# MTMEGPS: An R package for multi-trait and multi-environment genomic and phenomic selection using deep learning

## TL;DR

MTMEGPS is an R package that uses deep learning to improve genomic and phenomic selection in breeding by handling multiple traits and environments efficiently.

## Contribution

MTMEGPS introduces an accessible R package for deep learning-based multi-trait and multi-environment genomic and phenomic selection.

## Key findings

- MTMEGPS showed superior predictive ability compared to benchmark models in most scenarios.
- The package performed particularly well under uni-trait for internal datasets and multi-trait for independent multi-environment datasets.
- Mean squared error values were moderate across all models tested.

## Abstract

Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often fail to represent. However, despite its potential, the adoption of DL in breeding programs remains limited due to its computational demands and the lack of accessible tools for users without extensive programming experience. This study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction. The package supports data preparation, hyperparameter optimization, model training, and DL-based evaluation. To assess its performance, MTMEGPS was applied to the two default datasets included in the package: Maize (genomic data) and Eucalyptus (near-infrared spectroscopy, NIR, data), as well as to an independent publicly available multi-environment validation dataset. Across most scenarios, MTMEGPS showed superior predictive ability compared with all benchmark models, particularly under UT for the internal datasets and MT for the independent multi-environment dataset. Mean squared error (MSE) values were similar across models, all falling within a moderate range. Overall, these results demonstrate the efficiency and practical utility of MTMEGPS for genomic and phenomic selection, even in scenarios where prediction errors remain moderate.