Genetic Characterization of a Wheat Association Mapping Panel Relevant to Brazilian Breeding Using a High-Density Single Nucleotide Polymorphism Array

Mellers, Greg; Aguilera, Jorge González; Bird, Nick; Bonato, A. L. V.; Bonow, S.; Caierão, E.; Consoli, Luciano; Santana, F. M.; Simmonds, James; Steed, Andrew; Torres, G. A. M.; Uauy, Cristóbal; Wright, Tally I.C.; Scheeren, P. L.; Nicholson, P.; Cockram, James

doi:10.1534/g3.120.401234

Cited by 14 publications

(17 citation statements)

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“…Tgw . jic-6B interval sits within 10.18 Mb of Kukri_c25377_240 , the closest significant marker associated with FDK in the study by Mellers et al [ 18 ], and is 53.57 Mb outside the Fhb2 fine mapped region [ 81 , 82 ]. In our study, QME .…”

Section: Discussionmentioning

confidence: 99%

“…The major FHB resistance gene Fhb2 has been mapped to the short arm of 6B between the markers gwm644 and gwm133, with reduced FDK and FHB severity being derived from the cultivar Sumai 3 [81]. FDK has been previously mapped on the short arm of chromosome 6B within the Fhb2 region in Brazilian germplasm, using bi-parental mapping with the cultivar Frontana [23] and through genome wide association mapping using a panel of wheat breeding lines and varieties relevant to Brazilian wheat breeding [18]. The QME.Tgw.jic-6B interval sits within 10.18 Mb of Kukri_c25377_240, the closest significant marker associated with FDK in the study by Mellers et al [18], and is 53.57 Mb outside the Fhb2 fine mapped region [81,82].…”

Section: Plos Onementioning

confidence: 99%

“…FDK has been previously mapped on the short arm of chromosome 6B within the Fhb2 region in Brazilian germplasm, using bi-parental mapping with the cultivar Frontana [23] and through genome wide association mapping using a panel of wheat breeding lines and varieties relevant to Brazilian wheat breeding [18]. The QME.Tgw.jic-6B interval sits within 10.18 Mb of Kukri_c25377_240, the closest significant marker associated with FDK in the study by Mellers et al [18], and is 53.57 Mb outside the Fhb2 fine mapped region [81,82]. In our study, QME.Tgw.jic-6B was not associated with other FHB traits, such as FHB severity and DON accumulation, as reported in other studies [23,81], which may indicate that this QTL is not due to the presence of Fhb2.…”

Section: Plos Onementioning

confidence: 99%

“…The nature of the underlying gene(s) is disputed and has been suggested to be a pore-forming toxin-like ( PFT ) gene [ 14 ] or a histidine-rich calcium-binding-protein gene ( His or HRC ) gene [ 15 , 16 ]. Recent analysis has found that Fhb1 is present at a very low frequency in Brazilian germplasm [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Fusarium head blight resistance loci in two Brazilian wheat mapping populations

et al. 2021

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Fusarium head blight (FHB) is a disease of wheat (Triticum aestivum L.) that causes major yield losses in South America, as well as many other wheat growing regions around the world. FHB results in low quality, contaminated grain due to the production of mycotoxins such as deoxynivalenol (DON). In Brazil, FHB outbreaks are increasing in frequency and are currently controlled by fungicides which are costly and potentially harmful to the wider environment. To identify the genetic basis of resistance to FHB in Brazilian wheat, two mapping populations (Anahuac 75 × BR 18-Terena and BR 18-Terena × BRS 179) segregating for FHB resistance were phenotyped and quantitative trait loci (QTL) analysis was undertaken to identify genomic regions associated with FHB-related traits. A total of 14 QTL associated with FHB visual symptoms were identified, each of which explained 3.7–17.3% of the phenotypic variance. Two of these QTL were stable across environments. This suggests FHB resistance in Anahuac 75, BR 18-Terena and BRS 179 is controlled by multiple genetic loci that confer relatively minor differences in resistance. A major, novel QTL associated with DON accumulation was also identified on chromosome 4B (17.8% of the phenotypic variance), as well as a major QTL associated with thousand-grain weight on chromosome 6B (16.8% phenotypic variance). These QTL could be useful breeding targets, when pyramided with major sources of resistance such as Fhb1, to improve grain quality and reduce the reliance on fungicides in Brazil and other countries affected by FHB.

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Section: Discussionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Fusarium head blight resistance loci in two Brazilian wheat mapping populations

et al. 2021

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“…The Brazilian spring wheat panel included entries released between 1922 to 2013. Year of varietal release and trait data were obtained from (39). The UK wheat panel consists of winter wheat varieties that were either registered or in use from 1916 to 2010.…”

Section: Germplasm Age and Trait Datamentioning

confidence: 99%

Trends of genetic changes uncovered by Env- and Eigen-GWAS in wheat and barley

Sharma

Cockram

Gardner

et al. 2020

Preprint

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The process of crop breeding over the last century has delivered new varieties with increased genetic gains, resulting in higher crop performance and yield. However in many cases, the underlying alleles and genomic regions that have underpinned this success remain unknown. This is due, in part, to the difficulty in generating sufficient phenotypic data on large numbers of historical varieties to allow such analyses to be undertaken. Here we demonstrate the ability to circumvent such bottlenecks by identifying genomic regions selected over 100 years of crop breeding using the age of a variety as a surrogate for yield. Using ‘environmental genome-wide association scans’ (EnvGWAS) on variety age in two of the world’s most important crops, wheat and barley, we found strong signals of selection across the genomes of our target crops. EnvGWAS identified 16 genomic regions in barley and 10 in wheat with contrasting patterns between spring and winter types of the two crops. To further examine changes in genome structure in wheat and barley over the past century, we used the same genotypic data to derive eigenvectors for deployment in EigenGWAS. This resulted in the detection of seven major chromosomal introgressions that contributed to adaptation in wheat. The deployment of both EigenGWAS and EnvGWAS based on variety age avoids costly phenotyping and will facilitate the identification of genomic tracts that have been under selection during plant breeding in underutilized historical cultivar collections. Our results not only demonstrate the potential of using historical cultivar collections coupled with genomic data to identify chromosomal regions that have been under selection but to also guide future plant breeding strategies to maximise the rate of genetic gain and adaptation in crop improvement programs.Significance Statement100 years of plant breeding have greatly improved crop adaptation, resilience, and productivity. Generating the trait data required for these studies is prohibitively expensive and can be impossible on large historical traits. This study reports using variety age and eigenvectors of the genomic relationship matrix as surrogate traits in GWAS to locate the genomic regions that have undergone selection during varietal development in wheat and barley. In several cases these were confirmed as associated with yield and other selected traits. The success and the simplicity of the approach means it can easily be extended to other crops with a recent recorded history of plant breeding and available genomic resources.

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