Ir al menú de navegación principal Ir al contenido principal Ir al pie de página del sitio

Estimation and comparison of conventional and genomic breeding values in Holstein cattle of Antioquia, Colombia

Estimation and comparison of conventional and genomic breeding values in Holstein cattle of Antioquia, Colombia



Abrir | Descargar

Cómo citar
Zambrano A, J., Rincón F J., López H A., & Echeverri Z, J. (2015). Estimation and comparison of conventional and genomic breeding values in Holstein cattle of Antioquia, Colombia. Revista MVZ Córdoba, 20(3), 4739-4753. https://doi.org/10.21897/rmvz.44

Dimensions
PlumX
Juan Zambrano A
Juan Rincón F
Albeiro López H
Julián Echeverri Z

ABSTRACT

Objetive. To estimate and compare breeding values (EBV) using the conventional method (BLUP) and genomic breeding values (MEBV and GEBV) estimated through bayes C method for milk yield and milk quality traits in dairy cattle in Antioquia, Colombia. Materials and methods. Two methods were used to estimate breeding values: BLUP to estimate conventional breeding value (EBV) and bayes C to estimate genomic values (MEBV and GEBV). The traits evaluated were: milk yield (PL), protein percentage (PPRO), fat percentage (PGRA) and score somatic cell (SCS). The methods (BLUP and bayes C) were compared using Person correlation (rp), Spearman rank correlation (rs) and linear regression coefficient (b). Results. The Pearson and Spearman correlations among EBVs and genomic values (MEBV and GEBV) (rpMEBV;EBV and rsGEBV;EBV) were greater than 0.93 and the linear regression coefficients of EBVs on genomic values (MEBV and GEBV) (bMEBV;EBV, and bGEBV;EBV) ranged between 0.954 and 1.051 in all traits evaluated. Conclusions. The predictions of genomic values (MEBV and GEBV), using bayes C method were consistent with the predictions of the EBVs estimate through the conventional method (BLUP) in conditions of high Colombian tropic, allowing to obtain high associations between the breeding values.


Visitas del artículo 888 | Visitas PDF


Descargas

Los datos de descarga todavía no están disponibles.
  1. Henderson CR. Applications of linear models in animal breeding. Guelph: CGIL Publications; 1984.
  2. Fisher R. The correlation between relatives on the supposition of mendelian inheritance. Transactions of the Royal Society of Edinburgh 1918; 52:399-433. http://dx.doi.org/10.1017/S0080456800012163
  3. Cole JB, VanRaden PM, O'Connell JRO, Van Tassell CP, Sonstegard TS, Schnabel RD et al. Distribution and location of genetic effect for dairy traits. J Dairy Sci 2009; 92(6):2931-2946. http://dx.doi.org/10.3168/jds.2008-1762
  4. Kahi AK, Rewe TO, Kosgey IS. Sustainable community-based organizations for the genetic improvement of livestock in developing countries. Outlook Agric 2005; 34(4):261-270.
  5. http://dx.doi.org/10.5367/000000005775454706
  6. Godard ME, Hayes BJ. Genomic Selection. J Anim Breed Genet 2007; 124(6):323-330. http://dx.doi.org/10.1111/j.1439-0388.2007.00702.x
  7. Meuwissen THE, Hayes B, Goddard M. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001; 157(4):1819–1829.
  8. Dekkers JC. Commercial application of marker and gene assisted selection in livestock: strategies and lessons. J Anim Sci 2004; 82(E-Suppl):E313-328.
  9. Hayes BJ, Bowman PJ, Chamberlain AC, Goddard ME. Genomic selection in dairy cattle: progress and challenges. J Dairy Sci 2009; 92(2):433–443. http://dx.doi.org/10.3168/jds.2008-1646
  10. Schefers J, Wigel KA. Genomic selection in dairy cattle: Integration of DNA testing into breeding programs. Anim Front 2012; 12(1):4-9. http://dx.doi.org/10.2527/af.2011-0032
  11. Calus MPL. Genomic breeding value prediction: methods and procedures. Animal 2010; 4(2):157-164. http://dx.doi.org/10.1017/S1751731109991352
  12. Moser G, Tier B, Crump RE, Khatkar MS, Raadsma HW. A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genet Sel Evol 2009; 41:56. http://dx.doi.org/10.1186/1297-9686-41-56
  13. Meuwissen T, Hayes B, Goddard M. Accelerating Improvement of livestock with Genomic Selection. Annu Rev Anim Biosci 2013; 1:221-237. http://dx.doi.org/10.1146/annurev-animal-031412-103705
  14. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS. Invited review: Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 2009; 92 (1):16-24. http://dx.doi.org/10.3168/jds.2008-1514
  15. Duchemin SI, Colombani C, Legarra A, Baloche G, Larroque H, Astruc JM et al. Genomic selection in the French Lacaune dairy sheep breed. J Dairy Sci 2012; 95(5):2723-2733.
  16. http://dx.doi.org/10.3168/jds.2011-4980
  17. Echeverri J, Zambrano JC, López-Herrera A. Genomic evaluation of Holstein Cattle in Antioquia (Colombia): a case study. Rev Colomb Cienc Pecu 2014; 27:306-314.
  18. Ali AK, Shook GE. An Optimun transformation for somatic cell concentration in milk. J Dairy Sci 1980; 63(3): 487-490. http://dx.doi.org/10.3168/jds.S0022-0302(80)82959-6
  19. Echeverri J, López A, Parra J. Software control 1. [CD-ROM]. Versión 1. Medellín: Universidad Nacional de Colombia sede Medellín; 2010.
  20. SAS. Statistical Analysis Systems [CD-ROM]. Versión 9.1 Cary, NC, USA: SAS Inst, Inc; 2006.
  21. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M, Bender D et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet 2007; 81(3):559-575. http://dx.doi.org/10.1086/519795
  22. Mrode RA, Thompson R. Linear models for the prediction of animal breeding values. Cambridge: CABI Publishing; 2005.
  23. http://dx.doi.org/10.1079/9780851990002.0000
  24. Boldman K, Kriese L, Van Vleck L, Van Tassell C, Kachman S. MTDFREML: A Set of programs to obtain estimates of variances and covariances. [Programa de computadora]. Clay Center (NE): USDA-ARS; 1995.
  25. Kizilkaya k, Fernando RL, Garrick DJ. Genomic Prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. J Anim Sci 2010; 88(2):544-551. http://dx.doi.org/10.2527/jas.2009-2064
  26. Verbyla KL, Bowman PJ, Hayes BJ, Raadsma H, Goddard ME. Sensitivity of genomic selection to using different prior distributions. BMC Proc 2010; 4(1):S5. http://dx.doi.org/10.1186/1753-6561-4-S1-S5
  27. Legarra A, Ricard A, Filangi O. GS3: Genomic selection, Gibbs sampling, Gauss Seidel and Bayes Cpi. [Programa de computadora] Toulouse: Inra; 2013.
  28. Mäntysaari E, Zengting L, Van Raden P. Interbull Valdation Test for Genomics Evaluations. Interbull Bolletin 2010; 41:17-22.
  29. Lillehammer L, Meuwissen THE, Sonesson AK. A comparison of dairy cattle breeding designs that use genomic selection. J Dairy Sci 2010; 94:493-500. http://dx.doi.org/10.3168/jds.2010-3518
  30. Calus MPL, Veerkamp RF. Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. J Anim Breed Genet 2007; 124:362–368. http://dx.doi.org/10.1111/j.1439-0388.2007.00691.x
  31. Muir WM. Comparison of genomic and traditional BLUP estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J Anim Breed Genet 2007; 124:342–355. http://dx.doi.org/10.1111/j.1439-0388.2007.00700.x
  32. Visscher PM, Yang J, Goddard MEA. A commentary on "common SNPs explain a large proportion of the heritability for human height" by Yang et al. (2010). Twin Res Hum Genet 2012; 13:517–524. http://dx.doi.org/10.1375/twin.13.6.517
  33. Legarra A, Robert-Granié C, Croiseau P, Guillaume F, Fritz S. Improved Lasso for genomic selection. Genet Res Camb 2011; 93(1):77-87. http://dx.doi.org/10.1017/S0016672310000534

Sistema OJS 3.4.0.3 - Metabiblioteca |