META-GSA

• Verfasst von: Rosenberger, Albert [VerfasserIn]
• Verfasst von: Friedrichs, Stefanie [VerfasserIn]
• Verfasst von: Amos, Christopher I. [VerfasserIn]
• Verfasst von: Brennan, Paul [VerfasserIn]
• Verfasst von: Fehringer, Gordon [VerfasserIn]
• Verfasst von: Heinrich, Joachim [VerfasserIn]
• Verfasst von: Hung, Rayjean J. [VerfasserIn]
• Verfasst von: Muley, Thomas [VerfasserIn]
• Verfasst von: Müller-Nurasyid, Martina [VerfasserIn]
• Verfasst von: Risch, Angela [VerfasserIn]
• Verfasst von: Bickeböller, Heike [VerfasserIn]
• Titel: META-GSA
• Titelzusatz: combining findings from gene-set analyses across several genome-wide association studies
• Verf.angabe: Albert Rosenberger, Stefanie Friedrichs, Christopher I. Amos, Paul Brennan, Gordon Fehringer, Joachim Heinrich, Rayjean J. Hung, Thomas Muley, Martina Müller-Nurasyid, Angela Risch, Heike Bickeböller
• E-Jahr: 2015
• Jahr: October 26, 2015
• Umfang: 21 S.
• Illustrationen: Diagramme
• Fussnoten: Gesehen am 08.07.2020
• Titel Quelle: Enthalten in: PLOS ONE
• Ort Quelle: San Francisco, California, US : PLOS, 2006
• Jahr Quelle: 2015
• Band/Heft Quelle: 10(2015,10) Artikel-Nummer e0140179, 21 Seiten
• ISSN Quelle: 1932-6203
• DOI: doi:10.1371/journal.pone.0140179
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Genome-wide association studies
• Sach-SW: Lung and intrathoracic tumors
• Sach-SW: Meta-analysis
• Sach-SW: Permutation
• Sach-SW: Principal component analysis
• Sach-SW: Research errors
• Sach-SW: Statistical distributions
• Sach-SW: Test statistics
• K10plus-PPN: 1713902028

Abstract

Introduction 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. Simulation and Power 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. Application 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).