Bayesian methods for estimating GEBVs of threshold traits

• Published in: Heredity Vol. 110; no. 3; pp. 213 - 219
• Main Authors: Wang, C-L, Ding, X-D, Wang, J-Y, Liu, J-F, Fu, W-X, Zhang, Z, Yin, Z-J, Zhang, Q
• Format: Journal Article
• Language: English
• Published: England Springer Nature B.V 01.03.2013; Nature Publishing Group
• Subjects: Algorithms; Animals; Bayes Theorem; Bayesian analysis; Breeding - statistics & numerical data; Cattle; Computer Simulation; Female; Genomics; Genotype; Humans; Male; Markov Chains; Models, Genetic; Monte Carlo Method; Original; Phenotype; Quantitative Trait, Heritable; Selection, Genetic
• ISSN: 0018-067X; 1365-2540
• DOI: 10.1038/hdy.2012.65

Estimation of genomic breeding values is the key step in genomic selection (GS). Many methods have been proposed for continuous traits, but methods for threshold traits are still scarce. Here we introduced threshold model to the framework of GS, and specifically, we extended the three Bayesian methods BayesA, BayesB and BayesCπ on the basis of threshold model for estimating genomic breeding values of threshold traits, and the extended methods are correspondingly termed BayesTA, BayesTB and BayesTCπ. Computing procedures of the three BayesT methods using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the benefit of the presented methods in accuracy with the genomic estimated breeding values (GEBVs) for threshold traits. Factors affecting the performance of the three BayesT methods were addressed. As expected, the three BayesT methods generally performed better than the corresponding normal Bayesian methods, in particular when the number of phenotypic categories was small. In the standard scenario (number of categories=2, incidence=30%, number of quantitative trait loci=50, h² = 0.3), the accuracies were improved by 30.4%, 2.4%, and 5.7% points, respectively. In most scenarios, BayesTB and BayesTCπ generated similar accuracies and both performed better than BayesTA. In conclusion, our work proved that threshold model fits well for predicting GEBVs of threshold traits, and BayesTCπ is supposed to be the method of choice for GS of threshold traits.