<?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%">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%">Jung, In-Hyuk</style></author><author><style face="normal" font="default" size="100%">Elenbaas, Jared S</style></author><author><style face="normal" font="default" size="100%">Alisio, Arturo</style></author><author><style face="normal" font="default" size="100%">Santana, Katherine</style></author><author><style face="normal" font="default" size="100%">Young, Erica P</style></author><author><style face="normal" font="default" size="100%">Kang, Chul Joo</style></author><author><style face="normal" font="default" size="100%">Kachroo, Puja</style></author><author><style face="normal" font="default" size="100%">Lavine, Kory J</style></author><author><style face="normal" font="default" size="100%">Razani, Babak</style></author><author><style face="normal" font="default" size="100%">Mecham, Robert P</style></author><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SVEP1 is a human coronary artery disease locus that promotes atherosclerosis.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Transl Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Transl Med</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Mar 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A low-frequency variant of sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SVEP1), an extracellular matrix protein, is associated with risk of coronary disease in humans independent of plasma lipids. Despite a robust statistical association, if and how SVEP1 might contribute to atherosclerosis remained unclear. Here, using Mendelian randomization and complementary mouse models, we provide evidence that SVEP1 promotes atherosclerosis in humans and mice and is expressed by vascular smooth muscle cells (VSMCs) within the atherosclerotic plaque. VSMCs also interact with SVEP1, causing proliferation and dysregulation of key differentiation pathways, including integrin and Notch signaling. Fibroblast growth factor receptor transcription increases in VSMCs interacting with SVEP1 and is further increased by the coronary disease-associated  variant p.D2702G. These effects ultimately drive inflammation and promote atherosclerosis. Together, our results suggest that VSMC-derived SVEP1 is a proatherogenic factor and support the concept that pharmacological inhibition of SVEP1 should protect against atherosclerosis in humans.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">586</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33762433?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author><author><style face="normal" font="default" size="100%">Kanter, Jenny E</style></author><author><style face="normal" font="default" size="100%">Bornfeldt, Karin E</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Emerging Targets for Cardiovascular Disease Prevention in Diabetes.</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%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">744-757</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 1 and type 2 diabetes mellitus (T1DM and T2DM) increase the risk of atherosclerotic cardiovascular disease (CVD), resulting in acute cardiovascular events, such as heart attack and stroke. Recent clinical trials point toward new treatment and prevention strategies for cardiovascular complications of T2DM. New antidiabetic agents show unexpected cardioprotective benefits. Moreover, genetic and reverse translational strategies have revealed potential novel targets for CVD prevention in diabetes, including inhibition of apolipoprotein C3 (APOC3). Modeling and pharmacology-based approaches to improve insulin action provide additional potential strategies to combat CVD. The development of new strategies for improved diabetes and lipid control fuels hope for future prevention of CVD associated with diabetes.&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/32423639?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%">Chun, Sung</style></author><author><style face="normal" font="default" size="100%">Imakaev, Maxim</style></author><author><style face="normal" font="default" size="100%">Hui, Daniel</style></author><author><style face="normal" font="default" size="100%">Patsopoulos, Nikolaos A</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%">Stitziel, Nathan O</style></author><author><style face="normal" font="default" size="100%">Sunyaev, Shamil R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.</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%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus, Type 2</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%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Linkage Disequilibrium</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%">Models, Genetic</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%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">107</style></volume><pages><style face="normal" font="default" size="100%">46-59</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait's heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.&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/32470373?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%">Young, Erica P</style></author><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Capitalizing on Insights from Human Genetics to Identify Novel Therapeutic Targets for Coronary Artery Disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Annu Rev Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Annu. Rev. 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 01 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">70</style></volume><pages><style face="normal" font="default" size="100%">19-32</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Coronary artery disease (CAD) is a major cause of morbidity and mortality. Unfortunately, despite decades of research focused on disease pathogenesis, we still lack a sufficient pharmacopeia for preventing CAD. The failure of many novel cardiovascular drugs to improve clinical outcomes reflects the major substantial challenge of drug development: identifying causal mechanisms that can be therapeutically manipulated to lower disease risk. Identifying genetic variants that are associated with risk of CAD has emerged as a clear path toward improving our understanding of the underlying mechanisms that lead to disease and to the development of new therapies. Here, we review the potential utility and limitations of using human genetics to guide the identification of therapeutic targets for CAD.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30355262?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%">Lin, Chien-Jung</style></author><author><style face="normal" font="default" size="100%">Lin, Chieh-Yu</style></author><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetics of the extracellular matrix in aortic aneurysmal diseases.</style></title><secondary-title><style face="normal" font="default" size="100%">Matrix Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Matrix Biol.</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 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">71-72</style></volume><pages><style face="normal" font="default" size="100%">128-143</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Aortic aneurysms are morbid conditions that can lead to rupture or dissection and are categorized as thoracic (TAA) or abdominal aortic aneurysms (AAA) depending on their location. While AAA shares overlapping risk factors with atherosclerotic cardiovascular disease, TAA exhibits strong heritability. Human genetic studies in the past two decades have successfully identified numerous genes involved in both familial and sporadic forms of aortic aneurysm. In this review we will discuss the genetic basis of aortic aneurysm, focusing on the extracellular matrix and how insights from these studies have informed our understanding of human biology and disease pathogenesis.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/29656146?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%">Auer, Paul L</style></author><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic association studies in cardiovascular diseases: Do we have enough power?</style></title><secondary-title><style face="normal" font="default" size="100%">Trends Cardiovasc Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Trends Cardiovasc Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cardiovascular Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Accuracy</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Interpretation, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Association Studies</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%">Genetic Variation</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%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Research Design</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Assessment</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 Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">397-404</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Genetic association studies have a long history of delivering insightful results for cardiovascular disease (CVD) research. Beginning with early candidate gene studies, to genome-wide association studies, and now on to newer whole-genome sequencing studies, research in human genetics has enriched our understanding of the pathobiology of CVD. As these studies continue to expand, the issue of statistical power plays an important role in study design as well as the interpretation of results. We provide an overview of the component parts that determine statistical power and preview the future of CVD genetic association studies through this lens.&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/28456354?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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Human genetic insights into lipoproteins and risk of cardiometabolic disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Curr Opin Lipidol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Curr Opin Lipidol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cardiovascular Diseases</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%">Lipoproteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">113-119</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 OF REVIEW: &lt;/b&gt;Human genetic studies have been successfully used to identify genes and pathways relevant to human biology. Using genetic instruments composed of loci associated with human lipid traits, recent studies have begun to clarify the causal role of major lipid fractions in risk of cardiometabolic disease.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RECENT FINDINGS: &lt;/b&gt;The causal relationship between LDL cholesterol and coronary disease has been firmly established. Of the remaining two major fractions, recent studies have found that HDL cholesterol is not likely to be a causal particle in atherogenesis, and have instead shifted the causal focus to triglyceride-rich lipoproteins. Subsequent results are refining this view to suggest that triglycerides themselves might not be causal, but instead may be a surrogate for the causal cholesterol content within this fraction. Other studies have used a similar approach to address the association between lipid fractions and risk of type 2 diabetes. Beyond genetic variation in the target of statin medications, reduced LDL cholesterol associated with multiple genes encoding current or prospective drug targets associated with increased diabetic risk. In addition, genetically lower HDL cholesterol and genetically lower triglycerides both appear to increase risk of type 2 diabetes.&lt;/p&gt;&lt;p&gt;&lt;b&gt;SUMMARY: &lt;/b&gt;Results of these and future human genetic studies are positioned to provide substantive insights into the causal relationship between lipids and human disease, and should highlight mechanisms with important implications for our understanding of human biology and future lipid-altering therapeutic development.&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/28059951?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Natarajan, Pradeep</style></author><author><style face="normal" font="default" size="100%">Young, Robin</style></author><author><style face="normal" font="default" size="100%">Stitziel, Nathan O</style></author><author><style face="normal" font="default" size="100%">Padmanabhan, Sandosh</style></author><author><style face="normal" font="default" size="100%">Baber, Usman</style></author><author><style face="normal" font="default" size="100%">Mehran, Roxana</style></author><author><style face="normal" font="default" size="100%">Sartori, Samantha</style></author><author><style face="normal" font="default" size="100%">Fuster, Valentin</style></author><author><style face="normal" font="default" size="100%">Reilly, Dermot F</style></author><author><style face="normal" font="default" size="100%">Butterworth, Adam</style></author><author><style face="normal" font="default" size="100%">Rader, Daniel J</style></author><author><style face="normal" font="default" size="100%">Ford, Ian</style></author><author><style face="normal" font="default" size="100%">Sattar, Naveed</style></author><author><style face="normal" font="default" size="100%">Kathiresan, Sekar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Polygenic Risk Score Identifies Subgroup With Higher Burden of Atherosclerosis and Greater Relative Benefit From Statin Therapy in the Primary Prevention Setting.</style></title><secondary-title><style face="normal" font="default" size="100%">Circulation</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Circulation</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Atherosclerosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Cost of Illness</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Hydroxymethylglutaryl-CoA Reductase Inhibitors</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Multifactorial Inheritance</style></keyword><keyword><style  face="normal" font="default" size="100%">Primary Prevention</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 May 30</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">135</style></volume><pages><style face="normal" font="default" size="100%">2091-2101</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Relative risk reduction with statin therapy has been consistent across nearly all subgroups studied to date. However, in analyses of 2 randomized controlled primary prevention trials (ASCOT [Anglo-Scandinavian Cardiac Outcomes Trial-Lipid-Lowering Arm] and JUPITER [Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin]), statin therapy led to a greater relative risk reduction among a subgroup at high genetic risk. Here, we aimed to confirm this observation in a third primary prevention randomized controlled trial. In addition, we assessed whether those at high genetic risk had a greater burden of subclinical coronary atherosclerosis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We studied participants from a randomized controlled trial of primary prevention with statin therapy (WOSCOPS [West of Scotland Coronary Prevention Study]; n=4910) and 2 observational cohort studies (CARDIA [Coronary Artery Risk Development in Young Adults] and BioImage; n=1154 and 4392, respectively). For each participant, we calculated a polygenic risk score derived from up to 57 common DNA sequence variants previously associated with coronary heart disease. We compared the relative efficacy of statin therapy in those at high genetic risk (top quintile of polygenic risk score) versus all others (WOSCOPS), as well as the association between the polygenic risk score and coronary artery calcification (CARDIA) and carotid artery plaque burden (BioImage).&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Among WOSCOPS trial participants at high genetic risk, statin therapy was associated with a relative risk reduction of 44% (95% confidence interval [CI], 22-60; &lt;0.001), whereas in all others, the relative risk reduction was 24% (95% CI, 8-37; =0.004) despite similar low-density lipoprotein cholesterol lowering. In a study-level meta-analysis across the WOSCOPS, ASCOT, and JUPITER primary prevention, relative risk reduction in those at high genetic risk was 46% versus 26% in all others ( for heterogeneity=0.05). Across all 3 studies, the absolute risk reduction with statin therapy was 3.6% (95% CI, 2.0-5.1) among those in the high genetic risk group and 1.3% (95% CI, 0.6-1.9) in all others. Each 1-SD increase in the polygenic risk score was associated with 1.32-fold (95% CI, 1.04-1.68) greater likelihood of having coronary artery calcification and 9.7% higher (95% CI, 2.2-17.8) burden of carotid plaque.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Those at high genetic risk have a greater burden of subclinical atherosclerosis and derive greater relative and absolute benefit from statin therapy to prevent a first coronary heart disease event.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CLINICAL TRIAL REGISTRATION: &lt;/b&gt;URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00738725 (BioImage) and NCT00005130 (CARDIA). WOSCOPS was carried out and completed before the requirement for clinical trial registration.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">22</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28223407?dopt=Abstract</style></custom1></record></records></xml>