Fentaw Abegaz, Kridsadakorn Chaichoompu and Van Steen Kristel
GIGA-R Medical Genomics – BIO3, University of Liège, Liège, Belgium and WELBIO, University of Liège, Liège, Belgium
Epistasis, or interaction between genes, has been identified as a component of complex phenotypes in a number of studies . When explaining multifactorial trait variation in humans, gene-gene interactions should not be ignored and potentially complex population-dependent modes of inheritance need to be assumed. Even simple genetic diseases may be complex. For example, Mendelian disorders such as Hirschsprung’s disease and cystic fibrosis are documented examples of epistasis where modifier genes have been identified to affect phenotypic differences. In general, epistasis studies help to identify novel drug targets and biomarkers relevant to the underlying mechanisms of disease that are not captured by single locus analysis. Moreover, there have been various studies to find pharmacogenetic evidence of epistatic interactions underlying drug resistance, for example, in malaria , epilepsy  and influenza .
Despite the fact that more and more researchers explore epistasis or genome-wide association interaction (GWAI) studies in an attempt to discover more of the hidden or missing heritability of complex traits, there are still several important challenges and considerations to bear in mind. This study aims to investigate the effect of population substructures and admixture on epistasis detection and to develop and apply remedial measures to confounding by shared genetic ancestry in epistasis analyses. Both real-life data and synthetic data will be used for illustration. Starting point is the versatile epistasis analysis tool Model-Based Multifactor Dimensionality Reduction (MB-MDR) . It is non-parametric in that it does not make any assumptions about the epistasis inheritance model and has several advantages over classic regression-based detection tools.
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