The effects of natural selection on genome-wide patterns of genetic variation

众所周知，突变会造成个体之间的差异，因此为自然选择和进化提供了原材料。根据突变对宿主健康的影响，突变可分为三类：(1)有害突变，对宿主的适应性有害；(2)有利突变，可提高存活率或生育力；(3)中性突变，影响很小或没有影响。进化遗传学的一个核心问题是，自然选择对这三种类型的突变在塑造种群内遗传变异模式方面所起的作用。事实上，这是促使人类(例如，1000基因组计划)和其他一些生物，如果蝇(例如，果蝇种群基因组计划)和杂草植物拟南芥(例如，1001基因组计划)进行重大DNA测序工作的问题之一。上述问题的答案对于生物学家来说至关重要，他们打算使用这些大规模数据集来破译表型变异的遗传基础(例如，疾病易感性)，推断进化史，并确定帮助有机体更好地适应环境的关键功能创新背后的突变，因为它奠定了我们对遗传变异本质的理解，这对于开发可靠的方法从数据中收集准确的结果至关重要。不幸的是，尽管具有实际和理论意义，但我们仍然对针对有害突变的负选择(称为背景选择或BGS)和对有利突变的正选择(称为选择扫描或SSW)在控制种群的遗传构成方面所起的作用知之甚少。一个主要的绊脚石是缺乏合适的理论工具来预测BGS对序列变异性的影响。这阻碍了更好地理解遗传变异本质的进展，因为多条证据表明，大多数突变(特别是基因组功能部分的突变)是有害的，但由于缺乏理论工具，这些突变对附近基因组区域遗传变异的后果尚未得到很好的了解。因此，该项目的第一个目标是构建一套BGS模型，不仅在生物上真实和计算效率高，而且还适合于分析大规模数据集。我最近发布的一个BGS模型使这一点变得可行。我将通过开发一组扩展模型来改进这一点，这些模型包含了原始模型中缺失的几个基本生物学特征。我还将开发分析序列可变性的理论工具，包括BGS和SSW过程。我将把这些新模型应用于全基因组序列数据集，例如爱丁堡大学我的合作者正在生成的家鼠Mus Musculus Castaneus的数据。其目标是了解BGS和SSW在控制种群内变异模式方面的相对重要性，并加强新方法，使其成为分析研究人员目前正在各种不同生物体上产生的新的大规模测序数据集的有用工具。

It is well known that mutations create differences between individuals, and therefore provide the raw material for natural selection and evolution. Depending on their effects on the host's well-being, mutations can be divided into three categories: (1) deleterious mutations, which are harmful to the fitness of their host; (2) advantageous mutations, which increase survival or fertility; (3) neutral mutations, which have little or no effect. A question that has been central to evolutionary genetics is the role of natural selection on these three types of mutations in shaping patterns of genetic variation within populations. In fact, this is one of the questions that have motivated major ongoing DNA sequencing efforts in humans (e.g., the 1000 Genomes Project) and a number of other organisms, such as the fruit fly Drosophila (e.g., the Drosophila Population Genomics Project) and the weedy plant Arabidopsis (e.g., the 1001 Genomes Project). The answer to the above question is fundamentally important for biologists who intend to use these large-scale datasets to decipher the genetic basis of phenotypic variation (e.g., disease susceptibility), to infer evolutionary history, and to identify mutations underlying key functional innovations that have helped the organism better adapt to the environment, because it underlies our understanding of the nature of genetic variation, which is critical for developing reliable methods to gather accurate results from the data. Unfortunately, despite being of both practical and theoretical significance, we still know rather little about the roles that negative selection against deleterious mutations, referred to as background selection or BGS, and positive selection on advantageous mutations, referred to as selective sweeps or SSW, play in controlling the genetic make-up of a population.A major stumbling block is the lack of suitable theoretical tools for predicting the effects of BGS on sequence variability. This has hampered the progress towards a better understanding of the nature of genetic variation, because multiple lines of evidence have suggested that most mutations (especially those in functional parts of the genome) are deleterious, but the consequences of such mutations for genetic variability in nearby genomic regions are not well understood due to the lack of theoretical tools. The first objective of this project is, therefore, to construct a set of BGS models that are not only biologically realistic and computationally efficient, but are also suitable for analysing large-scale datasets. This is made feasible by a BGS model I have recently published. I will improve on this by developing a set of extended models that incorporate several essential biological features that are missing in the original model. I will also develop theoretical tools for analysing sequence variability that incorporate both of the BGS and SSW processes. I will apply these new models to whole-genome sequence datasets, such as that for the house mouse Mus musculus castaneus, which is being generated by my collaborators at the University of Edinburgh. The goal is to understand the relative importance of BGS and SSW in controlling patterns of variation within populations, and to enhance the new methods in such a way that they can become a useful set of tools for analysing the new large-scale sequencing datasets that are currently being generated by researchers on a variety of different organisms.