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dc.contributor.authorLin, Chin
dc.contributor.authorChu, Chi-Ming
dc.contributor.authorSu, Sui-Lung
dc.date.accessioned2020-02-18T04:47:33Z
dc.date.available2020-02-18T04:47:33Z
dc.date.issued2016
dc.identifier.issn1932-6203
dc.identifier.doi10.1371/journal.pone.0152891
dc.identifier.urihttp://hdl.handle.net/10072/391657
dc.description.abstractConventional genome-wide association studies (GWAS) have been proven to be a successful strategy for identifying genetic variants associated with complex human traits. However, there is still a large heritability gap between GWAS and transitional family studies. The “missing heritability” has been suggested to be due to lack of studies focused on epistasis, also called gene–gene interactions, because individual trials have often had insufficient sample size. Meta-analysis is a common method for increasing statistical power. However, sufficient detailed information is difficult to obtain. A previous study employed a meta-regression-based method to detect epistasis, but it faced the challenge of inconsistent estimates. Here, we describe a Markov chain Monte Carlo-based method, called “Epistasis Test in Meta-Analysis” (ETMA), which uses genotype summary data to obtain consistent estimates of epistasis effects in meta-analysis. We defined a series of conditions to generate simulation data and tested the power and type I error rates in ETMA, individual data analysis and conventional meta-regression-based method. ETMA not only successfully facilitated consistency of evidence but also yielded acceptable type I error and higher power than conventional meta-regression. We applied ETMA to three real meta-analysis data sets. We found significant gene–gene interactions in the renin–angiotensin system and the polycyclic aromatic hydrocarbon metabolism pathway, with strong supporting evidence. In addition, glutathione S-transferase (GST) mu 1 and theta 1 were confirmed to exert independent effects on cancer. We concluded that the application of ETMA to real meta-analysis data was successful. Finally, we developed an R package, etma, for the detection of epistasis in meta-analysis [etma is available via the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/etma/index.html].
dc.languageEnglish
dc.language.isoeng
dc.publisherPublic Library of Science (PLoS)
dc.relation.ispartofpagefrome0152891
dc.relation.ispartofissue4
dc.relation.ispartofjournalPLoS One
dc.relation.ispartofvolume11
dc.titleEpistasis Test in meta-analysis: A multi-parameter Markov chain Monte Carlo model for consistency of evidence
dc.typeJournal article
dcterms.bibliographicCitationLin, C; Chu, CM; Su, SL, Epistasis Test in meta-analysis: A multi-parameter Markov chain Monte Carlo model for consistency of evidence, PLoS One, 2016, 11 (4), pp. e0152891-
dcterms.dateAccepted2016-03-21
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-02-18T04:46:00Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2016 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
gro.hasfulltextFull Text
gro.griffith.authorChu, Cordia M.


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