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）：我们建议继续研究使用计算密集型方法来精细定位复杂性状中涉及的基因。这项工作以前得到了我们的资助“遗传流行病学的计算方法”（GM 58897，PI：D。托马斯）。现在的化身是这个早期授权的一个更集中的版本。我们特别强调的是利用感兴趣的样本的潜在谱系（祖先）的方法。在个体样本中看到的单倍型多样性模式D是样本中未观察到的系谱G（其本身是重组的函数）与作用于该系谱的突变力之间复杂相互作用的结果。这进一步受到由与感兴趣的性状相关的功能突变的存在诱导的共同祖先的影响。这导致靠近功能突变的区域出现连锁不平衡。原则上，人们希望探索可能产生特定数据集的可能谱系空间，并将其用作绘图方法的基础。这样的方法可能是不可能的，或者在计算上过于昂贵。 因此，我们建议采用以下两种近似方法来实现这一预期目标： - 谱系方法，其中我们明确包括感兴趣的样品的未观察到的谱系，但使用混合拒绝/MCMC方法来避免计算P（DIG），（该量只能针对一族简单的、不切实际的突变模型计算）。 - 贝叶斯方法，它使用层次聚类提取出系谱的影响 技术.