{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T19:52:07Z","timestamp":1773085927626,"version":"3.50.1"},"reference-count":54,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,7,16]],"date-time":"2023-07-16T00:00:00Z","timestamp":1689465600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["acsess.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Plant Genome"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Use of trifluoromethanesulfonamide (TFMSA), a male gametocide, increases the opportunities to identify promising B\u2010lines because large quantities of F\n                    <jats:sub>1<\/jats:sub>\n                    seed can be generated prior to the laborious task of B\u2010line sterilization. Combining TFMSA technology with genomic selection could efficiently evaluate sorghum B\u2010lines in hybrid combination to maximize the rates of genetic gain of the crop. This study used two recombinant inbred B\u2010line populations, consisting of 217 lines, which were testcrossed to two R\u2010lines to produce 434 hybrids. Each population of testcross hybrids were evaluated across five environments. Population\u2010based genomic prediction models were assessed across environments using three different cross\u2010validation (CV) schemes, each with 70% training and 30% validation sets. The validation schemes were as follows: CV1\u2014hybrids chosen randomly for validation; CV2\u2014B\u2010lines were randomly chosen, and each chosen B\u2010line had one of the two corresponding testcross hybrids randomly chosen for the validation; and CV3\u2014B\u2010lines were randomly chosen, and each chosen B\u2010line 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\u2010line 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\u2010line hybrid performance in early generations prior to the laborious task of generating A\/B\u2010line pairs.\n                  <\/jats:p>","DOI":"10.1002\/tpg2.20369","type":"journal-article","created":{"date-parts":[[2023,7,16]],"date-time":"2023-07-16T23:59:55Z","timestamp":1689551995000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Use of genomic prediction to screen sorghum B\u2010lines in hybrid testcrosses"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4853-9481","authenticated-orcid":false,"given":"Mitchell A.","family":"Kent","sequence":"first","affiliation":[{"name":"Department of Soil and Crop Sciences Texas A&amp;M University College Station TX USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1195-6193","authenticated-orcid":false,"given":"Jales M. 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