Predicting superior crosses in winter wheat using genomics: A retrospective study to assess accuracy

Authors: BALLÉN-TABORDA, CAROLINA - Clemson University; LYERLY, JEANETTE - North Carolina State University; Smith, Jared; Howell, Kimberly; Brown-Guedira, Gina; DEWITT, NOAH - Louisiana State University; WARD, BRIAN - Forage Genetics International; BABAR, MD ALI - University Of Florida; HARRISON, STEPHEN - Louisiana State University; MASON, RICHARD - Colorado State University; MERGOUM, MOHAMED - University Of Georgia; MURPHY, J. PAUL - North Carolina State University; SUTTON, RUSSELL - Texas A&M University; GRIFFEY, CARL - Virginia Tech; BOYLES, RICHARD - Clemson University

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/22/2024
Publication Date: 5/28/2024
Citation: Ballén-Taborda, C., Lyerly, J., Smith, J.H., Howell, K.D., Brown Guedira, G.L., Dewitt, N., Ward, B., Babar, M., Harrison, S.A., Mason, R.E., Mergoum, M., Murphy, J., Sutton, R., Griffey, C.A., Boyles, R.E. 2024. Predicting superior crosses in winter wheat using genomics: A retrospective study to assess accuracy. Crop Science. 64(4):2195-2211. https://doi.org/10.1002/csc2.21266.
DOI: https://doi.org/10.1002/csc2.21266

Interpretive Summary: In plant breeding selecting combinations of parents that are more likely to result in superior lines for cultivar development is critical; yet this step remains largely subjective. The process of genomic selection, that combines molecular marker data and performance information, provides new opportunities to accelerate genetic gain within modern crop breeding programs. This approach has been used to identify lines with outstanding performance that can be advanced for further evaluation. The application of genomic selection has also been expanded to identify superior parental combinations by simulating variation in performance for a trait of interest for the progeny from different crosses. In this context, the R package PopVar was developed to predict genetic variance and progeny means for all possible combinations from a set of phenotyped and genotyped parental lines. This study reports a retroactive PopVar analysis aimed at investigating whether the crosses made in a traditional program that produced superior wheat breeding lines would have been made if progeny simulations had guided breeders’ crossing decisions. Here, information about 217 parents of 670 historical winter wheat breeding lines were used to predict genetic variance and progeny means of 23,436 parental combinations for grain yield, test weight, days to heading, and plant height. Predicted and observed data for the 670 lines, including four released cultivars, were compared to assess the accuracy of PopVar predictions. The most advanced nursery each line entered was recorded to examine the overall usefulness of simulated variances and means to identify the most valuable crosses. Of the pedigrees that were predicted to give rise to progenies with above-average grain yield, 76% were selected and advanced (or released) by breeders in the Southeastern University Small Grains breeding cooperative (SunGrains). Simulation of progeny performance to select the most promising biparental crosses could accelerate and increase efficiency of the breeding process.

Technical Abstract: In plant breeding, selecting cross-combinations that are more likely to result in superior lines for cultivar development is critical. This step, however, is subjective with decisions being based on available genomic and phenotypic data for prospective parents. Genomic prediction (GP) provides new opportunities to accelerate genetic gain for a target trait by identifying superior crosses through simulation of progeny performance. In this context, this study deployed GP using the phenotype and genotype of potential parents to predict the progeny genetic variance (VG) and means of overall, inferior 10%, and superior 10% (µ, µip, and µsp, respectively). This retrospective experimental design investigated whether the crosses that produced superior soft red winter wheat breeding lines would have been made if progeny simulations had guided crossing decisions of breeding programs. Here, data from historical wheat breeding lines were used to train GP models and predict VG and means for yield, test weight, heading date, and plant height for all combinations of 217 parents. Predicted and observed data for 670 lines derived from biparental crosses were compared to assess the accuracy of progeny simulations, and low-to-moderate prediction accuracy was observed for the four traits (0.25–0.52). Of the pedigrees that produced lines that were selected and advanced into later stage nurseries, 76% were predicted to give rise to progeny with above-average yield. The moderate correlation found between predicted progeny means and observed line per se performance justifies using cross-combination prediction as a tool to reduce crossing number and focus on segregating populations that harbor future cultivars.