| Title: | A new method for multiancestry polygenic prediction improves performance across diverse populations |
| Journal: | Nature Genetics |
| Published: | 25 Sep 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37749244/ |
| DOI: | https://doi.org/10.1038/s41588-023-01501-z |
Publication 9478
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Abstract
Polygenic risk scores (PRSs) increasingly predict complex traits; however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction.</p>
16 Authors
- Haoyu Zhang
- Jianan Zhan
- Jin Jin
- Jingning Zhang
- Wenxuan Lu
- Ruzhang Zhao
- Thomas U. Ahearn
- Zhi Yu
- Jared O'Connell
- Yunxuan Jiang
- Tony Chen
- Dayne Okuhara
- Montserrat Garcia-Closas
- Xihong Lin
- Bertram L. Koelsch
- Nilanjan Chatterjee
Enabling scientific discoveries that improve human health