The goal of this R01 proposal is to use high-throughput DNA sequencing and genotyping technologies to identify genes and pathways that contribute to the risk for myocardial infarction (MI). Our research team combines strengths in cardiovascular disease, high-throughput genetics and genomics and development and application of innovative computational and statistical methods to maximize the benefits of next-generation sequencing technologies.
In Aim 1, we will sequence DNA from 1,000 individuals, 700 with MI and 300 controls, at ~5X coverage for the genome, and at ~100X average coverage for the exome. We will select MI cases with known family history of MI and additional cases with the earliest-onset from 3,100 MI cases collected by the HUNT Biobank.
In Aim 2, we will carry out MI association analyses based on sequence data from these 1,000 samples and an additional 1,000 samples (500 with early-onset MI) with publically-available exome data from the NHLBI Exome Sequencing Project (ESP). We will analyze variants with MAF>0.5% individually. For variants with MAF<1%, we will use """"""""burden"""""""" tests designed to identify regions where clusters of rare variants are more common in cases than controls (or vice versa).
In Aim 3, we will use the Metabochip custom SNP array to genotype dense marker sets from 94 MI-related loci in 1,044 additional MI cases and 4,919 MI controls from the Finnish FUSION and METSIM studies. Using reference haplotypes from this project (Aim 1) together with haplotypes from the 1000 Genomes Project, T2DGO and Sardinia whole genome sequencing studies, we will impute variants into samples with Metabochip genotypes and perform association testing in 2,244 MI cases and 5,719 controls.
In Aim 4, we will select for follow-up ~200 genes that show the strongest evidence for association with MI from Aims 2 and 3. These 200 genes will be sequenced in an additional 2,100 MI cases and 2,100 MI controls from the HUNT Biobank.
In Aim 5, we will share data and methods to support similar studies for MI and other cardiovascular phenotypes, for imputation into larger samples with GWAS data, and more broadly across the scientific community. Completion of these aims will provide new insights into disease mechanism that have the potential to catalyze breakthroughs in MI prevention, treatment, and diagnosis.
Myocardial infarction (MI) is the leading cause of death in the United States. Despite nearly 2400 Americans dying of cardiovascular disease every day, little is known about the etiology of MI. Preventive therapies currently include antiplatelet agents such as aspirin, and therapies aimed at reducing LDL cholesterol, a risk factor for atherosclerosis and CVD. Improved understanding of the genetic basis of MI may improve our understanding of disease etiology, support identification of novel drugs and therapies, and enable better targeting of preventive and therapeutic approaches.
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|Turcot, Valérie (see original citation for additional authors) (2018) Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet 50:26-41|
|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|
|Yang, Bo; Zhou, Wei; Jiao, Jiao et al. (2017) Protein-altering and regulatory genetic variants near GATA4 implicated in bicuspid aortic valve. Nat Commun 8:15481|
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|McCarthy, Shane; Das, Sayantan; Kretzschmar, Warren et al. (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48:1279-83|
|Ehret, Georg B (see original citation for additional authors) (2016) The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat Genet 48:1171-1184|
|Surendran, Praveen (see original citation for additional authors) (2016) Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat Genet 48:1151-1161|
|Schmidt, Ellen M; Willer, Cristen J (2015) Insights into blood lipids from rare variant discovery. Curr Opin Genet Dev 33:25-31|
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