="normal" font="default" size="100%">Cook, Edwin H</style></author><author><style face="normal" font="default" size="100%">Gallagher, Louise</style></author><author><style face="normal" font="default" size="100%">Gill, Michael</style></author><author><style face="normal" font="default" size="100%">Sutcliffe, James S</style></author><author><style face="normal" font="default" size="100%">Thurm, Audrey</style></author><author><style face="normal" font="default" size="100%">Zwick, Michael E</style></author><author><style face="normal" font="default" size="100%">Børglum, Anders D</style></author><author><style face="normal" font="default" size="100%">State, Matthew W</style></author><author><style face="normal" font="default" size="100%">Cicek, A Ercument</style></author><author><style face="normal" font="default" size="100%">Talkowski, Michael E</style></author><author><style face="normal" font="default" size="100%">Cutler, David J</style></author><author><style face="normal" font="default" size="100%">Devlin, Bernie</style></author><author><style face="normal" font="default" size="100%">Sanders, Stephan J</style></author><author><style face="normal" font="default" size="100%">Roeder, Kathryn</style></author><author><style face="normal" font="default" size="100%">Daly, Mark J</style></author><author><style face="normal" font="default" size="100%">Buxbaum, Joseph D</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Autism Sequencing Consortium</style></author><author><style face="normal" font="default" size="100%">iPSYCH-Broad Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autistic Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Lineage</style></keyword><keyword><style  face="normal" font="default" size="100%">Cerebral Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Developmental</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation, Missense</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurobiology</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurons</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Sex Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Single-Cell Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Exome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 02 06</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">180</style></volume><pages><style face="normal" font="default" size="100%">568-584.e23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n = 35,584 total samples, 11,986 with ASD). Using an enhanced analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neurodevelopmental delay, whereas 53 show higher frequencies in individuals ascertained to have ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In cells from the human cortex, expression of risk genes is enriched in excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31981491?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mak, Angel C Y</style></author><author><style face="normal" font="default" size="100%">Sajuthi, Satria</style></author><author><style face="normal" font="default" size="100%">Joo, Jaehyun</style></author><author><style face="normal" font="default" size="100%">Xiao, Shujie</style></author><author><style face="normal" font="default" size="100%">Sleiman, Patrick M</style></author><author><style face="normal" font="default" size="100%">White, Marquitta J</style></author><author><style face="normal" font="default" size="100%">Lee, Eunice Y</style></author><author><style face="normal" font="default" size="100%">Saef, Benjamin</style></author><author><style face="normal" font="default" size="100%">Hu, Donglei</style></author><author><style face="normal" font="default" size="100%">Gui, Hongsheng</style></author><author><style face="normal" font="default" size="100%">Keys, Kevin L</style></author><author><style face="normal" font="default" size="100%">Lurmann, Fred</style></author><author><style face="normal" font="default" size="100%">Jain, Deepti</style></author><author><style face="normal" font="default" size="100%">Abecasis, Gonçalo</style></author><author><style face="normal" font="default" size="100%">Kang, Hyun Min</style></author><author><style face="normal" font="default" size="100%">Nickerson, Deborah A</style></author><author><style face="normal" font="default" size="100%">Germer, Soren</style></author><author><style face="normal" font="default" size="100%">Zody, Michael C</style></author><author><style face="normal" font="default" size="100%">Winterkorn, Lara</style></author><author><style face="normal" font="default" size="100%">Reeves, Catherine</style></author><author><style face="normal" font="default" size="100%">Huntsman, Scott</style></author><author><style face="normal" font="default" size="100%">Eng, Celeste</style></author><author><style face="normal" font="default" size="100%">Salazar, Sandra</style></author><author><style face="normal" font="default" size="100%">Oh, Sam S</style></author><author><style face="normal" font="default" size="100%">Gilliland, Frank D</style></author><author><style face="normal" font="default" size="100%">Chen, Zhanghua</style></author><author><style face="normal" font="default" size="100%">Kumar, Rajesh</style></author><author><style face="normal" font="default" size="100%">Martínez, Fernando D</style></author><author><style face="normal" font="default" size="100%">Wu, Ann Chen</style></author><author><style face="normal" font="default" size="100%">Ziv, Elad</style></author><author><style face="normal" font="default" size="100%">Hakonarson, Hakon</style></author><author><style face="normal" font="default" size="100%">Himes, Blanca E</style></author><author><style face="normal" font="default" size="100%">Williams, L Keoki</style></author><author><style face="normal" font="default" size="100%">Seibold, Max A</style></author><author><style face="normal" font="default" size="100%">Burchard, Esteban G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand  and Gene-By-Air-Pollution Interaction.</style></title><secondary-title><style face="normal" font="default" size="100%">Genetics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genetics</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">215</style></volume><pages><style face="normal" font="default" size="100%">869-886</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Baseline lung function, quantified as forced expiratory volume in the first second of exhalation (FEV), is a standard diagnostic criterion used by clinicians to identify and classify lung diseases. Using whole-genome sequencing data from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine project, we identified a novel genetic association with FEV on chromosome 12 in 867 African American children with asthma ( = 1.26 × 10, β = 0.302). Conditional analysis within 1 Mb of the tag signal (rs73429450) yielded one major and two other weaker independent signals within this peak. We explored statistical and functional evidence for all variants in linkage disequilibrium with the three independent signals and yielded nine variants as the most likely candidates responsible for the association with FEV Hi-C data and expression QTL analysis demonstrated that these variants physically interacted with  (KIT ligand, also known as ), and their minor alleles were associated with increased expression of the  gene in nasal epithelial cells. Gene-by-air-pollution interaction analysis found that the candidate variant rs58475486 interacted with past-year ambient sulfur dioxide exposure ( = 0.003, β = 0.32). This study identified a novel protective genetic association with FEV, possibly mediated through , in African American children with asthma. This is the first study that has identified a genetic association between lung function and , which has established a role in orchestrating allergic inflammation in asthma.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32327564?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alonge, Michael</style></author><author><style face="normal" font="default" size="100%">Wang, Xingang</style></author><author><style face="normal" font="default" size="100%">Benoit, Matthias</style></author><author><style face="normal" font="default" size="100%">Soyk, Sebastian</style></author><author><style face="normal" font="default" size="100%">Pereira, Lara</style></author><author><style face="normal" font="default" size="100%">Zhang, Lei</style></author><author><style face="normal" font="default" size="100%">Suresh, Hamsini</style></author><author><style face="normal" font="default" size="100%">Ramakrishnan, Srividya</style></author><author><style face="normal" font="default" size="100%">Maumus, Florian</style></author><author><style face="normal" font="default" size="100%">Ciren, Danielle</style></author><author><style face="normal" font="default" size="100%">Levy, Yuval</style></author><author><style face="normal" font="default" size="100%">Harel, Tom Hai</style></author><author><style face="normal" font="default" size="100%">Shalev-Schlosser, Gili</style></author><author><style face="normal" font="default" size="100%">Amsellem, Ziva</style></author><author><style face="normal" font="default" size="100%">Razifard, Hamid</style></author><author><style face="normal" font="default" size="100%">Caicedo, Ana L</style></author><author><style face="normal" font="default" size="100%">Tieman, Denise M</style></author><author><style face="normal" font="default" size="100%">Klee, Harry</style></author><author><style face="normal" font="default" size="100%">Kirsche, Melanie</style></author><author><style face="normal" font="default" size="100%">Aganezov, Sergey</style></author><author><style face="normal" font="default" size="100%">Ranallo-Benavidez, T Rhyker</style></author><author><style face="normal" font="default" size="100%">Lemmon, Zachary H</style></author><author><style face="normal" font="default" size="100%">Kim, Jennifer</style></author><author><style face="normal" font="default" size="100%">Robitaille, Gina</style></author><author><style face="normal" font="default" size="100%">Kramer, Melissa</style></author><author><style face="normal" font="default" size="100%">Goodwin, Sara</style></author><author><style face="normal" font="default" size="100%">McCombie, W Richard</style></author><author><style face="normal" font="default" size="100%">Hutton, Samuel</style></author><author><style face="normal" font="default" size="100%">Van Eck, Joyce</style></author><author><style face="normal" font="default" size="100%">Gillis, Jesse</style></author><author><style face="normal" font="default" size="100%">Eshed, Yuval</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author><author><style face="normal" font="default" size="100%">van der Knaap, Esther</style></author><author><style face="normal" font="default" size="100%">Schatz, Michael C</style></author><author><style face="normal" font="default" size="100%">Lippman, Zachary B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Crops, Agricultural</style></keyword><keyword><style  face="normal" font="default" size="100%">Cytochrome P-450 Enzyme System</style></keyword><keyword><style  face="normal" font="default" size="100%">Ecotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Epistasis, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Fruit</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Duplication</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomic Structural Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Inbreeding</style></keyword><keyword><style  face="normal" font="default" size="100%">Lycopersicon esculentum</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Breeding</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07 09</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">182</style></volume><pages><style face="normal" font="default" size="100%">145-161.e23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Structural variants (SVs) underlie important crop improvement and domestication traits. However, resolving the extent, diversity, and quantitative impact of SVs has been challenging. We used long-read nanopore sequencing to capture 238,490 SVs in 100 diverse tomato lines. This panSV genome, along with 14 new reference assemblies, revealed large-scale intermixing of diverse genotypes, as well as thousands of SVs intersecting genes and cis-regulatory regions. Hundreds of SV-gene pairs exhibit subtle and significant expression changes, which could broadly influence quantitative trait variation. By combining quantitative genetics with genome editing, we show how multiple SVs that changed gene dosage and expression levels modified fruit flavor, size, and production. In the last example, higher order epistasis among four SVs affecting three related transcription factors allowed introduction of an important harvesting trait in modern tomato. Our findings highlight the underexplored role of SVs in genotype-to-phenotype relationships and their widespread importance and utility in crop improvement.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32553272?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abel, Haley J</style></author><author><style face="normal" font="default" size="100%">Larson, David E</style></author><author><style face="normal" font="default" size="100%">Regier, Allison A</style></author><author><style face="normal" font="default" size="100%">Chiang, Colby</style></author><author><style face="normal" font="default" size="100%">Das, Indraniel</style></author><author><style face="normal" font="default" size="100%">Kanchi, Krishna L</style></author><author><style face="normal" font="default" size="100%">Layer, Ryan M</style></author><author><style face="normal" font="default" size="100%">Neale, Benjamin M</style></author><author><style face="normal" font="default" size="100%">Salerno, William J</style></author><author><style face="normal" font="default" size="100%">Reeves, Catherine</style></author><author><style face="normal" font="default" size="100%">Buyske, Steven</style></author><author><style face="normal" font="default" size="100%">Matise, Tara C</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna M</style></author><author><style face="normal" font="default" size="100%">Zody, Michael C</style></author><author><style face="normal" font="default" size="100%">Lander, Eric S</style></author><author><style face="normal" font="default" size="100%">Dutcher, Susan K</style></author><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author><author><style face="normal" font="default" size="100%">Hall, Ira M</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">NHGRI Centers for Common Disease Genomics</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Mapping and characterization of structural variation in 17,795 human genomes.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nature</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Continental Population Groups</style></keyword><keyword><style  face="normal" font="default" size="100%">Epigenesis, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Dosage</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Population</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">583</style></volume><pages><style face="normal" font="default" size="100%">83-89</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7814</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32460305?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Emdin, Connor A</style></author><author><style face="normal" font="default" size="100%">Haas, Mary E</style></author><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author><author><style face="normal" font="default" size="100%">Aragam, Krishna</style></author><author><style face="normal" font="default" size="100%">Chaffin, Mark</style></author><author><style face="normal" font="default" size="100%">Klarin, Derek</style></author><author><style face="normal" font="default" size="100%">Hindy, George</style></author><author><style face="normal" font="default" size="100%">Jiang, Lan</style></author><author><style face="normal" font="default" size="100%">Wei, Wei-Qi</style></author><author><style face="normal" font="default" size="100%">Feng, Qiping</style></author><author><style face="normal" font="default" size="100%">Karjalainen, Juha</style></author><author><style face="normal" font="default" size="100%">Havulinna, Aki</style></author><author><style face="normal" font="default" size="100%">Kiiskinen, Tuomo</style></author><author><style face="normal" font="default" size="100%">Bick, Alexander</style></author><author><style face="normal" font="default" size="100%">Ardissino, Diego</style></author><author><style face="normal" font="default" size="100%">Wilson, James G</style></author><author><style face="normal" font="default" size="100%">Schunkert, Heribert</style></author><author><style face="normal" font="default" size="100%">McPherson, Ruth</style></author><author><style face="normal" font="default" size="100%">Watkins, Hugh</style></author><author><style face="normal" font="default" size="100%">Elosua, Roberto</style></author><author><style face="normal" font="default" size="100%">Bown, Matthew J</style></author><author><style face="normal" font="default" size="100%">Samani, Nilesh J</style></author><author><style face="normal" font="default" size="100%">Baber, Usman</style></author><author><style face="normal" font="default" size="100%">Erdmann, Jeanette</style></author><author><style face="normal" font="default" size="100%">Gupta, Namrata</style></author><author><style face="normal" font="default" size="100%">Danesh, John</style></author><author><style face="normal" font="default" size="100%">Saleheen, Danish</style></author><author><style face="normal" font="default" size="100%">Chang, Kyong-Mi</style></author><author><style face="normal" font="default" size="100%">Vujkovic, Marijana</style></author><author><style face="normal" font="default" size="100%">Voight, Ben</style></author><author><style face="normal" font="default" size="100%">Damrauer, Scott</style></author><author><style face="normal" font="default" size="100%">Lynch, Julie</style></author><author><style face="normal" font="default" size="100%">Kaplan, David</style></author><author><style face="normal" font="default" size="100%">Serper, Marina</style></author><author><style face="normal" font="default" size="100%">Tsao, Philip</style></author><author><style face="normal" font="default" size="100%">Mercader, Josep</style></author><author><style face="normal" font="default" size="100%">Hanis, Craig</style></author><author><style face="normal" font="default" size="100%">Daly, Mark</style></author><author><style face="normal" font="default" size="100%">Denny, Joshua</style></author><author><style face="normal" font="default" size="100%">Gabriel, Stacey</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Million Veteran Program</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">A missense variant in Mitochondrial Amidoxime Reducing Component 1 gene and protection against liver disease.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol, LDL</style></keyword><keyword><style  face="normal" font="default" size="100%">Coronary Artery Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Datasets as Topic</style></keyword><keyword><style  face="normal" font="default" size="100%">Fatty Liver</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Homozygote</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Liver</style></keyword><keyword><style  face="normal" font="default" size="100%">Liver Cirrhosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Liver Cirrhosis, Alcoholic</style></keyword><keyword><style  face="normal" font="default" size="100%">Loss of Function Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Mitochondrial Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation, Missense</style></keyword><keyword><style  face="normal" font="default" size="100%">Oxidoreductases</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 04</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">e1008629</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Analyzing 12,361 all-cause cirrhosis cases and 790,095 controls from eight cohorts, we identify a common missense variant in the Mitochondrial Amidoxime Reducing Component 1 gene (MARC1 p.A165T) that associates with protection from all-cause cirrhosis (OR 0.91, p = 2.3*10-11). This same variant also associates with lower levels of hepatic fat on computed tomographic imaging and lower odds of physician-diagnosed fatty liver as well as lower blood levels of alanine transaminase (-0.025 SD, 3.7*10-43), alkaline phosphatase (-0.025 SD, 1.2*10-37), total cholesterol (-0.030 SD, p = 1.9*10-36) and LDL cholesterol (-0.027 SD, p = 5.1*10-30) levels. We identified a series of additional MARC1 alleles (low-frequency missense p.M187K and rare protein-truncating p.R200Ter) that also associated with lower cholesterol levels, liver enzyme levels and reduced risk of cirrhosis (0 cirrhosis cases for 238 R200Ter carriers versus 17,046 cases of cirrhosis among 759,027 non-carriers, p = 0.04) suggesting that deficiency of the MARC1 enzyme may lower blood cholesterol levels and protect against cirrhosis.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32282858?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yin, Jiani</style></author><author><style face="normal" font="default" size="100%">Chun, Chun-An</style></author><author><style face="normal" font="default" size="100%">Zavadenko, Nikolay N</style></author><author><style face="normal" font="default" size="100%">Pechatnikova, Natalia L</style></author><author><style face="normal" font="default" size="100%">Naumova, Oxana Yu</style></author><author><style face="normal" font="default" size="100%">Doddapaneni, Harsha V</style></author><author><style face="normal" font="default" size="100%">Hu, Jianhong</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna M</style></author><author><style face="normal" font="default" size="100%">Schaaf, Christian P</style></author><author><style face="normal" font="default" size="100%">Grigorenko, Elena L</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Next Generation Sequencing of 134 Children with Autism Spectrum Disorder and Regression.</style></title><secondary-title><style face="normal" font="default" size="100%">Genes (Basel)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genes (Basel)</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autism Spectrum Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Progression</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Markers</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07 25</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Approximately 30% of individuals with autism spectrum disorder (ASD) experience developmental regression, the etiology of which remains largely unknown. We performed a complete literature search and identified 47 genes that had been implicated in such cases. We sequenced these genes in a preselected cohort of 134 individuals with regressive autism. In total, 16 variants in 12 genes with evidence supportive of pathogenicity were identified. They were classified as variants of uncertain significance based on ACMG standards and guidelines. Among these were recurring variants in  and , variants in genes that were linked to syndromic forms of ASD (, , , , , and ), and variants in the form of oligogenic heterozygosity (, , and ).&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32722525?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Grenn, Francis P</style></author><author><style face="normal" font="default" size="100%">Kim, Jonggeol J</style></author><author><style face="normal" font="default" size="100%">Makarious, Mary B</style></author><author><style face="normal" font="default" size="100%">Iwaki, Hirotaka</style></author><author><style face="normal" font="default" size="100%">Illarionova, Anastasia</style></author><author><style face="normal" font="default" size="100%">Brolin, Kajsa</style></author><author><style face="normal" font="default" size="100%">Kluss, Jillian H</style></author><author><style face="normal" font="default" size="100%">Schumacher-Schuh, Artur F</style></author><author><style face="normal" font="default" size="100%">Leonard, Hampton</style></author><author><style face="normal" font="default" size="100%">Faghri, Faraz</style></author><author><style face="normal" font="default" size="100%">Billingsley, Kimberley</style></author><author><style face="normal" font="default" size="100%">Krohn, Lynne</style></author><author><style face="normal" font="default" size="100%">Hall, Ashley</style></author><author><style face="normal" font="default" size="100%">Diez-Fairen, Monica</style></author><author><style face="normal" font="default" size="100%">Periñán, Maria Teresa</style></author><author><style face="normal" font="default" size="100%">Foo, Jia Nee</style></author><author><style face="normal" font="default" size="100%">Sandor, Cynthia</style></author><author><style face="normal" font="default" size="100%">Webber, Caleb</style></author><author><style face="normal" font="default" size="100%">Fiske, Brian K</style></author><author><style face="normal" font="default" size="100%">Gibbs, J Raphael</style></author><author><style face="normal" font="default" size="100%">Nalls, Mike A</style></author><author><style face="normal" font="default" size="100%">Singleton, Andrew B</style></author><author><style face="normal" font="default" size="100%">Bandres-Ciga, Sara</style></author><author><style face="normal" font="default" size="100%">Reed, Xylena</style></author><author><style face="normal" font="default" size="100%">Blauwendraat, Cornelis</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">International Parkinson's Disease Genomics Consortium (IPDGC)</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">The Parkinson's Disease Genome-Wide Association Study Locus Browser.</style></title><secondary-title><style face="normal" font="default" size="100%">Mov Disord</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mov Disord</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Age of Onset</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurodegenerative Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Parkinson Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">2056-2067</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome-wide association studies. The most recent large-scale PD genome-wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome-wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We included all significant genome-wide signals from multiple recent PD genome-wide association studies including themost recent PD risk genome-wide association study, age-at-onset genome-wide association study, progression genome-wide association study, and Asian population PD risk genome-wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self-ranked criteria. Multiple databases were queried for each gene to collect additional causal data.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We created a PD genome-wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow-up functional studies to identify potential therapeutic targets.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Our PD genome-wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large-scale PD genome-wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32864809?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zarate, Samantha</style></author><author><style face="normal" font="default" size="100%">Carroll, Andrew</style></author><author><style face="normal" font="default" size="100%">Mahmoud, Medhat</style></author><author><style face="normal" font="default" size="100%">Krasheninina, Olga</style></author><author><style face="normal" font="default" size="100%">Jun, Goo</style></author><author><style face="normal" font="default" size="100%">Salerno, William J</style></author><author><style face="normal" font="default" size="100%">Schatz, Michael C</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parliament2: Accurate structural variant calling at scale.</style></title><secondary-title><style face="normal" font="default" size="100%">Gigascience</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Gigascience</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Structural variants (SVs) are critical contributors to genetic diversity and genomic disease. To predict the phenotypic impact of SVs, there is a need for better estimates of both the occurrence and frequency of SVs, preferably from large, ethnically diverse cohorts. Thus, the current standard approach requires the use of short paired-end reads, which remain challenging to detect, especially at the scale of hundreds to thousands of samples.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FINDINGS: &lt;/b&gt;We present Parliament2, a consensus SV framework that leverages multiple best-in-class methods to identify high-quality SVs from short-read DNA sequence data at scale. Parliament2 incorporates pre-installed SV callers that are optimized for efficient execution in parallel to reduce the overall runtime and costs. We demonstrate the accuracy of Parliament2 when applied to data from NovaSeq and HiSeq X platforms with the Genome in a Bottle (GIAB) SV call set across all size classes. The reported quality score per SV is calibrated across different SV types and size classes. Parliament2 has the highest F1 score (74.27%) measured across the independent gold standard from GIAB. We illustrate the compute performance by processing all 1000 Genomes samples (2,691 samples) in &lt;1 day on GRCH38. Parliament2 improves the runtime performance of individual methods and is open source (https://github.com/slzarate/parliament2), and a Docker image, as well as a WDL implementation, is available.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Parliament2 provides both a highly accurate single-sample SV call set from short-read DNA sequence data and enables cost-efficient application over cloud or cluster environments, processing thousands of samples.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33347570?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Majidian, Sina</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PhaseME: Automatic rapid assessment of phasing quality and phasing improvement.</style></title><secondary-title><style face="normal" font="default" size="100%">Gigascience</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Gigascience</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The detection of which mutations are occurring on the same DNA molecule is essential to predict their consequences. This can be achieved by phasing the genomic variations. Nevertheless, state-of-the-art haplotype phasing is currently a black box in which the accuracy and quality of the reconstructed haplotypes are hard to assess.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FINDINGS: &lt;/b&gt;Here we present PhaseME, a versatile method to provide insights into and improvement of sample phasing results based on linkage data. We showcase the performance and the importance of PhaseME by comparing phasing information obtained from Pacific Biosciences including both continuous long reads and high-quality consensus reads, Oxford Nanopore Technologies, 10x Genomics, and Illumina sequencing technologies. We found that 10x Genomics and Oxford Nanopore phasing can be significantly improved while retaining a high N50 and completeness of phase blocks. PhaseME generates reports and summary plots to provide insights into phasing performance and correctness. We observed unique phasing issues for each of the sequencing technologies, highlighting the necessity of quality assessments. PhaseME is able to decrease the Hamming error rate significantly by 22.4% on average across all 5 technologies. Additionally, a significant improvement is obtained in the reduction of long switch errors. Especially for high-quality consensus reads, the improvement is 54.6% in return for only a 5% decrease in phase block N50 length.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;PhaseME is a universal method to assess the phasing quality and accuracy and improves the quality of phasing using linkage information. The package is freely available at https://github.com/smajidian/phaseme.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32706368?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Montenegro-Garreaud, Ximena</style></author><author><style face="normal" font="default" size="100%">Hansen, Adam W</style></author><author><style face="normal" font="default" size="100%">Khayat, Michael M</style></author><author><style face="normal" font="default" size="100%">Chander, Varuna</style></author><author><style face="normal" font="default" size="100%">Grochowski, Christopher M</style></author><author><style face="normal" font="default" size="100%">Jiang, Yunyun</style></author><author><style face="normal" font="default" size="100%">Li, He</style></author><author><style face="normal" font="default" size="100%">Mitani, Tadahiro</style></author><author><style face="normal" font="default" size="100%">Kessler, Elena</style></author><author><style face="normal" font="default" size="100%">Jayaseelan, Joy</style></author><author><style face="normal" font="default" size="100%">Shen, Hua</style></author><author><style face="normal" font="default" size="100%">Gezdirici, Alper</style></author><author><style face="normal" font="default" size="100%">Pehlivan, Davut</style></author><author><style face="normal" font="default" size="100%">Meng, Qingchang</style></author><author><style face="normal" font="default" size="100%">Rosenfeld, Jill A</style></author><author><style face="normal" font="default" size="100%">Jhangiani, Shalini N</style></author><author><style face="normal" font="default" size="100%">Madan-Khetarpal, Suneeta</style></author><author><style face="normal" font="default" size="100%">Scott, Daryl A</style></author><author><style face="normal" font="default" size="100%">Abarca-Barriga, Hugo</style></author><author><style face="normal" font="default" size="100%">Trubnykova, Milana</style></author><author><style face="normal" font="default" size="100%">Gingras, Marie-Claude</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna M</style></author><author><style face="normal" font="default" size="100%">Posey, Jennifer E</style></author><author><style face="normal" font="default" size="100%">Liu, Pengfei</style></author><author><style face="normal" font="default" size="100%">Lupski, James R</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phenotypic expansion in KIF1A-related dominant disorders: A description of novel variants and review of published cases.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mutat</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mutat</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">2094-2104</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;KIF1A is a molecular motor for membrane-bound cargo important to the development and survival of sensory neurons. KIF1A dysfunction has been associated with several Mendelian disorders with a spectrum of overlapping phenotypes, ranging from spastic paraplegia to intellectual disability. We present a novel pathogenic in-frame deletion in the KIF1A molecular motor domain inherited by two affected siblings from an unaffected mother with apparent germline mosaicism. We identified eight additional cases with heterozygous, pathogenic KIF1A variants ascertained from a local data lake. Our data provide evidence for the expansion of KIF1A-associated phenotypes to include hip subluxation and dystonia as well as phenotypes observed in only a single case: gelastic cataplexy, coxa valga, and double collecting system. We review the literature and suggest that KIF1A dysfunction is better understood as a single neuromuscular disorder with variable involvement of other organ systems than a set of discrete disorders converging at a single locus.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32935419?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chen, Guo-Chong</style></author><author><style face="normal" font="default" size="100%">Chai, Jin Choul</style></author><author><style face="normal" font="default" size="100%">Yu, Bing</style></author><author><style face="normal" font="default" size="100%">Michelotti, Gregory A</style></author><author><style face="normal" font="default" size="100%">Grove, Megan L</style></author><author><style face="normal" font="default" size="100%">Fretts, Amanda M</style></author><author><style face="normal" font="default" size="100%">Daviglus, Martha L</style></author><author><style face="normal" font="default" size="100%">Garcia-Bedoya, Olga L</style></author><author><style face="normal" font="default" size="100%">Thyagarajan, Bharat</style></author><author><style face="normal" font="default" size="100%">Schneiderman, Neil</style></author><author><style face="normal" font="default" size="100%">Cai, Jianwen</style></author><author><style face="normal" font="default" size="100%">Kaplan, Robert C</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Qi, Qibin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Serum sphingolipids and incident diabetes in a US population with high diabetes burden: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Clin Nutr</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am J Clin Nutr</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Hispanic Americans</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Prospective Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Sphingolipids</style></keyword><keyword><style  face="normal" font="default" size="100%">United States</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">112</style></volume><pages><style face="normal" font="default" size="100%">57-65</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Genetic or pharmacological inhibition of de novo sphingolipid synthases prevented diabetes in animal studies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;We sought to evaluate prospective associations of serum sphingolipids with incident diabetes in a population-based cohort.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We included 2010 participants of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) aged 18-74 y who were free of diabetes and other major chronic diseases at baseline (2008-2011). Metabolomic profiling of fasting serum was performed using a global, untargeted approach. A total of 43 sphingolipids were quantified and, considering subclasses and chemical structures of individual species, 6 sphingolipid scores were constructed. Diabetes status was assessed using standard procedures including blood tests. Multivariable survey Poisson regressions were applied to estimate RR and 95% CI of incident diabetes associated with individual sphingolipids or sphingolipid scores.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;There were 224 incident cases of diabetes identified during, on average, 6 y of follow-up. After adjustment for socioeconomic and lifestyle factors, a ceramide score (RR Q4 versus Q1 = 2.40; 95% CI: 1.24, 4.65; P-trend = 0.003) and a score of sphingomyelins with fully saturated sphingoid-fatty acid pairs (RR Q4 versus Q1 = 3.15; 95% CI: 1.75, 5.67; P-trend &lt;0.001) both were positively associated with risk of diabetes, whereas scores of glycosylceramides, lactosylceramides, or other unsaturated sphingomyelins (even if having an SFA base) were not associated with risk of diabetes. After additional adjustment for numerous traditional risk factors (especially triglycerides), both associations were attenuated and only the saturated-sphingomyelin score remained associated with risk of diabetes (RR Q4 versus Q1 = 1.98; 95% CI: 1.09, 3.59; P-trend = 0.031).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Our findings suggest that a cluster of saturated sphingomyelins may be associated with elevated risk of diabetes beyond traditional risk factors, which needs to be verified in other population studies. This study was registered at clinicaltrials.gov as NCT02060344.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32469399?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Collins, Ryan L</style></author><author><style face="normal" font="default" size="100%">Brand, Harrison</style></author><author><style face="normal" font="default" size="100%">Karczewski, Konrad J</style></author><author><style face="normal" font="default" size="100%">Zhao, Xuefang</style></author><author><style face="normal" font="default" size="100%">Alföldi, Jessica</style></author><author><style face="normal" font="default" size="100%">Francioli, Laurent C</style></author><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author><author><style face="normal" font="default" size="100%">Lowther, Chelsea</style></author><author><style face="normal" font="default" size="100%">Gauthier, Laura D</style></author><author><style face="normal" font="default" size="100%">Wang, Harold</style></author><author><style face="normal" font="default" size="100%">Watts, Nicholas A</style></author><author><style face="normal" font="default" size="100%">Solomonson, Matthew</style></author><author><style face="normal" font="default" size="100%">O'Donnell-Luria, Anne</style></author><author><style face="normal" font="default" size="100%">Baumann, Alexander</style></author><author><style face="normal" font="default" size="100%">Munshi, Ruchi</style></author><author><style face="normal" font="default" size="100%">Walker, Mark</style></author><author><style face="normal" font="default" size="100%">Whelan, Christopher W</style></author><author><style face="normal" font="default" size="100%">Huang, Yongqing</style></author><author><style face="normal" font="default" size="100%">Brookings, Ted</style></author><author><style face="normal" font="default" size="100%">Sharpe, Ted</style></author><author><style face="normal" font="default" size="100%">Stone, Matthew R</style></author><author><style face="normal" font="default" size="100%">Valkanas, Elise</style></author><author><style face="normal" font="default" size="100%">Fu, Jack</style></author><author><style face="normal" font="default" size="100%">Tiao, Grace</style></author><author><style face="normal" font="default" size="100%">Laricchia, Kristen M</style></author><author><style face="normal" font="default" size="100%">Ruano-Rubio, Valentin</style></author><author><style face="normal" font="default" size="100%">Stevens, Christine</style></author><author><style face="normal" font="default" size="100%">Gupta, Namrata</style></author><author><style face="normal" font="default" size="100%">Cusick, Caroline</style></author><author><style face="normal" font="default" size="100%">Margolin, Lauren</style></author><author><style face="normal" font="default" size="100%">Taylor, Kent D</style></author><author><style face="normal" font="default" size="100%">Lin, Henry J</style></author><author><style face="normal" font="default" size="100%">Rich, Stephen S</style></author><author><style face="normal" font="default" size="100%">Post, Wendy S</style></author><author><style face="normal" font="default" size="100%">Chen, Yii-Der Ida</style></author><author><style face="normal" font="default" size="100%">Rotter, Jerome I</style></author><author><style face="normal" font="default" size="100%">Nusbaum, Chad</style></author><author><style face="normal" font="default" size="100%">Philippakis, Anthony</style></author><author><style face="normal" font="default" size="100%">Lander, Eric</style></author><author><style face="normal" font="default" size="100%">Gabriel, Stacey</style></author><author><style face="normal" font="default" size="100%">Neale, Benjamin M</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author><author><style face="normal" font="default" size="100%">Daly, Mark J</style></author><author><style face="normal" font="default" size="100%">Banks, Eric</style></author><author><style face="normal" font="default" size="100%">MacArthur, Daniel G</style></author><author><style face="normal" font="default" size="100%">Talkowski, Michael E</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Genome Aggregation Database Production Team</style></author><author><style face="normal" font="default" size="100%">Genome Aggregation Database Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">A structural variation reference for medical and population genetics.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nature</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Continental Population Groups</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Testing</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Medical</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Population</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotyping Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Reference Standards</style></keyword><keyword><style  face="normal" font="default" size="100%">Selection, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 05</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">581</style></volume><pages><style face="normal" font="default" size="100%">444-451</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Structural variants (SVs) rearrange large segments of DNA and can have profound consequences in evolution and human disease. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) have become integral in the interpretation of single-nucleotide variants (SNVs). However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25-29% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9% of samples, and estimate that 0.13% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings. This SV resource is freely distributed via the gnomAD browser and will have broad utility in population genetics, disease-association studies, and diagnostic screening.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7809</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32461652?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ferraro, Nicole M</style></author><author><style face="normal" font="default" size="100%">Strober, Benjamin J</style></author><author><style face="normal" font="default" size="100%">Einson, Jonah</style></author><author><style face="normal" font="default" size="100%">Abell, Nathan S</style></author><author><style face="normal" font="default" size="100%">Aguet, François</style></author><author><style face="normal" font="default" size="100%">Barbeira, Alvaro N</style></author><author><style face="normal" font="default" size="100%">Brandt, Margot</style></author><author><style face="normal" font="default" size="100%">Bucan, Maja</style></author><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Davis, Joe R</style></author><author><style face="normal" font="default" size="100%">Greenwald, Emily</style></author><author><style face="normal" font="default" size="100%">Hess, Gaelen T</style></author><author><style face="normal" font="default" size="100%">Hilliard, Austin T</style></author><author><style face="normal" font="default" size="100%">Kember, Rachel L</style></author><author><style face="normal" font="default" size="100%">Kotis, Bence</style></author><author><style face="normal" font="default" size="100%">Park, YoSon</style></author><author><style face="normal" font="default" size="100%">Peloso, Gina</style></author><author><style face="normal" font="default" size="100%">Ramdas, Shweta</style></author><author><style face="normal" font="default" size="100%">Scott, Alexandra J</style></author><author><style face="normal" font="default" size="100%">Smail, Craig</style></author><author><style face="normal" font="default" size="100%">Tsang, Emily K</style></author><author><style face="normal" font="default" size="100%">Zekavat, Seyedeh M</style></author><author><style face="normal" font="default" size="100%">Ziosi, Marcello</style></author><author><style face="normal" font="default" size="100%">Ardlie, Kristin G</style></author><author><style face="normal" font="default" size="100%">Assimes, Themistocles L</style></author><author><style face="normal" font="default" size="100%">Bassik, Michael C</style></author><author><style face="normal" font="default" size="100%">Brown, Christopher D</style></author><author><style face="normal" font="default" size="100%">Correa, Adolfo</style></author><author><style face="normal" font="default" size="100%">Hall, Ira</style></author><author><style face="normal" font="default" size="100%">Im, Hae Kyung</style></author><author><style face="normal" font="default" size="100%">Li, Xin</style></author><author><style face="normal" font="default" size="100%">Natarajan, Pradeep</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author><author><style face="normal" font="default" size="100%">Mohammadi, Pejman</style></author><author><style face="normal" font="default" size="100%">Montgomery, Stephen B</style></author><author><style face="normal" font="default" size="100%">Battle, Alexis</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">TOPMed Lipids Working Group</style></author><author><style face="normal" font="default" size="100%">GTEx Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Transcriptomic signatures across human tissues identify functional rare genetic variation.</style></title><secondary-title><style face="normal" font="default" size="100%">Science</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Science</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Multifactorial Inheritance</style></keyword><keyword><style  face="normal" font="default" size="100%">Organ Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 09 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">369</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6509</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32913073?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sajuthi, Satria P</style></author><author><style face="normal" font="default" size="100%">DeFord, Peter</style></author><author><style face="normal" font="default" size="100%">Li, Yingchun</style></author><author><style face="normal" font="default" size="100%">Jackson, Nathan D</style></author><author><style face="normal" font="default" size="100%">Montgomery, Michael T</style></author><author><style face="normal" font="default" size="100%">Everman, Jamie L</style></author><author><style face="normal" font="default" size="100%">Rios, Cydney L</style></author><author><style face="normal" font="default" size="100%">Pruesse, Elmar</style></author><author><style face="normal" font="default" size="100%">Nolin, James D</style></author><author><style face="normal" font="default" size="100%">Plender, Elizabeth G</style></author><author><style face="normal" font="default" size="100%">Wechsler, Michael E</style></author><author><style face="normal" font="default" size="100%">Mak, Angel C Y</style></author><author><style face="normal" font="default" size="100%">Eng, Celeste</style></author><author><style face="normal" font="default" size="100%">Salazar, Sandra</style></author><author><style face="normal" font="default" size="100%">Medina, Vivian</style></author><author><style face="normal" font="default" size="100%">Wohlford, Eric M</style></author><author><style face="normal" font="default" size="100%">Huntsman, Scott</style></author><author><style face="normal" font="default" size="100%">Nickerson, Deborah A</style></author><author><style face="normal" font="default" size="100%">Germer, Soren</style></author><author><style face="normal" font="default" size="100%">Zody, Michael C</style></author><author><style face="normal" font="default" size="100%">Abecasis, Gonçalo</style></author><author><style face="normal" font="default" size="100%">Kang, Hyun Min</style></author><author><style face="normal" font="default" size="100%">Rice, Kenneth M</style></author><author><style face="normal" font="default" size="100%">Kumar, Rajesh</style></author><author><style face="normal" font="default" size="100%">Oh, Sam</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Santana, Jose</style></author><author><style face="normal" font="default" size="100%">Burchard, Esteban G</style></author><author><style face="normal" font="default" size="100%">Seibold, Max A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Angiotensin-Converting Enzyme 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Betacoronavirus</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Coronavirus Infections</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Epithelial Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Host-Pathogen Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Inflammation</style></keyword><keyword><style  face="normal" font="default" size="100%">Interferons</style></keyword><keyword><style  face="normal" font="default" size="100%">Interleukin-13</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Nasal Mucosa</style></keyword><keyword><style  face="normal" font="default" size="100%">Pandemics</style></keyword><keyword><style  face="normal" font="default" size="100%">Peptidyl-Dipeptidase A</style></keyword><keyword><style  face="normal" font="default" size="100%">Pneumonia, Viral</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Serine Endopeptidases</style></keyword><keyword><style  face="normal" font="default" size="100%">Virus Internalization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 10 12</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">5139</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33046696?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sajuthi, Satria P</style></author><author><style face="normal" font="default" size="100%">DeFord, Peter</style></author><author><style face="normal" font="default" size="100%">Jackson, Nathan D</style></author><author><style face="normal" font="default" size="100%">Montgomery, Michael T</style></author><author><style face="normal" font="default" size="100%">Everman, Jamie L</style></author><author><style face="normal" font="default" size="100%">Rios, Cydney L</style></author><author><style face="normal" font="default" size="100%">Pruesse, Elmar</style></author><author><style face="normal" font="default" size="100%">Nolin, James D</style></author><author><style face="normal" font="default" size="100%">Plender, Elizabeth G</style></author><author><style face="normal" font="default" size="100%">Wechsler, Michael E</style></author><author><style face="normal" font="default" size="100%">Mak, Angel Cy</style></author><author><style face="normal" font="default" size="100%">Eng, Celeste</style></author><author><style face="normal" font="default" size="100%">Salazar, Sandra</style></author><author><style face="normal" font="default" size="100%">Medina, Vivian</style></author><author><style face="normal" font="default" size="100%">Wohlford, Eric M</style></author><author><style face="normal" font="default" size="100%">Huntsman, Scott</style></author><author><style face="normal" font="default" size="100%">Nickerson, Deborah A</style></author><author><style face="normal" font="default" size="100%">Germer, Soren</style></author><author><style face="normal" font="default" size="100%">Zody, Michael C</style></author><author><style face="normal" font="default" size="100%">Abecasis, Gonçalo</style></author><author><style face="normal" font="default" size="100%">Kang, Hyun Min</style></author><author><style face="normal" font="default" size="100%">Rice, Kenneth M</style></author><author><style face="normal" font="default" size="100%">Kumar, Rajesh</style></author><author><style face="normal" font="default" size="100%">Oh, Sam</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Santana, Jose</style></author><author><style face="normal" font="default" size="100%">Burchard, Esteban G</style></author><author><style face="normal" font="default" size="100%">Seibold, Max A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium.</style></title><secondary-title><style face="normal" font="default" size="100%">bioRxiv</style></secondary-title><alt-title><style face="normal" font="default" size="100%">bioRxiv</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 Apr 10</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both  and , that vary in frequency across world populations. Importantly, we find  is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates  expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate  while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32511326?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wang, Minxian</style></author><author><style face="normal" font="default" size="100%">Menon, Ramesh</style></author><author><style face="normal" font="default" size="100%">Mishra, Sanghamitra</style></author><author><style face="normal" font="default" size="100%">Patel, Aniruddh P</style></author><author><style face="normal" font="default" size="100%">Chaffin, Mark</style></author><author><style face="normal" font="default" size="100%">Tanneeru, Deepak</style></author><author><style face="normal" font="default" size="100%">Deshmukh, Manjari</style></author><author><style face="normal" font="default" size="100%">Mathew, Oshin</style></author><author><style face="normal" font="default" size="100%">Apte, Sanika</style></author><author><style face="normal" font="default" size="100%">Devanboo, Christina S</style></author><author><style face="normal" font="default" size="100%">Sundaram, Sumathi</style></author><author><style face="normal" font="default" size="100%">Lakshmipathy, Praveena</style></author><author><style face="normal" font="default" size="100%">Murugan, Sakthivel</style></author><author><style face="normal" font="default" size="100%">Sharma, Krishna Kumar</style></author><author><style face="normal" font="default" size="100%">Rajendran, Karthikeyan</style></author><author><style face="normal" font="default" size="100%">Santhosh, Sam</style></author><author><style face="normal" font="default" size="100%">Thachathodiyl, Rajesh</style></author><author><style face="normal" font="default" size="100%">Ahamed, Hisham</style></author><author><style face="normal" font="default" size="100%">Balegadde, Aniketh Vijay</style></author><author><style face="normal" font="default" size="100%">Alexander, Thomas</style></author><author><style face="normal" font="default" size="100%">Swaminathan, Krishnan</style></author><author><style face="normal" font="default" size="100%">Gupta, Rajeev</style></author><author><style face="normal" font="default" size="100%">Mullasari, Ajit S</style></author><author><style face="normal" font="default" size="100%">Sigamani, Alben</style></author><author><style face="normal" font="default" size="100%">Kanchi, Muralidhar</style></author><author><style face="normal" font="default" size="100%">Peterson, Andrew S</style></author><author><style face="normal" font="default" size="100%">Butterworth, Adam S</style></author><author><style face="normal" font="default" size="100%">Danesh, John</style></author><author><style face="normal" font="default" size="100%">Di Angelantonio, Emanuele</style></author><author><style face="normal" font="default" size="100%">Naheed, Aliya</style></author><author><style face="normal" font="default" size="100%">Inouye, Michael</style></author><author><style face="normal" font="default" size="100%">Chowdhury, Rajiv</style></author><author><style face="normal" font="default" size="100%">Vedam, Ramprasad L</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author><author><style face="normal" font="default" size="100%">Gupta, Ravi</style></author><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Validation of a Genome-Wide Polygenic Score for Coronary Artery Disease in South Asians.</style></title><secondary-title><style face="normal" font="default" size="100%">J Am Coll Cardiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Am Coll Cardiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Bangladesh</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Coronary Artery Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">India</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Multifactorial Inheritance</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 08 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">76</style></volume><pages><style face="normal" font="default" size="100%">703-714</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Genome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population.&lt;/p&gt;&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;This analysis used summary statistics from a prior genome-wide association study to derive a new GPS for South Asians.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;This GPS was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPS reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPS reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The GPS, containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p &lt; 0.001 for each). Next, individuals of the Indian case-control study were projected onto static reference distributions, observing an OR/SD of 1.66 (p &lt; 0.001). Compared with the middle quintile, risk for CAD was most pronounced for those in the top 5% of the GPS distribution-ORs of 4.16, 2.46, and 3.22 in the South Asian UK Biobank, Bangladeshi, and Indian studies, respectively (p &lt; 0.05 for each).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;The new GPS has been developed and tested using 3 distinct South Asian studies, and provides a generalizable framework for ancestry-specific GPS assessment.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32762905?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Aguet, François</style></author><author><style face="normal" font="default" size="100%">Mohammadi, Pejman</style></author><author><style face="normal" font="default" size="100%">Ardlie, Kristin G</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">GTEx Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">A vast resource of allelic expression data spanning human tissues.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Biol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 09 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">234</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32912332?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Minardi, Raffaella</style></author><author><style face="normal" font="default" size="100%">Licchetta, Laura</style></author><author><style face="normal" font="default" size="100%">Baroni, Maria Chiara</style></author><author><style face="normal" font="default" size="100%">Pippucci, Tommaso</style></author><author><style face="normal" font="default" size="100%">Stipa, Carlotta</style></author><author><style face="normal" font="default" size="100%">Mostacci, Barbara</style></author><author><style face="normal" font="default" size="100%">Severi, Giulia</style></author><author><style face="normal" font="default" size="100%">Toni, Francesco</style></author><author><style face="normal" font="default" size="100%">Bergonzini, Luca</style></author><author><style face="normal" font="default" size="100%">Carelli, Valerio</style></author><author><style face="normal" font="default" size="100%">Seri, Marco</style></author><author><style face="normal" font="default" size="100%">Tinuper, Paolo</style></author><author><style face="normal" font="default" size="100%">Bisulli, Francesca</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Whole-exome sequencing in adult patients with developmental and epileptic encephalopathy: It is never too late.</style></title><secondary-title><style face="normal" font="default" size="100%">Clin Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Clin Genet</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">98</style></volume><pages><style face="normal" font="default" size="100%">477-485</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Developmental and epileptic encephalopathies (DEE) encompass rare, sporadic neurodevelopmental disorders and usually with pediatric onset. As these conditions are characterized by marked clinical and genetic heterogeneity, whole-exome sequencing (WES) represents the strategy of choice for the molecular diagnosis. While its usefulness is well established in pediatric DEE cohorts, our study is aimed at assessing the WES feasibility in adult DEE patients who experienced a diagnostic odyssey prior to the advent of this technique. We analyzed exomes from 71 unrelated adult DEE patients, consecutively recruited from an Italian cohort for the EPI25 Project. All patients underwent accurate clinical and electrophysiological characterization. An overwhelming percentage (90.1%) had already undergone negative genetic testing. Variants were classified according to the American College of Medical Genetics and Genomics guidelines. WES disclosed 24 (likely) pathogenic variants among 18 patients in epilepsy-related genes with either autosomal dominant, recessive or X-linked inheritance. Ten of these were novel. We obtained a diagnostic yield of 25.3%, higher among patients with brain malformations, early-onset epilepsy and dysmorphisms. Despite a median diagnostic delay of 38.7 years, WES analysis provided the long-awaited diagnosis for 18 adult patients, which also had an impact on the clinical management of 50% of them.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32725632?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Flex, Elisabetta</style></author><author><style face="normal" font="default" size="100%">Martinelli, Simone</style></author><author><style face="normal" font="default" size="100%">Van Dijck, Anke</style></author><author><style face="normal" font="default" size="100%">Ciolfi, Andrea</style></author><author><style face="normal" font="default" size="100%">Cecchetti, Serena</style></author><author><style face="normal" font="default" size="100%">Coluzzi, Elisa</style></author><author><style face="normal" font="default" size="100%">Pannone, Luca</style></author><author><style face="normal" font="default" size="100%">Andreoli, Cristina</style></author><author><style face="normal" font="default" size="100%">Radio, Francesca Clementina</style></author><author><style face="normal" font="default" size="100%">Pizzi, Simone</style></author><author><style face="normal" font="default" size="100%">Carpentieri, Giovanna</style></author><author><style face="normal" font="default" size="100%">Bruselles, Alessandro</style></author><author><style face="normal" font="default" size="100%">Catanzaro, Giuseppina</style></author><author><style face="normal" font="default" size="100%">Pedace, Lucia</style></author><author><style face="normal" font="default" size="100%">Miele, Evelina</style></author><author><style face="normal" font="default" size="100%">Carcarino, Elena</style></author><author><style face="normal" font="default" size="100%">Ge, Xiaoyan</style></author><author><style face="normal" font="default" size="100%">Chijiwa, Chieko</style></author><author><style face="normal" font="default" size="100%">Lewis, M E Suzanne</style></author><author><style face="normal" font="default" size="100%">Meuwissen, Marije</style></author><author><style face="normal" font="default" size="100%">Kenis, Sandra</style></author><author><style face="normal" font="default" size="100%">Van der Aa, Nathalie</style></author><author><style face="normal" font="default" size="100%">Larson, Austin</style></author><author><style face="normal" font="default" size="100%">Brown, Kathleen</style></author><author><style face="normal" font="default" size="100%">Wasserstein, Melissa P</style></author><author><style face="normal" font="default" size="100%">Skotko, Brian G</style></author><author><style face="normal" font="default" size="100%">Begtrup, Amber</style></author><author><style face="normal" font="default" size="100%">Person, Richard</style></author><author><style face="normal" font="default" size="100%">Karayiorgou, Maria</style></author><author><style face="normal" font="default" size="100%">Roos, J Louw</style></author><author><style face="normal" font="default" size="100%">Van Gassen, Koen L</style></author><author><style face="normal" font="default" size="100%">Koopmans, Marije</style></author><author><style face="normal" font="default" size="100%">Bijlsma, Emilia K</style></author><author><style face="normal" font="default" size="100%">Santen, Gijs W E</style></author><author><style face="normal" font="default" size="100%">Barge-Schaapveld, Daniela Q C M</style></author><author><style face="normal" font="default" size="100%">Ruivenkamp, Claudia A L</style></author><author><style face="normal" font="default" size="100%">Hoffer, Mariette J V</style></author><author><style face="normal" font="default" size="100%">Lalani, Seema R</style></author><author><style face="normal" font="default" size="100%">Streff, Haley</style></author><author><style face="normal" font="default" size="100%">Craigen, William J</style></author><author><style face="normal" font="default" size="100%">Graham, Brett H</style></author><author><style face="normal" font="default" size="100%">van den Elzen, Annette P M</style></author><author><style face="normal" font="default" size="100%">Kamphuis, Daan J</style></author><author><style face="normal" font="default" size="100%">Õunap, Katrin</style></author><author><style face="normal" font="default" size="100%">Reinson, Karit</style></author><author><style face="normal" font="default" size="100%">Pajusalu, Sander</style></author><author><style face="normal" font="default" size="100%">Wojcik, Monica H</style></author><author><style face="normal" font="default" size="100%">Viberti, Clara</style></author><author><style face="normal" font="default" size="100%">Di Gaetano, Cornelia</style></author><author><style face="normal" font="default" size="100%">Bertini, Enrico</style></author><author><style face="normal" font="default" size="100%">Petrucci, Simona</style></author><author><style face="normal" font="default" size="100%">De Luca, Alessandro</style></author><author><style face="normal" font="default" size="100%">Rota, Rossella</style></author><author><style face="normal" font="default" size="100%">Ferretti, Elisabetta</style></author><author><style face="normal" font="default" size="100%">Matullo, Giuseppe</style></author><author><style face="normal" font="default" size="100%">Dallapiccola, Bruno</style></author><author><style face="normal" font="default" size="100%">Sgura, Antonella</style></author><author><style face="normal" font="default" size="100%">Walkiewicz, Magdalena</style></author><author><style face="normal" font="default" size="100%">Kooy, R Frank</style></author><author><style face="normal" font="default" size="100%">Tartaglia, Marco</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aberrant Function of the C-Terminal Tail of HIST1H1E Accelerates Cellular Senescence and Causes Premature Aging.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am. J. Hum. Genet.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Sep 05</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">105</style></volume><pages><style face="normal" font="default" size="100%">493-508</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Histones mediate dynamic packaging of nuclear DNA in chromatin, a process that is precisely controlled to guarantee efficient compaction of the genome and proper chromosomal segregation during cell division and to accomplish DNA replication, transcription, and repair. Due to the important structural and regulatory roles played by histones, it is not surprising that histone functional dysregulation or aberrant levels of histones can have severe consequences for multiple cellular processes and ultimately might affect development or contribute to cell transformation. Recently, germline frameshift mutations involving the C-terminal tail of HIST1H1E, which is a widely expressed member of the linker histone family and facilitates higher-order chromatin folding, have been causally linked to an as-yet poorly defined syndrome that includes intellectual disability. We report that these mutations result in stable proteins that reside in the nucleus, bind to chromatin, disrupt proper compaction of DNA, and are associated with a specific methylation pattern. Cells expressing these mutant proteins have a dramatically reduced proliferation rate and competence, hardly enter into the S phase, and undergo accelerated senescence. Remarkably, clinical assessment of a relatively large cohort of subjects sharing these mutations revealed a premature aging phenotype as a previously unrecognized feature of the disorder. Our findings identify a direct link between aberrant chromatin remodeling, cellular senescence, and accelerated aging.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31447100?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Liu, Yaowu</style></author><author><style face="normal" font="default" size="100%">Chen, Sixing</style></author><author><style face="normal" font="default" size="100%">Li, Zilin</style></author><author><style face="normal" font="default" size="100%">Morrison, Alanna C</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Lin, Xihong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am. J. Hum. Genet.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Mar 07</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">104</style></volume><pages><style face="normal" font="default" size="100%">410-421</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30849328?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wenger, Aaron M</style></author><author><style face="normal" font="default" size="100%">Peluso, Paul</style></author><author><style face="normal" font="default" size="100%">Rowell, William J</style></author><author><style face="normal" font="default" size="100%">Chang, Pi-Chuan</style></author><author><style face="normal" font="default" size="100%">Hall, Richard J</style></author><author><style face="normal" font="default" size="100%">Concepcion, Gregory T</style></author><author><style face="normal" font="default" size="100%">Ebler, Jana</style></author><author><style face="normal" font="default" size="100%">Fungtammasan, Arkarachai</style></author><author><style face="normal" font="default" size="100%">Kolesnikov, Alexey</style></author><author><style face="normal" font="default" size="100%">Olson, Nathan D</style></author><author><style face="normal" font="default" size="100%">Töpfer, Armin</style></author><author><style face="normal" font="default" size="100%">Alonge, Michael</style></author><author><style face="normal" font="default" size="100%">Mahmoud, Medhat</style></author><author><style face="normal" font="default" size="100%">Qian, Yufeng</style></author><author><style face="normal" font="default" size="100%">Chin, Chen-Shan</style></author><author><style face="normal" font="default" size="100%">Phillippy, Adam M</style></author><author><style face="normal" font="default" size="100%">Schatz, Michael C</style></author><author><style face="normal" font="default" size="100%">Myers, Gene</style></author><author><style face="normal" font="default" size="100%">DePristo, Mark A</style></author><author><style face="normal" font="default" size="100%">Ruan, Jue</style></author><author><style face="normal" font="default" size="100%">Marschall, Tobias</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author><author><style face="normal" font="default" size="100%">Zook, Justin M</style></author><author><style face="normal" font="default" size="100%">Li, Heng</style></author><author><style face="normal" font="default" size="100%">Koren, Sergey</style></author><author><style face="normal" font="default" size="100%">Carroll, Andrew</style></author><author><style face="normal" font="default" size="100%">Rank, David R</style></author><author><style face="normal" font="default" size="100%">Hunkapiller, Michael W</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Biotechnol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat. Biotechnol.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">37</style></volume><pages><style face="normal" font="default" size="100%">1155-1162</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The DNA sequencing technologies in use today produce either highly accurate short reads or less-accurate long reads. We report the optimization of circular consensus sequencing (CCS) to improve the accuracy of single-molecule real-time (SMRT) sequencing (PacBio) and generate highly accurate (99.8%) long high-fidelity (HiFi) reads with an average length of 13.5 kilobases (kb). We applied our approach to sequence the well-characterized human HG002/NA24385 genome and obtained precision and recall rates of at least 99.91% for single-nucleotide variants (SNVs), 95.98% for insertions and deletions &lt;50 bp (indels) and 95.99% for structural variants. Our CCS method matches or exceeds the ability of short-read sequencing to detect small variants and structural variants. We estimate that 2,434 discordances are correctable mistakes in the 'genome in a bottle' (GIAB) benchmark set. Nearly all (99.64%) variants can be phased into haplotypes, further improving variant detection. De novo genome assembly using CCS reads alone produced a contiguous and accurate genome with a contig N50 of &gt;15 megabases (Mb) and concordance of 99.997%, substantially outperforming assembly with less-accurate long reads.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31406327?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chiang, Theodore</style></author><author><style face="normal" font="default" size="100%">Liu, Xiuping</style></author><author><style face="normal" font="default" size="100%">Wu, Tsung-Jung</style></author><author><style face="normal" font="default" size="100%">Hu, Jianhong</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author><author><style face="normal" font="default" size="100%">White, Simon</style></author><author><style face="normal" font="default" size="100%">Schaid, Daniel</style></author><author><style face="normal" font="default" size="100%">Andrade, Mariza de</style></author><author><style face="normal" font="default" size="100%">Jarvik, Gail P</style></author><author><style face="normal" font="default" size="100%">Crosslin, David</style></author><author><style face="normal" font="default" size="100%">Stanaway, Ian</style></author><author><style face="normal" font="default" size="100%">Carrell, David S</style></author><author><style face="normal" font="default" size="100%">Connolly, John J</style></author><author><style face="normal" font="default" size="100%">Hakonarson, Hakon</style></author><author><style face="normal" font="default" size="100%">Groopman, Emily E</style></author><author><style face="normal" font="default" size="100%">Gharavi, Ali G</style></author><author><style face="normal" font="default" size="100%">Fedotov, Alexander</style></author><author><style face="normal" font="default" size="100%">Bi, Weimin</style></author><author><style face="normal" font="default" size="100%">Leduc, Magalie S</style></author><author><style face="normal" font="default" size="100%">Murdock, David R</style></author><author><style face="normal" font="default" size="100%">Jiang, Yunyun</style></author><author><style face="normal" font="default" size="100%">Meng, Linyan</style></author><author><style face="normal" font="default" size="100%">Eng, Christine M</style></author><author><style face="normal" font="default" size="100%">Wen, Shu</style></author><author><style face="normal" font="default" size="100%">Yang, Yaping</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna M</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Salerno, William</style></author><author><style face="normal" font="default" size="100%">Venner, Eric</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel.</style></title><secondary-title><style face="normal" font="default" size="100%">Genet Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genet. Med.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Sep</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">2135-2144</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;PURPOSE: &lt;/b&gt;To provide a validated method to confidently identify exon-containing copy-number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;DNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples, when the target log ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies. We integrate an ExonQC threshold to lower FDR and compare performance with alternate software (VisCap).&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Thirteen CNVs were used as a truth set to validate Atlas-CNV and compared with VisCap. We demonstrated FDR reduction in validation, simulation, and 10,926 eMERGESeq samples without sensitivity loss. Sixty-four multiexon and 29 single-exon CNVs with high C-scores were assessed by Multiplex Ligation-dependent Probe Amplification (MLPA).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Atlas-CNV is validated as a method to identify exonic CNVs in targeted sequencing data generated in the clinical laboratory. The ExonQC and C-score assignment can reduce FDR (identification of targets with high variance) and improve calling accuracy of single-exon CNVs respectively. We propose guidelines and criteria to identify high confidence single-exon CNVs.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30890783?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karaca, Ender</style></author><author><style face="normal" font="default" size="100%">Posey, Jennifer E</style></author><author><style face="normal" font="default" size="100%">Bostwick, Bret</style></author><author><style face="normal" font="default" size="100%">Liu, Pengfei</style></author><author><style face="normal" font="default" size="100%">Gezdirici, Alper</style></author><author><style face="normal" font="default" size="100%">Yesil, Gozde</style></author><author><style face="normal" font="default" size="100%">Coban Akdemir, Zeynep</style></author><author><style face="normal" font="default" size="100%">Bayram, Yavuz</style></author><author><style face="normal" font="default" size="100%">Harms, Frederike L</style></author><author><style face="normal" font="default" size="100%">Meinecke, Peter</style></author><author><style face="normal" font="default" size="100%">Alawi, Malik</style></author><author><style face="normal" font="default" size="100%">Bacino, Carlos A</style></author><author><style face="normal" font="default" size="100%">Sutton, V Reid</style></author><author><style face="normal" font="default" size="100%">Kortüm, Fanny</style></author><author><style face="normal" font="default" size="100%">Lupski, James R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Biallelic and De Novo Variants in DONSON Reveal a Clinical Spectrum of Cell Cycle-opathies with Microcephaly, Dwarfism and Skeletal Abnormalities.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Med Genet A</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am. J. Med. Genet. A</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">179</style></volume><pages><style face="normal" font="default" size="100%">2056-2066</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Co-occurrence of primordial dwarfism and microcephaly together with particular skeletal findings are seen in a wide range of Mendelian syndromes including microcephaly micromelia syndrome (MMS, OMIM 251230), microcephaly, short stature, and limb abnormalities (MISSLA, OMIM 617604), and microcephalic primordial dwarfisms (MPDs). Genes associated with these syndromes encode proteins that have crucial roles in DNA replication or in other critical steps of the cell cycle that link DNA replication to cell division. We identified four unrelated families with five affected individuals having biallelic or de novo variants in DONSON presenting with a core phenotype of severe short stature (z score &lt; -3 SD), additional skeletal abnormalities, and microcephaly. Two apparently unrelated families with identical homozygous c.631C &gt; T p.(Arg211Cys) variant had clinical features typical of Meier-Gorlin syndrome (MGS), while two siblings with compound heterozygous c.346delG p.(Asp116Ile*62) and c.1349A &gt; G p.(Lys450Arg) variants presented with Seckel-like phenotype. We also identified a de novo c.683G &gt; T p.(Trp228Leu) variant in DONSON in a patient with prominent micrognathia, short stature and hypoplastic femur and tibia, clinically diagnosed with Femoral-Facial syndrome (FFS, OMIM 134780). Biallelic variants in DONSON have been recently described in individuals with microcephalic dwarfism. These studies also demonstrated that DONSON has an essential conserved role in the cell cycle. Here we describe novel biallelic and de novo variants that are associated with MGS, Seckel-like phenotype and FFS, the last of which has not been associated with any disease gene to date.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31407851?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Punetha, Jaya</style></author><author><style face="normal" font="default" size="100%">Karaca, Ender</style></author><author><style face="normal" font="default" size="100%">Gezdirici, Alper</style></author><author><style face="normal" font="default" size="100%">Lamont, Ryan E</style></author><author><style face="normal" font="default" size="100%">Pehlivan, Davut</style></author><author><style face="normal" font="default" size="100%">Marafi, Dana</style></author><author><style face="normal" font="default" size="100%">Appendino, Juan P</style></author><author><style face="normal" font="default" size="100%">Hunter, Jill V</style></author><author><style face="normal" font="default" size="100%">Akdemir, Zeynep C</style></author><author><style face="normal" font="default" size="100%">Fatih, Jawid M</style></author><author><style face="normal" font="default" size="100%">Jhangiani, Shalini N</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author><author><style face="normal" font="default" size="100%">Innes, A Micheil</style></author><author><style face="normal" font="default" size="100%">Posey, Jennifer E</style></author><author><style face="normal" font="default" size="100%">Lupski, James R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Biallelic CACNA2D2 variants in epileptic encephalopathy and cerebellar atrophy.</style></title><secondary-title><style face="normal" font="default" size="100%">Ann Clin Transl Neurol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Ann Clin Transl Neurol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">1395-1406</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;To characterize the molecular and clinical phenotypic basis of developmental and epileptic encephalopathies caused by rare biallelic variants in CACNA2D2.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Two affected individuals from a family with clinical features of early onset epileptic encephalopathy were recruited for exome sequencing at the Centers for Mendelian Genomics to identify their molecular diagnosis. GeneMatcher facilitated identification of a second family with a shared candidate disease gene identified through clinical gene panel-based testing.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Rare biallelic CACNA2D2 variants have been previously reported in three families with developmental and epileptic encephalopathy, and one family with congenital ataxia. We identified three individuals in two unrelated families with novel homozygous rare variants in CACNA2D2 with clinical features of developmental and epileptic encephalopathy and cerebellar atrophy. Family 1 includes two affected siblings with a likely damaging homozygous rare missense variant c.1778G&gt;C; p.(Arg593Pro) in CACNA2D2. Family 2 includes a proband with a homozygous rare nonsense variant c.485_486del; p.(Tyr162Ter) in CACNA2D2. We compared clinical and molecular findings from all nine individuals reported to date and note that cerebellar atrophy is shared among all.&lt;/p&gt;&lt;p&gt;&lt;b&gt;INTERPRETATION: &lt;/b&gt;Our study supports the candidacy of CACNA2D2 as a disease gene associated with a phenotypic spectrum of neurological disease that include features of developmental and epileptic encephalopathy, ataxia, and cerebellar atrophy. Age at presentation may affect apparent penetrance of neurogenetic trait manifestations and of a particular clinical neurological endophenotype, for example, seizures or ataxia.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31402629?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin, Sarah</style></author><author><style face="normal" font="default" size="100%">Strzelczyk, Adam</style></author><author><style face="normal" font="default" size="100%">Lindlar, Silvia</style></author><author><style face="normal" font="default" size="100%">Krause, Kristina</style></author><author><style face="normal" font="default" size="100%">Reif, Philipp S</style></author><author><style face="normal" font="default" size="100%">Menzler, Katja</style></author><author><style face="normal" font="default" size="100%">Chiocchetti, Andreas G</style></author><author><style face="normal" font="default" size="100%">Rosenow, Felix</style></author><author><style face="normal" font="default" size="100%">Knake, Susanne</style></author><author><style face="normal" font="default" size="100%">Klein, Karl Martin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Drug-Resistant Juvenile Myoclonic Epilepsy: Misdiagnosis of Progressive Myoclonus Epilepsy.</style></title><secondary-title><style face="normal" font="default" size="100%">Front Neurol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Front Neurol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">946</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Juvenile myoclonic epilepsy (JME) is a common epilepsy syndrome characterized by bilateral myoclonic and tonic-clonic seizures typically starting in adolescence and responding well to medication. Misdiagnosis of a more severe progressive myoclonus epilepsy (PME) as JME has been suggested as a cause of drug-resistance. Medical records of the Epilepsy Center Hessen-Marburg between 2005 and 2014 were automatically selected using keywords and manually reviewed regarding the presence of a JME diagnosis at any timepoint. The identified patients were evaluated regarding seizure outcome and drug resistance according to ILAE criteria. 87/168 identified JME patients were seizure-free at last follow-up including 61 drug-responsive patients (group NDR). Seventy-eight patients were not seizure-free including 26 drug-resistant patients (group DR). Valproate was the most efficacious AED. The JME diagnosis was revised in 7 patients of group DR including 6 in whom the diagnosis had already been questioned or revised during clinical follow-up. One of these was finally diagnosed with PME (genetically confirmed Lafora disease) based on genetic testing. She was initially reviewed at age 29 yrs and considered to be inconsistent with PME. Intellectual disability ( = 0.025), cognitive impairment ( &lt; 0.001), febrile seizures in first-degree relatives ( = 0.023) and prominent dialeptic seizures ( = 0.009) where significantly more frequent in group DR. Individuals with PME are rarely found among drug-resistant alleged JME patients in a tertiary epilepsy center. Even a very detailed review by experienced epileptologists may not identify the presence of PME before the typical features evolve underpinning the need for early genetic testing in drug-resistant JME patients.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31551911?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leija-Salazar, Melissa</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author><author><style face="normal" font="default" size="100%">Toffoli, Marco</style></author><author><style face="normal" font="default" size="100%">Mullin, Stephen</style></author><author><style face="normal" font="default" size="100%">Mokretar, Katya</style></author><author><style face="normal" font="default" size="100%">Athanasopoulou, Maria</style></author><author><style face="normal" font="default" size="100%">Donald, Aimee</style></author><author><style face="normal" font="default" size="100%">Sharma, Reena</style></author><author><style face="normal" font="default" size="100%">Hughes, Derralynn</style></author><author><style face="normal" font="default" size="100%">Schapira, Anthony H V</style></author><author><style face="normal" font="default" size="100%">Proukakis, Christos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of the detection of GBA missense mutations and other variants using the Oxford Nanopore MinION.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Genet Genomic Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Genet Genomic Med</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">e564</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Mutations in GBA cause Gaucher disease when biallelic and are strong risk factors for Parkinson's disease when heterozygous. GBA analysis is complicated by the nearby pseudogene. We aimed to design and validate a method for sequencing GBA using long reads.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We sequenced GBA on the Oxford Nanopore MinION as an 8.9 kb amplicon from 102 individuals, including patients with Parkinson's and Gaucher diseases. We used NanoOK for quality metrics, NGMLR to align data (after comparing with GraphMap), Nanopolish and Sniffles to call variants, and WhatsHap for phasing.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We detected all known missense mutations in these samples, including the common p.N409S (N370S) and p.L483P (L444P) in multiple samples, and nine rarer ones, as well as a splicing and a truncating mutation, and intronic SNPs. We demonstrated the ability to phase mutations, confirm compound heterozygosity, and assign haplotypes. We also detected two known risk variants in some Parkinson's patients. Rare false positives were easily identified and filtered, with the Nanopolish quality score adjusted for the number of reads a very robust discriminator. In two individuals carrying a recombinant allele, we were able to detect and fully define it in one carrier, where it included a 55-base pair deletion, but not in another one, suggesting a limitation of the PCR enrichment method. Missense mutations were detected at the correct zygosity, except for the case where the RecNciI one was missed.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;The Oxford Nanopore MinION can detect missense mutations and an exonic deletion in this difficult gene, with the added advantages of phasing and intronic analysis. It can be used as an efficient research tool, but additional work is required to exclude all recombinants.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30637984?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Li, Alexander H</style></author><author><style face="normal" font="default" size="100%">Hanchard, Neil A</style></author><author><style face="normal" font="default" size="100%">Azamian, Mahshid</style></author><author><style face="normal" font="default" size="100%">D'Alessandro, Lisa C A</style></author><author><style face="normal" font="default" size="100%">Coban-Akdemir, Zeynep</style></author><author><style face="normal" font="default" size="100%">Lopez, Keila N</style></author><author><style face="normal" font="default" size="100%">Hall, Nancy J</style></author><author><style face="normal" font="default" size="100%">Dickerson, Heather</style></author><author><style face="normal" font="default" size="100%">Nicosia, Annarita</style></author><author><style face="normal" font="default" size="100%">Fernbach, Susan</style></author><author><style face="normal" font="default" size="100%">Boone, Philip M</style></author><author><style face="normal" font="default" size="100%">Gambin, Tomaz</style></author><author><style face="normal" font="default" size="100%">Karaca, Ender</style></author><author><style face="normal" font="default" size="100%">Gu, Shen</style></author><author><style face="normal" font="default" size="100%">Yuan, Bo</style></author><author><style face="normal" font="default" size="100%">Jhangiani, Shalini N</style></author><author><style face="normal" font="default" size="100%">Doddapaneni, HarshaVardhan</style></author><author><style face="normal" font="default" size="100%">Hu, Jianhong</style></author><author><style face="normal" font="default" size="100%">Dinh, Huyen</style></author><author><style face="normal" font="default" size="100%">Jayaseelan, Joy</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna</style></author><author><style face="normal" font="default" size="100%">Lalani, Seema</style></author><author><style face="normal" font="default" size="100%">Towbin, Jeffrey</style></author><author><style face="normal" font="default" size="100%">Penny, Daniel</style></author><author><style face="normal" font="default" size="100%">Fraser, Charles</style></author><author><style face="normal" font="default" size="100%">Martin, James</style></author><author><style face="normal" font="default" size="100%">Lupski, James R</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Ware, Stephanie M</style></author><author><style face="normal" font="default" size="100%">Belmont, John W</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic architecture of laterality defects revealed by whole exome sequencing.</style></title><secondary-title><style face="normal" font="default" size="100%">Eur J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Eur. J. Hum. Genet.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">563-573</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Aberrant left-right patterning in the developing human embryo can lead to a broad spectrum of congenital malformations. The causes of most laterality defects are not known, with variants in established genes accounting for &lt;20% of cases. We sought to characterize the genetic spectrum of these conditions by performing whole-exome sequencing of 323 unrelated laterality cases. We investigated the role of rare, predicted-damaging variation in 1726 putative laterality candidate genes derived from model organisms, pathway analyses, and human phenotypes. We also evaluated the contribution of homo/hemizygous exon deletions and gene-based burden of rare variation. A total of 28 candidate variants (26 rare predicted-damaging variants and 2 hemizygous deletions) were identified, including variants in genes known to cause heterotaxy and primary ciliary dyskinesia (ACVR2B, NODAL, ZIC3, DNAI1, DNAH5, HYDIN, MMP21), and genes without a human phenotype association, but with prior evidence for a role in embryonic laterality or cardiac development. Sanger validation of the latter variants in probands and their parents revealed no de novo variants, but apparent transmitted heterozygous (ROCK2, ISL1, SMAD2), and hemizygous (RAI2, RIPPLY1) variant patterns. Collectively, these variants account for 7.1% of our study subjects. We also observe evidence for an excess burden of rare, predicted loss-of-function variation in PXDNL and BMS1- two genes relevant to the broader laterality phenotype. These findings highlight potential new genes in the development of laterality defects, and suggest extensive locus heterogeneity and complex genetic models in this class of birth defects.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30622330?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohammadi, Pejman</style></author><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Cummings, Beryl B</style></author><author><style face="normal" font="default" size="100%">Einson, Jonah</style></author><author><style face="normal" font="default" size="100%">Sousa, Christina</style></author><author><style face="normal" font="default" size="100%">Hoffman, Paul</style></author><author><style face="normal" font="default" size="100%">Donkervoort, Sandra</style></author><author><style face="normal" font="default" size="100%">Jiang, Zhuoxun</style></author><author><style face="normal" font="default" size="100%">Mohassel, Payam</style></author><author><style face="normal" font="default" size="100%">Foley, A Reghan</style></author><author><style face="normal" font="default" size="100%">Wheeler, Heather E</style></author><author><style face="normal" font="default" size="100%">Im, Hae Kyung</style></author><author><style face="normal" font="default" size="100%">Bonnemann, Carsten G</style></author><author><style face="normal" font="default" size="100%">MacArthur, Daniel G</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic regulatory variation in populations informs transcriptome analysis in rare disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Science</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Science</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 10 18</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">366</style></volume><pages><style face="normal" font="default" size="100%">351-356</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Transcriptome data can facilitate the interpretation of the effects of rare genetic variants. Here, we introduce ANEVA (analysis of expression variation) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application of ANEVA to the Genotype-Tissues Expression (GTEx) data showed that this variance estimate is robust and correlated with selective constraint in a gene. Using these variance estimates in a dosage outlier test (ANEVA-DOT) applied to AE data from 70 Mendelian muscular disease patients showed accuracy in detecting genes with pathogenic variants in previously resolved cases and led to one confirmed and several potential new diagnoses. Using our reference estimates from GTEx data, ANEVA-DOT can be incorporated in rare disease diagnostic pipelines to use RNA-sequencing data more effectively.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6463</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31601707?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Guo, Hui</style></author><author><style face="normal" font="default" size="100%">Duyzend, Michael H</style></author><author><style face="normal" font="default" size="100%">Coe, Bradley P</style></author><author><style face="normal" font="default" size="100%">Baker, Carl</style></author><author><style face="normal" font="default" size="100%">Hoekzema, Kendra</style></author><author><style face="normal" font="default" size="100%">Gerdts, Jennifer</style></author><author><style face="normal" font="default" size="100%">Turner, Tychele N</style></author><author><style face="normal" font="default" size="100%">Zody, Michael C</style></author><author><style face="normal" font="default" size="100%">Beighley, Jennifer S</style></author><author><style face="normal" font="default" size="100%">Murali, Shwetha C</style></author><author><style face="normal" font="default" size="100%">Nelson, Bradley J</style></author><author><style face="normal" font="default" size="100%">Bamshad, Michael J</style></author><author><style face="normal" font="default" size="100%">Nickerson, Deborah A</style></author><author><style face="normal" font="default" size="100%">Bernier, Raphael A</style></author><author><style face="normal" font="default" size="100%">Eichler, Evan E</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">University of Washington Center for Mendelian Genomics</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome sequencing identifies multiple deleterious variants in autism patients with more severe phenotypes.</style></title><secondary-title><style face="normal" font="default" size="100%">Genet Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genet. Med.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">1611-1620</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;PURPOSE: &lt;/b&gt;To maximize the discovery of potentially pathogenic variants to better understand the diagnostic utility of genome sequencing (GS) and to assess how the presence of multiple risk events might affect the phenotypic severity in autism spectrum disorders (ASD).&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;GS was applied to 180 simplex and multiplex ASD families (578 individuals, 213 patients) with exome sequencing and array comparative genomic hybridization further applied to a subset for validation and cross-platform comparisons.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We found that 40.8% of patients carried variants with evidence of disease risk, including a de novo frameshift variant in NR4A2 and two de novo missense variants in SYNCRIP, while 21.1% carried clinically relevant pathogenic or likely pathogenic variants. Patients with more than one risk variant (9.9%) were more severely affected with respect to cognitive ability compared with patients with a single or no-risk variant. We observed no instance among the 27 multiplex families where a pathogenic or likely pathogenic variant was transmitted to all affected members in the family.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;The study demonstrates the diagnostic utility of GS, especially for multiple risk variants that contribute to the phenotypic severity, shows the genetic heterogeneity in multiplex families, and provides evidence for new genes for follow up.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30504930?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leasure, Audrey C</style></author><author><style face="normal" font="default" size="100%">Sheth, Kevin N</style></author><author><style face="normal" font="default" size="100%">Comeau, Mary</style></author><author><style face="normal" font="default" size="100%">Aldridge, Chad</style></author><author><style face="normal" font="default" size="100%">Worrall, Bradford B</style></author><author><style face="normal" font="default" size="100%">Vashkevich, Anastasia</style></author><author><style face="normal" font="default" size="100%">Rosand, Jonathan</style></author><author><style face="normal" font="default" size="100%">Langefeld, Carl</style></author><author><style face="normal" font="default" size="100%">Moomaw, Charles J</style></author><author><style face="normal" font="default" size="100%">Woo, Daniel</style></author><author><style face="normal" font="default" size="100%">Falcone, Guido J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification and Validation of Hematoma Volume Cutoffs in Spontaneous, Supratentorial Deep Intracerebral Hemorrhage.</style></title><secondary-title><style face="normal" font="default" size="100%">Stroke</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stroke</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">2044-2049</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Background and Purpose- Clinical trials in spontaneous intracerebral hemorrhage (ICH) have used volume cutoffs as inclusion criteria to select populations in which the effects of interventions are likely to be the greatest. However, optimal volume cutoffs for predicting poor outcome in deep locations (thalamus versus basal ganglia) are unknown. Methods- We conducted a 2-phase study to determine ICH volume cutoffs for poor outcome (modified Rankin Scale score of 4-6) in the thalamus and basal ganglia. Cutoffs with optimal sensitivity and specificity for poor outcome were identified in the ERICH ([Ethnic/Racial Variations of ICH] study; derivation cohort) using receiver operating characteristic curves. The cutoffs were then validated in the ATACH-2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage-2) by comparing the c-statistic of regression models for outcome (including dichotomized volume) in the validation cohort. Results- Of the 3000 patients enrolled in ERICH, 1564 (52%) had deep ICH, of whom 1305 (84%) had complete neuroimaging and outcome data (660 thalamic and 645 basal ganglia hemorrhages). Receiver operating characteristic curve analysis identified 8 mL in thalamic (area under the curve, 0.79; sensitivity, 73%; specificity, 78%) and 18 mL in basal ganglia ICH (area under the curve, 0.79; sensitivity, 70%; specificity, 83%) as optimal cutoffs for predicting poor outcome. The validation cohort included 834 (84%) patients with deep ICH and complete neuroimaging data enrolled in ATACH-2 (353 thalamic and 431 basal ganglia hemorrhages). In thalamic ICH, the c-statistic of the multivariable outcome model including dichotomized ICH volume was 0.80 (95% CI, 0.75-0.85) in the validation cohort. For basal ganglia ICH, the c-statistic was 0.81 (95% CI, 0.76-0.85) in the validation cohort. Conclusions- Optimal hematoma volume cutoffs for predicting poor outcome in deep ICH vary by the specific deep brain nucleus involved. Utilization of location-specific volume cutoffs may improve clinical trial design by targeting deep ICH patients that will obtain maximal benefit from candidate therapies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31238829?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Posey, Jennifer E</style></author><author><style face="normal" font="default" size="100%">O'Donnell-Luria, Anne H</style></author><author><style face="normal" font="default" size="100%">Chong, Jessica X</style></author><author><style face="normal" font="default" size="100%">Harel, Tamar</style></author><author><style face="normal" font="default" size="100%">Jhangiani, Shalini N</style></author><author><style face="normal" font="default" size="100%">Coban Akdemir, Zeynep H</style></author><author><style face="normal" font="default" size="100%">Buyske, Steven</style></author><author><style face="normal" font="default" size="100%">Pehlivan, Davut</style></author><author><style face="normal" font="default" size="100%">Carvalho, Claudia M B</style></author><author><style face="normal" font="default" size="100%">Baxter, Samantha</style></author><author><style face="normal" font="default" size="100%">Sobreira, Nara</style></author><author><style face="normal" font="default" size="100%">Liu, Pengfei</style></author><author><style face="normal" font="default" size="100%">Wu, Nan</style></author><author><style face="normal" font="default" size="100%">Rosenfeld, Jill A</style></author><author><style face="normal" font="default" size="100%">Kumar, Sushant</style></author><author><style face="normal" font="default" size="100%">Avramopoulos, Dimitri</style></author><author><style face="normal" font="default" size="100%">White, Janson J</style></author><author><style face="normal" font="default" size="100%">Doheny, Kimberly F</style></author><author><style face="normal" font="default" size="100%">Witmer, P Dane</style></author><author><style face="normal" font="default" size="100%">Boehm, Corinne</style></author><author><style face="normal" font="default" size="100%">Sutton, V Reid</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna M</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Günel, Murat</style></author><author><style face="normal" font="default" size="100%">Nickerson, Deborah A</style></author><author><style face="normal" font="default" size="100%">Mane, Shrikant</style></author><author><style face="normal" font="default" size="100%">MacArthur, Daniel G</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author><author><style face="normal" font="default" size="100%">Hamosh, Ada</style></author><author><style face="normal" font="default" size="100%">Lifton, Richard P</style></author><author><style face="normal" font="default" size="100%">Matise, Tara C</style></author><author><style face="normal" font="default" size="100%">Rehm, Heidi L</style></author><author><style face="normal" font="default" size="100%">Gerstein, Mark</style></author><author><style face="normal" font="default" size="100%">Bamshad, Michael J</style></author><author><style face="normal" font="default" size="100%">Valle, David</style></author><author><style face="normal" font="default" size="100%">Lupski, James R</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Centers for Mendelian Genomics</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Insights into genetics, human biology and disease gleaned from family based genomic studies.</style></title><secondary-title><style face="normal" font="default" size="100%">Genet Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genet. Med.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 04</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">798-812</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Identifying genes and variants contributing to rare disease phenotypes and Mendelian conditions informs biology and medicine, yet potential phenotypic consequences for variation of &gt;75% of the ~20,000 annotated genes in the human genome are lacking. Technical advances to assess rare variation genome-wide, particularly exome sequencing (ES), enabled establishment in the United States of the National Institutes of Health (NIH)-supported Centers for Mendelian Genomics (CMGs) and have facilitated collaborative studies resulting in novel &quot;disease gene&quot; discoveries. Pedigree-based genomic studies and rare variant analyses in families with suspected Mendelian conditions have led to the elucidation of hundreds of novel disease genes and highlighted the impact of de novo mutational events, somatic variation underlying nononcologic traits, incompletely penetrant alleles, phenotypes with high locus heterogeneity, and multilocus pathogenic variation. Herein, we highlight CMG collaborative discoveries that have contributed to understanding both rare and common diseases and discuss opportunities for future discovery in single-locus Mendelian disorder genomics. Phenotypic annotation of all human genes; development of bioinformatic tools and analytic methods; exploration of non-Mendelian modes of inheritance including reduced penetrance, multilocus variation, and oligogenic inheritance; construction of allelic series at a locus; enhanced data sharing worldwide; and integration with clinical genomics are explored. Realizing the full contribution of rare disease research to functional annotation of the human genome, and further illuminating human biology and health, will lay the foundation for the Precision Medicine Initiative.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30655598?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Connaughton, Dervla M</style></author><author><style face="normal" font="default" size="100%">Kennedy, Claire</style></author><author><style face="normal" font="default" size="100%">Shril, Shirlee</style></author><author><style face="normal" font="default" size="100%">Mann, Nina</style></author><author><style face="normal" font="default" size="100%">Murray, Susan L</style></author><author><style face="normal" font="default" size="100%">Williams, Patrick A</style></author><author><style face="normal" font="default" size="100%">Conlon, Eoin</style></author><author><style face="normal" font="default" size="100%">Nakayama, Makiko</style></author><author><style face="normal" font="default" size="100%">van der Ven, Amelie T</style></author><author><style face="normal" font="default" size="100%">Ityel, Hadas</style></author><author><style face="normal" font="default" size="100%">Kause, Franziska</style></author><author><style face="normal" font="default" size="100%">Kolvenbach, Caroline M</style></author><author><style face="normal" font="default" size="100%">Dai, Rufeng</style></author><author><style face="normal" font="default" size="100%">Vivante, Asaf</style></author><author><style face="normal" font="default" size="100%">Braun, Daniela A</style></author><author><style face="normal" font="default" size="100%">Schneider, Ronen</style></author><author><style face="normal" font="default" size="100%">Kitzler, Thomas M</style></author><author><style face="normal" font="default" size="100%">Moloney, Brona</style></author><author><style face="normal" font="default" size="100%">Moran, Conor P</style></author><author><style face="normal" font="default" size="100%">Smyth, John S</style></author><author><style face="normal" font="default" size="100%">Kennedy, Alan</style></author><author><style face="normal" font="default" size="100%">Benson, Katherine</style></author><author><style face="normal" font="default" size="100%">Stapleton, Caragh</style></author><author><style face="normal" font="default" size="100%">Denton, Mark</style></author><author><style face="normal" font="default" size="100%">Magee, Colm</style></author><author><style face="normal" font="default" size="100%">O'Seaghdha, Conall M</style></author><author><style face="normal" font="default" size="100%">Plant, William D</style></author><author><style face="normal" font="default" size="100%">Griffin, Matthew D</style></author><author><style face="normal" font="default" size="100%">Awan, Atif</style></author><author><style face="normal" font="default" size="100%">Sweeney, Clodagh</style></author><author><style face="normal" font="default" size="100%">Mane, Shrikant M</style></author><author><style face="normal" font="default" size="100%">Lifton, Richard P</style></author><author><style face="normal" font="default" size="100%">Griffin, Brenda</style></author><author><style face="normal" font="default" size="100%">Leavey, Sean</style></author><author><style face="normal" font="default" size="100%">Casserly, Liam</style></author><author><style face="normal" font="default" size="100%">de Freitas, Declan G</style></author><author><style face="normal" font="default" size="100%">Holian, John</style></author><author><style face="normal" font="default" size="100%">Dorman, Anthony</style></author><author><style face="normal" font="default" size="100%">Doyle, Brendan</style></author><author><style face="normal" font="default" size="100%">Lavin, Peter J</style></author><author><style face="normal" font="default" size="100%">Little, Mark A</style></author><author><style face="normal" font="default" size="100%">Conlon, Peter J</style></author><author><style face="normal" font="default" size="100%">Hildebrandt, Friedhelm</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Monogenic causes of chronic kidney disease in adults.</style></title><secondary-title><style face="normal" font="default" size="100%">Kidney Int</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Kidney Int.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">95</style></volume><pages><style face="normal" font="default" size="100%">914-928</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Approximately 500 monogenic causes of chronic kidney disease (CKD) have been identified, mainly in pediatric populations. The frequency of monogenic causes among adults with CKD has been less extensively studied. To determine the likelihood of detecting monogenic causes of CKD in adults presenting to nephrology services in Ireland, we conducted whole exome sequencing (WES) in a multi-centre cohort of 114 families including 138 affected individuals with CKD. Affected adults were recruited from 78 families with a positive family history, 16 families with extra-renal features, and 20 families with neither a family history nor extra-renal features. We detected a pathogenic mutation in a known CKD gene in 42 of 114 families (37%). A monogenic cause was identified in 36% of affected families with a positive family history of CKD, 69% of those with extra-renal features, and only 15% of those without a family history or extra-renal features. There was no difference in the rate of genetic diagnosis in individuals with childhood versus adult onset CKD. Among the 42 families in whom a monogenic cause was identified, WES confirmed the clinical diagnosis in 17 (40%), corrected the clinical diagnosis in 9 (22%), and established a diagnosis for the first time in 16 families referred with CKD of unknown etiology (38%). In this multi-centre study of adults with CKD, a molecular genetic diagnosis was established in over one-third of families. In the evolving era of precision medicine, WES may be an important tool to identify the cause of CKD in adults.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30773290?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Donkervoort, S</style></author><author><style face="normal" font="default" size="100%">Sabouny, R</style></author><author><style face="normal" font="default" size="100%">Yun, P</style></author><author><style face="normal" font="default" size="100%">Gauquelin, L</style></author><author><style face="normal" font="default" size="100%">Chao, K R</style></author><author><style face="normal" font="default" size="100%">Hu, Y</style></author><author><style face="normal" font="default" size="100%">Al Khatib, I</style></author><author><style face="normal" font="default" size="100%">Töpf, A</style></author><author><style face="normal" font="default" size="100%">Mohassel, P</style></author><author><style face="normal" font="default" size="100%">Cummings, B B</style></author><author><style face="normal" font="default" size="100%">Kaur, R</style></author><author><style face="normal" font="default" size="100%">Saade, D</style></author><author><style face="normal" font="default" size="100%">Moore, S A</style></author><author><style face="normal" font="default" size="100%">Waddell, L B</style></author><author><style face="normal" font="default" size="100%">Farrar, M A</style></author><author><style face="normal" font="default" size="100%">Goodrich, J K</style></author><author><style face="normal" font="default" size="100%">Uapinyoying, P</style></author><author><style face="normal" font="default" size="100%">Chan, S H S</style></author><author><style face="normal" font="default" size="100%">Javed, A</style></author><author><style face="normal" font="default" size="100%">Leach, M E</style></author><author><style face="normal" font="default" size="100%">Karachunski, P</style></author><author><style face="normal" font="default" size="100%">Dalton, J</style></author><author><style face="normal" font="default" size="100%">Medne, L</style></author><author><style face="normal" font="default" size="100%">Harper, A</style></author><author><style face="normal" font="default" size="100%">Thompson, C</style></author><author><style face="normal" font="default" size="100%">Thiffault, I</style></author><author><style face="normal" font="default" size="100%">Specht, S</style></author><author><style face="normal" font="default" size="100%">Lamont, R E</style></author><author><style face="normal" font="default" size="100%">Saunders, C</style></author><author><style face="normal" font="default" size="100%">Racher, H</style></author><author><style face="normal" font="default" size="100%">Bernier, F P</style></author><author><style face="normal" font="default" size="100%">Mowat, D</style></author><author><style face="normal" font="default" size="100%">Witting, N</style></author><author><style face="normal" font="default" size="100%">Vissing, J</style></author><author><style face="normal" font="default" size="100%">Hanson, R</style></author><author><style face="normal" font="default" size="100%">Coffman, K A</style></author><author><style face="normal" font="default" size="100%">Hainlen, M</style></author><author><style face="normal" font="default" size="100%">Parboosingh, J S</style></author><author><style face="normal" font="default" size="100%">Carnevale, A</style></author><author><style face="normal" font="default" size="100%">Yoon, G</style></author><author><style face="normal" font="default" size="100%">Schnur, R E</style></author><author><style face="normal" font="default" size="100%">Boycott, K M</style></author><author><style face="normal" font="default" size="100%">Mah, J K</style></author><author><style face="normal" font="default" size="100%">Straub, V</style></author><author><style face="normal" font="default" size="100%">Foley, A Reghan</style></author><author><style face="normal" font="default" size="100%">Innes, A M</style></author><author><style face="normal" font="default" size="100%">Bönnemann, C G</style></author><author><style face="normal" font="default" size="100%">Shutt, T E</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Care4Rare Canada Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">MSTO1 mutations cause mtDNA depletion, manifesting as muscular dystrophy with cerebellar involvement.</style></title><secondary-title><style face="normal" font="default" size="100%">Acta Neuropathol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Acta Neuropathol.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Aug 29</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;MSTO1 encodes a cytosolic mitochondrial fusion protein, misato homolog 1 or MSTO1. While the full genotype-phenotype spectrum remains to be explored, pathogenic variants in MSTO1 have recently been reported in a small number of patients presenting with a phenotype of cerebellar ataxia, congenital muscle involvement with histologic findings ranging from myopathic to dystrophic and pigmentary retinopathy. The proposed underlying pathogenic mechanism of MSTO1-related disease is suggestive of impaired mitochondrial fusion secondary to a loss of function of MSTO1. Disorders of mitochondrial fusion and fission have been shown to also lead to mitochondrial DNA (mtDNA) depletion, linking them to the mtDNA depletion syndromes, a clinically and genetically diverse class of mitochondrial diseases characterized by a reduction of cellular mtDNA content. However, the consequences of pathogenic variants in MSTO1 on mtDNA maintenance remain poorly understood. We present extensive phenotypic and genetic data from 12 independent families, including 15 new patients harbouring a broad array of bi-allelic MSTO1 pathogenic variants, and we provide functional characterization from seven MSTO1-related disease patient fibroblasts. Bi-allelic loss-of-function variants in MSTO1 manifest clinically with a remarkably consistent phenotype of childhood-onset muscular dystrophy, corticospinal tract dysfunction and early-onset non-progressive cerebellar atrophy. MSTO1 protein was not detectable in the cultured fibroblasts of all seven patients evaluated, suggesting that pathogenic variants result in a loss of protein expression and/or affect protein stability. Consistent with impaired mitochondrial fusion, mitochondrial networks in fibroblasts were found to be fragmented. Furthermore, all fibroblasts were found to have depletion of mtDNA ranging from 30 to 70% along with alterations to mtDNA nucleoids. Our data corroborate the role of MSTO1 as a mitochondrial fusion protein and highlight a previously unrecognized link to mtDNA regulation. As impaired mitochondrial fusion is a recognized cause of mtDNA depletion syndromes, this novel link to mtDNA depletion in patient fibroblasts suggests that MSTO1-deficiency should also be considered a mtDNA depletion syndrome. Thus, we provide mechanistic insight into the disease pathogenesis associated with MSTO1 mutations and further define the clinical spectrum and the natural history of MSTO1-related disease.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31463572?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Staretz-Chacham, Orna</style></author><author><style face="normal" font="default" size="100%">Shukrun, Rachel</style></author><author><style face="normal" font="default" size="100%">Barel, Ortal</style></author><author><style face="normal" font="default" size="100%">Pode-Shakked, Ben</style></author><author><style face="normal" font="default" size="100%">Pleniceanu, Oren</style></author><author><style face="normal" font="default" size="100%">Anikster, Yair</style></author><author><style face="normal" font="default" size="100%">Shalva, Nechama</style></author><author><style face="normal" font="default" size="100%">Ferreira, Carlos R</style></author><author><style face="normal" font="default" size="100%">Ben-Haim Kadosh, Admit</style></author><author><style face="normal" font="default" size="100%">Richardson, Justin</style></author><author><style face="normal" font="default" size="100%">Mane, Shrikant M</style></author><author><style face="normal" font="default" size="100%">Hildebrandt, Friedhelm</style></author><author><style face="normal" font="default" size="100%">Vivante, Asaf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel homozygous ENPP1 mutation causes generalized arterial calcifications of infancy, thrombocytopenia, and cardiovascular and central nervous system syndrome.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Med Genet A</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am. J. Med. Genet. A</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">179</style></volume><pages><style face="normal" font="default" size="100%">2112-2118</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Generalized arterial calcifications of infancy (GACI) is caused by mutations in ENPP1. Other ENPP1-related phenotypes include pseudoxanthoma elasticum, hypophosphatemic rickets, and Cole disease. We studied four children from two Bedouin consanguineous families who presented with severe clinical phenotype including thrombocytopenia, hypoglycemia, hepatic, and neurologic manifestations. Initial working diagnosis included congenital infection; however, patients remained without a definitive diagnosis despite extensive workup. Consequently, we investigated a potential genetic etiology. Whole exome sequencing (WES) was performed for affected children and their parents. Following the identification of a novel mutation in the ENPP1 gene, we characterized this novel multisystemic presentation and revised relevant imaging studies. Using WES, we identified a novel homozygous mutation (c.556G &gt; C; p.Gly186Arg) in ENPP1 which affects a highly conserved protein domain (somatomedin B2). ENPP1-associated genetic diseases exhibit phenotypic heterogeneity depending on mutation type and location. Follow-up clinical characterization of these families allowed us to revise and detect new features of systemic calcifications, which established the diagnosis of GACI, expanding the phenotypic spectrum associated with ENPP1 mutations. Our findings demonstrate that this novel ENPP1 founder mutation can cause a fatal multisystemic phenotype, mimicking severe congenital infection. This also represents the first reported mutation affecting the SMB2 domain, associated with GACI.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31444901?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bryen, Samantha J</style></author><author><style face="normal" font="default" size="100%">Joshi, Himanshu</style></author><author><style face="normal" font="default" size="100%">Evesson, Frances J</style></author><author><style face="normal" font="default" size="100%">Girard, Cyrille</style></author><author><style face="normal" font="default" size="100%">Ghaoui, Roula</style></author><author><style face="normal" font="default" size="100%">Waddell, Leigh B</style></author><author><style face="normal" font="default" size="100%">Testa, Alison C</style></author><author><style face="normal" font="default" size="100%">Cummings, Beryl</style></author><author><style face="normal" font="default" size="100%">Arbuckle, Susan</style></author><author><style face="normal" font="default" size="100%">Graf, Nicole</style></author><author><style face="normal" font="default" size="100%">Webster, Richard</style></author><author><style face="normal" font="default" size="100%">MacArthur, Daniel G</style></author><author><style face="normal" font="default" size="100%">Laing, Nigel G</style></author><author><style face="normal" font="default" size="100%">Davis, Mark R</style></author><author><style face="normal" font="default" size="100%">Lührmann, Reinhard</style></author><author><style face="normal" font="default" size="100%">Cooper, Sandra T</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pathogenic Abnormal Splicing Due to Intronic Deletions that Induce Biophysical Space Constraint for Spliceosome Assembly.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am. J. Hum. Genet.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Sep 05</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">105</style></volume><pages><style face="normal" font="default" size="100%">573-587</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A precise genetic diagnosis is the single most important step for families with genetic disorders to enable personalized and preventative medicine. In addition to genetic variants in coding regions (exons) that can change a protein sequence, abnormal pre-mRNA splicing can be devastating for the encoded protein, inducing a frameshift or in-frame deletion/insertion of multiple residues. Non-coding variants that disrupt splicing are extremely challenging to identify. Stemming from an initial clinical discovery in two index Australian families, we define 25 families with genetic disorders caused by a class of pathogenic non-coding splice variant due to intronic deletions. These pathogenic intronic deletions spare all consensus splice motifs, though they critically shorten the minimal distance between the 5' splice-site (5'SS) and branchpoint. The mechanistic basis for abnormal splicing is due to biophysical constraint precluding U1/U2 spliceosome assembly, which stalls in A-complexes (that bridge the 5'SS and branchpoint). Substitution of deleted nucleotides with non-specific sequences restores spliceosome assembly and normal splicing, arguing against loss of an intronic element as the primary causal basis. Incremental lengthening of 5'SS-branchpoint length in our index EMD case subject defines 45-47 nt as the critical elongation enabling (inefficient) spliceosome assembly for EMD intron 5. The 5'SS-branchpoint space constraint mechanism, not currently factored by genomic informatics pipelines, is relevant to diagnosis and precision medicine across the breadth of Mendelian disorders and cancer genomics.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31447096?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pinard, Amélie</style></author><author><style face="normal" font="default" size="100%">Guey, Stéphanie</style></author><author><style face="normal" font="default" size="100%">Guo, Dongchuan</style></author><author><style face="normal" font="default" size="100%">Cecchi, Alana C</style></author><author><style face="normal" font="default" size="100%">Kharas, Natasha</style></author><author><style face="normal" font="default" size="100%">Wallace, Stephanie</style></author><author><style face="normal" font="default" size="100%">Regalado, Ellen S</style></author><author><style face="normal" font="default" size="100%">Hostetler, Ellen M</style></author><author><style face="normal" font="default" size="100%">Sharrief, Anjail Z</style></author><author><style face="normal" font="default" size="100%">Bergametti, Françoise</style></author><author><style face="normal" font="default" size="100%">Kossorotoff, Manoelle</style></author><author><style face="normal" font="default" size="100%">Hervé, Dominique</style></author><author><style face="normal" font="default" size="100%">Kraemer, Markus</style></author><author><style face="normal" font="default" size="100%">Bamshad, Michael J</style></author><author><style face="normal" font="default" size="100%">Nickerson, Deborah A</style></author><author><style face="normal" font="default" size="100%">Smith, Edward R</style></author><author><style face="normal" font="default" size="100%">Tournier-Lasserve, Elisabeth</style></author><author><style face="normal" font="default" size="100%">Milewicz, Dianna M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The pleiotropy associated with de novo variants in CHD4, CNOT3, and SETD5 extends to moyamoya angiopathy.</style></title><secondary-title><style face="normal" font="default" size="100%">Genet Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genet. Med.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Sep 02</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;PURPOSE: &lt;/b&gt;Moyamoya angiopathy (MMA) is a cerebrovascular disease characterized by occlusion of large arteries, which leads to strokes starting in childhood. Twelve altered genes predispose to MMA but the majority of cases of European descent do not have an identified genetic trigger.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Exome sequencing from 39 trios were analyzed.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We identified four de novo variants in three genes not previously associated with MMA: CHD4, CNOT3, and SETD5. Identification of additional rare variants in these genes in 158 unrelated MMA probands provided further support that rare pathogenic variants in CHD4 and CNOT3 predispose to MMA. Previous studies identified de novo variants in these genes in children with developmental disorders (DD), intellectual disability, and congenital heart disease.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;These genes encode proteins involved in chromatin remodeling, and taken together with previously reported genes leading to MMA-like cerebrovascular occlusive disease (YY1AP1, SMARCAL1), implicate disrupted chromatin remodeling as a molecular pathway predisposing to early onset, large artery occlusive cerebrovascular disease. Furthermore, these data expand the spectrum of phenotypic pleiotropy due to alterations of CHD4, CNOT3, and SETD5 beyond DD to later onset disease in the cerebrovascular arteries and emphasize the need to assess clinical complications into adulthood for genes associated with DD.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31474762?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aldinger, Kimberly A</style></author><author><style face="normal" font="default" size="100%">Timms, Andrew E</style></author><author><style face="normal" font="default" size="100%">Thomson, Zachary</style></author><author><style face="normal" font="default" size="100%">Mirzaa, Ghayda M</style></author><author><style face="normal" font="default" size="100%">Bennett, James T</style></author><author><style face="normal" font="default" size="100%">Rosenberg, Alexander B</style></author><author><style face="normal" font="default" size="100%">Roco, Charles M</style></author><author><style face="normal" font="default" size="100%">Hirano, Matthew</style></author><author><style face="normal" font="default" size="100%">Abidi, Fatima</style></author><author><style face="normal" font="default" size="100%">Haldipur, Parthiv</style></author><author><style face="normal" font="default" size="100%">Cheng, Chi V</style></author><author><style face="normal" font="default" size="100%">Collins, Sarah</style></author><author><style face="normal" font="default" size="100%">Park, Kaylee</style></author><author><style face="normal" font="default" size="100%">Zeiger, Jordan</style></author><author><style face="normal" font="default" size="100%">Overmann, Lynne M</style></author><author><style face="normal" font="default" size="100%">Alkuraya, Fowzan S</style></author><author><style face="normal" font="default" size="100%">Biesecker, Leslie G</style></author><author><style face="normal" font="default" size="100%">Braddock, Stephen R</style></author><author><style face="normal" font="default" size="100%">Cathey, Sara</style></author><author><style face="normal" font="default" size="100%">Cho, Megan T</style></author><author><style face="normal" font="default" size="100%">Chung, Brian H Y</style></author><author><style face="normal" font="default" size="100%">Everman, David B</style></author><author><style face="normal" font="default" size="100%">Zarate, Yuri A</style></author><author><style face="normal" font="default" size="100%">Jones, Julie R</style></author><author><style face="normal" font="default" size="100%">Schwartz, Charles E</style></author><author><style face="normal" font="default" size="100%">Goldstein, Amy</style></author><author><style face="normal" font="default" size="100%">Hopkin, Robert J</style></author><author><style face="normal" font="default" size="100%">Krantz, Ian D</style></author><author><style face="normal" font="default" size="100%">Ladda, Roger L</style></author><author><style face="normal" font="default" size="100%">Leppig, Kathleen A</style></author><author><style face="normal" font="default" size="100%">McGillivray, Barbara C</style></author><author><style face="normal" font="default" size="100%">Sell, Susan</style></author><author><style face="normal" font="default" size="100%">Wusik, Katherine</style></author><author><style face="normal" font="default" size="100%">Gleeson, Joseph G</style></author><author><style face="normal" font="default" size="100%">Nickerson, Deborah A</style></author><author><style face="normal" font="default" size="100%">Bamshad, Michael J</style></author><author><style face="normal" font="default" size="100%">Gerrelli, Dianne</style></author><author><style face="normal" font="default" size="100%">Lisgo, Steven N</style></author><author><style face="normal" font="default" size="100%">Seelig, Georg</style></author><author><style face="normal" font="default" size="100%">Ishak, Gisele E</style></author><author><style face="normal" font="default" size="100%">Barkovich, A James</style></author><author><style face="normal" font="default" size="100%">Curry, Cynthia J</style></author><author><style face="normal" font="default" size="100%">Glass, Ian A</style></author><author><style face="normal" font="default" size="100%">Millen, Kathleen J</style></author><author><style face="normal" font="default" size="100%">Doherty, Dan</style></author><author><style face="normal" font="default" size="100%">Dobyns, William B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Redefining the Etiologic Landscape of Cerebellar Malformations.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am. J. Hum. Genet.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Sep 05</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">105</style></volume><pages><style face="normal" font="default" size="100%">606-615</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cerebellar malformations are diverse congenital anomalies frequently associated with developmental disability. Although genetic and prenatal non-genetic causes have been described, no systematic analysis has been performed. Here, we present a large-exome sequencing study of Dandy-Walker malformation (DWM) and cerebellar hypoplasia (CBLH). We performed exome sequencing in 282 individuals from 100 families with DWM or CBLH, and we established a molecular diagnosis in 36 of 100 families, with a significantly higher yield for CBLH (51%) than for DWM (16%). The 41 variants impact 27 neurodevelopmental-disorder-associated genes, thus demonstrating that CBLH and DWM are often features of monogenic neurodevelopmental disorders. Though only seven monogenic causes (19%) were identified in more than one individual, neuroimaging review of 131 additional individuals confirmed cerebellar abnormalities in 23 of 27 genetic disorders (85%). Prenatal risk factors were frequently found among individuals without a genetic diagnosis (30 of 64 individuals [47%]). Single-cell RNA sequencing of prenatal human cerebellar tissue revealed gene enrichment in neuronal and vascular cell types; this suggests that defective vasculogenesis may disrupt cerebellar development. Further, de novo gain-of-function variants in PDGFRB, a tyrosine kinase receptor essential for vascular progenitor signaling, were associated with CBLH, and this discovery links genetic and non-genetic etiologies. Our results suggest that genetic defects impact specific cerebellar cell types and implicate abnormal vascular development as a mechanism for cerebellar malformations. We also confirmed a major contribution for non-genetic prenatal factors in individuals with cerebellar abnormalities, substantially influencing diagnostic evaluation and counseling regarding recurrence risk and prognosis.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31474318?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mahmoud, Medhat</style></author><author><style face="normal" font="default" size="100%">Gobet, Nastassia</style></author><author><style face="normal" font="default" size="100%">Cruz-Dávalos, Diana Ivette</style></author><author><style face="normal" font="default" size="100%">Mounier, Ninon</style></author><author><style face="normal" font="default" size="100%">Dessimoz, Christophe</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Structural variant calling: the long and the short of it.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Biol.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 11 20</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">246</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recent research into structural variants (SVs) has established their importance to medicine and molecular biology, elucidating their role in various diseases, regulation of gene expression, ethnic diversity, and large-scale chromosome evolution-giving rise to the differences within populations and among species. Nevertheless, characterizing SVs and determining the optimal approach for a given experimental design remains a computational and scientific challenge. Multiple approaches have emerged to target various SV classes, zygosities, and size ranges. Here, we review these approaches with respect to their ability to infer SVs across the full spectrum of large, complex variations and present computational methods for each approach.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31747936?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author><author><style face="normal" font="default" size="100%">Chaffin, Mark</style></author><author><style face="normal" font="default" size="100%">Zekavat, Seyedeh M</style></author><author><style face="normal" font="default" size="100%">Collins, Ryan L</style></author><author><style face="normal" font="default" size="100%">Roselli, Carolina</style></author><author><style face="normal" font="default" size="100%">Natarajan, Pradeep</style></author><author><style face="normal" font="default" size="100%">Lichtman, Judith H</style></author><author><style face="normal" font="default" size="100%">D'Onofrio, Gail</style></author><author><style face="normal" font="default" size="100%">Mattera, Jennifer</style></author><author><style face="normal" font="default" size="100%">Dreyer, Rachel</style></author><author><style face="normal" font="default" size="100%">Spertus, John A</style></author><author><style face="normal" font="default" size="100%">Taylor, Kent D</style></author><author><style face="normal" font="default" size="100%">Psaty, Bruce M</style></author><author><style face="normal" font="default" size="100%">Rich, Stephen S</style></author><author><style face="normal" font="default" size="100%">Post, Wendy</style></author><author><style face="normal" font="default" size="100%">Gupta, Namrata</style></author><author><style face="normal" font="default" size="100%">Gabriel, Stacey</style></author><author><style face="normal" font="default" size="100%">Lander, Eric</style></author><author><style face="normal" font="default" size="100%">Ida Chen, Yii-Der</style></author><author><style face="normal" font="default" size="100%">Talkowski, Michael E</style></author><author><style face="normal" font="default" size="100%">Rotter, Jerome I</style></author><author><style face="normal" font="default" size="100%">Krumholz, Harlan M</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Whole-Genome Sequencing to Characterize Monogenic and Polygenic Contributions in Patients Hospitalized With Early-Onset Myocardial Infarction.</style></title><secondary-title><style face="normal" font="default" size="100%">Circulation</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Circulation</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Mar 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">139</style></volume><pages><style face="normal" font="default" size="100%">1593-1602</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The relative prevalence and clinical importance of monogenic mutations related to familial hypercholesterolemia and of high polygenic score (cumulative impact of many common variants) pathways for early-onset myocardial infarction remain uncertain. Whole-genome sequencing enables simultaneous ascertainment of both monogenic mutations and polygenic score for each individual.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We performed deep-coverage whole-genome sequencing of 2081 patients from 4 racial subgroups hospitalized in the United States with early-onset myocardial infarction (age ≤55 years) recruited with a 2:1 female-to-male enrollment design. We compared these genomes with those of 3761 population-based control subjects. We first identified individuals with a rare, monogenic mutation related to familial hypercholesterolemia. Second, we calculated a recently developed polygenic score of 6.6 million common DNA variants to quantify the cumulative susceptibility conferred by common variants. We defined high polygenic score as the top 5% of the control distribution because this cutoff has previously been shown to confer similar risk to that of familial hypercholesterolemia mutations.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The mean age of the 2081 patients presenting with early-onset myocardial infarction was 48 years, and 66% were female. A familial hypercholesterolemia mutation was present in 36 of these patients (1.7%) and was associated with a 3.8-fold (95% CI, 2.1-6.8; P&lt;0.001) increased odds of myocardial infarction. Of the patients with early-onset myocardial infarction, 359 (17.3%) carried a high polygenic score, associated with a 3.7-fold (95% CI, 3.1-4.6; P&lt;0.001) increased odds. Mean estimated untreated low-density lipoprotein cholesterol was 206 mg/dL in those with a familial hypercholesterolemia mutation, 132 mg/dL in those with high polygenic score, and 122 mg/dL in those in the remainder of the population. Although associated with increased risk in all racial groups, high polygenic score demonstrated the strongest association in white participants ( P for heterogeneity=0.008).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Both familial hypercholesterolemia mutations and high polygenic score are associated with a &gt;3-fold increased odds of early-onset myocardial infarction. However, high polygenic score has a 10-fold higher prevalence among patients presents with early-onset myocardial infarction.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CLINICAL TRIAL REGISTRATION: &lt;/b&gt;URL: https://www.clinicaltrials.gov . Unique identifier: NCT00597922.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">13</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30586733?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Emdin, Connor A</style></author><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author><author><style face="normal" font="default" size="100%">Chaffin, Mark</style></author><author><style face="normal" font="default" size="100%">Klarin, Derek</style></author><author><style face="normal" font="default" size="100%">Natarajan, Pradeep</style></author><author><style face="normal" font="default" size="100%">Aragam, Krishna</style></author><author><style face="normal" font="default" size="100%">Haas, Mary</style></author><author><style face="normal" font="default" size="100%">Bick, Alexander</style></author><author><style face="normal" font="default" size="100%">Zekavat, Seyedeh M</style></author><author><style face="normal" font="default" size="100%">Nomura, Akihiro</style></author><author><style face="normal" font="default" size="100%">Ardissino, Diego</style></author><author><style face="normal" font="default" size="100%">Wilson, James G</style></author><author><style face="normal" font="default" size="100%">Schunkert, Heribert</style></author><author><style face="normal" font="default" size="100%">McPherson, Ruth</style></author><author><style face="normal" font="default" size="100%">Watkins, Hugh</style></author><author><style face="normal" font="default" size="100%">Elosua, Roberto</style></author><author><style face="normal" font="default" size="100%">Bown, Matthew J</style></author><author><style face="normal" font="default" size="100%">Samani, Nilesh J</style></author><author><style face="normal" font="default" size="100%">Baber, Usman</style></author><author><style face="normal" font="default" size="100%">Erdmann, Jeanette</style></author><author><style face="normal" font="default" size="100%">Gupta, Namrata</style></author><author><style face="normal" font="default" size="100%">Danesh, John</style></author><author><style face="normal" font="default" size="100%">Chasman, Daniel</style></author><author><style face="normal" font="default" size="100%">Ridker, Paul</style></author><author><style face="normal" font="default" size="100%">Denny, Joshua</style></author><author><style face="normal" font="default" size="100%">Bastarache, Lisa</style></author><author><style face="normal" font="default" size="100%">Lichtman, Judith H</style></author><author><style face="normal" font="default" size="100%">D'Onofrio, Gail</style></author><author><style face="normal" font="default" size="100%">Mattera, Jennifer</style></author><author><style face="normal" font="default" size="100%">Spertus, John A</style></author><author><style face="normal" font="default" size="100%">Sheu, Wayne H-H</style></author><author><style face="normal" font="default" size="100%">Taylor, Kent D</style></author><author><style face="normal" font="default" size="100%">Psaty, Bruce M</style></author><author><style face="normal" font="default" size="100%">Rich, Stephen S</style></author><author><style face="normal" font="default" size="100%">Post, Wendy</style></author><author><style face="normal" font="default" size="100%">Rotter, Jerome I</style></author><author><style face="normal" font="default" size="100%">Chen, Yii-Der Ida</style></author><author><style face="normal" font="default" size="100%">Krumholz, Harlan</style></author><author><style face="normal" font="default" size="100%">Saleheen, Danish</style></author><author><style face="normal" font="default" size="100%">Gabriel, Stacey</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus, Type 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Testing</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Obesity</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Respiratory Hypersensitivity</style></keyword><keyword><style  face="normal" font="default" size="100%">United Kingdom</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Apr 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">1613</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Less than 3% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In &gt;400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency &lt; 5%) pLOF variant-phenotype associations. pLOF variants in the gene GPR151 protect against obesity and type 2 diabetes, in the gene IL33 against asthma and allergic disease, and in the gene IFIH1 against hypothyroidism. In the gene PDE3B, pLOF variants associate with elevated height, improved body fat distribution and protection from coronary artery disease. Our findings prioritize genes for which pharmacologic mimics of pLOF variants may lower risk for disease.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29691411?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Murphy, Meredith P</style></author><author><style face="normal" font="default" size="100%">Kuramatsu, Joji B</style></author><author><style face="normal" font="default" size="100%">Leasure, Audrey</style></author><author><style face="normal" font="default" size="100%">Falcone, Guido J</style></author><author><style face="normal" font="default" size="100%">Kamel, Hooman</style></author><author><style face="normal" font="default" size="100%">Sansing, Lauren H</style></author><author><style face="normal" font="default" size="100%">Kourkoulis, Christina</style></author><author><style face="normal" font="default" size="100%">Schwab, Kristin</style></author><author><style face="normal" font="default" size="100%">Elm, Jordan J</style></author><author><style face="normal" font="default" size="100%">Gurol, M Edip</style></author><author><style face="normal" font="default" size="100%">Tran, Huy</style></author><author><style face="normal" font="default" size="100%">Greenberg, Steven M</style></author><author><style face="normal" font="default" size="100%">Viswanathan, Anand</style></author><author><style face="normal" font="default" size="100%">Anderson, Christopher D</style></author><author><style face="normal" font="default" size="100%">Schwab, Stefan</style></author><author><style face="normal" font="default" size="100%">Rosand, Jonathan</style></author><author><style face="normal" font="default" size="100%">Shi, Fu-Dong</style></author><author><style face="normal" font="default" size="100%">Kittner, Steven J</style></author><author><style face="normal" font="default" size="100%">Testai, Fernando D</style></author><author><style face="normal" font="default" size="100%">Woo, Daniel</style></author><author><style face="normal" font="default" size="100%">Langefeld, Carl D</style></author><author><style face="normal" font="default" size="100%">James, Michael L</style></author><author><style face="normal" font="default" size="100%">Koch, Sebastian</style></author><author><style face="normal" font="default" size="100%">Huttner, Hagen B</style></author><author><style face="normal" font="default" size="100%">Biffi, Alessandro</style></author><author><style face="normal" font="default" size="100%">Sheth, Kevin N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cardioembolic Stroke Risk and Recovery After Anticoagulation-Related Intracerebral Hemorrhage.</style></title><secondary-title><style face="normal" font="default" size="100%">Stroke</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stroke</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Nov</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">2652-2658</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Background and Purpose- Whether to resume oral anticoagulation treatment after intracerebral hemorrhage (ICH) remains an unresolved question. Previous studies focused primarily on recurrent stroke after ICH. We sought to investigate the association between cardioembolic stroke risk, oral anticoagulation therapy resumption, and functional recovery among ICH survivors in the absence of recurrent stroke. Methods- We conducted a joint analysis of 3 observational studies: (1) the multicenter RETRACE study (German-Wide Multicenter Analysis of Oral Anticoagulation Associated Intracerebral Hemorrhage); (2) the Massachusetts General Hospital ICH study (n=166); and (3) the ERICH study (Ethnic/Racial Variations of Intracerebral Hemorrhage; n=131). We included 941 survivors of ICH in the setting of active oral anticoagulation therapy for prevention of cardioembolic stroke because of nonvalvular atrial fibrillation and without evidence of ischemic stroke and recurrent ICH at 1 year from the index event. We created univariable and multivariable models to explore associations between cardioembolic stroke risk (based on CHADS-VASc scores) and functional recovery after ICH, defined as achieving modified Rankin Scale score of ≤3 at 1 year for participants with modified Rankin Scale score of &gt;3 at discharge. Results- In multivariable analyses, the CHADS-VASc score was associated with a decreased likelihood of functional recovery (odds ratio, 0.83 per 1 point increase; 95% CI, 0.79-0.86) at 1 year. Anticoagulation resumption was independently associated with a higher likelihood of recovery, regardless of CHADS-VASc score (odds ratio, 1.89; 95% CI, 1.32-2.70). We found an interaction between CHADS-VASc score and anticoagulation resumption in terms of association with increased likelihood of functional recovery (interaction P=0.011). Conclusions- Increasing cardioembolic stroke risk is associated with a decreased likelihood of functional recovery at 1 year after ICH, but this association was weaker among participants resuming oral anticoagulation therapy. These findings support, including recovery metrics, in future studies of anticoagulation resumption after ICH.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30355194?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, Yuan</style></author><author><style face="normal" font="default" size="100%">Xie, Shirley</style></author><author><style face="normal" font="default" size="100%">Darnell, Jennifer C</style></author><author><style face="normal" font="default" size="100%">Darnell, Andrew J</style></author><author><style face="normal" font="default" size="100%">Saito, Yuhki</style></author><author><style face="normal" font="default" size="100%">Phatnani, Hemali</style></author><author><style face="normal" font="default" size="100%">Murphy, Elisabeth A</style></author><author><style face="normal" font="default" size="100%">Zhang, Chaolin</style></author><author><style face="normal" font="default" size="100%">Maniatis, Tom</style></author><author><style face="normal" font="default" size="100%">Darnell, Robert B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cell type-specific CLIP reveals that NOVA regulates cytoskeleton interactions in motoneurons.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Biol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alternative Splicing</style></keyword><keyword><style  face="normal" font="default" size="100%">Amino Acid Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Cercopithecus aethiops</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosomes, Artificial, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">COS Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Cross-Linking Reagents</style></keyword><keyword><style  face="normal" font="default" size="100%">Cytoskeleton</style></keyword><keyword><style  face="normal" font="default" size="100%">Dendrites</style></keyword><keyword><style  face="normal" font="default" size="100%">Exons</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunoprecipitation</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipoylation</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Transgenic</style></keyword><keyword><style  face="normal" font="default" size="100%">Motor Neurons</style></keyword><keyword><style  face="normal" font="default" size="100%">Nerve Tissue Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">NIH 3T3 Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Pseudopodia</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-Binding Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Septins</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 08 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">117</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Alternative RNA processing plays an essential role in shaping cell identity and connectivity in the central nervous system. This is believed to involve differential regulation of RNA processing in various cell types. However, in vivo study of cell type-specific post-transcriptional regulation has been a challenge. Here, we describe a sensitive and stringent method combining genetics and CLIP (crosslinking and immunoprecipitation) to globally identify regulatory interactions between NOVA and RNA in the mouse spinal cord motoneurons.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We developed a means of undertaking motoneuron-specific CLIP to explore motoneuron-specific protein-RNA interactions relative to studies of the whole spinal cord in mouse. This allowed us to pinpoint differential RNA regulation specific to motoneurons, revealing a major role for NOVA in regulating cytoskeleton interactions in motoneurons. In particular, NOVA specifically promotes the palmitoylated isoform of the cytoskeleton protein Septin 8 in motoneurons, which enhances dendritic arborization.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Our study demonstrates that cell type-specific RNA regulation is important for fine tuning motoneuron physiology and highlights the value of defining RNA processing regulation at single cell type resolution.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/30111345?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Costello, Maura</style></author><author><style face="normal" font="default" size="100%">Fleharty, Mark</style></author><author><style face="normal" font="default" size="100%">Abreu, Justin</style></author><author><style face="normal" font="default" size="100%">Farjoun, Yossi</style></author><author><style face="normal" font="default" size="100%">Ferriera, Steven</style></author><author><style face="normal" font="default" size="100%">Holmes, Laurie</style></author><author><style face="normal" font="default" size="100%">Granger, Brian</style></author><author><style face="normal" font="default" size="100%">Green, Lisa</style></author><author><style face="normal" font="default" size="100%">Howd, Tom</style></author><author><style face="normal" font="default" size="100%">Mason, Tamara</style></author><author><style face="normal" font="default" size="100%">Vicente, Gina</style></author><author><style face="normal" font="default" size="100%">Dasilva, Michael</style></author><author><style face="normal" font="default" size="100%">Brodeur, Wendy</style></author><author><style face="normal" font="default" size="100%">DeSmet, Timothy</style></author><author><style face="normal" font="default" size="100%">Dodge, Sheila</style></author><author><style face="normal" font="default" size="100%">Lennon, Niall J</style></author><author><style face="normal" font="default" size="100%">Gabriel, Stacey</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Library</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 May 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">332</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Here we present an in-depth characterization of the mechanism of sequencer-induced sample contamination due to the phenomenon of index swapping that impacts Illumina sequencers employing patterned flow cells with Exclusion Amplification (ExAmp) chemistry (HiSeqX, HiSeq4000, and NovaSeq). We also present a remediation method that minimizes the impact of such swaps.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Leveraging data collected over a two-year period, we demonstrate the widespread prevalence of index swapping in patterned flow cell data. We calculate mean swap rates across multiple sample preparation methods and sequencer models, demonstrating that different library methods can have vastly different swapping rates and that even non-ExAmp chemistry instruments display trace levels of index swapping. We provide methods for eliminating sample data cross contamination by utilizing non-redundant dual indexing for complete filtering of index swapped reads, and share the sequences for 96 non-combinatorial dual indexes we have validated across various library preparation methods and sequencer models. Finally, using computational methods we provide a greater insight into the mechanism of index swapping.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Index swapping in pooled libraries is a prevalent phenomenon that we observe at a rate of 0.2 to 6% in all sequencing runs on HiSeqX, HiSeq 4000/3000, and NovaSeq. Utilizing non-redundant dual indexing allows for the removal (flagging/filtering) of these swapped reads and eliminates swapping induced sample contamination, which is critical for sensitive applications such as RNA-seq, single cell, blood biopsy using circulating tumor DNA, or clinical sequencing.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29739332?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanghvi, Rashesh V</style></author><author><style face="normal" font="default" size="100%">Buhay, Christian J</style></author><author><style face="normal" font="default" size="100%">Powell, Bradford C</style></author><author><style face="normal" font="default" size="100%">Tsai, Ellen A</style></author><author><style face="normal" font="default" size="100%">Dorschner, Michael O</style></author><author><style face="normal" font="default" size="100%">Hong, Celine S</style></author><author><style face="normal" font="default" size="100%">Lebo, Matthew S</style></author><author><style face="normal" font="default" size="100%">Sasson, Ariella</style></author><author><style face="normal" font="default" size="100%">Hanna, David S</style></author><author><style face="normal" font="default" size="100%">McGee, Sean</style></author><author><style face="normal" font="default" size="100%">Bowling, Kevin M</style></author><author><style face="normal" font="default" size="100%">Cooper, Gregory M</style></author><author><style face="normal" font="default" size="100%">Gray, David E</style></author><author><style face="normal" font="default" size="100%">Lonigro, Robert J</style></author><author><style face="normal" font="default" size="100%">Dunford, Andrew</style></author><author><style face="normal" font="default" size="100%">Brennan, Christine A</style></author><author><style face="normal" font="default" size="100%">Cibulskis, Carrie</style></author><author><style face="normal" font="default" size="100%">Walker, Kimberly</style></author><author><style face="normal" font="default" size="100%">Carneiro, Mauricio O</style></author><author><style face="normal" font="default" size="100%">Sailsbery, Joshua</style></author><author><style face="normal" font="default" size="100%">Hindorff, Lucia A</style></author><author><style face="normal" font="default" size="100%">Robinson, Dan R</style></author><author><style face="normal" font="default" size="100%">Santani, Avni</style></author><author><style face="normal" font="default" size="100%">Sarmady, Mahdi</style></author><author><style face="normal" font="default" size="100%">Rehm, Heidi L</style></author><author><style face="normal" font="default" size="100%">Biesecker, Leslie G</style></author><author><style face="normal" font="default" size="100%">Nickerson, Deborah A</style></author><author><style face="normal" font="default" size="100%">Hutter, Carolyn M</style></author><author><style face="normal" font="default" size="100%">Garraway, Levi</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna M</style></author><author><style face="normal" font="default" size="100%">Wagle, Nikhil</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">NHGRI Clinical Sequencing Exploratory Research (CSER) Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterizing reduced coverage regions through comparison of exome and genome sequencing data across 10 centers.</style></title><secondary-title><style face="normal" font="default" size="100%">Genet Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genet. Med.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Exome Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">855-866</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;PURPOSE: &lt;/b&gt;As massively parallel sequencing is increasingly being used for clinical decision making, it has become critical to understand parameters that affect sequencing quality and to establish methods for measuring and reporting clinical sequencing standards. In this report, we propose a definition for reduced coverage regions and describe a set of standards for variant calling in clinical sequencing applications.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;To enable sequencing centers to assess the regions of poor sequencing quality in their own data, we optimized and used a tool (ExCID) to identify reduced coverage loci within genes or regions of particular interest. We used this framework to examine sequencing data from 500 patients generated in 10 projects at sequencing centers in the National Human Genome Research Institute/National Cancer Institute Clinical Sequencing Exploratory Research Consortium.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;This approach identified reduced coverage regions in clinically relevant genes, including known clinically relevant loci that were uniquely missed at individual centers, in multiple centers, and in all centers.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;This report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/29144510?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nattestad, Maria</style></author><author><style face="normal" font="default" size="100%">Goodwin, Sara</style></author><author><style face="normal" font="default" size="100%">Ng, Karen</style></author><author><style face="normal" font="default" size="100%">Baslan, Timour</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author><author><style face="normal" font="default" size="100%">Rescheneder, Philipp</style></author><author><style face="normal" font="default" size="100%">Garvin, Tyler</style></author><author><style face="normal" font="default" size="100%">Fang, Han</style></author><author><style face="normal" font="default" size="100%">Gurtowski, James</style></author><author><style face="normal" font="default" size="100%">Hutton, Elizabeth</style></author><author><style face="normal" font="default" size="100%">Tseng, Elizabeth</style></author><author><style face="normal" font="default" size="100%">Chin, Chen-Shan</style></author><author><style face="normal" font="default" size="100%">Beck, Timothy</style></author><author><style face="normal" font="default" size="100%">Sundaravadanam, Yogi</style></author><author><style face="normal" font="default" size="100%">Kramer, Melissa</style></author><author><style face="normal" font="default" size="100%">Antoniou, Eric</style></author><author><style face="normal" font="default" size="100%">McPherson, John D</style></author><author><style face="normal" font="default" size="100%">Hicks, James</style></author><author><style face="normal" font="default" size="100%">McCombie, W Richard</style></author><author><style face="normal" font="default" size="100%">Schatz, Michael C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Res.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Amplification</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Rearrangement</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomic Structural Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">MCF-7 Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Oncogenes</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor, ErbB-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Repetitive Sequences, Nucleic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1126-1135</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The SK-BR-3 cell line is one of the most important models for HER2+ breast cancers, which affect one in five breast cancer patients. SK-BR-3 is known to be highly rearranged, although much of the variation is in complex and repetitive regions that may be underreported. Addressing this, we sequenced SK-BR-3 using long-read single molecule sequencing from Pacific Biosciences and develop one of the most detailed maps of structural variations (SVs) in a cancer genome available, with nearly 20,000 variants present, most of which were missed by short-read sequencing. Surrounding the important  oncogene (also known as ), we discover a complex sequence of nested duplications and translocations, suggesting a punctuated progression. Full-length transcriptome sequencing further revealed several novel gene fusions within the nested genomic variants. Combining long-read genome and transcriptome sequencing enables an in-depth analysis of how SVs disrupt the genome and sheds new light on the complex mechanisms involved in cancer genome evolution.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/29954844?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Regier, Allison A</style></author><author><style face="normal" font="default" size="100%">Farjoun, Yossi</style></author><author><style face="normal" font="default" size="100%">Larson, David E</style></author><author><style face="normal" font="default" size="100%">Krasheninina, Olga</style></author><author><style face="normal" font="default" size="100%">Kang, Hyun Min</style></author><author><style face="normal" font="default" size="100%">Howrigan, Daniel P</style></author><author><style face="normal" font="default" size="100%">Chen, Bo-Juen</style></author><author><style face="normal" font="default" size="100%">Kher, Manisha</style></author><author><style face="normal" font="default" size="100%">Banks, Eric</style></author><author><style face="normal" font="default" size="100%">Ames, Darren C</style></author><author><style face="normal" font="default" size="100%">English, Adam C</style></author><author><style face="normal" font="default" size="100%">Li, Heng</style></author><author><style face="normal" font="default" size="100%">Xing, Jinchuan</style></author><author><style face="normal" font="default" size="100%">Zhang, Yeting</style></author><author><style face="normal" font="default" size="100%">Matise, Tara</style></author><author><style face="normal" font="default" size="100%">Abecasis, Goncalo R</style></author><author><style face="normal" font="default" size="100%">Salerno, Will</style></author><author><style face="normal" font="default" size="100%">Zody, Michael C</style></author><author><style face="normal" font="default" size="100%">Neale, Benjamin M</style></author><author><style face="normal" font="default" size="100%">Hall, Ira M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Human Genetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 10 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">4038</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/30279509?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Li, Chang</style></author><author><style face="normal" font="default" size="100%">Grove, Megan L</style></author><author><style face="normal" font="default" size="100%">Yu, Bing</style></author><author><style face="normal" font="default" size="100%">Jones, Barbara C</style></author><author><style face="normal" font="default" size="100%">Morrison, Alanna</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Liu, Xiaoming</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic variants in microRNA genes and targets associated with cardiovascular disease risk factors in the African-American population.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3' Untranslated Regions</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Black or African American</style></keyword><keyword><style  face="normal" font="default" size="100%">Cardiovascular Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotyping Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">137</style></volume><pages><style face="normal" font="default" size="100%">85-94</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The purpose of this study is to identify microRNA (miRNA) related polymorphism, including single nucleotide variants (SNVs) in mature miRNA-encoding sequences or in miRNA-target sites, and their association with cardiovascular disease (CVD) risk factors in African-American population. To achieve our objective, we examined 1900 African-Americans from the Atherosclerosis Risk in Communities study using SNVs identified from whole-genome sequencing data. A total of 971 SNVs found in 726 different mature miRNA-encoding sequences and 16,057 SNVs found in the three prime untranslated region (3'UTR) of 3647 protein-coding genes were identified and interrogated their associations with 17 CVD risk factors. Using single-variant-based approach, we found 5 SNVs in miRNA-encoding sequences to be associated with serum Lipoprotein(a) [Lp(a)], high-density lipoprotein (HDL) or triglycerides, and 2 SNVs in miRNA-target sites to be associated with Lp(a) and HDL, all with false discovery rates of 5%. Using a gene-based approach, we identified 3 pairs of associations between gene NSD1 and platelet count, gene HSPA4L and cardiac troponin T, and gene AHSA2 and magnesium. We successfully validated the association between a variant specific to African-American population, NR_039880.1:n.18A&gt;C, in mature hsa-miR-4727-5p encoding sequence and serum HDL level in an independent sample of 2135 African-Americans. Our study provided candidate miRNAs and their targets for further investigation of their potential contribution to ethnic disparities in CVD risk factors.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29264654?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Cervera, Alejandra</style></author><author><style face="normal" font="default" size="100%">Mohammadi, Pejman</style></author><author><style face="normal" font="default" size="100%">Aguet, François</style></author><author><style face="normal" font="default" size="100%">Reverter, Ferran</style></author><author><style face="normal" font="default" size="100%">Wolman, Aaron</style></author><author><style face="normal" font="default" size="100%">Guigo, Roderic</style></author><author><style face="normal" font="default" size="100%">Iossifov, Ivan</style></author><author><style face="normal" font="default" size="100%">Vasileva, Ana</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat. Genet.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Sep</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">1327-1334</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Coding variants represent many of the strongest associations between genotype and phenotype; however, they exhibit inter-individual differences in effect, termed 'variable penetrance'. Here, we study how cis-regulatory variation modifies the penetrance of coding variants. Using functional genomic and genetic data from the Genotype-Tissue Expression Project (GTEx), we observed that in the general population, purifying selection has depleted haplotype combinations predicted to increase pathogenic coding variant penetrance. Conversely, in cancer and autism patients, we observed an enrichment of penetrance increasing haplotype configurations for pathogenic variants in disease-implicated genes, providing evidence that regulatory haplotype configuration of coding variants affects disease risk. Finally, we experimentally validated this model by editing a Mendelian single-nucleotide polymorphism (SNP) using CRISPR/Cas9 on distinct expression haplotypes with the transcriptome as a phenotypic readout. Our results demonstrate that joint regulatory and coding variant effects are an important part of the genetic architecture of human traits and contribute to modified penetrance of disease-causing variants.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/30127527?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jiang, Yunyun</style></author><author><style face="normal" font="default" size="100%">Wangler, Michael F</style></author><author><style face="normal" font="default" size="100%">McGuire, Amy L</style></author><author><style face="normal" font="default" size="100%">Lupski, James R</style></author><author><style face="normal" font="default" size="100%">Posey, Jennifer E</style></author><author><style face="normal" font="default" size="100%">Khayat, Michael M</style></author><author><style face="normal" font="default" size="100%">Murdock, David R</style></author><author><style face="normal" font="default" size="100%">Sanchez-Pulido, Luis</style></author><author><style face="normal" font="default" size="100%">Ponting, Chris P</style></author><author><style face="normal" font="default" size="100%">Xia, Fan</style></author><author><style face="normal" font="default" size="100%">Hunter, Jill V</style></author><author><style face="normal" font="default" size="100%">Meng, Qingchang</style></author><author><style face="normal" font="default" size="100%">Murugan, Mullai</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The phenotypic spectrum of Xia-Gibbs syndrome.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Med Genet A</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am. J. Med. Genet. A</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 06</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">176</style></volume><pages><style face="normal" font="default" size="100%">1315-1326</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Xia-Gibbs syndrome (XGS: OMIM # 615829) results from de novo truncating mutations within the AT-Hook DNA Binding Motif Containing 1 gene (AHDC1). To further define the phenotypic and molecular spectrum of this disorder, we established an XGS Registry and recruited patients from a worldwide pool of approximately 60 probands. Additional de novo truncating mutations were observed among 25 individuals, extending both the known number of mutation sites and the range of positions within the coding region that were sensitive to alteration. Detailed phenotypic examination of 20 of these patients via clinical records review and data collection from additional surveys showed a wider age range than previously described. Data from developmental milestones showed evidence for delayed speech and that males were more severely affected. Neuroimaging from six available patients showed an associated thinning of the corpus callosum and posterior fossa cysts. An increased risk of both scoliosis and seizures relative to the population burden was also observed. Data from a modified autism screening tool revealed that XGS shares significant overlap with autism spectrum disorders. These details of the phenotypic heterogeneity of XGS implicate specific genotype/phenotype correlations and suggest potential clinical management guidelines.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/29696776?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mak, Angel C Y</style></author><author><style face="normal" font="default" size="100%">White, Marquitta J</style></author><author><style face="normal" font="default" size="100%">Eckalbar, Walter L</style></author><author><style face="normal" font="default" size="100%">Szpiech, Zachary A</style></author><author><style face="normal" font="default" size="100%">Oh, Sam S</style></author><author><style face="normal" font="default" size="100%">Pino-Yanes, Maria</style></author><author><style face="normal" font="default" size="100%">Hu, Donglei</style></author><author><style face="normal" font="default" size="100%">Goddard, Pagé</style></author><author><style face="normal" font="default" size="100%">Huntsman, Scott</style></author><author><style face="normal" font="default" size="100%">Galanter, Joshua</style></author><author><style face="normal" font="default" size="100%">Wu, Ann Chen</style></author><author><style face="normal" font="default" size="100%">Himes, Blanca E</style></author><author><style face="normal" font="default" size="100%">Germer, Soren</style></author><author><style face="normal" font="default" size="100%">Vogel, Julia M</style></author><author><style face="normal" font="default" size="100%">Bunting, Karen L</style></author><author><style face="normal" font="default" size="100%">Eng, Celeste</style></author><author><style face="normal" font="default" size="100%">Salazar, Sandra</style></author><author><style face="normal" font="default" size="100%">Keys, Kevin L</style></author><author><style face="normal" font="default" size="100%">Liberto, Jennifer</style></author><author><style face="normal" font="default" size="100%">Nuckton, Thomas J</style></author><author><style face="normal" font="default" size="100%">Nguyen, Thomas A</style></author><author><style face="normal" font="default" size="100%">Torgerson, Dara G</style></author><author><style face="normal" font="default" size="100%">Kwok, Pui-Yan</style></author><author><style face="normal" font="default" size="100%">Levin, Albert M</style></author><author><style face="normal" font="default" size="100%">Celedón, Juan C</style></author><author><style face="normal" font="default" size="100%">Forno, Erick</style></author><author><style face="normal" font="default" size="100%">Hakonarson, Hakon</style></author><author><style face="normal" font="default" size="100%">Sleiman, Patrick M</style></author><author><style face="normal" font="default" size="100%">Dahlin, Amber</style></author><author><style face="normal" font="default" size="100%">Tantisira, Kelan G</style></author><author><style face="normal" font="default" size="100%">Weiss, Scott T</style></author><author><style face="normal" font="default" size="100%">Serebrisky, Denise</style></author><author><style face="normal" font="default" size="100%">Brigino-Buenaventura, Emerita</style></author><author><style face="normal" font="default" size="100%">Farber, Harold J</style></author><author><style face="normal" font="default" size="100%">Meade, Kelley</style></author><author><style face="normal" font="default" size="100%">Lenoir, Michael A</style></author><author><style face="normal" font="default" size="100%">Avila, Pedro C</style></author><author><style face="normal" font="default" size="100%">Sen, Saunak</style></author><author><style face="normal" font="default" size="100%">Thyne, Shannon M</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Cintron, William</style></author><author><style face="normal" font="default" size="100%">Winkler, Cheryl A</style></author><author><style face="normal" font="default" size="100%">Moreno-Estrada, Andrés</style></author><author><style face="normal" font="default" size="100%">Sandoval, Karla</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Santana, Jose R</style></author><author><style face="normal" font="default" size="100%">Kumar, Rajesh</style></author><author><style face="normal" font="default" size="100%">Williams, L Keoki</style></author><author><style face="normal" font="default" size="100%">Ahituv, Nadav</style></author><author><style face="normal" font="default" size="100%">Ziv, Elad</style></author><author><style face="normal" font="default" size="100%">Seibold, Max A</style></author><author><style face="normal" font="default" size="100%">Darnell, Robert B</style></author><author><style face="normal" font="default" size="100%">Zaitlen, Noah</style></author><author><style face="normal" font="default" size="100%">Hernandez, Ryan D</style></author><author><style face="normal" font="default" size="100%">Burchard, Esteban G</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Respir Crit Care Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am. J. Respir. Crit. Care Med.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Jun 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">197</style></volume><pages><style face="normal" font="default" size="100%">1552-1564</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;RATIONALE: &lt;/b&gt;Albuterol, a bronchodilator medication, is the first-line therapy for asthma worldwide. There are significant racial/ethnic differences in albuterol drug response.&lt;/p&gt;&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;To identify genetic variants important for bronchodilator drug response (BDR) in racially diverse children.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We performed the first whole-genome sequencing pharmacogenetics study from 1,441 children with asthma from the tails of the BDR distribution to identify genetic association with BDR.&lt;/p&gt;&lt;p&gt;&lt;b&gt;MEASUREMENTS AND MAIN RESULTS: &lt;/b&gt;We identified population-specific and shared genetic variants associated with BDR, including genome-wide significant (P &lt; 3.53 × 10) and suggestive (P &lt; 7.06 × 10) loci near genes previously associated with lung capacity (DNAH5), immunity (NFKB1 and PLCB1), and β-adrenergic signaling (ADAMTS3 and COX18). Functional analyses of the BDR-associated SNP in NFKB1 revealed potential regulatory function in bronchial smooth muscle cells. The SNP is also an expression quantitative trait locus for a neighboring gene, SLC39A8. The lack of other asthma study populations with BDR and whole-genome sequencing data on minority children makes it impossible to perform replication of our rare variant associations. Minority underrepresentation also poses significant challenges to identify age-matched and population-matched cohorts of sufficient sample size for replication of our common variant findings.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;The lack of minority data, despite a collaboration of eight universities and 13 individual laboratories, highlights the urgent need for a dedicated national effort to prioritize diversity in research. Our study expands the understanding of pharmacogenetic analyses in racially/ethnically diverse populations and advances the foundation for precision medicine in at-risk and understudied minority populations.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/29509491?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author><author><style face="normal" font="default" size="100%">Wang, Xiao</style></author><author><style face="normal" font="default" size="100%">Bierhals, Andrew J</style></author><author><style face="normal" font="default" size="100%">Vourakis, A Christina</style></author><author><style face="normal" font="default" size="100%">Sperry, Alexandra E</style></author><author><style face="normal" font="default" size="100%">Natarajan, Pradeep</style></author><author><style face="normal" font="default" size="100%">Klarin, Derek</style></author><author><style face="normal" font="default" size="100%">Emdin, Connor A</style></author><author><style face="normal" font="default" size="100%">Zekavat, Seyedeh M</style></author><author><style face="normal" font="default" size="100%">Nomura, Akihiro</style></author><author><style face="normal" font="default" size="100%">Erdmann, Jeanette</style></author><author><style face="normal" font="default" size="100%">Schunkert, Heribert</style></author><author><style face="normal" font="default" size="100%">Samani, Nilesh J</style></author><author><style face="normal" font="default" size="100%">Kraus, William E</style></author><author><style face="normal" font="default" size="100%">Shah, Svati H</style></author><author><style face="normal" font="default" size="100%">Yu, Bing</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Rader, Daniel J</style></author><author><style face="normal" font="default" size="100%">Gupta, Namrata</style></author><author><style face="normal" font="default" size="100%">Frossard, Philippe M</style></author><author><style face="normal" font="default" size="100%">Rasheed, Asif</style></author><author><style face="normal" font="default" size="100%">Danesh, John</style></author><author><style face="normal" font="default" size="100%">Lander, Eric S</style></author><author><style face="normal" font="default" size="100%">Gabriel, Stacey</style></author><author><style face="normal" font="default" size="100%">Saleheen, Danish</style></author><author><style face="normal" font="default" size="100%">Musunuru, Kiran</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">PROMIS and Myocardial Infarction Genetics Consortium Investigators</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">ANGPTL3 Deficiency and Protection Against Coronary Artery Disease.</style></title><secondary-title><style face="normal" font="default" size="100%">J Am Coll Cardiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Am Coll Cardiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Angiopoietin-Like Protein 3</style></keyword><keyword><style  face="normal" font="default" size="100%">Angiopoietin-like Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Angiopoietins</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Atherosclerosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Coronary Artery Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipids</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Inbred C57BL</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Knockout</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation, Missense</style></keyword><keyword><style  face="normal" font="default" size="100%">Myocardial Infarction</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Apr 25</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">69</style></volume><pages><style face="normal" font="default" size="100%">2054-2063</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Familial combined hypolipidemia, a Mendelian condition characterized by substantial reductions in all 3 major lipid fractions, is caused by mutations that inactivate the gene angiopoietin-like 3 (ANGPTL3). Whether ANGPTL3 deficiency reduces risk of coronary artery disease (CAD) is unknown.&lt;/p&gt;&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;The study goal was to leverage 3 distinct lines of evidence-a family that included individuals with complete (compound heterozygote) ANGPTL3 deficiency, a population based-study of humans with partial (heterozygote) ANGPTL3 deficiency, and biomarker levels in patients with myocardial infarction (MI)-to test whether ANGPTL3 deficiency is associated with lower risk for CAD.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We assessed coronary atherosclerotic burden in 3 individuals with complete ANGPTL3 deficiency and 3 wild-type first-degree relatives using computed tomography angiography. In the population, ANGPTL3 loss-of-function (LOF) mutations were ascertained in up to 21,980 people with CAD and 158,200 control subjects. LOF mutations were defined as nonsense, frameshift, and splice-site variants, along with missense variants resulting in &lt;25% of wild-type ANGPTL3 activity in a mouse model. In a biomarker study, circulating ANGPTL3 concentration was measured in 1,493 people who presented with MI and 3,232 control subjects.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The 3 individuals with complete ANGPTL3 deficiency showed no evidence of coronary atherosclerotic plaque. ANGPTL3 gene sequencing demonstrated that approximately 1 in 309 people was a heterozygous carrier for an LOF mutation. Compared with those without mutation, heterozygous carriers of ANGPTL3 LOF mutations demonstrated a 17% reduction in circulating triglycerides and a 12% reduction in low-density lipoprotein cholesterol. Carrier status was associated with a 34% reduction in odds of CAD (odds ratio: 0.66; 95% confidence interval: 0.44 to 0.98; p = 0.04). Individuals in the lowest tertile of circulating ANGPTL3 concentrations, compared with the highest, had reduced odds of MI (adjusted odds ratio: 0.65; 95% confidence interval: 0.55 to 0.77; p &lt; 0.001).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;ANGPTL3 deficiency is associated with protection from CAD.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">16</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28385496?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu, Hsiang-Chih</style></author><author><style face="normal" font="default" size="100%">Tan, Qiumin</style></author><author><style face="normal" font="default" size="100%">Rousseaux, Maxime W C</style></author><author><style face="normal" font="default" size="100%">Wang, Wei</style></author><author><style face="normal" font="default" size="100%">Kim, Ji-Yoen</style></author><author><style face="normal" font="default" size="100%">Richman, Ronald</style></author><author><style face="normal" font="default" size="100%">Wan, Ying-Wooi</style></author><author><style face="normal" font="default" size="100%">Yeh, Szu-Ying</style></author><author><style face="normal" font="default" size="100%">Patel, Jay M</style></author><author><style face="normal" font="default" size="100%">Liu, Xiuyun</style></author><author><style face="normal" font="default" size="100%">Lin, Tao</style></author><author><style face="normal" font="default" size="100%">Lee, Yoontae</style></author><author><style face="normal" font="default" size="100%">Fryer, John D</style></author><author><style face="normal" font="default" size="100%">Han, Jing</style></author><author><style face="normal" font="default" size="100%">Chahrour, Maria</style></author><author><style face="normal" font="default" size="100%">Finnell, Richard H</style></author><author><style face="normal" font="default" size="100%">Lei, Yunping</style></author><author><style face="normal" font="default" size="100%">Zurita-Jimenez, Maria E</style></author><author><style face="normal" font="default" size="100%">Ahimaz, Priyanka</style></author><author><style face="normal" font="default" size="100%">Anyane-Yeboa, Kwame</style></author><author><style face="normal" font="default" size="100%">Van Maldergem, Lionel</style></author><author><style face="normal" font="default" size="100%">Lehalle, Daphne</style></author><author><style face="normal" font="default" size="100%">Jean-Marcais, Nolwenn</style></author><author><style face="normal" font="default" size="100%">Mosca-Boidron, Anne-Laure</style></author><author><style face="normal" font="default" size="100%">Thevenon, Julien</style></author><author><style face="normal" font="default" size="100%">Cousin, Margot A</style></author><author><style face="normal" font="default" size="100%">Bro, Della E</style></author><author><style face="normal" font="default" size="100%">Lanpher, Brendan C</style></author><author><style face="normal" font="default" size="100%">Klee, Eric W</style></author><author><style face="normal" font="default" size="100%">Alexander, Nora</style></author><author><style face="normal" font="default" size="100%">Bainbridge, Matthew N</style></author><author><style face="normal" font="default" size="100%">Orr, Harry T</style></author><author><style face="normal" font="default" size="100%">Sillitoe, Roy V</style></author><author><style face="normal" font="default" size="100%">Ljungberg, M Cecilia</style></author><author><style face="normal" font="default" size="100%">Liu, Zhandong</style></author><author><style face="normal" font="default" size="100%">Schaaf, Christian P</style></author><author><style face="normal" font="default" size="100%">Zoghbi, Huda Y</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Disruption of the ATXN1-CIC complex causes a spectrum of neurobehavioral phenotypes in mice and humans.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Ataxin-1</style></keyword><keyword><style  face="normal" font="default" size="100%">Autism Spectrum Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">Cerebellum</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Intellectual Disability</style></keyword><keyword><style  face="normal" font="default" size="100%">Interpersonal Relations</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Nerve Tissue Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurodegenerative Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Nuclear Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Repressor Proteins</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">527-536</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gain-of-function mutations in some genes underlie neurodegenerative conditions, whereas loss-of-function mutations in the same genes have distinct phenotypes. This appears to be the case with the protein ataxin 1 (ATXN1), which forms a transcriptional repressor complex with capicua (CIC). Gain of function of the complex leads to neurodegeneration, but ATXN1-CIC is also essential for survival. We set out to understand the functions of the ATXN1-CIC complex in the developing forebrain and found that losing this complex results in hyperactivity, impaired learning and memory, and abnormal maturation and maintenance of upper-layer cortical neurons. We also found that CIC activity in the hypothalamus and medial amygdala modulates social interactions. Informed by these neurobehavioral features in mouse mutants, we identified five individuals with de novo heterozygous truncating mutations in CIC who share similar clinical features, including intellectual disability, attention deficit/hyperactivity disorder (ADHD), and autism spectrum disorder. Our study demonstrates that loss of ATXN1-CIC complexes causes a spectrum of neurobehavioral phenotypes.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28288114?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sabo, Aniko</style></author><author><style face="normal" font="default" size="100%">Mishra, Pamela</style></author><author><style face="normal" font="default" size="100%">Dugan-Perez, Shannon</style></author><author><style face="normal" font="default" size="100%">Voruganti, V Saroja</style></author><author><style face="normal" font="default" size="100%">Kent, Jack W</style></author><author><style face="normal" font="default" size="100%">Kalra, Divya</style></author><author><style face="normal" font="default" size="100%">Cole, Shelley A</style></author><author><style face="normal" font="default" size="100%">Comuzzie, Anthony G</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna M</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author><author><style face="normal" font="default" size="100%">Butte, Nancy F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exome sequencing reveals novel genetic loci influencing obesity-related traits in Hispanic children.</style></title><secondary-title><style face="normal" font="default" size="100%">Obesity (Silver Spring)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Obesity (Silver Spring)</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">ATPases Associated with Diverse Cellular Activities</style></keyword><keyword><style  face="normal" font="default" size="100%">Body Mass Index</style></keyword><keyword><style  face="normal" font="default" size="100%">Body Weight</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Hispanic or Latino</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Pediatric Obesity</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Waist Circumference</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">1270-1276</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;To perform whole exome sequencing in 928 Hispanic children and identify variants and genes associated with childhood obesity.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Single-nucleotide variants (SNVs) were identified from Illumina whole exome sequencing data using integrated read mapping, variant calling, and an annotation pipeline (Mercury). Association analyses of 74 obesity-related traits and exonic variants were performed using SeqMeta software. Rare autosomal variants were analyzed using gene-based association analyses, and common autosomal variants were analyzed at the SNV level.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;(1) Rare exonic variants in 10 genes and 16 common SNVs in 11 genes that were associated with obesity traits in a cohort of Hispanic children were identified, (2) novel rare variants in peroxisome biogenesis factor 1 (PEX1) associated with several obesity traits (weight, weight z score, BMI, BMI z score, waist circumference, fat mass, trunk fat mass) were discovered, and (3) previously reported SNVs associated with childhood obesity were replicated.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Convergence of whole exome sequencing, a family-based design, and extensive phenotyping discovered novel rare and common variants associated with childhood obesity. Linking PEX1 to obesity phenotypes poses a novel mechanism of peroxisomal biogenesis and metabolism underlying the development of childhood obesity.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28508493?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Battle, Alexis</style></author><author><style face="normal" font="default" size="100%">Brown, Christopher D</style></author><author><style face="normal" font="default" size="100%">Engelhardt, Barbara E</style></author><author><style face="normal" font="default" size="100%">Montgomery, Stephen B</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">GTEx Consortium</style></author><author><style face="normal" font="default" size="100%">Laboratory, Data Analysis &amp;Coordinating Center (LDACC)—Analysis Working Group</style></author><author><style face="normal" font="default" size="100%">Statistical Methods groups—Analysis Working Group</style></author><author><style face="normal" font="default" size="100%">Enhancing GTEx (eGTEx) groups</style></author><author><style face="normal" font="default" size="100%">NIH Common Fund</style></author><author><style face="normal" font="default" size="100%">NIH/NCI</style></author><author><style face="normal" font="default" size="100%">NIH/NHGRI</style></author><author><style face="normal" font="default" size="100%">NIH/NIMH</style></author><author><style face="normal" font="default" size="100%">NIH/NIDA</style></author><author><style face="normal" font="default" size="100%">Biospecimen Collection Source Site—NDRI</style></author><author><style face="normal" font="default" size="100%">Biospecimen Collection Source Site—RPCI</style></author><author><style face="normal" font="default" size="100%">Biospecimen Core Resource—VARI</style></author><author><style face="normal" font="default" size="100%">Brain Bank Repository—University of Miami Brain Endowment Bank</style></author><author><style face="normal" font="default" size="100%">Leidos Biomedical—Project Management</style></author><author><style face="normal" font="default" size="100%">ELSI Study</style></author><author><style face="normal" font="default" size="100%">Genome Browser Data Integration &amp;Visualization—EBI</style></author><author><style face="normal" font="default" size="100%">Genome Browser Data Integration &amp;Visualization—UCSC Genomics Institute, University of California Santa Cruz</style></author><author><style face="normal" font="default" size="100%">Lead analysts:</style></author><author><style face="normal" font="default" size="100%">Laboratory, Data Analysis &amp;Coordinating Center (LDACC):</style></author><author><style face="normal" font="default" size="100%">NIH program management:</style></author><author><style face="normal" font="default" size="100%">Biospecimen collection:</style></author><author><style face="normal" font="default" size="100%">Pathology:</style></author><author><style face="normal" font="default" size="100%">eQTL manuscript working group:</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic effects on gene expression across human tissues.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nature</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosomes, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Organ Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Oct 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">550</style></volume><pages><style face="normal" font="default" size="100%">204-213</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7675</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29022597?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kim-Hellmuth, Sarah</style></author><author><style face="normal" font="default" size="100%">Bechheim, Matthias</style></author><author><style face="normal" font="default" size="100%">Pütz, Benno</style></author><author><style face="normal" font="default" size="100%">Mohammadi, Pejman</style></author><author><style face="normal" font="default" size="100%">Nédélec, Yohann</style></author><author><style face="normal" font="default" size="100%">Giangreco, Nicholas</style></author><author><style face="normal" font="default" size="100%">Becker, Jessica</style></author><author><style face="normal" font="default" size="100%">Kaiser, Vera</style></author><author><style face="normal" font="default" size="100%">Fricker, Nadine</style></author><author><style face="normal" font="default" size="100%">Beier, Esther</style></author><author><style face="normal" font="default" size="100%">Boor, Peter</style></author><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Nöthen, Markus M</style></author><author><style face="normal" font="default" size="100%">Barreiro, Luis B</style></author><author><style face="normal" font="default" size="100%">Pickrell, Joseph K</style></author><author><style face="normal" font="default" size="100%">Müller-Myhsok, Bertram</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author><author><style face="normal" font="default" size="100%">Schumacher, Johannes</style></author><author><style face="normal" font="default" size="100%">Hornung, Veit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acetylmuramyl-Alanyl-Isoglutamine</style></keyword><keyword><style  face="normal" font="default" size="100%">Adjuvants, Immunologic</style></keyword><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Autoimmune Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Healthy Volunteers</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Indicators and Reagents</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipids</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipopolysaccharides</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Monocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Regulatory Sequences, Nucleic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Double-Stranded</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Aug 16</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">266</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The immune system plays a major role in human health and disease, and understanding genetic causes of interindividual variability of immune responses is vital. Here, we isolate monocytes from 134 genotyped individuals, stimulate these cells with three defined microbe-associated molecular patterns (LPS, MDP, and 5'-ppp-dsRNA), and profile the transcriptomes at three time points. Mapping expression quantitative trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with varying effects between conditions. We characterize the dynamics of genetic regulation on early and late immune response and observe an enrichment of reQTLs in distal cis-regulatory elements. In addition, reQTLs are enriched for recent positive selection with an evolutionary trend towards enhanced immune response. Finally, we uncover reQTL effects in multiple GWAS loci and show a stronger enrichment for response than constant eQTLs in GWAS signals of several autoimmune diseases. This demonstrates the importance of infectious stimuli in modifying genetic predisposition to disease.Insight into the genetic influence on the immune response is important for the understanding of interindividual variability in human pathologies. Here, the authors generate transcriptome data from human blood monocytes stimulated with various immune stimuli and provide a time-resolved response eQTL map.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28814792?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chiang, Colby</style></author><author><style face="normal" font="default" size="100%">Scott, Alexandra J</style></author><author><style face="normal" font="default" size="100%">Davis, Joe R</style></author><author><style face="normal" font="default" size="100%">Tsang, Emily K</style></author><author><style face="normal" font="default" size="100%">Li, Xin</style></author><author><style face="normal" font="default" size="100%">Kim, Yungil</style></author><author><style face="normal" font="default" size="100%">Hadzic, Tarik</style></author><author><style face="normal" font="default" size="100%">Damani, Farhan N</style></author><author><style face="normal" font="default" size="100%">Ganel, Liron</style></author><author><style face="normal" font="default" size="100%">Montgomery, Stephen B</style></author><author><style face="normal" font="default" size="100%">Battle, Alexis</style></author><author><style face="normal" font="default" size="100%">Conrad, Donald F</style></author><author><style face="normal" font="default" size="100%">Hall, Ira M</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">GTEx Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">The impact of structural variation on human gene expression.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">INDEL Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Linear Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 May</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">692-699</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5-6.8% of eQTLs-a substantially higher fraction than prior estimates-and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance of rare high-impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28369037?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Natarajan, Pradeep</style></author><author><style face="normal" font="default" size="100%">Young, Robin</style></author><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author><author><style face="normal" font="default" size="100%">Padmanabhan, Sandosh</style></author><author><style face="normal" font="default" size="100%">Baber, Usman</style></author><author><style face="normal" font="default" size="100%">Mehran, Roxana</style></author><author><style face="normal" font="default" size="100%">Sartori, Samantha</style></author><author><style face="normal" font="default" size="100%">Fuster, Valentin</style></author><author><style face="normal" font="default" size="100%">Reilly, Dermot F</style></author><author><style face="normal" font="default" size="100%">Butterworth, Adam</style></author><author><style face="normal" font="default" size="100%">Rader, Daniel J</style></author><author><style face="normal" font="default" size="100%">Ford, Ian</style></author><author><style face="normal" font="default" size="100%">Sattar, Naveed</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Polygenic Risk Score Identifies Subgroup With Higher Burden of Atherosclerosis and Greater Relative Benefit From Statin Therapy in the Primary Prevention Setting.</style></title><secondary-title><style face="normal" font="default" size="100%">Circulation</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Circulation</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Atherosclerosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Cost of Illness</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Hydroxymethylglutaryl-CoA Reductase Inhibitors</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Multifactorial Inheritance</style></keyword><keyword><style  face="normal" font="default" size="100%">Primary Prevention</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 May 30</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">135</style></volume><pages><style face="normal" font="default" size="100%">2091-2101</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Relative risk reduction with statin therapy has been consistent across nearly all subgroups studied to date. However, in analyses of 2 randomized controlled primary prevention trials (ASCOT [Anglo-Scandinavian Cardiac Outcomes Trial-Lipid-Lowering Arm] and JUPITER [Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin]), statin therapy led to a greater relative risk reduction among a subgroup at high genetic risk. Here, we aimed to confirm this observation in a third primary prevention randomized controlled trial. In addition, we assessed whether those at high genetic risk had a greater burden of subclinical coronary atherosclerosis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We studied participants from a randomized controlled trial of primary prevention with statin therapy (WOSCOPS [West of Scotland Coronary Prevention Study]; n=4910) and 2 observational cohort studies (CARDIA [Coronary Artery Risk Development in Young Adults] and BioImage; n=1154 and 4392, respectively). For each participant, we calculated a polygenic risk score derived from up to 57 common DNA sequence variants previously associated with coronary heart disease. We compared the relative efficacy of statin therapy in those at high genetic risk (top quintile of polygenic risk score) versus all others (WOSCOPS), as well as the association between the polygenic risk score and coronary artery calcification (CARDIA) and carotid artery plaque burden (BioImage).&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Among WOSCOPS trial participants at high genetic risk, statin therapy was associated with a relative risk reduction of 44% (95% confidence interval [CI], 22-60; &lt;0.001), whereas in all others, the relative risk reduction was 24% (95% CI, 8-37; =0.004) despite similar low-density lipoprotein cholesterol lowering. In a study-level meta-analysis across the WOSCOPS, ASCOT, and JUPITER primary prevention, relative risk reduction in those at high genetic risk was 46% versus 26% in all others ( for heterogeneity=0.05). Across all 3 studies, the absolute risk reduction with statin therapy was 3.6% (95% CI, 2.0-5.1) among those in the high genetic risk group and 1.3% (95% CI, 0.6-1.9) in all others. Each 1-SD increase in the polygenic risk score was associated with 1.32-fold (95% CI, 1.04-1.68) greater likelihood of having coronary artery calcification and 9.7% higher (95% CI, 2.2-17.8) burden of carotid plaque.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Those at high genetic risk have a greater burden of subclinical atherosclerosis and derive greater relative and absolute benefit from statin therapy to prevent a first coronary heart disease event.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CLINICAL TRIAL REGISTRATION: &lt;/b&gt;URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00738725 (BioImage) and NCT00005130 (CARDIA). WOSCOPS was carried out and completed before the requirement for clinical trial registration.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">22</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28223407?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Morrison, Alanna C</style></author><author><style face="normal" font="default" size="100%">Huang, Zhuoyi</style></author><author><style face="normal" font="default" size="100%">Yu, Bing</style></author><author><style face="normal" font="default" size="100%">Metcalf, Ginger</style></author><author><style face="normal" font="default" size="100%">Liu, Xiaoming</style></author><author><style face="normal" font="default" size="100%">Ballantyne, Christie</style></author><author><style face="normal" font="default" size="100%">Coresh, Josef</style></author><author><style face="normal" font="default" size="100%">Yu, Fuli</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna</style></author><author><style face="normal" font="default" size="100%">Feofanova, Elena</style></author><author><style face="normal" font="default" size="100%">Rustagi, Navin</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Black or African American</style></keyword><keyword><style  face="normal" font="default" size="100%">C-Reactive Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol, HDL</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol, LDL</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosomes, Human, Pair 9</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Hemoglobins</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Introns</style></keyword><keyword><style  face="normal" font="default" size="100%">Leukocyte Count</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipoprotein(a)</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnesium</style></keyword><keyword><style  face="normal" font="default" size="100%">Natriuretic Peptide, Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Neutrophils</style></keyword><keyword><style  face="normal" font="default" size="100%">Peptide Fragments</style></keyword><keyword><style  face="normal" font="default" size="100%">Phosphorus</style></keyword><keyword><style  face="normal" font="default" size="100%">Platelet Count</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Troponin T</style></keyword><keyword><style  face="normal" font="default" size="100%">White People</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Feb 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">100</style></volume><pages><style face="normal" font="default" size="100%">205-215</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Whole-genome sequencing (WGS) allows for a comprehensive view of the sequence of the human genome. We present and apply integrated methodologic steps for interrogating WGS data to characterize the genetic architecture of 10 heart- and blood-related traits in a sample of 1,860 African Americans. In order to evaluate the contribution of regulatory and non-protein coding regions of the genome, we conducted aggregate tests of rare variation across the entire genomic landscape using a sliding window, complemented by an annotation-based assessment of the genome using predefined regulatory elements and within the first intron of all genes. These tests were performed treating all variants equally as well as with individual variants weighted by a measure of predicted functional consequence. Significant findings were assessed in 1,705 individuals of European ancestry. After these steps, we identified and replicated components of the genomic landscape significantly associated with heart- and blood-related traits. For two traits, lipoprotein(a) levels and neutrophil count, aggregate tests of low-frequency and rare variation were significantly associated across multiple motifs. For a third trait, cardiac troponin T, investigation of regulatory domains identified a locus on chromosome 9. These practical approaches for WGS analysis led to the identification of informative genomic regions and also showed that defined non-coding regions, such as first introns of genes and regulatory domains, are associated with important risk factor phenotypes. This study illustrates the tractable nature of WGS data and outlines an approach for characterizing the genetic architecture of complex traits.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28089252?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohammadi, Pejman</style></author><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Brown, Andrew A</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantifying the regulatory effect size of -acting genetic variation using allelic fold change.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Nov</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">1872-1884</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Mapping -acting expression quantitative trait loci (-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic variant, as a biologically interpretable unit for quantifying the effect size of -eQTLs and a mathematically convenient approach for systematic modeling of -regulation. This measure is mathematically independent from expression level and allele frequency, additive, applicable to multiallelic variants, and generalizable to multiple independent variants. We provide efficient tools and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Expression (GTEx) data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two eQTLs act independently in regulating gene expression. In summary, aFC is a solid measure of -regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29021289?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Posey, Jennifer E</style></author><author><style face="normal" font="default" size="100%">Harel, Tamar</style></author><author><style face="normal" font="default" size="100%">Liu, Pengfei</style></author><author><style face="normal" font="default" size="100%">Rosenfeld, Jill A</style></author><author><style face="normal" font="default" size="100%">James, Regis A</style></author><author><style face="normal" font="default" size="100%">Coban Akdemir, Zeynep H</style></author><author><style face="normal" font="default" size="100%">Walkiewicz, Magdalena</style></author><author><style face="normal" font="default" size="100%">Bi, Weimin</style></author><author><style face="normal" font="default" size="100%">Xiao, Rui</style></author><author><style face="normal" font="default" size="100%">Ding, Yan</style></author><author><style face="normal" font="default" size="100%">Xia, Fan</style></author><author><style face="normal" font="default" size="100%">Beaudet, Arthur L</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna M</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Eng, Christine M</style></author><author><style face="normal" font="default" size="100%">Sutton, V Reid</style></author><author><style face="normal" font="default" size="100%">Shaw, Chad A</style></author><author><style face="normal" font="default" size="100%">Plon, Sharon E</style></author><author><style face="normal" font="default" size="100%">Yang, Yaping</style></author><author><style face="normal" font="default" size="100%">Lupski, James R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation.</style></title><secondary-title><style face="normal" font="default" size="100%">N Engl J Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">N Engl J Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Diseases, Inborn</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotyping Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Retrospective Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Jan 05</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">376</style></volume><pages><style face="normal" font="default" size="100%">21-31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Whole-exome sequencing can provide insight into the relationship between observed clinical phenotypes and underlying genotypes.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We conducted a retrospective analysis of data from a series of 7374 consecutive unrelated patients who had been referred to a clinical diagnostic laboratory for whole-exome sequencing; our goal was to determine the frequency and clinical characteristics of patients for whom more than one molecular diagnosis was reported. The phenotypic similarity between molecularly diagnosed pairs of diseases was calculated with the use of terms from the Human Phenotype Ontology.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;A molecular diagnosis was rendered for 2076 of 7374 patients (28.2%); among these patients, 101 (4.9%) had diagnoses that involved two or more disease loci. We also analyzed parental samples, when available, and found that de novo variants accounted for 67.8% (61 of 90) of pathogenic variants in autosomal dominant disease genes and 51.7% (15 of 29) of pathogenic variants in X-linked disease genes; both variants were de novo in 44.7% (17 of 38) of patients with two monoallelic variants. Causal copy-number variants were found in 12 patients (11.9%) with multiple diagnoses. Phenotypic similarity scores were significantly lower among patients in whom the phenotype resulted from two distinct mendelian disorders that affected different organ systems (50 patients) than among patients with disorders that had overlapping phenotypic features (30 patients) (median score, 0.21 vs. 0.36; P=1.77×10).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;In our study, we found multiple molecular diagnoses in 4.9% of cases in which whole-exome sequencing was informative. Our results show that structured clinical ontologies can be used to determine the degree of overlap between two mendelian diseases in the same patient; the diseases can be distinct or overlapping. Distinct disease phenotypes affect different organ systems, whereas overlapping disease phenotypes are more likely to be caused by two genes encoding proteins that interact within the same pathway. (Funded by the National Institutes of Health and the Ting Tsung and Wei Fong Chao Foundation.).&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27959697?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rusu, Victor</style></author><author><style face="normal" font="default" size="100%">Hoch, Eitan</style></author><author><style face="normal" font="default" size="100%">Mercader, Josep M</style></author><author><style face="normal" font="default" size="100%">Tenen, Danielle E</style></author><author><style face="normal" font="default" size="100%">Gymrek, Melissa</style></author><author><style face="normal" font="default" size="100%">Hartigan, Christina R</style></author><author><style face="normal" font="default" size="100%">DeRan, Michael</style></author><author><style face="normal" font="default" size="100%">von Grotthuss, Marcin</style></author><author><style face="normal" font="default" size="100%">Fontanillas, Pierre</style></author><author><style face="normal" font="default" size="100%">Spooner, Alexandra</style></author><author><style face="normal" font="default" size="100%">Guzman, Gaelen</style></author><author><style face="normal" font="default" size="100%">Deik, Amy A</style></author><author><style face="normal" font="default" size="100%">Pierce, Kerry A</style></author><author><style face="normal" font="default" size="100%">Dennis, Courtney</style></author><author><style face="normal" font="default" size="100%">Clish, Clary B</style></author><author><style face="normal" font="default" size="100%">Carr, Steven A</style></author><author><style face="normal" font="default" size="100%">Wagner, Bridget K</style></author><author><style face="normal" font="default" size="100%">Schenone, Monica</style></author><author><style face="normal" font="default" size="100%">Ng, Maggie C Y</style></author><author><style face="normal" font="default" size="100%">Chen, Brian H</style></author><author><style face="normal" font="default" size="100%">Centeno-Cruz, Federico</style></author><author><style face="normal" font="default" size="100%">Zerrweck, Carlos</style></author><author><style face="normal" font="default" size="100%">Orozco, Lorena</style></author><author><style face="normal" font="default" size="100%">Altshuler, David M</style></author><author><style face="normal" font="default" size="100%">Schreiber, Stuart L</style></author><author><style face="normal" font="default" size="100%">Florez, Jose C</style></author><author><style face="normal" font="default" size="100%">Jacobs, Suzanne B R</style></author><author><style face="normal" font="default" size="100%">Lander, Eric S</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">MEDIA Consortium</style></author><author><style face="normal" font="default" size="100%">SIGMA T2D Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Basigin</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Membrane</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosomes, Human, Pair 17</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus, Type 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockdown Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Haplotypes</style></keyword><keyword><style  face="normal" font="default" size="100%">Hepatocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Heterozygote</style></keyword><keyword><style  face="normal" font="default" size="100%">Histone Code</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Liver</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Molecular</style></keyword><keyword><style  face="normal" font="default" size="100%">Monocarboxylic Acid Transporters</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Jun 29</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">170</style></volume><pages><style face="normal" font="default" size="100%">199-212.e20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. VIDEO ABSTRACT.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28666119?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Khera, Amit V</style></author><author><style face="normal" font="default" size="100%">Emdin, Connor A</style></author><author><style face="normal" font="default" size="100%">Drake, Isabel</style></author><author><style face="normal" font="default" size="100%">Natarajan, Pradeep</style></author><author><style face="normal" font="default" size="100%">Bick, Alexander G</style></author><author><style face="normal" font="default" size="100%">Cook, Nancy R</style></author><author><style face="normal" font="default" size="100%">Chasman, Daniel I</style></author><author><style face="normal" font="default" size="100%">Baber, Usman</style></author><author><style face="normal" font="default" size="100%">Mehran, Roxana</style></author><author><style face="normal" font="default" size="100%">Rader, Daniel J</style></author><author><style face="normal" font="default" size="100%">Fuster, Valentin</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Melander, Olle</style></author><author><style face="normal" font="default" size="100%">Orho-Melander, Marju</style></author><author><style face="normal" font="default" size="100%">Ridker, Paul M</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease.</style></title><secondary-title><style face="normal" font="default" size="100%">N Engl J Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">N Engl J Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Coronary Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Cross-Sectional Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Healthy Lifestyle</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Incidence</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Multifactorial Inheritance</style></keyword><keyword><style  face="normal" font="default" size="100%">Patient Compliance</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Dec 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">375</style></volume><pages><style face="normal" font="default" size="100%">2349-2358</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Both genetic and lifestyle factors contribute to individual-level risk of coronary artery disease. The extent to which increased genetic risk can be offset by a healthy lifestyle is unknown.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Using a polygenic score of DNA sequence polymorphisms, we quantified genetic risk for coronary artery disease in three prospective cohorts - 7814 participants in the Atherosclerosis Risk in Communities (ARIC) study, 21,222 in the Women's Genome Health Study (WGHS), and 22,389 in the Malmö Diet and Cancer Study (MDCS) - and in 4260 participants in the cross-sectional BioImage Study for whom genotype and covariate data were available. We also determined adherence to a healthy lifestyle among the participants using a scoring system consisting of four factors: no current smoking, no obesity, regular physical activity, and a healthy diet.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The relative risk of incident coronary events was 91% higher among participants at high genetic risk (top quintile of polygenic scores) than among those at low genetic risk (bottom quintile of polygenic scores) (hazard ratio, 1.91; 95% confidence interval [CI], 1.75 to 2.09). A favorable lifestyle (defined as at least three of the four healthy lifestyle factors) was associated with a substantially lower risk of coronary events than an unfavorable lifestyle (defined as no or only one healthy lifestyle factor), regardless of the genetic risk category. Among participants at high genetic risk, a favorable lifestyle was associated with a 46% lower relative risk of coronary events than an unfavorable lifestyle (hazard ratio, 0.54; 95% CI, 0.47 to 0.63). This finding corresponded to a reduction in the standardized 10-year incidence of coronary events from 10.7% for an unfavorable lifestyle to 5.1% for a favorable lifestyle in ARIC, from 4.6% to 2.0% in WGHS, and from 8.2% to 5.3% in MDCS. In the BioImage Study, a favorable lifestyle was associated with significantly less coronary-artery calcification within each genetic risk category.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Across four studies involving 55,685 participants, genetic and lifestyle factors were independently associated with susceptibility to coronary artery disease. Among participants at high genetic risk, a favorable lifestyle was associated with a nearly 50% lower relative risk of coronary artery disease than was an unfavorable lifestyle. (Funded by the National Institutes of Health and others.).&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">24</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27959714?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Turner, Tychele N</style></author><author><style face="normal" font="default" size="100%">Hormozdiari, Fereydoun</style></author><author><style face="normal" font="default" size="100%">Duyzend, Michael H</style></author><author><style face="normal" font="default" size="100%">McClymont, Sarah A</style></author><author><style face="normal" font="default" size="100%">Hook, Paul W</style></author><author><style face="normal" font="default" size="100%">Iossifov, Ivan</style></author><author><style face="normal" font="default" size="100%">Raja, Archana</style></author><author><style face="normal" font="default" size="100%">Baker, Carl</style></author><author><style face="normal" font="default" size="100%">Hoekzema, Kendra</style></author><author><style face="normal" font="default" size="100%">Stessman, Holly A</style></author><author><style face="normal" font="default" size="100%">Zody, Michael C</style></author><author><style face="normal" font="default" size="100%">Nelson, Bradley J</style></author><author><style face="normal" font="default" size="100%">Huddleston, John</style></author><author><style face="normal" font="default" size="100%">Sandstrom, Richard</style></author><author><style face="normal" font="default" size="100%">Smith, Joshua D</style></author><author><style face="normal" font="default" size="100%">Hanna, David</style></author><author><style face="normal" font="default" size="100%">Swanson, James M</style></author><author><style face="normal" font="default" size="100%">Faustman, Elaine M</style></author><author><style face="normal" font="default" size="100%">Bamshad, Michael J</style></author><author><style face="normal" font="default" size="100%">Stamatoyannopoulos, John</style></author><author><style face="normal" font="default" size="100%">Nickerson, Deborah A</style></author><author><style face="normal" font="default" size="100%">McCallion, Andrew S</style></author><author><style face="normal" font="default" size="100%">Darnell, Robert</style></author><author><style face="normal" font="default" size="100%">Eichler, Evan E</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome Sequencing of Autism-Affected Families Reveals Disruption of Putative Noncoding Regulatory DNA.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autistic Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Pedigree</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Jan 07</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">98</style></volume><pages><style face="normal" font="default" size="100%">58-74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We performed whole-genome sequencing (WGS) of 208 genomes from 53 families affected by simplex autism. For the majority of these families, no copy-number variant (CNV) or candidate de novo gene-disruptive single-nucleotide variant (SNV) had been detected by microarray or whole-exome sequencing (WES). We integrated multiple CNV and SNV analyses and extensive experimental validation to identify additional candidate mutations in eight families. We report that compared to control individuals, probands showed a significant (p = 0.03) enrichment of de novo and private disruptive mutations within fetal CNS DNase I hypersensitive sites (i.e., putative regulatory regions). This effect was only observed within 50 kb of genes that have been previously associated with autism risk, including genes where dosage sensitivity has already been established by recurrent disruptive de novo protein-coding mutations (ARID1B, SCN2A, NR3C2, PRKCA, and DSCAM). In addition, we provide evidence of gene-disruptive CNVs (in DISC1, WNT7A, RBFOX1, and MBD5), as well as smaller de novo CNVs and exon-specific SNVs missed by exome sequencing in neurodevelopmental genes (e.g., CANX, SAE1, and PIK3CA). Our results suggest that the detection of smaller, often multiple CNVs affecting putative regulatory elements might help explain additional risk of simplex autism.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26749308?dopt=Abstract</style></custom1></record></records></xml>