Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

Clara Albiñana (), Zhihong Zhu, Andrew J. Schork, Andrés Ingason, Hugues Aschard, Isabell Brikell, Cynthia M. Bulik, Liselotte V. Petersen, Esben Agerbo, Jakob Grove, Merete Nordentoft, David M. Hougaard, Thomas Werge, Anders D. Børglum, Preben Bo Mortensen, John J. McGrath, Benjamin M. Neale, Florian Privé and Bjarni J. Vilhjálmsson ()
Additional contact information
Clara Albiñana: iPSYCH
Zhihong Zhu: Aarhus University
Andrew J. Schork: iPSYCH
Andrés Ingason: iPSYCH
Hugues Aschard: Université de Paris
Isabell Brikell: iPSYCH
Cynthia M. Bulik: Karolinska Institute
Liselotte V. Petersen: iPSYCH
Esben Agerbo: iPSYCH
Jakob Grove: iPSYCH
Merete Nordentoft: iPSYCH
David M. Hougaard: iPSYCH
Thomas Werge: iPSYCH
Anders D. Børglum: iPSYCH
Preben Bo Mortensen: iPSYCH
John J. McGrath: Aarhus University
Benjamin M. Neale: Massachusetts General Hospital
Florian Privé: iPSYCH
Bjarni J. Vilhjálmsson: iPSYCH

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract 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 R2 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.

Date: 2023
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DOI: 10.1038/s41467-023-40330-w

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