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, Perminder S. Sachdev, Karen A. Mather, Nicola J. Armstrong, Anbupalam Thalamuthu, Henry Brodaty, Loic Yengo, Jian Yang, Naomi R. Wray, Allan F. McRae and Peter M. Visscher ()
Additional contact information
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
Alison M. Goate: Icahn School of Medicine at Mount Sinai
Edoardo Marcora: Icahn School of Medicine at Mount Sinai
Kuan-lin Huang: Icahn School of Medicine at Mount Sinai
Tenielle Porter: Edith Cowan University
Simon M. Laws: Edith Cowan University
Perminder S. Sachdev: University of New South Wales
Karen A. Mather: University of New South Wales
Nicola J. Armstrong: Murdoch University
Anbupalam Thalamuthu: University of New South Wales
Henry Brodaty: University of New South Wales
Loic Yengo: The University of Queensland
Jian Yang: The University of Queensland
Naomi R. Wray: The University of Queensland
Allan F. McRae: The University of Queensland
Peter M. Visscher: The University of Queensland

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18534-1

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