randompat is a software of detecting disease-causing single-locus effects and gene-gene interactions. The method is based on finding differences of genotype pattern frequencies between case and control individuals. Those single-nucleotide polymorphism markers with largest single-locus association test statistics are included in a pattern. For a logistic regression model comprising three disease variants exerting main and epistatic interaction effects, we demonstrate that the method is vastly superior to the traditional approach of looking for single-locus effects. In addition, the method is suitable for estimating the number of disease variants in a dataset. We successfully apply our approach to data on Parkinson Disease and heroin addiction.
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Long, Q., Zhang, Q., and Ott, J. 2009.
Detecting disease-associated genotype patterns.
BMC Bioinformatics 10(Suppl 1): S75.