Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions
dc.contributor | "European Union (EU)" and "Horizon 2020" | |
dc.contributor.author | Shungin, Dmitry | |
dc.contributor.author | Deng, Wei Q. | |
dc.contributor.author | Varga, Tibor V. | |
dc.contributor.author | Luan, Jian'an | |
dc.contributor.author | Mihailov, Evelin | |
dc.contributor.author | Metspalu, Andres | |
dc.contributor.author | GIANT Consortium | |
dc.contributor.author | Morris, Andrew P. | |
dc.contributor.author | Forouhi, Nita G. | |
dc.contributor.author | Lindgren, Cecilia | |
dc.contributor.author | Magnusson, Patrik K. E. | |
dc.contributor.author | Pedersen, Nancy L. | |
dc.contributor.author | Hallmans, Göran | |
dc.contributor.author | Chu, Audrey Y. | |
dc.contributor.author | Justice, Anne E. | |
dc.contributor.author | Graff, Mariaelisa | |
dc.contributor.author | Winkler, Thomas W. | |
dc.contributor.author | Rose, Lynda M. | |
dc.contributor.author | Langenberg, Claudia | |
dc.contributor.author | Cupples, L. Adrienne | |
dc.contributor.author | Kilpeläinen, Tuomas O. | |
dc.contributor.author | Scott, Robert A. | |
dc.contributor.author | Mägi, Reedik | |
dc.contributor.author | Paré, Guillaume | |
dc.contributor.author | Franks, Paul W. | |
dc.contributor.author | Ridker, Paul M. | |
dc.contributor.author | Wareham, Nicholas J. | |
dc.contributor.author | Ong, Ken K. | |
dc.contributor.author | Loos, Ruth J. F. | |
dc.contributor.author | Chasman, Daniel I. | |
dc.contributor.author | Ingelsson, Erik | |
dc.date.accessioned | 2019-02-27T10:27:43Z | |
dc.date.available | 2019-02-27T10:27:43Z | |
dc.date.issued | 2017 | |
dc.description.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. | et |
dc.identifier.uri | https://doi.org/10.1371/journal.pgen.1006812 | |
dc.identifier.uri | http://hdl.handle.net/10062/63439 | |
dc.language.iso | eng | et |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/692065///WIDENLIFE | et |
dc.relation.ispartofseries | PLoS Genet. 2017 Jun;13(6) | |
dc.rights | info:eu-repo/semantics/openAccess | et |
dc.subject | Molecular genetics | et |
dc.subject | Physical activity | et |
dc.subject | Cholesterol | et |
dc.subject | Quantitative trait loci | et |
dc.subject | Meta-analysis | et |
dc.subject | Consortia | et |
dc.subject | Lipoproteins | et |
dc.title | Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions | et |
dc.type | info:eu-repo/semantics/article | et |
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