A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Funding Information: Xiang Zhu is supported by the Stein Fellowship from Stanford University and Institute for Computational and Data Sciences Seed Grant from The Pennsylvania State University. C.D.B. is supported by the NIH (R01-HL133218). Funding for the Global Lipids Genetics Consortium was provided by the NIH (R01-HL127564). This research was conducted using the UK Biobank Resource under application number 24460. This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by awards 2I01BX003362-03A1 and 1I01BX004821-01A1. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. We thank Bethany Klunder for administrative support. Study-specific acknowledgments are provided in the supplemental information. G.C.-P. is currently an employee of 23andMe Inc. M.J.C. is the Chief Scientist for Genomics England, a UK Government company. B.M. Psaty serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. G. Thorleifsson, A.H. D.F.G. H. Holm, U.T. and K.S. are employees of deCODE/Amgen Inc. V.S. has received honoraria for consultations from Novo Nordisk and Sanofi and has an ongoing research collaboration with Bayer Ltd. M. McCarthy has served on advisory panels for Pfizer, NovoNordisk, and Zoe Global and has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. M. McCarthy and A. Mahajan are employees of Genentech and holders of Roche stock. M.S. receives funding from Pfizer Inc. for a project unrelated to this work. M.E.K. is employed by SYNLAB MVZ Mannheim GmbH. W.M. has received grants from Siemens Healthineers, grants and personal fees from Aegerion Pharmaceuticals, grants and personal fees from AMGEN, grants from Astrazeneca, grants and personal fees from Sanofi, grants and personal fees from Alexion Pharmaceuticals, grants and personal fees from BASF, grants and personal fees from Abbott Diagnostics, grants and personal fees from Numares AG, grants and personal fees from Berlin-Chemie, grants and personal fees from Akzea Therapeutics, grants from Bayer Vital GmbH, grants from bestbion dx GmbH, grants from Boehringer Ingelheim Pharma GmbH Co KG, grants from Immundiagnostik GmbH, grants from Merck Chemicals GmbH, grants from MSD Sharp and Dohme GmbH, grants from Novartis Pharma GmbH, grants from Olink Proteomics, and other from Synlab Holding Deutschland GmbH, all outside the submitted work. A.V.K. has served as a consultant to Sanofi, Medicines Company, Maze Pharmaceuticals, Navitor Pharmaceuticals, Verve Therapeutics, Amgen, and Color Genomics; received speaking fees from Illumina and the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research, and reports a patent related to a genetic risk predictor (20190017119). S. Kathiresan is an employee of Verve Therapeutics and holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis, and San Therapeutics. He is a member of the scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; he has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, and Medscape; and he reports patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119). D.K. accepts consulting fees from Regeneron Pharmaceuticals. D.O.M.-K. is a part-time clinical research consultant for Metabolon, Inc. D. Saleheen has received support from the British Heart Foundation, Pfizer, Regeneron, Genentech, and Eli Lilly pharmaceuticals. P.N. reports investigator-initated grants from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Novartis, Roche / Genentech, is a co-founder of TenSixteen Bio, is a scientific advisory board member of Esperion Therapeutics, geneXwell, and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. The spouse of C.J.W. is employed by Regeneron. Funding Information: Xiang Zhu is supported by the Stein Fellowship from Stanford University and Institute for Computational and Data Sciences Seed Grant from The Pennsylvania State University . C.D.B. is supported by the NIH ( R01-HL133218 ). Funding for the Global Lipids Genetics Consortium was provided by the NIH ( R01-HL127564 ). This research was conducted using the UK Biobank Resource under application number 24460. This research is based on data from the Million Veteran Program, Office of Research and Development , Veterans Health Administration, and was supported by awards 2I01BX003362-03A1 and 1I01BX004821-01A1 . This publication does not represent the views of the Department of Veteran Affairs or the United States Government. We thank Bethany Klunder for administrative support. Study-specific acknowledgments are provided in the supplemental information . Publisher Copyright: © 2022 American Society of Human GeneticsA major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.Peer reviewe

Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data

Promoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood ([Formula: see text]), we predict 34,797 PAIs which show strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites are enriched in enhancers or near expression QTLs. Genes whose promoters are involved in PAIs are more actively expressed, and gene pairs with promoter-promoter interactions are enriched for co-expression. Integration of the predicted PAIs with GWAS data highlight interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation

Multivariate discovery and replication of five novel loci associated with Immunoglobulin G <i>N</i>-glycosylation

Multivariate analysis methods can uncover the relationship between phenotypic measures characterised by modern omic techniques. Here the authors conduct a multivariate GWAS on IgG N-glycosylation phenotypes and identify 5 novel loci enriched in immune system genes

Rare variant in scavenger receptor BI raises HDL cholesterol and increases risk of coronary heart disease.

Scavenger receptor BI (SR-BI) is the major receptor for high-density lipoprotein (HDL) cholesterol (HDL-C). In humans, high amounts of HDL-C in plasma are associated with a lower risk of coronary heart disease (CHD). Mice that have depleted Scarb1 (SR-BI knockout mice) have markedly elevated HDL-C levels but, paradoxically, increased atherosclerosis. The impact of SR-BI on HDL metabolism and CHD risk in humans remains unclear. Through targeted sequencing of coding regions of lipid-modifying genes in 328 individuals with extremely high plasma HDL-C levels, we identified a homozygote for a loss-of-function variant, in which leucine replaces proline 376 (P376L), in SCARB1, the gene encoding SR-BI. The P376L variant impairs posttranslational processing of SR-BI and abrogates selective HDL cholesterol uptake in transfected cells, in hepatocyte-like cells derived from induced pluripotent stem cells from the homozygous subject, and in mice. Large population-based studies revealed that subjects who are heterozygous carriers of the P376L variant have significantly increased levels of plasma HDL-C. P376L carriers have a profound HDL-related phenotype and an increased risk of CHD (odds ratio = 1.79, which is statistically significant)

Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits

The identification of genes and regulatory elements underlying the associations discovered by GWAS is essential to understanding the aetiology of complex traits (including diseases). Here, we demonstrate an analytical paradigm of prioritizing genes and regulatory elements at GWAS loci for follow-up functional studies. We perform an integrative analysis that uses summary-level SNP data from multi-omics studies to detect DNA methylation (DNAm) sites associated with gene expression and phenotype through shared genetic effects (i.e., pleiotropy). We identify pleiotropic associations between 7858 DNAm sites and 2733 genes. These DNAm sites are enriched in enhancers and promoters, and >40% of them are mapped to distal genes. Further pleiotropic association analyses, which link both the methylome and transcriptome to 12 complex traits, identify 149 DNAm sites and 66 genes, indicating a plausible mechanism whereby the effect of a genetic variant on phenotype is mediated by genetic regulation of transcription through DNAm

Association of Rare and Common Variation in the Lipoprotein Lipase Gene With Coronary Artery Disease.

IMPORTANCE: The activity of lipoprotein lipase (LPL) is the rate-determining step in clearing triglyceride-rich lipoproteins from the circulation. Mutations that damage the LPL gene (LPL) lead to lifelong deficiency in enzymatic activity and can provide insight into the relationship of LPL to human disease. OBJECTIVE: To determine whether rare and/or common variants in LPL are associated with early-onset coronary artery disease (CAD). DESIGN, SETTING, AND PARTICIPANTS: In a cross-sectional study, LPL was sequenced in 10 CAD case-control cohorts of the multinational Myocardial Infarction Genetics Consortium and a nested CAD case-control cohort of the Geisinger Health System DiscovEHR cohort between 2010 and 2015. Common variants were genotyped in up to 305 699 individuals of the Global Lipids Genetics Consortium and up to 120 600 individuals of the CARDIoGRAM Exome Consortium between 2012 and 2014. Study-specific estimates were pooled via meta-analysis. EXPOSURES: Rare damaging mutations in LPL included loss-of-function variants and missense variants annotated as pathogenic in a human genetics database or predicted to be damaging by computer prediction algorithms trained to identify mutations that impair protein function. Common variants in the LPL gene region included those independently associated with circulating triglyceride levels. MAIN OUTCOMES AND MEASURES: Circulating lipid levels and CAD. RESULTS: Among 46 891 individuals with LPL gene sequencing data available, the mean (SD) age was 50 (12.6) years and 51% were female. A total of 188 participants (0.40%; 95% CI, 0.35%-0.46%) carried a damaging mutation in LPL, including 105 of 32 646 control participants (0.32%) and 83 of 14 245 participants with early-onset CAD (0.58%). Compared with 46 703 noncarriers, the 188 heterozygous carriers of an LPL damaging mutation displayed higher plasma triglyceride levels (19.6 mg/dL; 95% CI, 4.6-34.6 mg/dL) and higher odds of CAD (odds ratio = 1.84; 95% CI, 1.35-2.51; P < .001). An analysis of 6 common LPL variants resulted in an odds ratio for CAD of 1.51 (95% CI, 1.39-1.64; P = 1.1 × 10-22) per 1-SD increase in triglycerides. CONCLUSIONS AND RELEVANCE: The presence of rare damaging mutations in LPL was significantly associated with higher triglyceride levels and presence of coronary artery disease. However, further research is needed to assess whether there are causal mechanisms by which heterozygous lipoprotein lipase deficiency could lead to coronary artery disease

Genome-wide analyses identify common variants associated with macular telangiectasia type 2

Idiopathic juxtafoveal retinal telangiectasis type 2 (macular telangiectasia type 2; MacTel) is a rare neurovascular degenerative retinal disease. To identify genetic susceptibility loci for MacTel, we performed a genome-wide association study (GWAS) with 476 cases and 1,733 controls of European ancestry. Genome-wide significant associations (P < 5 × 10−8) were identified at three independent loci (rs73171800 at 5q14.3, P = 7.74 × 10−17; rs715 at 2q34, P = 9.97 × 10−14; rs477992 at 1p12, P = 2.60 × 10−12) and then replicated (P < 0.01) in an independent cohort of 172 cases and 1,134 controls. The 5q14.3 locus is known to associate with variation in retinal vascular diameter, and the 2q34 and 1p12 loci have been implicated in the glycine/serine metabolic pathway. We subsequently found significant differences in blood serum levels of glycine (P = 4.04 × 10−6) and serine (P = 2.48 × 10−4) between MacTel cases and controls

The power of genetic diversity in genome-wide association studies of lipids

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4–23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice. © 2021, The Author(s), under exclusive licence to Springer Nature Limited

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology. © 2022 American Society of Human Genetic