Large-scale composite hypothesis testing procedure for omics data analyses

doi:10.1093/nargab/lqaf118

2025-09-01

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DOI：10.1093/nargab/lqaf118

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基于汇总统计的复合假设检验是一种评估单个标记或基因在多个性状或组学水平上效应的成熟方法。为此任务已开发出多种方法，并成功应用于识别性状、条件或表型之间复杂的关联模式。然而，现有方法在大数据集上常面临可扩展性挑战，或未能考虑性状或组学水平间的依赖关系，从而限制了其有效控制假阳性的能力。为克服这些挑战，我们提出qch_copula方法，该方法整合了混合模型与Copula函数以捕捉性状或组学间的依赖关系，并为任何复合假设提供严格定义的P值。通过与八种当前先进方法的全面基准测试，我们证明qch_copula在有效控制第一类错误率的同时，增强了联合关联模式的检测。与其他基于混合模型的方法相比，我们的方法在EM算法中显著降低了内存使用量，允许分析高达20个性状和105-106个标记。qch_copula的有效性进一步通过人类和植物遗传学中的两个应用案例得到验证。该方法可在R包qch中获取，该包可在CRAN上访问。

Keyword:

GENOME-WIDE ASSOCIATION

FALSE DISCOVERY RATE

METAANALYSIS

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NAR Genomics and Bioinformatics

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2.8

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259

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universite paris saclay

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