Polygenic risk score prediction accuracy convergence

Léo Henches

doi:10.1016/j.xhgg.2025.100457

2025-05-14

Bjarni J. Vilhjálmsson

Florian Privé

Hugues Aschard

DOI：10.1016/j.xhgg.2025.100457

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摘要

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多基因风险评分（PRSs）模型基于全基因组关联研究（GWAS）结果进行训练，有望在针对多因素人类疾病的生物医学研究中发挥关键作用。这些风险评分在临床护理和公共卫生中的应用前景引发了热情与争议，专家们对其优缺点的看法存在分歧。现有多基因评分的性能仍有限，但随着GWAS样本量的增加和新方法的开发，预计将得到改善。理论上，PRS解释的方差可高达总加性遗传方差，但尚不清楚其中有多少方差已被PRS捕获。在此，我们进行了回顾性分析，以评估自首次大规模GWAS发表以来PRS预测准确性的进展，使用了六种常见人类疾病且GWAS信息充足的疾病数据。我们发现，尽管PRS准确性逐年快速增长，但近期GWAS带来的改进速度已大幅减缓，表明单纯增加GWAS样本量可能仅能带来适度的风险区分改进。接着，我们利用12.5万名英国生物银行参与者的全基因组测序数据和先进的多基因结局建模方法，探讨了影响最大可达成预测的因素。我们的分析表明，增加PRS的变异覆盖范围，无论是通过更多推断变异还是测序数据，是未来提高预测准确性的关键因素。

Keyword:

polygenic risk prediction

GWAS

sample size

polygenicity

期刊

Human Genetics and Genomics Advances

IF:

3.6

论文数:

536

被引数:

775

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暂无机构信息

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