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，基因互作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性状，它们的探索很差。在BBSRC资助的GridQTL项目的支持下，我们开发了一种工具，可以在用低密度遗传标记进行基因分型的实验群体中进行基因互作的高通量分析。然而，该工具不适用于自然/商业人群的全基因组关联研究提供的大型数据集。这些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性状的个体。全基因组关联研究在家畜、植物和人类等复杂性状的遗传学研究中越来越受欢迎。尽管付出了很多努力，但由于其过度的计算需求和缺乏处理数十亿个标记组合测试的算法，即使是单个性状（在CPU月的水平上），在这些大型数据集中对基因相互作用的全面分析仍然是棘手的。一种新的高通量分析工具已经成为研究这些大数据集中基因相互作用的必要条件。我们建议开发Epicluster，这是一种新的工具，用于支持大型关联研究数据集中基因相互作用的常规高通量分析。Epicluster将有效地选择具有一致基因型分布模式的候选标记，而不是直接测试数十亿个标记组合，这些模式将具有高性状值的个体组与具有低性状值的个体组区分开来。然后，它只在选定的候选标记之间进行全面的统计测试，从而可以将分析一个性状的基因相互作用的速度提高到CPU小时。Epicluster开发将采用已成功应用于基因表达研究的双聚类算法。主成分检验证明表明，双聚类算法可以在几分钟内聚类50万个标记的大数据集。完成后，Epicluster将作为分布式软件（即自动分析）在高性能计算机环境中使用。总之，我们预计Epicluster预示着跨物种大型数据集的基因相互作用分析的突破。因此，Epicluster将有助于更全面地理解基因相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.