META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies.
BACKGROUND
Gene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a predefined set of genes are either contrasted with those of all remaining genes or with a null non-associated background. To pool the p-values from several GSAs, it is important to take into account the concordance of the observed patterns resulting from single marker association point estimates across any given gene set. Here we propose an enhanced version of Fisher's inverse χ2-method META-GSA, however weighting each study to account for imperfect correlation between association patterns.
We investigated the performance of META-GSA by simulating GWASs with 500 cases and 500 controls at 100 diallelic markers in 20 different scenarios, simulating different relative risks between 1 and 1.5 in gene sets of 10 genes. Wilcoxon's rank sum test was applied as GSA for each study. We found that META-GSA has greater power to discover truly associated gene sets than simple pooling of the p-values, by e.g. 59% versus 37%, when the true relative risk for 5 of 10 genes was assume to be 1.5. Under the null hypothesis of no difference in the true association pattern between the gene set of interest and the set of remaining genes, the results of both approaches are almost uncorrelated. We recommend not relying on p-values alone when combining the results of independent GSAs.


CONCLUSIONS
We applied META-GSA to pool the results of four case-control GWASs of lung cancer risk (Central European Study and Toronto/Lunenfeld-Tanenbaum Research Institute Study; German Lung Cancer Study and MD Anderson Cancer Center Study), which had already been analyzed separately with four different GSA methods (EASE; SLAT, mSUMSTAT and GenGen). This application revealed the pathway GO0015291 "transmembrane transporter activity" as significantly enriched with associated genes (GSA-method: EASE, p = 0.0315 corrected for multiple testing). Similar results were found for GO0015464 "acetylcholine receptor activity" but only when not corrected for multiple testing (all GSA-methods applied; p ≈ 0.02).
Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany.
Albert
A
Rosenberger
Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany.
Stefanie
S
Friedrichs
Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States of America.
Christopher I
CI
Amos
International Agency for Research on Cancer (IARC), Lyon, France.
Paul
P
Brennan
Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.
Gordon
G
Fehringer
Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Joachim
J
Heinrich
Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.
Rayjean J
RJ
Hung
Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), Heidelberg, Germany; Thoraxklinik at University of Heidelberg, Heidelberg, Germany.
Thomas
T
Muley
Department of Medicine I, Ludwig-Maximilians-University Munich, Munich, Germany; Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-University, Munich, Germany; Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.
Martina
M
Müller-Nurasyid
Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), Heidelberg, Germany; Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany; Division of Molecular Biology, University Salzburg, Salzburg, Austria.
Angela
A
Risch
Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany.
Heike
H
Bickeböller
D000483:Alleles; D005796:Genes; D005819:Genetic Markers / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods; D006801:Humans; D015201:Meta-Analysis as Topic; D008962:Models, Theoretical; D018709:Statistics, Nonparametric
D005819:Genetic Markers