Epistasis Analysis Using an Improved Fuzzy C-Means-Based Entropy Approach

Abstract

Epistasis detection is vital to determining disease susceptibility in the human genome. With rapid advances in technology, multifactor dimensionality reduction (MDR) has become an effective algorithm for epistasis detection. Classification of high-risk (H) and low-risk (L) groups in MDR operations is a key topic, but it has not been thoroughly investigated. In this paper, we propose an improved fuzzy c-means-based entropy (FCME) approach to address the limitations of binary classification. For this approach, the degree of membership in MDR, referred to as FCMEMDR, was used. The FCME approach and MDR measure were integrated to enable more precise differentiation between similar frequencies of multifactor genotypes in the cases of possible epistasis. We used the MDR measures of correct classification rate and likelihood ratio. Numerous simulated datasets were applied, and the experimental results revealed two measures of FCMEMDR with higher detection rates than those of other MDR-based algorithms. Our analysis of binary and fuzzy classifications in MDR operations may offer insights into the problem of uncertainty in H/L classification. Two measures of FCMEMDR detected significant instances of epistasis associated with coronary artery disease in the Wellcome Trust Case Control Consortium dataset. FCMEMDR is freely available at https://gitlab.com/yudalinemail/fcmemdr .