Wu Baolin, Pankow James S, Guan Weihua
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America.
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America.
Genet Epidemiol. 2015 Sep;39(6):399-405. doi: 10.1002/gepi.21913.
Recent sequencing efforts have focused on exploring the influence of rare variants on the complex diseases. Gene level based tests by aggregating information across rare variants within a gene have become attractive to enrich the rare variant association signal. Among them, the sequence kernel association test (SKAT) has proved to be a very powerful method for jointly testing multiple rare variants within a gene. In this article, we explore an alternative SKAT. We propose to use the univariate likelihood ratio statistics from the marginal model for individual variants as input into the kernel association test. We show how to compute its significance P-value efficiently based on the asymptotic chi-square mixture distribution. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to associations between rare exonic variants and type 2 diabetes (T2D) in the Atherosclerosis Risk in Communities (ARIC) study. We identified an exome-wide significant rare variant set in the gene ZZZ3 worthy of further investigations.
最近的测序工作集中在探索罕见变异对复杂疾病的影响。通过整合基因内多个罕见变异的信息进行基于基因水平的检测，已成为增强罕见变异关联信号的有效手段。其中，序列核关联检验（SKAT）已被证明是一种用于联合检测基因内多个罕见变异的强大方法。在本文中，我们探索了一种替代的SKAT方法。我们建议将单个变异边际模型的单变量似然比统计量作为核关联检验的输入。我们展示了如何基于渐近卡方混合分布有效地计算其显著性P值。通过大量数值研究，我们证明了所提出的方法具有竞争力。在社区动脉粥样硬化风险（ARIC）研究中，将其应用于罕见外显子变异与2型糖尿病（T2D）之间的关联，进一步说明了其有效性。我们在ZZZ3基因中鉴定出一组全外显子显著的罕见变异，值得进一步研究。
