Epicluster: A novel tool for high throughput detection of epistasis in studies of the genetics of complex traits

在农业、模式生物和人类疾病遗传学中，基因相互作用被认为是形成复杂性状变异的重要因素。然而，由于缺乏高通量工具来分析许多不同的特征，对它们的研究一直很少。在BBSRC资助的GridQTL项目的支持下，我们开发了一个工具，可以对使用低密度遗传标记进行基因分型的实验群体进行高通量基因交互分析。然而，该工具不适用于由自然/商业人口中的全基因组关联研究提供的大型数据集。这样的数据集通常包括数十万个遗传标记和数千个具有大量表型特征的个体。全基因组关联研究已经变得越来越流行，用于研究牲畜、植物和人类领域中复杂性状的遗传学。尽管做了很多工作，但对这些大型数据集中的基因交互作用进行全面分析仍然是困难的，即使是单个性状(在CPU月的水平上)也是如此，因为它们对计算的要求过高，而且缺乏处理数十亿个标记组合测试的算法。一种新的高通量分析工具已经成为研究这些大型数据集中基因相互作用的必要工具。我们建议开发Epicluster，这是一种新的工具，可以支持大型关联研究数据集中基因交互作用的常规高通量分析。Epicluster不会直接详尽地测试数十亿个标记组合，而是有效地选择具有一致的基因分布模式的候选标记，以区分高特征值个体组和低特征值个体组。然后，它只在选定的候选标记中执行全面的统计测试，从而可以将分析一个性状的基因交互作用的速度提高到CPU小时。Epicluster的开发将采用已成功应用于基因表达研究的双向聚类算法。一项主要测试的证明表明，双聚类算法可以在几分钟内对具有50万个标记的大型数据集进行聚类。完成后，Epicluster将作为分布式软件(即自动化分析)实施，用于高性能计算机环境。总而言之，我们预计Epicluster将预示着在跨物种的大型数据集中进行基因交互分析方面的突破。因此，Epicluster将有助于更全面地理解基因交互作用在复杂性状中的重要性。

Gene interactions are thought to be important in shaping complex trait variation in agricultural, model organism and human disease genetics. They have been poorly explored, however, because of the lack of high throughput tools to analyse many different traits. With the support from the GridQTL project funded by BBSRC, we have developed a tool that can perform high throughput analyses of gene interactions in experimental populations genotyped with low density genetic markers. The tool however is not applicable to large datasets provided by genome-wide association studies in natural/commercial populations. Such datasets typically include hundreds of thousands of genetic markers and thousands of individuals with a large number of phenotypic traits. Genome-wide association studies have become increasingly popular for the investigation of the genetics of complex traits in livestock, plant, and human sectors. Despite much effort, a comprehensive analysis of gene interactions in those large datasets is still intractable for even a single trait (at levels of CPU months) due to their excessive computing demand and the lack of algorithms to handle billions of tests of marker combinations. A new high throughput analysis tool has become a necessity to study gene interactions in these large datasets. We propose the development of Epicluster, a novel tool to support routine high throughput analysis of gene interactions in large association study datasets. Instead of directly testing billions of marker combinations exhaustively, Epicluster will effectively select candidate markers with consistent genotype distribution patterns that differentiate the group of individuals with high trait values from the group with low trait values. It then performs comprehensive statistical tests only among the selected candidate markers and thus can improve the speed of analysing gene interactions for one trait to CPU hours. Epicluster development will adapt a bi-clustering algorithm that has been successfully applied in gene expression studies. A proof of principal test showed that the bi-clustering algorithm could cluster a large dataset with 500,000 markers in minutes. On completion Epicluster will be implemented as distributed software (i.e. automated analysis) to be used in high performance computer environments. In summary we expect Epicluster to herald a breakthrough in gene interaction analyses in large datasets across species. Hence Epicluster will facilitate a fuller understanding of the importance of gene interactions in complex traits.