Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-25T15:04:31.620Z Has data issue: false hasContentIssue false
  • English
  • Français

Variance heterogeneity and genotype by environment interactions in native Black and White dual-purpose cattle for different herd allocation schemes

Published online by Cambridge University Press:  11 March 2019

M. Jaeger ,
K. Brügemann ,
S. Naderi ,
H. Brandt  and
S. König
M. Jaeger
Affiliation:
Institute of Animal Breeding and Genetics, University of Gießen, 35390 Gießen, Germany
K. Brügemann
Affiliation:
Institute of Animal Breeding and Genetics, University of Gießen, 35390 Gießen, Germany
S. Naderi
Affiliation:
Institute of Animal Breeding and Genetics, University of Gießen, 35390 Gießen, Germany
H. Brandt
Affiliation:
Institute of Animal Breeding and Genetics, University of Gießen, 35390 Gießen, Germany
S. König*
Affiliation:
Institute of Animal Breeding and Genetics, University of Gießen, 35390 Gießen, Germany
*
†E-mail: sven.koenig@agrar.uni-giessen.de
Get access

Abstract

Black and White dual-purpose cattle (DSN) are kept in diverse production systems, but the same set of genetic parameters is used for official national genetic evaluations, neglecting the herd or production system characteristics. The aim of the present study was to infer genetic (co)variance components within and across defined herd descriptor groups or clusters, considering only herds keeping the local and endangered DSN breed. The study considered 3659 DSN and 2324 Holstein Friesian (HF) cows from parities one to three. The 46 herds always kept DSN cows, but in most cases, herds were ‘mixed’ herds (Mixed), including both genetic lines HF and DSN. In order to study environmental sensitivity, we had a focus on the naturally occurring negative energy balance in the early lactation period. In consequence, traits were records from the 1st official test-day after calving for milk yield (Milk-kg), somatic cell score (SCS) and fat-to-protein ratio (FPR). Genetic parameters were estimated in bivariate runs (separate runs for the three genetic lines Mixed, HF and DSN), defining the same trait from different herd groups or clusters as different traits. Additive-genetic variances and heritabilities were larger in herd groups that indicated superior herd management, implying that cow records from these herds allow a better genetic differentiation. Superior herd management included larger herds, low calving age, high herd production levels and low intra-herd somatic cell count. Herd descriptor group differences in additive-genetic variances for Milk-kg were stronger in HF than in DSN, indicating environmental sensitivity for DSN. Similar variance components and heritabilities across groups, clusters and genetic lines were found for data stratification according to geographical descriptors altitude and latitude. Considering 72 bivariate herd group runs, 29 genetic correlations were very close to 1 (mostly for Milk-kg). Somatic cell score was the trait showing the smallest genetic correlations, especially in the DSN analyses, and when stratifying herds according to genetic line compositions (rg=0.11), or according to the percentage of natural service sires (rg=0.08). For estimations based on the results of a cluster analysis considering several herd descriptors simultaneously, indications for genotype × environment interactions could be found for SCS, but genetic correlations were larger than 0.80 for Milk-kg and FPR. In conclusion, we suggest multiple-trait animal model applications in genetic evaluations, in order to select the best sires for specific herd environments or herd clusters.

Keywords

herd descriptorsherd clusteringlocal breedmultiple-trait model
Type
Research Article
Information
animal , Volume 13 , Issue 10 , October 2019 , pp. 2146 - 2155
Copyright
© The Animal Consortium 2019 

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

Byrant, JR, López-Villalobos, N, Pryce, JE, Holmes, CW, Johnson, DL and Carrick, DJ 2006. Environmental sensitivity in New Zealand dairy cattle. Journal of Dairy Science 90, 1538–1547.CrossRefGoogle Scholar
Calus, MPL, Janss, LLG and Veerkamp, RF 2006. Genotype by environment Interaction for somatic cell score across bulk milk somatic cell count and days in milk. Journal of Dairy Science 89, 4846–4857.CrossRefGoogle ScholarPubMed
Clément V, Bibé B, Verrier É, Elsen JM, Manfredi E, Bouix J and Hanocq É 2001. Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects. Genetics Selection Evolution 33, 369–395.CrossRefGoogle Scholar
Dillon, P, Buckley, F, O’Connor, P, Hegarty, D and Rath, M 2003. A comparison of different dairy cow breeds on a seasonal grass-based system of milk production. Livestock Production Science 83, 21–33.CrossRefGoogle Scholar
Falconer, DS and Mackay, TFC 1996. Introduction to quantitative genetics, 4th edition. Pearson Education Limited, Harlow, England.Google Scholar
Fatehi, J, Stella, A, Shannon, JJ and Boettcher, PJ 2003. Genetic parameters for feet and leg traits evaluated in different environments. Journal of Dairy Science 86, 661–666.CrossRefGoogle ScholarPubMed
Fikse, WF, Rekaya, R and Weigel, KA 2003. Assessment of environmental descriptors for studying genotype by environment interaction. Livestock Production Science 82, 223–231.CrossRefGoogle Scholar
Freyer, G, König, S, Fischer, B, Bergfeld, U and Cassell, BG 2008. Invited review: Crossbreeding in dairy cattle from a German perspective of the past and today. Journal of Dairy Science 91, 3725–3743.CrossRefGoogle ScholarPubMed
Gernand, E, Waßmuth, R, von Borstel, UU and König, S 2007. Heterogeneity of variance components for production traits in large-scale dairy farms. Livestock Science 112, 78–89.CrossRefGoogle Scholar
Groeneveld, E, Kovac, M and Mielenz, M 2010. VCE user’s guide and reference manual version 6.0. Institute of Farm Animal Genetics Friedrich Loeffler Institute (FLI), Neustadt, Germany.Google Scholar
Hammami, H 2009. Genotype by environment interaction in dairy cattle. Biotechnology, Agronomy, Society and Environment 13, 155–164.Google Scholar
Jaeger, M, Scheper, C, König, S and Brügemann, K 2018. Inbreeding and genetic relationships of the endangered dual-purpose Black and White cattle breed (DNS) based on own genetic breed percentage calculations. Züchtungskunde 4, 262–279.Google Scholar
Kolmodin, R, Strandberg, E, Madsen, P, Jensen, J and Jorjani, H 2002. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agriculturae Scandinavica 52, 11–24.CrossRefGoogle Scholar
Kolver, ES, Rocher, JR, De Veth, MJ, Thorne, PL and Napper, AR 2002. Total mixed rations versus pasture diets: evidence for a genotype x diet interaction in dairy cow performance. Proceedings of the New Zealand Society of Animal Production 62, 246–251.Google Scholar
König, S 2017. Genetic background of resistance, tolerance and resilience traits in dairy cattle. In Proceedings of the 68th Annual EAAP Meeting, 28 August to 1 September 2017, Tallinn, Estonia, 461 pp.Google Scholar
König, S, Lessner, S and Simianer, H 2007. Application of controlling instruments for improvements in cow sire selection. Journal of Dairy Science 90, 1967–1980.CrossRefGoogle ScholarPubMed
König, S and Simianer, H. 2006. Approaches to the management of inbreeding and relationship in the German Holstein dairy cattle population. Livestock Science 103, 40–53.CrossRefGoogle Scholar
König, S, Simianer, H and Swalve, HH 2002. Genetic relationships between dairy performance under large-scale farm and family farm conditions estimated from different groups of common sires. In Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, 19–23 August 2002, Montpellier, France. Book of abstracts, ISBN 9782738010520, INRA, Castanet-Tolosan, France.Google Scholar
Lynch, M and Walsh, B. 1998. Genetics and analysis of quantitative traits. Sinauer Associates, Sunderland, MA, USA.Google Scholar
Madsen, P and Jensen, J 2012. A user’s guide to DMU. A package for analysing multivariate mixed models. Version 6, release 5.1. University of Aarhus, Tjele, Denmark.Google Scholar
May, K, Brügemann, K, Yin, T, Scheper, C, Strube, C and König, S 2017. Genetic line comparisons and genetic parameters for endoparasite infections and test-day milk production traits. Journal of Dairy Science 100, 7330–7344.CrossRefGoogle ScholarPubMed
Naderi, S 2018. Exploring the potential of machine learning methods and selection signature analyses for the estimation of genomic breeding values, the estimation of SNP effects and the identification of possible candidate genes in dairy cattle. PhD thesis, Faculty of Agricultural Sciences, University of Gießen, Gießen, Germany.Google Scholar
Nauta, WJ, Veerkamp, RF, Brascamp, EW and Bovenhuis, H 2006. Genotype by environment interaction for milk production traits between organic and conventional dairy cattle production in the Netherlands. Journal of Dairy Science 89, 2729–2737.CrossRefGoogle ScholarPubMed
Ojango, JMK and Pollott, GE 2002. The relationship between Holstein bull breeding values for milk yield derived in both the UK and Kenya. Livestock Production Science 74, 1–12.CrossRefGoogle Scholar
Osorio-Avalos, J, Menéndez-Buxadera, A, Serradilla, JM and Molina, A 2015. Use of descriptors to define clusters of herds under similar environmental conditions to improve the level of connection among contemporary groups of mutton type merino sheep under an extensive production system. Livestock Science 176, 54–60.CrossRefGoogle Scholar
Robertson, A 1959. The sampling variance of the genetic correlation coefficient. Biometrics 15, 469–485.CrossRefGoogle Scholar
Sargolzaei, M, Iwaisaki, H and Colleau, JJ 2006. CFC - a software package for pedigree analysis and monitoring genetic diversity. User’s manual. Release 1.0. Retrieved 5 January 2018 from http://animalbiosciences.uoguelph.ca/~msargol/.Google Scholar
SAS Institute Inc 2008. The cluster procedure. SAS/STAT® 9.2 user’s guide. SAS Institute Inc., Cary, NC, USA.Google Scholar
Schaeffer, LR 1994. Multiple-country comparison of dairy sires. Journal of Dairy Science 77, 2671–2678.CrossRefGoogle ScholarPubMed
Schaeffer, LR 2004. Application of random regression models in animal breeding. Livestock Production Science 86, 35–45.CrossRefGoogle Scholar
Streit, M, Reinhardt, F, Thaller, G and Bennewitz, J 2013. Genome-wide association analysis to identify genotype x environment interaction for milk protein yield and level of somatic cell score as environmental descriptors in German Holsteins. Journal of Dairy Science 96, 7318–7324.CrossRefGoogle ScholarPubMed
Weigel, KA and Rekaya, R 2000. A multiple-trait herd cluster model for international dairy sire evaluation. Journal of Dairy Science 83, 815–821.CrossRefGoogle ScholarPubMed
Yin, T and König, S 2018. Heritabilities and genetic correlations in the same traits across different strata of herds created according to continuous genomic, genetic and phenotypic descriptors. Journal of Dairy Science 101, 2171–2186.CrossRefGoogle ScholarPubMed
Zwald, NR, Weigel, KA, Fikse, WF and Rekaya, R 2003. Identification of factors that cause genotype by environment interaction between herds of Holstein cattle in seventeen countries. Journal of Dairy Science 86, 1009–1018.CrossRefGoogle ScholarPubMed
Supplementary material: File

Jaeger et al. supplementary material

Tables S1-S5 and Figure S1

Download Jaeger et al. supplementary material(File)
File 371.2 KB