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Comparative phylogeographic inference with genome-wide data from aggregated population pairs
Authors:Alexander T Xue  Michael J Hickerson
Institution:1. Subprogram in Ecology, Evolutionary Biology, and Behavior, Department of Biology, Graduate Center of City University of New York, New York, NY, 10016;2. Subprogram in Ecology, Evolutionary Biology, and Behavior, Department of Biology, Graduate Center of City University of New York, New York, NY, 10016

Subprogram in Ecology, Evolutionary Biology, and Behavior, Department of Biology, City College of City University of New York, New York, NY, 10031

Division of Invertebrate Zoology, American Museum of Natural History, New York, NY, 10024

Abstract:Comparing divergences across multiple sister population pairs has been a focus in phylogeography since its inception. Initial approaches used organelle genetic data and involved qualitative comparisons of phylogenetic patterns to evaluate hypotheses of shared and variable evolutionary responses. This endeavor has progressed with coalescent model-based statistical techniques and advances in next-generation sequencing, yet there remains a need for methods that can exploit aggregated genomic-scale data within a unified analytical framework. To this end, we introduce the aggregate joint site frequency spectrum (ajSFS) by validating its use within a hierarchical Bayesian framework through several in silico experiments. Subsequently, we applied our method against two published restriction site–associated DNA marker datasets consisting of eight local replicates of a lamprey species pair and six co-distributed passerine taxon pairs, respectively, with the aim of inferring variability in co-divergence and co-migration histories. We found that the lamprey population pairs exhibited temporal synchrony in both co-divergence and collective secondary contact times, yet an idiosyncratic pattern in secondary migration intensities. In contrast, the bird population pairs displayed thoroughly asynchronous co-divergence histories. Our results demonstrate that the ajSFS can be exploited for complex and flexible co-demographic inference, opening up new possibilities for comparative phylogeography and population genomic studies.
Keywords:Aggregate site frequency spectrum (aSFS)  approximate Bayesian computation (ABC)  hierarchical co-demographic modeling  Multi-DICE  population genomic methods  random forest
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