Inbred phenotypic data and non‐additive effects can enhance genomic prediction models for hybrid grain sorghum

Abstract

Implementation of genomic prediction can bolster rates of genetic gain in sorghum improvement and permit more efficient allocation of resources within hybrid breeding programs. In the present study, alternative genomic prediction models were compared to assess the potential benefits of including inbred phenotypic records, dominance effects, and genotype-by-environment (G×E) interactions in predicting hybrid grain sorghum performance. Comparisons were made in a set of 395 hybrid combinations derived from 92 parental inbred lines tested in a sparse multienvironment trial. Phenotypic data were collected on hybrids and inbreds for days to mid-anthesis, grain yield, and plant height, and genomic data on parental inbreds were collected by genotyping × sequencing. A significant increase in prediction accuracy was observed when modeling G×E effects; however, dominance effects did not contribute to the overall predictive ability of models in this data set. Including phenotypic data from parental lines significantly improved the prediction of hybrid merit by as much as 17% for days to mid-anthesis, 14% for grain yield, and 33% for plant height when there were no testcross records for a given parental line. Alternatively, similar improvements were not as consistent when the training set included lines already tested in hybrid combinations. Thus, hybrid crop breeders can further optimize genomic predictions for un-testcrossed lines by including non-additive effects and inbred data. INTRODUCTIONGrain sorghum [Sorghum bicolor (L.)] is an ancient cereal grain that is the second most widely grown feed grain in the United States on a production basis (USDA, 2021) and ranks as one of the top 25 major commodities in the world since 1961 (Monk et al., 2015). As a drought-tolerant crop Abbreviations: BLUE, best linear unbiased estimator; BLUP, best linear unbiased predictor; CV, coefficient of variation; GBLUP, genomic best linear unbiased prediction; G×E, genotype-by-environment.

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 ].…”

“…From observation, there is a large amount of variation in plant height, and it is likely that a few of the families are segregating for major dwarfing genes. Studies reporting high prediction accuracy for plant height had lines subject to more intense selection pressure, and it is likely that large-effect genes that control height were fixed [ 16 , 21 , 23 ], whereas studies with large variation in height, exotic germplasms, and minimal selection reported lower prediction accuracies [ 5 , 22 ]. Within this study, the GBLUP approach is limited in its ability to capture large single gene effects due to the distributed weight of effects across the genome [ 11 , 24 ].…”

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

“…Additionally, Crozier et al. (2023) demonstrated that genomic prediction of hybrid performance can be improved by adding inbred data into the training data. This may be further useful if predicted inbred phenotypes could be used to improve the prediction of hybrids as this would save resources by reducing the need of inbred phenotyping.…”

“…Although the observations made herein regarding training set size were demonstrated in inbred lines, it is conceivable that similar trends would apply to sorghum hybrids, although studies are needed to support this claim. Additionally, Crozier et al (2023) demonstrated that genomic prediction of hybrid performance can be improved by adding inbred data into the training data. This may be further useful if predicted inbred phenotypes could be used to improve the prediction of hybrids as this would save resources by reducing the need of inbred phenotyping.…”

Genomic prediction of sweet sorghum agronomic performance under drought and irrigated environments in Haiti

“…However, the broad application of GS in sorghum breeding programs might be hindered by G × E in certain regions (Hunt et al., 2018). To account for multiple environmental factors, ongoing research on genomic prediction in sorghum is exploring the incorporation of G × E into prediction models (Crozier et al., 2023; Fonseca et al., 2021). While G × E effects seem to positively impact the prediction accuracy of critical sorghum traits (Crozier et al., 2023), the incongruous outcomes from G × E suggest that factors beyond environment correlations drive genomic predictions in sorghum hybrids for grain production (Fonseca et al., 2021).…”