Volume 8, Issue 15 (6-2017)                   rap 2017, 8(15): 149-154 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Genomic Evaluation of Threshold Traits with Different Genetic Architecture using Bayesian Approaches. rap. 2017; 8 (15) :149-154
URL: http://rap.sanru.ac.ir/article-1-761-en.html
Abstract:   (2152 Views)

The current study was carried out to evaluate accuracy of some Bayesian methods for genomic breeding values prediction for threshold traits with different types of genetic architecture based on distribution of gene effect and QTL numbers. A genome consisted of 3 chromosomes of 100 CM with 2000 single nucleotide polymorphisms (SNP) was simulated. The QTL numbers were 0.01, 0.05 and 0.1 of total number of SNPs whose effects were simulated by uniform, normal and gamma distributions. The studied threshold traits were either one-threshold (survival) or two-threshold (litter size). Genomic estimated breeding values were predicted by five regression methods including Bayesian Ridge Regression (BRR), Bayes A, Bayes B, Bayes C, and Bayes LASSO. Comparison of prediction accuracy of these methods (correlation between real and estimated breeding value) showed that Bayesian methods are powerful for genomic evaluation and there were no significant differences among them. The proficiency of these methods for one-threshold trait was significantly higher compared to two-threshold trait. Non-significant and irregular variance was observed in accuracy of prediction these methods between different QTL numbers and statistical distributions. Also the results showed that increasing in distance (generation) between reference and target populations will lead to decline in accuracy of prediction due to breakdown of LD between QTL and marker.

Full-Text [PDF 400 kb]   (1010 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/06/17 | Revised: 2017/06/20 | Accepted: 2017/06/17 | Published: 2017/06/17

1. Abdollahi-Arpanahi, R., A. Pakdel, A. Nejati-Javaremi and M. Moradi Shahrbabak. 2013. Comparison of Genomic Evaluation Methods in Complex Traits with Different Genetic Architecture. Journal of Animal Production, 15: 65-77.
2. Bastiaansen, J.W.M., A. Coster, M.P.L. Calus, J.A.M. Van Arendonk and H. Bovenhuis. 2012. Longterm Response to Genomic Selection: Effects of Estimation Method and Reference Population Structure for Different Genetic Architectures. Genetics Selection Evolution, 44: 1-13. [DOI:10.1186/1297-9686-44-3]
3. Calus, M.P.L., 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]
4. Calus, M.P.L. and R.F. Veerkamp. 2007. Accuracy of Breeding Values when Using and Ignoring the Polygenic Effect in Genomic Breeding Value Estimation with a Marker Density of One SNP per cM. Journal of Animal Breeding and Genetics, 124: 362-368. [DOI:10.1111/j.1439-0388.2007.00691.x]
5. Clark, S.A., J.M. Hickey and J.H.J. van der Werf. 2011. Different Models of Genetic Variation and Their Effect on Genomic Evaluation. Genetics Selection Evolution, 43: 18-27. [DOI:10.1186/1297-9686-43-18]
6. Daetwyler, H.D., R. Pong-Wong, B. Villanueva and J.A. Woolliams. 2010. Theimpactof genetic architecture on genome-wide evaluation methods. Genetics, 185: 1021-31. [DOI:10.1534/genetics.110.116855]
7. De losCampos, G. and P. Pérez. 2013a. BGLR=Bayesian Generalized Linear Regression. R Package Version 1.0. https://r-forge.r-project.org/R/?group_id=1525.
8. De los Campos, G., J.M. Hickey, R. Pong-Wong, H.D. Daetwyler and M.P.L. Calus. 2013b. Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding. Genetics, 193: 327-345. [DOI:10.1534/genetics.112.143313]
9. Dekkers, J.C. 2004. Commercial Application of Marker- and Gene-Assisted Selection in Livestock: Strategies and Lessons. Journal of Animal Science, 82: E-Suppl: E313-E328.
10. Gianola, D. (1982). Theory and Analysis of Threshold Characters Journal of Science, 54: 1079-1096. [DOI:10.2527/jas1982.5451079x]
11. Goddard, M. 2008. Genomic Selection Prediction of Accuracy and Maximisation of Long Term Response. Genetica, 136: 245-257. [DOI:10.1007/s10709-008-9308-0]
12. Goddard, M.E. and B.J. Hayes. 2007. Genomic Selection. Journal of Animal Breeding and Genetics, 124: 323-330. [DOI:10.1111/j.1439-0388.2007.00702.x]
13. González-Recio, O. and S. Forni. 2011. Genome-wide Prediction of Discrete Traits Using Bayesian Regressions and Machine Learning. Genetics Selection Evolution, 43: 1-12. [DOI:10.1186/1297-9686-43-7]
14. Habier, D., R.L. Fernando, K. Kizilkayaand D.J. Garrick. 2011. Extension of the Bayesian alphabet for Genomic Selection. BMC Bioinformatics, 12: 186-197. [DOI:10.1186/1471-2105-12-186]
15. Heslot, N., M.E. Sorrells, J.L. Jannink and H.P. Yang. 2012. Genomic Selection in Plant Breeding: a Comparison of Models. Crop Science, 52: 146-160. [DOI:10.2135/cropsci2011.09.0297]
16. Lund, M.S., G. Sahana, D.J. De Koning, G. Su and Ö. Carlborg. 2009 Comparison of Analyses of the QTLMAS XII Common Dataset. I: Genomic selection. BMC Proc. 3(Suppl. 1): S1. [DOI:10.1186/1753-6561-3-S1-S1]
17. McRae, A.F., J.C. McEwan, K.G. Dodds, T. Wilson, A.M. Crawford and J. Slate. 2002. Linkage Disequilibrium in Domestic Sheep. Genetics, 160: 1113-1122.
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. Meuwissen, T., T.R. Solberg, R. Shepherd and J.A. Woolliams. 2009. A Fast Algorithm for BayesB Type of Prediction of Genome-Wide Estimates of Genetic Value. Genetics Selection Evolution, 41: 1-10. [DOI:10.1186/1297-9686-41-2]
20. Muir, W.M. 2007. Comparison of Genomic and Traditional BLUP-Estimated Breeding Value Accuracy and Selection Response under Alternative Trait and Genomic Parameters. Animal Breeding and Genetics, 124: 342-355. [DOI:10.1111/j.1439-0388.2007.00700.x]
21. Nejati-Javaremi, A., C. Smith and J.P. Gibson. 1997. Effect of Total Allelic Relationship on Accuracy of Evaluation and Response to Selection. Journal of Animal Science, 75: 1738-1745. [DOI:10.2527/1997.7571738x]
22. Park, T. and G. Casella. 2008. The Bayesian Lasso. American Statistical Association, 103: 681-686. [DOI:10.1198/016214508000000337]
23. Pszczola, M., T. Strabel, A. Wolc, S. Mucha and M. Szydlowski. 2011. Comparison of Analyses of the QTLMAS XIV Common Dataset. I: Genomic Selection. BMC Proc. 5(Suppl. 3): S1. [DOI:10.1186/1753-6561-5-S3-S1]
24. Resende, M.F.R. Jr., P. Muñoz, M.D.V. Resende, D.J. Garrick, R.L. Fernando, J M. Davis, E.J. Jokela, T.A. Martin, G.F. Peter and M.Kirst. 2012 Accuracy of Genomic Selection Methods in a Standard Dataset of Loblolly Pine (Pinus taeda L.).Genetics, 190: 1503-1510. [DOI:10.1534/genetics.111.137026]
25. Shirali, M., S.R. Miraei-Ashtiani, A. Pakdel, C. Haley and R. Pong-Wong. 2012. Comparison between Bayes C. and GBLUP in Estimating Genomic Breeding Values under Different QTL Variance Distributions, in Abstract from ICQG2012-4th International Conference on Quantitative Genetics, Edinburgh, United Kingdom, pp: 261-268.
26. Solberg, T.R., A.K. Sonesson, J.A. Woolliams and T.H.E. Meuwissen. 2008. Genomic Selection using Different Marker Types and Densities. Journal of Animal Science, 86: 2447-2454. [DOI:10.2527/jas.2007-0010]
27. Son, J., H. Kang, J. Kim, J.E. Park and D. Lee. 2014. Accuracy of Genomic Prediction in Simulated Pig Populations. Proceedings, 10th World Congress of Genetics Applied to Livestock Production, Canada.

Add your comments about this article : Your username or Email:

© 2020 All Rights Reserved | Research On Animal Production(Scientific and Research)

Designed & Developed by : Yektaweb