Hypertension and its sequelae constitute a major public-health burden and, therefore-, identification of the causal variants for hypertension could lead to the development of novel interventions to control or treat the adverse outcomes. Although Genome-Wide Association Studies (GWAS) have successfully identified 29 common variants for hypertension, their effects collectively explain less than 2.5% of blood pressure (BP) variance, with most of the heritability still missing. We propose a comprehensive study to identify rare and low frequency variants with supposedly larger effects for hypertension and high BP in highly enriched Taiwan Chinese hypertensive families using whole exome sequencing and state-of-the-art statistical methods. The SAPPHIRe Network in the Family Blood Pressure Program (FBPP) recruited Taiwan families with multiple hypertensive sibs. Thus, by the very design of the study, the family sample is highly enriched with hypertension and BP segregating variants. We show that this type of recruitment and further selection of a subset of the families with strong linkage evidence vastly enriches rare and low frequency variants for hypertension/BP by several fold as compared to the general population. Therefore, we propose to carry out exome sequencing in 150 highly enriched Taiwan sib-pairs (300 subjects) and 300 unrelated controls from Taiwan, and genotype the top 6,000 SNPs in all SAPPHIRe families (N=1,200) and 1,200 unrelated matched controls. Finally, the 50 variants most associated with hypertension/BP will then be replicated in large multi- ethnic cohorts, including Chinese and U.S. populations, with nearly 45,000 subjects. This study can potentially explain a large proportion of the missing heritability, which could then lead to important translational research o considerable public health significance. Therefore, the potential impact is very high.

Public Health Relevance

The primary goal of the proposed research is to identify rare and low frequency variants that have large effects on blood pressure and hypertension by carrying out exome sequencing in 150 highly enriched Taiwan Chinese sib-pairs (300 subjects) and 300 unrelated controls, then to validate the top 6,000 SNPs in larger samples, and finally replicate the top 50 SNPs in nearly 45,000 multi-ethnic subjects. Any success with hypertension can lead to important translational research of considerable public health significance and will likely motivate similar approaches for other common complex diseases.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL111249-04
Application #
8874266
Study Section
Cardiovascular and Sleep Epidemiology Study Section (CASE)
Program Officer
Jaquish, Cashell E
Project Start
2012-07-15
Project End
2017-06-30
Budget Start
2015-07-01
Budget End
2017-06-30
Support Year
4
Fiscal Year
2015
Total Cost
Indirect Cost
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
Li, Changwei; He, Jiang; Chen, Jing et al. (2017) Genome-Wide Gene-Potassium Interaction Analyses on Blood Pressure: The GenSalt Study (Genetic Epidemiology Network of Salt Sensitivity). Circ Cardiovasc Genet 10:
Sung, Yun Ju; Basson, Jacob; Cheng, Nuo et al. (2015) The role of rare variants in systolic blood pressure: analysis of ExomeChip data in HyperGEN African Americans. Hum Hered 79:20-7
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
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