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

Citation

Xue, Alexander T.; Hickerson, Michael J. (2015), Data from: The aggregate site frequency spectrum (aSFS) for comparative population genomic inference, Dryad, Dataset, https://doi.org/10.5061/dryad.b6vh6

Abstract

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.

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