Bayesian models

Genome-wide prediction of maize single-cross performance, considering non-additive genetic effects

J. P. R. Santos, Pereira, H. D., Von Pinho, R. G., Pires, L. P. M., Camargos, R. B., and Balestre, M., Genome-wide prediction of maize single-cross performance, considering non-additive genetic effects, vol. 14, pp. 18471-18484, 2015.

The prediction of single-cross hybrids in maize is a promising technique for optimizing the use of financial resources in a breeding program. This study aimed to evaluate Genomic Best Linear Unbiased Predictors models for hybrid prediction and compare them with the Bayesian Ridge Regression, Bayes A, Bayesian LASSO, Bayes C, Bayes B, and Reproducing Kernel Hilbert Spaces Regression models, with inclusion or absence of non-additive effects under three heritability scenarios.

Subscribe to Bayesian models