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Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

Author

Listed:
  • Qian Zhang

    (The University of Queensland)

  • Julia Sidorenko

    (The University of Queensland)

  • Baptiste Couvy-Duchesne

    (The University of Queensland)

  • Riccardo E. Marioni

    (University of Edinburgh)

  • Margaret J. Wright

    (The University of Queensland
    The University of Queensland)

  • Alison M. Goate

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Edoardo Marcora

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Kuan-lin Huang

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Tenielle Porter

    (Edith Cowan University)

  • Simon M. Laws

    (Edith Cowan University
    Curtin University)

  • Perminder S. Sachdev

    (University of New South Wales
    Prince of Wales Hospital)

  • Karen A. Mather

    (University of New South Wales
    Neuroscience Research Australia)

  • Nicola J. Armstrong

    (Murdoch University)

  • Anbupalam Thalamuthu

    (University of New South Wales
    Neuroscience Research Australia)

  • Henry Brodaty

    (University of New South Wales
    University of New South Wales)

  • Loic Yengo

    (The University of Queensland)

  • Jian Yang

    (The University of Queensland)

  • Naomi R. Wray

    (The University of Queensland
    The University of Queensland)

  • Allan F. McRae

    (The University of Queensland)

  • Peter M. Visscher

    (The University of Queensland)

Abstract

Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.

Suggested Citation

  • Qian Zhang & Julia Sidorenko & Baptiste Couvy-Duchesne & Riccardo E. Marioni & Margaret J. Wright & Alison M. Goate & Edoardo Marcora & Kuan-lin Huang & Tenielle Porter & Simon M. Laws & Perminder S. , 2020. "Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18534-1
    DOI: 10.1038/s41467-020-18534-1
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    Cited by:

    1. Hans Kippersluis & Pietro Biroli & Rita Dias Pereira & Titus J. Galama & Stephanie Hinke & S. Fleur W. Meddens & Dilnoza Muslimova & Eric A. W. Slob & Ronald Vlaming & Cornelius A. Rietveld, 2023. "Overcoming attenuation bias in regressions using polygenic indices," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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