<?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%">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%">Zarate, Samantha</style></author><author><style face="normal" font="default" size="100%">Carroll, Andrew</style></author><author><style face="normal" font="default" size="100%">Mahmoud, Medhat</style></author><author><style face="normal" font="default" size="100%">Krasheninina, Olga</style></author><author><style face="normal" font="default" size="100%">Jun, Goo</style></author><author><style face="normal" font="default" size="100%">Salerno, William J</style></author><author><style face="normal" font="default" size="100%">Schatz, Michael C</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard A</style></author><author><style face="normal" font="default" size="100%">Sedlazeck, Fritz J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parliament2: Accurate structural variant calling at scale.</style></title><secondary-title><style face="normal" font="default" size="100%">Gigascience</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Gigascience</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Structural variants (SVs) are critical contributors to genetic diversity and genomic disease. To predict the phenotypic impact of SVs, there is a need for better estimates of both the occurrence and frequency of SVs, preferably from large, ethnically diverse cohorts. Thus, the current standard approach requires the use of short paired-end reads, which remain challenging to detect, especially at the scale of hundreds to thousands of samples.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FINDINGS: &lt;/b&gt;We present Parliament2, a consensus SV framework that leverages multiple best-in-class methods to identify high-quality SVs from short-read DNA sequence data at scale. Parliament2 incorporates pre-installed SV callers that are optimized for efficient execution in parallel to reduce the overall runtime and costs. We demonstrate the accuracy of Parliament2 when applied to data from NovaSeq and HiSeq X platforms with the Genome in a Bottle (GIAB) SV call set across all size classes. The reported quality score per SV is calibrated across different SV types and size classes. Parliament2 has the highest F1 score (74.27%) measured across the independent gold standard from GIAB. We illustrate the compute performance by processing all 1000 Genomes samples (2,691 samples) in &lt;1 day on GRCH38. Parliament2 improves the runtime performance of individual methods and is open source (https://github.com/slzarate/parliament2), and a Docker image, as well as a WDL implementation, is available.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Parliament2 provides both a highly accurate single-sample SV call set from short-read DNA sequence data and enables cost-efficient application over cloud or cluster environments, processing thousands of samples.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33347570?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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%">Liu, Yaowu</style></author><author><style face="normal" font="default" size="100%">Chen, Sixing</style></author><author><style face="normal" font="default" size="100%">Li, Zilin</style></author><author><style face="normal" font="default" size="100%">Morrison, Alanna C</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Lin, Xihong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am. J. Hum. Genet.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Mar 07</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">104</style></volume><pages><style face="normal" font="default" size="100%">410-421</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30849328?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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%">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%">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%">Li, Chang</style></author><author><style face="normal" font="default" size="100%">Grove, Megan L</style></author><author><style face="normal" font="default" size="100%">Yu, Bing</style></author><author><style face="normal" font="default" size="100%">Jones, Barbara C</style></author><author><style face="normal" font="default" size="100%">Morrison, Alanna</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Liu, Xiaoming</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic variants in microRNA genes and targets associated with cardiovascular disease risk factors in the African-American population.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3' Untranslated Regions</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Black or African American</style></keyword><keyword><style  face="normal" font="default" size="100%">Cardiovascular Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotyping Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">137</style></volume><pages><style face="normal" font="default" size="100%">85-94</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The purpose of this study is to identify microRNA (miRNA) related polymorphism, including single nucleotide variants (SNVs) in mature miRNA-encoding sequences or in miRNA-target sites, and their association with cardiovascular disease (CVD) risk factors in African-American population. To achieve our objective, we examined 1900 African-Americans from the Atherosclerosis Risk in Communities study using SNVs identified from whole-genome sequencing data. A total of 971 SNVs found in 726 different mature miRNA-encoding sequences and 16,057 SNVs found in the three prime untranslated region (3'UTR) of 3647 protein-coding genes were identified and interrogated their associations with 17 CVD risk factors. Using single-variant-based approach, we found 5 SNVs in miRNA-encoding sequences to be associated with serum Lipoprotein(a) [Lp(a)], high-density lipoprotein (HDL) or triglycerides, and 2 SNVs in miRNA-target sites to be associated with Lp(a) and HDL, all with false discovery rates of 5%. Using a gene-based approach, we identified 3 pairs of associations between gene NSD1 and platelet count, gene HSPA4L and cardiac troponin T, and gene AHSA2 and magnesium. We successfully validated the association between a variant specific to African-American population, NR_039880.1:n.18A&gt;C, in mature hsa-miR-4727-5p encoding sequence and serum HDL level in an independent sample of 2135 African-Americans. Our study provided candidate miRNAs and their targets for further investigation of their potential contribution to ethnic disparities in CVD risk factors.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29264654?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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%">Morrison, Alanna C</style></author><author><style face="normal" font="default" size="100%">Huang, Zhuoyi</style></author><author><style face="normal" font="default" size="100%">Yu, Bing</style></author><author><style face="normal" font="default" size="100%">Metcalf, Ginger</style></author><author><style face="normal" font="default" size="100%">Liu, Xiaoming</style></author><author><style face="normal" font="default" size="100%">Ballantyne, Christie</style></author><author><style face="normal" font="default" size="100%">Coresh, Josef</style></author><author><style face="normal" font="default" size="100%">Yu, Fuli</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna</style></author><author><style face="normal" font="default" size="100%">Feofanova, Elena</style></author><author><style face="normal" font="default" size="100%">Rustagi, Navin</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Black or African American</style></keyword><keyword><style  face="normal" font="default" size="100%">C-Reactive Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol, HDL</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol, LDL</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosomes, Human, Pair 9</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Hemoglobins</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Introns</style></keyword><keyword><style  face="normal" font="default" size="100%">Leukocyte Count</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipoprotein(a)</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnesium</style></keyword><keyword><style  face="normal" font="default" size="100%">Natriuretic Peptide, Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Neutrophils</style></keyword><keyword><style  face="normal" font="default" size="100%">Peptide Fragments</style></keyword><keyword><style  face="normal" font="default" size="100%">Phosphorus</style></keyword><keyword><style  face="normal" font="default" size="100%">Platelet Count</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Troponin T</style></keyword><keyword><style  face="normal" font="default" size="100%">White People</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Feb 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">100</style></volume><pages><style face="normal" font="default" size="100%">205-215</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Whole-genome sequencing (WGS) allows for a comprehensive view of the sequence of the human genome. We present and apply integrated methodologic steps for interrogating WGS data to characterize the genetic architecture of 10 heart- and blood-related traits in a sample of 1,860 African Americans. In order to evaluate the contribution of regulatory and non-protein coding regions of the genome, we conducted aggregate tests of rare variation across the entire genomic landscape using a sliding window, complemented by an annotation-based assessment of the genome using predefined regulatory elements and within the first intron of all genes. These tests were performed treating all variants equally as well as with individual variants weighted by a measure of predicted functional consequence. Significant findings were assessed in 1,705 individuals of European ancestry. After these steps, we identified and replicated components of the genomic landscape significantly associated with heart- and blood-related traits. For two traits, lipoprotein(a) levels and neutrophil count, aggregate tests of low-frequency and rare variation were significantly associated across multiple motifs. For a third trait, cardiac troponin T, investigation of regulatory domains identified a locus on chromosome 9. These practical approaches for WGS analysis led to the identification of informative genomic regions and also showed that defined non-coding regions, such as first introns of genes and regulatory domains, are associated with important risk factor phenotypes. This study illustrates the tractable nature of WGS data and outlines an approach for characterizing the genetic architecture of complex traits.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28089252?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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%">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></records></xml>