A Belief Degree-Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection.

Abstract

Epistasis is a challenge in prediction, classification, and suspicion of human genetic diseases. Many technologies, methods, and tools have been developed for epistasis detection. Multifactor dimensionality reduction (MDR) is the method commonly used in epistasis detection. It uses two class groups-high risk and low risk-in human genetic disease and complex genetic traits. However, it cannot handle uncertainties from genetic information. This chapter describes the fuzzy sigmoid membership-based MDR (FSMDR) method of epistasis detection. The algorithmic steps in FSMDR are also elaborated with simulated data generated from GAMETES and a real coronary artery disease patient epistasis data set obtained from the Wellcome Trust Case Control Consortium(WTCCC). Moreover, a belief degree-associated fuzzy MDR framework is also proposed for epistasis detection, which can overcome the uncertainties of MDR-based methods. This framework improves the detection efficiency. It works like fuzzy set-based MDR methods. Simulated epistasis data sets are used to compare different MDR-based methods. Belief degree-associated fuzzy MDR was shown togives good results by taking into account the uncertainly of the high/low risk classification.