Using biological pathways in Machine Learning methods for Alzheimer's disease risk prediction. (20th December 2022) - Rowe, Thomas; Leonenko, Ganna; Williams, Julie; Holmans, Peter; Ivanov, Dobril; Escott‐Price, Valentina
Abstract: Background: 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. Method: We used the Genetic and Environmental Risk for Alzheimer's Disease consortium dataset (Harold et al. 2009). SNPs were selected from the AD associated pathways in Kunkle et al. 2019. Two decision tree‐based ML algorithms were used, Random Forests (RFs) and Gradient Boosting (GB). The prediction ability of these methods was compared to Polygenic Risk Score (PRS). RFs and GB models were developed using the Python library sklearn . These were trained and tested using 5‐fold cross‐validation. Clumping and thresholding (CT), as implemented in PLINK, was used to generate PRS, with logistic regression used for prediction. CT PRS was compared to the PRS generated by the PRS‐CS method (Ge, T., Chen, CY., Ni, Y. et al). Result: Initial results demonstrate that PRS, PRS‐CS and ML perform similarly for pathway specific analysis (AUC∼69%) when pathways include APOE . Conclusion: Subsequent analyses will compare the performance of the methods when APOE SNPs are removed from the pathways, and in multivariate analyses modelling multiple pathway effects simultaneously.