Optimizing sparse testing for genomic prediction of plant breeding crops

Montesinos-López, O. A., Saint Pierre, C., Gezan, S. A., Bentley, A. R., Mosqueda-González, B. A., Montesinos-López, A., van Eeuwijk, F., Beyene, Y., Gowda, M., Gardner, K., Gerard, G. S., Crespo-Herrera, L., & Crossa, J. (2023). Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops. Genes, 14(4), 927. https://doi.org/10.3390/genes14040927

While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1–M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15–85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis.