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Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients.

Publication ,  Journal Article
Manning, AK; LaValley, M; Liu, C-T; Rice, K; An, P; Liu, Y; Miljkovic, I; Rasmussen-Torvik, L; Harris, TB; Province, MA; Borecki, IB ...
Published in: Genet Epidemiol
January 2011

INTRODUCTION: Genetic discoveries are validated through the meta-analysis of genome-wide association scans in large international consortia. Because environmental variables may interact with genetic factors, investigation of differing genetic effects for distinct levels of an environmental exposure in these large consortia may yield additional susceptibility loci undetected by main effects analysis. We describe a method of joint meta-analysis (JMA) of SNP and SNP by Environment (SNP × E) regression coefficients for use in gene-environment interaction studies. METHODS: In testing SNP × E interactions, one approach uses a two degree of freedom test to identify genetic variants that influence the trait of interest. This approach detects both main and interaction effects between the trait and the SNP. We propose a method to jointly meta-analyze the SNP and SNP × E coefficients using multivariate generalized least squares. This approach provides confidence intervals of the two estimates, a joint significance test for SNP and SNP × E terms, and a test of homogeneity across samples. RESULTS: We present a simulation study comparing this method to four other methods of meta-analysis and demonstrate that the JMA performs better than the others when both main and interaction effects are present. Additionally, we implemented our methods in a meta-analysis of the association between SNPs from the type 2 diabetes-associated gene PPARG and log-transformed fasting insulin levels and interaction by body mass index in a combined sample of 19,466 individuals from five cohorts.

Duke Scholars

Published In

Genet Epidemiol

DOI

EISSN

1098-2272

Publication Date

January 2011

Volume

35

Issue

1

Start / End Page

11 / 18

Location

United States

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • PPAR gamma
  • Middle Aged
  • Meta-Analysis as Topic
  • Mathematical Computing
  • Male
  • Least-Squares Analysis
  • Insulin
  • Humans
  • Genotype
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Manning, A. K., LaValley, M., Liu, C.-T., Rice, K., An, P., Liu, Y., … Dupuis, J. (2011). Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet Epidemiol, 35(1), 11–18. https://doi.org/10.1002/gepi.20546
Manning, Alisa K., Michael LaValley, Ching-Ti Liu, Kenneth Rice, Ping An, Yongmei Liu, Iva Miljkovic, et al. “Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients.Genet Epidemiol 35, no. 1 (January 2011): 11–18. https://doi.org/10.1002/gepi.20546.
Manning AK, LaValley M, Liu C-T, Rice K, An P, Liu Y, et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet Epidemiol. 2011 Jan;35(1):11–8.
Manning, Alisa K., et al. “Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients.Genet Epidemiol, vol. 35, no. 1, Jan. 2011, pp. 11–18. Pubmed, doi:10.1002/gepi.20546.
Manning AK, LaValley M, Liu C-T, Rice K, An P, Liu Y, Miljkovic I, Rasmussen-Torvik L, Harris TB, Province MA, Borecki IB, Florez JC, Meigs JB, Cupples LA, Dupuis J. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet Epidemiol. 2011 Jan;35(1):11–18.
Journal cover image

Published In

Genet Epidemiol

DOI

EISSN

1098-2272

Publication Date

January 2011

Volume

35

Issue

1

Start / End Page

11 / 18

Location

United States

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • PPAR gamma
  • Middle Aged
  • Meta-Analysis as Topic
  • Mathematical Computing
  • Male
  • Least-Squares Analysis
  • Insulin
  • Humans
  • Genotype