PRISM:组织特异性基因组注释的祖先意识集成增强了多基因分数的可转移性
Xiaohe Tian1,2,3, Tabassum Fabiha4, William F Li1,2
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
bioRxiv : the preprint server for biology
|November 26, 2025
概括
PRISM是一种新的转移学习方法,可以提高多基因分数 (PGS) 跨祖先的可转移性. 它通过整合组织特定的基因组注释,提高了预测准确性,特别是对于非洲血统.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 人口遗传学 人口遗传学
背景情况:
- 多基因分数 (PGS) 在不同人群中显示出有限的可转移性,阻碍了临床使用,并可能加剧健康差异.
- 多祖先培训和变异优先级的挑战来自特定人群的稀缺或不可用的基因组资源.
- 现有的方法难以有效地整合生物相关注释,用于代表性不足的群体.
研究的目的:
- 引入PRISM,一种新的转移学习方法,旨在增强多基因分数在不同祖先之间的可转移性.
- 通过利用基因组注释的祖先意识整合来应对多祖先培训和变异优先级的挑战.
- 通过加强交叉祖先预测,提高PGS的临床实用性并减轻健康差异.
主要方法:
- 开发了PRISM,一个转移学习框架,集成祖先意识,组织特定的基因组注释.
- 将PRISM应用到精细映射的变体,ENCODE注释和来自超过40万个人的英国生物库数据.
- 将PRISM的性能与注释无关的多祖先PGS模型进行了比较.
主要成果:
- 在非洲祖先的预测性能方面,PRISM显著提高,平均改善13.10% (p=1.6×10-5).
- 最有效的PRISM模型使用了比非特定模型少102倍的注释,突出了高效的特征选择.
- 组织特异性注释的祖先意识整合在改善非洲祖先队列中的PGS预测方面产生了最大影响.
结论:
- PRISM提供了一种可立即应用的策略,通过整合生物相关的,特定于种群的基因组资源来增强PGS的可转移性.
- 这种方法补充了对基因组数据集多样化的努力,促进了更大的基因组健康公平.
- PRISM使用特定注释的方法提高了预测准确度,并减少了对广泛,非特定注释集的依赖.
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