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

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. While polygenic risk scores have been shown to be correlated with disease risk, there is little agreement on how the score should be calculated. Here the authors investigate risk scores for Alzheimer's disease, finding that the most effective approach includes an APOE score and a polygenic score excluding APOE.

Automated summary

Polygenic Risk Scores for Alzheimer's disease offer unique possibilities for reliably identifying individuals at high and low risk. The study shows that the best prediction accuracy is achieved with a model including APOE and a polygenic score excluding APOE. Standardizing PRS against the population mean makes individuals' scores comparable between studies.