Multi-PGS enhances polygenic prediction by combining 937 polygenic scores
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The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R 2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | 4702 |
| Tidsskrift | Nature Communications |
| Vol/bind | 14 |
| Antal sider | 11 |
| ISSN | 2041-1723 |
| DOI | |
| Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
C.A., B.J.V. and F.P. were supported by the Danish National Research Foundation (Niels Bohr Professorship to Prof. John McGrath), the Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH (R102-A9118, R155-2014-1724 and R248-2017-2003), and a Lundbeck Foundation Fellowship (R335-2019-2339). C.A. was supported by a Willam Demant Fonden fellowship. I.B. was also supported by the Swedish Brain Foundation and Fredrik och Ingrid Thurings Stiftelse. AN data are from the Anorexia Nervosa Genetics Initiative, an initiative of the Klarman Family Foundation, and extendented with support from the Lundbeck foundation (R276-2017-4581). High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to A.D.B.).
Funding Information:
C.M.B. reports: Lundbeckfonden (grant recipient); Pearson (author, royalty recipient); Equip Health Inc. (Stakeholder Advisory Board). B.M.N. is a member of the scientific advisory board at Deep Genomics and Neumora, consultant of the scientific advisory board for Camp4 Therapeutics and consultant for Merck. B.J.V. is on Allelica’s international advisory board. The remaining authors declare no competing interests.
Publisher Copyright:
© 2023, Springer Nature Limited.
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