Increasing power in association studies by using linkage disequilibrium structure and molecular function as prior information
Increasing power in association studies by using linkage disequilibrium structure and molecular function as prior information
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DOI：
10.1101/gr.072785.107
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发表时间：
2008-04-01
期刊：
影响因子：
7
通讯作者：
Eskin, Eleazar
中科院分区：
文献类型：
--
作者：
Eskin, Eleazar
各种类型基因组数据的可获得性为将这些数据作为先验信息纳入遗传关联研究提供了机会。这些信息包括连锁不平衡结构的知识以及哪些区域可能涉及疾病。在这篇文章中，我们提出了一种方法，通过重新审视我们如何执行多假设校正来整合这些信息。在传统的关联研究中，为了纠正多假设检验，每个标记的显著性阈值t被设置来控制总的假阳性率。在我们的框架中，我们改变每个标记t(I)处的阈值，并使用这些阈值来合并先验信息。我们提出了一种利用先验信息最大化关联学习能力的阈值求解的数值方法。我们还给出了使用HapMap数据的基准模拟实验结果，表明在该框架下关联学习能力显著提高。我们提供了一个Web服务器，用于使用我们的方法执行关联研究，并提供了针对Affymetrix 500k和Illumina HumanHap 550芯片进行优化的阈值，并演示了我们的框架在Wellcome Trust Case Control Consortium数据分析中的应用。
The availability of various types of genomic data provides an opportunity to incorporate this data as prior information in genetic association studies. This information includes knowledge of linkage disequilibrium structure as well as which regions are likely to be involved in disease. In this paper, we present an approach for incorporating this information by revisiting how we perform multiple-hypothesis correction. In a traditional association study, in order to correct for multiple-hypothesis testing, the significance threshold at each marker, t, is set to control the total false-positive rate. In our framework, we vary the threshold at each marker t(i) and use these thresholds to incorporate prior information. We present a numerical procedure for solving for thresholds that maximizes association study power using prior information. We also present the results of benchmark simulation experiments using the HapMap data, which demonstrate a significant increase in association study power under this framework. We provide a Web server for performing association studies using our method and provide thresholds optimized for the Affymetrix 500k and Illumina HumanHap 550 chips and demonstrate the application of our framework to the analysis of the Wellcome Trust Case Control Consortium data.