The goal of this R35 proposal is to uncover novel genetic discoveries and biological mechanisms underlying association with devastating cardiovascular diseases. This proposal builds on strengths in high-throughput genetics and genomics and development and application of innovative computational and statistical methods and genomics technology to maximize the benefits of genetic studies of cardiovascular disease. We will continue our discovery efforts to uncover genetic variants associated with a variety of cardiovascular diseases including atrial fibrillation, aortic aneurysm and dissection, and myocardial infarction and coronary artery disease. Building on our previous work where we identified a number of new genes for coronary artery disease and lipids, we also propose to uncover the mechanisms underlying association at known loci using genetics and epigenomics. We propose to assess the phenotypic impact of the ~19 million variants and 20k indels and SVs identified from whole genome sequenced samples by imputing them into 70,000 new GWAS samples with many cardiovascular phenotypes. We will perform integrated analyses with epigenomics data to highlight clusters of loci with related function. We also propose to perform targeted sequencing of 300 genes in 30,000 CAD cases and controls to search for loss of function variants at CAD loci that implicate CAD genes. We will continue to search for mechanistic insight by performing a PheWAS for all CAD-associated variants identified, disentangling multiple independent signals and correlated traits and clinical endpoints using conditional testing. Completion of these studies will provide new insights into disease mechanisms that have the potential to catalyze breakthroughs in cardiovascular disease prevention, treatment, and diagnosis.

Public Health Relevance

Cardiovascular disease is the leading cause of death in the United States. We propose a variety of experimental, bioinformatics, and statistical methods to identify specific genes that cause cardiovascular diseases, including heart attack and atrial fibrillation. Improved understanding of the genes that cause cardiovascular diseases may improve our understanding of how diseases begin, support identification of novel drugs and therapies, and enable better targeting of preventive and therapeutic approaches.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Unknown (R35)
Project #
5R35HL135824-05
Application #
10094225
Study Section
Special Emphasis Panel (ZHL1)
Program Officer
Luo, James
Project Start
2017-02-01
Project End
2024-01-31
Budget Start
2021-02-01
Budget End
2022-01-31
Support Year
5
Fiscal Year
2021
Total Cost
Indirect Cost
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
Hornsby, Whitney E; Sareini, Mohamed-Ali; Golbus, Jessica R et al. (2018) Lower Extremity Function Is Independently Associated With Hospitalization Burden in Heart Failure With Preserved Ejection Fraction. J Card Fail :
Nielsen, Jonas B; Thorolfsdottir, Rosa B; Fritsche, Lars G et al. (2018) Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat Genet 50:1234-1239
Wolford, Brooke N; Willer, Cristen J; Surakka, Ida (2018) Electronic health records: the next wave of complex disease genetics. Hum Mol Genet 27:R14-R21
Thorolfsdottir, Rosa B; Sveinbjornsson, Gardar; Sulem, Patrick et al. (2018) Coding variants in RPL3L and MYZAP increase risk of atrial fibrillation. Commun Biol 1:68
Gusarova, Viktoria; O'Dushlaine, Colm; Teslovich, Tanya M et al. (2018) Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes. Nat Commun 9:2252
Mahajan, Anubha (see original citation for additional authors) (2018) Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet 50:559-571
Trenkwalder, Teresa; Nelson, Christopher P; Musameh, Muntaser D et al. (2018) Effects of the coronary artery disease associated LPA and 9p21 loci on risk of aortic valve stenosis. Int J Cardiol :
Zhou, Wei; Nielsen, Jonas B; Fritsche, Lars G et al. (2018) Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet 50:1335-1341
Helgadottir, Anna; Thorleifsson, Gudmar; Gretarsdottir, Solveig et al. (2018) Genome-wide analysis yields new loci associating with aortic valve stenosis. Nat Commun 9:987
Nielsen, Jonas B; Fritsche, Lars G; Zhou, Wei et al. (2018) Genome-wide Study of Atrial Fibrillation Identifies Seven Risk Loci and Highlights Biological Pathways and Regulatory Elements Involved in Cardiac Development. Am J Hum Genet 102:103-115

Showing the most recent 10 out of 17 publications