Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
권호 표지
DOI QR코드

DOI QR Code

Single-step genomic evaluation for growth traits in a Mexican Braunvieh cattle population

  • Received : 2022.04.19
  • Accepted : 2023.02.27
  • Published : 2023.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. Results: The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. Conclusion: The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

Keywords

Acknowledgement

The authors acknowledge the Asociacion Mexicana de Criadores de Ganado Suizo de Registro (AMCGSR, Mexico City) and the collaborating breeders for permitting the use of their databases for this study. Special thanks to the Consejo Nacional de Ciencia y Tecnologia, Mexico, for providing financial support to the first author for his Doctorate studies.

References

  1. Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics 1975;31:423-47. https://doi.org/10.2307/2529430
  2. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org/10.3168/jds.2007-0980
  3. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001;157:1819-29. https://doi.org/10.1093/genetics/157.4.1819
  4. Gianola D. Priors in whole-genome regression: the bayesian alphabet. Genetics 2013;194:573-96. https://doi.org/10.1534/genetics.113.151753
  5. Legarra A. Christensen OF, Aguilar I, Misztal I. Single Step, a general approach for genomic selection. Livest Sci 2014;166:54-65. https://doi.org/10.1016/j.livsci.2014.04.029
  6. Christensen O, Lund M. Genomic prediction when some animals are not genotyped. Genet Sel Evol 2010;42:2. https://doi.org/10.1186/1297-9686-42-2
  7. Aguilar I, Misztal I, Legarra A, Tsuruta S. Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. J Anim Breed Genet 2011; 128:422-8. https://doi.org/10.1111/j.1439-0388.2010.00912.x
  8. Liu Z, Goddard ME, Reinhardt F, Reents R. A single-step genomic model with direct estimation of marker effects. J Dairy Sci 2014;97:5833-50. https://doi.org/10.3168/jds.2014-7924
  9. Fernando RL, Dekkers JCM, Garrick DJ. A class of Bayesian methods to combine large numbers of genotyped and nongenotyped animals for whole-genome analyses. Genet Sel Evol 2014; 46:50. https://doi.org/10.1186/1297-9686-46-50
  10. Fernando RL, Cheng H, Golden BL, Garrick DJ. Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals. Genet Sel Evol 2016;48:96. https://doi.org/10.1186/s12711-016-0273-2
  11. Ashraf B, Edriss V, Akdemir D, et al. Genomic prediction using phenotypes from pedigreed lines with no marker data. Crop Sci 2016;56:957-64. https://doi.org/10.2135/cropsci2015.02.0111
  12. Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G. Single-step methods for genomic evaluation in pigs. Animal 2012; 6:1565-71. https://doi.org/10.1017/s1751731112000742
  13. Perez-Rodriguez P, Crossa J, Rutkoski J, et al. Single-step genomic and pedigree Genotype × Environment interaction models for predicting wheat lines in international environments. Plant Genome 2017;10:plantgenome2016.09.0089. https://doi.org/10.3835/plantgenome2016.09.0089
  14. Yoshida GM, Carvalheiro R, Rodriguez FH, Lhorente JP. Genomics single-step genomic evaluation improves accuracy of breeding value predictions for resistance to infectious pancreatic necrosis virus in rainbow trout. Genomics 2019; 111:127-32. https://doi.org/10.1016/j.ygeno.2018.01.008
  15. Cornelissen MAMC, Mullaart E, Van der Linde C, Mulder HA. Estimating variance components and breeding values for number of oocytes and number of embryos in dairy cattle using a single-step genomic evaluation. J Dairy Sci 2017;100:4698-705. https://doi.org/10.3168/jds.2016-12075
  16. Tiezzi F, Parker-Gaddis KL, Cole JB, Clay JS, Maltecca C. A genome-wide association study for clinical mastitis in first parity US Holstein cows using single-step approach and genomic matrix re-weighting procedure. PLoS ONE 2015;10:e0114919. https://doi.org/10.1371/journal.pone.0114919
  17. Koivula M, Stranden I, Poso J, Aamand GP, Mantysaari EA. Single-step genomic evaluation using multitrait random regression model and test-day data. J Dairy Sci 2015;98:2775-84. https://doi.org/10.3168/jds.2014-8975
  18. Croue I, Ducrocq V. Genomic and single-step evaluations of carcass traits of young bulls in dual-purpose cattle. J Anim Breed Genet 2017;134:300-7. https://doi.org/10.1111/jbg.12261
  19. Abdalla EEA, Schenkel FS, Begli HE, et al. Single-step methodology for genomic evaluation in turkeys (Meleagris gallopavo). Front Genet 2019;10:1248. https://doi.org/10.3389/fgene.2019.01248
  20. Nilforooshan MA. Application of single-step GBLUP in New Zealand Romney sheep. Anim Prod Sci 2020;60:1136-44. https://doi.org/10.1071/AN19315
  21. Nilforooshan MA, Lee M. The quality of the algorithm for proven and young with various sets of core animals in a multibreed sheep population. J Anim Sci 2019;97:1090-100. https://doi.org/10.1093/jas/skz010
  22. Carrilier C, Larroque H, Robert-Granie C. Comparison of joint versus purebred genomic evaluation in the French multi-breed dairy goat population. Gen Sel Evol 2014;46:67. https://doi.org/10.1186/s12711-014-0067-3
  23. Nunez-Dominguez R, Ramirez-Valverde R, Ruiz-Flores A, Dominguez-Viveros J. Genetic evaluation of Braunvieh sires. Chapingo, Mexico: Zootechnics Department, Chapingo Autonomous University; 2014.
  24. Jarquin D, Crossa J, Lacaxe X, et al. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet 2014;127:595-607. https://doi.org/10.1007/s00122-013-2243-1
  25. Jarquin D, Howard R, Graef G, Lorenz A. Response surface analysis of genomic prediction accuracy values using quality control covariates in soybean. Evol Bioinform Online. 2019; 15:1176934319831307. https://doi.org/10.1177/1176934319831307
  26. Vazquez AI, Bates DM, Rosa GJM, Gianola D, Weigel KA. Technical Note: An R package for fitting generalized linear mixed models in animal breeding. J Anim Sci 2013;88:497-504. https://doi.org/10.2527/jas.2009-1952
  27. Lopez-Cruz M, Crossa J, Bonnett D, et al. Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model. G3 (Bethesda) 2015;5:569-82. https://doi.org/10.1534/g3.114.016097
  28. Perez P, de los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics 2014; 198:483-95. https://doi.org/10.1534/genetics.114.164442
  29. Montes VD, Barragan HW, Vergara GO. Genetic parameters for productive and reproductive characteristics for beef cattle in Colombia. Revista Colombiana de Ciencia Animal-RECIA 2009;1:302-18. https://doi.org/10.24188/recia.v1.n2.2009.374
  30. Utrera AR, Velazquez GM, Murillo VEV, Bermudez MM. Genetic effects for growth traits of Mexican Charolais and Charbray cattle estimated with alternative models. Rev Mex Cienc Pec 2012;3:275-90.
  31. Ma D, Yu Q, Hedrick VE, et al. Proteomic and metabolomic profiling reveals the involvement of apoptosis in meat quality characteristics of ovine M. longissimus from different callipyge genotypes. Meat Sci 2020;166:108140. https://doi.org/10.1016/j.meatsci.2020.108140
  32. Park MN, Alam M, Kim S, Park B, Lee SH, Lee SS. Genomic selection through single-step genomic best linear unbiased prediction improves the accuracy of evaluation in Hanwoo cattle. Asian-Australas J Anim Sci 2020;33:1544-57. https://doi.org/10.5713/ajas.18.0936