Using biological pathways in Machine Learning methods for Alzheimer’s disease risk prediction

Recent genome‐wide studies have identified over 70 risk loci for late onset Alzheimer’s Disease (AD) (Kunkle et al. 2019, Bellenguez et el. 2021, Wightman et al. 2021). Analysis in this study focused on developing Machine Learning (ML) models to predict AD risk from genetic data. We compared the prediction accuracy of ML to polygenic risk scores (PRS) using SNPs in disease‐associated biological pathways.