Institute of Evolution and Ecology, Departments of Mathematics and Biology, University of Oregon, Eugene, Oregon 97405
Department of Ecology and Evolutionary Biology, University of California, Irvine, California 92697.
Genetics. 2020 Jul;215(3):779-797. doi: 10.1534/genetics.120.303253. Epub 2020 May 1.
As a genetic mutation is passed down across generations, it distinguishes those genomes that have inherited it from those that have not, providing a glimpse of the genealogical tree relating the genomes to each other at that site. Statistical summaries of genetic variation therefore also describe the underlying genealogies. We use this correspondence to define a general framework that efficiently computes single-site population genetic statistics using the succinct tree sequence encoding of genealogies and genome sequence. The general approach accumulates sample weights within the genealogical tree at each position on the genome, which are then combined using a summary function; different statistics result from different choices of weight and function. Results can be reported in three ways: by , which corresponds to statistics calculated as usual from genome sequence; by , which gives the expected value of the dual site statistic under the infinite sites model of mutation, and by , which summarizes the contribution of each ancestor to these statistics. We use the framework to implement many currently defined statistics of genome sequence (making the statistics' relationship to the underlying genealogical trees concrete and explicit), as well as the corresponding branch statistics of tree shape. We evaluate computational performance using simulated data, and show that calculating statistics from tree sequences using this general framework is several orders of magnitude more efficient than optimized matrix-based methods in terms of both run time and memory requirements. We also explore how well the duality between site and branch statistics holds in practice on trees inferred from the 1000 Genomes Project data set, and discuss ways in which deviations may encode interesting biological signals.
随着基因突变在世代间传递，它将那些遗传了该突变的基因组与没有遗传该突变的基因组区分开来，从而提供了一个关于在该位置基因组之间亲缘关系的 glimpses。因此，遗传变异的统计摘要还描述了潜在的亲缘关系。我们利用这种对应关系来定义一个通用框架，该框架使用简洁的树序列编码对基因组序列进行单一位点群体遗传统计的高效计算。该通用方法在基因组上的每个位置在亲缘关系树上累积样本权重，然后使用摘要函数对其进行组合；不同的权重和函数会产生不同的统计结果。结果可以通过三种方式报告：通过，它对应于通常从基因组序列计算的统计数据；通过，它给出了在突变的无限位点模型下对偶位点统计量的期望值，通过，它总结了每个祖先对这些统计数据的贡献。我们使用该框架实现了目前定义的许多基因组序列统计量（使统计量与潜在的亲缘关系树之间的关系具体化和明确化），以及树形状的相应分支统计量。我们使用模拟数据评估计算性能，并表明使用该通用框架从树序列计算统计数据在运行时间和内存需求方面比基于矩阵的优化方法高效几个数量级。我们还探讨了在从 1000 基因组计划数据集推断的树上，位点和分支统计量之间的对偶性在实践中的吻合程度，并讨论了偏差可能编码有趣的生物学信号的方式。
