In this proposal, we will perform integrative analysis for multi-omics data generated in the Trans-Omics for Precision Medicine (TOPMed) study to identify the molecular mechanism driving multiple blood lipid traits and coronary heart disease (CHD). Blood lipids are highly heritable and modifiable biomarkers for CHD, and therapeutic modification of blood lipid levels is an effective strategy for reducing CHD risk. TOPMed is expected to generate >100,000 sequenced whole genomes, and epigenomics, transcriptomics, metabolomics, and proteomics data on >10,000 of these individuals. These rich datasets will provide an outstanding opportunity to better understand underlying biology and provide novel molecular targets. We will integratively analyze multi-omics data to 1) identify genetic variants, methylation sites and genes that are associated with lipid and CHD phenotypes, and evaluate their total contribution to the phenotypic variation as well as their ability to predict phenotypes; 2) identify functional variants associated with methylation and gene expression/translation levels, and incorporate such functional information to improve the power of genome- wide association mapping; and 3) identify the causal networks from genetic variations to phenotypic variations, and incorporate the network structure to improve power for identifying novel genes associated with the phenotypes. Successful completion of these aims will provide new insights into molecular mechanisms and biomarkers that can be translated into the prevention and treatment of CHD.

Public Health Relevance

Blood lipid levels are widely measured and treatable risk factors for coronary heart disease, the leading cause of death in the world. Here we propose to analyze multi-omics data from TOPMed to uncover the molecular basis of blood lipid levels and CHD. The proposed research will also provide an exemplary framework and software packages for the analysis of multi-omics data, which can facilitate large-scale integrative analyses for other phenotypes in TOPMed and other genetic studies.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL142023-01
Application #
9525025
Study Section
Special Emphasis Panel (ZHL1)
Program Officer
Mussolino, Michael Eugene
Project Start
2018-05-01
Project End
2020-04-30
Budget Start
2018-05-01
Budget End
2019-04-30
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109