Key message The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Abstract Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat (Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes.

In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical model to estimate all marker effects for an individual simultaneously to determine a genome estimated breeding value (GEBV). Breeders are thus able to select for performance based on GEBVs in the absence of phenotypic data. In wheat, genomic selection has been successfully implemented for a number of key traits including grain yield, grain quality and quantitative disease resistance, such as that for Fusarium head blight. For this review, we focused on the ways to modify genomic selection to maximize prediction accuracy, including prediction model selection, marker density, trait heritability, linkage disequilibrium, the relationship between training and validation sets, population structure, and training set optimization methods. Altogether, the effects of these different factors on the accuracy of predictions should be thoroughly considered for the successful implementation of GS strategies in wheat breeding programs.

Developing cultivars with improved adaptation to drought and heat stressed environments is a priority for plant breeders. Canopy temperature (CT) is a useful tool for phenotypic selection of tolerant genotypes, as it integrates many physiological responses into a single low-cost measurement. The objective of this study was to determine the ability of CT to predict grain yield within the flow of a wheat breeding program and assess its utility as a tool for indirect selection. CT was measured in both heat and drought stressed field experiments in northwest Mexico on 18 breeding trials totaling 504 spring wheat lines from the International Maize and Wheat Improvement Center (CIMMYT) Irrigated Bread Wheat program. In the heat treatment, CT was significantly correlated with yield (r = −0.26) across all trials, with a maximum coefficient of determination within the individual trials of R² = 0.36. In the drought treatment, a significant correlation across all trials was only observed when days to heading or plant height was used as a covariate. However, the coefficient of determination within individual trials had a maximum of R² = 0.54, indicating that genetic background may impact the ability of CT to predict yield. Overall a negative slope in the heat treatment indicated that a cooler canopy provided a yield benefit under stress, and implementing selection strategies for CT may have potential for breeding tolerant genotypes.