BMTME

BMTME implements Bayesian multi-trait and multi-environment genomic selection models in R to analyze genotype-by-environment interactions and predict multiple trait responses.

Key Features:

• Bayesian Framework: Employs a Bayesian approach to incorporate prior information and quantify uncertainty in genomic predictions.
• Multi-trait and Multi-environment Analysis: Models multiple correlated traits and multiple environments simultaneously to capture genotype-by-environment and trait correlations.
• Modeling Diverse Response Variables: Supports continuous, binary, ordinal, and count response variables.
• Bayesian Multi-output Regressor Stacking (BMORS): Includes BMORS functions BMORS() and BMORS_Env() to provide computationally efficient multi-output regression stacking.
• Parameter Estimation and Prediction Performance Evaluation: Performs parameter estimation and evaluates prediction performance for multi-trait multi-environment models.

Scientific Applications:

• Plant Breeding: Dissects genotype-by-environment interactions and informs selection of genotypes that perform across environments in crop breeding.
• Animal Breeding: Applies multi-trait multi-environment genomic prediction to improve selection decisions in animal populations.
• Medical Genetics: Supports genomic prediction tasks involving multiple correlated phenotypes across varying conditions in medical genetics research.

Methodology:

Implements Bayesian multi-trait multi-environment models, includes BMORS() and BMORS_Env() for multi-output regressor stacking, and conducts parameter estimation and prediction performance evaluation.