Year: 2023
Type: article
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 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.
Source: Nature Communications
Authors: 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
Institutions: Aarhus University, Lundbeck Foundation, Translational Genomics Research Institute, Mental Health Services, Copenhagen University Hospital, Institut Pasteur, Université Paris Cité, Karolinska Institutet, University of North Carolina at Chapel Hill, University of Copenhagen, Statens Serum Institut, The University of Queensland, Park Centre for Mental Health, Queensland Centre for Mental Health Research, Broad Institute, Massachusetts General Hospital, Stanley Center for Psychiatric Research
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,290
OpenAlex ID: W4385607015