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Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions
Umeå University, Faculty of Medicine, Department of Odontology. Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden; Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, United States of America.
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2017 (English)In: PLoS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 13, no 6, article id e1006812Article in journal (Refereed) Published
Abstract [en]

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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Public Library Science , 2017. Vol. 13, no 6, article id e1006812
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Medical Genetics Genetics
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URN: urn:nbn:se:umu:diva-140239DOI: 10.1371/journal.pgen.1006812ISI: 000404512600010PubMedID: 28614350OAI: oai:DiVA.org:umu-140239DiVA, id: diva2:1151012
Available from: 2017-10-20 Created: 2017-10-20 Last updated: 2018-08-31Bibliographically approved

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Shungin, DmitryHallmans, GöranFranks, Paul W.
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