Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning

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Item Type:	Article
Title:	Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning
Creators Name:	Monti, R., Eick, L., Hudjashov, G., Läll, K., Kanoni, S., Wolford, B.N., Wingfield, B., Pain, O., Wharrie, S., Jermy, B., McMahon, A., Hartonen, T., Heyne, H., Mars, N., Lambert, S., Hveem, K., Inouye, M., van Heel, D.A., Mägi, R., Marttinen, P., Ripatti, S., Ganna, A. and Lippert, C.
Abstract:	Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.
Keywords:	Polygenic Scores, PGS, Genome-Wide Association Studies, GWAS, Biobank Studies, Genetic Risk, Ensemble Learning, Method Evaluation, Cross-Biobank Analysis, Autoimmune Diseases, Genetic Variability, Phenotype Prediction
Source:	American Journal of Human Genetics
ISSN:	0002-9297
Publisher:	Elsevier / Cell Press
Volume:	111
Number:	7
Page Range:	1431-1447
Date:	11 July 2024
Additional Information:	Copyritgh © 2024 American Society of Human Genetics. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Official Publication:	https://doi.org/10.1016/j.ajhg.2024.06.003
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