Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

TLDR

Polygenic risk scores (PRS) for Alzheimer’s disease can identify individuals at high or low risk, yet consensus on calculation methods, APOE modeling, SNP selection thresholds, and cross‑study comparisons remains lacking. Our study shows that a two‑predictor model (APOE plus PRS excluding the APOE region with pT < 0.1) yields the highest prediction accuracy, and that standardizing PRS to the population mean enables comparable risk ranking across studies, providing optimal strategies for AD risk profiling.

Abstract

Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals' scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals' scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.