2021
DOI: 10.20900/cbgg20210007
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Genome-Wide Association Studies Combined with Genomic Selection as a Tool to Increase Fusarium Head Blight Resistance in Wheat

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Cited by 5 publications
(5 citation statements)
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“…GWAS is a useful technique to identify significant markers for characteristics in wheat ( Verges et al., 2021 ). GWASs have been reported against the most threatening fungal diseases to wheat production, including rust, smut, and bunt diseases ( Goutam et al., 2015 ).…”
Section: Genome Wide Association Studies For Disease-resistance In Wheatmentioning
confidence: 99%
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“…GWAS is a useful technique to identify significant markers for characteristics in wheat ( Verges et al., 2021 ). GWASs have been reported against the most threatening fungal diseases to wheat production, including rust, smut, and bunt diseases ( Goutam et al., 2015 ).…”
Section: Genome Wide Association Studies For Disease-resistance In Wheatmentioning
confidence: 99%
“…Leaf (Dababat et al, 2016) resistance (Lr) genes have been reported to be linked with a wide range of markers (Imbaby et al, 2014). GWAS also reported in fusarium head blight (FHB) (Verges et al, 2021) resistance in winter wheat lines, suggesting that GWAS is the most effective approach for FHB resistance (Savadi et al, 2018). Furthermore, there are sixteen race-specific resistance genes to common bunts from Bt1 to Bt15 that have been reported (Goates, 2012).…”
Section: Genome Wide Association Studies For Disease-resistance In Wheatmentioning
confidence: 99%
“…Historical data sets are a rich resource for genomic selection, as they are frequently comprised of quality data for traits of interest over a range of environments (Boyles et al, 2019), and studies have demonstrated their utility in genomics‐assisted breeding (Dawson et al, 2013; Rutkoski et al, 2015; Sarinelli et al, 2019; Storlie & Charmet, 2013; Verges et al, 2020). Research has examined various strategies for optimizing training populations based on genetic relationships, population size and structure, marker density and significance, heritability, and environment similarity (Berro et al, 2019; Lozada et al, 2019; Norman et al, 2018; Verges et al, 2021). In the SUWWSN, environments were selected for inclusion based on biplot analysis and performance of the resistant and susceptible check varieties in a given location and year.…”
Section: Implementation Of Genomic Selection For Fhb Resistancementioning
confidence: 99%
“…Exploring methods to increase the accuracy of predictions is a continuous, iterative process. Including significant markers as fixed effects in prediction models and selecting markers based on GWAS analysis has achieved some success in improving prediction accuracy (Larkin et al, 2020; Lozada et al, 2019; Verges et al, 2020; Verges et al, 2021). Cluster analysis to select environments for training population construction has also been shown to improve prediction accuracy for FDK and DON (Winn et al, 2023) and provide additional support concerning decisions about including the highest‐quality data.…”
Section: Implementation Of Genomic Selection For Fhb Resistancementioning
confidence: 99%
“…The associated loci were distributed across all chromosomes except 2D, 6A, 6D and 7D ( Hu et al, 2020 ). In addition, numerous recent studies have used GWAS in FHB resistance ( Verges, Brown-Guedira & Van Sanford, 2021 ; Gaire et al, 2021 ; Ghimire et al, 2022 ; Wu et al, 2019 ). It appears that using GWAS to investigate wheat FHB is a very promising endeavor.…”
Section: Introductionmentioning
confidence: 99%