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

• Published in: Nature communications Vol. 11; no. 1; p. 4799
• Main Authors: Zhang, Qian, Sidorenko, Julia, Couvy-Duchesne, Baptiste, Marioni, Riccardo E., Wright, Margaret J., Goate, Alison M., Marcora, Edoardo, Huang, Kuan-lin, Porter, Tenielle, Laws, Simon M., Sachdev, Perminder S., Mather, Karen A., Armstrong, Nicola J., Thalamuthu, Anbupalam, Brodaty, Henry, Yengo, Loic, Yang, Jian, Wray, Naomi R., McRae, Allan F., Visscher, Peter M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23-09-2020; Nature Publishing Group; Nature Portfolio
• Subjects: 45/43; 631/114/2785; 631/208; 631/208/205/2138; 692/699/375/132/1283; Adult; Age of Onset; Aged; Alzheimer Disease - genetics; Female; Genetic Association Studies; Genetic Predisposition to Disease - genetics; Human health and pathology; Humanities and Social Sciences; Humans; Life Sciences; Male; Middle Aged; multidisciplinary; Polymorphism, Single Nucleotide; Risk Factors; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-18534-1

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 (
P
optimal
) 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.
Despite the identification of genetic risk loci for late-onset Alzheimer’s disease (LOAD), the genetic architecture and prediction remains unclear. Here, the authors use genetic risk scores for prediction of LOAD across three datasets and show evidence suggesting oligogenic variant architecture for this disease.