Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-28T02:24:58.292Z Has data issue: false hasContentIssue false
  • English
  • Français

Statistical tools to select for robustness and milk quality

Published online by Cambridge University Press:  30 July 2013

E. Strandberg ,
M. Felleki ,
W. F. Fikse ,
J. Franzén ,
H. A. Mulder ,
L. Rönnegård ,
J. I. Urioste  and
J. J. Windig
E. Strandberg*
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, SE-75007 Uppsala, Sweden
M. Felleki
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, SE-75007 Uppsala, Sweden School of Technology and Business Studies, Dalarna University, SE-79188 Falun, Sweden
W. F. Fikse
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, SE-75007 Uppsala, Sweden
J. Franzén
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, SE-75007 Uppsala, Sweden Department of Statistics, Stockholm University, 106 91 Stockholm, Sweden
H. A. Mulder
Affiliation:
Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH, Wageningen, The Netherlands
L. Rönnegård
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, SE-75007 Uppsala, Sweden School of Technology and Business Studies, Dalarna University, SE-79188 Falun, Sweden
J. I. Urioste
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, SE-75007 Uppsala, Sweden Dept Prod. Animal y Pasturas, Facultad de Agronomia, UDELAR, Garzón 780, 12900 Montevideo, Uruguay
J. J. Windig
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands
*
†E-mail: [email protected]
Get access

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.

Keywords

genotype by environment interactionenvironmental sensitivityreaction normsomatic cell countmastitis
Type
Full Paper
Information
Advances in Animal Biosciences , Volume 4 , Issue 3: Proceedings of the ROBUSTMILK project, an EU wide programme which was financially supported by the European Commission under the Seventh Research Framework Program, Grant Agreement KBBE–211708 , July 2013 , pp. 606 - 611
Copyright
Copyright © The Animal Consortium 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bishop, SC, Woolliams, JA 2010. On the genetic interpretation of disease data. PLoS One 5(1), e8940.Google Scholar
Felleki, M, Lee, D, Lee, Y, Gilmour, AR, Rönnegård, L 2012. Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models. Genetics Research 94, 307–317.Google Scholar
Fikse, WF, Rönnegård, L, Mulder, HA, Strandberg, E 2012. Genome-wide assocation study for genetic heterogeneity for milk yield and somatic cell score. In Proceedings of the Annual meeting of the European Federation of Animal Science (EAAP), p. 239. Wageningen Academic Publishers, Bratislava.Google Scholar
Franzén, J, Thorburn, D, Urioste, JI, Strandberg, E 2012. Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities. Genetics, Selection, Evolution 44, 10.Google Scholar
Hill, WG, Mulder, HA 2010. Genetic analysis of environmental variation. Genetics Research 92, 381–395.Google Scholar
Ibanez-Escriche, N, Varona, L, Sorensen, D, Noguera, JL 2008a. A study of heterogeneity of environmental variance for slaughter weight in pigs. Animal 2, 19–26.Google Scholar
Ibanez-Escriche, N, Moreno, A, Nieto, B, Piqueras, P, Salgado, C, Gutierrez, J 2008b. Genetic parameters related to environmental variability of weight traits in a selection experiment for weight gain in mice: signs of correlated canalised response. Genetics Selection Evolution 40, 279–293.Google Scholar
Lee, Y, Nelder, JA 2006. Double hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society: Series C (Applied Statistics) 55, 139–185.Google Scholar
Mulder, HA, Bijma, P 2005. Effects of genotype by environment interaction on genetic gain in breeding programs. Journal of Animal Science 83, 49–61.Google Scholar
Mulder, HA, Bijma, P 2006. Benefits of cooperation between breeding programs in the presence of genotype by environment interaction. Journal of Dairy Science 89, 1727–1739.Google Scholar
Mulder, HA, Bijma, P, Hill, WG 2008. Selection for uniformity in livestock by exploiting genetic heterogeneity of residual variance. Genetics, Selection, Evolution 40, 37–59.Google Scholar
Mulder, HA, Veerkamp, RF, Ducro, BJ, van Arendonk, JAM, Bijma, P 2006. Optimization of dairy cattle breeding programs for different environments with genotype by environment interaction. Journal of Dairy Science 89, 1740–1752.Google Scholar
Mulder, HA, Rönnegård, L, Fikse, F, Veerkamp, RF, Strandberg, E 2011. Estimation of genetic variation in macro- and micro-environmental sensitivity. In Proceedings of the European Association for Animal Production, Stavanger, Norway, p. 108.Google Scholar
Mulder, HA, Rönnegård, L, Fikse, F, Veerkamp, RF, Strandberg, E 2012. Estimation of genetic variance in macro- and micro-environmental sensitivity using double hierarchical generalized linear models. Genetics, Selection, Evolution (accepted).Google Scholar
Rönnegård, L, Valdar, W 2011. Detecting major genetic loci controlling phenotypic variability in experimental crosses. Genetics 188, 435–447.Google Scholar
Rönnegård, L, Felleki, M, Fikse, F, Mulder, H, Strandberg, E 2010. Genetic heterogeneity of residual variance – estimation of variance components using double hierarchical generalized linear models. Genetics, Selection, Evolution 42, 8.Google Scholar
Rönnegård, L, Felleki, M, Fikse, F, Mulder, HA, Strandberg, E 2012. Variance component and breeding value estimation for environmental sensitivity in Swedish Holstein dairy cattle. Journal of Dairy Science 96, 2627–2636.Google Scholar
Sorensen, D, Waagepetersen, R 2003. Normal linear models with genetically structured residual variance heterogeneity: a case study. Genetical Research 92, 207–222.Google Scholar
Urioste, JI, Franzén, J, Strandberg, E 2010. Phenotypic and genetic characterization of novel somatic cell count traits from weekly or monthly observations. Journal of Dairy Science 93, 2757–2764.Google Scholar
Urioste, JI, Franzén, J, Windig, JJ, Strandberg, E 2012. Genetic relationships among mastitis and alternative somatic cell count traits in the first three lactations of Swedish Holsteins. Journal of Dairy Science 95, 3428–3434.Google Scholar
Vandenplas, J, Bastin, C, Gengler, N, Mulder, HA 2012. Genetic variance in environmental sensitivity for milk and milk quality in Walloon Holstein cattle. In Proceedings of the Annual Meeting of the European Federation of Animal Science (EAAP), p. 15. Wageningen Academic Publishers, Bratislava.Google Scholar
Windig, JJ, Mulder, HA, Bohthe-Wilhelmus, DI, Veerkamp, RF 2011. Simultaneous estimation of genotype by environment interaction accounting for discrete and continuous environmental descriptors in Irish dairy cattle. Journal of Dairy Science 94, 3137–3147.Google Scholar