Contenido principal del artículo

Las evaluaciones genéticas convencionales han estado enmarcadas en la estimación de valores genéticos a partir de los sistemas de ecuaciones de modelos mixtos que consideran efectos aleatorios y fijos simultáneamente. En los últimos años, el desarrollo en tecnologías de secuenciación del genoma ha permitido obtener información genómica que puede ser incluida en las evaluaciones genéticas para incrementar las confiabilidades, el progreso genético y disminuir el intervalo generacional. El mejor predictor lineal insesgado en una etapa es una metodología que incluye información genómica reemplazando la matriz de parentesco por una matriz que combina el parentesco por pedigrí y genómico de una población genotipada, permitiendo la estimación de valores genéticos para animales no genotipados. El objetivo de este artículo de revisión fue la descripción de la metodología, sus recientes avances, y conocer algunas de las estrategias que podrían ser llevadas a cabo cuando el número de animales genotipados es bajo.

Fenotipos, ganadería, genómica, marcadores genéticos, mejoramiento genético

Alejandro Amaya Martínez, Universidad de Ciencias Aplicadas y Ambientales U.D.C.A.

Docente e investigador

Rodrigo Martínez Sarmiento, Corporación Colombiana de Investigación Agropecuaria AGROSAVIA.

Investigador

Mario Cerón Muñoz, Universidad de Antioquia

Docente e investigador

Amaya Martínez, A., Martínez Sarmiento, R., & Cerón Muñoz, M. (2019). Evaluaciones genéticas usando el mejor predictor lineal insesgado genómico en una etapa en bovinos. Ciencia & Tecnología Agropecuaria, 21(1), 1-13. https://doi.org/10.21930/rcta.vol21_num1_art:1548

1. Aguilar, I., Misztal, I., Johnson D., legarra, A., Tsuruta, S., & Lawlor, T. (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(2), 743-752. https://doi.org/10.3168/jds.2009-2730.

2. Andonov, S., Lourenco, D. A. L., Fragomeni, B. O., Masuda, Y., Pocrnic, I., Tsuruta, S., & Misztal, I. (2016). Accuracy of breeding values in small genotyped populations using different sources of external information-a simulation study. Journal of Dairy Science, 100(1), 395-401. https://doi.org/10.3168/jds.2016-11335.

3. Chen, J., Wang, Y., Zhang, Y., Sun, D., Zhang, S., & Zhang, Y. (2011). Evaluation of breeding programs combining genomic information in Chinese Holstein. Agricultural Sciences in China, 10(12), 1949-1957. https://doi.org/10.1016/S1671-2927(11)60196-X.

4. Christensen, O., Madsen, P., Nielsen, B., Ostersen, T., & Su, G. (2012). Single-step methods for genomic evaluation in pigs. Animal, 6(10), 1565-1571. https://doi.org/10.1017/S1751731112000742.

5. de Roos, A. P. W., Schrooten, C., Veerkamp, R. F., & Van Arendonk, J. A. M. (2011). Effects of genomic selection on genetic improvement, inbreeding, and merit of young versus proven bulls. Journal of Dairy Science, 94(3), 1559-1567. https://doi.org/10.3168/jds.2010-3354.

6. Elzo, M. A, Thomas, M. G, Johnson, D. D., Martinez, C. A., Lamb, G. C., Rae, D. O., & Driver, J. D. (2015). Genetic parameters and predictions for direct and maternal growth traits in a multibreed angus-brahman cattle population using genomic-polygenic and polygenic models. Livestock Science, 178, 43-51. https://doi.org/10.1016/j.livsci.2015.06.015.

7. Fragomeni, B. O., Lourenco, D. A. L., Tsuruta, S., Masuda, Y., Aguilar, I., Legarra, A., & Misztal, I. (2015). Hot topic: use of genomic recursions in single-step genomic best linear unbiased predictor (blup) with a large number of genotypes. Journal of Dairy Science, 98(6), 4090-4094. https://doi.org/10.3168/jds.2014-9125.

8. Garrick, D., Dekkers, J., & Fernando, R. (2014). The evolution of methodologies for genomic prediction. Livestock Science, 166(1), 10-18. https://doi.org/10.1016/j.livsci.2014.05.031.

9. Garrick, D., Taylor, J. F., & Fernando, R. L. (2009). Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics Selection Evolution, 31, 41-55. https://doi.org/10.1186/1297-9686-41-55.

10. Goddard, M. (2009). Genomic selection: prediction of accuracy and maximisation of long-term response. Genetica, 136(2), 245-257. https://doi.org/10.1007/s10709-008-9308-0.

11. Goddard, M. E., & Hayes, B. J. (2007). Genomic selection. Journal of Animal Breeding and Genetics, 124(6), 323-330. https://doi.org/10.1111/j.1439-0388.2007.00702.x.

12. Goddard, M. E., & Hayes, B. J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews, 10(6), 381-391. https://doi.org/10.1038/nrg2575.

13. Haile-Mariam, M., Nieuwhof, G. J., Beard, K. T., Konstatinov, K. V., & Hayes, B. J. (2013). Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations. Journal of Animal Breeding and Genetics, 130(1), 20-31. https://doi.org/10.1111/j.1439-0388.2013.01001.x.

14. Henderson, C. R. (1984). Applications of linear models in animal breeding. 2nd. printing. Guelph, Canada: University of Guelph, Press.

15. Howard, R., Carriquiry, A. L., & Beavis, W. D. (2014). Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures. Genes Genomes Genetics, 4(6), 1027-1046. https://doi.org/10.1534/g3.114.010298.

16. Jattawa, D., Elzo, M. A., Koonawootrittriron, S., & Suwanasopee, T. (2015). Comparison of genetic evaluations for milk yield and fat yield using a polygenic model and three genomic-polygenic models with different sets of snp genotypes in Thai multibreed dairy cattle. Livestock Science, 181, 58-64. https://doi.org/10.1016/j.livsci.2015.10.008.

17. König, S., & Swalve, H. H. (2009). Application of selection index calculations to determine selection strategies in genomic breeding programs. Journal of Dairy Science, 92(10), 5292-5303. https://doi.org/10.3168/jds.2009-2232.

18. Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 92(9), 4656-4663. https://doi.org/10.3168/jds.2009-2061.

19. Legarra, A., & Ducrocq, V. (2012). Computational strategies for national integration of phenotypic, genomic, and pedigree data in a single-step best linear unbiased prediction. Journal of Dairy Science, 95(8), 4629-4645. https://doi.org/10.3168/jds.2011-4982.

20. Liu, Z.; Goddard, M. E., Hayes, B. J., Reinhardt, F., & Reents, R. (2016). Technical note: equivalent genomic models with a residual polygenic effect. Journal of Dairy Science, 99(3), 2016-2025. https://doi.org/10.3168/jds.2015-10394.

21. Liu, Z., Goddard, M. E., Reinhardt, F., & Reents, R. (2014). A single-step genomic model with direct estimation of marker effects. Journal of Dairy Science, 97(9), 5833-5850. https://doi.org/10.3168/jds.2014-7924.

22. Loberg, A., Durr, J. W., Fikse, W. F., Jorjani, H., & Crooks, L. (2015). Estimates of genetic variance and variance of predicted genetic merits using pedigree or genomic relationship matrices in six brown swiss cattle populations for different traits. Journal of Animal Breeding and Genetics, 132(5), 376-385. https://doi.org/10.1111/jbg.12142.

23. Lourenco, D. A. L., Misztal, I., Tsuruta, S., Aguilar, I., Ezra, E., Ron, M., & Weller, J. I. (2014a). 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(3), 1742-1752. https://doi.org/10.3168/jds.2013-6916.

24. Lourenco, D. A. L., Misztal, I., Tsuruta, S., Aguilar, I., Lawlor, T. J., Forni, S., & Weller, J. I. (2014b). Are evaluations on young genotyped animals benefiting from the past generations?. Journal of Dairy Science, 97(6), 3930-3942. https://doi.org/10.3168/jds.2013-7769.

25. Lourenco, D. A. L., Tsuruta, S., Fragomeni, B. O., Masuda, Y., Aguilar, I., Legarra, A., & Misztal, I. (2015). Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. Journal of Dairy Science, 93(6), 2653-2662. https://doi.org/10.2527/jas.2014-8836.

26. Mc Hugh, N., Meuwissen, T. H. E., Cromie, C. R., & Sonesson, A. K. (2011). Use of female information in dairy cattle genomic breeding programs. Journal of Dairy Science, 94(8), 4109-4118. https://doi.org/10.3168/jds.2010-4016.

27. Meuwissen, T. H. E. (2009). Accuracy of breeding values of "unrelated" individuals predicted by dense snp genotyping. Genetics Selection Evolution, 41, 35. https://doi.org/10.1186/1297-9686-41-35.

28. Meuwissen, T. H., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819–1829.

29. Misztal, I. (2016). Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size. Genetics, 202(2), 401-409. https://doi.org/10.1534/genetics.115.182089.

30. Misztal, I., & Legarra, A. (2016). Invited review: efficient computation strategies in genomic selection. Animal, 11(5), 731-736. https://doi.org/10.1017/S1751731116002366.

31. Misztal, I., Legarra, A., & Aguilar, I. (2009). Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. Journal of Dairy Science, 92(9), 4648-4655. https://doi.org/10.3168/jds.2009-2064.

32. Misztal, I., Legarra, A., & Aguilar, I. (2014). Using recursion to compute the inverse of the genomic relationship matrix. Journal of Dairy Science, 97(6), 3943-3952. https://doi.org/10.3168/jds.2013-7752.

33. Misztal, I., Tsuruta, S., Aguilar, I., Legarra, A., Vanraden, P. M., & Lawlor, T. J. (2013). Methods to approximate reliabilities in single-step genomic evaluation. Journal of Dairy Science, 96(1), 647-654. https://doi.org/10.3168/jds.2012-5656.

34. Moser, G., Khatkar, M. S., Hayes, B. J., & Raadsma, H. W. (2010). Accuracy of direct genomic values in Holstein bulls and cows using subsets of snp markers. Genetics Selection Evolution, 42(1), 37. https://doi.org/10.1186/1297-9686-42-37.

35. Muir, W. M. (2007). Comparison of genomic and traditional blup-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. Journal of Animal Breeding and Genetics, 124(6), 342-355. https://doi.org/10.1111/j.1439-0388.2007.00700.x.

36. Mulder, H. A., Calus, M. P. L., Druet, T., & Schrooten, C. (2012). Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle. Journal of Dairy Science, 95(2), 876-889. https://doi.org/10.3168/jds.2011-4490.

37. Patry, C., Ducrocq, V. (2011). Evidence of biases in genetic evaluations due to genomic preselection in dairy cattle. Journal of Dairy Science, 94(2), 1011-1020. https://doi.org/10.3168/jds.2010-3804.

38. Pintus, M., Gaspa, G., Nicolazzi, E., Vicario, D., Rossoni, A., Ajmone-Marsan, P., & Macciotta, N. P. (2012). Prediction of genomic breeding values for dairy traits in Italian brown and Simmental bulls using a principal component approach. Journal of Dairy Science, 95(6), 3390-3400. https://doi.org/10.3168/jds.2011-4274.

39. Pocrnic, I.; Lourenco, D. A. L., Masuda, Y., Legarra, A., & Misztal, I. (2016). The dimensionality of genomic information and its effect on genomic prediction. Genetics, 203(1), 573-581. https://doi.org/10.1534/genetics.116.187013.

40. Schaeffer, L. R. (2006). Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics, 123(4), 218-223. https://doi.org/10.1111/j.1439-0388.2006.00595.x.

41. Su, G., Madsen, P., Nielsen, U. S., Mäntysaari, E. A., Aamand, G. P., Christensen, O. F., & Lund, M. S. (2012). Genomic prediction for Nordic red cattle using one-step and selection index blending. Journal of Dairy Science, 95(2), 909-917. https://doi.org/10.3168/jds.2011-4804.

42. Tsuruta, S., Misztal, I., Aguilar, I., & Lawlor, T. J. (2011). Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in us Holsteins. Journal of Dairy Science, 94(8), 4198-4204. https://doi.org/10.3168/jds.2011-4256.

43. Tsuruta, S., Misztal, I., & Lawlor, T. J. (2013). Short communication: genomic evaluations of final score for us Holsteins benefit from the inclusion of genotypes on cows. Journal of Dairy Science, 96(5), 3332-3335. https://doi.org/10.3168/jds.2012-6272.

44. Uemoto, Y., Sasaki, S., Sugimoto, Y., & Watanabe, T. (2015). Accuracy of high-density genotype imputation in Japanese black cattle. Animal Genetics, 46(4), 388-394. https://doi.org/10.1111/age.12314.

45. Vanraden, P. M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91(11), 4414-4423. https://doi.org/10.3168/jds.2007-0980.

46. Vanraden, P. M. (2016). Practical implications for genetic modeling in the genomics era. Journal of Dairy Science, 99(3), 2405-2412. https://doi.org/10.3168/jds.2015-10038.

47. Vanraden, P. M., Null, D. J., Sargolzaei, M., Wiggans, G. R., Tooker, M. E., Cole, J. B., & Doak, G. A. (2013). Genomic imputation and evaluation using high-density Holstein genotypes. Journal of Dairy Science, 96(1), 668-678. https://doi.org/10.3168/jds.2012-5702.

48. Wang, H., Misztal, I., Aguilar, I., Legarra, A., & Muir, W. M. (2012). Genome-wide association mapping including phenotypes from relatives without genotypes. Genetics Research, 94(2), 73-83. https://doi.org/10.1017/S0016672312000274.

49. Wensch-Dorendorf, M., Yin, T., Swalve, H. H., & König, S. (2011). Optimal strategies for the use of genomic selection in dairy cattle breeding programs. Journal of Dairy Science, 94(8), 4140-4151. https://doi.org/10.3168/jds.2010-4101.

50. Wiggans, G. R., Su, G., Cooper, T. A., & Nielsen, U. S., Aamand, G. P., Guldbrandtsen, B., & Vanraden, P. M. (2015). Short communication: improving accuracy of jersey genomic evaluations in the United States and Denmark by sharing reference population bulls. Journal of Dairy Science, 98(5), 3508-3513. https://doi.org/10.3168/jds.2014-8874.

51. Zhang, Z., Liu, J., Ding, X., Bijma, P., de Koning, D. J., & Zhang, Q. (2010). Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. Plos One, 5(9), 1-8. https://doi.org/10.1371/journal.pone.0012648.

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