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bioRxiv · 10.1101/2023.09.25.559307

Ensembled best subset selection using summary statistics for polygenic risk prediction

Chen, T.·Zhang, H.·Mazumder, R.·Lin, X.
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Abstract

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L0L2 penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.

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Chen, T., Zhang, H., Mazumder, R., Lin, X.. 2023-09-26. Ensembled best subset selection using summary statistics for polygenic risk prediction. https://doi.org/10.1101/2023.09.25.559307

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