The importance of pedigree information and the role of un-genotyped individuals on ‎the neccesity of repeating the genomic selection of binary threshold traits‎

Document Type : Research Paper

Author

Associate Professor, Department of Animal Science, Astara Branch, Islamic Azad University, Astara, Iran

Abstract

Single-step genomic prediction models became the prevailing tool in genetic evaluations of livestock due to high genomic performance, high genotyping costs for some animals, and utilizing both genotyped and un-genotyped animals. The objective of the current study was to investigate the effect of genetic relationships between the training and validation populations on the performance of single and multi-step GBLUP and BayesB, which in this regard the importance of un-genotyped individuals in generations after genomic selection and their roles on the necessity of reusing genomic selection in binary threshold traits was investigated. For this purpose, genomic populations were simulated to reflect the different number of QTL (33 and 534) with two different distributions of QTL (normal and gamma) on 29 chromosomes. Four statistical models (GBLUP, SS-GBLUP, BayesB and SS-BayesB) were implemented to analyze the simulated data. According to the results, GEBV accuracy was influenced by the relationships between the training and validation populations more significantly for multi-step models than for single-step models. Multi-step and single-step BayesB methods showed an obvious advantage over GBLUP and SSGBLUP when the trait is controlled by fewer QTLs. For the normal QTL effects distribution, BayesB methods (especially multi-step BayesB) obtained the lower accuracy than that of if more QTLs affecting the trait. Generally, since the increasing generation distance is a major challenge in the genomic selection of threshold traits, therefore the use of single-step methods makes possible reuse of genomic selection during the early generations of selection unnecessary. However, using single-step BayesB methods and its advantages in genomic evaluation depends on the identification of trait genetic architecture.

Keywords

References

  1. Aguilar, I., Misztal, I., & Tsuruta, S. (2010). Genetic trends of milk yield under heat stress for US Holsteins. Journal of Dairy Science, 93, 1754-1758.
  2. Bermann, M., Legarra, A., Hollifield, M.K., Masuda, Y., Lourenco, D., & Misztal, I. (2021). Validation of single-step GBLUP genomic predictions from threshold models using the linear regression method: An application in chicken mortality. Journal of Animal Breeding and Genetics, 138(1), 4-13.
  3. Chen, C. Y., Misztal, I., Aguilar, I., Tsuruta, S., Meuwissen, T. H. E., Aggrey, S. E., ... & Muir, W. M. (2011) Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: An example using broiler chickens. Journal of Animal Science, 89, 23-28.
  4. Christensen, O.F., & Lund, M.S. (2010). Genomic prediction when some animals are not genotyped. Genetics Selection Evolution, 42, 2.
  5. Clark, S.A., Hickey, J.M., Daetwyler, H.D., & van der Werf, J.H. (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. Genetics Selection Evolution, 44, 4.
  6. Daetwyler, H. D., Kemper, K. E., Van Der Werf, J. H. J., & Hayes, B. J. (2012) Components of the accuracy of genomic prediction in a multi-breed sheep population. Journal of Animal Science 90: 3375-3384.
  7. Fernando, R.L., Dekkers, J.C., & Garrick, D.J. (2014). A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses. Genetics Selection Evolution, 46, 50.
  8. González-Recio, O., & Forni, S. (2011). Genome-wide prediction of discrete traits using Bayesian regressions and machine learning. Genetics Selection Evolution, 43, 7.
  9. Habier, D., Fernando, R., & Dekkers, J. (2007). The impact of genetic relationship information on genome-assisted breeding values. Genetics, 1 (77), 2389-2397.
  10. Habier, D., Tetens, J., Seefried, F-R., Lichtner, P., & Thaller, G. (2010). The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics Selection Evolution, 42 5.
  11. Hayes, B.J., Visscher, P.M., & Goddard, M.E. (2009). Increased accuracy of artificial selection by using the realized relationship matrix. Genetics Research, 91, 47-60.
  12. Kang, H., Zhou, L., Mrode, R., Zhang, Q., & Liu, J. (2017). Incorporating the single-step strategy into a random regression model to enhance genomic prediction of longitudinal traits. Heredity, 119, 459.
  13. Kappes, S.M., Keele, J.W., Stone, R.T., McGraw, R.A., Sonstegard, T.S., Smith, T., Lopez-Corrales, N.L., & Beattie, C.W. (1997). A second-generation linkage map of the bovine genome. Genome Research, 7, 235-249.
  14. Lee, J., Cheng, H., Garrick, D., Golden, B., Dekkers, J., Park, K., Lee, D., & Fernando, R. (2017). Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle. Genetics Selection Evolution, 49, 2.
  15. Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 92, 4656-4663.
  16. Liu, Z., Goddard, M., Reinhardt, F., & Reents, R. (2014). A single-step genomic model with direct estimation of marker effects. Journal of Dairy Science, 97, 5833-5850.
  17. Lourenco, D., Tsuruta, S., Fragomeni, B., Masuda, Y., Aguilar, I., Legarra, A., Bertrand, J., Amen, T., Wang, L., & Moser, D. (2015). Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. Journal of Animal Science, 93, 2653-2662.
  18. Madsen, P., & Jensen, J. (2010). A Users Guide to DMU. A Package for Analysing Multivariate Mixed Models, Version 6.
  19. Meuwissen, T., Hayes, B., & Goddard, M. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819-1829.
  20. Naderi, Y., & Sadeghi, S. (2019). Assessment of the genomic prediction accuracy of discrete traits with imputation of missing genotypes. Animal Science Papers and Reports, 37, 149-168.
  21. Sadeghi, S., Rafat, S.A., & Alijani, S. (2018). Evaluation of imputed genomic data in discrete traits using random forest and Bayesian threshold methods. Acta Scientiarum Animal Sciences, 40, e39007.
  22. Sargolzaei, M., & Schenkel, F.S. (2009). QMSim: a large-scale genome simulator for livestock. Bioinformatics, 2 (5), 680-681
  23. Solberg, T., Sonesson, A., & Woolliams, J. (2008). Genomic selection using different marker types and densities. Journal of Animal Science, 86, 2447-2454.
  24. Su, G., & Madsen, P. (2013). User’s Guide for GMATRIX version 2, a Program for Computing Genomic Relationship Matrix.
  25. VanRaden, P.M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91, 4414-4423.
  26. VanRaden, P.M., & Sullivan, P.G. (2010). International genomic evaluation methods for dairy cattle. Genetics Selection Evolution, 42, 7.
  27. Yin, T., Pimentel, E., Borstel, U.Kv., & König, S. (2014). Strategy for the simulation and analysis of longitudinal phenotypic and genomic data in the context of a temperature× humidity-dependent covariate. Journal of Dairy Science, 97, 2444-2454.
  28. Zhang, X., Lourenco, D., Aguilar, I., Legarra, A., & Misztal, (2016). Weighting strategies for single-step genomic BLUP: an iterative approach for accurate calculation of GEBV and GWAS. Frontiers in Genetics, 7, 151.
  29. Zhou, L., Mrode, R., Zhang, S., Zhang, Q., Li, B., & Liu, J-F. (2018). Factors affecting GEBV accuracy with single-step Bayesian models. Heredity, 120, 100.
Iranian Journal of animal Science
Volume 53, Issue 3
December 2022
Pages 141-152

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History

  • Receive Date: 03 February 2022
  • Accept Date: 05 March 2022

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