Cardiovascular disease (CVD) and management of its risk factors such as blood pressure (BP) and plasma lipids continue to be a major public health problem. Therefore, understanding the genetic basis of these traits and how the environment modulates genetic effects in influencing these traits is important because it may provide important clues for interventions. Over the past several years, the advent of Genome-Wide Association Studies (GWAS) has revolutionized the field with rapid progress in identifying hundreds of common genetic variants associated with many common complex diseases and disease-related traits including cardiovascular traits. Despite these extraordinary accomplishments, most of the identified genetic variants have small effect sizes, confer relatively small increments of risk and, for the most part, explain only small proportions of the trait variance. It is increasingly recognized that the near-exclusive focus on main effects of common variants (which generally have small effects) has become a barrier to the identification of additional genes (with larger effects) underlying these disease traits. These discovery efforts need more sophisticated approaches such as gene- environment interactions, analysis of pleiotropic effects on correlated traits, and pathway analysis. Although important research involving gene-environment interactions is being reported in the literature, this is the first systematic, well-powered large consortium-level effort for systematically evaluating gene- lifestyle interactions using very large sample sizes. We propose to investigate gene-lifestyle interactions, the genetic architecture of correlated traits with pleiotropy analysis, and pathway analysis, each as a means for uncovering more of the unexplained genetic variance in BP and lipids. We will do this by leveraging the extraordinary resources of existing multi-ethnic studies/cohorts that have the phenotypes, relevant lifestyle data and dense genotype data on common (GWAS) as well as rare variants (Exome chip). Our application involves 25 cohorts with GWAS data on 90,673 European Americans, 34,543 African Americans, 13,174 Hispanic Americans, and 12,375 Asians, for an overall total of N=150,765, with Exome Chip data on about 61% of the samples. Replication will be sought from two large consortia with an aggregate sample size of 160,958 subjects (Global BP Genetics or GBPgen, and Global Lipids Genetics Consortium or GLGC). This would represent the most significant effort to date to investigate interactions with an aggregate sample size over 300,000 in either discovery or replication. Timely funding can sustain the great momentum generated by putting together this application, which could ultimately lead to novel therapeutic approaches thus potentially impacting clinical practice.

Public Health Relevance

The primary goal of the proposed research is to leverage existing GWAS and Exome Chip data in 25 large multi-ethnic cohorts to discover additional genetic loci for cardiovascular traits by modeling gene-lifestyle interactions, using pleiotropy analysis of correlated traits, and pathway analysis. The investigation will be carried out in 150,765 samples of European Americans, African Americans, Hispanic Americans, and Asians. Approximately equal sample sizes will be used for replication.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL118305-01A1
Application #
8630851
Study Section
Special Emphasis Panel (ZRG1-PSE-S (03))
Program Officer
Jaquish, Cashell E
Project Start
2014-01-15
Project End
2017-12-31
Budget Start
2014-01-15
Budget End
2014-12-31
Support Year
1
Fiscal Year
2014
Total Cost
$1,952,964
Indirect Cost
$427,680
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
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
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