Efficient simulation and inference under approximate models of ancestry

虽然现在正在为许多分类群和种群例行地生成大型全基因组数据集，但对这些数据的分析仍然很肤浅，主要是描述性的。为了理解基因组样本中存在的遗传变异，我们需要在数学上将其与产生它的进化过程联系起来。这需要遗传祖先的数学模型，这些模型易于处理，但又现实，而且足够普遍，足以捕捉所有基本的进化力量。至少，从种群中采集的基因组的零模型应该捕捉到减数分裂重组的随机性，以及大多数突变要么是中性的，要么是有害的事实，因此很可能由于遗传漂移和(背景)选择而从种群中移除。虽然重组基因组样本的祖先可以用数学图形来描述，但这种完整的时间倒退描述不能扩展到大种群，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看似合理的零模型下，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，从基因组数据推断过去的选择或人口统计学的最先进的统计方法是基于对基因组范围变异的粗略(和极具损耗的)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，并在最近的突破基础上开发了有效的近似算法，准确地捕捉到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的一部分和计算序列数据与过去人口统计和选择模型的匹配度的工具来实施。这些工具对于解释现在正在生命之树上产生的海量基因组序列数据是基本的。虽然将要开发的算法和工具是通用的，但这个项目将立即提高我们扫描基因组数据以寻找过去积极选择信号的能力，同时考虑到祖先的随机性。

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.