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Epistasis Detection using Model-Based Multifactor Dimensionality Reduction in Structured Populations

By Fentaw Abegaz, Fran├žois Van Lishout, Jestinah M Mahachie John, Kridsadakorn Chiachoompu, Archana Bhardwaj, Elena S. Gusareva, Zhi Wei, Hakon Hakonarson, Kristel Van Steen

Posted 05 Feb 2019
bioRxiv DOI: 10.1101/541946

In genome-wide association studies, the extent and impact of confounding due population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of non-linear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework. In order to identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on model-based multifactor dimensionality reduction (MB-MDR) approach for structured populations. We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and non-linear sample genetic similarity.

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