Florian Privé

Nov 8, 2023 | cell.com | Florian Privé |Clara Albiñana |Grenoble INP

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Aug 5, 2023 | nature.com | Clara Albiñana |Hugues Aschard |Cynthia Bulik |Jakob Grove |David M. Hougaard |Thomas Werge | +5 more

AbstractThe 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.