Chen Zhongxue, Wang Kai
Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, Indiana.
Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa.
Stat Med. 2019 Jun 15;38(13):2353-2363. doi: 10.1002/sim.8111. Epub 2019 Jan 31.
Detecting the association between a set of variants and a phenotype of interest is the first and important step in genetic and genomic studies. Although it attracted a large amount of attention in the scientific community and several related statistical approaches have been proposed in the literature, powerful and robust statistical tests are still highly desired and yet to be developed in this area. In this paper, we propose a powerful and robust association test, which combines information from each individual single-nucleotide polymorphisms based on sequential independent burden tests. We compare the proposed approach with some popular tests through a comprehensive simulation study and real data application. Our results show that, in general, the new test is more powerful; the gain in detecting power can be substantial in many situations, compared to other methods.
检测一组变异与感兴趣的表型之间的关联是遗传和基因组研究的首要且重要步骤。尽管它在科学界引起了大量关注，文献中也提出了几种相关的统计方法，但在该领域仍非常需要强大且稳健的统计检验，并且有待开发。在本文中，我们提出了一种强大且稳健的关联检验方法，该方法基于顺序独立负担检验，结合了来自每个个体单核苷酸多态性的信息。我们通过全面的模拟研究和实际数据应用，将所提出的方法与一些常用检验进行了比较。我们的结果表明，总体而言，新检验更具功效；与其他方法相比，在许多情况下检测功效的提升可能相当显著。
