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Enregistrement W3099857046 · doi:10.15414/afz.2020.23.mi-fpap.233-240

Breeding values prediction for clinical mastitis in Czech Holstein cattle

2020· article· en· W3099857046 sur OpenAlexaboutno aff
Ludmila Zavadilová

Notice bibliographique

RevueActa fytotechnica et zootechnica/Acta fytotechnica et zootechnica · 2020
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenetic and phenotypic traits in livestock
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHeritabilityTraitRepeatabilityBiologyHerdGenetic correlationRandom effects modelPopulationMastitisDairy cattleAnimal scienceIce calvingLactationVeterinary medicineStatisticsGenetic variationMathematicsGeneticsMedicinePregnancyMeta-analysisInternal medicine

Résumé

récupéré en direct d'OpenAlex

Submitted 2020-07-16 | Accepted 2020-08-26 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.233-240 This study aims to genetically evaluate clinical mastitis (CM) in Holstein cattle using a two-trait repeatability animal model with the average lactation somatic cell score (LSCS) as an indicator trait of mastitis. The data set included 21,786 Holsteins with 29,110 lactations in 59 herds and with a calving date between 2015 and 2019. CM was considered as an all-or-none trait (values 0 or 1) in the period from calving to 305 days in milk, and the LSCS was obtained by logarithmic transformation of the average of the individual test-day records for somatic cell count over lactation. Heritability of CM was estimated using a single-trait repeatability animal model, whereas the genetic correlation between CM and LSCS was assessed through a two-trait repeatability animal model. Fixed effects included in the analyses were parity-age and herd-year-season, and the random effects were the permanent environment and the animal. The (co)variance matrix was employed in breeding values estimation for both single-trait (only CM) and bivariate models (CM and LSCS) including genomic prediction. Only genotyped sires formed the reference population for the single-step genomic evaluation. The heritability for CM was 0.04 in the single-trait and 0.05 in the two-trait analysis. Genetic correlation between CM and LSCS was 0.80. The employment of the two-trait model had a considerably strong influence on reliability. The reliability increased for cows with records as well as for the genotyped sires. This study indicates that the two-trait analysis of CM and LSCS is feasible and improves the reliability of the estimated breeding values. Keywords: mastitis, cow, genomic breeding value, multi-trait model, somatic cell score References Aguilar, I. et al. (2010). Hot topic: A unified approach to utilise phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 93, 743–752. https://doi.org/10.3168/jds.2009-2730 Ali, A. K. A. and Shook, G. E. (1980). An optimum transformation for somatic-cell concentration in milk. Journal of Dairy Science, 63, 487–490. https://doi.org/10.3168/jds.S0022-0302(80)82959-6 Buch, L. H. et al. (2011). Udder health and female fertility traits are favourably correlated and support each other in multi-trait evaluations. Journal of Animal Breeding and Genetics, 128, 174-182. https://doi.org/10.1111/j.1439-0388.2010.00904.x Carlén, E. et al. (2004). Genetic parameters for clinical mastitis, somatic cell score, and production in the first three lactations of Swedish Holstein cows. Journal of Dairy Science, 87, 3062–3070. https://doi.org/10.3168/jds.S0022-0302(04)73439-6 Christensen, O. F. and Lund, M. S. (2010). Genomic prediction when some animals are not genotyped. Genetics Selection Evolution, 42, 2. https://doi.org/10.1186/1297-9686-42-2 Forni, S. et al. (2011). Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution, 43, 1. https://doi.org/10.1186/1297-9686-43-1 Heringstad, B. et al. (2001). Responses to selection against clinical mastitis in the Norwegian cattle population. Acta Agriculturæ Scandinavica, Section A – Animal Science, 51(2), 155–160. https://doi.org/10.1080/090647001750193503 Heringstad, B. et al. (2006). Genetic associations between clinical mastitis and somatic cell score in early first-lactation cows. Journal of Dairy Science, 89, 2236–2244. https://doi.org/10.3168/jds.S0022-0302(06)72295-0 Jamrozik, J. et al. (2013). Genetic and genomic evaluation of mastitis resistance in Canada. Interbull Bulletin, 47, 43–51. Kašná, E. et al. (2018). Genetic evaluation of the clinical mastitis in Holstein cattle. Czech Journal of Animal Science, 63, 443- 451. https://doi.org/10.17221/105/2018-CJAS Kvapilík, J. et al. (eds) (2016). Yearbook. Raising Cattle in the Czech Republic – Main Results and Indicators for 2016. CMSCH a.s., Prague, Czech Republic. In Czech. Martin, P. et al. (2018). Novel strategies to genetically improve mastitis resistance in dairy cattle. Journal of Dairy Science, 101, 2724-2736. https://doi.org/10.3168/jds.2017-13554 Misztal, I. et al. (2018). Manual for BLUPF90 family programs. University of Georgia, Athens, USA, 142 p. Mrode, R. et al. (2012). Joint estimation of genetic parameters for test-day somatic cell count and mastitis in the United Kingdom. Journal of Dairy Science, 95, 4618–4628. https://doi.org/10.3168/jds.2011-4971 Negussie, E. et al. (2005). Genetic parameters and single versus multitrait evaluation of udder health traits. Acta Agriculturæ Scandinavica, Section A – Animal Science, 56, 73-82. https://doi.org/10.1080/09064700600979693 Negussie, E. et al. (2010). Combining test day LSCS with clinical mastitis and udder type traits: A random regression model for joint genetic evaluation of udder health in Denmark Finland and Sweden. Interbull Bulletin, 42, 25-32. Neuenschwander, T. F.-O. et al. (2012). Genetic parameters for producer-recorded health data in Canadian Holstein cattle. Animal, 6(4), 571–578. https://doi.org/10.1017/S1751731111002059 Ødegård, J. et al. (2004). Short communication: Bivariate genetic analysis of clinical mastitis and somatic cell count in Norwegian dairy cattle, J. Dairy Sci., 87, 3515–3517. https://doi.org/10.3168/jds.S0022-0302(04)73487-6 Oltenacu, P. A. and Broom, D. M. (2010). The impact of genetic selection for increased milk yield on the welfare of dairy cows. Animal Welfare, 19, 39–49. Pérez-Cabal, M. A., and Charfeddine, N. (2013) Genetic relationship between clinical mastitis and several traits of interest in Spanish Holstein dairy cattle. Interbull Bulletin, 47, 77–81. Rilanto, T. et al. (2020). Culling reasons and risk factors in Estonian dairy cows. BMC Veterinary Research, 16, 173. https://doi.org/10.1186/s12917-020-02384-6 Rupp, R. and Boichard, D. (2003). Genetics of resistance to mastitis in dairy cattle. Veterinary Research, 34, 671–688. https://doi.org/10.1051/vetres:2003020 Schaeffer, L. R. (1984). Sire and cow evaluation under multiple trait models. Journal of Dairy Science, 67, 1567-1580. https://doi.org/10.3168/jds.S0022-0302(84)81479-4 Šlosárková, S. et al. (2016). Monitoring of dairy cattle diseases in the Czech Republic. Veterinářství, 66(11), 859–866. In Czech. Vazquez, A. I. et al. (2009). Assessment of Poisson, logit, and linear models for genetic analysis of clinical mastitis in Norwegian Red cows. Journal of Dairy Science, 92, 739–748. https://doi.org/10.3168/jds.2008-1325 Vitezica, Z. G. et al. (2011). Bias in genomic predictions for populations under selection. Genetic Research, 93, 357-366. https://doi.org/10.1017/S001667231100022X Wolf, J. et al. (2010). A model for the genetic evaluation of number of clinical mastitis cases per lactation in Czech Holstein cows. Journal of Dairy Science, 93, 1193–1204. https://doi.org/10.3168/jds.2009-2443 Zavadilová, L. et al. (2017). Genetic parameters for clinical mastitis, fertility and somatic cell score in Czech Holstein cattle. Annals of Animal Science, 17, 1007-1018. https://doi.org/10.1515/aoas-2017-0006 Zavadilová, L. et al. (2015). Genetic analysis of clinical mastitis data for Holstein cattle in the Czech Republic. Archives Animal Breeding, 58, 199-204. https://doi.org/10.5194/aab-58-199-2015

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesMéta-épidémiologie (sens strict), Intégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,717
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,004
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0040,002
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,053
Tête enseignante GPT0,333
Écart entre enseignants0,280 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission1
Résumé présentoui

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