@article {893, title = {Association of structural variation with cardiometabolic traits in Finns.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 04 01}, pages = {583-596}, abstract = {

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~{\texttimes} 10) and is also associated with increased levels of total cholesterol (p = 1.22~{\texttimes} 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~{\texttimes} 10) and alanine (p = 6.14~{\texttimes} 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~{\texttimes} 10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53~{\texttimes} 10). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

}, keywords = {Alleles, Cardiovascular Diseases, Cholesterol, DNA Copy Number Variations, Female, Finland, Genome, Human, Genomic Structural Variation, Genotype, High-Throughput Nucleotide Sequencing, Humans, Male, Mitochondrial Proteins, Promoter Regions, Genetic, Pyruvate Dehydrogenase (Lipoamide)-Phosphatase, Pyruvic Acid, Serum Albumin, Human}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.03.008}, author = {Chen, Lei and Abel, Haley J and Das, Indraniel and Larson, David E and Ganel, Liron and Kanchi, Krishna L and Regier, Allison A and Young, Erica P and Kang, Chul Joo and Scott, Alexandra J and Chiang, Colby and Wang, Xinxin and Lu, Shuangjia and Christ, Ryan and Service, Susan K and Chiang, Charleston W K and Havulinna, Aki S and Kuusisto, Johanna and Boehnke, Michael and Laakso, Markku and Palotie, Aarno and Ripatti, Samuli and Freimer, Nelson B and Locke, Adam E and Stitziel, Nathan O and Hall, Ira M} } @article {902, title = {SVEP1 is a human coronary artery disease locus that promotes atherosclerosis.}, journal = {Sci Transl Med}, volume = {13}, year = {2021}, month = {2021 Mar 24}, abstract = {

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.

}, issn = {1946-6242}, doi = {10.1126/scitranslmed.abe0357}, author = {Jung, In-Hyuk and Elenbaas, Jared S and Alisio, Arturo and Santana, Katherine and Young, Erica P and Kang, Chul Joo and Kachroo, Puja and Lavine, Kory J and Razani, Babak and Mecham, Robert P and Stitziel, Nathan O} } @article {862, title = {Emerging Targets for Cardiovascular Disease Prevention in Diabetes.}, journal = {Trends Mol Med}, volume = {26}, year = {2020}, month = {2020 08}, pages = {744-757}, abstract = {

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.

}, issn = {1471-499X}, doi = {10.1016/j.molmed.2020.03.011}, author = {Stitziel, Nathan O and Kanter, Jenny E and Bornfeldt, Karin E} } @article {839, title = {Mapping and characterization of structural variation in 17,795 human genomes.}, journal = {Nature}, volume = {583}, year = {2020}, month = {2020 07}, pages = {83-89}, abstract = {

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.

}, keywords = {Alleles, Case-Control Studies, Continental Population Groups, Epigenesis, Genetic, Female, Gene Dosage, Genetic Variation, Genetics, Population, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Male, Molecular Sequence Annotation, Quantitative Trait Loci, Software, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2371-0}, author = {Abel, Haley J and Larson, David E and Regier, Allison A and Chiang, Colby and Das, Indraniel and Kanchi, Krishna L and Layer, Ryan M and Neale, Benjamin M and Salerno, William J and Reeves, Catherine and Buyske, Steven and Matise, Tara C and Muzny, Donna M and Zody, Michael C and Lander, Eric S and Dutcher, Susan K and Stitziel, Nathan O and Hall, Ira M} } @article {875, title = {Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.}, journal = {Am J Hum Genet}, volume = {107}, year = {2020}, month = {2020 07 02}, pages = {46-59}, abstract = {

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{\textquoteright}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.

}, keywords = {Aged, Cohort Studies, Diabetes Mellitus, Type 2, Female, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.05.004}, author = {Chun, Sung and Imakaev, Maxim and Hui, Daniel and Patsopoulos, Nikolaos A and Neale, Benjamin M and Kathiresan, Sekar and Stitziel, Nathan O and Sunyaev, Shamil R} }