Multi-trait multi-environment genomic prediction of preliminary yield trials in pulse crops

Phenotypic selection in preliminary yield trials (PYT) is challenged by limited seeds, resulting in trials with few replications and environments. The emergence of multi-trait multi-environment enabled genomic prediction (MTME-GP) offers opportunity for enhancing prediction accuracy and genetic gain across multiple traits and diverse environments. Using a set of 300 advanced breeding lines in the North Dakota State University (NDSU) pulse crop breeding program, we assessed the efficiency of a MTME-GP model for improving seed yield and protein content in field peas in stress and non-stress environments. MTME-GP significantly improved predictive ability, improving up to 2.5-fold, particularly when a significant number of genotypes overlapped across environments. Heritability of the training environments contributed significantly to the overall prediction of the model. Average predictive ability ranged from 3 to 7-folds when environments with low heritability were excluded from the training set. Overall, the Reproducing Kernel Hilbert Spaces (RKHS) model consistently resulted in improved predictive ability across all breeding scenarios considered in our study. Our results lay the groundwork for further exploration, including integration of diverse traits, incorporation of deep learning techniques, and the utilization of multi-omics data in predictive modeling. Core ideasO_LIPhenotypic selection in PYT is challenged by limited seeds, resulting to few replications and environments. C_LIO_LIMTME-GP offers opportunity for enhancing prediction accuracy of multi-trait and diverse environments in PYT. C_LIO_LIMTME-GP enhances prediction by up to 2.5-fold, especially with numerous overlapping genotypes in various tested environments. C_LIO_LIRKHS MTME-GP models, excels in low-heritability, negatively correlated traits, like drought-affected conditions. C_LI