Cardiovascular disease is the leading cause of death worldwide and elevated blood lipid levels are the strongest risk factor. The goal of this R01 proposal is to identify genes and genetic variants associated with blood lipid levels to inform our understanding of the biology of lipids and cardiovascular disease and identify new targets for therapies for cardiovascular disease. Our research team combines strengths in cardiovascular disease, high-throughput genetics and genomics and development and application of innovative computational and statistical methods.
In Aim 1, we will assess ~40m genetic variants for association in very large samples after imputation from large, sequenced reference panels. We expect to meta-analyze data for ~400,000 individuals.
In Aim 2, we will examine coding variation for association with blood lipid levels, with a particular focus on low frequency variation that cannot be imputed well in GWAS cohorts. We anticipate the exome survey will include data for ~300,000 individuals from different ancestries.
In Aim 3, we will evaluate rare and novel variation, not reachable using chip or imputation-based approaches, by leveraging exome or whole genome sequence data from 10,000s sequenced samples.
In Aim 4, we will examine null alleles in lipid- associated genes we identify in coronary artery disease or myocardial infarction case-control studies, to better understand their role in cardiovascular disease and as potential drug targets. We are leaders in the development and implementation of tools, methods and websites for statistical analysis of genetic data and sharing of study results. Here, we will also develop a web portal to facilitate public sharing, interpretation, and experimental follow-up of results from our genetic discovery aims. Funding this proposal will allow continued effort and co-ordination of the Global Lipids Genetics Consortium (GLGC). Completion of our aims will provide new insights into disease mechanisms that have the potential to catalyze breakthroughs in prevention, treatment, and diagnosis of cardiovascular disease and may serve as a model for other large-scale genetic studies.

Public Health Relevance

Blood lipid levels are heritable, treatable risk factors for cardiovascular disease, the leading cause of death in the United States. To identify novel lipid genes and variants, we propose large-scale assessment of the genetic architecture of lipid levels using the latest in technological, statistical and bioinformatics innovation. We propose to target important common, low frequency and rare variants using sequencing, imputation, and array-based surveys of coding variation. The goal is that these genes will inform about biological mechanisms and potentially become targets of novel drug therapies that reduce the prevalence of cardiovascular disease.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL127564-01
Application #
8870798
Study Section
Genetics of Health and Disease Study Section (GHD)
Program Officer
Papanicolaou, George
Project Start
2015-04-01
Project End
2019-02-28
Budget Start
2015-04-01
Budget End
2016-02-29
Support Year
1
Fiscal Year
2015
Total Cost
$817,960
Indirect Cost
$150,288
Name
University of Michigan Ann Arbor
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Natarajan, Pradeep; Young, Robin; Stitziel, Nathan O et al. (2017) Polygenic Risk Score Identifies Subgroup With Higher Burden of Atherosclerosis and Greater Relative Benefit From Statin Therapy in the Primary Prevention Setting. Circulation 135:2091-2101
Marouli, Eirini (see original citation for additional authors) (2017) Rare and low-frequency coding variants alter human adult height. Nature 542:186-190
Liu, Dajiang J (see original citation for additional authors) (2017) Exome-wide association study of plasma lipids in >300,000 individuals. Nat Genet 49:1758-1766
Stitziel, Nathan O; Khera, Amit V; Wang, Xiao et al. (2017) ANGPTL3 Deficiency and Protection Against Coronary Artery Disease. J Am Coll Cardiol 69:2054-2063
Klarin, Derek; Emdin, Connor A; Natarajan, Pradeep et al. (2017) Genetic Analysis of Venous Thromboembolism in UK Biobank Identifies the ZFPM2 Locus and Implicates Obesity as a Causal Risk Factor. Circ Cardiovasc Genet 10:
Emdin, Connor A; Klarin, Derek; Natarajan, Pradeep et al. (2017) Genetic Variation at the Sulfonylurea Receptor, Type 2 Diabetes, and Coronary Heart Disease. Diabetes 66:2310-2315
Webb, Thomas R; Erdmann, Jeanette; Stirrups, Kathleen E et al. (2017) Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease. J Am Coll Cardiol 69:823-836
Klarin, Derek; Zhu, Qiuyu Martin; Emdin, Connor A et al. (2017) Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat Genet 49:1392-1397
Khera, Amit V; Won, Hong-Hee; Peloso, Gina M et al. (2017) Association of Rare and Common Variation in the Lipoprotein Lipase Gene With Coronary Artery Disease. JAMA 317:937-946
Khera, Amit V; Kathiresan, Sekar (2017) Is Coronary Atherosclerosis One Disease or Many? Setting Realistic Expectations for Precision Medicine. Circulation 135:1005-1007

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