Document Type

Journal Article

Publication Title

Nature Communications

Volume

11

Issue

1

PubMed ID

32968074

Publisher

Springer Nature

School

Centre of Excellence for Alzheimer's Disease Research and Care / School of Medical and Health Sciences

RAS ID

32211

Funders

Commonwealth Scientific and Industrial Research Organisation

Comments

Zhang, Q., Sidorenko, J., Couvy-Duchesne, B., Marioni, R. E., Wright, M. J., Goate, A. M., ... & Visscher, P.M. (2020). Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture. Nature Communications, 11(1), article 4799. https://doi.org/10.1038/s41467-020-18534-1

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.

DOI

10.1038/s41467-020-18534-1

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Research Themes

Health

Priority Areas

Multidisciplinary biological approaches to personalised disease diagnosis, prognosis and management

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