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

Author

Listed:
  • Ganna Leonenko

    (Cardiff University)

  • Emily Baker

    (Cardiff University)

  • Joshua Stevenson-Hoare

    (Cardiff University)

  • Annerieke Sierksma

    (VIB Center for Brain & Disease Research
    KU Leuven (University of Leuven))

  • Mark Fiers

    (VIB Center for Brain & Disease Research
    KU Leuven (University of Leuven)
    University College London)

  • Julie Williams

    (Cardiff University
    Cardiff University)

  • Bart Strooper

    (VIB Center for Brain & Disease Research
    KU Leuven (University of Leuven)
    University College London)

  • Valentina Escott-Price

    (Cardiff University
    Cardiff University)

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

Suggested Citation

  • Ganna Leonenko & Emily Baker & Joshua Stevenson-Hoare & Annerieke Sierksma & Mark Fiers & Julie Williams & Bart Strooper & Valentina Escott-Price, 2021. "Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24082-z
    DOI: 10.1038/s41467-021-24082-z
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    Cited by:

    1. Vinay, Rasita & Biller-Andorno, Nikola, 2023. "A critical analysis of national dementia care guidances," Health Policy, Elsevier, vol. 130(C).
    2. Wei Jiang & Ling Chen & Matthew J. Girgenti & Hongyu Zhao, 2024. "Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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