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.

Evaluating and Predicting the Performance of Sorghum Lines in an Elite by Exotic Backcross-Nested Association Mapping Population

“…The prediction accuracies for days to mid-anthesis and grain yield ( Figure 4 ) are like those reported in other studies in sorghum [ 5 , 15 , 16 , 20 , 21 , 22 , 23 ]. However, the prediction accuracies for plant height are lower than in previous studies, much like what was observed by Winans et al [ 5 ] and Sapkota et al [ 22 ].…”

“…Modest increases in prediction accuracy do not always translate into large gains in selection efficiency [ 5 , 20 ]; however, it may also be beneficial to expend effort to create more robust training sets for a few reasons. One major goal of the GUESS program was to introgress novel genetic diversity into sorghum without sacrificing the agronomic performance of the resulting lines.…”

Assessing the agronomic potential of sorghum B‐lines using genomic prediction

“…The BTx623/BTx642 population had a wider range of genetic variation than the BTx623/BTx378 population. Similar results are reported in Kent et al (2023) using a smaller set of these lines that were evaluated in hybrid test crosses.…”

“…This would also explain why the prediction accuracies for PH did not improve when the populations were combined. When a select number of lines from these two populations in hybrid test crosses for grain yield and DY were assessed (Kent et al, 2023 ), it was observed that combining the two populations improved in some scenarios. Similarly, literature has shown both moderate increases and decreases in prediction accuracy when populations are combined (Brandariz & Bernardo, 2019;Haile et al, 2021;Heffner et al, 2011).…”

“…Since its inception, genomic prediction has been shown effective at predicting animals' GEBVs and crop phenotypes (Crossa et al, 2010;de los Campos et al, 2015;De Roos et al, 2007;Garrick, 2011;Heffner et al, 2009;Massman et al, 2013). Genomic prediction has been shown to be effective at predicting phenotypes of sorghum hybrids (Fonseca et al, 2021;Fonseca et al, 2022;Hunt et al, 2018;Kent et al, 2023;Maulana et al, 2023;Winans et al, 2023). Similarly, genomic prediction has been shown to be effective at predicting phenotypes of sorghum inbred/parental lines (Dos Santos et al, 2020;Habyarimana & Lopez-Cruz, 2019;Sakamoto et al, 2019;Sapkota, Boatwright et al, 2020 ).…”