The Impact of Different Genetic Architectures on Accuracy of Genomic Selection Using Three Bayesian Methods

الموضوعات :

ف. علاء نوشهر ¹ (Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
س.ع. رأفت ² (Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
ر. ایمانی-نبئی ³ (Department of Animal Science, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran)
ص. علیجانی ⁴ (Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
ک. روبرت گرنیه ⁵ (INRA-INPT-ENSAT-INPT-ENVT, Université de Toulouse, UMR 1388 GenPhySE, Castanet Tolosan, France)

تاريخ الإرسال : 26 الجمعة , رمضان, 1437 تاريخ التأكيد : 14 الخميس , ذو الحجة, 1437 تاريخ الإصدار : 13 الخميس , جمادى الثانية, 1439

الکلمات المفتاحية: LASSO, BayesA, marker density, BayesB, genomic accuracy, Ne,

ملخص المقالة :

Genome-wide evaluation uses the associations of a large number of single nucleotide polymorphism (SNP) markers across the whole genome and then combines the statistical methods with genomic data to predict the genetic values. Genomic predictions relieson linkage disequilibrium (LD) between genetic markers and quantitative trait loci (QTL) in a population. Methods that use all markers simultaneously may therefore result in greater reliabilities of predictions of the total genetic merit, indicating that a larger proportion of the genetic variance is explained. This is hypothesized that the genome-wide methods deal differently with genetic architecture of quantitative traits and genome. The genomic nonlinear Bayesian variable selection methods (BayesA, BayesBand Bayesian LASSO) are compared using the stochastic simulation across three effective population sizes (Ne). Thereby, a genome with three chromosomes, 100 cM each was simulated. For each animal, a trait was simulated with heritability of 0.50, three different marker densities (1000, 2000 and 3000 markers) and number of the QTL was assumed to be either 100, 200 or 300. The data were simulated with two different QTL distributionswhich were uniform and gamma (α=1.66, β=0.4). Marker density, number of the QTL and the QTL effect distributions affected the genomic estimated breeding value accuracy with different Ne (P<0.05). In comparison of three methods, the greatest genomic accuracy obtained by BayesB method for traits influenced by a low number of the QTL, high marker density, gamma QTL distribution and high Ne.

المصادر:

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.

Goddard M. (2009). 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.

Nadaf J. and Pong-Wong R. (2011). Applying different genomic evaluation approaches on QTLMAS2010 dataset. BMC Proc. 5(3), 9-16.

Park T. and Casella G. (2008). The Bayesian LASSO. J. Am. Stat. Assoc. 103, 681-686.

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. (2015). 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. Iranian J. Appl. Anim. Sci. 5(1), 41-46.

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.

Wimmer V., Lehermeier C., Albrecht T., Auinger H.J., Wang Y. and Schön C.C. (2013). Genome-wide prediction of traits with different genetic architecture through efficient variable selection. Genetics. 195, 573-587.

Yi N. and Xu S. (2008). Bayesian LASSO for quantitative trait loci mapping. Genetics. 179, 1045-1055.