Wang Shuaicheng, Fang Shurong, Sha Qiuying, Zhang Shuanglin
Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA.
BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S91. doi: 10.1186/1753-6561-8-S1-S91. eCollection 2014.
Increasing evidence shows that complex diseases are caused by both common and rare variants. Recently, several statistical methods for detecting associations of rare variants have been developed, including the test for testing the effect of an optimally weighted combination of variants (TOW) developed by our group in 2012. These methodologies consider phenotype measurement at only one time point. Because many sequence data have been developed on population cohorts that contain phenotype measurements at multiple time points, such as the data set provided in the Genetic Analysis Workshop 18 (GAW18), we extend TOW from phenotype measurement at one time point to phenotype measurements at multiple time points. We then apply the newly proposed method to the GAW18 data set and compare the power of the new method with TOW using only one phenotype measurement. The application results show that the newly proposed method jointly modeling phenotype measurements at all time points has increased power over TOW.
越来越多的证据表明，复杂疾病是由常见变异和罕见变异共同引起的。最近，已经开发了几种用于检测罕见变异关联的统计方法，包括我们团队在2012年开发的用于测试变异最佳加权组合效应的检验（TOW）。这些方法仅考虑在一个时间点的表型测量。由于已经在包含多个时间点表型测量的人群队列中开发了许多序列数据，例如遗传分析研讨会18（GAW18）提供的数据集，我们将TOW从一个时间点的表型测量扩展到多个时间点的表型测量。然后，我们将新提出的方法应用于GAW18数据集，并将新方法的功效与仅使用一个表型测量的TOW进行比较。应用结果表明，新提出的在所有时间点联合建模表型测量的方法比TOW具有更高的功效。
