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Multi-polygenic score approach to trait prediction

Krapohl, E; Patel, H; Newhouse, S; Curtis, CJ; von Stumm, S; Dale, PS; Zabaneh, D; ... Plomin, R; + view all (2018) Multi-polygenic score approach to trait prediction. Molecular Psychiatry , 23 (5) pp. 1368-1374. 10.1038/mp.2017.163. Green open access

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Abstract

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene–environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

Type: Article
Title: Multi-polygenic score approach to trait prediction
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/mp.2017.163
Publisher version: https://doi.org/10.1038/mp.2017.163
Language: English
Additional information: © The Author(s) 2018. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10064293
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