MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.

IF 11.1 Q1 CELL BIOLOGY Cell genomics Pub Date : 2024-04-10 DOI:10.1016/j.xgen.2024.100539
Jin Jin, Jianan Zhan, Jingning Zhang, Ruzhang Zhao, Jared O'Connell, Yunxuan Jiang, Steven Buyske, Christopher Gignoux, Christopher Haiman, Eimear E Kenny, Charles Kooperberg, Kari North, Bertram L Koelsch, Genevieve Wojcik, Haoyu Zhang, Nilanjan Chatterjee
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Abstract

Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.

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MUSSEL:利用多个祖先群体信息的增强型贝叶斯多基因风险预测。
目前,多基因风险评分(PRSs)在各种复杂性状和疾病方面显示出了良好的预测性能,但在不同人群之间还存在很大的性能差距。我们提出的 MUSSEL 是一种针对特定祖先的多基因预测方法,它通过贝叶斯分层建模和集合学习,从多个祖先群体的全基因组关联研究(GWAS)中借用汇总统计信息。在我们的模拟研究和四项不同研究的数据分析中,与其他替代方案相比,MUSSEL 表现出了良好的性能,这些研究的参与者总计 570 万人,其祖先具有很大的多样性。例如,在非洲血统人群中,与 PRS-CSx 和 CT-SLEB 相比,MUSSEL 在 11 个连续性状上的预测 R2 平均增益分别为 40.2% 和 49.3%。然而,表现最好的方法因 GWAS 样本大小、目标祖先、性状结构和连锁不平衡参考样本而异;因此,最终可能需要结合多种方法才能在不同人群中生成最稳健的 PRS。
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