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

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, Australian Imaging Biomarkers and Lifestyle (AIBL) Study, Perminder S. Sachdev, Karen A. Mather, Nicola J. Armstrong, Anbupalam Thalamuthu, Henry Brodaty, Loic Yengo, Jian Yang, Naomi R. Wray, Allan F. McRaePeter M. Visscher*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    89 Citations (Scopus)
    75 Downloads (Pure)


    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.

    Original languageEnglish
    Article number4799
    Pages (from-to)1-11
    Number of pages11
    JournalNature Communications
    Issue number1
    Publication statusPublished - 23 Sept 2020

    Bibliographical note

    Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


    Dive into the research topics of 'Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture'. Together they form a unique fingerprint.

    Cite this