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Synchronized file of allele frequencies for: Using seasonal genomic changes to understand historical adaptation to new environments: parallel selection on stickleback in highly-variable estuaries

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NIAID Data Ecosystem2026-03-12 收录
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http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.fbg79cntg
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Parallel evolution is considered strong evidence for natural selection. However, few studies have investigated the process of parallel selection as it plays out in real time. The common approach is to study historical signatures of selection in populations already well adapted to different environments. Here, to document selection under natural conditions, we study six populations of threespine stickleback (Gasterosteus aculeatus) inhabiting bar-built estuaries that undergo seasonal cycles of environmental changes. Estuaries are periodically isolated from the ocean due to sandbar formation during dry summer months, with concurrent environmental shifts that resemble the long-term changes associated with postglacial colonization of freshwater habitats by marine populations. We used pooled whole-genome sequencing (Pool-WGS) to track seasonal allele frequency changes in six of these populations and search for signatures of natural selection. We found consistent changes in allele frequency across estuaries, suggesting a potential role for parallel selection. Functional enrichment among candidate genes included transmembrane ion transport and calcium binding, which are important for osmoregulation and ion balance. The genomic changes that occur in threespine stickleback from bar-built estuaries could provide a glimpse into the early stages of adaptation that have occurred in many historical marine to freshwater transitions. Methods We sampled stickleback from six bar-built estuaries at two time points (i.e., 12 pools). We applied pooled sequencing of whole-genomes (12 pools). Fst estimates were produced using the fst-sliding.pl program of Popoolation2 (https://sourceforge.net/p/popoolation2/wiki/Manual/). The scripts used to analyze the results are available at https://github.com/garfring.
创建时间:
2021-02-23
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