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Comprehensive benchmarking single and multi ancestry polygenic score methods with the PGS-hub platform.

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Název: Comprehensive benchmarking single and multi ancestry polygenic score methods with the PGS-hub platform.
Autoři: Chen, Xingyu, Wang, Fei, Zhao, Hongqiang, Hao, Jing, A, Yunga, Yang, Xiong, Xu, Tingfeng, Zhou, Yubo, Chen, Qiuli, Zhang, Rufan, Yu, Kang, Zaib, Komal, Fahed, Akl C., Zhai, Guangyao, Wang, Minxian
Zdroj: Nature Communications; 1/25/2026, Vol. 17 Issue 1, p1-17, 17p
Témata: GENETIC risk score, COMPUTING platforms, BENCHMARK problems (Computer science), OPTIMIZATION algorithms, BIOBANKS
Abstrakt: Polygenic scores (PGS) quantify genetic contributions to complex traits, yet existing single- and multi-ancestry methods lack multi-dimensional evaluation within a unified framework. Here, we benchmarked 13 state-of-the-art PGS methods across 36 traits in UK Biobank European and African samples. The prediction performance, computational efficiency, the number of variants, and the impact of different linkage disequilibrium (LD) reference sizes were thoroughly assessed for each method. Results of single-ancestry methods demonstrate that LDpred2 has superior performance across a broad spectrum of complex traits in terms of accuracy and computational efficiency; however, other methods remain valuable for specific traits. For multi-ancestry methods, PRS-CSx and X-Wing have comparable performance, whereas LDpred2-multi outperforms both. Notably, we find that increasing the panel size of the LD reference significantly elevates PGS performance for sample sizes below 1,000, and it reaches a plateau when it exceeds 5,000 samples. Furthermore, implementing PGS calculation methods requires considerable technical effort and resource allocation. To support easy use of these PGS methods, we developed a user-friendly online computing platform, PGS-hub, that integrates all evaluated methods and is pre-configured with ancestry-stratified LD panels. This resource enables a scalable and harmonized PGS computation platform for the PGS community. Here the authors systematically benchmark 13 polygenic score (PGS) methods in UK Biobank European and African populations, revealing factors that affect prediction accuracy. They also introduce an automated platform for standardized and reproducible PGS computation. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Polygenic scores (PGS) quantify genetic contributions to complex traits, yet existing single- and multi-ancestry methods lack multi-dimensional evaluation within a unified framework. Here, we benchmarked 13 state-of-the-art PGS methods across 36 traits in UK Biobank European and African samples. The prediction performance, computational efficiency, the number of variants, and the impact of different linkage disequilibrium (LD) reference sizes were thoroughly assessed for each method. Results of single-ancestry methods demonstrate that LDpred2 has superior performance across a broad spectrum of complex traits in terms of accuracy and computational efficiency; however, other methods remain valuable for specific traits. For multi-ancestry methods, PRS-CSx and X-Wing have comparable performance, whereas LDpred2-multi outperforms both. Notably, we find that increasing the panel size of the LD reference significantly elevates PGS performance for sample sizes below 1,000, and it reaches a plateau when it exceeds 5,000 samples. Furthermore, implementing PGS calculation methods requires considerable technical effort and resource allocation. To support easy use of these PGS methods, we developed a user-friendly online computing platform, PGS-hub, that integrates all evaluated methods and is pre-configured with ancestry-stratified LD panels. This resource enables a scalable and harmonized PGS computation platform for the PGS community. Here the authors systematically benchmark 13 polygenic score (PGS) methods in UK Biobank European and African populations, revealing factors that affect prediction accuracy. They also introduce an automated platform for standardized and reproducible PGS computation. [ABSTRACT FROM AUTHOR]
ISSN:20411723
DOI:10.1038/s41467-026-68599-7