Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

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 R² 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.

Polygenic scores (PGS) have high potential for clinical use but are currently underpowered for many applications. Here, the authors develop an approach that leverages an agnostic library of hundreds of PGS to increase prediction of complex diseases and other traits. This multi-PGS framework is ideal for emerging biobank data.