Automated Author Profile

Daetwyler, Hans D.

Department of Environment, Land, Water and Planning

Current S-Index

2.0

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

2.0

Average Dataset Index per dataset

Total Datasets

1

Total datasets for this author

Average FAIR Score

76.9%

Average FAIR Score per dataset

Total Citations

2

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Data from: Genomic selection for recovery of original genetic background from hybrids of endangered and common breeds (Version: 1)

Critically endangered breeds and populations are often crossed with more common breeds or subspecies. This results in genetic admixture that can be undesirable when it challenges the genetic integrity of wild and domestic populations, causing a loss in special characteristics or unique genetic material and ultimately extinction. Here, we present two genomic selection strategies, using genome-wide DNA markers, to recover the genomic content of the original endangered population from admixtures. Each strategy relies on the estimation of the proportion of nonintrogressed genome in individuals based on a different method: either genomic prediction or identification of breed-specific haplotypes. Then, breeding programs that remove introgressed genomic information can be designed. To test these strategies, we used empirical 50K SNP array data from two pure sheep breeds, Merino (used as target breed), Poll Dorset and an existing admixed population of both breeds. Sheep populations with varying degrees of introgression and admixture were simulated starting from these real genotypes. Both strategies were capable of identifying segment origin, and both removed up to the 100% of the Poll Dorset segments. While the selection process led to substantial inbreeding, we controlled it by imposing a minimum number of individuals contributing to the next generation.

Authors

  • Amador, Carmen ;
  • Hayes, Ben J. ;
  • Daetwyler, Hans D.
2 Citations0 Mentions77% FAIR2.3 Dataset Index
10.5061/dryad.mn0d52013