Problems and issues in forecasting the genetic breeding value of agricultural animals
https://doi.org/10.31677/2072-6724-2022-65-4-77-96
Abstract
Methods for forecasting genetic value in cattle breeding are widely recognized in countries with developed cattle breeding. They are used and improved in continuous evolution by genetic breeders and statisticians. The unbiased linear estimation method (BLUP/BLUE) is very flexible because it has many alternatives tailored to different breeding objectives, animal species, production conditions, and calculation methods. Today it is relevant to the search for new, faster, and more economical algorithms for inverting dominant and additive kinship relationships between individuals using additive kinship matrices. For a new search, it is necessary to select (create) a suitable selection model to solve the problem of cow culling, the non-random influence of sires. Further, it is essential to relate this to the fixed factors of the animal’s life cycle HYS (herd-year-season, herd-year-season) and the preferred production environment factors. The purpose of this paper is to address several issues related to the problems of animal breeding. First, an overview of a historically powerful method for assessing the genetic value of cattle (and other species by analogy) based on multifactorial regression models is presented, and simple examples of selection using it are given. Over the past decade, many new methodological techniques, programs, databases, patents, and review articles have been published in this area. By the requirements of national economic development, the material presented can serve as a guide for the study of new, modern methods of assessing the value of animals and the formation of new breeding indices.
About the Authors
A. E. KalashnikovRussian Federation
Ph.D. in Biological Sciences, Moscow;
Arkhangelsk
A. I. Golubkov
Russian Federation
Doctor of Agricultural Sciences, Professor,
Moscow
N. F. Schegolkov
Russian Federation
Ph.D. in Agricultural Sciences, Associate Professor,
Moscow
E. R. Gosteva
Russian Federation
Doctor of Agricultural Sciences,
Saratov
References
1. Lush J.L., The bull index problem in the light of modern genetics, Journal of Dairy Science, 1933, Vol. 16, No. 6, P. 501–522, https://doi.org/10.3168/jds.S0022-0302(33)93369-X.
2. Mood A.M.F., Introduction to the Theory of Statistics, 1950, pp. 1–17.
3. Henderson C.R., Estimation of general, specific and maternal combining abilities in crosses among inbred lines of swine, Iowa State University, 1948, pp. 25–27.
4. Henderson C.R. Estimation of changes in herd environment, J. Dairy Sci, 1949, Vol. 32, No. 8, pp. 706–706.
5. Misztal I., Stein Y., Lourenco D.A.L., Genomic evaluation with multibreed and crossbred data, JDS Communications, 2022, pp. 1–10, https://doi.org/10.3168/jdsc.2021-0177.
6. Barwick S.A., Henzell A.L., Development successes and issues for the future in deriving and applying selection indexes for beef breeding, Australian Journal of Experimental Agriculture, 2005, Vol. 45, No. 8, pp. 923–933, https://doi.org/10.1071/EA05068.
7. Misztal I., Aggrey S.E., Muir W.M., Experiences with a single-step genome evaluation, Poultry science, 2013, Vol. 92, No. 9, pp. 2530–2534, https://doi.org/10.3382/ps.2012-02739.
8. Shook G.E., Major advances in determining appropriate selection goals, Journal of dairy science, 2006, Vol. 89, No. 4, pp. 1349–1361, https://doi.org/10.3168/jds.S0022-0302(06)72202-0.
9. Lourenco D.A.L., Misztal I., Tsuruta S., Aguilar I., Ezra E., Ron M., Weller J.I., Lourenco D.A.L. [et al.], Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses, Journal of dairy science, 2014, Vol. 97, No. 3, pp. 1742–1752, https://doi.org/10.3168/jds.2013-6916.
10. Oliveira E.J., Santana F.A., Oliveira L.A., Santos V.S. Oliveira E.J. [et al.], Genetic parameters and prediction of genotypic values for root quality traits in cassava using REML/BLUP, Genetics and Molecular Research, 2014, Vol. 13, No. 3, pp. 6683–6700, http://dx.doi.org/10.4238/2014. August.28.13.
11. Lund M.S., Su G., Janss L., Guldbrandtsen B., Brøndum R.F., Lund M.S. [et al.] Genomic evaluation of cattle in a multi-breed context, Livestock Science, 2014, Vol. 166, pp. 101–110, https://doi.org/10.1016/j.livsci.2014.05.008.
12. Henderson C.R., General flexibility of linear model techniques for sire evaluation, Journal of Dairy Science, 1974, Vol. 57, No. 8, pp. 963–972, https://doi.org/10.3168/jds.S0022-0302(74)84993-3.
13. Henderson C.R., Comparison of alternative sire evaluation methods, Journal of Animal Science, 1975, Vol. 41, No. 3, pp. 760–770, https://doi.org/10.2527/jas1975.413760x.
14. Henderson C.R., Best linear unbiased prediction of breeding values not in the model for records, Journal of Dairy Science, 1977, Vol. 60, No 5, pp. 783–787, https://doi.org/10.3168/jds.S0022-0302(77)83935-0.
15. Schaeffer L.R., Application of random regression models in animal breeding, Livestock Production Science, 2004, Vol. 86, No. 1–3, pp. 35–45, https://doi.org/10.1016/S0301-6226(03)00151-9.
16. Henderson C.R., Quaas R.L., Multiple trait evaluation using relatives’ records, Journal of animal science, 1976, Vol. 43, No. 6, pp. 1188–1197, https://doi.org/10.2527/jas1976.4361188x.
17. Quaas R.L., Additive genetic model with groups and relationships, Journal of Dairy Science, 1988, Vol. 71, No. 5, pp. 1338–1345, https://doi.org/10.3168/jds.S0022-0302(88)79691-5.
18. Yu C.R., Zou K.H., Carlsson M.O., Weerahandi S., Generalized estimation of the BLUP in mixedeffects models: A comparison with ML and REML, Communications in Statistics-Simulation and Computation, 2015, Vol. 44, No. 3, pp. 694–704, https://doi.org/10.1080/03610918.2013.790445.
19. Schaeffer L.R. CR Henderson: Contributions to predicting genetic merit, Journal of dairy science, 1991, Vol. 74, No. 11, pp. 4052–4066, https://doi.org/10.3168/jds.S0022-0302(91)78601-3.
20. Calus M. P.L., De Haas Y., Veerkamp R.F., Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies, Journal of Dairy Science, 2013, Vol. 96, No. 10, pp. 6703–6715, https://doi.org/10.3168/jds.2012-6013.
21. Gianola D., Foulley J. L., Fernando R.L., Prediction of breeding values when variances are not known, Génétique sélection évolution, 1986, Vol. 18, No. 4, pp. 485–498.
22. Yang H., Su G., Impact of phenotypic information of previous generations and depth of pedigree on estimates of genetic parameters and breeding values, Livestock Science, 2016, Vol. 187, pp. 61–67, https://doi.org/10.1016/j.livsci.2016.03.001.
23. Henderson C.R., Equivalent linear models to reduce computations, Journal of Dairy Science, 1985, Vol. 68, No. 9, pp. 2267–2277, https://doi.org/10.3168/jds.S0022-0302(85)81099-7.
24. Quaas R.L., Pollak E.J., Mixed model methodology for farm and ranch beef cattle testing programs, Journal of Animal Science, 1980, Vol. 51, No. 6, pp. 1277–1287. https://doi.org/10.2527/jas1981.5161277x.
25. Gianola D., Fernando R.L., Bayesian methods in animal breeding theory, Journal of Animal Science, 1986, Vol. 63, No. 1, pp. 217–244, https://doi.org/10.2527/jas1986.631217x.
26. Henderson C.R., Use of all relatives in intraherd prediction of breeding values and producing abilities, Journal of Dairy Science, 1975, Vol. 58, No. 12, pp. 1910–1916, https://doi.org/10.3168/jds.S0022-0302(75)84808-9.
27. Van Vleck L.D., Derivation of Henderson’s method of incorporating artificial insemination sire evaluations into intraherd prediction of breeding values, Journal of Dairy Science, 1982, Vol. 65, No. 2, pp. 284–286, https://doi.org/10.3168/jds.S0022-0302(82)82190-5.
28. Chen X., Liang H., Wang Y., Total positivity of recursive matrices, Linear Algebra and its Applications, 2015, Vol. 471, pp. 383–393. https://doi.org/10.1016/j.laa.2015.01.009.
29. Misztal I., Legarra A., Invited review: efficient computation strategies in genomic selection, Animal, 2017, Vol. 11, No. 5, pp. 731–736, doi:10.1017/S1751731116002366.
30. Misztal I., Aggrey S.E., Muir W.M., Experiences with a single-step genome evaluation, Poultry science, 2013, Vol. 92, No. 9, pp. 2530–2534, https://doi.org/10.3382/ps.2012-02739.
31. Henderson C.R., Simple method to compute biases and mean squared errors of linear estimators and predictors in a selection model assuming multivariate normality, Journal of Dairy Science, 1988, Vol. 71, No. 11, pp. 3135–3142, https://doi.org/10.3168/jds.S0022-0302(88)79914-2.
32. Henderson C.R., A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values, Biometrics, 1976, pp. 69–83. https://doi.org/10.2307/2529339.
33. Darvasi A., Experimental strategies for the genetic dissection of complex traits in animal models, Nature genetics, 1998, Vol. 18, No. 1, pp. 19–24.
34. Pollak E.J., Quaas R.L., Definition of group effects in sire evaluation models, Journal of Dairy Science, 1983, Vol. 66, No. 7, pp. 1503–1509, https://doi.org/10.3168/jds.S0022-0302(83)81965-1.
35. Quaas R.L., Computing the diagonal elements and inverse of a large numerator relationship matrix, Biometrics, 1976, pp. 949–953, https://doi.org/10.2307/2529279.
36. Misztal I., Legarra A., Aguilar I., Using recursion to compute the inverse of the genomic relationship matrix, Journal of dairy science, 2014, Vol. 97, No. 6, pp. 3943–3952, https://doi.org/10.3168/jds.2013-7752.
37. Misztal I., Restricted maximum likelihood estimation of variance components in animal model using sparse matrix inversion and a supercomputer, Journal of Dairy Science, 1990, Vol. 73, No. 1, pp. 163–172, https://doi.org/10.3168/jds.S0022-0302(90)78660-2.
38. Henderson C.R., Best linear unbiased prediction using relationship matrices derived from selected base populations, Journal of Dairy Science, 1985, Vol. 68, No. 2, pp. 443–448, https://doi.org/10.3168/jds.S0022-0302(85)80843-2.
39. Eding H., Crooijmans R.P., Groenen M.A., Meuwissen T.H. Eding H. [et al.], Assessing the contribution of breeds to genetic diversity in conservation schemes, Genetics Selection Evolution, 2002, Vol. 34, No. 5, pp. 613–633, https://doi.org/10.1051/gse:2002027.
40. Lourenco D.A.L., Misztal I., Tsuruta S., Aguilar I., Ezra E., Ron M., Weller J.I., Lourenco D.A.L. [et al.], Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses, Journal of dairy science, 2014, Vol. 97, No. 3, pp. 1742–1752, https://doi.org/10.3168/jds.2013-6916.
41. Laodim T., Elzo M.A., Koonawootrittriron S., Suwanasopee T., Jattawa D., Genomic-polygenic and polygenic predictions for milk yield, fat yield, and age at first calving in Thai multibreed dairy population using genic and functional sets of genotypes, Livestock Science, 2019, Vol. 219, pp. 17–24, https://doi.org/10.1016/j.livsci.2018.11.008.
42. Zhou J., Advances in Pedigree Analysis: Hardy-Weinberg Equilibrium, Strain Imputation, and Maternal Effects, University of California, Los Angeles, 2011.
43. Ahlborn-Breier G., Hohenboken W.D., Additive and nonadditive genetic effects on milk production in dairy cattle: evidence for major individual, Journal of Dairy science, 1991, Vol. 74, No. 2, pp. 592–602, https://doi.org/10.3168/jds.S0022-0302(91)78206-4.
44. Smith S.P., Mäki-Tanila A., Genotypic covariance matrices and their inverses for models allowing dominance and inbreeding, Genetics Selection Evolution, 1990, Vol. 22, No. 1, pp. 65–91.
45. VanRaden P.M., Hoeschele I., Rapid inversion of additive by additive relationship matrices by including sire-dam combination effects, Journal of dairy science, 1991, Vol. 74, No. 2, pp. 570– 579, https://doi.org/10.3168/jds.S0022-0302(91)78204-0.
46. Schaeffer L.R., CR Henderson: Contributions to predicting genetic merit, Journal of dairy science, 1991, Vol. 74, No. 11, pp. 4052–4066, https://doi.org/10.3168/jds.S0022-0302(91)78601-3.
47. Kahneman D., Thaler R.H., Anomalies: Utility maximization and experienced utility, Journal of economic perspectives, 2006, Vol. 20, No. 1, pp. 221–234, DOI: 10.1257/089533006776526076.
48. Koivula M., Strandén I., Pösö J., Aamand G.P., Mäntysaari E.A., Koivula M. [et al.], Single-step genomic evaluation using multitrait random regression model and test-day data, Journal of Dairy Science, 2015, Vol. 98, No. 4, pp. 2775–2784, https://doi.org/10.3168/jds.2014-8975.
49. Gianola D., Fernando R.L., Im S., Foulley J.L., Gianola D. [et al.], Likelihood estimation of quantitative genetic parameters when selection occurs: models and problems, Genome, 1989, Vol. 31, No. 2, pp. 768–777, https://doi.org/10.1139/g89-136.
50. Gianola D., Im S., Fernando R.L., Prediction of breeding value under Henderson’s selection model: a revisitation, Journal of dairy science, 1988, Vol. 71, No. 10, pp. 2790–2798., https://doi.org/10.3168/jds.S0022-0302(88)79873-2.
51. Legarra A., Christensen O.F., Aguilar I., Misztal I., Legarra A. [et al.], Single Step, a general approach for genomic selection, Livestock Science, 2014, Vol. 166, pp. 54–65, https://doi.org/10.1016/j.livsci.2014.04.029.
Review
For citations:
Kalashnikov A.E., Golubkov A.I., Schegolkov N.F., Gosteva E.R. Problems and issues in forecasting the genetic breeding value of agricultural animals. Bulletin of NSAU (Novosibirsk State Agrarian University). 2022;(4):77-96. (In Russ.) https://doi.org/10.31677/2072-6724-2022-65-4-77-96