title: Epistasis Test in Meta-Analysis: A Multi-Parameter Markov Chain Monte Carlo Model for Consistency of Evidence.
abstract: Conventional 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].
authors.affiliation: Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, ROC.
authors.forename: Chin
authors.initials: C
authors.lastname: Lin
authors.affiliation: Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, ROC.|School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC.
authors.forename: Chi-Ming
authors.initials: CM
authors.lastname: Chu
authors.affiliation: Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, ROC.|School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC.
authors.forename: Sui-Lung
authors.initials: SL
authors.lastname: Su
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study / Q000379:methods; D005838:Genotype; D005982:Glutathione Transferase / Q000235:genetics; D006801:Humans; D008390:Markov Chains; D015201:Meta-Analysis as Topic; D053858:Metabolic Networks and Pathways; D009010:Monte Carlo Method; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D011084:Polycyclic Aromatic Hydrocarbons / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D012084:Renin-Angiotensin System / Q000235:genetics
chemical_list: D011084:Polycyclic Aromatic Hydrocarbons; D005982:Glutathione Transferase