Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Paul Franks

Paul Franks

Principal investigator

Paul Franks

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

Author

  • Dmitry Shungin
  • Wei Q. Deng
  • Tibor V. Varga
  • Jian'an Luan
  • Evelin Mihailov
  • Andres Metspalu
  • Andrew P. Morris
  • Nita G. Forouhi
  • Cecilia Lindgren
  • Patrik K. E. Magnusson
  • Nancy L. Pedersen
  • Göran Hallmans
  • Audrey Y Chu
  • Anne E. Justice
  • Mariaelisa Graff
  • Thomas W Winkler
  • Lynda M Rose
  • Claudia Langenberg
  • Adrienne L. Cupples
  • Paul M Ridker
  • Nicholas J Wareham
  • Ken K. Ong
  • Ruth J F Loos
  • Daniel I Chasman
  • Erik Ingelsson
  • Tuomas O Kilpeläinen
  • Robert A. Scott
  • Reedik Mägi
  • Guillaume Paré
  • Paul W. Franks

Summary, in English

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.

Department/s

  • Genetic and Molecular Epidemiology
  • EXODIAB: Excellence in Diabetes Research in Sweden
  • EpiHealth: Epidemiology for Health

Publishing year

2017

Language

English

Publication/Series

PLoS Genetics

Volume

13

Issue

6

Document type

Journal article

Publisher

Public Library of Science

Topic

  • Medical Genetics

Status

Published

Research group

  • Genetic and Molecular Epidemiology

ISBN/ISSN/Other

  • ISSN: 1553-7390