Effects of Marker Density, Number of Quantitative Trait Loci and Heritability of Trait on Genomic Selection Accuracy
الموضوعات :ف. علاء نوشهر 1 , س.ع. رأفت 2 , ر. ایمانی-نبئی 3 , ص. علیجانی 4 , ک. روبرت گرنیه 5
1 - Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
2 - Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
3 - Department of Animal Science, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran
4 - Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
5 - INRA-INPT-ENSAT-INPT-ENVT, Université de Toulouse, UMR 1388 GenPhySE, Castanet Tolosan, France
الکلمات المفتاحية: GBLUP, heritability, BayesA, genomic breeding value, marker density, quantitative trait loci,
ملخص المقالة :
The success of genomic selection mainly depends on the extent of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), number of QTL and heritability (h2) of the traits. The extent of LD depends on the genetic structure of the population and marker density. This study was conducted to determine the effects of marker density, level of heritability, number of QTL, and to compare the accuracy of predicting breeding values using two diverse approaches: GBLUP and BayesA using simulated data under two different distributions of the QTL effect. Thereby, three traits (milk production, carcass weight and mature body weight) were simulated with the heritability of 0.10, 0.30 and 0.50, respectively; for each ovine animal, a genome with three chromosomes, 100 cM each. Three different marker densities was considered (1000, 2000 and 3000 markers) and the number of QTL was assumed to be either 100, 200 or 300. Data were simulated with two different distributions of the QTL effect which were uniform and gamma (α=1.66 and β=0.4) the marker density, number of the QTL, the QTL effect distributions and heritability levels significantly affected the accuracy of genomic breeding values (P<0.05). The BayesA produced estimates with greater accuracies in traits influenced by a low number of the QTL and with the gamma QTL effects distribution. Based on the findings of this simulation, heritability, as well as dense marker panels, aiming to increase the level of LD between the markers and QTL, is likely to be needed for successful implementation of the genomic selection.
Calus M.P.L., Meuwissen T.H.E., De Roos A.P.W. and Veerkamp R.F. (2008). Accuracy of genomic selection using different methods to define haplotypes. Genetics. 178(1), 553-561.
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.
De los Campos G., Naya H., Gianola D., Crossa J., Legarra A., Manfredi E., Weigel K. and Cotes J.M. (2009). Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics. 182, 375-385.
Gianola D. and van Kaam J. (2008). Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics. 178(4), 2289-2303.
Gilmour A.R., Thompson R. and Cullis B.R. (1995). Average information REML: an efficient algorithm for variance pa-rameter estimation in linear mixed models. Biometrics. 51, 1440-1450.
Goddard M. (2008). Genomic selection prediction of accuracy and maximisation of long term response. Genetics. 136, 245-257.
Habier D., Fernando R.L., Kizilkaya K. and Garrick D.J. (2011). Extension of the Bayesian alphabet for genomic selection. BMC Bioinform. 12, 186-193.
Haldane J.B.S. (1919). The combination of linkage values and the calculation of distances between the loci of linked factors. Genetics. 8, 299-309.
Hill W.G. and Robertson A. (1968). Linkage disequilibrium in finite populations. Theor. Appl. Genet. 38, 226-231.
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.
Nejati Javaremi A., Smith C. and Gibson P.J. (1997). Effect of total allelic relationship on accuracy of evaluation and re-sponse to selection. J. Anim. Sci. 75, 1738-1745.
Sargolzaei M. and Schenkel F.S. (2009). QMSim: a large-scale genome simulator for livestock. Bioinformatics. 25, 680-681.
SAS Institute. (2003). SAS®/STAT Software, Release 9.1. SAS Institute, Inc., Cary, NC. USA.
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 vari-ance distributions. Iranian J. Anim. Appl. 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.
Whittaker J.C., Thompson R. and Denham M.C. (2000). Marker-assisted selection using ridge regression. Genet. Res. 75(2), 249-252.