<?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%">Emdin, Connor A</style></author><author><style face="normal" font="default" size="100%">Haas, Mary</style></author><author><style face="normal" font="default" size="100%">Ajmera, Veeral</style></author><author><style face="normal" font="default" size="100%">Simon, Tracey G</style></author><author><style face="normal" font="default" size="100%">Homburger, Julian</style></author><author><style face="normal" font="default" size="100%">Neben, Cynthia</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%">Zhou, Alicia</style></author><author><style face="normal" font="default" size="100%">Denny, Joshua</style></author><author><style face="normal" font="default" size="100%">Corey, Kathleen</style></author><author><style face="normal" font="default" size="100%">Loomba, Rohit</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></contributors><titles><title><style face="normal" font="default" size="100%">Association of Genetic Variation With Cirrhosis: A Multi-Trait Genome-Wide Association and Gene-Environment Interaction Study.</style></title><secondary-title><style face="normal" font="default" size="100%">Gastroenterology</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Gastroenterology</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 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">160</style></volume><pages><style face="normal" font="default" size="100%">1620-1633.e13</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;In contrast to most other common diseases, few genetic variants have been identified that impact risk of cirrhosis. We aimed to identify new genetic variants that predispose to cirrhosis, to test whether such variants, aggregated into a polygenic score, enable genomic risk stratification, and to test whether alcohol intake or body mass index interacts with polygenic predisposition.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We conducted a multi-trait genome-wide association study combining cirrhosis and alanine aminotransferase levels performed in 5 discovery studies (UK Biobank, Vanderbilt BioVU, Atherosclerosis Risk in Communities study, and 2 case-control studies including 4829 individuals with cirrhosis and 72,705 controls and 362,539 individuals with alanine aminotransferase levels). Identified variants were replicated in 3 studies (Partners HealthCare Biobank, FinnGen, and Biobank Japan including 3554 individuals with cirrhosis and 343,826 controls). A polygenic score was tested in Partners HealthCare Biobank.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Five previously reported and 7 newly identified genetic variants were associated with cirrhosis in both the discovery studies multi-trait genome-wide association study (P &lt; 5 × 10) and the replication studies (P &lt; .05), including a missense variant in the APOE gene and a noncoding variant near EFN1A. These 12 variants were used to generate a polygenic score. Among Partners HealthCare Biobank individuals, high polygenic score-defined as the top quintile of the distribution-was associated with significantly increased risk of cirrhosis (odds ratio, 2.26; P &lt; .001) and related comorbidities compared with the lowest quintile. Risk was even more pronounced among those with extreme polygenic risk (top 1% of the distribution, odds ratio, 3.16; P &lt; .001). The impact of extreme polygenic risk was substantially more pronounced in those with elevated alcohol consumption or body mass index. Modeled as risk by age 75 years, probability of cirrhosis with extreme polygenic risk was 13.7%, 20.1%, and 48.2% among individuals with no or modest, moderate, and increased alcohol consumption, respectively (P &lt; .001). Similarly, probability among those with extreme polygenic risk was 6.5%, 10.3%, and 19.5% among individuals with normal weight, overweight, and obesity, respectively (P &lt; .001).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Twelve independent genetic variants, 7 of which are newly identified in this study, conferred risk for cirrhosis. Aggregated into a polygenic score, these variants identified a subset of the population at substantially increased risk who are most susceptible to the hepatotoxic effects of excess alcohol consumption or obesity.&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/33310085?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, Lei</style></author><author><style face="normal" font="default" size="100%">Abel, Haley J</style></author><author><style face="normal" font="default" size="100%">Das, Indraniel</style></author><author><style face="normal" font="default" size="100%">Larson, David E</style></author><author><style face="normal" font="default" size="100%">Ganel, Liron</style></author><author><style face="normal" font="default" size="100%">Kanchi, Krishna L</style></author><author><style face="normal" font="default" size="100%">Regier, Allison A</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%">Scott, Alexandra J</style></author><author><style face="normal" font="default" size="100%">Chiang, Colby</style></author><author><style face="normal" font="default" size="100%">Wang, Xinxin</style></author><author><style face="normal" font="default" size="100%">Lu, Shuangjia</style></author><author><style face="normal" font="default" size="100%">Christ, Ryan</style></author><author><style face="normal" font="default" size="100%">Service, Susan K</style></author><author><style face="normal" font="default" size="100%">Chiang, Charleston W K</style></author><author><style face="normal" font="default" size="100%">Havulinna, Aki S</style></author><author><style face="normal" font="default" size="100%">Kuusisto, Johanna</style></author><author><style face="normal" font="default" size="100%">Boehnke, Michael</style></author><author><style face="normal" font="default" size="100%">Laakso, Markku</style></author><author><style face="normal" font="default" size="100%">Palotie, Aarno</style></author><author><style face="normal" font="default" size="100%">Ripatti, Samuli</style></author><author><style face="normal" font="default" size="100%">Freimer, Nelson B</style></author><author><style face="normal" font="default" size="100%">Locke, Adam E</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></contributors><titles><title><style face="normal" font="default" size="100%">Association of structural variation with cardiometabolic traits in Finns.</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%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Cardiovascular Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol</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%">Finland</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%">Genotype</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%">Mitochondrial Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Pyruvate Dehydrogenase (Lipoamide)-Phosphatase</style></keyword><keyword><style  face="normal" font="default" size="100%">Pyruvic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Serum Albumin, Human</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 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">108</style></volume><pages><style face="normal" font="default" size="100%">583-596</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 contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47 × 10) and is also associated with increased levels of total cholesterol (p = 1.22 × 10) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81 × 10) and alanine (p = 6.14 × 10) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24 × 10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53 × 10). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.&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/33798444?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%">Rubinacci, Simone</style></author><author><style face="normal" font="default" size="100%">Ribeiro, Diogo M</style></author><author><style face="normal" font="default" size="100%">Hofmeister, Robin J</style></author><author><style face="normal" font="default" size="100%">Delaneau, Olivier</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient phasing and imputation of low-coverage sequencing data using large reference panels.</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%">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%">Likelihood Functions</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%">Sequence Analysis, DNA</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</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">53</style></volume><pages><style face="normal" font="default" size="100%">120-126</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Low-coverage whole-genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. However, its competitiveness against SNP arrays is undermined because current imputation methods are computationally expensive and unable to leverage large reference panels. Here, we describe a method, GLIMPSE, for phasing and imputation of low-coverage sequencing datasets from modern reference panels. We demonstrate its remarkable performance across different coverages and human populations. GLIMPSE achieves imputation of a genome for less than US$1 in computational cost, considerably outperforming other methods and improving imputation accuracy over the full allele frequency range. As a proof of concept, we show that 1× coverage enables effective gene expression association studies and outperforms dense SNP arrays in rare variant burden tests. Overall, this study illustrates the promising potential of low-coverage imputation and suggests a paradigm shift in the design of future genomic 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%">https://www.ncbi.nlm.nih.gov/pubmed/33414550?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%">Akçimen, Fulya</style></author><author><style face="normal" font="default" size="100%">Ross, Jay P</style></author><author><style face="normal" font="default" size="100%">Liao, Calwing</style></author><author><style face="normal" font="default" size="100%">Spiegelman, Dan</style></author><author><style face="normal" font="default" size="100%">Dion, Patrick A</style></author><author><style face="normal" font="default" size="100%">Rouleau, Guy A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expanded CAG Repeats in ATXN1, ATXN2, ATXN3, and HTT in the 1000 Genomes Project.</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%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Ataxin-1</style></keyword><keyword><style  face="normal" font="default" size="100%">Ataxin-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Ataxin-3</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Huntingtin Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Huntington Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Repressor Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Spinocerebellar Ataxias</style></keyword><keyword><style  face="normal" font="default" size="100%">Trinucleotide Repeat Expansion</style></keyword><keyword><style  face="normal" font="default" size="100%">Trinucleotide Repeats</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 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">514-518</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;Spinocerebellar ataxia types 1, 2, 3 and Huntington disease are neurodegenerative disorders caused by expanded CAG repeats.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We performed an in-silico analysis of CAG repeats in ATXN1, ATXN2, ATXN3, and HTT using 30× whole-=genome sequencing data of 2504 samples from the 1000 Genomes Project.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Seven HTT-positive, 3 ATXN2-positive, 1 ATXN3-positive, and 6 possibly ATXN1-positive samples were identified. No correlation was found between the repeat sizes of the different genes. The distribution of CAG alleles varied by ethnicity.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Our results suggest that there may be asymptomatic small expanded repeats in almost 0.5% of these populations. © 2020 International Parkinson and Movement Disorder Society.&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/33159825?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%">Dron, Jacqueline S</style></author><author><style face="normal" font="default" size="100%">Wang, Minxian</style></author><author><style face="normal" font="default" size="100%">Patel, Aniruddh P</style></author><author><style face="normal" font="default" size="100%">Kartoun, Uri</style></author><author><style face="normal" font="default" size="100%">Ng, Kenney</style></author><author><style face="normal" font="default" size="100%">Hegele, Robert A</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%">Genetic Predictor to Identify Individuals With High Lipoprotein(a) Concentrations.</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%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">e003182</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><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/33522245?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%">De Coster, Wouter</style></author><author><style face="normal" font="default" size="100%">Weissensteiner, Matthias H</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%">Towards population-scale long-read sequencing.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Rev Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Rev Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</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%">Industrial Development</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%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 09</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">572-587</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Long-read sequencing technologies have now reached a level of accuracy and yield that allows their application to variant detection at a scale of tens to thousands of samples. Concomitant with the development of new computational tools, the first population-scale studies involving long-read sequencing have emerged over the past 2 years and, given the continuous advancement of the field, many more are likely to follow. In this Review, we survey recent developments in population-scale long-read sequencing, highlight potential challenges of a scaled-up approach and provide guidance regarding experimental design. We provide an overview of current long-read sequencing platforms, variant calling methodologies and approaches for de novo assemblies and reference-based mapping approaches. Furthermore, we summarize strategies for variant validation, genotyping and predicting functional impact and emphasize challenges remaining in achieving long-read sequencing at a population scale.&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/34050336?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%">Fahed, Akl C</style></author><author><style face="normal" font="default" size="100%">Aragam, Krishna G</style></author><author><style face="normal" font="default" size="100%">Hindy, George</style></author><author><style face="normal" font="default" size="100%">Chen, Yii-Der Ida</style></author><author><style face="normal" font="default" size="100%">Chaudhary, Kumardeep</style></author><author><style face="normal" font="default" size="100%">Dobbyn, Amanda</style></author><author><style face="normal" font="default" size="100%">Krumholz, Harlan M</style></author><author><style face="normal" font="default" size="100%">Sheu, Wayne H H</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%">Chowdhury, Rajiv</style></author><author><style face="normal" font="default" size="100%">Cho, Judy</style></author><author><style face="normal" font="default" size="100%">Do, Ron</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%">Khera, Amit V</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transethnic Transferability of a Genome-Wide Polygenic Score for 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%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">e003092</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><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/33284643?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%">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%">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%">Demanelis, Kathryn</style></author><author><style face="normal" font="default" size="100%">Jasmine, Farzana</style></author><author><style face="normal" font="default" size="100%">Chen, Lin S</style></author><author><style face="normal" font="default" size="100%">Chernoff, Meytal</style></author><author><style face="normal" font="default" size="100%">Tong, Lin</style></author><author><style face="normal" font="default" size="100%">Delgado, Dayana</style></author><author><style face="normal" font="default" size="100%">Zhang, Chenan</style></author><author><style face="normal" font="default" size="100%">Shinkle, Justin</style></author><author><style face="normal" font="default" size="100%">Sabarinathan, Mekala</style></author><author><style face="normal" font="default" size="100%">Lin, Hannah</style></author><author><style face="normal" font="default" size="100%">Ramirez, Eduardo</style></author><author><style face="normal" font="default" size="100%">Oliva, Meritxell</style></author><author><style face="normal" font="default" size="100%">Kim-Hellmuth, Sarah</style></author><author><style face="normal" font="default" size="100%">Stranger, Barbara E</style></author><author><style face="normal" font="default" size="100%">Lai, Tsung-Po</style></author><author><style face="normal" font="default" size="100%">Aviv, Abraham</style></author><author><style face="normal" font="default" size="100%">Ardlie, Kristin G</style></author><author><style face="normal" font="default" size="100%">Aguet, François</style></author><author><style face="normal" font="default" size="100%">Ahsan, Habibul</style></author><author><style face="normal" font="default" size="100%">Doherty, Jennifer A</style></author><author><style face="normal" font="default" size="100%">Kibriya, Muhammad G</style></author><author><style face="normal" font="default" size="100%">Pierce, Brandon L</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%">Determinants of telomere length 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%">Aging</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Markers</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%">Organ Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Telomere</style></keyword><keyword><style  face="normal" font="default" size="100%">Telomere Homeostasis</style></keyword><keyword><style  face="normal" font="default" size="100%">Telomere Shortening</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;Telomere shortening is a hallmark of aging. Telomere length (TL) in blood cells has been studied extensively as a biomarker of human aging and disease; however, little is known regarding variability in TL in nonblood, disease-relevant tissue types. Here, we characterize variability in TLs from 6391 tissue samples, representing &gt;20 tissue types and 952 individuals from the Genotype-Tissue Expression (GTEx) project. We describe differences across tissue types, positive correlation among tissue types, and associations with age and ancestry. We show that genetic variation affects TL in multiple tissue types and that TL may mediate the effect of age on gene expression. Our results provide the foundational knowledge regarding TL in healthy tissues that is needed to interpret epidemiological studies of TL and human health.&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/32913074?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%">Niestroj, Lisa-Marie</style></author><author><style face="normal" font="default" size="100%">Perez-Palma, Eduardo</style></author><author><style face="normal" font="default" size="100%">Howrigan, Daniel P</style></author><author><style face="normal" font="default" size="100%">Zhou, Yadi</style></author><author><style face="normal" font="default" size="100%">Cheng, Feixiong</style></author><author><style face="normal" font="default" size="100%">Saarentaus, Elmo</style></author><author><style face="normal" font="default" size="100%">Nürnberg, Peter</style></author><author><style face="normal" font="default" size="100%">Stevelink, Remi</style></author><author><style face="normal" font="default" size="100%">Daly, Mark J</style></author><author><style face="normal" font="default" size="100%">Palotie, Aarno</style></author><author><style face="normal" font="default" size="100%">Lal, Dennis</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Epi25 Collaborative</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Epilepsy subtype-specific copy number burden observed in a genome-wide study of 17 458 subjects.</style></title><secondary-title><style face="normal" font="default" size="100%">Brain</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brain</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">DNA Copy Number Variations</style></keyword><keyword><style  face="normal" font="default" size="100%">Epilepsy</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%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</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%">143</style></volume><pages><style face="normal" font="default" size="100%">2106-2118</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cytogenic testing is routinely applied in most neurological centres for severe paediatric epilepsies. However, which characteristics of copy number variants (CNVs) confer most epilepsy risk and which epilepsy subtypes carry the most CNV burden, have not been explored on a genome-wide scale. Here, we present the largest CNV investigation in epilepsy to date with 10 712 European epilepsy cases and 6746 ancestry-matched controls. Patients with genetic generalized epilepsy, lesional focal epilepsy, non-acquired focal epilepsy, and developmental and epileptic encephalopathy were included. All samples were processed with the same technology and analysis pipeline. All investigated epilepsy types, including lesional focal epilepsy patients, showed an increase in CNV burden in at least one tested category compared to controls. However, we observed striking differences in CNV burden across epilepsy types and investigated CNV categories. Genetic generalized epilepsy patients have the highest CNV burden in all categories tested, followed by developmental and epileptic encephalopathy patients. Both epilepsy types also show association for deletions covering genes intolerant for truncating variants. Genome-wide CNV breakpoint association showed not only significant loci for genetic generalized and developmental and epileptic encephalopathy patients but also for lesional focal epilepsy patients. With a 34-fold risk for developing genetic generalized epilepsy, we show for the first time that the established epilepsy-associated 15q13.3 deletion represents the strongest risk CNV for genetic generalized epilepsy across the whole genome. Using the human interactome, we examined the largest connected component of the genes overlapped by CNVs in the four epilepsy types. We observed that genetic generalized epilepsy and non-acquired focal epilepsy formed disease modules. In summary, we show that in all common epilepsy types, 1.5-3% of patients carry epilepsy-associated CNVs. The characteristics of risk CNVs vary tremendously across and within epilepsy types. Thus, we advocate genome-wide genomic testing to identify all disease-associated types of CNVs.&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/32568404?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%">Palencia-Madrid, Leire</style></author><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%">Hohoff, Carsten</style></author><author><style face="normal" font="default" size="100%">Phillips, Christopher</style></author><author><style face="normal" font="default" size="100%">Kayser, Manfred</style></author><author><style face="normal" font="default" size="100%">Parson, Walther</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of the VISAGE Basic Tool for Appearance and Ancestry Prediction Using PowerSeq Chemistry on the MiSeq FGx System.</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%">DNA Fingerprinting</style></keyword><keyword><style  face="normal" font="default" size="100%">Eye Color</style></keyword><keyword><style  face="normal" font="default" size="100%">Forensic Genetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Markers</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Hair Color</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%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Pigmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</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 06 26</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;The study of DNA to predict externally visible characteristics (EVCs) and the biogeographical ancestry (BGA) from unknown samples is gaining relevance in forensic genetics. Technical developments in Massively Parallel Sequencing (MPS) enable the simultaneous analysis of hundreds of DNA markers, which improves successful Forensic DNA Phenotyping (FDP). The EU-funded VISAGE (VISible Attributes through GEnomics) Consortium has developed various targeted MPS-based lab tools to apply FDP in routine forensic analyses. Here, we present an evaluation of the VISAGE Basic tool for appearance and ancestry prediction based on PowerSeq chemistry (Promega) on a MiSeq FGx System (Illumina). The panel consists of 153 single nucleotide polymorphisms (SNPs) that provide information about EVCs (41 SNPs for eye, hair and skin color from HIrisPlex-S) and continental BGA (115 SNPs; three overlap with the EVCs SNP set). The assay was evaluated for sensitivity, repeatability and genotyping concordance, as well as its performance with casework-type samples. This targeted MPS assay provided complete genotypes at all 153 SNPs down to 125 pg of input DNA and 99.67% correct genotypes at 50 pg. It was robust in terms of repeatability and concordance and provided useful results with casework-type samples. The results suggest that this MPS assay is a useful tool for basic appearance and ancestry prediction in forensic genetics for users interested in applying PowerSeq chemistry and MiSeq for this purpose.&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/32604780?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%">Darnell, Robert B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Genetic Control of Stoichiometry Underlying Autism.</style></title><secondary-title><style face="normal" font="default" size="100%">Annu Rev Neurosci</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Annu Rev Neurosci</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 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">509-533</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Autism is a common and complex neurologic disorder whose scientific underpinnings have begun to be established in the past decade. The essence of this breakthrough has been a focus on families, where genetic analyses are strongest, versus large-scale, case-control studies. Autism genetics has progressed in parallel with technology, from analyses of copy number variation to whole-exome sequencing (WES) and whole-genome sequencing (WGS). Gene mutations causing complete loss of function account for perhaps one-third of cases, largely detected through WES. This limitation has increased interest in understanding the regulatory variants of genes that contribute in more subtle ways to the disorder. Strategies combining biochemical analysis of gene regulation, WGS analysis of the noncoding genome, and machine learning have begun to succeed. The emerging picture is that careful control of the amounts of transcription, mRNA, and proteins made by key brain genes-stoichiometry-plays a critical role in defining the clinical features of autism.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32640929?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%">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%">Orange, Dana E</style></author><author><style face="normal" font="default" size="100%">Yao, Vicky</style></author><author><style face="normal" font="default" size="100%">Sawicka, Kirsty</style></author><author><style face="normal" font="default" size="100%">Fak, John</style></author><author><style face="normal" font="default" size="100%">Frank, Mayu O</style></author><author><style face="normal" font="default" size="100%">Parveen, Salina</style></author><author><style face="normal" font="default" size="100%">Blachère, Nathalie E</style></author><author><style face="normal" font="default" size="100%">Hale, Caryn</style></author><author><style face="normal" font="default" size="100%">Zhang, Fan</style></author><author><style face="normal" font="default" size="100%">Raychaudhuri, Soumya</style></author><author><style face="normal" font="default" size="100%">Troyanskaya, Olga G</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%">RNA Identification of PRIME Cells Predicting Rheumatoid Arthritis Flares.</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%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Arthritis, Rheumatoid</style></keyword><keyword><style  face="normal" font="default" size="100%">B-Lymphocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Fibroblasts</style></keyword><keyword><style  face="normal" font="default" size="100%">Flow Cytometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression</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%">Mesenchymal Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Patient Acuity</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Surveys and Questionnaires</style></keyword><keyword><style  face="normal" font="default" size="100%">Symptom Flare Up</style></keyword><keyword><style  face="normal" font="default" size="100%">Synovial Fluid</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 16</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">383</style></volume><pages><style face="normal" font="default" size="100%">218-228</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;Rheumatoid arthritis, like many inflammatory diseases, is characterized by episodes of quiescence and exacerbation (flares). The molecular events leading to flares are unknown.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We established a clinical and technical protocol for repeated home collection of blood in patients with rheumatoid arthritis to allow for longitudinal RNA sequencing (RNA-seq). Specimens were obtained from 364 time points during eight flares over a period of 4 years in our index patient, as well as from 235 time points during flares in three additional patients. We identified transcripts that were differentially expressed before flares and compared these with data from synovial single-cell RNA-seq. Flow cytometry and sorted-blood-cell RNA-seq in additional patients were used to validate the findings.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Consistent changes were observed in blood transcriptional profiles 1 to 2 weeks before a rheumatoid arthritis flare. B-cell activation was followed by expansion of circulating CD45-CD31-PDPN+ preinflammatory mesenchymal, or PRIME, cells in the blood from patients with rheumatoid arthritis; these cells shared features of inflammatory synovial fibroblasts. Levels of circulating PRIME cells decreased during flares in all 4 patients, and flow cytometry and sorted-cell RNA-seq confirmed the presence of PRIME cells in 19 additional patients with rheumatoid arthritis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Longitudinal genomic analysis of rheumatoid arthritis flares revealed PRIME cells in the blood during the period before a flare and suggested a model in which these cells become activated by B cells in the weeks before a flare and subsequently migrate out of the blood into the synovium. (Funded by the National Institutes of Health and others.).&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/32668112?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%">Chen, Xiao</style></author><author><style face="normal" font="default" size="100%">Sanchis-Juan, Alba</style></author><author><style face="normal" font="default" size="100%">French, Courtney E</style></author><author><style face="normal" font="default" size="100%">Connell, Andrew J</style></author><author><style face="normal" font="default" size="100%">Delon, Isabelle</style></author><author><style face="normal" font="default" size="100%">Kingsbury, Zoya</style></author><author><style face="normal" font="default" size="100%">Chawla, Aditi</style></author><author><style face="normal" font="default" size="100%">Halpern, Aaron L</style></author><author><style face="normal" font="default" size="100%">Taft, Ryan J</style></author><author><style face="normal" font="default" size="100%">Bentley, David R</style></author><author><style face="normal" font="default" size="100%">Butchbach, Matthew E R</style></author><author><style face="normal" font="default" size="100%">Raymond, F Lucy</style></author><author><style face="normal" font="default" size="100%">Eberle, Michael A</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">NIHR BioResource</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data.</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%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Muscular Atrophy, Spinal</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival of Motor Neuron 1 Protein</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%">22</style></volume><pages><style face="normal" font="default" size="100%">945-953</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;Spinal muscular atrophy (SMA), caused by loss of the SMN1 gene, is a leading cause of early childhood death. Due to the near identical sequences of SMN1 and SMN2, analysis of this region is challenging. Population-wide SMA screening to quantify the SMN1 copy number (CN) is recommended by the American College of Medical Genetics and Genomics.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We developed a method that accurately identifies the CN of SMN1 and SMN2 using genome sequencing (GS) data by analyzing read depth and eight informative reference genome differences between SMN1/2.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We characterized SMN1/2 in 12,747 genomes, identified 1568 samples with SMN1 gains or losses and 6615 samples with SMN2 gains or losses, and calculated a pan-ethnic carrier frequency of 2%, consistent with previous studies. Additionally, 99.8% of our SMN1 and 99.7% of SMN2 CN calls agreed with orthogonal methods, with a recall of 100% for SMA and 97.8% for carriers, and a precision of 100% for both SMA and carriers.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;This SMN copy-number caller can be used to identify both carrier and affected status of SMA, enabling SMA testing to be offered as a comprehensive test in neonatal care and an accurate carrier screening tool in GS sequencing projects.&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/32066871?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%">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%">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%">Tusso, Sergio</style></author><author><style face="normal" font="default" size="100%">Nieuwenhuis, Bart P S</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author><author><style face="normal" font="default" size="100%">Davey, John W</style></author><author><style face="normal" font="default" size="100%">Jeffares, Daniel C</style></author><author><style face="normal" font="default" size="100%">Wolf, Jochen B W</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ancestral Admixture Is the Main Determinant of Global Biodiversity in Fission Yeast.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Biol Evol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol. Biol. Evol.</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 09 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">1975-1989</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Mutation and recombination are key evolutionary processes governing phenotypic variation and reproductive isolation. We here demonstrate that biodiversity within all globally known strains of Schizosaccharomyces pombe arose through admixture between two divergent ancestral lineages. Initial hybridization was inferred to have occurred ∼20-60 sexual outcrossing generations ago consistent with recent, human-induced migration at the onset of intensified transcontinental trade. Species-wide heritable phenotypic variation was explained near-exclusively by strain-specific arrangements of alternating ancestry components with evidence for transgressive segregation. Reproductive compatibility between strains was likewise predicted by the degree of shared ancestry. To assess the genetic determinants of ancestry block distribution across the genome, we characterized the type, frequency, and position of structural genomic variation using nanopore and single-molecule real-time sequencing. Despite being associated with double-strand break initiation points, over 800 segregating structural variants exerted overall little influence on the introgression landscape or on reproductive compatibility between strains. In contrast, we found strong ancestry disequilibrium consistent with negative epistatic selection shaping genomic ancestry combinations during the course of hybridization. This study provides a detailed, experimentally tractable example that genomes of natural populations are mosaics reflecting different evolutionary histories. Exploiting genome-wide heterogeneity in the history of ancestral recombination and lineage-specific mutations sheds new light on the population history of S. pombe and highlights the importance of hybridization as a creative force in generating biodiversity.&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/31225876?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%">van Karnebeek, Clara D M</style></author><author><style face="normal" font="default" size="100%">Ramos, Rúben J</style></author><author><style face="normal" font="default" size="100%">Wen, Xiao-Yan</style></author><author><style face="normal" font="default" size="100%">Tarailo-Graovac, Maja</style></author><author><style face="normal" font="default" size="100%">Gleeson, Joseph G</style></author><author><style face="normal" font="default" size="100%">Skrypnyk, Cristina</style></author><author><style face="normal" font="default" size="100%">Brand-Arzamendi, Koroboshka</style></author><author><style face="normal" font="default" size="100%">Karbassi, Farhad</style></author><author><style face="normal" font="default" size="100%">Issa, Mahmoud Y</style></author><author><style face="normal" font="default" size="100%">van der Lee, Robin</style></author><author><style face="normal" font="default" size="100%">Drögemöller, Britt I</style></author><author><style face="normal" font="default" size="100%">Koster, Janet</style></author><author><style face="normal" font="default" size="100%">Rousseau, Justine</style></author><author><style face="normal" font="default" size="100%">Campeau, Philippe M</style></author><author><style face="normal" font="default" size="100%">Wang, Youdong</style></author><author><style face="normal" font="default" size="100%">Cao, Feng</style></author><author><style face="normal" font="default" size="100%">Li, Meng</style></author><author><style face="normal" font="default" size="100%">Ruiter, Jos</style></author><author><style face="normal" font="default" size="100%">Ciapaite, Jolita</style></author><author><style face="normal" font="default" size="100%">Kluijtmans, Leo A J</style></author><author><style face="normal" font="default" size="100%">Willemsen, Michel A A P</style></author><author><style face="normal" font="default" size="100%">Jans, Judith J</style></author><author><style face="normal" font="default" size="100%">Ross, Colin J</style></author><author><style face="normal" font="default" size="100%">Wintjes, Liesbeth T</style></author><author><style face="normal" font="default" size="100%">Rodenburg, Richard J</style></author><author><style face="normal" font="default" size="100%">Huigen, Marleen C D G</style></author><author><style face="normal" font="default" size="100%">Jia, Zhengping</style></author><author><style face="normal" font="default" size="100%">Waterham, Hans R</style></author><author><style face="normal" font="default" size="100%">Wasserman, Wyeth W</style></author><author><style face="normal" font="default" size="100%">Wanders, Ronald J A</style></author><author><style face="normal" font="default" size="100%">Verhoeven-Duif, Nanda M</style></author><author><style face="normal" font="default" size="100%">Zaki, Maha S</style></author><author><style face="normal" font="default" size="100%">Wevers, Ron A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bi-allelic GOT2 Mutations Cause a Treatable Malate-Aspartate Shuttle-Related Encephalopathy.</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%">534-548</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Early-infantile encephalopathies with epilepsy are devastating conditions mandating an accurate diagnosis to guide proper management. Whole-exome sequencing was used to investigate the disease etiology in four children from independent families with intellectual disability and epilepsy, revealing bi-allelic GOT2 mutations. In-depth metabolic studies in individual 1 showed low plasma serine, hypercitrullinemia, hyperlactatemia, and hyperammonemia. The epilepsy was serine and pyridoxine responsive. Functional consequences of observed mutations were tested by measuring enzyme activity and by cell and animal models. Zebrafish and mouse models were used to validate brain developmental and functional defects and to test therapeutic strategies. GOT2 encodes the mitochondrial glutamate oxaloacetate transaminase. GOT2 enzyme activity was deficient in fibroblasts with bi-allelic mutations. GOT2, a member of the malate-aspartate shuttle, plays an essential role in the intracellular NAD(H) redox balance. De novo serine biosynthesis was impaired in fibroblasts with GOT2 mutations and GOT2-knockout HEK293 cells. Correcting the highly oxidized cytosolic NAD-redox state by pyruvate supplementation restored serine biosynthesis in GOT2-deficient cells. Knockdown of got2a in zebrafish resulted in a brain developmental defect associated with seizure-like electroencephalography spikes, which could be rescued by supplying pyridoxine in embryo water. Both pyridoxine and serine synergistically rescued embryonic developmental defects in zebrafish got2a morphants. The two treated individuals reacted favorably to their treatment. Our data provide a mechanistic basis for the biochemical abnormalities in GOT2 deficiency that may also hold for other MAS defects.&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/31422819?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%">Saito, Yuhki</style></author><author><style face="normal" font="default" size="100%">Yuan, Yuan</style></author><author><style face="normal" font="default" size="100%">Zucker-Scharff, Ilana</style></author><author><style face="normal" font="default" size="100%">Fak, John J</style></author><author><style face="normal" font="default" size="100%">Jereb, Saša</style></author><author><style face="normal" font="default" size="100%">Tajima, Yoko</style></author><author><style face="normal" font="default" size="100%">Licatalosi, Donny D</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%">Differential NOVA2-Mediated Splicing in Excitatory and Inhibitory Neurons Regulates Cortical Development and Cerebellar Function.</style></title><secondary-title><style face="normal" font="default" size="100%">Neuron</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Neuron</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 Feb 20</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">101</style></volume><pages><style face="normal" font="default" size="100%">707-720.e5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;RNA-binding proteins (RBPs) regulate genetic diversity, but the degree to which they do so in individual cell types in vivo is unknown. We developed NOVA2 cTag-crosslinking and immunoprecipitation (CLIP) to generate functional RBP-RNA maps from different neuronal populations in the mouse brain. Combining cell type datasets from Nova2-cTag and Nova2 conditional knockout mice revealed differential NOVA2 regulatory actions on alternative splicing (AS) on the same transcripts expressed in different neurons. This includes functional differences in transcripts expressed in cortical and cerebellar excitatory versus inhibitory neurons, where we find NOVA2 is required for, respectively, development of laminar structure, motor coordination, and synapse formation. We also find that NOVA2-regulated AS is coupled to NOVA2 regulation of intron retention in hundreds of transcripts, which can sequester the trans-acting splicing factor PTBP2. In summary, cTag-CLIP complements single-cell RNA sequencing (RNA-seq) studies by providing a means for understanding RNA regulation of functional cell diversity.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/30638744?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%">Zeng, Xue</style></author><author><style face="normal" font="default" size="100%">Hunt, Ava</style></author><author><style face="normal" font="default" size="100%">Jin, Sheng Chih</style></author><author><style face="normal" font="default" size="100%">Duran, Daniel</style></author><author><style face="normal" font="default" size="100%">Gaillard, Jonathan</style></author><author><style face="normal" font="default" size="100%">Kahle, Kristopher T</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EphrinB2-EphB4-RASA1 Signaling in Human Cerebrovascular Development and Disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Trends Mol Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Trends Mol 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 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">265-286</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 whole exome sequencing studies in humans have provided novel insight into the importance of the ephrinB2-EphB4-RASA1 signaling axis in cerebrovascular development, corroborating and extending previous work in model systems. Here, we aim to review the human cerebrovascular phenotypes associated with ephrinB2-EphB4-RASA1 mutations, including those recently discovered in Vein of Galen malformation: the most common and severe brain arteriovenous malformation in neonates. We will also discuss emerging paradigms of the molecular and cellular pathophysiology of disease-causing ephrinB2-EphB4-RASA1 mutations, including the potential role of somatic mosaicism. These observations have potential diagnostic and therapeutic implications for patients with rare congenital cerebrovascular diseases and their families.&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/30819650?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%">Dennenmoser, Stefan</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author><author><style face="normal" font="default" size="100%">Schatz, Michael C</style></author><author><style face="normal" font="default" size="100%">Altmüller, Janine</style></author><author><style face="normal" font="default" size="100%">Zytnicki, Matthias</style></author><author><style face="normal" font="default" size="100%">Nolte, Arne W</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome-wide patterns of transposon proliferation in an evolutionary young hybrid fish.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Ecol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol. Ecol.</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</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1491-1505</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Hybridization can induce transposons to jump into new genomic positions, which may result in their accumulation across the genome. Alternatively, transposon copy numbers may increase through nonallelic (ectopic) homologous recombination in highly repetitive regions of the genome. The relative contribution of transposition bursts versus recombination-based mechanisms to evolutionary processes remains unclear because studies on transposon dynamics in natural systems are rare. We assessed the genomewide distribution of transposon insertions in a young hybrid lineage (&quot;invasive Cottus&quot;, n = 11) and its parental species Cottus rhenanus (n = 17) and Cottus perifretum(n = 9) using a reference genome assembled from long single molecule pacbio reads. An inventory of transposable elements was reconstructed from the same data and annotated. Transposon copy numbers in the hybrid lineage increased in 120 (15.9%) out of 757 transposons studied here. The copy number increased on average by 69% (range: 10%-197%). Given the age of the hybrid lineage, this suggests that they have proliferated within a few hundred generations since admixture began. However, frequency spectra of transposon insertions revealed no increase in novel and rare insertions across assembled parts of the genome. This implies that transposons were added to repetitive regions of the genome that remain difficult to assemble. Future studies will need to evaluate whether recombination-based mechanisms rather than genomewide transposition may explain the majority of the recent transposon proliferation in the hybrid lineage. Irrespectively of the underlying mechanism, the observed overabundance in repetitive parts of the genome suggests that gene-rich regions are unlikely to be directly affected.&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/30520198?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%">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%">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%">Larson, David E</style></author><author><style face="normal" font="default" size="100%">Abel, Haley J</style></author><author><style face="normal" font="default" size="100%">Chiang, Colby</style></author><author><style face="normal" font="default" size="100%">Badve, Abhijit</style></author><author><style face="normal" font="default" size="100%">Das, Indraniel</style></author><author><style face="normal" font="default" size="100%">Eldred, James M</style></author><author><style face="normal" font="default" size="100%">Layer, Ryan 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%">svtools: population-scale analysis of structural variation.</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%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Nov 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">4782-4787</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;SUMMARY: &lt;/b&gt;Large-scale human genetics studies are now employing whole genome sequencing with the goal of conducting comprehensive trait mapping analyses of all forms of genome variation. However, methods for structural variation (SV) analysis have lagged far behind those for smaller scale variants, and there is an urgent need to develop more efficient tools that scale to the size of human populations. Here, we present a fast and highly scalable software toolkit (svtools) and cloud-based pipeline for assembling high quality SV maps-including deletions, duplications, mobile element insertions, inversions and other rearrangements-in many thousands of human genomes. We show that this pipeline achieves similar variant detection performance to established per-sample methods (e.g. LUMPY), while providing fast and affordable joint analysis at the scale of ≥100 000 genomes. These tools will help enable the next generation of human genetics studies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;AVAILABILITY AND IMPLEMENTATION: &lt;/b&gt;svtools is implemented in Python and freely available (MIT) from https://github.com/hall-lab/svtools.&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><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/31218349?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%">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%">Jereb, Saša</style></author><author><style face="normal" font="default" size="100%">Hwang, Hun-Way</style></author><author><style face="normal" font="default" size="100%">Van Otterloo, Eric</style></author><author><style face="normal" font="default" size="100%">Govek, Eve-Ellen</style></author><author><style face="normal" font="default" size="100%">Fak, John J</style></author><author><style face="normal" font="default" size="100%">Yuan, Yuan</style></author><author><style face="normal" font="default" size="100%">Hatten, Mary E</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%">Differential 3' Processing of Specific Transcripts Expands Regulatory and Protein Diversity Across Neuronal Cell Types.</style></title><secondary-title><style face="normal" font="default" size="100%">Elife</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Elife</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 Mar 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Alternative polyadenylation (APA) regulates mRNA translation, stability, and protein localization. However, it is unclear to what extent APA regulates these processes uniquely in specific cell types. Using a new technique, cTag-PAPERCLIP, we discovered significant differences in APA between the principal types of mouse cerebellar neurons, the Purkinje and granule cells, as well as between proliferating and differentiated granule cells. Transcripts that differed in APA in these comparisons were enriched in key neuronal functions and many differed in coding sequence in addition to 3'UTR length. We characterize , a transcript that shifted from expressing a short 3'UTR isoform to a longer one during granule cell differentiation. We show that  regulates granule cell precursor proliferation and that its long 3'UTR isoform is targeted by miR-124, contributing to its downregulation during development. Our findings provide insight into roles for APA in specific cell types and establish a platform for further functional studies.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/29578408?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%">Hwang, Hun-Way</style></author><author><style face="normal" font="default" size="100%">Saito, Yuhki</style></author><author><style face="normal" font="default" size="100%">Park, Christopher Y</style></author><author><style face="normal" font="default" size="100%">Blachère, Nathalie E</style></author><author><style face="normal" font="default" size="100%">Tajima, Yoko</style></author><author><style face="normal" font="default" size="100%">Fak, John J</style></author><author><style face="normal" font="default" size="100%">Zucker-Scharff, Ilana</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%">cTag-PAPERCLIP Reveals Alternative Polyadenylation Promotes Cell-Type Specific Protein Diversity and Shifts Araf Isoforms with Microglia Activation.</style></title><secondary-title><style face="normal" font="default" size="100%">Neuron</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Neuron</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Antigens, Neoplasm</style></keyword><keyword><style  face="normal" font="default" size="100%">Astrocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Cells, Cultured</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%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Microglia</style></keyword><keyword><style  face="normal" font="default" size="100%">Nerve Tissue Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuro-Oncological Ventral Antigen</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurons</style></keyword><keyword><style  face="normal" font="default" size="100%">Organ Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Polyadenylation</style></keyword><keyword><style  face="normal" font="default" size="100%">Polypyrimidine Tract-Binding Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Isoforms</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Serine-Threonine Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-Binding 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 Sep 13</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">95</style></volume><pages><style face="normal" font="default" size="100%">1334-1349.e5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Alternative polyadenylation (APA) is increasingly recognized to regulate gene expression across different cell types, but obtaining APA maps from individual cell types typically requires prior purification, a stressful procedure that can itself alter cellular states. Here, we describe a new platform, cTag-PAPERCLIP, that generates APA profiles from single cell populations in intact tissues; cTag-PAPERCLIP requires no tissue dissociation and preserves transcripts in native states. Applying cTag-PAPERCLIP to profile four major cell types in the mouse brain revealed common APA preferences between excitatory and inhibitory neurons distinct from astrocytes and microglia, regulated in part by neuron-specific RNA-binding proteins NOVA2 and PTBP2. We further identified a role of APA in switching Araf protein isoforms during microglia activation, impacting production of downstream inflammatory cytokines. Our results demonstrate the broad applicability of cTag-PAPERCLIP and a previously undiscovered role of APA in contributing to protein diversity between different cell types and cellular states within the brain.&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/28910620?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%">Turner, Tychele N</style></author><author><style face="normal" font="default" size="100%">Coe, Bradley P</style></author><author><style face="normal" font="default" size="100%">Dickel, Diane E</style></author><author><style face="normal" font="default" size="100%">Hoekzema, Kendra</style></author><author><style face="normal" font="default" size="100%">Nelson, Bradley J</style></author><author><style face="normal" font="default" size="100%">Zody, Michael C</style></author><author><style face="normal" font="default" size="100%">Kronenberg, Zev N</style></author><author><style face="normal" font="default" size="100%">Hormozdiari, Fereydoun</style></author><author><style face="normal" font="default" size="100%">Raja, Archana</style></author><author><style face="normal" font="default" size="100%">Pennacchio, Len A</style></author><author><style face="normal" font="default" size="100%">Darnell, Robert B</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%">Genomic Patterns of De Novo Mutation in Simplex 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%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Autistic Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Copy Number Variations</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Mutational Analysis</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%">INDEL Mutation</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%">Polymorphism, Single Nucleotide</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 19</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">171</style></volume><pages><style face="normal" font="default" size="100%">710-722.e12</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 further our understanding of the genetic etiology of autism, we generated and analyzed genome sequence data from 516 idiopathic autism families (2,064 individuals). This resource includes &gt;59 million single-nucleotide variants (SNVs) and 9,212 private copy number variants (CNVs), of which 133,992 and 88 are de novo mutations (DNMs), respectively. We estimate a mutation rate of ∼1.5 × 10 SNVs per site per generation with a significantly higher mutation rate in repetitive DNA. Comparing probands and unaffected siblings, we observe several DNM trends. Probands carry more gene-disruptive CNVs and SNVs, resulting in severe missense mutations and mapping to predicted fetal brain promoters and embryonic stem cell enhancers. These differences become more pronounced for autism genes (p = 1.8 × 10, OR = 2.2). Patients are more likely to carry multiple coding and noncoding DNMs in different genes, which are enriched for expression in striatal neurons (p = 3 × 10), suggesting a path forward for genetically characterizing more complex cases of autism.&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/28965761?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%">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>