Disordered lipid metabolism (dyslipidemia) is a critical risk factor for cardiovascular disease. Although dyslipidemia can be reduced by dietary interventions, the changes in lipid profile that occur in response to diet are highly variable. Prior studies have identified genetic factors that contribute to interindividual variation, but due to insufficient genomic coverage, lack of integration with epigenetic data, and reliance on traditional lipid measures, they were unable to capture the full range of heritable influences or to distinguish between lipoproteins with differential impact on disease risk. This project will capitalize on the whole-genome sequencing data generated by the NHLBI TOPMed program to identify and characterize novel genetic predictors of lipoprotein response to a high-fat meal. Using nuclear magnetic resonance (NMR)- based measurements of lipoprotein subfractions taken at baseline and after a high-fat meal, this project aims to: 1) identify and validate novel predictors of postprandial lipoprotein response via whole-genome sequencing analysis of ~1800 participants of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) and the Heredity and Phenotype (HAPI) Heart cohorts; 2) test for associations between postprandial changes in lipoprotein subfractions and those in small molecule lipids, as well as identify shared genetic determinants of these phenotypes; and 3) conduct follow-up analysis of top genetic regions implicated in lipoprotein subfraction response by bisulfite sequencing and tests for association between DNA sequence variation, DNA methylation, and gene expression. The proposed project leverages the rich multilayered ? omics data available in GOLDN and HAPI Heart and emerging methods of integrated analysis, providing Dr. Aslibekyan's with crucial tools and experience to become an independent `big data' cardiovascular scientist and a successful TOPMed investigator.
Disordered metabolism of dietary fats is a well-known risk factor for cardiovascular disease. This project uses cutting-edge genomic and lipids data to identify novel genes that may be important to dietary fat metabolism, and to investigate whether biochemical modification of these genes (methylation) is correlated with changes in lipid profile following a high-fat meal. My findings could lead to a deeper understanding of how genes impact fat metabolism and may suggest new, personalized ways of preventing cardiovascular disease.
