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Genomic selection in dairy cattle simulated populations

Published online by Cambridge University Press:  22 May 2018

Leonardo de Oliveira Seno ,
Diego Gomes Freire Guidolin ,
Rusbel Raul Aspilcueta-Borquis ,
Guilherme Batista do Nascimento ,
Thiago Bruno Ribeiro da Silva ,
Henrique Nunes de Oliveira  and
Danísio Prado Munari
Leonardo de Oliveira Seno*
Affiliation:
Grande Dourados Federal University (UFGD) – Dourados, MS, Brazil
Diego Gomes Freire Guidolin
Affiliation:
Universidade Anhanguera (Uniderp) – Campo Grande, MS, Brazil
Rusbel Raul Aspilcueta-Borquis
Affiliation:
Grande Dourados Federal University (UFGD) – Dourados, MS, Brazil
Guilherme Batista do Nascimento
Affiliation:
Universidade Estadual Paulista (UNESP) – Jaboticabal, SP, Brazil
Thiago Bruno Ribeiro da Silva
Affiliation:
Mato Grosso Federal University (UFMT) – Rondonópolis, MT, Brazil
Henrique Nunes de Oliveira
Affiliation:
Universidade Estadual Paulista (UNESP) – Jaboticabal, SP, Brazil
Danísio Prado Munari
Affiliation:
Universidade Estadual Paulista (UNESP) – Jaboticabal, SP, Brazil
*
*For correspondence; e-mail: leonardoseno@ufgd.edu.br
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Abstract

Genomic selection is arguably the most promising tool for improving genetic gain in domestic animals to emerge in the last few decades, but is an expensive process. The aim of this study was to evaluate the economic impact related to the implementation of genomic selection in a simulated dairy cattle population. The software QMSim was used to simulate genomic and phenotypic data. The simulated genome contained 30 chromosomes with 100 cm each, 1666 SNPs markers equally spread and 266 QTLs randomly designated for each chromosome. The numbers of markers and QTLs were designated according to information available from Animal QTL (http://www.animalgenome.org/QTLdb) and Bovine QTL (http://bovineqtl.tamu.edu/). The allelic frequency changes were assigned in a gamma distribution with alpha parameters equal to 0·4. Recurrent mutation rates of 1·0e−4 were assumed to apply to markers and QTLs. A historic population of 1000 individuals was generated and the total number of animals was reduced gradually along 850 generations until we obtained a number of 200 animals in the last generation, characterizing a bottleneck effect. Progenies were created along generations from random mating of the male and female gametes, assuming the same proportion of both genders. Than the population was extended for another 150 generations until we obtained 17 000 animals, with only 320 male individuals in the last generation. After this period a 25 year of selection was simulated taking into account a trait limited by sex with heritability of 0·30 (i.e. milk yield), one progeny/cow/year and variance equal to 1·0. Annually, 320 bulls were mated with 16 000 dams, assuming a replacement rate of 60 and 40% for males and females, respectively. Selection and discard criteria were based in four strategies to obtain the EBVs assuming as breeding objective to maximize milk yield. The progeny replaced the discarded animals creating an overlapping generation structure. The selection strategies were: RS is selection based on random values; PS is selection based on phenotypic values; Blup is selection based on EBVs estimated by BLUP; and GEBV is selection based on genomic estimated breeding values in one step, using high (GBlup) and low (GBlupi) density panels. Results indicated that the genetic evaluation using the aid of genomic information could provide better genetic gain rates in dairy cattle breeding programs as well as reduce the average inbreeding coefficient in the population. The economic viability indicators showed that only Blup and GBlup/GBlupi strategies, the ones that used milk control and genetic evaluation were economic viable, considering a discount rate of 6·32% per year.

Keywords

Cost-benefit analysisgenetic parameterssingle nucleotide polymorphisms
Type
Research Article
Information
Journal of Dairy Research , Volume 85 , Issue 2 , May 2018 , pp. 125 - 132
Copyright
Copyright © Hannah Dairy Research Foundation 2018 

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References

Aguilar, I, Misztal, I, Johnson, DL, Legarra, A, Tsuruta, S, Lawlor, TJ 2010 A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal Dairy Science 93 743–752Google Scholar
Andreazza, J, Rorato, PRN, El Faro, L, Boligon, AA, Weber, T, Kippert, CJ, Lopes, JS 2008 Parâmetros genéticos e eficiência relativa de seleção para a produção de leite no dia do controle para vacas da raça Holandesa. Ciência Rural 38(2) 451–456Google Scholar
ANUALPEC 2013 Anuário da Pecuária Brasileira. São Paulo: FNP Consultoria e ComércioGoogle Scholar
APCBRH/UFPR 2013 Associação Paranaense de criadores de bovinos da raça Holandesa Programa de análise de rebanhos leiteiros do Paraná. Disponível em: http://www.holandesparana.com.br/controle/parlpr.html. Acesso em: 11 nov 2013Google Scholar
BACEN 2013 Banco Central do Brasil. Captação em depósitos de poupança – dados mensais. Disponível em: http://www.bcb.gov.br/. Acesso em: 11 nov 2013Google Scholar
Barbosa Silveira, ID, Peters, MDP, Storch, T, Ziguer, EA, Fisher, V 2011 Simulação da rentabilidade e viabilidade econômica de um modelo de produção de leite em free-stall. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, Belo Horizonte 63(2) 392–398Google Scholar
Boligon, AA, Long, N, Albuquerque, LG, Weigel, KA, Gianola, D, Rosa, GJM 2012 Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection. Journal of Animal Science 90 4716–4722Google Scholar
Cardoso, VL, Cassoli, LD, Guilhermino, MM, Machado, PF, Nogueira, JR, Freitas, MAR 2005 Análise econômica de esquemas alternativos de controle leiteiro. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, Belo Horizonte 57(1) 85–92Google Scholar
Christensen, O, Lund, M 2010 Genomic prediction when some animals are not genotyped. Genetics Selection Evolution 42 2Google Scholar
Chud, TCS, Ventura, RV, Schenkel, FS, Carvalheiro, R, Buzanskas, ME, Rosa, JO, Mudadu, MA, da Silva, MVGB, Mokry, FB, Marcondes, CR, Regitano, LCA, Munari, DP 2015 Strategies for genotype imputation in composite beef cattle. BMC Genetics 16 99. DOI: 10.1186/s12863-015-0251-7Google Scholar
Daetwyler, HD, Villanueva, B, Bijma, P, Woolliams, JA 2007 Inbreeding in genome-wide selection. Journal of Animal Breeding and Genetics 124 369–376Google Scholar
De Roos, APW, Schrooten, C, Mullaart, E, Calus, MPL, Veerkamp, RF 2007 Breeding value estimation for fat percentage using dense markers on Bos Taurus autosome 14. Journal Dairy Science 90 4821–4829Google Scholar
DEOXI 2013 Deoxi Biotecnologia Ltda. Biotecnologia Aplicada ao Agronegócio. Disponível em: www.deoxi.com.br. Acesso em: 11 novGoogle Scholar
Garrick, DJ 2011 The nature, scope and impact of genomic prediction in beef cattle in the United States. Genetics, Selection, Evolution 43(1) 17. 10.1186/1297-9686-43-17Google Scholar
Gengler, N, Mayeres, P, Szydalowski, M 2007 A simple method to approximate gene content in large pedigree populations: application to the myostatin gene in dual-purpose Belgian blue cattle. Animal 1 21–28Google Scholar
GESTOR LEITE 2013 Gestor Leite – CRV Lagoa Ltda. Programa de melhoramento genético em leite da CRV Lagoa. Disponível em: www.crvlagoa.com.br/gestorleite.asp. Acesso em: 11 nov 2013Google Scholar
Harris, BL, Johnson, DL, Spelman, RJ 2008 Genomic selection in New Zealand and the implications for national genetic evaluation. Proceedings of the Interbull Meeting, Niagara Falls, NYGoogle Scholar
Hayes, B, Goddard, ME 2001 The distribution of the effects of genes affecting quantitative traits in livestock. Genetics Selection Evolution 33(3) 209–229Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ, Goddard, ME 2009 Invited review: genomic selection in dairy cattle: progress and challenges. Journal Dairy Science 92 433–444Google Scholar
Huirne, RBM, Dijkhuizen, AA 1997 Basic methods of economic analysis. In Animal Health Economics: Principles and Applications, pp. 25–39 (Eds Dijkhuizen, AA, Morris, RS). Sydney: University of Sydney, cap. 03Google Scholar
IEA 2013 Instituto de Economia Agrícola. Banco de dados: preços médios mensais pagos pela agricultura. Disponível em: http://www.iea.sp.br. Acesso em: 11 nov 2013Google Scholar
Jiménez-Montero, JA, González-Recio, O, Alenda, R 2012 Genotyping strategies for genomic selection in small dairy cattle populations. Animal 6(8) 1216–1224Google Scholar
Legarra, A, Aguilar, I, Misztal, I 2009 A relationship matrix including full pedigree and genomic information. Journal Dairy Science 92, 4656–4663Google Scholar
Misztal, I, Legarra, A, Aguilar, I 2009 Computing procedures for genetic evaluation including phenotypic, full pedigree and genomic information. Journal Dairy Science 92 4648–4655Google Scholar
NRC 1989 Nutrient Requeriments of Dairy Cattle, 157p, 6th rev edition. Washinton, DC: National Research CouncilGoogle Scholar
Pszczola, M, Mulder, HA, Calus, MP 2011 Effect of enlarging the reference population with (un)genotyped animals on the accuracy of genomic selection in dairy cattle. Journal Dairy Science 94 431–441Google Scholar
Quinton, M, Smith, C, Goddard, ME 1992 Comparison of selection methods at the same level of inbreeding. Journal of Animal Science 70(4) 1060–1067. doi: 10.2527/1992.7041060xGoogle Scholar
Resende, MDV, Lopes, PS, Silva, RL, Pires, IE 2008 Seleção genômica ampla (GWS) e maximização da eficiência do melhoramento genético. Pesquisa florestal brasileira 56 63–77Google Scholar
Sargolzaei, M, Schenkel, FS 2009 QMSim: a large-scale genome simulator for livestock. Bioinformatics 25 680–681Google Scholar
Sargolzaei, M, Schenkel, FS, Jansen, GB, Schaeffer, LR 2008 Extent of linkage disequilibrium in Holstein cattle in North America. Journal Dairy Science 91 2106–2117. doi: 10.3168/jds.2007-0553Google Scholar
Schaeffer, LR 2006 Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123 218–223. doi: 10.1111/j.1439-0388.2006.00595.xGoogle Scholar
Segelke, D, Chen, J, Liu, Z, Reinhardt, F, Thaller, G, Reents, R 2012 Reliability of genomic prediction for German Holsteins using imputed genotypes from low-density chips. Journal Dairy Science 95(9) 5403–5411. doi: 10.3168/jds.2012-5466Google Scholar
Seno, LO, Fernández, J, Cardoso, VL, García-Cortes, LA, Toro, M, Santos, DO, Albuquerque, LG, de Camargo, GMF, Tonhati, H 2012 Selection strategies for dairy buffaloes: economic and genetic consequences. Journal of Animal Science 129 1–13, doi: 10.1111/j.1439-0388.2012.00992.xGoogle ScholarPubMed
Valadares Filho, SC 2000 Nutrição, avaliação de alimentos e tabelas de composição de alimentos. In REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, pp. 267–340 37, 2000, Viçosa, MG. Anais… Viçosa, MG: Sociedade Brasileira de ZootecniaGoogle Scholar
VanRaden, PM, VanTassell, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF, Schenkel, FS 2009 Invited review: reliability of genomic predictions for North American Holstein bulls. Journal Dairy Science 92 16–24Google Scholar