Detecting gene–gene interactions that underlie human diseases

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Abstract
Interactions between genetic loci might reduce the power to detect genetic effects in genetic association studies, if these interactions are not allowed for. Statistical interaction corresponds to a departure from the additive effects of two or more variables in a linear model describing the relationship between an outcome and predictor variables. A variety of methods can be used to test for statistical interaction between predictor variables that encode the genotype and an outcome variable corresponding to the disease phenotype. Logistic regression is one method that can be used either to test for interaction, or to test for association while allowing for interaction. Given genome-wide data, an exhaustive search is feasible for investigating two-way interactions (that is, all pairwise combinations of loci) but not for investigation of higher-order interactions. Filtering approaches allow one to reduce the number of loci considered and thus the number of interaction tests performed. Data-mining or machine-learning methods, such as random forests and Multifactor Dimensionality Reduction (MDR), can allow one to search through the space of possible interactions. Bayesian model selection approaches offer an alternative approach for searching through the space of possible interactions. The biological interpretation of statistical interactions is complex. The degree to which statistical interaction implies interaction or synergism in a causal sense might be extremely limited.