title: Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA. amanning@bu.edu
authors.forename: Alisa K
authors.initials: AK
authors.lastname: Manning
authors.forename: Michael
authors.initials: M
authors.lastname: LaValley
authors.forename: Ching-Ti
authors.initials: CT
authors.lastname: Liu
authors.forename: Kenneth
authors.initials: K
authors.lastname: Rice
authors.forename: Ping
authors.initials: P
authors.lastname: An
authors.forename: Yongmei
authors.initials: Y
authors.lastname: Liu
authors.forename: Iva
authors.initials: I
authors.lastname: Miljkovic
authors.forename: Laura
authors.initials: L
authors.lastname: Rasmussen-Torvik
authors.forename: Tamara B
authors.initials: TB
authors.lastname: Harris
authors.forename: Michael A
authors.initials: MA
authors.lastname: Province
authors.forename: Ingrid B
authors.initials: IB
authors.lastname: Borecki
authors.forename: Jose C
authors.initials: JC
authors.lastname: Florez
authors.forename: James B
authors.initials: JB
authors.lastname: Meigs
authors.forename: L Adrienne
authors.initials: LA
authors.lastname: Cupples
authors.forename: Josée
authors.initials: J
authors.lastname: Dupuis
mesh_terms: D000328:Adult; D000368:Aged; D015992:Body Mass Index; D016001:Confidence Intervals; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000235:genetics*; D004777:Environment; D005215:Fasting; D005260:Female; D015894:Genome, Human; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D007328:Insulin / Q000097:blood*; D016018:Least-Squares Analysis; D008297:Male; D008432:Mathematical Computing; D015201:Meta-Analysis as Topic; D008875:Middle Aged; D047495:PPAR gamma / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide
chemical_list: D007328:Insulin; D047495:PPAR gamma