subject area of
- Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments
- Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R
- Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis
- Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials.
- Phantom Epistasis in Genomic Selection: On the Predictive Ability of Epistatic Models.
- Simulating the effects of dominance and epistasis on selection response in the CIMMYT wheat breeding program using QuCim