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Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture
Authors
Nicola J. Armstrong
Australian Imaging Biomarkers and Lifestyle
+27 more
Henry Brodaty
Steven Collins
Baptiste Couvy-Duchesne
Alison M. Goate
Kuan-lin Huang
Simon M. Laws
Kate Lennon
Qiao Xin Li
Edoardo Marcora
Riccardo E. Marioni
Karen A. Mather
Allan F. McRae
Tenielle Porter
Naomi R. Wray Robertson
Perminder S. Sachdev
Julia Sidorenko
Christine Thai
Anbupalam Thalamuthu
Brett Trounson
Fernanda Yevenes Ugarte
Peter M. Visscher
Irene Volitakis
Michael Vovos
Margaret J. Wright
Jian Yeng
Loic Yengo
Qian Zhang
Publication date
23 September 2020
Publisher
Edith Cowan University, Research Online, Perth, Western Australia
Abstract
© 2020, The Author(s). 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
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Last time updated on 19/11/2020