Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions.
PLoS genetics 2016 ; 13: e1006812.
Shungin D, Deng WQ, Varga TV, Luan J, Mihailov E, Metspalu A, GIANT Consortium, Morris AP, Forouhi NG, Lindgren C, Magnusson PKE, Pedersen NL, Hallmans G, Chu AY, Justice AE, Graff M, Winkler TW, Rose LM, Langenberg C, Cupples LA, Ridker PM, Wareham NJ, Ong KK, Loos RJF, Chasman DI, Ingelsson E, Kilpeläinen TO, Scott RA, Mägi R, Pare G, and Franks PW
DOI : 10.1371/journal.pgen.1006812
PubMed ID : 28614350
PMCID : PMC5489225
URL : https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006812
Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) 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 (Pv), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (Pm). Correlations between Pv and Pm were stronger for SNPs with established marginal effects (Spearman's ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When Pv and Pm were compared for all pruned SNPs, only BMI was statistically significant (Spearman's ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the Pv distribution (Pbinomial <0.05). SNPs from the top 1% of the Pm distribution for BMI had more significant Pv values (PMann-Whitney = 1.46×10-5), and the odds ratio of SNPs with nominally significant (<0.05) Pm and Pv was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (Pint<0.05) were enriched with nominally significant Pv values (Pbinomial = 8.63×10-9 and 8.52×10-7 for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them.