评估和改善多基因分数的健康公平性和公平性
Tianyu Zhang1, Geyu Zhou2, Lambertus Klei3
1Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
HGG advances
|February 25, 2024
概括
联合-拉索索 (JLS) 通过整合来自多个群体的全基因组关联研究数据,提高了多基因分数 (PGS) 在祖先之间的可移植性. 这种方法提高了预测的准确性和公平性,解决了现有的PGS方法的局限性.
科学领域:
- 遗传学和生物信息学 遗传学和生物信息学
- 统计基因组学 统计基因组学
- 计算生物学 计算生物学
背景情况:
- 多基因分数 (PGS) 预测表型值,但在接受欧洲祖先全基因组关联研究 (GWAS) 数据训练时,往往缺乏跨祖先的可移植性.
- 像Lassosum这样的现有方法虽然有效,但继承了这种祖先特定的预测偏差,限制了它们在不同人群中的临床实用性.
研究的目的:
- 开发一种新的PGS方法,联合拉索索 (JLS),可以提高不同祖先的分数的可移植性和公平性.
- 引入一种新的数据驱动方法来选择适用于任何PGS方法的调参数.
主要方法:
- 拟议的联合拉索索姆 (JLS),一种利用GWAS总结统计和来自多个祖先的链接不平衡信息的方法.
- 开发了一种新的调参数选择策略,用于PGS模型选择.
- 通过模拟和分析英国生物库数据来评估JLS的性能.
主要成果:
- 模拟表明,与现有方法相比,JLS产生更准确和更公平的多基因分数,特别是对于非欧洲祖先.
- 英国生物库的分析表明,JLS在计算上是高效的,其性能与SDPRX等贝叶斯方法相美.
- 新的调参数选择方法对于一般的PGS模型选择证明是相关的.
结论:
- 联合拉索 (JLS) 通过整合多祖先GWAS数据,在创建更便携和公平的多基因分数方面取得了重大进展.
- 通过减少不同人群的预测偏见,JLS可以帮助减轻与PGS在临床环境中的使用相关的与公平性相关的危害.
- 拟议的调参数选择方法为优化PGS模型提供了有价值的工具.
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