Data from: The aggregate site frequency spectrum (aSFS) for comparative population genomic inference

Understanding how assemblages of species responded to past climate change is a central goal of comparative phylogeography and comparative population genomics, and an endeavor that has increasing potential to integrate with community ecology. New sequencing technology now provides the potential to gain complex demographic inference at unprecedented resolution across assemblages of non-model species. To this end, we introduce the aggregate site frequency spectrum (aSFS), an expansion of the site frequency spectrum to use single nucleotide polymorphism (SNP) datasets collected from multiple, co-distributed species for assemblage-level demographic inference. We describe how the aSFS is constructed over an arbitrary number of independent population samples and then demonstrate how the aSFS can differentiate various multi-species demographic histories under a wide range of sampling configurations while allowing effective population sizes and expansion magnitudes to vary independently. We subsequently couple the aSFS with a hierarchical approximate Bayesian computation (hABC) framework to estimate degree of temporal synchronicity in expansion times across taxa, including an empirical demonstration with a dataset consisting of five populations of the threespine stickleback (Gasterosteus aculeatus). Corroborating what is generally understood about the recent post-glacial origins of these populations, the joint aSFS/hABC analysis strongly suggests that the stickleback data are most consistent with synchronous expansion after the Last Glacial Maximum (posterior probability = 0.99). The aSFS will have general application for multi-level statistical frameworks to test models involving assemblages and/or communities and as large-scale SNP data from non-model species become routine, the aSFS expands the potential for powerful next-generation comparative population genomic inference.

探究物种集合对既往气候变化的响应规律，是比较系统地理学（comparative phylogeography）与比较种群基因组学的核心研究目标，亦是一项与群落生态学融合潜力不断提升的研究工作。如今，新兴测序技术为在前所未有的分辨率下，针对非模式物种种群集合开展复杂种群历史推断提供了可能。为此，我们提出聚合等位基因频率谱（aggregate site frequency spectrum, aSFS）——这是对等位基因频率谱（site frequency spectrum, SFS）的拓展方法，可利用从多个同分布物种中采集的单核苷酸多态性（single nucleotide polymorphism, SNP）数据集，开展物种集合水平的种群历史推断。我们详述了aSFS如何基于任意数量的独立种群样本完成构建，并展示了在多样本配置场景下，aSFS可在允许有效种群大小与扩张幅度独立变化的前提下，区分不同类群的多物种种群历史。随后，我们将aSFS与分层近似贝叶斯计算（hierarchical approximate Bayesian computation, hABC）框架相结合，以估算不同类群扩张时间的时序同步性，并以包含三刺鱼（Gasterosteus aculeatus）5个种群的数据集开展了实证验证。针对该类群冰期后近期起源的已有学术认知，联合aSFS/hABC分析强有力地表明，三刺鱼数据与末次冰盛期（Last Glacial Maximum）后同步扩张的模型最为契合（后验概率=0.99）。aSFS可广泛应用于多水平统计框架，以检验涉及物种集合或群落的相关模型；随着来自非模式物种的大规模SNP数据日趋常规化，aSFS将为开展兼具统计效力的下一代比较种群基因组学推断拓展应用潜力。