Accuracy of Genomic Prediction under Different Genetic Architectures and Estimation Methods

Subject Areas : Camel

ع. عاطفی ¹ , ع.ا. شادپرور ² , ن. قوی حسین-زاده ³

1 - Department of Animal Science, Faculty of Agricultural Science, University of Guilan, Rasht, Iran
2 - Department of Animal Science, Faculty of Agricultural Science, University of Guilan, Rasht, Iran
3 - Department of Animal Science, Faculty of Agricultural Science, University of Guilan, Rasht, Iran

Received: 2017-02-14 Accepted : 2017-07-01 Published : 2018-03-01

Keywords: Accuracy, heritability, QTL, bayesian, genomic, genetic architecture,

Abstract :

The accuracy of genomic breeding value prediction was investigated in various levels of reference population size, trait heritability and the number of quantitative trait locus (QTL). Five Bayesian methods, including Bayesian Ridge regression, BayesA, BayesB, BayesC and Bayesian LASSO, were used to estimate the marker effects for each of 27 scenarios resulted from combining three levels for heritability (0.1, 0.3 and 0.5), training population size (600, 1000 and 1600) and QTL numbers (50, 100 and 150). A finite locus model was used to simulate stochastically a historical population consisting 100 animals at first 100 generations. Through next 100 generations, the population size gradually increased to 1000 individuals. Then the animals in generations 201 and 202 having both known genotypic and phenotypic records were assigned as reference population, and individuals at generations 203 and 204 were considered as validation population. The genome comprised five chromosomes of 100 cM length and 500 single nucleotide polymorphism markers for each chromosome that distributed through the genome randomly. The QTLs and markers were bi-allelic. In this study, the heritability had great significant positive effect on the accuracy (P<0.001). By increasing the size of the reference population, the average genomic accuracy increased from 0.64±0.03 to 0.70 ± 0.04 (P<0.001). The accuracy responded to increasing number of QTLs non-linearly. The highest and lowest accuracies of Bayesian methods were 0.40 ± 0.04 and 0.84 ± 0.05, respectively. The results showed having the greatest amount of information (i.e. highest heritability, highest contribution of gene action in phenotypic variation and large reference population size), the highest accuracy (0.84) was obtained, with all investigated methods of estimation.

References:

Bastiaansen J.W., Bink M.C., Coster A., Maliepaard C. and Calus M.P. (2010). Comparison of analyses of the QTLMAS XIII common dataset. I: genomic selection. BMC Proc. 4(1), 1-11.

Calus M. and Veerkamp R. (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. J. Anim. Breed. Genet. 124(6), 362-368.

Clark S.A., Hickey J.M. and Van der Werf J.H. (2011). Different models of genetic variation and their effect on genomic evaluation. Genet. Sel. Evol. 43(18), 12.

Coster A., Bastiaansen J.W., Calus M.P., van Arendonk J.A. 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. Sel. Evol. 42, 9.

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(3), 1021-1031.

Daetwyler H.D., Villanueva B., Bijma P. and Woolliams J.A. (2007). Inbreeding in genome wide selection. J. Anim. Breed. Genet. 124(6), 369-376.

Daetwyler H.D., Villanueva B. and Woolliams J.A. (2008). Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One. 3(10), e3395.

De Los Campos G., Hickey J.M., Pong-Wong R., Daetwyler H.D. and Calus M.P. (2013). Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics. 193(2), 327-345.

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(1), 375-385.

Dekkers J. (2007). Prediction of response to marker assisted and genomic selection using selection index theory. J. Anim. Breed. Genet. 124(6), 331-341.

Dekkers J. (2012). Application of genomics tools to animal breeding. Curr. Genom. 13(3), 207-212.

Gianola D., Fernando R.L. and Stella A. (2006). Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics. 173(3), 1761-1776.

Goddard M. (2009). Genomic selection: Prediction of accuracy and maximisation of long term response. Genetica. 136(2), 245-257.

Habier D., Fernando R. and Dekkers J. (2007). The impact of genetic relationship information on genome-assisted breeding values. Genetics. 177(4), 2389-2397.

Hayes B., Bowman P., Chamberlain A. and Goddard M. (2009). Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 92(2), 433-443.

Heslot N., Yang H.P., Sorrells M.E. and Jannink J.L. (2012). Genomic selection in plant breeding: a comparison of models. Crop. Sci. 52(1), 146-160.

Lorenzana R.E. and Bernardo R. (2009). Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor. Appl. Genet. 120(1), 151-161.

Luan T., Woolliams J.A., Lien S., Kent M., Svendsen M. and Meuwissen T.H. (2009). The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genet. 183(3), 1119-1126.

Meuwissen T.H.E.,HayesB.J.andGoddardM.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157(4), 1819-1829.

Moser G., Tier B., Crump R.E., Khatkar M.S. and Raadsma H.W. (2009). A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genet. Sel. Evol. 41, 56.

Muir W. (2007). Comparison of genomic and traditional BLUP estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J. Anim. Breed. Genet. 124(6), 342-355.

Park T. and Casella G. (2008). The bayesian lasso. J. Am. Stat. Assoc. 103(482), 681-686.

Pérez P. and De Los Campos G. (2014). Genome-wide regression and prediction with the BGLR statistical package. Genet. Genet. 114, 164442.

Piyasatian N. and Dekkers J. (2013). Accuracy of genomic prediction when accounting for population structure and polygenic effects. Anim. Ind. Rep. 659(1), 68.

Tibshirani R. (1996). Regression shrinkage and selection via the lasso. J. Royal Stat. Soc. Series B (Methodological). 58(1), 267-288.

VanRaden P.M. and Sullivan P.G. (2010). International genomic evaluation methods for dairy cattle. Genet. Sel. Evol. 42, 7.

Verbyla K.L., Bowman P.J., Hayes B.J. and Goddard M.E. (2010). Sensitivity of genomic selection to using different prior distributions. BMC Proc. 4(5), 34-39.

Wientjes Y.C., Veerkamp R.F., Bijma P., Bovenhuis H., Schrooten C. and Calus M.P. (2015). Empirical and deterministic accuracies of across population genomic prediction. Genet. Sel. Evol. 47, 5.