Skip to main content Accessibility help
×
Home

Random regression models for genetic evaluation of clinical mastitis in dairy cattle

  • E. Carlén (a1), K. Grandinson (a1), U. Emanuelson (a2) and E. Strandberg (a1)

Abstract

A genetic analysis of longitudinal binary clinical mastitis (CM) data recorded on about 90 000 first-lactation Swedish Holstein cows was carried out using linear random regression models (RRM). This method for genetic evaluation of CM has theoretical advantages compared to the method of linear cross-sectional models (CSM), which is currently being used. The aim of this study was to investigate the feasibility and suitability of estimating genetic parameters and predicting breeding values for CM with a linear sire RRM. For validation purposes, the estimates and predictions from the RRM were compared to those from linear sire longitudinal multivariate models (LMVM) and CSM. For each cow, the period from 10 days before to 241 days after calving was divided into four 1-week intervals followed by eight 4-week intervals. Within each interval, presence or absence of CM was scored as ‘1’ or ‘0’. The linear RRM used to explain the trajectory of CM over time included a set of explanatory variables plus a third-order Legendre polynomial function of time for the sire effect. The time-dependent heritabilities and genetic correlations from the chosen RRM corresponded fairly well with estimates obtained from the linear LMVM for the separate intervals. Some discrepancy between the two methods was observed, with the more unstable results being obtained from the linear LMVM. Both methods indicated clearly that CM was not genetically the same trait throughout lactation. The correlations between predicted sire breeding values from the RRM, summarized over different time periods, and from linear CSM were rather high. They were, however, less than unity (0.74 to 0.96), which indicated some re-ranking of sires. Sire curves based on the time-specific breeding values from the RRM illustrated differences in intercept and slope among the best and the worst sires. To conclude, a linear sire RRM seemed to work well for genetic evaluation purposes, but was sensitive for estimation of genetic parameters.

Copyright

Corresponding author

References

Hide All
Averill, T, Rekaya, R, Weigel, K 2006. Random regression models for male and female fertility evaluation using longitudinal binary data. Journal of Dairy Science 89, 36813689.
Carlén, E, del Schneider, MP, Strandberg, E 2005. Comparison between linear models and survival analysis for genetic evaluation of clinical mastitis in dairy cattle. Journal of Dairy Science 88, 797803.
Carlén, E, Emanuelson, U, Strandberg, E 2006. Genetic evaluation of mastitis in dairy cattle using linear models, threshold models, and survival analysis: a simulation study. Journal of Dairy Science 89, 40494057.
Carlén, E, Strandberg, E, Roth, A 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, 30623070.
Chang, YM 2002. Multivariate and longitudinal models for binary data with applications to clinical mastitis in Norwegian cattle. PhD, University of Wisconsin.
Chang, YM, Gianola, D, Heringstad, B, Klemetsdal, G 2004a. Effects of trait definition on genetic parameter estimates and sire evaluation for clinical mastitis with threshold models. Animal Science 79, 355363.
Chang, YM, Gianola, D, Heringstad, B, Klemetsdal, G 2004b. Longitudinal analysis of clinical mastitis at different stages of lactation in Norwegian cattle. Livestock Production Science 88, 251261.
Heringstad, B, Chang, YM, Gianola, D, Klemetsdal, G 2003. Genetic analysis of longitudinal trajectory of clinical mastitis in first-lactation Norwegian cattle. Journal of Dairy Science 86, 26762683.
Heringstad, B, Chang, YM, Gianola, D, Klemetsdal, G 2004. Multivariate threshold model analysis of clinical mastitis in multiparous Norwegian dairy cattle. Journal of Dairy Science 87, 30383046.
Heringstad, B, Klemetsdal, G, Ruane, J 2000. Selection for mastitis resistance in dairy cattle: a review with focus on the situation in the Nordic countries. Livestock Production Science 64, 95106.
Hinrichs, D, Stamer, E, Junge, W, Kalm, E 2005. Genetic analyses of mastitis data using animal threshold models and genetic correlation with production traits. Journal of Dairy Science 88, 22602268.
Hogan, JS, Smith, KL, Hoblet, KH, Schoenberger, PS, Todhunter, DS, Hueston, WD, Pritchard, DE, Bowman, GL, Heider, LE, Brockett, BL, Conrad, HR 1989. Field survey of clinical mastitis in low somatic cell count herds. Journal of Dairy Science 72, 15471556.
Interbull 2008. Description of National Genetic Evaluation Systems for dairy cattle traits as applied in different Interbull member countries. Retrieved August 4, 2008, from http://www-interbull.slu.se/national_ges_info2/framesida-ges.htm
International Dairy Federation 1997. Recommendations for presentation of mastitis-related data. Bulletin of the IDF 321, 625.
Jensen, J 2001. Genetic evaluation of dairy cattle using test-day models. Journal of Dairy Science 84, 28032812.
Kadarmideen, HN, Thompson, R, Simm, G 2000. Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle. Animal Science 71, 411419.
Lund, MS, Jensen, J, Petersen, PH 1999. Estimation of genetic and phenotypic parameters for clinical mastitis, somatic cell production deviance, and protein yield in dairy cattle using Gibbs sampling. Journal of Dairy Science 82, 10451051.
Madsen, P, Jensen, J 2008. An user’s guide to DMU. A package for analysing multivariate mixed models. Version 6, release 4.7. University of Aarhus, Tjele, Denmark.
Negussie, E, Strandén, I, Mäntysaari, EA 2008. Genetic associations of clinical mastitis with test-day somatic cell count and milk yield during first lactation of Finnish Ayrshire. Journal of Dairy Science 91, 11891197.
Negussie, E, Strandén, I, Mäntysaari, EA, Tsuruta, S 2006. Genetic parameters for clinical mastitis in Finnish Ayrshire: a longitudinal threshold model analysis. Proc. 8th WCGALP, Belo Horizonte, Brazil. CD-ROM Commun. No. 24-11.
Rauw, WM, Kanis, E, Noordhuizen-Stassen, EN, Grommers, FJ 1998. Undesirable side effects of selection for high production efficiency in farm animals: a review. Livestock Production Science 56, 1533.
Rekaya, R, Gianola, D, Weigel, K, Shook, G 2003. Longitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle. Genetics, Selection, Evolution 35, 457468.
Saebø, S, Frigessi, A 2004. A genetic and spatial Bayesian analysis of mastitis resistance. Genetics, Selection, Evolution 36, 527542.
SAS 2002. SAS Release 9.1, 2002–2003. SAS Inst. Inc., Cary, NC, USA.
Schaeffer, LR 2004. Application of random regression models in animal breeding. Livestock Production Science 86, 3545.
Veerkamp, RF, Brotherstone, S, Engel, B, Meuwissen, THE 2001. Analysis of censored survival data using random regression models. Animal Science 72, 110.
Zwald, NR, Weigel, KA, Chang, YM, Welper, RD, Clay, JS 2006. Genetic analysis of clinical mastitis data from on-farm management software using threshold models. Journal of Dairy Science 89, 330336.

Keywords

Random regression models for genetic evaluation of clinical mastitis in dairy cattle

  • E. Carlén (a1), K. Grandinson (a1), U. Emanuelson (a2) and E. Strandberg (a1)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed