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Updated: Dec 17, 2025

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
Published on: February 10, 2023
A demonstration of conservation genomics for threatened species management
Belinda R Wright1, Katherine A Farquharson1, Elspeth A McLennan1
1School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW, Australia.
Abstract:
As species extinction rates increase, genomics provides a powerful tool to support intensive management of threatened species. We use the Tasmanian devil (Sarcophilus harrisii) to demonstrate how conservation genomics can be implemented in threatened species management. We conducted whole genome sequencing (WGS) of 25 individuals from the captive breeding programme and reduced-representation sequencing (RRS) of 98 founders of the same programme. A subset of the WGS samples was also sequenced by RRS, allowing us to directly compare genome-wide heterozygosity with estimates from RRS data. We found good congruence in interindividual variation and gene-ontology classifications between the two data sets, indicating that our RRS data reflect the genome well. We also attempted genome-wide association studies with both data sets (regarding breeding success), but the genomic data suffered from small sample size, while the RRS data suffered from lack of precision, highlighting a key trade-off in the design of conservation genomic research. Nevertheless, we identified a number of candidate genes that may be associated with variation in breeding success. Individual heterozygosity, as measured by WGS or RRS, was not associated with breeding success in captivity but was negatively associated with litter sizes of breeding females in the RRS data set. Our findings enable conservation managers to have confidence in RRS data while understanding its limitations, and provide avenues for further investigation into which processes underlie variation in breeding success in captive Tasmanian devils. We caution, however, that deep functional insights using RRS may be impaired by a lack of precision, especially when marker density is low.
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