Abstract
Landscape genomics studies focus on identifying candidate genes under selection via spatial variation in abiotic environmental variables, but rarely by biotic factors (i.e., disease). The Tasmanian devil (Sarcophilus harrisii) is found only on the environmentally heterogeneous island of Tasmania and is threatened with extinction by a transmissible cancer, devil facial tumor disease (DFTD). Devils persist in regions of long-term infection despite epidemiological model predictions of species’ extinction, suggesting possible adaptation to DFTD. Here, we test the extent to which spatial variation and genetic diversity are associated with the abiotic environment (i.e., climatic variables, elevation, vegetation cover) and/or DFTD. We employ genetic-environment association analyses using 6886 SNPs from 3287 individuals sampled pre- and post-disease arrival across the devil's geographic range. Pre-disease, we find significant correlations of allele frequencies with environmental variables, including 365 unique loci linked to 71 genes, suggesting local adaptation to abiotic environment. The majority of candidate loci detected pre-DFTD are not detected post-DFTD arrival. Several post-DFTD candidate loci are associated with disease prevalence and were in linkage disequilibrium with genes involved in tumor suppression and immune response. Loss of apparent signal of abiotic local adaptation post-disease suggests swamping by strong selection resulting from the rapid onset of DFTD.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1392-1408 |
| Number of pages | 17 |
| Journal | Evolution |
| Volume | 74 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 1 2020 |
Bibliographical note
Funding Information:We thank Omar Cornejo, and his laboratory, David Crowder, and Joanna Kelley's laboratory for their helpful review and insightful discussion. We are grateful to the anonymous reviewers for feedback on our manuscript. This work was funded by NSF grant DEB‐1316549 and NIH grant R01‐GM126563 to A.S., P.A.H., M.J., and H.M. as part of the joint NSF‐NIH‐USDA Ecology and Evolution of Infectious Diseases program. Bioinformatics work was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the NIH under grant number P30 GM103324.
Funding Information:
A.K.F. conducted the analyses and wrote the paper; M.J.M., S.B., and B.E. assisted with the analyses and helped write the paper; B.S., S.H., A.V., and A.S. assisted with the sample preparation and helped write the paper; R.H. and M.J. collected the samples, contributed to the study design, managed field surveys, and helped write the paper; H.M. helped write the paper; E.L.-C. and S.J.K. assisted with the analyses; P.H., J.L.K., and A.S. directed the project, supervised this work and helped write the paper. We thank Omar Cornejo, and his laboratory, David Crowder, and Joanna Kelley's laboratory for their helpful review and insightful discussion. We are grateful to the anonymous reviewers for feedback on our manuscript. This work was funded by NSF grant DEB-1316549 and NIH grant R01-GM126563 to A.S., P.A.H., M.J., and H.M. as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases program. Bioinformatics work was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the NIH under grant number P30 GM103324. The sequence data has been deposited at NCBI under BioProject PRJNA306495 (http://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA306495) and BioProject PRJNA634071 (http://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA634071). Code is available at github.com/jokelley/devil-landscape-genomics.
Publisher Copyright:
© 2020 The Authors. Evolution © 2020 The Society for the Study of Evolution.
