Novel Methods for Characterizing Recombination and Selection

描述（申请人提供）：两种已知的减数分裂重组类型是交换和基因转换，它们对连锁不平衡（LD）模式有不同的影响。推断历史重组模式的努力是疾病关联研究设计和分析的核心，这取决于对LD在人群数据中结构的理解。PI目前研究的重点是开发有效的算法，用于重建重组的简约进化历史。PI的长期目标是定量描述各种进化力量对人类基因组中LD结构的影响。本研究的动机如下：（1）由于缺乏分析工具和精细数据，基因转换在人群中的研究一直很困难。然而，在未来几年中产生的基因组数据应该允许量化基因转换的基本参数，以及基因转换对群体中序列变异的总体模式的贡献。(2)自然选择是一种重要的进化力量，它塑造了物种内的基因组变异和物种间的分化。最近已经表明，强阳性选择产生的LD模式可以类似于交叉热点产生的LD模式。 该奖项独立阶段的具体目标是： (1)开发新的统计方法来估计交叉和基因转换率。基于扩散近似的数学框架将被用来获得新的多轨迹采样分布。基因转换将包括在该框架内。一个可能性的方法，利用新的抽样分布将开发，使交叉和基因转换率的联合估计。 (2)研究自然选择对LD模式的影响。在多个位点的选择的相互作用将进行分析研究，并通过相互作用的选择形成LD的结构将被表征。 相关性：了解人类基因组中的变异结构对于研究疾病风险和药物反应变异的遗传基础至关重要。这项研究的目的，这是相关的疾病相关性研究，是表征各种进化机制，在基因组中的不同位置的遗传形式的非独立性模式的形状。

DESCRIPTION (provided by applicant): Two known types of meiotic recombination are crossovers and gene conversions, which have different effects on the pattern of linkage disequilibrium (LD). Efforts to deduce patterns of historical recombination are central to the design and analysis of disease association studies, which depend on understanding the structure of LD in population data. The focus of the PI's current research is on developing efficient algorithms for reconstructing parsimonious evolutionary histories with recombination. The PI's long-term objective is to characterize quantitatively the effect of various evolutionary forces on shaping the structure of LD in the human genome. Some motivations for the proposed research are as follows: (1) Gene conversion has been hard to study in populations because of the lack of analytical tools and the lack of fine-scale data. However, genomic data produced over the next several years should allow quantification of the fundamental parameters of gene conversion, and the contribution of gene conversion to the overall patterns of sequence variations in a population. (2) Natural selection is an important evolutionary force that shapes genomic variation within species and the divergence between species. It has been shown recently that the patterns of LD generated by strong positive selection can resemble that generated by crossover hotspots. The specific aims of the independent phase of the award are: (1) Develop novel statistical methods for estimating crossover and gene conversion rates. A mathematical framework based on diffusion approximation will be used to obtain novel multi-locus sampling distributions. Gene conversion will be included in that framework. A likelihood method that utilizes the new sampling distributions will be developed to enable joint estimation of crossover and gene conversion rates. (2) Study the effects of natural selection on the pattern of LD. The interaction of selection at multiple loci will be studied analytically and the structure of LD shaped by interacting selection will be characterized. Relevance: Understanding the structure of variation in the human genome is central to the study of the genetic basis of disease risk and variation in drug response. The aim of this research, which is relevant to disease association studies, is to characterize various evolutionary mechanisms that shape the pattern of non-independence of genetic forms at different positions in the genome.