Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th street, Bloomington, IN 47405, USA.
Department of Computer Science, Sam Houston State University, 1803 Avenue I, Huntsville, TX 77341, USA.
Genomics. 2019 Sep;111(5):1152-1159. doi: 10.1016/j.ygeno.2018.07.010. Epub 2018 Jul 17.
Gene- and pathway-based variant association tests are important tools in finding genetic variants that are associated with phenotypes of interest. Although some methods have been proposed in the literature, powerful and robust statistical tests are still desirable in this area. In this study, we propose a statistical test based on decomposing the genotype data into orthogonal parts from which powerful and robust independent p-value combination approaches can be utilized. Through a comprehensive simulation study, we compare the proposed test with some existing popular ones. Our simulation results show that the new test has great performance in terms of controlling type I error rate and statistical power. Real data applications are also conducted to illustrate the performance and usefulness of the proposed test.
基于基因和通路的变异关联测试是发现与感兴趣表型相关的遗传变异的重要工具。尽管文献中已经提出了一些方法，但在这个领域仍然需要强大和稳健的统计测试。在这项研究中，我们提出了一种基于将基因型数据分解为正交部分的统计测试，从中可以利用强大和稳健的独立 p 值组合方法。通过全面的模拟研究，我们将提出的测试与一些现有的流行测试进行了比较。我们的模拟结果表明，新测试在控制Ⅰ型错误率和统计功效方面具有优异的性能。还进行了真实数据应用，以说明所提出的测试的性能和有用性。
