Evaluating and predicting the performance of sorghum lines in an elite by exotic BC-NAM population

Authors: CROZIER, DANIEL - Texas A&M University; WINANS, NOAH - Texas A&M University; HOFFMAN, LEO - University Of Florida; PATIL, NKHIL - Texas A&M University; KLEIN, PATRICIA - Texas A&M University; Klein, Robert - Bob; ROONER, WILLIAM - Texas A&M University

Submitted to: Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/13/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: Maintaining or introducing genetic diversity into plant breeding programs is necessary for continual genetic gain; however, diversity at the cost of reduced performance is not an acceptable trade off. To this end, we implemented a breeding process in which we transferred genetic diversity of unadapted sorghum germplasm into elite inbreds while selecting the resulting offspring for acceptable plant performance and testing their potential as parents of hybrid sorghum. This study will provide the necessary knowledge to breeders who labor to introduce novel genetic diversity into their sorghum hybrid to improve grain yield of hybrid cereal crops including sorghum.

Technical Abstract: Maintaining or introducing genetic diversity into plant breeding programs is necessary for continual genetic gain; however, diversity at the cost of reduced performance is not an acceptable tradeoff for breeders. To this end, backcross-nested association mapping (BC-NAM) populations, in which the recurrent parent is an elite line, is a strategy often employed to introgress diversity from unadapted accessions while also attempting to maintain elite agronomic performance. This study evaluates (i) the performance of sorghum lines from BC1F5-NAM families in hybrid trials, and (ii) the potential of utilizing genomic prediction to select BC1F5-NAM lines for elite hybrid performance. Despite the diverse geographical origins and agronomic performance of the unadapted parental for each BC1F5-NAM population, many BC1F# lines performed significantly better in hybrid trials than did the elite recurrent parent, RTx436. Genomic prediction accuracies for grain yield, plant height, and days to mid-anthesis were acceptable, but prediction accuracies for plant height were lower than expected. The results suggest that genomic prediction for hybrid performance may be an effective method of pre-screening lines within a BC-NAM population prior to evaluation in extensive hybrid trials. While prediction accuracies increased when including more individuals in the training set, improvement tended to plateau between two and five lines per BC1 family with larger training sets being required for complex traits such as grain yield. Therefore, genomic prediction models with a relatively low fraction of related lines for large BC-NAM population can be an effective prescreening tool for hybrid performance without extensive field evaluation.