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

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 varianc...

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Detalles Bibliográficos
Autores principales: Shungin, Dmitry, Deng, Wei Q., Varga, Tibor V., Luan, Jian'an, Mihailov, Evelin, Metspalu, Andres, 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, Ridker, Paul M., Wareham, Nicholas J., Ong, Ken K., Loos, Ruth J. F., Chasman, Daniel I., Ingelsson, Erik, Kilpeläinen, Tuomas O., Scott, Robert A., Mägi, Reedik, Paré, Guillaume, Franks, Paul W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5489225/
https://www.ncbi.nlm.nih.gov/pubmed/28614350
http://dx.doi.org/10.1371/journal.pgen.1006812
Descripción
Sumario: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 (P(v)), G×E 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 ρ = 0.401 for triglycerides, and ρ = 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 ρ = 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 (P(Mann–Whitney) = 1.46×10(−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 G×E interaction P-values (P(int)<0.05) were enriched with nominally significant P(v) values (P(binomial) = 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.