Volume 8, Issue 18 (3-2018)
rap 2018, 8(18): 161-167 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mohammadi Y, Sattaei Mokhtari M. (2018). Genomic Selection Accuracy Parametric and Nonparametric Statistical Methods with Additive and Dominance Genetic Architectures. rap. 8(18), 161-167. doi:10.29252/rap.8.18.161
URL: http://rap.sanru.ac.ir/article-1-908-en.html
URL: http://rap.sanru.ac.ir/article-1-908-en.html
Abstract: (3744 Views)
In most genomic prediction studies only additive effects will be used in models for estimating genomic breeding values (GEBV). However, dominance genetic effects are an important source of variation for complex traits, considering them into account may improve the accuracy of GEBV. In the present study, performed applying simulated data, the effect of different heritability values (0.1, 0.3 and 0.5) and different values for the proportion of dominance variance to phenotypic variance (0, 0.05 and 0.15) on genomic selection accuracy in parametric (LASSO, A and B Bayes) and non-parametric (RKHS) statistical methods were studied. Correlations between the true and genomic breeding values, as a measure for the accuracy of genomic predictions under different scenarios were calculated using R software. The results of the present study revealed that, under all statistical methods as heritability values increased, the accuracy of genomic predictability increased. Also, as the value of dominance variance to phenotype variance increased genomic accuracy slant was slow under parametric methods but in the non-parametric method accuracy continued to increase. Under non parametric method the average mean square error was more reduced as the ratio of dominance variance to phenotype variance increased. Therefore, it may be concluded that under non- parametric method as the ratio of dominance variance to phenotype variance increased the accuracy of genomic predictions would be more increased than that of under parametric methods.
Type of Study: Research |
Subject:
Special
Received: 2018/02/28 | Revised: 2018/03/3 | Accepted: 2018/02/28 | Published: 2018/02/28
Received: 2018/02/28 | Revised: 2018/03/3 | Accepted: 2018/02/28 | Published: 2018/02/28
References
1. Aliloo, H., J.E. Pryce, O. Gonzalez‑Recio, B.G. Cocks and B.J. Hayes. 2016. Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits. Genetic Selection Evolution, 48: 8-11. [DOI:10.1186/s12711-016-0186-0]
2. Bolormaa, S., J.E. Pryce1, Y. Zhang, A. Reverter, W. Barendse, B.J. Hayes and M.E. Goddard. 2015. Non-additive genetic variation in growth, carcass and fertility traits of beef cattle. Genetic Selection Evolution, 47)26): 12 pp. [DOI:10.1186/s12711-015-0114-8]
3. Boysen, T.J., C. Heuer, J. Tetens, F. Reinhardt and G. Thaller. 2013. Novel use of derived genotype probabilities to discover significant dominance effects for milk production traits in dairy cattle. Genetics, 193: 431-42. [DOI:10.1534/genetics.112.144535]
4. Calus, M.P.L. 2010. Genomic breeding value prediction: methods and procedures. Animal, 4(02): 157-164. [DOI:10.1017/S1751731109991352]
5. Calus, M.P.L., T.H.E. Meuwissen, A.P.W. De Roos and R.F. Veerkamp. 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics, 178: 553-561. [DOI:10.1534/genetics.107.080838]
6. Carlborg, Ö., S. Kerje, K. Schütz,L. Jacobsson, P. Jensen and L. A. Andersson.2003.Global search reveals epistatic interaction between QTL for early growth in the chicken. Genome Research, 13: 413-421. [DOI:10.1101/gr.528003]
7. de los Campos, G., D. Gianola, G.J.M. Rosa, K.A. Weigel and J. Crossa. 2010. Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genetic Research, 92: 295-308. [DOI:10.1017/S0016672310000285]
8. Falconer, D.S. and T.F.C. Mackay. 1996. Introduction to Quantitative Genetics. 4thed. Harlow: Pearson Education Limited.
9. Gao, H., O.F. Christensen, P. Madsen, U.S. Nielsen, Y. Zhang, M.S. Lund and G. Su. 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 pp. [DOI:10.1186/1297-9686-44-8]
10. Gianola, D. and J.B. Van Kaam. 2008. Reproducing Kernel Hilbert Spaces Regression methods for genomic assisted prediction of quantitative traits. Genetics, 178: 2289-2303. [DOI:10.1534/genetics.107.084285]
11. Goddard, M.E. and B.J. Hayes. 2007. Genomic selection. Journal Animal Breeding and Genetics, 124: 323-330. [DOI:10.1111/j.1439-0388.2007.00702.x]
12. González-Recio, O., D. Gianola, N. Long, K.A. Weigel, G.J.M. Rosa and S. Avendaño. 2008. Nonparametric methods for incorporating genomic information into genetic evaluations: An Application to Mortality in Broilers. Genetics, 178: 2305-2313. [DOI:10.1534/genetics.107.084293]
13. Hayes, B.J., P.J. Bowman, A.J. Chamberlain and M.E. Goddard. 2009. Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal Dairy Science, 92: 433-443. [DOI:10.3168/jds.2008-1646]
14. Heidaritabar, M., A. Wolc, J. Arango, J. Zeng, P. Settar, J.E. Fulton, N.P. Osullivan, J.W.M. Bastiaansen. R.L. Fernando, D.J. Garrick and J.C.M. Dekkers. 2016. Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers. Journal Animal Breeding and Genetics, 1-13. [DOI:10.1111/jbg.12225]
15. Heslot, N., H.P. Yang, M.E. Sorrells and J.L. Jannink. 2012. Genomic selection in plant breeding: A Comparison of Models. Crop Science, 52: 146-160. [DOI:10.2135/cropsci2011.09.0297]
16. Howard, R., A.L. Carriquiry and W.D. Beavis. 2014. Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures. G3, 4: 1027-1046. [DOI:10.1534/g3.114.010298]
17. Lee, S. H., J.H.J. Vander Werf, B.J. Hayes, M.E. Goddard and P.M. Visscher. 2008. Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLoS Genetic, 4: e1000231. [DOI:10.1371/journal.pgen.1000231]
18. Meuwissen, T.H., B.J. Hayes and M.E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157: 1819-1829.
19. Neves, H., R. Carvalheiro and S.A. Queiroz. 2012. A comparison of statistical methods for genomic selection in a mice population. BMC Genetics, 13:100. 17 pp. [DOI:10.1186/1471-2156-13-100]
20. Ogutu, J.O., T. Schulz-Streeck and H.P. Piepho. 2012. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proceedings. 6(Suppl 2): S10. [DOI:10.1186/1753-6561-6-S2-S10]
21. Perez, P., G. de los Campos, J. Crossa and D. Gianola. 2010. Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. Plant Genome, 3: 106-16. [DOI:10.3835/plantgenome2010.04.0005]
22. Sargolzaei, M. and F.S. Schenkel. 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics, 25: 680-1. [DOI:10.1093/bioinformatics/btp045]
23. Saatchi, M., S.R. Miraei- Ashtiani, A. Nejati- Javaremi, M. Moradi-Shahrebabak and H. Mehrabani-Yeghaneh. 2010. The impact of information quantity and strength of relationship between training set and validation set on accuracy of genomic estimated breeding values. African Journal of Biotechnology, 9: 438-442.
24. Su, G., O.F. Christensen, T. Ostersen, M. Henryon and M.S. Lund. 2012. Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS One, 7(45): 293. [DOI:10.1371/journal.pone.0045293]
25. Wittenburg, D., N. Melzer and N. Reinsch. 2011. Including non-additive genetic effects in Bayesian methods for the prediction of genetic values based on genome-wide markers. BMC Genetics, 12-74. [DOI:10.1186/1471-2156-12-74]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
