Multi-PGS enhances polygenic prediction by combining 937 polygenic scores.
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ABSTRACT:
SUBMITTER: Albinana C
PROVIDER: S-EPMC10404269 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
Publications
Multi-PGS enhances polygenic prediction by combining 937 polygenic scores.
Albiñana Clara C Zhu Zhihong Z Schork Andrew J AJ Ingason Andrés A Aschard Hugues H Brikell Isabell I Bulik Cynthia M CM Petersen Liselotte V LV Agerbo Esben E Grove Jakob J Nordentoft Merete M Hougaard David M DM Werge Thomas T Børglum Anders D AD Mortensen Preben Bo PB McGrath John J JJ Neale Benjamin M BM Privé Florian F Vilhjálmsson Bjarni J BJ
Nature communications 20230805 1
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, th ...[more]
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