Journal article

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, Peter M Visscher

Nature Communications | NATURE RESEARCH | Published : 2020

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 expla..

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Grants

Awarded by Alzheimer's Research UK


Awarded by Cooperative Research Centre (CRC) for Mental Health-funded through the CRC Program


Awarded by Australian National Health and Medical Research Council (NHMRC)


Awarded by Australian National Health and Medical Research Council


Awarded by Australian Research Council


Awarded by NIH


Funding Acknowledgements

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).