As genetic epidemiology looks beyond mapping single disease susceptibility loci, interest in detecting epistatic interactions between genes has grown. The dimensionality and comparisons required to search the epistatic space and the inference for a significant result pose challenges for testing epistatic disease models. The multifactor dimensionality reduction-pedigree disequilibrium test (MDR-PDT) was developed to test for multilocus models in pedigree data. In the present study we rigorously tested MDR-PDTwith new cross-validation (CV) (both 5- and 10-fold) and omnibus model selection algorithms by simulating a range of heritabilities, odds ratios, minor allele frequencies, sample sizes, and numbers of interacting loci. Power was evaluated using 100, 500, and 1,000 families, with minor allele frequencies 0.2 and 0.4 and broad-sense heritabilities of 0.005, 0.01, 0.03, 0.05, and 0.1 for 2- and 3-locus purely epistatic penetrance models. We also compared the prediction error (PE) measure of effect with a predicted matched odds ratio (MOR) for final model selection and testing. We report that the CV procedure is valid with the permutation test, MDR-PDT performs similarly with 5- and 10-fold CV, and that the MOR is more powerful than PE as the fitness metric for MDR-PDT.
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