A Novel Powerful Joint Analysis with Data Fusion in Two-stage Case–Control Genome-wide Association Studies
A Novel Powerful Joint Analysis with Data Fusion in Two-stage Case–Control Genome-wide Association Studies
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DOI：
10.1080/03610918.2014.901360
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Dong-Dong Pan-Dong;Zhengbang Li;Qizhai Li;W. Fung
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文献类型：
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作者：
Dong-Dong Pan-Dong;Zhengbang Li;Qizhai Li;W. Fung
在检测疾病相关遗传变异的全基因组关联研究（GWASs）中，两阶段设计因其成本效益和效率高而备受关注。在两阶段设计的框架下，联合分析比基于复制的分析更强大。为了解决遗传模式未知的问题，提出了几种鲁棒性检验来进行联合分析。然而，如果合并的测试统计数据不够充分，或者每个阶段的统计数据的权重不合适，那么现有的结合两个阶段的测试统计数据的联合分析可能会损失效率。在本文中，我们提出了一种新的联合分析策略，即结合原始数据而不是跨阶段的测试统计数据，并构建了一个基于max3的两阶段GWASs测试，该测试可以充分利用两阶段数据的信息。数值结果表明，所提出的方法比现有的联合分析方法更强大，计算速度更快。一个2型糖尿病数据集的应用程序被用来说明所提出的方法。
In genome-wide association studies (GWASs) to detect the disease-associated genetic variants, two-stage design has received much attention because of its cost effectiveness and high efficiency. Under the framework of a two-stage design, it has been shown that joint analysis is more powerful than replication-based analysis. Several robust tests have been proposed for joint analysis to handle the problem of unknown genetic mode of inheritance. However, existing joint analysis of combining test statistics from both stages might suffer from a loss of efficiency if the combined test statistics are not sufficient or the weight of the statistic for each stage is not appropriate. In this article, we propose a new strategy for joint analysis by combining the raw data rather than the test statistics across stages and construct a robust MAX3-based test for two-staged GWASs, which can make full use of the information of the data from both stages. Our numerical results show that the proposed procedure is more powerful and computationally much faster than the existing joint analysis procedures. An application to a type 2 diabetes dataset is used to illustrate the proposed approach.