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Shungin, D.* ; Deng, W.Q.* ; Varga, T.V.* ; Luan, J.* ; Mihailov, E.* ; Metspalu, A.* ; Morris, A.P.* ; Forouhi, N.G.* ; Lindgren, C.* ; Magnusson, P.K.E.* ; Pedersen, N.L.* ; Hallmans, G.* ; Chu, A.Y.* ; Justice, A.E.* ; Graff, M.* ; Winkler, T.W.* ; Rose, L.M.* ; Langenberg, C.* ; Cupples, L.A.* ; Ridker, P.M.* ; Wareham, N.J.* ; Ong, K.K.* ; Loos, R.J.F.* ; Chasman, D.I.* ; Ingelsson, E.* ; Kilpeläinen, T.O.* ; Scott, R.A.* ; Mägi, R.* ; Paré, G.* ; Franks, P.W.* ; GIANT Consortium (Gieger, C. ; Grallert, H. ; Holzapfel, C. ; Rawal, R. ; Huth, C. ; Peters, A. ; Thorand, B. ; Müller-Nurasyid, M. ; Strauch, K.)

Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions.

PLoS Genet. 13:e1006812 (2017)

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Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (GxE) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (P-v), GxE interaction effects (with smoking and physical activity), and marginal genetic effects (P-m). Correlations between P-v and P-m were stronger for SNPs with established marginal effects (Spearman's rho = 0.401 for triglycerides, and rho = 0.236 for BMI) compared to all SNPs. When P-v and P-m were compared for all pruned SNPs, only BMI was statistically significant (Spearman's rho = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the P-v distribution (P-binomial < 0.05). SNPs from the top 1% of the P-m distribution for BMI had more significant P-v values (Pmann-Whitney = 1.46x10(-5)), and the odds ratio of SNPs with nominally significant (< 0.05) P-m and P-v was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant GxE interaction P-values (Pint < 0.05) were enriched with nominally significant P-v values (P-binomial = 8.63x10(-9) and 8.52x10(-7) for SNP x smoking and SNP x physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for GxE, and variance-based prioritization can be used to identify them.

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