CBGG

Diseases are among the most important limiting factors for wheat production. Breeding for fungal diseases of wheat, primarily for rusts and Fusarium head blight (FHB), are major resource consuming activities in most breeding programs which prevent breeders from focusing entirely on improving yield. Breeding for these diseases is challenging because resistance is inherited mostly in a quantitative fashion and is greatly influenced by weather conditions. Recent advances in genomics, phenomics and big-data analysis provide opportunities for accelerating the development of low-cost and efficient selection methods for such complex traits. Genomic selection (GS) may provide opportunities for reducing the time and cost of making selections. By appropriately integrating GS in the breeding workflow, it is possible to select new parents purely based on genomic estimated breeding values before breeding materials are entered into nurseries and field trials. Due to reduced selection cycle time, annual genetic gain for GS is predicted to be two to threefold greater than for a conventional phenotypic selection program. In this paper, we review the recent GS studies focusing on the prediction of resistance to rusts and FHB including those that benefits from modeling multiple phenological traits correlated with the resistance. In addition, we discuss the potential of integrating phenomics and machine learning for evaluating plant disease and the integration of multiple “omics” data in genomic prediction to improve the applicability of GS for disease resistance breeding in wheat.

disease resistance; genomic selection; genetic gain; genotyping; machine learning; “omics” data; phenomics; wheat