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Assessing the impact of natural service bulls and genotype by environment interactions on genetic gain and inbreeding in organic dairy cattle genomic breeding programs

Published online by Cambridge University Press:  04 April 2014

T. Yin ,
M. Wensch-Dorendorf ,
H. Simianer ,
H. H. Swalve  and
S. König
T. Yin*
Affiliation:
Department of Animal Breeding, University of Kassel, 37213 Witzenhausen, Germany Department of Animal Sciences, Animal Breeding and Genetics Group, Georg-August-University, 37075 Göttingen, Germany
M. Wensch-Dorendorf
Affiliation:
Institute of Agricultural and Nutritional Sciences, University of Halle, 06099 Halle, Germany
H. Simianer
Affiliation:
Department of Animal Sciences, Animal Breeding and Genetics Group, Georg-August-University, 37075 Göttingen, Germany
H. H. Swalve
Affiliation:
Institute of Agricultural and Nutritional Sciences, University of Halle, 06099 Halle, Germany
S. König
Affiliation:
Department of Animal Breeding, University of Kassel, 37213 Witzenhausen, Germany
*
†E-mail: tong.yin@uni-kassel.de
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Abstract

The objective of the present study was to compare genetic gain and inbreeding coefficients of dairy cattle in organic breeding program designs by applying stochastic simulations. Evaluated breeding strategies were: (i) selecting bulls from conventional breeding programs, and taking into account genotype by environment (G×E) interactions, (ii) selecting genotyped bulls within the organic environment for artificial insemination (AI) programs and (iii) selecting genotyped natural service bulls within organic herds. The simulated conventional population comprised 148 800 cows from 2976 herds with an average herd size of 50 cows per herd, and 1200 cows were assigned to 60 organic herds. In a young bull program, selection criteria of young bulls in both production systems (conventional and organic) were either ‘conventional’ estimated breeding values (EBV) or genomic estimated breeding values (GEBV) for two traits with low (h2=0.05) and moderate heritability (h2=0.30). GEBV were calculated for different accuracies (rmg), and G×E interactions were considered by modifying originally simulated true breeding values in the range from rg=0.5 to 1.0. For both traits (h2=0.05 and 0.30) and rmg⩾0.8, genomic selection of bulls directly in the organic population and using selected bulls via AI revealed higher genetic gain than selecting young bulls in the larger conventional population based on EBV; also without the existence of G×E interactions. Only for pronounced G×E interactions (rg=0.5), and for highly accurate GEBV for natural service bulls (rmg>0.9), results suggests the use of genotyped organic natural service bulls instead of implementing an AI program. Inbreeding coefficients of selected bulls and their offspring were generally lower when basing selection decisions for young bulls on GEBV compared with selection strategies based on pedigree indices.

Keywords

genomic breeding programorganic farminggenotype × environment interaction
Type
Full Paper
Information
animal , Volume 8 , Issue 6 , June 2014 , pp. 877 - 886
Copyright
© The Animal Consortium 2014 

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References

Albuquerque, LG, Keown, JF and Van Vleck, LD 1998. Variances of direct genetic effects, maternal genetic effects, and cytoplasmic inheritance effects for milk yield, fat yield, and fat percentage. Journal of Dairy Science 81, 544–549.Google Scholar
Baars, T 2002. Naturalness and breeding in organic farming. In Genetic engineering and the intrinsic value and integrity of animals and plants (ed. D Heaf and J Wirz), pp. 58–61. Ifgene – International Forum for Genetic Engineering, Edinburgh, UK.Google Scholar
Bates, D and Vazquez, AI 2009. Package ‘pedigreemm’. Retrieved December 7, 2011, from http://cran.r-project.org/web/packages/pedigreemm/pedigreemm.pdf Google Scholar
Buch, LH, Kargo, M, Berg, P, Lassen, J and Sørensen, AC 2011. The value of cows in reference populations for genomic selection of new functional traits. Animal 6, 880–886.Google Scholar
Calus, MPL, Groen, AF and de Jong, G 2002. Genotype×environment interaction for protein yield in Dutch dairy cattle as quantified by different models. Journal of Dairy Science 85, 3115–3123.Google Scholar
Daetwyler, HD, Villanueva, B, Bijma, P and Woolliams, JA 2007. Inbreeding in genome-wide selection. Journal of Animal Breeding and Genetics 124, 369–376.Google Scholar
de Haas, Y, Smolders, EAA, Hoorneman, JN, Nauta, WJ and Veerkamp, RF 2013. Suitability of cross-bred cows for organic farms based on cross-breeding effects on production and functional traits. Animal 7, 655–665.Google Scholar
Egger-Danner, C, Fuerst-Waltl, B, Obritzhauser, W, Fuerst, C, Schwarzenbacher, H, Grassauer, B, Mayerhofer, M and Koeck, A 2012. Recording of direct health traits in Austria – experience report with emphasis on aspects of availability for breeding purposes. Journal of Dairy Science 95, 2765–2777.Google Scholar
Emanuelson, U, Danell, B and Philipsson, J 1988. Genetic parameters for clinical mastitis, somatic cell counts, and milk production estimated by multiple-restricted maximum likelihood. Journal of Dairy Science 71, 467–476.Google Scholar
Goddard, M 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257.Google Scholar
Heringstad, B, Klemetsdal, G and Ruane, J 2000. Selection for mastitis resistance in dairy cattle: a review with focus on the situation in Nordic countries. Livestock Production Science 64, 95–106.Google Scholar
Hunt, MS, Burnside, EB, Freeman, MG and Wilton, JW 1974. Genetic gain when sire sampling and proving programs vary in different artificial insemination population sizes. Journal of Dairy Science 57, 251–257.Google Scholar
Kearney, JF, Schutz, MM and Boettcher, PJ 2004. Genotype×environment interaction for grazing vs. confinement II. Health and reproduction traits. Journal of Dairy Science 87, 501–509.Google Scholar
König, S 2001. Untersuchungen zu einem kooperativen Zuchtprogramm der Rasse Holstein Friesian. PhD thesis, University of Göttingen, Göttingen, Germany.Google Scholar
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.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.Google Scholar
König, S, Simianer, H and Willam, A 2009. Economic evaluation of genomic breeding programs. Journal of Dairy Science 92, 382–391.Google Scholar
König, S, Dietl, G, Raeder, I and Swalve, HH 2005. Genetic relationships for dairy performance between large-scale and small-scale farm conditions. Journal of Dairy Science 88, 4087–4096.Google Scholar
König, S, Yin, T, Wensch-Dorendorf, M and Swalve, HH 2011. Selection of young sires in genomic breeding programs. Interbull Bulletin 44, 134–137.Google Scholar
Lillehammer, M, Meuwissen, THE and Sonesson, AK 2011. A comparison of dairy cattle breeding designs that use genomic selection. Journal of Dairy Science 94, 493–500.Google Scholar
Nauta, WJ, Groen, AF, Veerkamp, RF, Roep, D and Baars, T 2005. Animal breeding in organic dairy farming: an inventory of farmers’ views and difficulties to overcome. Wageningen Journal of Life Sciences 53, 19–34.Google Scholar
Ojango, JMK and Pollot, 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.Google Scholar
Oltenacu, PA and Young, CW 1974. Genetic optimization of a young bull sampling program in dairy cattle. Journal of Dairy Science 57, 894–897.Google Scholar
Pimentel, ECG and König, S 2012. Genomic selection for meat quality in beef. Jounal of Animal Science 90, 3418–3426.Google Scholar
Powell, RL, Sanders, AH and Normann, HD 2003. Progeny testing and selection intensity for Holstein bulls in different countries. Journal of Dairy Science 86, 2614–2620.Google Scholar
Pryce, JE, Goddard, ME, Raadsma, HW and Hayes, BJ 2010. Deterministic models of breeding scheme designs that incorporate genomic selection. Journal of Dairy Science 93, 5455–5466.Google Scholar
Rolf, MM, McKay, SD, McClure, MC, Decker, JE, Taxis, TM, Chappl, RH, Vasco, DA, Gregg, SJ, Kim, JW, Schnabel, RD and Taylor, JF 2010. How the next generation of genetic technologies will impact beef cattle selection. In Proceedings Beef Improvement Federation Research Symposium & Annual Meeting, 28 June to 1 July, Columbia, USA, pp. 46–56.Google Scholar
Rozzi, P, Miglior, F and Hand, KJ 2007. A total merit selection index for Ontario organic dairy farmers. Journal of Dairy Science 90, 1584–1593.Google Scholar
Rutgers, LJ, Grommers, FJ and Colenbrander, B 1996. Ethical aspects of invasive reproduction techniques in farm animals. Reproduction in Domestic Animals 31, 651–655.Google Scholar
Sanders, K, Bennewitz, J and Kalm, E 2006. Wrong and missing sire information affects genetic gain in the Angeln Dairy cattle population. Journal of Dairy Science 89, 315–321.Google Scholar
Sargolzaei, M and Schenkel, FS 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics 25, 680–681.Google Scholar
Schierenbeck, S, Pimentel, ECG, Körte, J, Reents, R, Reinhardt, F, Simianer, H and König, S 2011. Controlling inbreeding and maximizing genetic gain using semi-definite programming with pedigree based and genomic relationships. Journal of Dairy Science 94, 6143–6152.Google Scholar
Strandén, I, Mäki-Tanila, A and Mäntysaari, EA 1991. Genetic progress and rate of inbreeding in a closed adult MOET nucleus under different mating strategies and heritabilities. Journal of Animal Breeding and Genetics 108, 401–411.Google Scholar
Terawaki, Y, Suzuki, M and Fukui, Y 1998. Genetic response and inbreeding in a sub-population mating system for dairy cattle. In Proceedings 6th World Congress on Genetics Applied to Livestock Production, 11 to 16 January, Armidale, Australia, pp. 471–474.Google Scholar
Van Tassel, CP and Van Vleck, LD 1991. Estimates of genetic selection differentials and generation intervals for four paths of selection. Journal of Dairy Science 74, 1078–1086.Google Scholar
Veerkamp, RF, Berry, DP, Wall, E, de Haas, Y, McParland, S, Coffey, M and Calus, MPL 2011. Use of phenotypes from research herds to develop genomic selection for scarcely recorded traits like feed efficiency. Interbull Bulletin 44, 249–254.Google Scholar
Verbyla, KL, Calus, MP, Mulder, HA, de Haas, Y and Veerkamp, RF 2010. Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information. Journal of Dairy Science 93, 2757–2764.Google Scholar
Wensch-Dorendorf, M, Yin, T, Swalve, HH and König, S 2011. Optimal strategies for the use of genomic selection in dairy cattle breeding programs. Journal of Dairy Science 94, 4140–4151.Google Scholar
Yin, T, Pimentel, ECG, von Borstel, UU and König, S 2014. Strategy for the simulation and analysis of longitudinal phenotypic and genomic data in the context of a temperature×humidity-dependent covariate. Journal of Dairy Science 97, 2444–2454.Google Scholar
Yin, T, Bapst, B, von Borstel, U, Simianer, H and König, S 2012. Genetic parameters for Gaussian and categorical in organic and low input dairy herds based on random regression methodology. Livestock Science 147, 159–169.Google Scholar