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.

解析物种集合对过往气候变化的响应模式，是比较系统地理学（comparative phylogeography）与比较种群基因组学（comparative population genomics）的核心研究目标，且该研究方向与群落生态学的融合潜力正日益显现。新兴测序技术如今为在前所未有的分辨率下，针对多组非模式物种集合开展复杂种群历史推断提供了可能。为此，我们提出聚合位点频率频谱（aggregate site frequency spectrum, aSFS）——这一位点频率频谱的扩展方法，可利用从多个共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。我们首先阐述了如何基于任意数量的独立种群样本构建aSFS，随后证明在多种采样配置下，aSFS能够区分不同的多物种种群历史模式，同时允许有效种群大小与扩张幅度独立变化。我们进一步将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同步性程度，并以包含5个三刺鱼（Gasterosteus aculeatus）种群的数据集开展了实证演示。该研究验证了学界对这些种群末次冰盛期（Last Glacial Maximum, LGM）后近期起源的普遍认知，联合aSFS与hABC的分析结果强烈表明，三刺鱼数据集最符合末次冰盛期后同步扩张的模式（后验概率=0.99）。aSFS将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模型；随着来自非模式物种的大规模SNP数据日趋普及，aSFS将为开展高效的下一代比较种群基因组推断拓展潜力。