Bayesian inference of mixed models in quantitative genetics of crop species

• Published in: Theoretical and applied genetics Vol. 126; no. 7; pp. 1749 - 1761
• Main Authors: e Silva, Fabyano Fonseca, Viana, José Marcelo Soriano, Faria, Vinícius Ribeiro, de Resende, Marcos Deon Vilela
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.07.2013; Springer; Springer Nature B.V
• Subjects: Agriculture; Barley; Bayes Theorem; Bayesian analysis; Bayesian statistical decision theory; Biochemistry; Biomedical and Life Sciences; Biotechnology; Breeding; computer software; Crops; Crops, Agricultural - genetics; Genetic aspects; grain yield; heritability; Life Sciences; meta-analysis; Models, Genetic; Original Paper; Physiological aspects; Plant Biochemistry; Plant Breeding/Biotechnology; Plant Genetics and Genomics; popcorn; Quantitative genetics; Quantitative Trait, Heritable; Software; variance; Zea mays - genetics; Brazil; Bayesian Inference; Markov Chain Monte Carlo; Posterior Distribution; Additive Genetic Variance; Markov Chain Monte Carlo Method
• ISSN: 0040-5752; 1432-2242
• DOI: 10.1007/s00122-013-2089-6

The objectives of this study were to implement a Bayesian framework for mixed models analysis in crop species breeding and to exploit alternatives for informative prior elicitation. Bayesian inference for genetic evaluation in annual crop breeding was illustrated with the first two half-sib selection cycles in a popcorn population. The Bayesian framework was based on the Just Another Gibbs Sampler software and the R2jags package. For the first cycle, a non-informative prior for the inverse of the variance components and an informative prior based on meta-analysis were used. For the second cycle, a non-informative prior and an informative prior defined as the posterior from the non-informative and informative analyses of the first cycle were used. Regarding the first cycle, the use of an informative prior from the meta-analysis provided clearly distinct results relative to the analysis with a non-informative prior only for the grain yield. Regarding the second cycle, the results for the expansion volume and grain yield showed differences among the three analyses. The differences between the non-informative and informative prior analyses were restricted to variance components and heritability. The correlations between the predicted breeding values from these analyses were almost perfect.