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

Dmitry Shungin, Wei Q. Deng, Tibor V. Varga, Jian'an Luan, Evelin Mihailov, Andres Metspalu, Andrew P. Morris, Nita G. Forouhi, Cecilia Lindgren, Patrik K. E. Magnusson, Nancy L. Pedersen, Göran Hallmans, Audrey Y Chu, Anne E. Justice, Mariaelisa Graff, Thomas W Winkler, Lynda M Rose, Claudia Langenberg, Adrienne L. Cupples, Paul M RidkerNicholas J Wareham, Ken K. Ong, Ruth J F Loos, Daniel I Chasman, Erik Ingelsson, Tuomas O Kilpeläinen, Robert A. Scott, Reedik Mägi, Guillaume Paré, Paul W. Franks, GIANT Consortium

Research output: Contribution to journalArticlepeer-review

Abstract

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 Pvand Pmwere 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 Pvand Pmwere 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 Pvdistribution (Pbinomial<0.05). SNPs from the top 1% of the Pmdistribution for BMI had more significant Pvvalues (PMann–Whitney= 1.46×10−5), and the odds ratio of SNPs with nominally significant (<0.05) Pmand Pvwas 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 Pvvalues (Pbinomial= 8.63×10−9and 8.52×10−7for 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.

Original languageEnglish
Article numbere1006812
JournalPLoS Genetics
Volume13
Issue number6
DOIs
Publication statusPublished - 2017

Subject classification (UKÄ)

  • Medical Genetics and Genomics (including Gene Therapy)

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