Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.
BMC Bioinformatics. 2010 Jan 27;11:58. doi: 10.1186/1471-2105-11-58.
The success achieved by genome-wide association (GWA) studies in the identification of candidate loci for complex diseases has been accompanied by an inability to explain the bulk of heritability. Here, we describe the algorithm V-Bay, a variational Bayes algorithm for multiple locus GWA analysis, which is designed to identify weaker associations that may contribute to this missing heritability.
V-Bay provides a novel solution to the computational scaling constraints of most multiple locus methods and can complete a simultaneous analysis of a million genetic markers in a few hours, when using a desktop. Using a range of simulated genetic and GWA experimental scenarios, we demonstrate that V-Bay is highly accurate, and reliably identifies associations that are too weak to be discovered by single-marker testing approaches. V-Bay can also outperform a multiple locus analysis method based on the lasso, which has similar scaling properties for large numbers of genetic markers. For demonstration purposes, we also use V-Bay to confirm associations with gene expression in cell lines derived from the Phase II individuals of HapMap.
V-Bay is a versatile, fast, and accurate multiple locus GWA analysis tool for the practitioner interested in identifying weaker associations without high false positive rates.
全基因组关联 (GWA) 研究在鉴定复杂疾病的候选基因座方面取得了成功,但仍无法解释大部分遗传率。在这里,我们描述了一种称为 V-Bay 的算法,这是一种用于多基因座 GWA 分析的变分贝叶斯算法,旨在识别可能导致遗传率缺失的较弱关联。
V-Bay 为大多数多基因座方法的计算扩展限制提供了一种新颖的解决方案,并且可以在使用桌面计算机时在几个小时内完成对一百万个遗传标记的同时分析。通过一系列模拟的遗传和 GWA 实验场景,我们证明 V-Bay 具有高度准确性,并可靠地识别出那些太弱而无法通过单标记测试方法发现的关联。V-Bay 还可以优于基于lasso 的多基因座分析方法,lasso 对于大量遗传标记具有相似的扩展特性。为了演示目的,我们还使用 V-Bay 来确认与 HapMap 二期个体来源的细胞系中的基因表达的关联。
V-Bay 是一种通用、快速且准确的多基因座 GWA 分析工具,适用于有兴趣识别较弱关联而又不产生高假阳性率的从业者。