Benchmarking Alzheimer's disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies

Background The success of selecting high risk or early-stage Alzheimer’s disease individuals for the delivery of clinical trials depends on the design and the appropriate recruitment of participants. Polygenic risk scores (PRS) show potential for identifying individuals at risk for Alzheimer’s disease (AD). Our study comprehensively examines AD PRS utility using various methods and models. Methods We compared the PRS prediction accuracy in ADNI (N = 568) and BioFINDER (N = 766) cohorts using five disease risk modelling approaches, three PRS derivation methods, two AD genome-wide association study (GWAS) statistics and two sets of SNPs: the whole genome and microglia-selective regions only. Results The best prediction accuracy was achieved when modelling genetic risk by using two predictors: APOE and remaining PRS (AUC = 0.72–0.76). Microglial PRS showed comparable accuracy to the whole genome (AUC = 0.71–0.74). The individuals’ risk scores differed substantially, with the largest discrepancies (up to 70%) attributable to the GWAS statistics used. Conclusions Our work benchmarks the best PRS derivation and modelling strategies for AD genetic prediction.