Browsing by Author "Lindgren, Cecilia"

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    Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions
    (2017) 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
    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.