Multi-PGS enhances polygenic prediction: weighting 937 polygenic scores
Albinana, C.; Zhu, Z.; Schork, A. J.; Ingason, A.; Aschard, H.; Brikell, I.; Bulik, C. M.; Pedersen, L. V.; Agerbo, E.; Grove, J.; Nordentoft, M.; Hougaard, D. M.; Werge, T.; Boerglum, A. D.; Mortensen, P. B.; McGrath, J. J.; Neale, B. M.; Prive, F.; Vilhjalmsson, B. J.
Show 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 increased 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 (ADHD) compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions, with up to 15-fold increases in prediction accuracy. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.
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