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An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions.

TitleAn empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions.
Publication TypeJournal Article
Year of Publication2014
AuthorsSung YJu, Schwander K, Arnett DK, Kardia SLR, Rankinen T, Bouchard C, Boerwinkle E, Hunt SC
Secondary AuthorsRao DC
JournalGenet Epidemiol
Volume38
Issue4
Pagination369-78
Date Published2014 May
ISSN1098-2272
KeywordsDiabetes Mellitus, Exercise, Gene-Environment Interaction, Genome-Wide Association Study, Health Surveys, Heart Diseases, Humans, Hypertension, Meta-Analysis as Topic, Polymorphism, Single Nucleotide, Research Design
Abstract

For analysis of the main effects of SNPs, meta-analysis of summary results from individual studies has been shown to provide comparable results as "mega-analysis" that jointly analyzes the pooled participant data from the available studies. This fact revolutionized the genetic analysis of complex traits through large GWAS consortia. Investigations of gene-environment (G×E) interactions are on the rise since they can potentially explain a part of the missing heritability and identify individuals at high risk for disease. However, for analysis of gene-environment interactions, it is not known whether these methods yield comparable results. In this empirical study, we report that the results from both methods were largely consistent for all four tests; the standard 1 degree of freedom (df) test of main effect only, the 1 df test of the main effect (in the presence of interaction effect), the 1 df test of the interaction effect, and the joint 2 df test of main and interaction effects. They provided similar effect size and standard error estimates, leading to comparable P-values. The genomic inflation factors and the number of SNPs with various thresholds were also comparable between the two approaches. Mega-analysis is not always feasible especially in very large and diverse consortia since pooling of raw data may be limited by the terms of the informed consent. Our study illustrates that meta-analysis can be an effective approach also for identifying interactions. To our knowledge, this is the first report investigating meta-versus mega-analyses for interactions.

DOI10.1002/gepi.21800
Alternate JournalGenet Epidemiol
PubMed ID24719363
PubMed Central IDPMC4332385
Grant ListR01 HL107552 / HL / NHLBI NIH HHS / United States
R01 HL111249 / HL / NHLBI NIH HHS / United States
HL118305 / HL / NHLBI NIH HHS / United States
R01 HL086694 / HL / NHLBI NIH HHS / United States
HL055673 / HL / NHLBI NIH HHS / United States
R01 HL118305 / HL / NHLBI NIH HHS / United States
HL111249 / HL / NHLBI NIH HHS / United States
HL086694 / HL / NHLBI NIH HHS / United States
HL107552 / HL / NHLBI NIH HHS / United States
R01 HL055673 / HL / NHLBI NIH HHS / United States