Abstract
Access to dense panels of molecular markers has facilitated genomic selection in animal breeding. The purpose of this study was to compare the nonparametric (random forest and support vector machine), semiparametric reproducing kernel Hilbert spaces (RKHS), and parametric methods (ridge regression and Bayes A) in prediction of genomic breeding values for traits with different genetic architecture. The predictive performance of different methods was compared in different combinations of distribution of QTL effects (normal and uniform), two levels of QTL numbers (50 and 200), three levels of heritability (0.1, 0.3 and 0.5), and two levels of training set individuals (1000 and 2000). To do this, a genome containing four chromosomes each 100-cM long was simulated on which 500, 1000 and 2000 evenly spaced single-nucleotide markers were distributed. With an increase in heritability and the number of markers, all the methods showed an increase in prediction accuracy (P < 0.05). By increasing the number of QTLs from 50 to 200, we found a significant decrease in the prediction accuracy of breeding value in all methods (P < 0.05). Also, with the increase in the number of training set individuals, the prediction accuracy increased significantly in all statistical methods (P < 0.05). In all the various simulation scenarios, parametric methods showed higher prediction accuracy than semiparametric and nonparametric methods. This superior mean value of prediction accuracy for parametric methods was not statistically significant compared to the semiparametric method, but it was statistically significant compared to the nonparametric method. Bayes A had the highest accuracy of prediction among all the tested methods and, is therefore, recommended for genomic evaluation.
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Abdollahi-Arpanahi R., Pakdel A., Nejati-Javaremi A. and Moradi Shahre Babak M. 2013 Comparison of different methods of genomic evaluation in traits with different genetic architecture. J. Anim. Prod. 15, 65–77 (in Persian with English abstract).
Combs E. and Bernardo R. 2012 Accuracy of genome wide selection for different traits with constant population size, heritability, and number of markers. Plant Genome 6, 1–7.
Daetwyler H. D., Pong-wong R., Villanueva B. and Woolliams J. A. 2010 The impact of genetic architecture on genome-wide evaluation methods. Genetics 185, 1021–1031.
Daetwyler H. D., Calus M. P. L., Pong-wong R., de los Campos G. and Hickey J. M. 2013 Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking. Genetics 193, 347–365.
De los Campos G., Hickey J. M., Pong-Wong R., Daetwyler H. D. and Calus M. P. 2013 Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327–345.
Ghafouri-Kesbi F., Rahimi-Mianji G., Honarvar M. and Nejati-Javaremi A. 2017 Predictive ability of random forests, boosting, support vector machines and genomic best linear unbiased prediction in different scenarios of genomic evaluation. Anim. Prod. Sci. 57, 229–236.
Gianola D., Fernando R. L. and Stella A. 2006 Genomic-assisted prediction of genetic value with semiparametric procudures. Genetics 173, 1761–1776.
Goddard M. 2009 Genomic selection: prediction of accuracy and maximisation of long term response. Genetics 136, 245–257.
Habier D., Fernando R. L. and Dekkers J. C. M. 2009 Genomic selection using low-density marker panels. Genetics 182, 343–353.
Hastie T. J., Tibshirani R. and Friedman J. 2009 The elements of statistical learning, 2nd edition. Springer-Verlag, New York.
Hayes B. 2007 QTL mapping, MAS, and genomic selection. A short-course, Animal Breeding and Genetics Department of Animal Science, Iowa State University 1, 3–4.
Hayes B., Bowman P., Chamberlain A., Verbyla K. and Goddard, M. 2009 Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet. Sel. Evol. 41, 51.
Hayes B. J., Daetwyler H. D., Bowman P., Moser G., Tier B., Crump R. et al. 2010 Accuracy of genomic selection: comparing theory and results. Proc. Assoc. Advmt. Anim. Breed. Genet. 18, 34–37.
Hill W. and Robertson A. 1968 Linkage disequilibrium in finite populations. Theor. Appl. Genet. 38, 226–231.
Hoerl A. E. and Kennard R. W. 1970 Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67.
Howard R., Carriquiry A. L. and Beavis W. D. 2014 Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures. G3 (Bethesda) 4, 1027–1046.
Liaw A. 2013 Breiman and Cutler’s random forests for classification and regression. Available 403 at: http://cran.r-project.org/web/packages/randomForest/index.html.
Meuwissen T. H. 2013 The accuracy of genomic selection. Available at: http://www.umb.no/statisk/husdyrforsoksmoter/2013/1_1.pdf.
Meuwissen T. H., Hayes B. J. and Goddard M. E. 2001 Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
Meyer D., Dimitriadou E., Hornik K., Weingessel A. and Leisch K. 2013 Misc functions of the department of statistics (e1071), TU Wien. Available at: http://cran.rproject.org/web/packages/e1071/index.html.
Perez P. and De los Campos G. 2014 Genome-wide regression and prediction with the BGLR statistical package. Genetics 198, 483–495.
Piyasation N. and Dekkers J. 2013 Accuracy of genomic prediction when accounting for population structure and polygenic effects. Anim. Industry Rep. 659, 68.
Samuel A. C., Hickey J. M., Daetwyler H. D. and van der Werf J. H. J. 2012 The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genet. Sel. Evol. 44, 4–13.
Schrooten C., Bovenhuis H., Van Arendonk J. A. M. and Bijma P. 2005 Genetic progress in multistage dairy cattle breeding schemes using genetic markers. J. Dairy Sci. 88, 1569–1581.
Technow F. 2013 hypred: Simulation of genomic data in applied genetics. Available at: 433 http://cran.r-project.org/web/packages/hypred/index.html.
Toosi A., Fernando R., Dekkers J. and Quaas R. 2010 Genomic selection inadmixed and crossbred populations. J. Anim. Sci. 88, 32.
VanRaden P. M., Van Tassell C. P., Wiggans G. R., Sonstegard T. S., Schnabel R. D., Taylor J. F. et al. 2009 Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92, 16–24.
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Sahebalam, H., Gholizadeh, M., Hafezian, H. et al. Comparison of parametric, semiparametric and nonparametric methods in genomic evaluation. J Genet 98, 102 (2019). https://doi.org/10.1007/s12041-019-1149-3
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DOI: https://doi.org/10.1007/s12041-019-1149-3