Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-18T22:38:49.628Z Has data issue: false hasContentIssue false
  • English
  • Français

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values

Published online by Cambridge University Press:  02 July 2015

J. Přibyl ,
J. Bauer ,
V. Čermák ,
P. Pešek ,
J. Přibylová ,
J. Šplíchal ,
H. Vostrá-Vydrová ,
L. Vostrý  and
L. Zavadilová
J. Přibyl
Affiliation:
Institute of Animal Science, Přátelství 815, Prague-Uhříněves 104 00, Czech Republic
J. Bauer
Affiliation:
Institute of Animal Science, Přátelství 815, Prague-Uhříněves 104 00, Czech Republic
V. Čermák
Affiliation:
Czech Moravian Breeding Corporation, Hradištko 123, 252 09, Czech Republic
P. Pešek
Affiliation:
Institute of Animal Science, Přátelství 815, Prague-Uhříněves 104 00, Czech Republic
J. Přibylová
Affiliation:
Institute of Animal Science, Přátelství 815, Prague-Uhříněves 104 00, Czech Republic
J. Šplíchal
Affiliation:
Czech Moravian Breeding Corporation, Hradištko 123, 252 09, Czech Republic
H. Vostrá-Vydrová
Affiliation:
Institute of Animal Science, Přátelství 815, Prague-Uhříněves 104 00, Czech Republic
L. Vostrý
Affiliation:
Institute of Animal Science, Přátelství 815, Prague-Uhříněves 104 00, Czech Republic
L. Zavadilová*
Affiliation:
Institute of Animal Science, Přátelství 815, Prague-Uhříněves 104 00, Czech Republic
*
†E-mail: zavadilova.ludmila@vuzv.cz
Get access

Abstract

Estimated breeding values (EBVs) and genomic enhanced breeding values (GEBVs) for milk production of young genotyped Holstein bulls were predicted using a conventional BLUP – Animal Model, a method fitting regression coefficients for loci (RRBLUP), a method utilizing the realized genomic relationship matrix (GBLUP), by a single-step procedure (ssGBLUP) and by a one-step blending procedure. Information sources for prediction were the nation-wide database of domestic Czech production records in the first lactation combined with deregressed proofs (DRP) from Interbull files (August 2013) and domestic test-day (TD) records for the first three lactations. Data from 2627 genotyped bulls were used, of which 2189 were already proven under domestic conditions. Analyses were run that used Interbull values for genotyped bulls only or that used Interbull values for all available sires. Resultant predictions were compared with GEBV of 96 young foreign bulls evaluated abroad and whose proofs were from Interbull method GMACE (August 2013) on the Czech scale. Correlations of predictions with GMACE values of foreign bulls ranged from 0.33 to 0.75. Combining domestic data with Interbull EBVs improved prediction of both EBV and GEBV. Predictions by Animal Model (traditional EBV) using only domestic first lactation records and GMACE values were correlated by only 0.33. Combining the nation-wide domestic database with all available DRP for genotyped and un-genotyped sires from Interbull resulted in an EBV correlation of 0.60, compared with 0.47 when only Interbull data were used. In all cases, GEBVs had higher correlations than traditional EBVs, and the highest correlations were for predictions from the ssGBLUP procedure using combined data (0.75), or with all available DRP from Interbull records only (one-step blending approach, 0.69). The ssGBLUP predictions using the first three domestic lactation records in the TD model were correlated with GMACE predictions by 0.69, 0.64 and 0.61 for milk yield, protein yield and fat yield, respectively.

Keywords

genomic breeding valuesingle-step predictiontest-day modelanimal modeldairy cattle
Type
Research Article
Information
animal , Volume 9 , Issue 10 , October 2015 , pp. 1635 - 1642
Copyright
© The Animal Consortium 2015 

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

Aguilar, I, Misztal, I, Johnson, DL, Legarra, A, Tsuruta, S and Lawlor, TJ 2010. Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93, 743–752.CrossRefGoogle Scholar
Christensen, OF and Lund, MS 2010. Genomic prediction when some animals are not genotyped. Genetics Selection Evolution 42, 2.CrossRefGoogle Scholar
Christensen, OF, Madsen, P, Nielsen, B, Ostersen, T and Su, G 2012. Single-step methods for genomic evaluation in pigs. Animal 6, 1565–1571.CrossRefGoogle ScholarPubMed
Colombani, C, Legarra, A, Fritz, S, Guillaume, F, Croiseau, P, Ducrocq, V and Robert-Granie, C 2013. Application of Bayesian least absolute shrinkage and selection operator (LASSO) and BayesCπ methods for genomic selection in French Holstein and Montbéliarde breeds. Journal of Dairy Science 96, 575–591.CrossRefGoogle ScholarPubMed
Forni, S, Aguilar, I and Misztal, I 2011. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution 43, 1.CrossRefGoogle ScholarPubMed
Gao, H, Christensen, OF, Madsen, P, Nielsen, US, Zhang, Y, Lund, MS and Su, G 2012. Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genetics Selection Evolution 44, 8.CrossRefGoogle ScholarPubMed
Jiménes-Montero, JA, Gonzáles-Recio, O and Alenda, R 2012. Genotyping strategies for genomic selection in small dairy cattle populations. Animal 6, 1216–1224.CrossRefGoogle Scholar
Kirkpatrick, M, Lofsvold, D and Bulmer, M 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124, 979–993.CrossRefGoogle ScholarPubMed
Legarra, A and Ducrocq, V 2012. Computation strategies for national integration of phenotypic, genomic, and pedigree data in a single-step best linear unbiased prediction. Journal of Dairy Science 95, 4629–4654.CrossRefGoogle Scholar
Liu, ZT, Seefried, FR, Reinhardt, F, Rensing, S, Thaller, G and Reents, R 2011. Impact of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction. Genetics Selection Evolution 43, 19.CrossRefGoogle ScholarPubMed
Lourenco, DAL, Misztal, I, Tsuruta, S, Aguilar, I, Ezra, E, Ron, M, Shirak, A and Weller, JL 2014. Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. Journal of Dairy Science 97, 1742–1752.CrossRefGoogle ScholarPubMed
Madsen, P and Jensen, J 2010. A user guide to DMU, version 6, release 5.0. Manual, Faculty of Agricultural Science, University of Aarhus. Retrieved September 15, 2012, from http://dmu.agrsci.dk Google Scholar
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.CrossRefGoogle ScholarPubMed
Misztal, I, Legarra, A and Aguilar, I 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. Journal of Dairy Science 92, 4648–4655.CrossRefGoogle ScholarPubMed
Misztal, I, Tsuruta, S, Strabel, T, Auvray, B, Druet, T and Lee, DH 2002. BLUPF90 and related programs (BGF90). In the 7th World Congress on Genetics Applied to Livestock Production (WCGALP), 19–23 August 2002, Montpellier, France, 28pp.Google Scholar
Patry, C and Ducrocq, V 2011. Accounting for genomic pre-selection in national genetic dairy cattle evaluations. Genetics Selection Evolution 43, 30.CrossRefGoogle Scholar
Patry, C, Hossein, J and Ducrocq, V 2013. Effects of a national genomic preselection on the international genetic evaluations. Journal of Dairy Science 96, 3272–3284.CrossRefGoogle ScholarPubMed
Pešek, P, Přibyl, J and Vostrý, L. Genetic variances of SNP loci for milk yield in dairy cattle. Journal of Applied Genetics. doi: 10.1007/s13353-014-0257-2. Published online by Springer 16 November 2014.CrossRefGoogle Scholar
Plemdat 2014. Descriptions of breeding values evaluation. Retrieved July 15, 2014, from www.plemdat.cz Google Scholar
Přibyl, J, Bauer, J, Pešek, P, Přibylová, J, Vostrý, L and Zavadilová, L 2014. Domestic and Interbull information in the single step genomic evaluation of Holstein milk production. Czech Journal of Animal Science 59, 409–415.CrossRefGoogle Scholar
Přibyl, J, Madsen, P, Bauer, J, Přibylová, J, Šimečková, M, Vostrý, L and Zavadilová, L 2013a. Contribution of domestic production records, Interbull estimated breeding values, and single nucleotide polymorphism genetic markers to the single-step genomic evaluation of milk production. Journal of Dairy Science 96, 1865–1873.CrossRefGoogle Scholar
Přibyl, J, Bauer, J, Přibylová, J, Vostrý, L, Zavadilová, L, Čermák, V, Růžička, Z, Šplíchal, J, Verner, M, Motyčka, J and Vondrášek, L 2013b. Global Interbull EBV in domestic single step genomic evaluation. Interbull Bulletin 47, 132–137.Google Scholar
Přibyl, J, Haman, J, Kott, T, Přibylová, J, Šimečková, M, Vostrý, L, Zavadilová, L, Čermák, V, Růžička, Z, Šplíchal, J, Verner, M, Motyčka, J and Vondrášek, L 2012. Single-step prediction of genomic breeding value in a small dairy cattle population with strong import of foreign genes. Czech Journal of Animal Science 57, 151–159.CrossRefGoogle Scholar
Rektorys, K (ed.) 1963. Přehled užité matematiky. Státní nakladatelství technické literatury, Praha, Czech Republic.Google Scholar
Rozzi, P, Schaeffer, LR, Burnside, EB and Schlote, W 1990. International evaluation of Holstein–Friesian dairy sires from three countries. Livestock Production Science 24, 15–28.CrossRefGoogle Scholar
Schaeffer, LR 1994. Multiple-country comparison of dairy sire. Journal of Dairy Science 77, 2671–2678.CrossRefGoogle Scholar
Schaeffer, LR, Jamrozik, J, Kistemaker, GJ and Van Doormaal, BJ 2000. Experience with a test-day model. Journal of Dairy Science 83, 1135–1144.CrossRefGoogle ScholarPubMed
Su, G and Madsen, P 2011. User’s guide for Gmatrix. A program for computing genomic relationship matrix. Retrieved September 15, 2012, from http://dmu.agrsci.dk Google Scholar
Su, G, Madsen, P, Nielsen, US, Mäntysaari, EA, Aamand, GP, Christensen, OF and Lund, MS 2012. Genomic prediction for Nordic Red Cattle using one-step and selection index blending. Journal of Dairy Science 95, 909–917.CrossRefGoogle ScholarPubMed
Sullivan, PG and Wilton, JW 2001. Multiple-trait MACE with a variable number of traits per country. Interbull Bulletin 27, 68–72.Google Scholar
Sullivan, PG and Jakobsen, JH 2012. Robust GMACE for young bulls – methodology. Interbull Bulletin 45, 3–7.Google Scholar
Szyda, J, Żarnecki, A, Suchocki, T and Kaminski, S 2011. Fitting and validating the genomic evaluation model to Polish Holstein–Friesian cattle. Journal of Applied Genetics 52, 363–366.CrossRefGoogle ScholarPubMed
Thomasen, JR, Guldbrandsen, B, Su, G, Brøndum, RF and Lund, MS 2012. Reliabilities of genomic estimated breeding values in Danish Jersey. Animal 6, 789–796.CrossRefGoogle ScholarPubMed
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
Vitezica, ZG, Aguilar, I, Misztal, I and Legarra, A 2011. Bias in genomic predictions for populations under selection. Genetics Research 93, 357–366.CrossRefGoogle ScholarPubMed
Zavadilová, L, Jamrozik, J and Schaeffer, LR 2005a. Genetic parameters for test-day model with random regressions for production traits of Czech Holstein cattle. Czech Journal of Animal Science 50, 142–154.CrossRefGoogle Scholar
Zavadilová, L, Němcová, E, Přibyl, J and Wolf, J 2005b. Definition of subgroups for fixed regression in the test-day animal model for milk production of Holstein cattle in the Czech Republic. Czech Journal of Animal Science 50, 7–13.CrossRefGoogle Scholar