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

journal contribution
posted on 21.12.2020, 03:32 by Q Zhang, J Sidorenko, B Couvy-Duchesne, RE Marioni, MJ Wright, AM Goate, E Marcora, KL Huang, T Porter, SM Laws, CL Masters, AI Bush, C Fowler, D Darby, K Pertile, C Restrepo, B Roberts, J Robertson, R Rumble, T Ryan, S Collins, C Thai, B Trounson, K Lennon, QX Li, FY Ugarte, I Volitakis, M Vovos, R Williams, J Baker, A Russell, M Peretti, L Milicic, L Lim, M Rodrigues, K Taddei, T Taddei, E Hone, F Lim, S Fernandez, S Rainey-Smith, S Pedrini, R Martins, J Doecke, P Bourgeat, J Fripp, S Gibson, H Leroux, D Hanson, V Dore, P Zhang, S Burnham, CC Rowe, VL Villemagne, P Yates, SB Pejoska, G Jones, D Ames, E Cyarto, N Lautenschlager, K Barnham, L Cheng, Andrew HillAndrew Hill, N Killeen, P Maruff, B Silbert, B Brown, H Sohrabi, G Savage, M Vacher, PS Sachdev, KA Mather, NJ Armstrong, A Thalamuthu, H Brodaty, L Yengo, J Yang, NR Wray, AF McRae, PM Visscher
© 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.

Funding

R.E.M. is supported by Alzheimer's Research UK grant ARUK-PG2017B-10. We thank all those who took part as a participant in the AIBL study for their commitment and dedication to helping advance research into the early detection and causation of AD. Funding for the AIBL study was provided in part by the study partners (Commonwealth Scientific Industrial and research Organization (CSIRO), Edith Cowan University (ECU), Mental Health Research institute (MHRI), National Ageing Research Institute (NARI), Austin Health and CogState Ltd). The AIBL study has also received support from the National Health and Medical Research Council (NHMRC) and the Dementia Collaborative Research Centres program (DCRC2), as well as funding from the Science and Industry Endowment Fund (SIEF) and the Cooperative Research Centre (CRC) for Mental Health-funded through the CRC Program (Grant ID:20100104), an Australian Government Initiative. We acknowledge and thank the Sydney MAS participants, their supporters, and the Sydney MAS Research Team (current and former staff and students) for their contributions. Funding was awarded from the Australian National Health and Medical Research Council (NHMRC) Program Grants (350833, 568969, 109308). The authors from the University of Queensland are supported by the Australian National Health and Medical Research Council (1078037, 1078901, 1161356 and 1113400) and the Australian Research Council (FT180100186 and FL180100072). E.M. and A.M.G. are supported by NIH (U01AG058635 and U01AG052411).

History

Publication Date

23/09/2020

Journal

Nature Communications

Volume

11

Article Number

4799

Pagination

11p. (p. 1-11)

Publisher

Springer Nature

ISSN

2041-1723

Rights Statement

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