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

Fonseca, Jales Mendes Oliveira; Perumal, Ramasamy; Klein, Patricia E.; Klein, Robert R.; Rooney, William L.

doi:10.1002/csc2.20624

Cited by 4 publications

(8 citation statements)

References 80 publications

(84 reference statements)

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“…The grain sorghum hybrids utilized in this study are as previously described by Fonseca et al (2021a). In brief, 100 hybrids were generated from crossing ten female with ten male lines in a factorial mating scheme (Comstock and Robinson 1952).…”

Section: Genetic Materialsmentioning

confidence: 99%

“…Hybrids were planted in a randomized complete block design (RCBD) with sets in replicate adjustment; each set was composed of one of the four groups of 25 hybrids (Figure 1), with randomization occurring within and between sets. In 2018 trials, each location had test, agronomic practices standard to the location were followed (Table 2) (Fonseca et al, 2021a). GPS coordinates of each environment were used to collect weather data from the NASA POWER database (NASA 2021).…”

Section: Experimental Datamentioning

confidence: 99%

“…Many studies have reported on the benefits of using linear mixed models to assess MET (Piepho 1997(Piepho , 1998Smith et al 2001Smith et al , 2019Resende and Thompson 2004;Piepho and Möhring 2005;Burgueño et al 2008;Balestre et al 2010;Hu 2015;Jarquin et al 2020). Bayesian models have also demonstrated their advantages for analyzing genotypic data (Sorensen and Gianola, 2007;Crossa et al, 2010;Alves et al, 2019;Fonseca et al, 2021a). For instance, Lian and de los Campos (2016) reported the stability of wheat varieties using a novel approach of the Finlay and Wilkinson stability test under a Bayesian framework.…”

Section: Introductionmentioning

confidence: 99%

“…Although this approach is still generally applicable, breeding pipelines are being modified to accommodate predictive breeding technologies (Crossa et al 2021). For instance, genomic-enabled prediction is most effective if it replaces the first phase of hybrid trials in grain sorghum breeding schemes (Fonseca et al, 2021a). Further, MET permit the development of genomic-enabled prediction models to include GxE, and results indicate better prediction accuracy (Burgueño et al 2012;Lopez-Cruz et al 2015;Lado et al 2016;Cuevas et al 2016;Acosta-Pech et al 2017;Basnet et al 2019;Costa-Neto et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Studies have demonstrated the potential advantages of utilizing this strategy in hybrid crops (Podlich and Cooper, 1998;Technow et al, 2020). This strategy is less common in public institutions (Fonseca et al 2021a), because these programs have more limited target area with fewer genetic resources. Nevertheless, the exchange of germplasm between distinct breeding programs could foster a collaborative system between public institutions and mitigate the decrease in number and funding of public breeding programs (Shelton and Tracy, 2017;Coe et al, 2020;Fonseca et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

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Mega-environment analysis to assess adaptability, stability, and genomic predictions in grain sorghum hybrids

Fonseca

Perumal²,

Klein³

et al. 2022

Euphytica

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Multi-environment trials (MET) are fundamental for assessing genotype-byenvironment interaction (GxE) effects, adaptability and stability of genotypes and provide valuable information about target regions. As such, a MET involving grain sorghum hybrid combinations derived from elite inbred lines adapted to diverse sorghum production regions was developed to assess agronomic performance, stability, and genomic-enabled prediction accuracies within mega-environments (ME). Ten females and ten males from the Texas A&M and Kansas State sorghum breeding programs were crossed following a factorial mating scheme to generate 100 hybrids. Grain yield, plant height, and days to anthesis were assessed in a MET consisting of ten environments across Texas and Kansas over two years.Genotype plus Genotype-by-block-of-environment biplot (GGB) assessed ME, while the "mean-vs-stability" view of the biplot and the Bayesian Finlay-Wilkinson regression evaluated hybrid adaptability and stability. A genomic prediction model including the GxE effect was applied within ME to assess prediction accuracy. Results suggest that grain sorghum hybrid combinations involving lines adapted to different target regions can produce superior hybrids. GGB confirmed distinct regions of sorghum adaption in the U.S. Further, genomic predictions within ME reported inconsistent results, suggesting that additional effects rather than the correlations between environments are influencing genomic prediction of grain sorghum hybrids.

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Section: Genetic Materialsmentioning

confidence: 99%

Section: Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mega-environment analysis to assess adaptability, stability, and genomic predictions in grain sorghum hybrids

Fonseca

Perumal²,

Klein³

et al. 2022

Euphytica

Self Cite

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Envirotyping can increase genomic prediction accuracy of new environments in grain sorghum trials depending on mega‐environment

Winans,

Fonseca,

Perumal

et al. 2024

Crop Science

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Grain sorghum [Sorghum bicolor (L.) Moench] is an important crop native to Africa and grown in many subtropical and temperate regions worldwide. The variability in production environments underscores the plasticity of sorghum genotypes and provides an opportunity to predict sorghum hybrid performance in novel environments. Reaction norms informed by envirotype data can aid in modeling the differential responses of genotypes across multi‐environment trials and ultimately increase the prediction accuracies of hybrid performance. In this study, a combination of genomic and enviromic information was applied to predict grain sorghum hybrid performance across United States sorghum production environments. Seven models based on additive, dominance, environment, and envirotype effects in two forms, and their interactions were tested under three cross‐validation schemes that simulate genomic prediction scenarios encountered by breeding programs. Relationship matrices for hybrids and environments were created from molecular and envirotypic data, respectively, to predict hybrid performance under a hierarchal Bayesian framework. When predicting unobserved environments, the envirotypic reaction norm model based on typologies produced the highest prediction accuracies for every temperate environment, but results vary in the subtropical environments, which has not been demonstrated in prior enviromic research. This study also demonstrated for the first time that envirotype data can effectively define established United States sorghum production regions and may assist breeders in resource allocation. Results support using sparse hybrid trials when breeding programs apply genomic predictions. Therefore, envirotype‐assisted genomic prediction models can expand the information available to a sorghum breeding program and aid breeders in efficiently designing hybrid trials.

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Combining abilities and quantitative inheritance of sorgoleone exudation in Sorghum bicolor

Maharjan,

Leon,

Rooney

et al. 2024

Crop Science

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Sorghum (Sorghum bicolor) has the ability to inhibit the conversion of ammonium to nitrate (biological nitrification inhibition [BNI]) in the rhizosphere, which in turn prevents the loss of bioavailable nitrogen. Sorgoleone is a lipidic compound secreted by sorghum root hairs and is responsible for roughly 60% of BNI activity in sorghum. Previous studies revealed variation in sorgoleone secretion among different accessions and cultivars. However, little information is available regarding the genetic inheritance of sorgoleone secretion in sorghum. To increase sorgoleone through breeding, an understanding of the inheritance of this trait is required. In this study, 21 seed parents and 21 pollinator parents from the Texas AgriLife Research sorghum breeding program were crossed in an incomplete factorial design to generate 158 hybrids, and sorgoleone secretion from both hybrids and inbreds was quantified. There was significant variation in sorgoleone secretion across hybrids and inbred lines, and small but significant mid‐parent heterosis was observed in the hybrids. A linear mixed model analysis to calculate general and specific combining abilities for inbred parents and hybrids detected significant genetic effects for the male, the female, and male × female interactions (p < 0.001). Broad‐sense heritability was high ( = 0.87), while narrow‐sense heritability for the seed parents and pollinator parents was moderate (= 0.35 and = 0.39, respectively). These results indicate that sorgoleone exudation is primarily driven by additive genetic effects, but dominance effects are important for optimum production. These findings indicate that selection for increased sorgoleone root exudation among elite, adapted grain sorghum hybrids and inbred should be effective.

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

Cited by 4 publications

References 80 publications

Mega-environment analysis to assess adaptability, stability, and genomic predictions in grain sorghum hybrids

Mega-environment analysis to assess adaptability, stability, and genomic predictions in grain sorghum hybrids

Envirotyping can increase genomic prediction accuracy of new environments in grain sorghum trials depending on mega‐environment

Combining abilities and quantitative inheritance of sorgoleone exudation in Sorghum bicolor

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