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Zhou et al. utilise deep learning to improve polygenic risk analysis for Alzheimer’s disease. Their computational approach outperforms existing statistical methods and helps to identify potential biological mechanisms of Alzheimer’s disease risk.

Abstract Introduction Dozens of Alzheimer's disease (AD)‐associated loci have been identified in European‐descent populations, but their effects have not been thoroughly investigated in the Hong Kong Chinese population. Methods TaqMan array genotyping was performed for known AD‐associated variants in a Hong Kong Chinese cohort. Regression analysis was conducted to study the associations of variants with AD‐associated traits and biomarkers. Lasso regression was applied to establish a polygenic risk score (PRS) model for AD risk prediction. Results SORL1 is associated with AD in the Hong Kong Chinese population. Meta‐analysis corroborates the AD‐protective effect of the SORL1 rs11218343 C allele. The PRS is developed and associated with AD risk, cognitive status, and AD‐related endophenotypes. TREM2 H157Y might influence the amyloid beta 42/40 ratio and levels of immune‐associated proteins in plasma. Discussion SORL1 is associated with AD in the Hong Kong Chinese population. The PRS model can predict AD risk and cognitive status in this population.