Comparing genomic prediction models for genomic selection of traits with additive and dominance genetic architecture

Document Type : Original Research Article (Regular Paper)

Authors

Department of Animal Science, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

Abstract

ABSTRACT
The purpose of this research was to compare different statistical methods such as GBLUP, BayesA, BayesB, BayesC, BayesL, Ridge regression, Boosting and SVM for genomic evaluation of traits with additive and dominance genetic architecture. A genome consisting of 5 chromosomes was simulated, with 1000 single nucleotide polymorphism markers (SNP) uniformly distributed on each chromosome. In two different scenarios, 50 and 500 quantitative trait loci (QTL) were considered and in each scenario of QTL number, 0.00, 10, 20, 50 and 100% of QTLs were given dominance genetic effect. The prediction accuracy, bias and reliability of genomic breeding values were used for analyzing the results and comparing the methods. The results showed that not separating the dominance effects from the additive effects lead to a decrease in the accuracy and reliability and an increase in the bias of the predicted genomic breeding values. In all examined scenarios of the QTL number and percentages of QTLs with dominance effect, the Bayesian methods had higher prediction accuracy and reliability and their predictions had the least bias. Boosting predicted the genomic breeding values with the lowest accuracy and reliability and highest bias. The performance of SVM and Ridge regression was better than Boosting, but lower than Bayesian methods and GBLUP. In terms of computing speed, GBLUP and Boosting were, respectively, the fastest and the slowest method. It can be concluded that to increase the efficiency of genomic selection, first, the dominance genetic effects need to be included in the model and, second, methods with the highest predictive performance should be used. 

Keywords

Main Subjects

References

References
Abdollahi-Arpanahi, R., Pakdel A., Nejati-Javaremi, A., Moradi Shahre Babak, M., 2013. Comparison of different methods of genomic evaluation in traits with different genetic architecture. Journal of Animal Production 15, 65-77.
Ahmadi, Z., Ghafouri-Kesbi, F., Zamani, P., 2021. Assessing the performance of a novel method for genomic selection: rrBLUP method6. Journal of Genetics 100, 24.
Aliloo, H., Pryce, J.E., González-Recio, O., Cocks, B.G., Hayes, B.J., 2016. Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits. Genetic Selection Evolution 48, 8.
Andrade, L.R.B., Sousa, M.B., Oliveira, E.J., Resende M.D.V., Azevedo, C.F., 2019. Cassava yield traits predicted by genomic selection methods. PLoS One 14, e0224920.
Amiri Roudbar, M., Mohammadabadi, M.R., Mehrgardi, A.A., Abdollahi-Arpanahi, R., 2017. Estimates of variance components due to parent-of-origin effects for body weight in Iran-Black sheep. Small Ruminant Research 149, 1-5.
Amiri Roudbar, M., Abdollahi-Arpanahi, R., Mehrgardi A.A., Mohammadabadi, M.R., Taheri Yeganeh, A., Rosa, G.J.M., 2018. Estimation of the variance due to parent-of-origin effects for productive and reproductive traits in Lori-Bakhtiari sheep. Small Ruminant Research 160, 95-102
Baneh, H., Nejati, Javaremi A., Honarvar, M., Rahimi, G.H., 2017. Genomic evaluation of threshold traits with different genetic architecture using Bayesian approaches. Research on Animal Production 8, 149-154.
Boison, S.A., Santos, D.J.A., Utsunomiya, A.H.T, Carvalheiro, R., Neves, H.H.R., O’Brien, A.M.P., Garcia, J.F., Sölkner, J., Da Silva, M.V.G.B., 2015. Strategies for single nucleotide polymorphism (SNP) genotyping to enhance genotype imputation in Gyr (Bos indicus) dairy cattle: Comparison of commercially available SNP chips. Journal of Dairy Science 98, 4969-4989.
Boser, B., Guyon, I.M., Vapnik, V., 1992. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory. Pittsburgh, USA.
de los Campos G., Perez-Rodriguez, P., 2020. BGLR: Bayesian generalized linear regression. https://cran.r-project.org/web/packages/BGLR/BGLR.pdf
Doublet, A.C., Croiseau, P., Fritz S., Michenet, A., Hozé, C., Danchin-Burge, C., Laloë D., Restoux, G., 2019. The impact of genomic selection on genetic diversity and genetic gain in three French dairy cattle breeds. Genetic Selection Evolution 51, 52.
 Ebrahimi, K., Dashab, G.R., Faraji-Arough, H., Rokouei, M., 2018 Estimation of additive and non-additive genetic variances of body weight in crossbreed populations of the Japan Quail. Poultry Science 1, 46-55.
Falconer, D.S., Mackay, T.F.C., 1996. Introduction to Quantitative Genetics, 4th ed. Longman Group. Harlow, Essex, UK.
Fernando, R.L., Grossman, M., 1989. Marker-assisted selection using best linear unbiased prediction. Genetic Selection Evolution 2, 467.
Ghafouri-Kesbi, F., Rahimi-Mianji, G., Honarvar, M., Nejati-Javaremi, A., 2016. Predictive ability of random forests, boosting, support vector machines and genomic best linear unbiased prediction in different scenarios of genomic evaluation. Animal Production. Science 57, 229-236.
Ghasemi, M., 2019. Genomic evaluation of threshold traits considering different number of threshold using some parametric and non-parametric statistical methods. M.Sc. Thesis. Bu-Ali Sina University, Hamedan, Iran.
Hastie, T.J., Tibshirani, R., Friedman, J., 2009. The elements of statistical learning. Springer Publishing. New York, USA.
Hill, W.G., 2008. Estimation, effectiveness and opportunities of long term genetic improvement in animals and maize. Lohman Information 43, 3-19.
Hill, W.G., Robertson, A., 1968. Linkage disequilibrium in finite populations. Theoretical and Applied Genetics 38, 226-231.
Howard, R., Carriquiry, A.L., Beavis, W.D., 2014. Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures. Genetics 4, 1027-1046.
Legarra, A., Reverter, A., 2018. Semi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method. Genetic Selection Evolution 50, 53.
Macedo, F.L., Christensen, O.F., Astruc, J.M., Aguilar, I., Masuda, Y., Legarra, A., 2020. Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups. Genetic Selection Evolution 52, 47.
Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829.
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, K., 2013. Misc publication of the Department of Statistics (e1071), TU Wien. Available at: http://cran.r-project.org/web/packages/e1071/index.html
Mohammadi, Y., Sattaei Mokhtari, M., 2018. Genomic selection accuracy parametric and nonparametric statistical methods with additive and dominance genetic architectures. Research on Animal Production 8, 161-167.
Mohammadabadi, M.R., Torabi, A., Tahmourespoor, M., Baghizadeh, A., Esmailizadeh Koshkoie, A., Mohammadi, A., 2010. Analysis of bovine growth hormone gene polymorphism of local and Holstein cattle breeds in Kerman province of Iran using polymerase chain reaction restriction fragment length. African Journal of Biotechnology 9, 6848-6852.
Moradi, M., Abdollahi-Arpanahi, R., Hemati, B., Lavvaf, A., 2017. Comparison of parametric and resampling methods in genetic evaluation of quantitative traits with different genetic structure. Journal of Animal Production 19, 1-12.
Neves, H.H.R., Carvalheiro, R., Queiroz, S.A., 2012. A comparison of statistical methods for genomic selection in a mice population. BMC Genetics 13, 100.
Sadeghi, S.A.T., Rokoue, M., Vafaye Valleh, M., Abbasi, M.A., Faraji-Arough, H., 2019. Estimation of additive and non-additive genetic variance component for growth traits in Adani goats. Tropical Animal Health and Production 52, 733-742.
Sahebalam, H., Gholizadeh, M., Hafezian, H., Ebrahimi, F., 2022. Evaluation of Bagging approach versus GBLUP and Bayesian LASSO in genomic prediction Journal of Genetics 101, 19.
Sahebalam, H., Gholizadeh, M., Hafezian H., Farhadi, A., 2019. Comparison of parametric, semiparametric and nonparametric methods in genomic evaluation. Journal of Genetics 98, 102.
Salehi, A., Bazrafshan, M., Abdollahi-Arpanahi, R., 2021. Assessment of parametric and non-parametric methods for prediction of quantitative traits with non-additive genetic architecture. Annals of Animal Science 21, 469-484.
Schefers, J.M., Weigel, K.A., 2012. Genomic selection in dairy cattle: Integration of DNA testing into breeding programs. Animal Frontiers 2, 4-9.
Schölkopf, B., Smola, A., 2005. Support Vector Machines. Encyclopedia of Biostatistics, 1 5328-5335.
Technow, F., 2013. hypred: simulation of genomic data in applied genetics. Available at: http://cran.r-project.org/web/packages/hypred/index.html.
VanRaden, P.M., 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414-4423.
Wang, C.L., Ding, X.D., Wang, J.Y., Liu, J.F., Fu, W.X., Zhang, Z., Yin, J., Zhang, Q., 2013. Bayesian methods for estimating GEBVs of threshold traits. Heredity 110, 213-219.
Zhang, A., Wang, H., Beyene, Y., Semagn, K., 2017. Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations. Frontiers in Plant Science 8, 1916.

Files

Share

How to cite

Statistics