Computational Aspects of Haplotype Maps

描述(由申请人提供)：广泛的遗传学研究的新兴范例依赖于单倍型图谱来指导研究设计和分析。这项研究旨在解决以下对此类研究至关重要的计算问题：1.建立选择和利用标签SNP的方法学，以便在关联研究中进行分型。具体地说，建议将检测关联的统计能力作为候选标签SNP集合的优值系数，以及优化这样的集合的目标。此外，开发了贝叶斯框架来概括和综合当前对标记变体的识别、优先排序和评估的方法。2.单倍型祖先精细化模型的建立。提出了一种两级隐马尔可夫模型。“底部”模型描述了现代样本从构成常见单倍型的祖先染色体片段进化而来；顶部模型描绘了导致这些祖先染色体片段的系谱。这种无区块的框架有可能在关联研究中帮助识别致病等位基因。3.参与本实验室正在进行的II型糖尿病和炎症性肠病的基因发现研究。这些数据驱动的项目将把方法开发的努力集中到实际的生物应用上。

DESCRIPTION (provided by applicant): The emerging paradigm for extensive genetic studies relies on a haplotype map to guide study design and analysis. The research aims to tackle the following computational issues that are essential for such studies: 1. Development of methodology for selection and utilization of tag SNPs to be typed in an association study. Specifically, the statistical power to detect association is suggested as a figure of merit for candidate tag SNP sets, and a goal for optimizing such a set. Furthermore Bayesian framework is developed to generalize and synthesize current approaches to the identity, prioritization, and evaluation of the tagged variants. 2. Development of refined models for haplotype ancestry. A model of two HMM levels is proposed. The "bottom" model describes the descent of modern samples from ancestral chromosome segments that underlie common haplotypes; the top model portrays genealogies leading to those ancestral segments. This block-free framework has potential to help identify the causative allele in association studies. 3. Participation in gene discovery studies of Type II Diabetes and Inflamatory Bowel Disease, ongoing in our lab. These data-driven projects will focus method development efforts to the practical biological applications.