| Title: | Identifying individuals with high risk of Alzheimer's disease using polygenic risk scores |
| Journal: | Nature Communications |
| Published: | 23 Jul 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34301930/ |
| DOI: | https://doi.org/10.1038/s41467-021-24082-z |
| URL: | https://www.nature.com/articles/s41467-021-24082-z.pdf |
Publication 9775
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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.</p>
8 Authors
- Ganna Leonenko
- Emily Baker
- Joshua Stevenson-Hoare
- Annerieke Sierksma
- Mark Fiers
- Julie Williams
- Bart de Strooper
- Valentina Escott-Price
1 Application
| Application ID | Title |
|---|---|
| 15175 | Further defining the genetic architecture of Alzheimer's disease |
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