Efficient simulation and inference under approximate models of ancestry

虽然现在正在为许多分类群和种群常规生成大型全基因组数据集，但对这些数据的分析仍然是肤浅的，主要是描述性的。为了弄清楚基因组样本中存在的遗传变异，我们需要将其与产生它的进化过程联系起来，这就需要遗传祖先的数学模型，这些模型既要易于处理，又要现实，而且要足够普遍，以捕捉所有基本的进化力量。至少，从群体中采样的基因组的零模型应该捕捉减数分裂重组的随机性以及大多数突变要么是中性的要么是有害的这一事实，因此很可能由于遗传漂变和（背景）选择而从群体中删除。虽然重组基因组样本的祖先可以用数学方法描述为一个图表，但这种完整的时间回溯描述并不能扩展到大的群体，目前也不包括背景选择。这意味着，即使在最简单的生物学上合理的空模型下，目前也不可能有效地模拟基因组变异。来自基因组数据的统计推断甚至更加有限，并且用于从基因组数据推断过去的选择或人口统计学的现有技术统计方法是基于全基因组变异的粗略（并且极其有损）总结。这个跨学科的项目汇集了计算机科学和数学生物学的专家，并建立在最近的突破，以开发有效的近似算法，准确地捕捉重组和背景选择对全基因组祖先和序列变异的影响。这些算法将作为标准模拟软件和工具的一部分来实现，这些软件和工具计算序列数据与过去人口统计和选择模型的拟合。这些工具对于解释现在在生命之树中产生的大量基因组序列数据至关重要。虽然要开发的算法和工具是通用的，但该项目将立即提高我们扫描基因组数据以寻找过去正选择信号的能力，同时考虑祖先的随机性。

While large whole genome data sets are now being generated routinely for many taxa and populations, analyses of these data remain superficial and largely descriptive. In order to make sense of the genetic variation present in samples of genomes, we need to relate it mathematically to the evolutionary processes that generated it. This requires mathematical models of genetic ancestry that are tractable, yet realistic, and general enough to capture all fundamental evolutionary forces. At a minimum, a null model of genomes sampled from a population should capture the randomness of meiotic recombination and the fact that most mutations are either neutral or deleterious, and so are likely to be removed from the population as a result of genetic drift and (background) selection. Although the ancestry for a sample of recombining genomes can be described mathematically as a graph, this full backward-in-time description does not scale to large populations and currently does not include background selection. This means that it is currently impossible to efficiently simulate genomic variation even under the simplest biologically plausible null model. Statistical inference from genomic data is even more limited and state of the art statistical approaches for inferring past selection or demography from genomic data are based on crude (and extremely lossy) summaries of genome-wide variation. This cross-disciplinary project brings together experts in computer science and mathematical biology and builds on recent breakthroughs to develop efficient approximate algorithms that accurately capture the effect of recombination and background selection on genome-wide ancestry and sequence variation. These algorithms will be implemented both as part of a standard simulation software and tools that calculate the fit of sequence data to models of past demography and selection. Such tools are fundamental for interpreting the vast volumes of genome sequence data that are now being generated across the tree of life. While the algorithms and tools to be developed are general, this project will immediately improve our ability to scan genomic data for signals of past positive selection whilst accounting for the randomness of ancestry.