Fiche publication
Date publication
décembre 2017
Journal
American journal of human genetics
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MEYRE David
Tous les auteurs :
Abadi A, Alyass A, Robiou du Pont S, Bolker B, Singh P, Mohan V, Diaz R, Engert JC, Yusuf S, Gerstein HC, Anand SS, Meyre D
Lien Pubmed
Résumé
A growing number of single-nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effects of these obesity-susceptibility loci are uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI-associated SNPs in 75,230 adults of European ancestry across BMI percentiles by using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of nine SNPs (24%)-rs1421085 (FTO; p = 8.69 × 10), rs6235 (PCSK1; p = 7.11 × 10), rs7903146 (TCF7L2; p = 9.60 × 10), rs11873305 (MC4R; p = 5.08 × 10), rs12617233 (FANCL; p = 5.30 × 10), rs11672660 (GIPR; p = 1.64 × 10), rs997295 (MAP2K5; p = 3.25 × 10), rs6499653 (FTO; p = 6.23 × 10), and rs3824755 (NT5C2; p = 7.90 × 10)-increased significantly across the sample BMI distribution. We showed that such increases stemmed from unadjusted gene interactions that enhanced the effects of SNPs in persons with a high BMI. When 125 height-associated SNPs were analyzed for comparison, only one (<1%), rs6219 (IGF1, p = 1.80 × 10), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-height) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p = 7.03 × 10; height: p = 0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method for detecting such interactions by using only the sample outcome distribution.
Mots clés
body mass index, conditional quantile regression, epistasis, gene score, gene-environment interactions, height, missing heritability, polygenic inheritance, quantitative trait distribution, variable allele penetrance
Référence
Am J Hum Genet. 2017 12 7;101(6):925-938
