Cardiovascular disease (CVD) remains the leading cause of death in the United States. Although CVD risk is heritable, identification of causal genes in risk pathways has been slow. This project focuses on the identification of causal genes that influence variation in susceptibility to CVD by concentrating on genetic dissection of quantitative endophenotypes including carotid wall thickness, lipids, obesity-related phenotypes, blood pressure-related phenotypes, the insulin/glucose axis, inflammatory markers, oxidative stress markers, hemostasis/coagulation factors, and measures of brain white matter hyperintensities that are genetically correlated with CVD risk. We will utilize existing samples/data from a valuable genetic resource, the San Antonio Family Study (SAFS), involving large extended pedigrees of Mexican American individuals. This long- running highly successful project has produced a large number of quantitative trait locus (QTL) localizations of relevance for CVD risk. In this project, we move from QTL localization to causal gene identification. Our approach to CVD-risk gene discovery is comprehensive;we will utilize whole genome sequencing to capture all possible functional variants in 1,957 individuals from 45 large pedigrees. The large pedigrees to be used represent an optimal study design for the detection of rare functional variants. Advanced statistical genetic methods will be employed to identify the likely causal genes/variants in quantitative trait locus (QTL) regions influencing CVD risk. To achieve our objectives, we will (1) localize additional CVD-related QTLs due to rare functional variants using novel pedigree-specific localization methods, (2) obtain whole genome sequence information for 1,957 Mexican American individuals, (3) identify causal genes underlying existing QTLs influencing CVD risk using WGS information, (4) perform agnostic genome-wide direct association scans using non-synonymous coding variants to identify novel rare functional protein-altering variants influencing CVD risk and, (5) use a novel whole genome assay measuring variant-specific functional regulatory potential to permit genome-wide direct association scans using the predicted functional variants to identify novel rare regulatory variants influencing CVD risk. Given the enormous impact of CVD to mortality rates and the economic burden this disease imposes, it is clear that new methods of genomic analysis are necessary to enable the identification of novel genes and pathways involved in disease risk. The results of this project should identify causal genes underlying CVD risk. Identification of the causal genes will obligately generate on the pathways of these genes and will directly identify novel drug targets.

Public Health Relevance

Cardiovascular disease (CVD) is the most common cause of death in the United States and also poses a huge economic burden. In this project, we focus on the identification of novel genes underlying the CVD risk, using a powerful whole genome sequencing approach in extended pedigrees. Such an approach will lead us to the identification of novel genes and potential new therapeutic targets influencing CVD risk.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01HL113323-03
Application #
8644878
Study Section
Special Emphasis Panel (ZHL1)
Program Officer
Papanicolaou, George
Project Start
Project End
Budget Start
Budget End
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Texas Biomedical Research Institute
Department
Type
DUNS #
City
San Antonio
State
TX
Country
United States
Zip Code
78245
Glahn, David C; Williams, Jeff T; McKay, D Reese et al. (2015) Discovering schizophrenia endophenotypes in randomly ascertained pedigrees. Biol Psychiatry 77:75-83
Bellis, Claire; Kulkarni, Hemant; Mamtani, Manju et al. (2014) Human plasma lipidome is pleiotropically associated with cardiovascular risk factors and death. Circ Cardiovasc Genet 7:854-63
Glahn, David C; Knowles, Emma E M; McKay, D Reese et al. (2014) Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics. Am J Med Genet B Neuropsychiatr Genet 165B:122-30
Thompson, Paul M; Ge, Tian; Glahn, David C et al. (2013) Genetics of the connectome. Neuroimage 80:475-88