Alternative somatic cell count traits exploitable in genetic selection for mastitis resistance in Italian Holstein and Jersey cows
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".