Computational Aspects of Haplotype Maps

描述（由申请人提供）：广泛遗传研究的新兴范式依赖于单倍型图谱来指导研究设计和分析。该研究旨在解决以下对此类研究至关重要的计算问题：1。开发用于选择和利用标签SNP的方法，以在关联研究中进行分型。具体地，检测关联的统计能力被建议作为候选标签SNP集合的品质因数，以及优化这样的集合的目标。此外，贝叶斯框架的发展，概括和综合目前的方法的身份，优先级，和评价的标记的变体。2.单倍型祖先精细模型的开发。提出了一种两层HMM模型。“底部”模型描述了现代样本从共同单倍型下的祖先染色体片段的血统;顶部模型描绘了导致这些祖先片段的系谱。这种无块的框架有可能帮助识别关联研究中的致病等位基因。3.参与我们实验室正在进行的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.