Computational Methods for Fine Mapping

精细测绘计算方法

• 批准号: 7103429
• 负责人: Paul Marjoram
• 金额: $ 50.38万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7103429
• 关键词: bioinformatics; clinical research; computational biology; computer assisted sequence analysis; epidemiology; family genetics; genetic mapping; human data; human population genetics; method development; statistics /biometry

DESCRIPTION (provided by applicant): We propose to continue work on the use of computationally intensive methods to fine-map genes involved in complex traits. This work was previously supported under our grant "'Computational methods in genetic epidemiology" (GM58897, PI: D. Thomas). The present incarnation is a more focused version of this earlier grant. Our particular emphasis is on methods that exploit the underlying genealogy (ancestry) of a sample of interest. The pattern of haplotype diversity, D, one sees in a sample of individuals is the result of a complex interaction between the unobserved genealogy, G, of the sample (which itself is a function of recombination) and the forces of mutation acting upon that genealogy. This is further influenced by the shared ancestry induced by the presence of the functional mutation(s) related to the trait of interest. This results in linkage disequilibrium in regions close to functional mutations. In principle, one wishes to explore the space of possible genealogies that might give rise to a particular data set and use this as a basis for an approach to mapping. Such an approach is likely to be impossible, or prohibitively expensive computationally. We therefore propose to adopt the following two approximations to this desired goal: - Genealogical methods in which we explicitly include the unobserved genealogy of the sample of interest, but use hybrid rejection/MCMC methods to avoid calculating P (DIG), (this quantity can only be calculated for a family of simplistic, unrealistic mutation models). - Bayes methods, which abstract out the effects of the genealogy using hierarchical clustering techniques.

描述（由申请人提供）：我们建议继续使用计算密集型方法来使用涉及复杂性状的细胞基因。以前，这项工作是根据我们的授予“遗传流行病学计算方法”（GM58897，PI：D。Thomas）支持的。目前的化身是此较早赠款的更为集中版本。我们特别强调的是利用感兴趣样本的基本家谱（祖先）的方法。单倍型多样性D的模式D在个体样本中看到的是样品未观察到的谱系G（本身是重组的函数）与作用于该家谱的突变力之间的复杂相互作用的结果。这进一步受到与感兴趣特征相关的功能突变的存在所引起的共同血统的影响。这导致在接近功能突变的地区的连锁不平衡。原则上，人们希望探索可能引起特定数据集的可能家谱的空间，并将其用作映射方法的基础。这种方法可能是不可能的，或者在计算上非常昂贵。 因此，我们建议对这个期望的目标采用以下两个近似值： - 我们明确包含感兴趣样本的未观察到的家谱的族谱方法，但使用混合拒绝/MCMC方法避免计算P（DIG）（DIG）（此数量只能针对一个简单，不切实际的突变模型的家族计算）。 - 贝叶斯方法，该方法使用分层聚类来抽象谱系的效果 技术。