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

Leonenko, Ganna ORCID:, Baker, Emily, Stevenson-Hoare, Joshua, Sierksma, Annerieke, Fiers, Mark, Williams, Julie ORCID:, de Strooper, Bart, Escott-Price, Valentina ORCID: and Alzheimer's Disease Neuroimaging Initiative 2021. Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores. Nature Communications 12 , 4506.

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit
Publisher: Nature Research
ISSN: 2041-1723
Funders: MRC
Date of First Compliant Deposit: 10 June 2021
Date of Acceptance: 2 June 2021
Last Modified: 18 May 2023 14:24

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