Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat

doi:10.20900/cbgg20200016

Cited by 6 publications

(7 citation statements)

References 141 publications

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“…One major target of genomic selection is the genetic improvement of resistance against fungal diseases [2]. Within this framework the resistance of wheat against Fusarium head blight (FHB) is probably one of the most thoroughly studied pathosystems [3]. FHB of wheat is induced by several species among which Fusarium graminearum, Fusarium culmorum, and Fusarium sporotrichioides are the most prominent and agronomically important causal agents conferring large losses in grain yield and contamination of harvested grain with mycotoxins [4].…”

Section: Introductionmentioning

confidence: 99%

“…Besides the genome, other biological strata like the transcriptome and metabolome can serve as an alternative or complement to genomic data in predictive breeding [14,15]. However, the latter two "omics"-methods have not been assessed for predicting FHB resistance in wheat until now [3]. Albeit metabolites can be considered closest to the actual phenotype, and in theory unite the previous biological strata, on the other hand they are also subject to practical constrains like fast turn-over rates.…”

Section: Introductionmentioning

confidence: 99%

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Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat

Michel

Wagner

Nosenko

et al. 2021

Genes

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Genomic selection with genome-wide distributed molecular markers has evolved into a well-implemented tool in many breeding programs during the last decade. The resistance against Fusarium head blight (FHB) in wheat is probably one of the most thoroughly studied systems within this framework. Aside from the genome, other biological strata like the transcriptome have likewise shown some potential in predictive breeding strategies but have not yet been investigated for the FHB-wheat pathosystem. The aims of this study were thus to compare the potential of genomic with transcriptomic prediction, and to assess the merit of blending incomplete transcriptomic with complete genomic data by the single-step method. A substantial advantage of gene expression data over molecular markers has been observed for the prediction of FHB resistance in the studied diversity panel of breeding lines and released cultivars. An increase in prediction ability was likewise found for the single-step predictions, although this can mostly be attributed to an increased accuracy among the RNA-sequenced genotypes. The usage of transcriptomics can thus be seen as a complement to already established predictive breeding pipelines with pedigree and genomic data, particularly when more cost-efficient multiplexing techniques for RNA-sequencing will become more accessible in the future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat

Michel

Wagner

Nosenko

et al. 2021

Genes

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“…This population was the subject of a GS study with encouraging results (Nyine et al, 2018). In wheat, Haile et al (2020) reported that it was possible to extract the useful alleles in the germplasm population. For this purpose, prediction error variance (PEV) and generalized coefficient of determination (CD) methods have been reported to optimize the TP from germplasm collection.…”

Section: Design Of the Training Population From Germplasm And Clonall...mentioning

confidence: 99%

“…This population was the subject of a GS study with encouraging results ( Nyine et al., 2018 ). In wheat, Haile et al. (2020) reported that it was possible to extract the useful alleles in the germplasm population.…”

Section: Perspectives To Succeed Gs On Polyploid Cropsmentioning

confidence: 99%

“…GS approach developed by Meuwissen et al (2001) is capable of selecting a large number of individuals for the traits of interest in a short time, with reduced costs. Because of its potential, GS is considered to be one of the most promising post-1990 approaches in plant breeding, along with genome editing, Quantitative Trait Locus (QTL) mapping, envirotyping, and Genetically Modified Organisms (GMOs) (Haile et al, 2020). The genetic gain of the GS can be two to three times greater than conventional selection.…”

Section: Introductionmentioning

confidence: 99%

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Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

Nkoulou

Ngalle²,

Cros³

et al. 2022

Front. Plant Sci.

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Genomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas (Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought—two major threats to banana production—used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.

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Genome‐wide association mapping for field and seedling resistance to the emerging Puccinia graminis f. sp. tritici race TTRTF in wheat

et al. 2022

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Stem rust of wheat (Triticum spp.), caused by Puccinia graminis f. sp. tritici (Pgt), is one of the most impactful wheat diseases because of its threat to global wheat production. While disease mitigation has primarily been achieved through the deployment of resistant wheat varieties, emerging new virulent races continue to pose risks to the crop. For example, races such as Ug99 (TTKSK), TKTTF, and TTRTF have caused epidemics in different wheat growing regions of the world in recent years. A continual search for new and effective sources of resistance is therefore necessary to safeguard wheat production. This study assessed a breeding panel from the Ethiopian Institute of Agricultural Research (EIAR) wheat breeding program for seedling and field plant resistance to TTRTF and reports genomic regions conferring resistance to TTRTF. Trait correlations (r) were medium to strong (range = .38-.71) and heritabilities were moderate (.32-.56). Association analysis for resistance to TTRTF resulted in detection of 20 markers in 11 chromosomes; the marker S1B_175439851 was associated with resistance at both seedling and adult plant stages. Models with two to four QTL combinations reduced seedling and field disease severity by 12-48 and 9-17%, respectively. Genomic prediction for TTRTF resistance resulted in low to moderately-high predictions (mean correlations of .25-.47). Identification of resistant lines and QTL in the EIAR population is expected to assist in selection toward improved resistance to TTRTF. Specifically, the application of genomic selection (GS) in identifying resistant lines in future related breeding populations will further assist breeding efforts against this new stem rust pathogen race.

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Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat

Cited by 6 publications

References 141 publications

Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat

Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat

Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

Genome‐wide association mapping for field and seedling resistance to the emerging Puccinia graminis f. sp. tritici race TTRTF in wheat

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