GENE-GENE INTERACTION NETWORKS IN GENOME WIDE ASSOCIATION STUDIES

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 全基因组关联研究（GWAS）涉及50万至100万个称为单核苷酸多态性（SNP）的DNA序列变异，这是人类基因组中最常见的变异形式。单变量过滤器具有去除对疾病风险重要的变异的风险，因为它们与基因网络中的其他变异相互作用。我们已经开发了一种计算方法，该方法在对单个SNP的疾病风险重要性进行排名时考虑了所有SNP的背景。然而，上下文依赖的优势是以大量内存需求为代价的。黑光超级计算机的大型共享内存将使我们有机会发现隐藏在这些大型数据集中的已知知识。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Genome-wide association studies (GWAS) involve 500,000 to 1 million DNA sequence variants called single nucleotide polymorphisms (SNPs), which are the most common form of variation in the human genome. A univariate filter has the risk of removing variants that are important to disease risk due to their interactions with other variants in the gene network. We have developed a computational approach that takes the context of all SNPs into account when ranking the importance to disease risk of an individual SNP. However, the advantage of context dependence comes at the cost of large memory requirements. The large shared memory of the Blacklight supercomputer will give us the opportunity to discover knew knowledge hidden in these large data sets.