We conducted a gene-environment interaction study to evaluate whether the association of body mass index (BMI) associated meta genome-wide association study single-nucleotide polymorphisms (SNPs) (as a genetic risk score) and BMI is modified by physical activity and age.
In 8,206 women of European ancestry from the Women's Health Initiative (WHI), we used linear regression to examine main effects of the 95 SNP BMI genetic risk score (GRS) and physical activity on BMI, and evaluated whether genetic associations are modified by physical activity (two-way interaction) and age (three-way interaction).
We found evidence for modification of the BMI GRS-BMI association according to both physical activity and age. We observed a significant two-way interaction of BMI GRS × physical activity in the crude model (P interaction = 0.05), where a smaller effect of the BMI GRS on BMI with increasing physical activity. The beta coefficient was 0.05 (standard error [SE] = 0.02, P = 0.01) for the high-activity group compared with beta = 0.13 (SE = 0.02, P = 4.8 × 10−9) for the sedentary group. The three-way interaction was statistically significant (adjusted P interaction = 0.01). Notably, in the 70+ age group, the BMI GRS-BMI association was attenuated and no longer significant in the high-activity group; the beta coefficient for the 70+ high-activity group was relatively small and nonsignificant (beta = 0.02, SE = 0.03, P = 0.58) compared with 70+ sedentary group (beta = 0.17, SE = 0.03, P = 2.5 × 10−7).
Our study suggests that physical activity attenuates the influence of genetic predisposition to obesity, and this effect is more profound in the oldest age group.
1Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY
2Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
3Joseph J. Zilber School of Public Health, University of Wisconsin, Milwaukee, Milwaukee, WI
4Department of Preventive Medicine, Rush University Medical Center, Chicago, IL
5Department of Public Health Sciences, University of California Davis, Davis, CA
6College of Pharmacy, The Ohio State University, Columbus, OH
7Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH.
Address correspondence to: Heather M. Ochs-Balcom, PhD, Department of Epidemiology and Environmental Health, 270 Farber Hall, University at Buffalo, Buffalo, NY 14214-8001. E-mail: email@example.com
Received 28 February, 2018
Revised 28 March, 2018
Accepted 28 March, 2018
Funding/support: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services through contracts HHSN268201600001C, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. Funding support for WHI GARNET was provided through the NHGRI Genomics and Randomized Trials Network (GARNET) (U01 HG005152). Assistance with phenotype harmonization and genotype cleaning, and also with general study coordination, was provided by the GARNET Coordinating Center (U01 HG005157). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the NIH Genes, Environment and Health Initiative [GEI] (U01 HG004424).
Financial disclosure/conflicts of interest: None reported.
This manuscript was prepared in collaboration with WHI investigators, and has been reviewed and approved by the WHI. The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGaP accession numbers phs000200.v10.p3, phs000675.v2.p3 and phs000315.v6.p3.