Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance

Abstract

Genomic selection in maize (Zea mays L.) has been one factor that has increased the rate of genetic gain when compared with other cereals. However, the technological foundations in maize also exist in other cereal crops that would allow prediction of hybrid performance based on general (GCA) and specific (SCA) combining abilities applied through genomic-enabled prediction models. Further, the incorporation of genotype × environment (G × E) interaction effects present an opportunity to deploy hybrids to targeted environments. To test these concepts, a factorial mating design of elite yet divergent grain sorghum lines generated hybrids for evaluation. Inbred parents were genotyped, and markers were used to assess population structure and develop the genomic relationship matrix (GRM). Grain yield, height, and days to anthesis were collected for hybrids in replicated trials, and best linear unbiased estimates were used to train classical GCA-SCA-based and genomic (GB) models under a hierarchical Bayesian framework. To incorporate population structure, GB was fitted using the GRM of both parents and hybrids. For GB models, G × E interaction effects were included by the Hadamard product between GRM and environments. A leave-one-out cross-validation scheme was used to study the prediction capacity of models. Classical and genomic models effectively predicted hybrid performance and prediction accuracy increased by including genomic data. Genomic models effectively partitioned the variation due to GCA, SCA, and their interaction with the environment. A strategy to implement genomic selection for hybrid sorghum [Sorghum bicolor (L.) Moench] breeding is presented herein.

Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize

“…Perhaps one of the major contributions of the predictive tools is the better use of good quality phenotypic records for feeding in silico platforms, aimed at screening a large number of genotypes and candidate cultivars (Crossa et al, 2017;Messina et al, 2018;Rogers et al, 2021). Whole-genome prediction (GP, Meuwissen et al, 2001) is the most extensively used predictive tool that is already developed and validated for several crop species and application scenarios (e.g., Lorenzana and Bernardo, 2009;Windhausen et al, 2012;Crossa et al, 2017;Morais Júnior et al, 2018;Fonseca et al, 2021). In crops such as maize, its uses have been consolidated to support diverse stages of breeding programs, from the selection of individuals among breeding populations to advanced stages aimed at predicting the performance of single crosses across multiple environments (e.g., Dias et al, 2018;Messina et al, 2018;Alves et al, 2019;Costa-Neto et al, 2021a;Rogers et al, 2021).…”

Combining abilities and elite germplasm enhancement across U.S. public sorghum breeding programs

“…Another important aspect of the combined analysis is the proportion of the total variation explained by the G×E effect for GY (Table 4). This demonstrates the potential of developing specific high-yielding hybrid combinations for target environments (Fonseca et al, 2021). Also of interest, the magnitude of female GCA × environment interaction effect is greater than the male GCA × environment interaction which implies that female parents are either more adapted to specific production regions or that male parents are more stable across all environments (or a combination of these two hypothesis).…”

“…Genetic variation consists of additive and nonadditive components (Falconer & Mackay, 1996), and the proportion of additive effects typically account for most of the total genetic variation (Falconer & Mackay, 1996;Fischer et al, 2008;Hill et al, 2008;Lynch & Walsh, 1998). Since additive effects reflect heritable variation, they predict breeding crosses and hybrid performance (Basnet et al, 2019;Bernardo, 1994;Fonseca et al, 2021;Piepho et al, 2008;Technow et al, 2014). In the absence of epistasis, general and specific combining abilities reflect the magnitudes of additive and dominance effects, respectively (Falconer & Mackay, 1996).…”

“…Several mating schemes are frequently applied to generate GCA and SCA estimates (Comstock & Robinson, 1952;Griffing, 1956). Currently, combining ability estimates can be obtained in an array of ways using more flexible models (Alves et al, 2019;Fonseca et al, 2021;Möhring et al, 2011) and modern statistical tools (Bates et al, 2015;Covarrubias-Pazaran, 2016;Onofri et al, 2020;Pérez & de los Campos, 2014).…”

“…The authors highlighted the importance of useful genetic variation within and across programs to make breeding crosses and develop improved lines with the potential to meet local needs. The combination of exchanging elite germplasm across public institutions with the application of new technologies, including modern sequencing techniques and robust statistical models, can enhance the rates of genetic gain in public sector breeding programs (Cobb et al, 2019;Fonseca et al, 2021;Xu et al, 2017).…”