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

Document Type : Original Research Article (Regular Paper)


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


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. 


Main Subjects

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