학술논문
Multi-PGS enhances polygenic prediction by combining 937 polygenic scores
Document Type
article
Author
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
Source
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Subject
Language
English
ISSN
2041-1723
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 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.