The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study

Abstract

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR<5%) age-specific effects, of which 11 had larger effects in younger (<50y) than in older adults (≥50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may provide further insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.

Genome-Wide Association Study of Body Fat Distribution identifies Novel Adiposity Loci and Sex-Specific Genetic Effects

“…In agreement with this, a larger fraction of the variance in fat distribution to different compartments could also be attributed to the SNPs investigated in women, as compared to men. These results are consistent with previous GWAS that have revealed sexual dimorphisms in genetic loci for adiposityrelated phenotypes, such as waist-circumference, waist-to-hip ratio, and visceral fat mass [14,[30][31][32]. In our study we find evidence for 43 loci whose effects differed between the males and females, of which one overlapped with a locus (LYPLAL1) that has previously been reported to display a different effect between sexes.…”

“…In our study we find evidence for 43 loci whose effects differed between the males and females, of which one overlapped with a locus (LYPLAL1) that has previously been reported to display a different effect between sexes. Our lead SNP (rs1415287) at the LYPLAL1 locus is in strong LD with rs2820443 and rs4846567 (R 2 =1.00 and 0.99), which have been associated with stronger effects on WHR and WHR adjusted for BMI in women [14,32].…”

60 YEARS OF POMC: The proopiomelanocortin gene: discovery, deletion and disease

“…In fact, this does not appear to be the case in most studies. For example, in one recent report POMC was not among 100 QTLs that were identified or confirmed in a population of over 320,000 individuals (Winkler et al 2015). Voisin et al (2015) have reported that methylation of a single nucleotide polymorphism within the POMC promoter region was among methylation site variants from 28 genes associated with obesity.…”

Exploiting single-cell quantitative data to map genetic variants having probabilistic effects

“…Currently, the only way this issue is handled is by scaling up sample size. For example, human Genome-Wide Association Studies (GWAS) now typically recruit tens of thousands of individuals in a hunt for small-effect variants [2][3][4]. Practically, however, cohort size cannot be infinitely increased.…”