在五个生物库中对多基因评分方法的评估显示,生物库之间的差异比方法更大,并发现集体学习的好处
Remo Monti1, Lisa Eick2, Georgi Hudjashov3
1Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.
American journal of human genetics
|June 22, 2024
概括
在五个生物库中评估了七种多基因评分 (PGS) 方法,没有发现单一的最佳表现者. 在英国生物银行调整的集体PGS模型显示出对复杂的特征和疾病的优越,可转移的性能.
科学领域:
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
- 统计遗传学 统计遗传学
背景情况:
- 来自全基因组关联研究 (GWAS) 的多基因分数 (PGS) 得到广泛使用.
- 独立评估和对PGS方法的比较是有限的,阻碍了最佳应用.
研究的目的:
- 综合评估和比较七种PGS估计方法跨不同的生物库和特征.
- 确定影响PGS性能的因素,包括方法选择,调和组合.
主要方法:
- 在五个大型生物库研究中使用参考标准化框架评估七种PGS方法 (约. 120万名参与者).
- 进行了元分析,以评估方法选择,超参数调整,方法组合和生物库对PGS性能的影响.
- 交叉验证与超参数优化的自动调整进行了比较.
主要成果:
- 在所有评估中,没有一种PGS方法在所有评估中始终超过所有其他方法.
- 在生物银行之间,PGS的表现比在生物银行内部调整良好的方法之间更有差异.
- 在英国生物银行调整的集体PGS模型显示出一致,高和可转移的性能,比顶级单一方法平均提高了5.0%的效果大小.
- 对大多数方法的超参数调整,交叉验证比自动方法更可靠.
结论:
- 选择PGS方法显著影响性能,没有通用最佳方法.
- 综合PGS方法提供了一个强大的策略,以提高预测准确性和跨不同人群的可转移性.
- 该研究提供了宝贵的资源,包括在线结果和开源工作流,以指导未来的PGS分析.
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