The effect of different selection and evaluation approaches on the accuracy of genomic prediction in a simulated population

Document Type : (original research)

Authors

1 Department of Animal Science, Faculty of Agriculture, Ilam University, Ilam, Iran

2 Department of Animal Science, Ilam Branch, Islamic Azad University, Ilam, Iran

3 Department of Laboratory and Clinical Sciences, Faculty of Para veterinary Medicine, Ilam University, Ilam, Iran

10.22034/aej.2022.337577.2787

Abstract

In this study, assessed genomic prediction accuracies based on different selection methods (phenotypic and estimated breeding value), evaluation procedures (GBLUP and ssGBLUP),training population (TP) sizes, heritability (h2) levels, marker densities and pedigree error (PE) rates in a simulated population. QMSim software was used to create a reference database of 1000 and number of animals was reduced to 200 (100 males and 100 females) during 95 generations to create LD and mutation-drift equilibrium. The heritability of the trait was 0.1, 0.3 and 0.5 and the marker density was simulated for three strategies of 1, 5 and 10 K. The proportions of errors substituted were 10%, 20% and 30%, respectively.The results of this study showed that, compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection.With increasing reference population size and trait heritability, genomic prediction accuracy increased in all strategies.When errors were introduced into the pedigree dataset from 0 to 30%, the prediction accuracies were only minimally influenced across all scenarios.Our study suggests that the use of ssGBLUP, EBV sThe results of this study showed that, compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection.With increasing reference population size and trait heritability, genomic prediction accuracy increased in all strategies.When errors were introduced into the pedigree dataset from 0 to 30%, the prediction accuracies were only minimally influenced across all scenarios.Our study suggests that the use of ssGBLUP, EBV selection, and high marker density could help improve genetic gainseven in the case of pedigree error in cattle.election, and high marker density could help improve genetic gainseven in the case of pedigree error in cattle.The results of this study showed that, compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection.With increasing reference population size and trait heritability, genomic prediction accuracy increased in all strategies.When errors were introduced into the pedigree dataset from 0 to 30%, the prediction accuracies were only minimally influenced across all scenarios.Our study suggests that the use of ssGBLUP, EBV selection, and high marker density could help improve genetic gainseven in the case of pedigree error in cattle.

Keywords

Main Subjects

References

  1. Hayes, B.J., Bowman, P.J., Chamberlain, A.J. and Goddard M.E., 2009. Invited review: Genomic selection in dairy cattle: progress and challenges. Journal Dairy Science. 92: 433-443.
  2. Henderson, C.R., 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics. 31: 423-447.
  3. Van der Werf, J., 2015. Principles of estimation of breeding values. In: Genetic evaluation and breeding program design. Armidale, Australia: University of New England. 1-17.
  4. Meuwissen, T.H.E., Hayes, B.J. and Goddard M.E., 2016. Genomic selection: a paradigm shifts in animal breeding. Animal Fronters. 6: 6-14.
  5. VanRaden, P.M., Van Tassell, C.P. and Wiggans, G.R., 2009. Invited review: Reliability of genomic predictions for North American Holstein bulls. Journal Dairy Science. 92: 16-24.
  6. Calus, M.P.L., 2010. Genomic breeding value prediction: methods and procedures. Animal. 4: 157-64.
  7. Gao, H., Christensen O.F. and Madsen, P., 2012. Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genetic Selection Evolution. 44: 8.
  8. Meuwissen, T.H.E., Hayes, B.J. and Goddard M.E., 2001. Prediction of total genetic value using genome wide dense marker maps. Genetics. 157: 1819-1829.
  9. Solberg, T.R., Sonesson, A.K., Woolliams, J.A. and Meuwissen, T.H.E., 2008. Genomic selection using different marker types and densities. Journal Animal Science. 86: 2447-2454.
  10. VanRaden, P.M., 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science. 91: 4414-4423.
  11. Habier, D., Fernando R.L. and Dekkers J.C.M., 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics. 177: 2389-2397.
  12. Legarra, A., Aguilar I. and Misztal I., 2009. A relationship matrix including full pedigree and genomic information. Journal Dairy Science. 92: 4656-4663.
  13. Christensen, O.F. and Lund, M.S., 2010. Genomic prediction when some animals are not genotyped. Genetic Selection Evolution. 42: 2.
  14. Piccoli, M.L., Britoand, L.F. and Braccini, J., 2018. A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population. Canadian Journal Animal Science. 98: 565-575.
  15. Legarra, A., Christensen, O.F., Aguilar, I. and Misztal, I., 2014. Single step, a general approach for genomic selection. Livestock Science. 166: 54-65.
  16. Nwogwugwu, C.P., Kim, Y. and Chung, Y.J., 2020. Effect of errors in pedigree on the accuracy of estimated breeding value for carcass traits in Korean Hanwoo cattle. Asian-Australas Journal Animal Science. 33: 1057-1067.
  17. Rogers, A.R., Wooding, S., Huff Batzerand, C.D.M.A. and Jorde, L.B., 2007. Ancestral alleles and population origins: inferences depend on mutation rate. Molecular Biologyand Evolution. 24: 990-997.
  18. Kizilkaya, K., Fernando, R.L. and Garrick. D.J., 2013. Genomic prediction of simulated multi-breed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. Journal Animal Science. 88: 521-544.
  19. Sargolzaei, M. and Schenkel, F.S., 2009. QMSim: A large-scale genome simulator for livestock. Bioinformatics. 25: 680-861.
  20. Misztal, I., Tsuruta, S., Strabel, T., Auvray, B., Druet, T. and Lee, D.H., 2002. BLUPF90 and related programs (BGF90). In: Proceedings of 7th World Congress on Genetics Applied to Livestock Production. 19-23 Aug. Montpellier, France. 1-2.
  21. Misztal, I. and Wiggans, G.R., 1988. Approximation of prediction error variance in large-scale animal models. Journal Dairy Science. 71: 27-32.
  22. Gowane, G.R., Lee, S.H., Clark, S., Moghaddar, N., Al-Mamunand, H.A. and Van der Werf J.H.J., 2018. Effect of selection on bias and accuracy in genomic prediction of breeding values. bioRxiv. 298042.
  23. Brito, F.V., Neto, J.B., Sargolzaei, M., Cobuciand J.A. and Schenkel F.S., 2011. Accuracy of genomic selection in simulated populations mimicking the extent of linkage disequilibrium in beef cattle. BMC Genetic. 12:80.
  24. Gowane, G.R, Swarnkar, C., Prince, L. and Kumar, A., 2018. Geneticparameters for neonatal mortality in lambs at semiarid region of Rajasthan India. Livestock science. 210: 85-92.
  25. Nwogwugwu, C.P., Kim, Y., Choi, H., Lee, J.H. and Lee. S.H., 2020b. Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population. Asian-Australas Journal Animal Science. 33: 1912-1921.
  26. Zhu, B., Zhang, J. and Niu, H., 2017. Effects of marker density and minor allele frequency on genomic prediction for growth traits in Chinese Simmental beef cattle. Journal of Integrative Agriculture. 16: 911-920.
  27. Vitezica, Z.G., Aguilar, I. and Legarra, A., 2010. One step vs. multi-step methods for genomic prediction in presence of selection. In: Proceedings of the World Congress on Genetics Applied to Livestock Production. Volume genetic improvement programmers: selection using molecular information - lecture sessions. 0131.
  28. Israel, C. and Weller J.I., 2000. Effect of misidentification on genetic gain and estimation of breeding value in dairy cattle populations. Journal Dairy Science. 83: 181-187.
Journal of Animal Environment
Volume 15, Issue 1
June 2023
Pages 93-100

Files

Share

How to cite

Statistics