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Genetic correlations between production and disease traits during first lactation in Holstein cows

Published online by Cambridge University Press:  14 November 2013

K. Hagiya ,
T. Yamazaki ,
Y. Nagamine ,
K. Togashi ,
S. Yamaguchi ,
Y. Gotoh ,
T. Kawahara ,
Y. Masuda  and
M. Suzuki
K. Hagiya*
Affiliation:
NARO Hokkaido Agricultural Research Center, Sapporo 062-8555 Japan
T. Yamazaki
Affiliation:
NARO Hokkaido Agricultural Research Center, Sapporo 062-8555 Japan
Y. Nagamine
Affiliation:
Nihon University, Fujisawa 252-8510, Japan
K. Togashi
Affiliation:
Livestock Improvement Association of Japan, Tokyo 135-0041, Japan
S. Yamaguchi
Affiliation:
Hokkaido Dairy Milk Recording and Testing Association, Sapporo 060-0004, Japan
Y. Gotoh
Affiliation:
Holstein Cattle Association of Japan, Hokkaido Branch, Sapporo 001-8555, Japan
T. Kawahara
Affiliation:
Holstein Cattle Association of Japan, Hokkaido Branch, Sapporo 001-8555, Japan
Y. Masuda
Affiliation:
Obihiro University of Agriculture and Veterinary Medicine, Obihiro 080-8555, Japan
M. Suzuki
Affiliation:
Obihiro University of Agriculture and Veterinary Medicine, Obihiro 080-8555, Japan
*
† E-mail: [email protected]
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Abstract

The aim of this study was to estimate genetic correlations between milk yield, somatic cell score (SCS), mastitis, and claw and leg disorders (CLDs) during first lactation in Holstein cows by using a threshold–linear random regression test-day model. We used daily records of milk, fat and protein yields; somatic cell count (SCC); and mastitis and CLD incidences from 46 771 first-lactation Holstein cows in Hokkaido, Japan, that calved between 2000 and 2009. A threshold animal model for binary records (mastitis and CLDs) and linear animal model for yield traits were applied in our multiple trait analysis. For both liabilities and yield traits, additive genetic effects were used as random regression on cubic Legendre polynomials of days on milk. The highest positive genetic correlations between yields and disease incidences (0.36 for milk and mastitis, 0.56 for fat and mastitis, 0.24 for protein and mastitis, 0.32 for milk and CLD, 0.44 for fat and CLD and 0.31 for protein and CLD) were estimated at about the time of peak milk yield (36 to 65 days in milk). Selection focused on early lactation yield may therefore increase the risk of mastitis and CLDs. The positive genetic correlations of SCS with mastitis or CLD incidence imply that selection to reduce SCS in the early stages of lactation would decrease the incidence of both mastitis and CLD.

Keywords

dairy cattlegenetic correlationdisease resistancerandom regressionthreshold
Type
Full Paper
Information
animal , Volume 8 , Issue 2 , February 2014 , pp. 217 - 223
Copyright
Copyright © The Animal Consortium 2013 

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References

Ali, AKA and Shook, GE 1980. An optimum transformation for somatic cell concentration in milk. Journal of Dairy Science 63, 487–490.CrossRefGoogle Scholar
Appuhamy, JADRN, Cassell, BG and Cole, JB 2009. Phenotypic and genetic relationships of common health disorders with milk and fat yield persistencies from producer-recorded health data and test-day yields. Journal of Dairy Science 92, 1785–1795.CrossRefGoogle ScholarPubMed
Booth, CJ, Warnick, LD, Gröhn, YT, Maizon, DO, Guard, CL and Janssen, D 2004. Effect of lameness on culling in dairy cows. Journal of Dairy Science 87, 4115–4122.CrossRefGoogle ScholarPubMed
Buch, LH, Sørensen, AC, Lassen, J, Berg, P, Eriksson, J-Å, Jakobsen, JH and Sørensen, MK 2011. Hygiene-related and feed-related hoof diseases show different patterns of genetic correlations to clinical mastitis and female fertility. Journal of Dairy Science 94, 1540–1551.CrossRefGoogle ScholarPubMed
Carlén, E, Strandberg, E and 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, 3062–3070.CrossRefGoogle ScholarPubMed
Enting, H, Kooij, D, Dijkhuizen, AA, Huirne, RBM and Noordhuizen-Stassen, EN 1997. Economic losses due to clinical lameness in dairy cattle. Livestock Production Science 49, 259–267.CrossRefGoogle Scholar
Hagiya, K, Togashi, K, Takeda, H, Yamasaki, T, Shirai, T, Saburi, J, Masuda, Y and Suzuki, M 2009. Genetic correlation between persistency and calving interval of Holsteins in Japan. EAAP publication no. 126, 129–135. Wageningen Academic Publishers, Wageningen, the Netherlands.Google Scholar
Harder, B, Bennewitz, J, Hinrichs, D and Kalm, E 2006. Genetic parameters for health traits and their relationship to different persistency traits in German Holstein dairy cattle. Journal of Dairy Science 89, 3202–3212.CrossRefGoogle ScholarPubMed
Hinrichs, D, Bennewitz, J, Stamer, E, Junge, W, Kalm, E and Thaller, G 2011. Genetic analysis of mastitis data with different models. Journal of Dairy Science 94, 471–478.CrossRefGoogle ScholarPubMed
Koenig, S, Sharifi, AR, Wentrot, H, Landmann, D, Eise, M and Simianer, H 2005. Genetic parameters of claw and foot disorders estimated with logistic models. Journal of Dairy Science 88, 3316–3325.CrossRefGoogle ScholarPubMed
Laursen, MV, Boelling, D and Mark, T 2009. Genetic parameters for claw and leg health, foot and leg conformation, and locomotion in Danish Holsteins. Journal of Dairy Science 92, 1770–1777.CrossRefGoogle ScholarPubMed
Lund, MS, Jensen, J and Petersen, H 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, 1045–1051.CrossRefGoogle ScholarPubMed
Miglior, F, Muir, BL and Van Doormaal, BJ 2005. Selection indices in Holstein cattle of various countries. Journal of Dairy Science 88, 1255–1263.CrossRefGoogle ScholarPubMed
Misztal, I, Tsuruta, S, Strabel, T, Auvray, B, Druet, T and Lee, DH 2002. BLUPF90 and Related Programs (BGF90). Proceedings of the 7th World Congress on Genetic Applied to Livestock Production, CD-ROM Communication no. 28, 07, Montpellier, France.Google Scholar
Muir, BL, Fatehi, J and Schaeffer, LR 2004. Genetic relationships between persistency and reproductive performance in first-lactation Canadian Holsteins. Journal of Dairy Science 87, 3029–3037.CrossRefGoogle ScholarPubMed
Negussie, E, Strandén, I and Mäntysaari, EA 2008. Genetic analysis of liability to clinical mastitis, with somatic cell score and production traits using bivariate threshold–linear and linear–linear models. Livestock Science 117, 52–59.CrossRefGoogle Scholar
Onyiro, OM, Andrews, LJ and Brotherstone, S 2008. Genetic parameters for digital dermatitis and correlations with locomotion, production, fertility traits and longevity in Holstein–Friesian dairy cows. Journal of Dairy Science 91, 4037–4046.CrossRefGoogle ScholarPubMed
Pérez-Cabal, MA, de los Campos, G, Vazquez, AI, Gianola, D, Rosa, GJM, Weigel, KA and Alenda, R 2009. Genetic evaluation of susceptibility to clinical mastitis in Spanish Holstein cows. Journal of Dairy Science 92, 3472–3480.CrossRefGoogle ScholarPubMed
Pritchard, T, Coffey, M, Mrode, R and Wall, E 2013. Genetic parameters for production, health, fertility and longevity traits in dairy cows. Animal 7, 34–46.CrossRefGoogle ScholarPubMed
Sewalem, A, Kistemaker, GJ and Van Doormaal, BJ 2005. Relationship between type traits and longevity in Canadian Jerseys and Ayrshires using a Weibull proportional hazards model. Journal of Dairy Science 88, 1552–1560.CrossRefGoogle ScholarPubMed
Shaeffer, LR, Jamrozik, J, Kistemaker, GJ and Van Doormaal, BJ 2000. Experience with a Test-Day Model. Journal of Dairy Science 83, 1135–1144.CrossRefGoogle Scholar
Shim, EH, Shanks, RD and Morin, DE 2004. Milk loss and treatment costs associated with two treatment protocols for clinical mastitis in dairy cows. Journal of Dairy Science 87, 2702–2708.CrossRefGoogle ScholarPubMed
Togashi, K and Lin, CY 2004. Development of an optimal index to improve lactation yield and persistency with the least selection intensity. Journal of Dairy Science 87, 3047–3052.CrossRefGoogle ScholarPubMed
Tsuruta, S, Misztal, I, Huang, C and Lawlor, TJ 2009. Bivariate analysis of conception rates and test-day milk yields in Holsteins using threshold–linear model with random regressions. Journal of Dairy Science 92, 2922–2930.CrossRefGoogle ScholarPubMed
van der Waaij, EH, Holzhauer, M, Ellen, E, Kamphuis, C and de Jong, G 2005. Genetic parameters for claw disorders in Dutch dairy cattle and correlations with conformation traits. Journal of Dairy Science 88, 3672–3678.CrossRefGoogle ScholarPubMed
Van Dorp, TE, Dekkers, JCM, Martin, SW and Noordhuizen, JPTM 1998. Genetic parameters of health disorders, and relationships with 305-day milk yield and conformation traits of registered Holstein cows. Journal of Dairy Science 81, 2264–2270.CrossRefGoogle ScholarPubMed
Wilmink, JBM 1987. Adjustment of test-day milk, fat and protein yield for age, season and stage of lactation. Livestock Production Science 16, 335–348.CrossRefGoogle Scholar
Yamazaki, T, Hagiya, K, Takeda, H, Sasaki, O, Yamaguchi, S, Sogabe, M, Saito, Y, Nakagawa, S, Togashi, K, Suzuki, K and Nagamine, Y 2013. Genetic correlations between lactation persistency and somatic cell scores on test day within and across first and second lactations in Holstein cows. Livestock Science 152, 120–126.CrossRefGoogle Scholar
Zwald, NR, Weigel, KA, Chang, YM, Welper, RD and Clay, JS 2006. Genetic analysis of clinical mastitis data from on-farm management software using threshold models. Journal of Dairy Science 89, 330–336.CrossRefGoogle ScholarPubMed