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Genomic Evaluation of Colombian Holstein Cattle Using Imputed Genotypes at Medium Density

Evaluación genómica en ganado Holstein Colombiano, usando genotipos imputados a densidad media



How to Cite
Zambrano, J. C., Echeverri Zuluaga, J. J., & López Herrera, A. (2019). Genomic Evaluation of Colombian Holstein Cattle Using Imputed Genotypes at Medium Density. Journal MVZ Cordoba, 24(2), 7248-7255. https://doi.org/10.21897/rmvz.1704

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Juan C. Zambrano
José Julián Echeverri Zuluaga
Albeiro López Herrera

Juan C. Zambrano,

Institución Universitaria Colegio Mayor de Antioquia, Facultad de Ciencias de la Salud, Medellín, Colombia.
Fundación Universitaria Navarra. Facultad de Ciencias de la Salud, Neiva, Colombia.


Objective. The goal of this study was to determine the accuracy and bias of direct genomic values (DGV) using imputed genotypes at medium density in yield- and reproduction-related traits for Holstein cattle from Antioquia, Colombia. Materials and Methods. A total of 31 animals were genotyped with the Illumina BovineLD chip, 64 with Illumina BovineSNP50v2 and 48 with Illumina BovineHD. Two SNP panels (6K and 40K) were imputed to a density of 44K using the FINDHAP.f90 v4 program. The effects of the SNPs were estimated using the Bayes C method, using low-density (6K) genotypes as well as medium-density imputed genotypes (44_imputed). The accuracy and bias of the DGVs were determined by cross-validation. The evaluated traits were: milk yield (MY), percentage of protein (PP), percentage of fat (PF), somatic cell score (SCS), calving interval (CI) and open days (OD). Results. When using the 6K panel, the accuracy values for DGV (rpDGV;EBV) in all the studied traits ranged from 0.19 to 0.24, and the bias (bDGV;EBV) from 0.03 to 0.16. In contrast, using the 44K_imputed panel generated higher accuracy values ranging from 0.24 to 0.33 and a bias ranging from 0.03 to 0.26. Conclusions. The accuracy of prediction the DGV was higher with genotypes imputed to medium densities when compared to the accuracy of prediction obtained using low-density genotypes. Therefore, in this study it is concluded that the imputation of genotypes is very useful, because it improves the reliability of the genomic evaluation.


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  1. Goddard ME, Hayes BJ. Genomic Selection. J Anim Breed Genet. 2007; 124(6):323-330. https://doi.org/10.1111/j.1439-0388.2007.00702.x
  2. Wang L, Zhu G, Johnson W, Kher M. Three new approaches to genomic selection. Plant Breeding. 2018;137(5):673–681. https://doi.org/10.1111/pbr.12640
  3. Meuwissen TH, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001; 157(4):1819–1829. https://www.ncbi.nlm.nih.gov/pubmed/11290733
  4. Boichard D, Chung H, Dassonneville R, David X, Eggen A, Fritz S. et al. Design of a bovine low-density SNP array optimized for imputation. PLoS ONE. 2012; 7(3):e34130. https://doi.org/10.1371/journal.pone.0034130
  5. Weng Z, Zhang Z, Ding X, Fu W, Ma P, Wang C, Zhang Q. Application of imputation methods to genomic selection in Chinese Holstein cattle. J Anim Sci Biotechnol. 2012, 3(1):6. https://doi.org/10.1186/2049-1891-3-6
  6. Khatkar MS, Moser G, Hayes BJ, Raadsma HW. Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle. BMC Genomics. 2012; 13(1):538. https://doi.org/10.1186/1471-2164-13-538
  7. Schefers J, Weigel KA. Genomic selection in dairy cattle: Integration of DNA testing into breeding programs. Anim Front. 2012; 12(1):4-9. https://doi.org/10.2527/af.2011-0032
  8. Huang YJ, Hickey JM, Cleveland MA, Maltecca C. Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost. Genet Sel Evol. 2012; 44(1):25. https://doi.org/10.1186/1297-9686-44-25
  9. Scheet P, Stephens M. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet. 2006; 78(4):629-644. https://doi.org/10.1086/502802
  10. Browning BL, Browning SR. A unified approach to genotype imputation and haplotype phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2009; 84(2):210-223. https://doi.org/10.1016/j.ajhg.2009.01.005
  11. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009; 5(6):e1000529. https://doi.org/10.1371/journal.pgen.1000529
  12. VanRaden PM, Null DJ, Sargolzaei M, Wiggans GR, Tooker ME, Cole JB, et al. Genomic imputation and evaluation using high-density Holstein genotypes. J Dairy Sci. 2013; 96(1):668–678. https://doi.org/10.3168/jds.2012-5702
  13. Sargolzaei M, Chesnais JP, Schenkel FS. A new approach for efficient genotype imputation using information from relatives. BMC Genomics. 2014; 15: 478. https://doi.org/10.1186/1471-2164-15-478
  14. Weigel KA, Van Tassell CP, O’Connell JR, VanRaden PM, Wiggans GR. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. J Dairy Sci. 2010; 93(5):2229-2238. https://doi.org/10.3168/jds.2009-2849
  15. Zhang Z, Druet T. Marker imputation with low-density marker panels in Dutch Holstein cattle. J Dairy Sci. 2010; 93(11):5487-5494. https://doi.org/10.3168/jds.2010-3501
  16. Wiggans GR, Cole JB, Hubbard SM, Sonstegard TS. Genomic Selection in Dairy Cattle: The USDA Experience. Ann Rev Anim Biosci. 2017; 5(1):309–327. https://doi.org/10.1146/annurev-animal-021815-111422
  17. Rincón JC, Zambrano JC, Echeverri JJ. Estimation of genetic and phenotypic parameters for production traits in Holstein and Jersey from Colombia. Rev MVZ Córdoba. 2015; 20(Supl):4962-4973. https://doi.org/10.21897/rmvz.11
  18. Echeverri J, Zambrano JC, López-Herrera A. Genomic evaluation of Holstein Cattle in Antioquia (Colombia): a case study. Rev Colomb Cienc Pecu. 2014; 27(4):306-314. http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-06902014000400009
  19. Zambrano JC, Rincón JC, López A, Echeverri JJ. Estimation and comparison of conventional and genomic breeding values in Holstein cattle of Antioquia, Colombia. Rev MVZ Córdoba. 2015; 20(3):4739-4753. https://doi.org/10.21897/rmvz.44
  20. Martínez R, Gómez Y, Rocha JFM. Genome-wide association study on growth traits in Colombian creole breeds and crossbreeds with Zebu cattle. Genet Mol Res. 2014; 13(3):6420-6432. https://doi.org/10.4238/2014.august.25.5
  21. Martínez R, Mar JF, Bejarano D, Burgos W. Genomic predictions and accuracy of weight traits in a breeding program for Colombian Zebu Brahman [On line]. Proceedings of the World Congress on Genetics Applied to Livestock Production. 2018. http://www.wcgalp.org/system/files/proceedings/2018/genomic-predictions-and-accuracy-weight-traits-breeding-program-colombian-zebu-brahman.pdf
  22. Ali AK, Shook GE. An Optimun transformation for somatic cell concentration in milk. J Dairy Sci. 1980; 63(3):487-490. https://doi.org/10.3168/jds.s0022-0302(80)82959-6
  23. 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. https://doi.org/10.2527/jas.2009-2064
  24. 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. https://doi.org/10.1186/1753-6561-4-s1-s5
  25. Meuwissen T, Hayes B, Goddard M. Accelerating Improvement of livestock with Genomic Selection. Annu Rev Anim Biosci. 2013; 1(1):221-237. https://doi.org/10.1146/annurev-animal-031412-103705
  26. Chen L, Li C, Zargolzaei M, Schenkel F. Impact of genotypes imputation on the performance of GBLUP and bayesian methods for genomic prediction. PLoS ONE. 2014; 9(7):e101544. https://doi.org/10.1371/journal.pone.0101544
  27. Vázquez AI, Rosa GJ, Weigel KA, de los Campos G, Gianola D, Allison DB. Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins. J Dairy Sci. 2010; 93(12):5942–5949. https://doi.org/10.3168/jds.2010-3335
  28. Habier D, Rohan LF, Kizilkaya K, Garrick DJ. Extension of the bayesian alphabet for genomic Selection. BMC Bioinformatics. 2011; 12(1):186. https://doi.org/10.1186/1471-2105-12-186
  29. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: Genomic selection in dairy cattle: Progress and challenges. J Dairy Sci 2009; 92(2):433–443. https://doi.org/10.3168/jds.2008-1646 https://doi.org/pdf/10.4081/ijas.2013.e91
  30. Nicolazzi EL, Negrini R, Chamberlain AJ, Goddard ME, Marsan PA, Hayes BJ. Effect of Prior Distributions on Accuracy of Genomic Breeding Values for Two Dairy Traits. Ital J Anim Sci 2013; 12(e91):555-561. https://www.tandfonline.com/doi/pdf/10.4081/ijas.2013.e91
  31. Colombani C, Legarra A, Fritz S, Guillaume F, Croiseau P, Ducrocq V, et al. Application of Bayesian least absolute shrinkage and selection operator (LASSO) and BayesCπ methods for genomic selection in French Holstein and Montbéliarde breeds. J Dairy Sci. 2013; 96(1):575–591. https://doi.org/10.3168/jds.2011-5225

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