权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

Epicluster：在复杂性状遗传学研究中高通量检测上位性的新工具

基本信息

批准号：
BB/H024484/1
负责人：
Wenhua Wei
金额：
$ 13.67万
依托单位：
MRC Institute of Genetics and Molecular Medicine
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FH024484%2F1
关键词：
Epicluster novel tool throughput detection

项目摘要

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.

基因相互作用被认为对于农业、模式生物和人类疾病遗传学中复杂性状变异的形成非常重要。然而，由于缺乏高通量工具来分析许多不同的性状，因此对它们的探索很少。在 BBSRC 资助的 GridQTL 项目的支持下，我们开发了一种工具，可以对低密度遗传标记基因分型的实验群体中的基因相互作用进行高通量分析。然而，该工具不适用于自然/商业群体中全基因组关联研究提供的大型数据集。此类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。全基因组关联研究在牲畜、植物和人类复杂性状的遗传学研究中变得越来越流行。尽管付出了很多努力，但由于计算需求过多且缺乏处理数十亿次标记组合测试的算法，即使是单个性状（CPU 月的水平），对这些大型数据集中的基因相互作用进行全面分析仍然很困难。一种新的高通量分析工具已成为研究这些大型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的新工具。 Epicluster 将有效地选择具有一致基因型分布模式的候选标记，从而区分具有高性状值的个体组和具有低性状值的个体组，而不是直接彻底地测试数十亿个标记组合。然后，它仅在选定的候选标记中执行全面的统计测试，从而可以提高分析某一性状的基因相互作用的速度至 CPU 小时。 Epicluster 的开发将采用已成功应用于基因表达研究的双聚类算法。主体测试证明表明，双聚类算法可以在几分钟内对包含 500,000 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（即自动分析）实施，用于高性能计算机环境。总之，我们预计 Epicluster 预示着跨物种大型数据集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。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

BiForce Toolbox: powerful high-throughput computational analysis of gene-gene interactions in genome-wide association studies.

DOI：
10.1093/nar/gks550
发表时间：
2012-07
期刊：
Nucleic acids research
影响因子：
14.9
作者：
Gyenesei A;Moody J;Laiho A;Semple CA;Haley CS;Wei WH
通讯作者：
Wei WH

Characterisation of genome-wide association epistasis signals for serum uric acid in human population isolates.

DOI：
10.1371/journal.pone.0023836
发表时间：
2011
期刊：
PloS one
影响因子：
3.7
作者：
Wei W;Hemani G;Hicks AA;Vitart V;Cabrera-Cardenas C;Navarro P;Huffman J;Hayward C;Knott SA;Rudan I;Pramstaller PP;Wild SH;Wilson JF;Campbell H;Dunlop MG;Hastie N;Wright AF;Haley CS
通讯作者：
Haley CS

High-throughput analysis of epistasis in genome-wide association studies with BiForce.