Advantages and limitations of multiple-trait genomic prediction for Fusarium head blight severity in hybrid wheat (Triticum aestivum L.)

Schulthess, Albert W.; Zhao, Yusheng; Longin, C. Friedrich H.; Reif, Jochen C.

doi:10.1007/s00122-017-3029-7

Cited by 74 publications

(83 citation statements)

References 81 publications

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“…As discussed previously, PH can affect disease pressure (Buerstmayr et al, 2002;Mao et al, 2010;Buerstmayr and Buerstmayr, 2016;Schulthess et al, 2018). Correlations between PH and FHB traits were negative in both years of our study, which agrees with the literature (Mao et al, 2010;Yan et al, 2011;Buerstmayr and Buerstmayr, 2016).…”

Section: Phenotypic Characteristicssupporting

confidence: 93%

GWAS for Fusarium Head Blight Traits in a Soft Red Winter Wheat Mapping Panel

2019

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Fusarium head blight (FHB) is an important disease of wheat (Triticum aestivum L.) that has caused billions of dollars in losses in recent decades. Although a massive breeding effort has been undertaken on multiple continents, there are no wheat cultivars with immunity to the disease. Resistance is conditioned by multiple loci and is further complicated by the role of the environment in expression of the disease phenotype. The objectives of our study were (i) to evaluate the phenotypic response to FHB in a large, diverse soft red winter wheat mapping panel; and (ii) to identify promising quantitative trait loci (QTL) associated with FHB resistance based on a genome‐wide association study (GWAS). We evaluated the mapping panel in 2014–2015 and 2015–2016 in an irrigated, inoculated scab nursery near Lexington, KY. Traits evaluated were heading date, plant height, FHB rating, severity, incidence, index, Fusarium‐damaged kernels (FDK), and deoxynivalenol (DON). There were significant (p < 0.05) differences among genotypes for all traits measured. The GWAS (based on 2‐yr entry means) identified 16 significant (p < 0.001) single nucleotide polymorphisms (SNPs) associated with disease traits on multiple chromosomes. Single nucleotide polymorphism association ranged from −2.14 to 4.01% of the mean of a given trait. We detected SNPs associated with FDK and DON on chromosomes 4A, 5B and 6B, and these SNPs decreased DON levels by 1.5, 2.1 and 3.2 mg kg−1, respectively. Our study demonstrated that even small‐effect QTL can potentially decrease disease levels and thus be useful in breeding programs.

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Section: Phenotypic Characteristicssupporting

confidence: 93%

GWAS for Fusarium Head Blight Traits in a Soft Red Winter Wheat Mapping Panel

2019

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“…For example, Schulthess et al (2017) reported that multivariate analysis improves parameter estimates. Authors like Calus and Veerkamp (2011), Jia and Jannink (2012), Jiang et al (2015), Montesinos‐López et al (2016), He et al (2016) and Schulthess et al (2017) reported better prediction accuracy of multivariate analysis compared to univariate analysis. These authors also agree that the genetic correlation between traits is the basis for the benefit of multivariate analysis; that is, that the larger the degree of relatedness between traits, the greater the benefit of multivariate analysis.…”

Section: Discussionmentioning

confidence: 99%

Multivariate Bayesian Analysis of On‐Farm Trials with Multiple‐Trait and Multiple‐Environment Data

Montesinos‐López

Vargas-Hernández

et al. 2019

Agronomy Journal

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Multivariate analysis is preferred over univariate analysis in plant breeding studies because it can exploit correlated traits and environments, whereas Bayesian analysis provides a natural way to incorporate prior knowledge and inferences that are conditional on the observed data. The objective of this paper is to show how to use multivariate Bayesian analysis for estimating random effects of genotype × environment and genotype × environment × trait combinations, and for computing genotypic and phenotypic correlations among traits and environments. Data were collected from on-farm trials conducted by the International Maize and Wheat Improvement Center (CIM-MYT) to evaluate bread and durum wheat lines in the Yaqui Valley of southern Sonora, México, during three crop seasons (2012, 2013, and 2015). The Bayesian multi-trait and multienvironment model with Gibbs sampler provides an analytic solution that can be used as an alternative for analyzing on-farm multiple-trait and multiple-environment data because it allows making parsimonious, precise and simultaneous estimations of random effects, genetic correlations (of traits and environments) and residual correlations of traits. The multivariate Bayesian model successfully fitted three of the four data sets (2012, 2015, and combined cropping seasons), but did not fit well the data of crop season 2013. For three out of five traits under study in this crop season, the correlations between the observed and predicted phenotypic values were lower than 0.6, suggesting that the predicted values were not very close to the observed values.

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“…Heritability for single locations was estimated following the concept of genomic heritability to evaluate the quality of field trials. This was done by estimating the prediction abilities applying fivefold crossvalidation as outlined in detail elsewhere (Schulthess et al 2018).…”

Section: Analysis Of Phenotypic Datamentioning

confidence: 99%

The potential of hybrid breeding to enhance leaf rust and stripe rust resistance in wheat

Beukert

Liu²,

Thorwarth

et al. 2020

Theor Appl Genet

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Key message Hybrid wheat breeding is a promising strategy to improve the level of leaf rust and stripe rust resistance in wheat. Abstract Leaf rust and stripe rust belong to the most important fungal diseases in wheat production. Due to a dynamic development of new virulent races, epidemics appear in high frequency and causes significant losses in grain yield and quality. Therefore, research is needed to develop strategies to breed wheat varieties carrying highly efficient resistances. Stacking of dominant resistance genes through hybrid breeding is such an approach. Within this study, we investigated the genetic architecture of leaf rust and stripe rust resistance of 1750 wheat hybrids and their 230 parental lines using a genome-wide association study. We observed on average a lower rust susceptibility for hybrids in comparison to their parental inbred lines and some hybrids outperformed their better parent with up to 56%. Marker-trait associations were identified on chromosome 3D and 4A for leaf rust and on chromosome 2A, 2B, and 6A for stripe rust resistance by using a genome-wide association study with a Bonferroni-corrected threshold of P < 0.10. Detected loci on chromosomes 4A and 2A were located within previously reported genomic regions affecting leaf rust and stripe rust resistance, respectively. The degree of dominance was for most associations favorable in the direction of improved resistance. Thus, resistance can be increased in hybrid wheat breeding by fixing complementary leaf rust and stripe rust resistance genes with desired dominance effects in opposite parental pools.

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Advantages and limitations of multiple-trait genomic prediction for Fusarium head blight severity in hybrid wheat (Triticum aestivum L.)

Cited by 74 publications

References 81 publications

GWAS for Fusarium Head Blight Traits in a Soft Red Winter Wheat Mapping Panel

GWAS for Fusarium Head Blight Traits in a Soft Red Winter Wheat Mapping Panel

Multivariate Bayesian Analysis of On‐Farm Trials with Multiple‐Trait and Multiple‐Environment Data

The potential of hybrid breeding to enhance leaf rust and stripe rust resistance in wheat

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