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.
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.
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