Computational Methods for Fine Mapping

精细测图计算方法

基本信息

批准号：
7278157
负责人：
Paul Marjoram
金额：
$ 46.72万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2004
资助国家：
美国
起止时间：
2004-09-01 至 2009-08-31
项目状态：
已结题

项目摘要

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精细绘制涉及复杂性状的基因。这项工作之前得到了我们的资助“遗传流行病学的计算方法”（GM58897，PI：D. Thomas）的支持。目前的版本是早期拨款的更集中的版本。我们特别强调的是利用感兴趣样本的潜在谱系（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谱系 G（其本身是重组的函数）与作用于该谱系的突变力量之间复杂相互作用的结果。这进一步受到与感兴趣的性状相关的功能突变的存在引起的共同祖先的影响。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谱系空间，并将其用作绘图方法的基础。这种方法可能是不可能的，或者计算成本过高。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样本的未观察到的谱系，但使用混合拒绝/MCMC 方法来避免计算 P (DIG)，（该数量只能针对一系列简单化、不切实际的突变模型进行计算）。 - 贝叶斯方法，使用层次聚类抽象出谱系的影响技术。