Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA.
Genet Epidemiol. 2011 Jul;35(5):350-9. doi: 10.1002/gepi.20583. Epub 2011 Apr 11.
Large-scale genome-wide association studies (GWAS) have become feasible recently because of the development of bead and chip technology. However, the success of GWAS partially depends on the statistical methods that are able to manage and analyze this sort of large-scale data. Currently, the commonly used tests for GWAS include the Cochran-Armitage trend test, the allelic χ(2) test, the genotypic χ(2) test, the haplotypic χ(2) test, and the multi-marker genotypic χ(2) test among others. From a methodological point of view, it is a great challenge to improve the power of commonly used tests, since these tests are commonly used precisely because they are already among the most powerful tests. In this article, we propose an improved score test that is uniformly more powerful than the score test based on the generalized linear model. Since the score test based on the generalized linear model includes the aforementioned commonly used tests as its special cases, our proposed improved score test is thus uniformly more powerful than these commonly used tests. We evaluate the performance of the improved score test by simulation studies and application to a real data set. Our results show that the power increases of the improved score test over the score test cannot be neglected in most cases.
近年来，由于珠子和芯片技术的发展，大规模全基因组关联研究（GWAS）变得可行。然而，GWAS 的成功部分取决于能够管理和分析这种大规模数据的统计方法。目前，GWAS 常用的检验包括 Cochran-Armitage 趋势检验、等位基因 χ(2)检验、基因型 χ(2)检验、单倍型 χ(2)检验和多标记基因型 χ(2)检验等。从方法学的角度来看，提高常用检验的功效是一个巨大的挑战，因为这些检验之所以被广泛使用，正是因为它们已经是最强大的检验之一。在本文中，我们提出了一种改进的得分检验，它比基于广义线性模型的得分检验具有更强的一致性。由于基于广义线性模型的得分检验包括上述常用检验作为其特例，因此我们提出的改进得分检验在一致性上比这些常用检验更强大。我们通过模拟研究和对真实数据集的应用来评估改进得分检验的性能。我们的结果表明，在大多数情况下，改进得分检验相对于得分检验的功效增加不容忽视。
