A Versatile Gene-Based Test for Genome-wide Association Studies
A Versatile Gene-Based Test for Genome-wide Association Studies
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DOI：
10.1016/j.ajhg.2010.06.009
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发表时间：
2010-07-09
影响因子：
9.8
通讯作者：
Macgregor, Stuart
中科院分区：
文献类型：
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作者：
Liu, Jimmy Z.;Mcrae, Allan F.;Macgregor, Stuart
我们已经推导出一个通用的基因为基础的测试全基因组关联研究（GWAS）。我们的方法，称为VEGAS（多功能基因关联研究），适用于所有GWAS设计，包括基于家族的GWAS，基于汇总数据的GWAS荟萃分析，基于DNA池的GWAS，其中现有的基于排列的方法是不可能的，以及单例数据，它们是。该测试结合了来自基因内的全套标记（或定义的子集）的信息，并通过使用来自多变量正态分布的模拟来解释标记之间的连锁不平衡。我们表明，对于使用单例的关联研究，我们的方法产生的结果相当于通过置换在一小部分的计算时间。我们通过使用基于基因的测试来复制几个已知相关的基因，证明了原理的证明，这些基因是基于11，536个个体中基于家庭的身高GWAS和基于DNA池的黑色素瘤GWAS的结果，这些结果与1300例病例和对照相似。我们的方法有可能识别新的相关基因;为选择用于复制的SNP提供基础;并直接用于需要每个基因关联检验统计的网络（途径）方法。我们已经实现了一个易于使用的Web界面，它只需要上传标记与他们的关联p值，和一个单独的可下载的应用程序的方法。
We have derived a versatile gene-based test for genome-wide association studies (GWAS). Our approach, called VEGAS (versatile gene-based association study), is applicable to all GWAS designs, including family-based GWAS, meta-analyses of GWAS on the basis of summary data, and DNA-pooling-based GWAS, where existing approaches based on permutation are not possible, as well as singleton data, where they are. The test incorporates information from a full set of markers (or a defined subset) within a gene and accounts for linkage disequilibrium between markers by using simulations from the multivariate normal distribution. We show that for an association study using singletons, our approach produces results equivalent to those obtained via permutation in a fraction of the computation time. We demonstrate proof-of-principle by using the gene-based test to replicate several genes known to be associated on the basis of results from a family-based GWAS for height in 11,536 individuals and a DNA-pooling-based GWAS for melanoma in similar to 1300 cases and controls. Our method has the potential to identify novel associated genes; provide a basis for selecting SNPs for replication; and be directly used in network (pathway) approaches that require per-gene association test statistics. We have implemented the approach in both an easy-to-use web interface, which only requires the uploading of markers with their association p-values, and a separate downloadable application.