Cardiovascular (CVD) and metabolic (MetS) diseases constitute a major public-health burden and, therefore, understanding the genetic basis of these traits is very important. The recent Genome-Wide Association Studies (GWAS) made good progress, but not enough. For example, two large consortia (CHARGE and ICBP) identified 13 loci for blood pressure (BP) which collectively explain less than 2% of BP variance (with most of the heritability still """"""""missing""""""""). The near-exclusive reliance on main effects of single genetic markers has been one of the major barriers to identifying more of the genes underlying these disease traits. Against this background, Gene-Environment Interaction (GEI) methods are known to vastly increase the statistical power for gene discovery (preliminary studies). Also, analysis of longitudinal data can be much more powerful than that of cross-sectional data. Combining GEI and longitudinal data should yield even more power. Despite the knowledge that GEI and longitudinal data both vastly improve the statistical power for gene discovery, this knowledge has not been leveraged yet, thus missing an opportunity to find (hopefully many) novel disease loci from large volumes of existing GWAS data. Thusly motivated, we propose to harness the large existing longitudinal Framingham Heart Study (FHS) data using our GEI methods including gene-age, gene-sex, gene-obesity, and gene-lifestyle interactions. This study will use FHS data on the 7 visits of the 'Offspring Cohort', the corresponding 7 visits of the 'Original Cohort', and the 'Generation 3 Cohort'(G3), to a total of 9,168 discovery subjects with GWAS data. Data include GWAS data, cardiovascular and metabolic phenotypes, and lifestyle data on physical activity, alcohol consumption, smoking, and socio-economic status (SES) in 2 to 3 generation families. All significant results will be replicated in four external studies involving 22,824 replication subjects. Completion of this project has the potential to discover (hopefully many) novel disease loci, which could effectively motivate others to re-analyze large volumes of existing GWAS data using GEI methods, ultimately leading to new diagnostic tools and therapeutic interventions.

Public Health Relevance

The primary goal of the proposed research is to exploit large existing genome-wide association study (GWAS) data using gene-environment interaction (GEI) methods to discover (hopefully many) more genetic loci for cardiovascular and metabolic disease traits. The investigation will be carried out in a large existing longitudinal Framingham Heart Study with 9,168 subjects and 4 external replication studies with 22,824 subjects.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL107552-02
Application #
8309925
Study Section
Genetics of Health and Disease Study Section (GHD)
Program Officer
Jaquish, Cashell E
Project Start
2011-08-01
Project End
2014-03-31
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
2
Fiscal Year
2012
Total Cost
$342,000
Indirect Cost
$117,000
Name
Washington University
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Taylor, Jacquelyn Y; Schwander, Karen; Kardia, Sharon L R et al. (2016) A Genome-wide study of blood pressure in African Americans accounting for gene-smoking interaction. Sci Rep 6:18812
Basson, Jacob; Sung, Yun Ju; de Las Fuentes, Lisa et al. (2016) Three Approaches to Modeling Gene-Environment Interactions in Longitudinal Family Data: Gene-Smoking Interactions in Blood Pressure. Genet Epidemiol 40:73-80
Sung, Yun J; de Las Fuentes, Lisa; Schwander, Karen L et al. (2015) Gene-smoking interactions identify several novel blood pressure loci in the Framingham Heart Study. Am J Hypertens 28:343-54
Basson, Jacob; Sung, Yun Ju; de las Fuentes, Lisa et al. (2015) Influence of Smoking Status and Intensity on Discovery of Blood Pressure Loci Through Gene-Smoking Interactions. Genet Epidemiol 39:480-8
Sung, Yun Ju; Korthauer, Keegan D; Swartz, Michael D et al. (2014) Methods for collapsing multiple rare variants in whole-genome sequence data. Genet Epidemiol 38 Suppl 1:S13-20
Sung, Yun Ju; Schwander, Karen; Arnett, Donna K et al. (2014) An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions. Genet Epidemiol 38:369-78
Simino, Jeannette; Kume, Rezart; Kraja, Aldi T et al. (2014) Linkage analysis incorporating gene-age interactions identifies seven novel lipid loci: the Family Blood Pressure Program. Atherosclerosis 235:84-93
Basson, Jacob; Sung, Yun Ju; Schwander, Karen et al. (2014) Gene-education interactions identify novel blood pressure loci in the Framingham Heart Study. Am J Hypertens 27:431-44
Sung, Yun Ju; Simino, Jeannette; Kume, Rezart et al. (2014) Comparison of two methods for analysis of gene-environment interactions in longitudinal family data: the Framingham heart study. Front Genet 5:9
Simino, Jeannette; Sung, Yun Ju; Kume, Rezart et al. (2013) Gene-alcohol interactions identify several novel blood pressure loci including a promising locus near SLC16A9. Front Genet 4:277

Showing the most recent 10 out of 11 publications