bioRxivEvolutionary BiologyPreprint
Contemporary and historical selection in Tasmanian devils (Sarcophilus harrisii) support novel, polygenic response to transmissible cancer
A. R. Stahlke, B. Epstein, S. Barbosa, M. J. Margres, A. Patton, S. A. Hendricks, A. Veillet, A. K. Fraik, B. Schonfeld, H. McCallum, R. Hamede, M. E. Jones, A. Storfer, P. A. Hohenlohe
Abstract
Tasmanian devils (Sarcophilus harrisii) are evolving in response to a unique transmissible cancer, devil facial tumour disease (DFTD), first described in 1996. Persistence of wild populations and the recent emergence of a second independently evolved transmissible cancer suggest that transmissible cancers may be a recurrent feature in devils. Here we compared signatures of selection across temporal scales to determine whether genes or gene pathways under contemporary selection (6-8 generations) have also been subject to historical selection (65-85 million years), and test for recurrent selection in devils. First, we used a targeted sequencing approach, RAD-capture, to identify genomic regions subject to rapid evolution in approximately 2,500 devils in six populations as DFTD spread across the species range. We documented genome-wide contemporary evolution, including 186 candidate genes related to cell cycling and immune response. Then we used a molecular evolution approach to identify historical positive selection in devils compared to other marsupials and found evidence of selection in 1,773 genes. However, we found limited overlap across time scales, with historical selection detected in only 16 contemporary candidate genes, and no overlap in enriched functional gene sets. Our results are consistent with a novel, multi-locus evolutionary response of devils to DFTD. Our results can inform management actions to conserve adaptive potential of devils by identifying high priority targets for genetic monitoring and maintenance of functional diversity in managed populations.
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
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