Department of Statistics, Yunnan University, Kunming 650091, China.
Comput Math Methods Med. 2013;2013:843563. doi: 10.1155/2013/843563. Epub 2013 Aug 12.
Genome-wide association studies (GWASs) in identifying the disease-associated genetic variants have been proved to be a great pioneering work. Two-stage design and analysis are often adopted in GWASs. Considering the genetic model uncertainty, many robust procedures have been proposed and applied in GWASs. However, the existing approaches mostly focused on binary traits, and few work has been done on continuous (quantitative) traits, since the statistical significance of these robust tests is difficult to calculate. In this paper, we develop a powerful F-statistic-based robust joint analysis method for quantitative traits using the combined raw data from both stages in the framework of two-staged GWASs. Explicit expressions are obtained to calculate the statistical significance and power. We show using simulations that the proposed method is substantially more robust than the F-test based on the additive model when the underlying genetic model is unknown. An example for rheumatic arthritis (RA) is used for illustration.
全基因组关联研究（GWAS）在鉴定与疾病相关的遗传变异方面已被证明是一项伟大的开拓性工作。GWAS 通常采用两阶段设计和分析。考虑到遗传模型的不确定性，已经提出并应用了许多稳健的程序。然而，现有的方法主要集中在二元性状上，很少有关于连续（定量）性状的工作，因为这些稳健检验的统计显著性很难计算。在本文中，我们在两阶段 GWAS 的框架下，使用两阶段中组合的原始数据，为定量性状开发了一种基于 F 统计量的强大稳健联合分析方法。获得了计算统计显著性和功效的显式表达式。我们通过模拟表明，当潜在的遗传模型未知时，与基于加性模型的 F 检验相比，所提出的方法具有更高的稳健性。以风湿性关节炎（RA）为例进行说明。
