期刊
PLOS ONE
卷 10, 期 10, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0140179
关键词
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资金
- National Institute of Health (NIH) [U19CA148127]
- Canadian Cancer Society Research Institute [020214]
- Ontario Institute of Cancer
- Cancer Care Ontario Chair Award
- Deutsche Krebshilfe [70-2919]
- Helmholtz-DAAD fellowship [A/07/97379]
- National Institutes of Health (USA) [U19CA148127]
- Helmholtz-Zentrum Munchen (HMGU)
- German Federal Ministry of Education, Science, Research and Technology
- State of Bavaria
- National Genome Research Network (NGFN)
- DFG [BI 576/2-1, BI 576/2-2]
- Helmholtzgemeinschaft (HGF)
- Federal office for Radiation Protection [BfS:STSch4454]
- NIH [U19CA148127, P50 CA70907, R01CA121197, RO1 CA127219]
- CPRIT [RP100443]
- National Institutes of Health to The Johns Hopkins University [HHSN268200782096C]
- Institut National du Cancer, France
- Central Europe study, Czech Republic
- European Regional Development Fund
- State Budget of the Czech Republic (RE-CAMO) [CZ.1.05/2.1.00/03.0101]
Introduction Gene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a prede-fined 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 chi(2)-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 approximate to 0.02).
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