Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui 230026, China.
Comput Biol Chem. 2013 Oct;46:32-8. doi: 10.1016/j.compbiolchem.2013.04.001. Epub 2013 May 9.
Genome-wide association studies, as a powerful approach for detecting common variants associated with diseases, have revealed many disease-associated loci. However, the traditional association analysis methods do not have enough power for detecting the effects of rare variants with limited sample size. As a solution to this problem, pooling rare variants by their functions into a composite variant provides an alternative way for identifying susceptible genes. In this paper, we propose a new pooling method to test the variant-disease association and to identify the functional rare variants related with the disease. Variants with smaller and larger risk measures defined as the ratio of allele frequencies between cases and controls are pooled and a chi-square test of the resultant pooled table is calculated. We vary the threshold of pooling over all possible values and use the maximal chi-square as test statistic. The maximal chi-square is in fact the global maximum over all possible poolings. Our approach is similar to the existing variable-threshold method, but we threshold on the risk measure instead of allele frequencies of controls. Simulation results show that our method performs better in both association testing and variant selection.
全基因组关联研究作为一种检测与疾病相关的常见变异的有效方法，已经揭示了许多与疾病相关的基因座。然而，传统的关联分析方法在检测样本量有限的稀有变异的效应时，其效能不足。为了解决这个问题，通过将稀有变异按其功能合并成复合变异，为鉴定易感基因提供了一种替代方法。在本文中，我们提出了一种新的合并方法来检验变异与疾病的关联，并识别与疾病相关的功能稀有变异。我们合并了风险度量较小和较大的变异，风险度量定义为病例和对照之间等位基因频率的比值，并计算所得合并表的卡方检验。我们在所有可能的值上改变合并的阈值，并使用最大卡方作为检验统计量。最大卡方实际上是所有可能合并的全局最大值。我们的方法类似于现有的可变阈值方法，但我们是在风险度量上而不是在对照的等位基因频率上进行阈值处理。模拟结果表明，我们的方法在关联检验和变异选择方面都表现更好。
