Alternative somatic cell count traits exploitable in genetic selection for mastitis resistance in Italian Holstein and Jersey cows
Notice bibliographique
Résumé
Breeding for mastitis resistance represents an important strategy to decrease the incidence of the disease in the farm. However, as routine disease-recording systems are currently not widely implemented, genetic selection for mastitis resistance is mostly based on test-day (TD) or lactation-mean somatic cell count (SCC) (Martin et al., 2018). Nevertheless, alternative traits derived from SCC and genetically correlated with clinical mastitis have been suggested to better describe SCC variation throughout the lactation and the dynamic of infection (de Haas et al., 2008; \nUrioste et al., 2010; Koeck et al., 2012). Therefore, the aim of this study was to characterize alternative SCC traits, derived from TD data from routine recording system, and to estimate phenotypic and genetic correlations with milk production traits in Italian Holstein Friesian (HF) and Jersey (JE) cows. Test-day records of 66,407 primiparous HF cows from 404 herds sampled between 1999 and 2014 and TD records of 12,754 primiparous JE cows from 428 herds sampled between 2004 and 2016 were extracted from the databases of the Italian Holstein Association (Cremona, Italy). Along with the traditional lactation-mean somatic cell score (SCS), analyzed traits included average SCS in early lactation (SCS_150), standard deviation of SCS of the whole lactation (SCS_SD), presence or absence of at least one TD SCC above 400,000 cells/mL (Infection) and the ratio of number of TD with SCC above 400,000 cells/mL to total number of TD (Severity). A subset of 22,695 HF and a subset of 8,133 JE cows were randomly extracted from the edited original databases and used for genetic analysis. Multivariate animal models were run to estimate heritability of and genetic correlations between alternative SCC traits, and genetic correlations between alternative SCC traits and milk production traits. Herd-year-season of calving and number of TD were included as fixed effects, and additive genetic animal (n = 62,146 for HF and n = 18,314 for JE cows) as random terms. Holsteins had lower SCS than JE cows, both when averaged over the entire lactation or over the first 150 days in milk: 2.86 vs 3.09 for SCS and 2.66 vs 3.01 for SCS_150, respectively. However, compared to JE, HF showed greater SCS_SD (1.29 vs 1.10), higher Infection (47.4% vs 42.9%) and greater Severity (14% vs 11%). Heritability of novel SCC traits was smaller in comparison to traditional lactation-mean SCS (0.13 for HF and 0.14 for JE), ranging from 0.04 (SCS_SD) to 0.11 (SCS_150) in both breeds. With the only exception of SCS_SD, genetic correlations between SCC traits were strong and comprised between 0.79 and 0.99. Regardless the breed, negative phenotypic correlations were estimated between milk yield and SCC traits, whereas positive genetic relationships were observed, especially with those traits related to variation of SCC (SCS_SD, Infection and Severity). Phenotypic and genetic correlations between SCC traits and milk composition (fat and protein percentage) were mostly close to zero. In conclusion, alternative SCC traits analyzed in the present study showed enough genetic variation to be exploited in breeding programs for mastitis resistance. Moreover, our findings confirmed that high milk SCC is associated with reduced milk production (negative phenotypic correlation) and support the hypothesis that high producing cows could be more susceptible to mastitis (positive genetic correlation). The unfavorable genetic correlation between SCC traits and production highlights the need of improving selection for mastitis resistance. A comparison of alternative SCC traits with clinical mastitis information would be required. de Haas, Y., W. Ouweltjes, J. ten Napel, J. J. Windig, and G. de Jong. 2008. Alternative somatic cell count traits as mastitis indicators for genetic selection. J. Dairy Sci. 91:2501–2511. Koeck, A., F. Miglior, D. F. Kelton, and F. S. Schenkel. 2012. Alternative somatic cell count traits to improve \nmastitis resistance in Canadian Holsteins. J. Dairy Sci. 95:432–439. Martin, P., H. W. Barkema, L. F. Brito, S. G. Narayana, and F. Miglior. 2018. Symposium review: Novel strategies to genetically improve mastitis resistance in dairy cattle. J. Dairy Sci. 101:2724-2736. Urioste, J. I., J. Franzén, and E. Strandberg. 2010. Phenotypic and genetic characterization of novel somatic cell count traits from weekly or monthly observations. J. Dairy Sci. 93:5930–5941.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».