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

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Date
2017
Author
Shungin, Dmitry
Deng, Wei Q.
Varga, Tibor V.
Luan, Jian'an
Mihailov, Evelin
Metspalu, Andres
GIANT Consortium
Morris, Andrew P.
Forouhi, Nita G.
Lindgren, Cecilia
Magnusson, Patrik K. E.
Pedersen, Nancy L.
Hallmans, Göran
Chu, Audrey Y.
Justice, Anne E.
Graff, Mariaelisa
Winkler, Thomas W.
Rose, Lynda M.
Langenberg, Claudia
Cupples, L. Adrienne
Kilpeläinen, Tuomas O.
Scott, Robert A.
Mägi, Reedik
Paré, Guillaume
Franks, Paul W.
Ridker, Paul M.
Wareham, Nicholas J.
Ong, Ken K.
Loos, Ruth J. F.
Chasman, Daniel I.
Ingelsson, Erik
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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 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.
URI
https://doi.org/10.1371/journal.pgen.1006812
http://hdl.handle.net/10062/63439
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