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Abstract:
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A method to detect relationships between disease susceptibility and multilocus genetic interactions is the Multifactor-Dimensionality Reduction (MDR) technique pioneered by Ritchie et. al. (2001). Since its introduction, many extensions have been pursued to deal with non-binary outcomes and/or account for multiple interactions simultaneously. Studying the effects of multilocus genetic interactions on continuous traits (blood pressure, weight, etc.) is one case that MDR does not handle. Culverhouse et. al. (2004) and Gui et. al. (2013) proposed two different methods to analyze such a case. We propose a new algorithm that uses a series of T tests, based on ascending order of multilocus means, to identify best interactions of different orders. Ten-fold cross validation is used to choose from among the resulting models, and permutation testing is used to assess the significance of the chosen model. We present results from a simulation study to illustrate the performance of the algorithm.
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