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- Strandberg, Erling, et al.
(författare)
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Statistical tools to select for robustness and milk quality
- 2013
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Ingår i: Advances in Animal Biosciences. - : Cambridge University Press. - 2040-4700 .- 2040-4719. ; 4:3, s. 606-611
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Tidskriftsartikel (refereegranskat)abstract
- This work was part of the EU RobustMilk project. In this work package, we have focused on two aspects of robustness, micro- and macro-environmental sensitivity and applied these to somatic cell count (SCC), one aspect of milk quality. We showed that it is possible to combine both categorical and continuous descriptions of the environment in one analysis of genotype by environment interaction. We also developed a method to estimate genetic variation in residual variance and applied it to both simulated and a large field data set of dairy cattle. We showed that it is possible to estimate genetic variation in both micro- and macro-environmental sensitivity in the same data, but that there is a need for good data structure. In a dairy cattle example, this would mean at least 100 bulls with at least 100 daughters each. We also developed methods for improved genetic evaluation of SCC. We estimated genetic variance for some alternative SCC traits, both in an experimental herd data and in field data. Most of them were highly correlated with subclinical mastitis (>0.9) and clinical mastitis (0.7 to 0.8), and were also highly correlated with each other. We studied whether the fact that animals in different herds are differentially exposed to mastitis pathogens could be a reason for the low heritabilities for mastitis, but did not find strong evidence for that. We also created a new model to estimate breeding values not only for the probability of getting mastitis but also for recovering from it. In a progeny-testing situation, this approach resulted in accuracies of 0.75 and 0.4 for these two traits, respectively, which means that it is possible to also select for cows that recover more quickly if they get mastitis.
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| 2. |
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| 3. |
- Welderufael, Berihu, et al.
(författare)
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Genetic Evaluation of Mastistis Liability and Recovery through Longitudinal Models of SCC
- 2013
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Konferensbidrag (refereegranskat)abstract
- Genetic evaluation of mastitis is performed either with cross-sectional or longitudinal models. In this study we aim to develop better longitudinal models using simulated SCC (Somatic Cell Count) which usually is used as a proxy to label clinical mastitis. Data was simulated for mastitis liability and recovery for two scenarios (28% and 95% mastitis cases/lactation) and two daughter groups of 60 and 150 per sire in 1200 herds. Weekly observations for SCC were simulated assuming a baseline curve for non-mastitic cows and deviations in case of a mastitis event. Binary data was created to define presence or absence of mastitis as 1 if the simulated SCC was above pre-specified boundary and 0 otherwise. The boundary was allowed to vary along the lactation curve modeled by a spline function with a multiple of 10 or 15. The dynamic nature of the SCC was taken in to consideration with the longitudinal approach and the patterns were captured by modelling transition probability of moving across the boundary. Thus, a transition from below to above the boundary is an indicator of the probability to contract mastitis, and a transition from above to below the boundary is an indicator of the recovery process. Estimated breeding values for mastitis liabilities and recovery were calculated in DMU. Our preliminary results showed the correlation between true and estimated breeding value for the simulated mastitis liability was 0.72 which is as good as the estimations based on clinical mastitis. Though the estimation accuracy for recovery (0.42) was not as high as for mastitis liability the transition probability model enables us to generate breeding values for mastitis recovery process.
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| 4. |
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| 5. |
- Welderufael, Berihu, et al.
(författare)
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Genetic evaulation of getting and recovering from an intramammary infection through longitudinal models of simulated somatic cell count
- 2013
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Mastitis, a bacterial intramammary infection, is one of the recently prioritized research thematic areas in the dairy cattle breeding programs. Genetic evaluation of mastitis is performed either with crosssectional or longitudinal models. The objective of this study was to develop better longitudinal models through the analysis of simulated somatic cell count (SC C) which is often used as a proxy to label clinical mastitis. Data were simulated for 2 traits: intramammary infection and recovery, for two scenarios (28% and 95% cases/lactation) and for two daughter groups of 60 and 150 per sire distributed over 1200 herds. Weekly observations for SCC were simulated assuming a baseline curve for non-mastitis cows and deviations in the case of a mastitis event. Binary data were created to define presence or absence of mastitis as 1 if the SCC was above pre-specified boundary (200000 cells/mL), and 0 otherwise. The boundary was allowed to vary along the lactation curve modeled by a spline function with a multiple of 10 or 15. The dynamic nature of the SCC was taken into consideration with the longitudinal approach; and the patterns were captured by modelling transition probabilities of moving across the boundary. Thus, a transition from below to above the boundary is an indicator of the probability to contract mastitis, and a transition from above to below the boundary is an indicator of the recovery process. Sire model with mean, fixed herd and random sire effects was fitted to calculate the estimated breeding values for intramammary infection and recovery using Bayesian inference and MCMC simulations in DMU, a statistical package for analyzing multivariate mixed models. Our preliminary results showed that the estimation accuracy or the correlation between true and estimated breeding value for the simulated intramammary infection mastitis was 0.72, which is as high as the estimations based on clinical mastitis. The estimation accuracy for recovery (0.42) was not as high as for getting infection. However, the transition probability model enables us to generate breeding values for the recovery process. The MCMC nonlinear and longitudinal approach leads to more precise genetic evaluation. This is because the MCMC fits well to the binary nature of getting infection; and the longitudinal approach uses more available information for the analysis.
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