Advanced

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

Shungin, Dmitry LU ; Deng, Wei Q.; Varga, Tibor V. LU ; Luan, Jian'an; Mihailov, Evelin; Metspalu, Andres; Morris, Andrew P.; Forouhi, Nita G.; Lindgren, Cecilia and Magnusson, Patrik K. E., et al. (2017) In PLoS Genetics 13(6).
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 Pvand Pmwere 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 Pvand Pmwere compared for all pruned SNPs, only BMI was statistically significant... (More)

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 Pvand Pmwere 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 Pvand Pmwere 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 Pvdistribution (Pbinomial<0.05). SNPs from the top 1% of the Pmdistribution for BMI had more significant Pvvalues (PMann–Whitney= 1.46×10−5), and the odds ratio of SNPs with nominally significant (<0.05) Pmand Pvwas 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 Pvvalues (Pbinomial= 8.63×10−9and 8.52×10−7for 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.

(Less)
Please use this url to cite or link to this publication:
@article{bd8f70ab-a353-4a59-8cc7-e05a7f65f4dd,
  abstract     = {<p>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<sub>v</sub>), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (P<sub>m</sub>). Correlations between P<sub>v</sub>and P<sub>m</sub>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<sub>v</sub>and P<sub>m</sub>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<sub>v</sub>distribution (P<sub>binomial</sub>&lt;0.05). SNPs from the top 1% of the P<sub>m</sub>distribution for BMI had more significant P<sub>v</sub>values (P<sub>Mann–Whitney</sub>= 1.46×10<sup>−5</sup>), and the odds ratio of SNPs with nominally significant (&lt;0.05) P<sub>m</sub>and P<sub>v</sub>was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (P<sub>int</sub>&lt;0.05) were enriched with nominally significant P<sub>v</sub>values (P<sub>binomial</sub>= 8.63×10<sup>−9</sup>and 8.52×10<sup>−7</sup>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.</p>},
  articleno    = {e1006812},
  author       = {Shungin, Dmitry and Deng, Wei Q. and Varga, Tibor V. and Luan, Jian'an and Mihailov, Evelin and Metspalu, Andres and Morris, Andrew P. and Forouhi, Nita G. and Lindgren, Cecilia and Magnusson, Patrik K. E. and Pedersen, Nancy L. and Hallmans, Göran and Chu, Audrey Y and Justice, Anne E. and Graff, Mariaelisa and Winkler, Thomas W and Rose, Lynda M and Langenberg, Claudia and Cupples, Adrienne L. and Ridker, Paul M and Wareham, Nicholas J and Ong, Ken K. and Loos, Ruth J F and Chasman, Daniel I and Ingelsson, Erik and Kilpeläinen, Tuomas O and Scott, Robert A. and Mägi, Reedik and Paré, Guillaume and Franks, Paul W. and , },
  issn         = {1553-7390},
  language     = {eng},
  number       = {6},
  publisher    = {Public Library of Science},
  series       = {PLoS Genetics},
  title        = {Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions},
  url          = {http://dx.doi.org/10.1371/journal.pgen.1006812},
  volume       = {13},
  year         = {2017},
}