Approaching 25 years of progress towards <i>Fusarium</i> head blight resistance in southern soft red winter wheat (<scp><i>Triticum aestivum</i></scp> L.)

Boyles, Richard; Ballén‐Taborda, Carolina; Brown‐Guedira, Gina; Costa, José Maria Correia da; Cowger, Christina; DeWitt, Noah; Griffey, Carl A.; Harrison, Stephen A.; Ibrahim, Amir M. H.; Johnson, J. W.; Lyerly, Jeanette; Marshall, David; Mason, Richard E.; Mohamed, Maryati; Murphy, J. Paul; Santantonio, Nicholas; Saripalli, Gautam; Sutton, Russell; Tiwari, Vijay K.; Sanford, David A. Van; Winn, Zachary J.

doi:10.1111/pbr.13137

Cited by 3 publications

(3 citation statements)

References 117 publications

(184 reference statements)

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“…Currently, the two main strategies for building training sets are based on accumulating historical phenotypic records from multiple years (Rutkoski et al 2015;Beyene et al 2021) and the test-and-shelf (Boyles et al 2024). The advantage of the first approach is that the estimation of markers effects is based on multiple-year records, leading to a more accurate estimation of year effect and its interaction with genotypes.…”

Section: Introductionmentioning

confidence: 99%

Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops

Fritsche-Neto,

Yassue,

Silva

et al. 2024

Preprint

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In genomic selection, the prediction accuracy is heavily influenced by the training set (TS) composition. Currently, two primary strategies for building TS are in use: one involves accumulating historical phenotypic records from multiple years, while the other is the “test-and-shelf” approach. Additionally, studies have suggested that optimizing TS composition using genetic algorithms can improve the accuracy of prediction models. Most breeders operate in open systems, introducing new genetic variability into their populations as needed. However, the impact of elite germplasm introduction in GS models remains unclear. Therefore, we conducted a case study in self-pollinated crops using stochastic simulations to understand the effects of elite germplasm introduction, TS composition, and its optimization in long-term breeding programs. Overall, introducing external elite germplasm reduces the prediction accuracy. In this context, Test and Shelf seem more stable regarding accuracy in dealing with introductions despite the origin and rate, being useful in programs where the introductions come from different sources over the years. Conversely, using historical data, if the introductions come from the same source over the cycles, this negative effect is reduced as long as the cycles and this approach become the best. Thus, it may support public breeding programs in establishing networks of collaborations, where the exchange of germplasm will occur at a pre-defined rate and flow. In either case, the use of algorithms of optimization to trim the genetic variability does not bring a substantial advantage in the medium to long term.

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Section: Introductionmentioning

confidence: 99%

Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops

Fritsche-Neto,

Yassue,

Silva

et al. 2024

Preprint

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“…This approach helps reduce the risk of encountering negative accuracies observed in pairwise and leave-one-family-out (LOFO) cross-validations. Strategies such as test-and-shelf may present a viable alternative for implementing genomic selection in raspberry breeding(Boyles et al 2024). Genetic structure can arise from different levels of genetic relatedness between individuals, including separating individuals into families(Würschum et al 2017).…”

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confidence: 99%

Comparing strategies for genomic predictions in interspecific biparental populations: a case study with the Rubus genus

Silva,

Prado,

Campos

et al. 2024

Preprint

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Genomic selection (GS) is becoming increasingly widespread and applied due to the promising results obtained, cost savings in generating single nucleotide polymorphism (SNP) markers, and the development of statistical models that allow to improve the analysis robustness and accuracy. GS might shorten the selection cycle, which has a major impact, especially for perennial species. The composition and size of the training population have a major influence on GS, which poses challenges for interspecific biparental populations. Another factor is the use of different reference genomes from other species to perform SNP calling, which could make it possible to explore variability in interspecific crosses comprehensively. Late leaf rust is a disease caused by the pathogen Acculeastrum americanum, and the rare reports on genetic resistance to this pathogen are related to the species Rubus occidentalis, which leads to the need for interspecific hybridizations, aiming to combine the fruit quality of R. idaeus with the resistance of R. occidentalis. Given the above, we evaluated the effect of different reference genomes on the SNP markers discovery, as well as training population optimization (TPO) strategies on the accuracy of genomic predictions, namely the CV-α, leaving-one-family-out (LOFO), pairwise families, and stratified k-fold. The composition of the training set in a stratified manner, together with a matrix of markers combined with the reference genomes, increased the model's predictive capacity. These results corroborate that genomic prediction aligned with SNP calling and training population optimization strategies can significantly increase genetic gains in interspecific biparental crosses.

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“…In such a way, they developed adapted peanut lines with high cultivated genome recovery, improved yield potential, and harbored the resistance QTL. Fusarium height blight (FHB) is a yield and quality-limiting disease of wheat ( Boyles et al., 2023 ). Less FHB incidence has been observed in taller and later-maturity plants ( Sari et al., 2020 ).…”

mentioning

confidence: 99%

Editorial: Characterizing and improving traits for resilient crop development

Roychowdhury,

Ballén-Taborda,

Chaturvedi

2023

Front. Plant Sci.

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Approaching 25 years of progress towards Fusarium head blight resistance in southern soft red winter wheat (Triticum aestivum L.)

Cited by 3 publications

References 117 publications

Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops

Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops

Comparing strategies for genomic predictions in interspecific biparental populations: a case study with the Rubus genus

Editorial: Characterizing and improving traits for resilient crop development

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