A Comparison of GBLUP and Bayesian Methods in Prediction of Genomic Breeding Values under Different Genetic Architectures

Document Type : Genetics & breeding


1 Animal Science Department of University of Mohaghegh Ardebili, Ardebil, Iran.

2 Department of Animal Sciences, Faculty of Agricultural Sciences, University of Tabriz, Tabriz, Iran

3 Department of Animal Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.


Introduction[1] Genomic Selection (GS) has been proved to be a powerful tool for estimating genetic values in livestock breeding. Newly developed sequencing technologies have dramatically reduced the cost of genotyping and significantly increased the scale of genotype data that used for GS. The estimation of breeding values in order to select the best animals as parents of the next generation is the main goal of animal breeding programs. Traditional methods of genetic evaluation were performed using a combination of phenotypic and pedigree information to produce estimated breeding values. Most simulation studies of genomic selection (GS) methods have considered genetic architectures in which the number and relative magnitudes of quantitative trait loci (QTL) have varied. Among the Bayesian methods, those using marker-specific shrinkage of effects (e.g., BayesA or BayesB of or the Bayesian LASSO are commonly used in animal breeding applications. The Bayesian methods proposed differ in the way of looking at the variances of parameters. In classical livestock breeding methods, selection for important economic traits using pedigree information with individual phenotypic records was performed and best Linear Prediction of Breeding Values (BLUP) is achieved. In genome selection, genomic breeding values of all individuals can be predicted with high accuracy using a linear model. Various factors can be affecting the accuracy of genomic breeding values. Therefore, the present study aimed to evaluate the accuracy of estimating genomic breeding values in different genetic architectures including different distributions of gene effects, different numbers of QTL, different levels of heritability and different marker densities using GBLUP and Bayesian methods including Bayes A, Bayes B, Bayes C and Bayes LASSO. In addition to comparing the performance of different methods in different genetic architectures, a marker density and QTL numbers were introduced for simulation programs of sheep populations.
Materials and Methods To create a basic population (G0), 100 heads of livestock, including 50 males and 50 females, were considered. The frequency of primary alleles for single-nucleotide polymorphisms in the basal generation was considered to be 0.5. To create the first generation (G1), the parents were randomly selected from the males and females of the G0 generation. Parental gametes were simulated based on the assumption of disconnection imbalance using the Halldan location function method, and then randomly generated gametes were randomly selected and mixed to create a new generation of G1 generation. A genome with a length of 300 cM was simulated and 500, 1000 and 1500 SNPs were equally spaced over the chromosome. Three different numbers of QTL (50, 100 and 150) were considered and QTLs were uniformly distributed over the chromosome. One hundred individuals, including 50 males and 50 females, were simulated for the base population. The first generation structure was followed through to the 50th generation of random mating to make linkage disequilibrium populations. Generation 51 was assumed as a training population and the other generations (52 to 60) as validation populations. Five methods, GBLUP, Bayes A, Bayes B, Bayes C and Bayesian LASSO, were used to estimate genomic breeding values.
Results and Discussion In all five methods, the accuracy of genomic values decreased as the number of QTLs increased from 50 to 150. The reason for this can be attributed to the limited amount of genetic variance distributed over many QTLs. Also predicting accuracy of all five methods increased with increasing marker density. Results showed that increasing marker density at low (0.1) and high (0.5) heritability levels, increased genomic accuracy but increasing at moderate heritability (0.3) traits did not affect the accuracy of genomic evaluation. Accuracy of genomic breeding values in the gamma distribution provides better gene effects to uniform distributions.
Conclusion The results showed that factors such as marker density, QTL numbers, distribution QTL effect and trait heritability were effective in estimating the accuracy of genomic breeding values. In high heritability traits, the higher markers density and lower QTL numbers, leading to increase accuracy of estimating genomic breeding values. In genomic studies, if the trait is affected by a small number of QTLs, estimation of breeding values by Bayes B method can yield a more favorable result. Marker densities did not affect the accuracy of genomic evaluation in traits of moderate heritability, and since most of the economic traits in native species of sheep are moderate heritability, 500 to 1000 markers can be used to estimate breeding values in simulation programs.



1- Buch, L. H., M. K. Sørensen, P. Berg, L. D. Pedersen, and A. C. Sørensen. 2012. Genomic selection strategies in dairy cattle: strong positive interaction between use of genotypic information and intensive use of young bulls. Journal of Animal Breeding and Genetics, 129: 138-151.
2- Calus, M. P. L., T. H. E, Meuwissen, A. P. W, De Roos, and R. F. Veerkamp. 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics, 178:553-561.
3- Chamberlain, A. J., H. C. McPartlan, and M. E. Goddard.2007. The Number of Loci That Affect Milk Production Traits in Dairy Cattle. Genetics, 177: 1117-1123.
4- Coster, A., J. W. M, Bastiaansen, M. P. L, Calus, J. A. M, Van Arendonk, and H. Bovenhuis. 2010. Sensitivity of methods for estimating breeding values using genetic markers to the num-ber of QTL and distribution of QTL variance. Genetic Selection Evolution, 42: 9-15.
5- Daetwyler, H. D., R. Pong-Wong, B. Villanueva and J. A. Woolliams. 2010. The impact of genetic architecture on genome-wide evaluation methods. Genetics, 185: 1021-1031.
6- Gilmour, A. R., R. Thompson, B. R. Cullis. 1995. Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biomet, 51: 1440-1450.
7- Goddard, M.E. 2008. Genomic selection prediction of accuracy and maximization of long term response. Genetics, 136: 245-257.
8- Haldane, J. B. S. 1919. The combination of linkage values and the calculation of distance between the loci of linked factors. Genetics, 8: 299–309.
9- Meuwissen, T. H. E. 2009. Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genetic Selection Evolution, 41: 35-47.
10- Meuwissen, T. H. E., B. Hayes, and M. E .Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157: 1819–1829.
11- Muir, W. M., G. K. S. Wong, Y. Zhang, J. Wang, M. A. M Groenen, R. Crooijmans, H. J. Megens, H. Zhang, R. Okimoto, A. Vereijken, A. Jungerius, G. A. A. Albers, C. T. Lawley, M. E .Delany, S. MacEachern, and H. H. Cheng. 2007. Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds. Proceedings of the National Academy of Sciences of the United States of America, 105: 17312-17317.
12- Nejati-Javaremi, A., C. Smith, and P. J. Gibson.1997. Effect of total allelic relationship on accuracy of evaluation and response to selection. Journal of Animal Science, 75: 1738-1745.
13- R Core, T.2015. R A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
14- Saatchi, M., J. Ward, and D. J. Garrick. 2013. Accuracies of direct genomic breeding values in Hereford beef cattle using national or international training populations. Journal of Animal Science, 91: 1538–1551.
15- Sargolzaei, M. and F.S. Schenkel.2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics, 25: 680-681.
16- 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.
17- Sved, J.A. 1971. Linkage disequilibrium and homozygosity of chromosome segments in finite populations. Theorical Population Biology, 2: 125-141.
18- Villumsen, T. M., L. Janss, and M. S. Lund.2009. The importance of haplotype length and heritability using genomic selection in dairy cattle. Journal of Animal Breeding and Genetics, 126: 3-13.
19- Wimmer, V., C. Lehermeier, T. Albrecht, H. J .Auinger, Y. Wang, and C. C. Schön. 2013. Genome-wide prediction of traits with different genetic architecture through efficient variable selection. Genetics, 195: 573-587.
  • Receive Date: 22 May 2019
  • Revise Date: 07 September 2019
  • Accept Date: 29 September 2019
  • First Publish Date: 21 June 2020