bioRxivEvolutionary Biology
Selection at behavioral, developmental and metabolic genes is associated with the northward expansion of a successful tropical colonizer
Abstract
What makes a species able to colonize novel environments? This question is key to understand the dynamics of adaptive radiations and ecological niche shifts, but the mechanisms that underlie expansion into novel habitats remain poorly understood at a genomic scale. Lizards from the genus Anolis are typically tropical and the green anole (Anolis carolinensis) constitutes an exception since it expanded into temperate North America from subtropical Florida. Thus, we used the green anole as a model to investigate signatures of selection associated with colonization of a new environment, namely temperate North America. To this end, we analyzed 29 whole genome sequences, representing the entire genetic diversity of the species. We used a combination of recent methods to quantify both positive and balancing selection in northern populations, including FST outlier methods, machine learning and ancestral recombination graphs. We naively scanned for genes of interest and assessed the overlap between multiple tests. Strikingly, we identified many genes involved in behavior, suggesting that the recent successful colonization of northern environments may have been linked to behavioral shifts as well as physiological adaptation. These results were robust to recombination, gene length and clustering. Using a candidate genes strategy, we determined that genes involved in response to cold or behavior displayed more frequently signals of selection, while controlling for local recombination rate and gene length. In addition, we found signatures of balancing selection at immune genes in all investigated genetic groups, but also at genes involved in neuronal and anatomical development in Florida.
- organism: non-human primate
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: development & morphogenesis
- research focus: metabolism & bioenergetics
- approach: machine learning (classical)
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