| Literature DB >> 28426822 |
Liesbeth François1, Katrien Wijnrocx1, Frédéric G Colinet2, Nicolas Gengler2, Bettine Hulsegge3,4, Jack J Windig3,4, Nadine Buys1, Steven Janssens1.
Abstract
Through centuries of both natural and artificial selection, a variety of local cattle populations arose with highly specific phenotypes. However, the intensification and expansion of scale in animal production systems led to the predominance of a few highly productive cattle breeds. The loss of local populations is often considered irreversible and with them specific qualities and rare variants could be lost as well. Over these last years, the interest in these local breeds has increased again leading to increasing efforts to conserve these breeds or even revive lost populations, e.g. through the use of crosses with similar breeds. However, the remaining populations are expected to contain crossbred individuals resulting from introgressions. They are likely to carry exogenous genes that affect the breed's authenticity on a genomic level. Using the revived Campine breed as a case study, 289 individuals registered as purebreds were genotyped on the Illumina BovineSNP50. In addition, genomic information on the Illumina BovineHD and Illumina BovineSNP50 of ten breeds was available to assess the current population structure, genetic diversity, and introgression with phenotypically similar and/or historically related breeds. Introgression with Holstein and beef cattle genotypes was limited to only a few farms. While the current population shows a substantial amount of within-breed variation, the majority of genotypes can be separated from other breeds in the study, supporting the re-establishment of the Campine breed. The majority of the population is genetically close to the Deep Red (NL), Improved Red (NL) and Eastern Belgium Red and White (BE) cattle, breeds known for their historical ties to the Campine breed. This would support an open herdbook policy, thereby increasing the population size and consequently providing a more secure future for the breed.Entities:
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Year: 2017 PMID: 28426822 PMCID: PMC5398708 DOI: 10.1371/journal.pone.0175916
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Detailed description of the number of samples selected for the different analyses per farm.
| Farmer | Breeding objective | |||
|---|---|---|---|---|
| 28 | Dairy | 8 | 3 | |
| 48 | Dairy | 12 | 4 | |
| 104 | Dairy | 81 | 28 | |
| 48 | Dual-purpose | 24 | 8 | |
| 214 | Dairy | 51 | 18 | |
| 59 | Beef | 37 | 13 | |
| 33 | Dairy | 17 | 6 | |
| 9 | Dual-purpose | 5 | 2 | |
| 8 | Dairy | 4 | 2 | |
| 66 | Dairy | 45 | 16 |
1Number of animals present at the farm at time of sampling
2Breeding objective of the farm
3Number of animals selected for genotyping
4Number of genotyped individuals selected to be included in the within breed analysis
Description of the number of samples available per breed and the source.
| Breed | Source | |
|---|---|---|
| 289 | KU Leuven | |
| 50 | Gembloux Agro-Bio Tech, AWE | |
| 53 | Centre for Genetic Resources (CGN) | |
| 18 | Centre for Genetic Resources (CGN), Gembloux Agro-Bio Tech | |
| 151 | Wageningen Livestock Research | |
| 50 | AWE | |
| 34 | AWE, Gautier et al. [ | |
| 28 | CRV | |
| 61 | Gautier et al. [ | |
| 20 | CRV |
1Number of genotyped individuals
Fig 1Principal component analysis within the Campine population indicating the position of the different farms on PC1, PC2 (a) and PC1, PC3 (b). The breeding bulls belonging to each farm are indicated using larger points.
Fig 2Principal component analysis showing the relation between the Campine population and nine additional breeds.
Fig 3Discriminant analysis of principal components (DAPC) based on the between-breed analysis.
Fig 4Principal component analysis of the between-breed analysis (similar to Fig 2) with emphasis on the Campine population (all other breeds colored in grey) and the position of each farm and respective breeding bulls (squares).
Fig 5FastSTRUCTURE hierarchical clustering method for the between-breed analysis with additional information on position of each Campine farm (1–10) using K = 5.
Fig 6FastSTRUCTURE hierarchical clustering for the three historically related breeds: Campine, Deep Red, and EBRW using K = 4.
Unsupervised hierarchical clustering of three historically related cattle breeds (Campine, Deep Red, Improved Red, and EBRW).
| Breed | Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 |
|---|---|---|---|---|
| Campine | 16 | 0 | 51 | 33 |
| Deep Red | 0 | 35 | 9 | 0 |
| EBRW | 0 | 0 | 50 | 0 |
| Improved Red | 0 | 0 | 18 | 0 |