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, Bjarni J Vilhjálmsson

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


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.

Original languageEnglish
Article number4702
JournalNature Communications
Publication statusPublished - Aug 2023


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