Computational Aspects of Haplotype Maps

描述（由申请人提供）：广泛遗传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。本研究旨在解决以下计算问题，这些问题对此类研究至关重要：1。在关联研究中选择和利用标签snp的方法的发展。具体来说，检测关联的统计能力被建议作为候选标签SNP集的优点值，以及优化该集的目标。此外，开发了贝叶斯框架来概括和综合当前的方法来识别，优先排序和评估标记变体。2. 单倍型祖先精细模型的发展。提出了一种二层HMM模型。“底部”模型描述了现代样本从构成普通单倍型的祖先染色体片段中进化而来；最上面的模型描绘了通向这些祖先部分的家谱。这种无块框架有潜力帮助在关联研究中确定致病等位基因。3. 参与II型糖尿病和炎症性肠病的基因发现研究，正在我们的实验室进行。这些数据驱动的项目将把方法开发工作的重点放在实际的生物学应用上。

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.