The objective of Project 1 is to detect, map and characterize genes that contribute to variation in risk ofcardiovascular disease (CVD). The focus in this Program Project (the San Antonio Family Heart Study,SAFHS) is on extended Mexican American families ascertained without regard to disease status. Each ofthe -1400 family members has been genotyped for >400 microsatellite markers in an 8 centimorgan map. Inthe current grant period significant evidence has been obtained for QTLs that influence HDL-C and otherlipoprotein measures; phenotypes related to metabolic syndrome, diabetes, and insulin; obesity-relatedtraits; phenotypes related to blood pressure and its change with age; and several novel CVD-relatedphenotypes, including risk of infection with Chlamydia pneumoniae. Identification of the functional alleles fora few of the best characterized of these genes will be pursued in Projects 2 and 3, and novel candidategenes will be identified in Project 4. In Project 1, taking advantage of the resource of extensive genotypicand phenotypic data that has been created in the past 15 years as well as a wealth of newly generatedexpression data and a 550K SNP panel currently being genotyped under other funding, we will(1) utilize the longitudinal data to investigate the genetic basis of phenotypic change, and in particular, toask whether QTLs identified through analyses of cross-sectional data also influence change over time.(2) perform multivariate analyses of newly available transcript data that yield more than 20,000 quantitativeexpression phenotypes, and a set of carefully selected CVD risk factors and environmental variables togenerate hypotheses concerning gene action.(3) conduct genome-wide association screens with a 550K SNP panel for clinically important traits withsignificant heritabilities for which no QTLs have yet been localized in the SAFHS.(4) use association with a dense set of SNPs, drawn from the 550K SNP panel, to follow-up suggestivelinkage peaks from the current grant period.Thus, Project 1 will be devoted to follow-up of intriguing findings from the current grant period; detection ofnew QTLs in analyses of newly-available data; genetic analyses of age-related changes in risk factors; andgenetic analysis of new risk factors, including information on levels of 20,000+ transcripts.
Showing the most recent 10 out of 258 publications
