Use of genomic prediction to screen sorghum B‐lines in hybrid testcrosses

Abstract Use of trifluoromethanesulfonamide (TFMSA), a male gametocide, increases the opportunities to identify promising B‐lines because large quantities of F1 seed can be generated prior to the laborious task of B‐line sterilization. Combining TFMSA technology with genomic selection could efficiently evaluate sorghum B‐lines in hybrid combination to maximize the rates of genetic gain of the crop. This study used two recombinant inbred B‐line populations, consisting of 217 lines, which were testcrossed to two R‐lines to produce 434 hybrids. Each population of testcross hybrids were evaluated across five environments. Population‐based genomic prediction models were assessed across environments using three different cross‐validation (CV) schemes, each with 70% training and 30% validation sets. The validation schemes were as follows: CV1—hybrids chosen randomly for validation; CV2—B‐lines were randomly chosen, and each chosen B‐line had one of the two corresponding testcross hybrids randomly chosen for the validation; and CV3—B‐lines were randomly chosen, and each chosen B‐line had both corresponding testcross hybrids chosen for the validation. CV1 and CV2 presented the highest prediction accuracies; nonetheless, the prediction accuracies of the CV schemes were not statistically different in many environments. We determined that combining the B‐line populations could improve prediction accuracies, and the genomic prediction models were able to effectively rank the poorest 70% of hybrids even when genomic prediction accuracies themselves were low. Results indicate that combining genomic prediction models and TFMSA technology can effectively aid breeders in predicting B‐line hybrid performance in early generations prior to the laborious task of generating A/B‐line pairs