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

Yun Ju Sung, Karen Schwander, Donna K. Arnett, Sharon L R Kardia, Tuomo Rankinen, Claude Bouchard, Eric Boerwinkle, Steven Hunt, Dabeeru C. Rao

Research output: Contribution to journalArticle

22 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)369-378
Number of pages10
JournalGenetic Epidemiology
Volume38
Issue number4
DOIs
Publication statusPublished - 2014
Externally publishedYes

Fingerprint

Gene-Environment Interaction
Meta-Analysis
Single Nucleotide Polymorphism
Genome-Wide Association Study
Economic Inflation
Informed Consent
Joints

Keywords

  • Gene-environment interactions (GEI)
  • Mega-analysis
  • Meta-analysis

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

Cite this

Sung, Y. J., Schwander, K., Arnett, D. K., Kardia, S. L. R., Rankinen, T., Bouchard, C., ... Rao, D. C. (2014). An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions. Genetic Epidemiology, 38(4), 369-378. https://doi.org/10.1002/gepi.21800

An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions. / Sung, Yun Ju; Schwander, Karen; Arnett, Donna K.; Kardia, Sharon L R; Rankinen, Tuomo; Bouchard, Claude; Boerwinkle, Eric; Hunt, Steven; Rao, Dabeeru C.

In: Genetic Epidemiology, Vol. 38, No. 4, 2014, p. 369-378.

Research output: Contribution to journalArticle

Sung, YJ, Schwander, K, Arnett, DK, Kardia, SLR, Rankinen, T, Bouchard, C, Boerwinkle, E, Hunt, S & Rao, DC 2014, 'An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions', Genetic Epidemiology, vol. 38, no. 4, pp. 369-378. https://doi.org/10.1002/gepi.21800
Sung, Yun Ju ; Schwander, Karen ; Arnett, Donna K. ; Kardia, Sharon L R ; Rankinen, Tuomo ; Bouchard, Claude ; Boerwinkle, Eric ; Hunt, Steven ; Rao, Dabeeru C. / An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions. In: Genetic Epidemiology. 2014 ; Vol. 38, No. 4. pp. 369-378.
@article{a41b2752fd33477189f0fa498e221762,
title = "An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions",
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.",
keywords = "Gene-environment interactions (GEI), Mega-analysis, Meta-analysis",
author = "Sung, {Yun Ju} and Karen Schwander and Arnett, {Donna K.} and Kardia, {Sharon L R} and Tuomo Rankinen and Claude Bouchard and Eric Boerwinkle and Steven Hunt and Rao, {Dabeeru C.}",
year = "2014",
doi = "10.1002/gepi.21800",
language = "English",
volume = "38",
pages = "369--378",
journal = "Genetic Epidemiology",
issn = "0741-0395",
publisher = "Wiley-Liss Inc.",
number = "4",

}

TY - JOUR

T1 - An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions

AU - Sung, Yun Ju

AU - Schwander, Karen

AU - Arnett, Donna K.

AU - Kardia, Sharon L R

AU - Rankinen, Tuomo

AU - Bouchard, Claude

AU - Boerwinkle, Eric

AU - Hunt, Steven

AU - Rao, Dabeeru C.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Gene-environment interactions (GEI)

KW - Mega-analysis

KW - Meta-analysis

UR - http://www.scopus.com/inward/record.url?scp=84899112197&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84899112197&partnerID=8YFLogxK

U2 - 10.1002/gepi.21800

DO - 10.1002/gepi.21800

M3 - Article

C2 - 24719363

AN - SCOPUS:84899112197

VL - 38

SP - 369

EP - 378

JO - Genetic Epidemiology

JF - Genetic Epidemiology

SN - 0741-0395

IS - 4

ER -