Discovering epistasis interactions in Alzheimer’s disease using integrated framework of ensemble learning and multifactor dimensionality reduction (MDR)

Abstract

Alzheimer's disease (AD) is a complex disorder with strong genetic factors. The proposed framework is applied to Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We present a novel framework integrating ensemble learning and MDR constructive induction algorithm to discover epistasis interactions associated with AD in a computationally efficient method. Discovering epistasis interactions is a big challenge and significantly impacts personalized medicine (PM). The applied ensemble learning algorithms are random forests (RF) with Gini index and permutation importance, Extreme Gradient Boosting (XGBoost), and classification and regression trees (CART). The classification accuracy of 5-way models varied between (0.8674–0.8758), whereas the accuracy of 2-way, 3-way, and 4-way models varied between (0.6515–0.6649), (0.7071–0.7170), and (0.7811–0.7878) respectively. The promising results of this proposed framework show high-ranked risk genes and up to 5-way epistasis models that contribute to the disease risk efficiently and at higher accuracy.

Keywords

• Epistasis Interactions
• Alzheimer's disease
• Personalized Medicine
• Ensemble learning techniques