A Comparison of the Sensitivity of the BayesC and Genomic Best Linear Unbiased Prediction(GBLUP) Methods of Estimating Genomic Breeding Values under Different Quantitative Trait Locus(QTL) Model Assumptions
الموضوعات :M. Shirali 1 , S.R. Miraei-Ashtiani 2 , A. Pakdel 3 , C. Haley 4 , P. Navarro 5 , R. Pong-Wong 6
1 - Department of Animal Science, Faculty ofAgriculture and Natural Resources, University of Tehran, Karaj, Iran
2 - Department of Animal Science, Faculty ofAgriculture and Natural Resources, University of Tehran, Karaj, Iran
3 - Department of Animal Science, Faculty ofAgriculture and Natural Resources, University of Tehran, Karaj, Iran
4 - Division of Genetics and Genomics, The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, United Kingdom
5 - Medical Research Council Human Genetics (MRC) Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
6 - Division of Genetics and Genomics, The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, United Kingdom
الکلمات المفتاحية: BayesC, breeding value, GBLUP, number of QTL, QTL effect distribution,
ملخص المقالة :
The objective of this study was to compare the accuracy of estimating and predicting breeding values using two diverse approaches, GBLUP and BayesC, using simulated data under different quantitative trait locus(QTL) effect distributions. Data were simulated with three different distributions for the QTL effect which were uniform, normal and gamma (1.66, 0.4). The number of QTL was assumed to be either 5, 10 or 20. In total, 9 different scenarios were generated to compare the markers estimated breeding values obtained from these scenarios using t-tests. In comparisons between GBLUP and BayesC within different scenarios for a trait of interest, the genomic estimated breeding values produced and the true breeding values in a training set were highly correlated (r>0.80), despite diverse assumptions and distributions. BayesC produced more accurate estimations than GBLUP in most simulated traits. In all scenarios, GBLUP had a consistently high accuracy independent of different distributions of QTL effects and at all numbers of QTL. BayesC produced estimates with higher accuracies in traits influenced by a low number of QTL and with gamma QTL effects distribution. In conclusion, GBLUP and BayesC had persistent high accuracies in all scenarios, although BayesC performed better in traits with low numbers of QTL and a Gamma effect distribution.
Coster A., Bastiaansen J.W.M., Calus M.P.L., Van Arendonk J.A.M. and Bovenhuis H. (2010). Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance. Genet. Select. Evol. 42, 9-15.
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., Villanueva B., Bijma P. and Woolliams J.A. (2007). Inbreeding in genome-wide selection. J. Anim. Breed. Genet. 124, 369-376.
Dekkers J.C.M. (2007). Prediction of response from marker-assisted and genomic selection using selection index theory. J. Anim. Breed. Genet. 124, 331-341.
Gilmour A.R., Thompson R. and Cullis B.R. (1995). Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics. 51, 1440-1450.
Goddard M. (2008). Genomic selection prediction of accuracy and maximisation of long term response. Genetica. 136, 245-257.
Haldane J.B.S. (1919). The combination of linkage values and the calculation of distances between the loci of linked factors. J. Genet. 8, 299-309.
Hayes B.J., Visscher P.M. and Goddard M.E. (2009). Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. 91, 47-60.
Henderson C.R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics. 31, 423-447.
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, 321-322.
Nadaf J. and Pong-Wong R. (2011). Applying different genomic evaluation approaches on QTLMAS2010 dataset. BMC Proc. 5(3), 9-16.
Nejati Javaremi A., Smith C. and Gibson P.J. (1997). Effect of total allelic relationship on accuracy of evaluation and response to selection. J. Anim. Sci. 75, 1738-1745.
Shirali M., Miraei-Ashtiani S.R., Pakdel A., Haley C. and Pong-Wong R. (2012). Comparison between Bayesc and GBLUP in estimating genomic breeding values under different QTL variance distributions. Iranian J. Anim. Sci. 43, 261-268.
Solberg T.R., Sonesson A.K., Woolliams J.A. and Meuwissen T.H.E. (2008). Genomic selection using different marker types and densities. J. Anim. Sci. 86, 2447-2454.
Sved J.A. (1971). Linkage disequilibrium and homozygosity of chromosome segments in finite populations. Theor. Popul. Biol. 2, 125-141.
Tibshirani R. (1996). Regression shrinkage and selection via the Lasso. J. Roy. Stat. Soc. B Met. 58, 267-288.
Villanueva B., Pong-Wong R., Fernandez J. and Toro M.A. (2005). Benefits from marker-assisted selection under an additive polygenic genetic model. J. Anim. Sci. 83, 1747-1752.
Visscher P.M. and Haley C.S. (1998). Strategies for marker-assisted selection in pig breeding programmes. Pp. 503-510 in Proc. 6th World Cong. Genet. Appl. Livest. Prod. WCGALP, Armidale, Australia.
Woolliams J.A., Pong-Wong R. and Villanueva B. (2002). Strategic optimisation of short- and long-term gain and inbreeding in MAS and non-MAS schemes. Pp. 23-25 in Proc. 7th World Cong. Genet. Appl. Livest. Prod. Montpellier, France.
