Genetic pathway analysis has become an important tool for investigating the association between a group of genetic variants and traits. With dense genotyping and extensive imputation, the number of genetic variants in biological pathways has increased considerably and sometimes exceeds the sample size [Formula: see text]. Conducting genetic pathway analysis and statistical inference in such settings is challenging. We introduce an approach that can handle pathways whose dimension [Formula: see text] could be greater than [Formula: see text]. Our method can be used to detect pathways that have nonsparse weak signals, as well as pathways that have sparse but stronger signals. We establish the asymptotic distribution for the proposed statistic and conduct theoretical analysis on its power. Simulation studies show that our test has correct Type I error control and is more powerful than existing approaches. An application to a genome-wide association study of high-density lipoproteins demonstrates the proposed approach.

中文翻译：

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高维遗传通路分析的统计推断

遗传通路分析已成为研究一组遗传变异和性状之间关联的重要工具。通过密集的基因分型和广泛的插补，生物途径中的遗传变异数量显着增加，有时甚至超过样本量[公式：见正文]。在这样的环境中进行遗传通路分析和统计推断具有挑战性。我们引入了一种方法，可以处理维度 [Formula: see text] 可能大于 [Formula: see text] 的路径。我们的方法可用于检测具有非稀疏弱信号的路径，以及具有稀疏但更强信号的路径。我们为提出的统计量建立渐近分布，并对其功效进行理论分析。仿真研究表明，我们的测试具有正确的 I 类错误控制，并且比现有方法更强大。应用于高密度脂蛋白的全基因组关联研究证明了所提出的方法。