Multi-point and multi-locus analysis of genomic association data

描述(由申请人提供)： 全基因组关联研究为研究遗传变异对复杂疾病风险的影响提供了一种新的、强有力的方法。随着基因分型技术的最新进展，全基因组关联研究现在正在成为现实。来自GWAS的数据预计将以加速的速度增长。尽管在开发有效的算法来定位复杂的疾病/特征方面做出了巨大的努力，但基于单基因座的方法仍然是GWAS的主要方法。然而，人们已经知道，在复杂疾病的病因中，通常多种遗传因素、环境因素及其相互作用起着重要作用。迫切需要新的和实用的方法来同时对数十万个单核苷酸多态(SNPs)中的多个变量及其相互作用进行建模。在这个项目中，我们建议开发有效的算法和实用的统计工具来解决全基因组关联研究中的两个重要问题：多点分析和多位点分析。对于多点分析，我们的动态隐链马尔可夫模型可以联合模拟历史重组和突变、单倍型结构和频率以及关联，预计将比现有方法更有效。对于多基因座分析，我们建议使用一种先进的机器学习方法来联合筛选可以预测疾病的SNP。我们的集成软件系统MAVEN将利用尖端技术促进GWA数据的管理、分析、可视化和结果共享。全球气候变化分析的真正价值还有待于开发出有效的计算模型和工具。我们预计，这项研究项目将极大地促进对复杂疾病遗传结构的理解。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) provide a new and powerful approach to investigate the effect of inherited genetic variation on risks of complex diseases. With recent advances in genotyping technology, genome-wide association studies are now becoming a reality. Data from GWAS are expected in an accelerated rate. Despite tremendous efforts in developing efficient algorithms for mapping complex diseases/traits, single-locus based approaches are still the primary method for GWAS. However, it is known that usually multiple genetic factors, environmental factors as well as their interactions play an important role in the etiology of complex diseases. Novel and practical approaches to simultaneously model multiple variables and their interactions from hundreds of thousands single nucleotide polymorphisms (SNPs) are greatly needed. In this project, we propose to develop efficient algorithms and practical statistical tools to address two important problems in the context of genome- wide association studies: multi-point analysis and multi-locus analysis. For multi-point analysis, our Dynamic Hidden Chain Markov Model (DHCMM) can jointly model historical recombination and muta- tions, haplotype structures and frequencies, and associations, which is expected to be more effective than existing approaches. For multi-locus analysis, we propose to use an advanced machine learning approach to jointly screen SNPs that are predictive of diseases. Our integrated software system MAVEN will facilitate management, analysis, visualization and results sharing of GWA data using cut- ting edge technologies. The true value of GWAS is pending the development of effective computational models and tools. We anticipate that this research project will greatly accelerate the understanding of the genetic architecture of complex diseases.