Genetic Dissection of End-Use Quality Traits in Adapted Soft White Winter Wheat

Jernigan, Kendra L.; Godoy, Jayfred; Huang, Meng; Zhou, Yao; Morris, Craig F.; Garland‐Campbell, Kimberly; Zhang, Zhiwu; Carter, Arron H.

doi:10.3389/fpls.2018.00271

Cited by 40 publications

(53 citation statements)

References 62 publications

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“…The majority of these lines were common wheat (284; 62%), whereas the remaining lines were club wheat (172; 38%). This population has been characterized previously for different traits such as grain yield [35], snow mold tolerance [11], eyespot resistance [36], and end-use quality traits [37].…”

Section: Experimental Materialsmentioning

confidence: 99%

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Lozada

Carter

2020

Agronomy

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Examining the architecture of traits through genomics is necessary to gain a better understanding of the genetic loci affecting important traits to facilitate improvement. Genomewide association study (GWAS) and genomic selection (GS) were implemented for grain yield, heading date, and plant height to gain insights into the genetic complexity of phenotypic stability of traits in a diverse population of US Pacific Northwest winter wheat. Analysis of variance using the Additive Main Effect and Multiplicative Interaction (AMMI) approach revealed significant genotype and genotype by environment interactions. GWAS identified 12 SNP markers distributed across 10 chromosomes affecting variation for both trait and phenotypic stability, indicating potential pleiotropic effects and signifying that similar genetic loci could be associated with different aspects of stability. The lack of stable and major effect loci affecting phenotypic variation supports the complexity of stability of traits. Accuracy of GS was low to moderate, between 0.14 and 0.66, indicating that phenotypic stability is under genetic control. The moderate to high correlation between trait and trait stability suggests the potential of simultaneous selection for trait and trait stability. Our results demonstrate the complex genetic architecture of trait stability and show the potential for improving stability in winter wheat using genomic-assisted approaches.Agronomy 2020, 10, 368 2 of 15 the strength of linkage disequilibrium (i.e., the non-random association of alleles at multiple loci) between markers and functional polymorphism across diverse germplasm [10]. In wheat, GWAS has been conducted in different traits such as grain yield, yield components (thousand kernel weight and kernel number), Fusarium head blight resistance, snow mold tolerance, plant height, and heading date [11][12][13][14][15][16]. Compared with biparental mapping, GWAS results in a higher mapping resolution as it exploits the majority of the recombination histories of the individuals belonging to the diverse population used [17,18]. The main drawback of GWAS, nonetheless, is that it might not able to capture the causative and rare variants (loci) with small effects, and therefore would be a special case of missing heritability, i.e., the portion of genetic variance that cannot be explained by all significant loci [19]. In this case, genomic selection (GS) is seen as a complementary approach to GWAS.GS uses genomewide markers to estimate the marker effects and calculate genomic estimated breeding values (GEBV), which represents the genetic "worth" or merit of an individual. A high GEBV for grain yield, for example, would mean that a line is predicted to have better grain yield compared with others. In contrast to GWAS, GS does not test for significance but rather uses genomewide marker data to calculate the GEBV. More genetic variation could be captured in GS compared with marker-assisted selection as all markers are used in estimating breeding values [20]. In GS, a prediction model ...

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Section: Experimental Materialsmentioning

confidence: 99%

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Lozada

Carter

2020

Agronomy

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“…All these results confirmed the significant positive association between HMW-GS loci and gluten strength in durum wheat. QTL for SV associated with Glu-A1 and Glu-B1 (Jernigan et al, 2018) were also reported in bread wheat. However, a weaker association was reported between HMW-GS loci and gluten strength in modern durum wheat cultivars likely as a result of limited genetic variation at Glu-1 (Sissons, 2008).…”

Section: Quantitative Trait Loci Associated With High Molecular Weighmentioning

confidence: 74%

“…The QTL QGlu.spa-1B.1 (peak SNP: Kukri_c38353_67) was projected on the short arm of chromosome 1B on the durum wheat consensus map, approximately 4.4 cM away from SSR marker gwm550 reported by Patil et al (2009). Likewise, QTL QGlu.spa-1B.2 (peak SNP: Excalibur_c50079_420) on the long arm of chromosome 1B was 6 cM away from the QTL interval (barc181-psr162) identified by Zhang et al (2008) and Conti et al (2011) in durum wheat, and 0.5 cM apart from the QTL (peak SNP: CAP8_c818_370) identified by Jernigan et al (2018) in bread wheat. Of the two QTL reported by Roselló et al (2018), QTL associated with marker wPt-1140 was located within QGlu.spa-2B.2 (peak SNP: Kukri_c25868_56) and another QTL associated with marker wPt-6894 within QGlu.spa-2B.3 (peak SNP: Excalibur_c91034_141).…”

Section: Comparison With Previously Reported Quantitative Trait Locimentioning

confidence: 80%

High Density Mapping of Quantitative Trait Loci Conferring Gluten Strength in Canadian Durum Wheat

Ruan

Knox

et al. 2020

Front. Plant Sci.

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Gluten strength is one of the factors that determine the end-use quality of durum wheat and is an important breeding target for this crop. To characterize the quantitative trait loci (QTL) controlling gluten strength in Canadian durum wheat cultivars, a population of 162 doubled haploid (DH) lines segregating for gluten strength and derived from cv. Pelissier × cv. Strongfield was used in this study. The DH lines, parents, and controls were grown in 3 years and two seeding dates in each year and gluten strength of grain samples was measured by sodium dodecyl sulfate (SDS)-sedimentation volume (SV). With a genetic map created by genotyping the DH lines using the Illumina Infinium iSelect Wheat 90K SNP (single nucleotide polymorphism) chip, QTL contributing to gluten strength were detected on chromosome 1A, 1B, 2B, and 3A. Two major and stable QTL detected on chromosome 1A (QGlu.spa-1A) and 1B (QGlu.spa-1B.1) explaining 13.7-18.7% and 25.4-40.1% of the gluten strength variability respectively were consistently detected over 3 years, with the trait increasing alleles derived from Strongfield. Putative candidate genes underlying the major QTL were identified. Two novel minor QTL (QGlu.spa-3A.1 and QGlu.spa-3A.2) with the trait increasing allele derived from Pelissier were mapped on chromosome 3A explaining up to 8.9% of the phenotypic variance; another three minor QTL (QGlu.spa-2B.1, QGlu.spa-2B.2, and QGlu.spa-2B.3) located on chromosome 2B explained up to 8.7% of the phenotypic variance with the trait increasing allele derived from Pelissier. QGlu.spa-2B.1 is a new QTL and has not been reported in the literature. Multi-environment analysis revealed genetic (QTL) × environment interaction due to the difference of effect in magnitude rather than the direction of the QTL. Eleven pairs of digenic epistatic QTL were identified, with an epistatic effect between the two major QTL of QGlu.spa-1A and QGlu.spa-1B.1 detected in four out of six environments. The peak SNPs and SNPs flanking the QTL interval of QGlu.spa-1A and QGlu.spa-1B.1 were converted to Kompetitive Allele Specific PCR (KASP) markers, which can be deployed in marker-assisted breeding to increase the efficiency and accuracy of phenotypic selection for gluten strength in durum wheat. The QTL that were expressed consistently across

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“…This study was performed with collected data from a panel of 480 advanced soft white winter wheat varieties from U.S. Pacific Northwest breeding programs (Oregon State University, University of Idaho, Washington State University, USDA-ARS, and private breeding companies) [10]. The panel was grown in an unreplicated augmented block design near Pullman, WA (46 • 7 N; −117 • 1 W) during 2015, 2016, and 2017.…”

Section: Methodsmentioning

confidence: 99%

Toward a New Use for Carbon Isotope Discrimination in Wheat Breeding

Dixon

Carter

2019

Agronomy

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A major obstacle in the effort to develop drought tolerant varieties of wheat (Triticum aestivum L.) is phenotyping. Traits known to contribute to improved drought tolerance, such as water-use behavior, reliance on stem reserve carbohydrates, and the ability to develop deep roots, require time and resource-intensive screening techniques. Plant breeding programs often have many thousands of experimental genotypes, which makes testing for each of these traits impractical. This work proposes that carbon isotope discrimination (∆) analysis of mature grains may serve as a relatively high-throughput approach to identify genotypes exhibiting traits associated with drought tolerance. Using ∆ as a proxy for stomatal conductance and photosynthetic capacity, assumptions can be made regarding fundamental plant physiological responses. When combined with knowledge of the terminal drought severity experienced in a particular environment, genotypes exhibiting conservative and rapid water use, deep roots, and reliance on stem reserve carbohydrates may be identified. Preliminary data in support of this idea are presented. Further verification of this use for grain ∆ will better equip wheat breeding programs to develop more drought tolerant varieties.

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Genetic Dissection of End-Use Quality Traits in Adapted Soft White Winter Wheat

Cited by 40 publications

References 62 publications

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

High Density Mapping of Quantitative Trait Loci Conferring Gluten Strength in Canadian Durum Wheat

Toward a New Use for Carbon Isotope Discrimination in Wheat Breeding

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