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

Ruan, Yuefeng; Yu, Bianyun; Knox, Ron; Singh, Asheesh K.; DePauw, R. M.; Cuthbert, Richard D.; Zhang, Wentao; Piché, Isabelle; Gao, Peng; Sharpe, Andrew; Fobert, Pierre R.

doi:10.3389/fpls.2020.00170

Cited by 16 publications

(12 citation statements)

References 60 publications

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“…Regarding the stability of GY, Hou et al (2014) showed that TaSus1-7A carrying Hap-1/2 had a higher 1000-grain weight in multiple environments, which indicated the stable expression of the grain weight trait of Hap-1/2 [ 73 ]. Regarding the stability of PC, Ruan et al (2020) conducted a two-year trial with 162 double haploid durum wheat populations and found that the favorable allele of Glu-B3 was stably expressed in all environments tested [ 78 ]. In the present study, wheat cultivars Liangxing 66 and Xinmai 18 contained both stable genes, TaSus1-7A and Glu-B3, which indicates that their PC and GY can be stably expressed under low-input management.…”

Section: Discussionmentioning

confidence: 99%

Combining Protein Content and Grain Yield by Genetic Dissection in Bread Wheat under Low-Input Management

Xiao

Hou

et al. 2021

Foods

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The simultaneous improvement of protein content (PC) and grain yield (GY) in bread wheat (Triticum aestivum L.) under low-input management enables the development of resource-use efficient varieties that combine high grain yield potential with desirable end-use quality. However, the complex mechanisms of genotype, management, and growing season, and the negative correlation between PC and GY complicate the simultaneous improvement of PC and GY under low-input management. To identify favorable genotypes for PC and GY under low-input management, this study used 209 wheat varieties, including strong gluten, medium-strong gluten, medium gluten, weak gluten, winter, semi-winter, weak-spring, and spring types, which has been promoted from the 1980s to the 2010s. Allelic genotyping, performed using kompetitive allele-specific polymerase chain reaction (KASP) technology, found 69 types of GY-PC allelic combinations in the tested materials. Field trials were conducted with two growing season treatments (2018–2019 and 2019–2020) and two management treatments (conventional management and low-input management). Multi-environment analysis of variance showed that genotype, management, and growing season had extremely substantial effects on wheat GY and PC, respectively, and the interaction of management × growing season also had extremely significant effects on wheat GY. According to the three-sigma rule of the normal distribution, the GY of wheat varieties Liangxing 66 and Xinmai 18 were stable among the top 15.87% of all tested materials with high GY, and their PC reached mean levels under low-input management, but also stably expressed high GY and high PC under conventional management, which represents a great development potential. These varieties can be used as cultivars of interest for breeding because TaSus1-7A, TaSus1-7B, TaGW2-6A, and TaGW2-6B, which are related to GY, and Glu-B3, which is related to PC, carry favorable alleles, among which Hap-1/2, the allele of TaSus1-7A, and Glu-B3b/d/g/i, the allele of Glu-B3, can be stably expressed. Our results may be used to facilitate the development of high-yielding and high-quality wheat varieties under low-input management, which is critical for sustainable food and nutrition security.

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Section: Discussionmentioning

confidence: 99%

Combining Protein Content and Grain Yield by Genetic Dissection in Bread Wheat under Low-Input Management

Xiao

Hou

et al. 2021

Foods

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“…Analysis of variance (ANOVA) and heritability estimate were performed using the PROC MIXED procedure of SAS 9.3 (SAS Institute, Cary, NC, USA) as described by Ruan et al ( 2020 ). In the mixed model, DH lines (genotypes, G) were considered as fixed effects, while environments (E), genotype × environment (G × E) interactions and replications nested in environments were considered as random effects.…”

Section: Methodsmentioning

confidence: 99%

“…QTL mapping was performed using the genetic map of Pelissier × Strongfield reported by Ruan et al ( 2020 ). The Infinium iSelect Wheat 90K SNP chip was used for genotyping.…”

Section: Methodsmentioning

confidence: 99%

“…QTL detection was performed using composite interval mapping (CIM) in WinQTL Cartographer v.2.5 software (Wang S. et al, 2012 ) ( http://statgen.ncsu.edu/qtlcart/WQTLCart.htm ). The same parameters as described by Ruan et al ( 2020 ) were used for CIM. QTL detected in different environments were considered the same if the confidence intervals (CI) overlapped and the additive effect was contributed by the same parent.…”

Section: Methodsmentioning

confidence: 99%

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Conditional Mapping Identified Quantitative Trait Loci for Grain Protein Concentration Expressing Independently of Grain Yield in Canadian Durum Wheat

Ruan

Knox

et al. 2021

Front. Plant Sci.

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Grain protein concentration (GPC) is an important trait in durum cultivar development as a major determinant of the nutritional value of grain and end-use product quality. However, it is challenging to simultaneously select both GPC and grain yield (GY) due to the negative correlation between them. To characterize quantitative trait loci (QTL) for GPC and understand the genetic relationship between GPC and GY in Canadian durum wheat, we performed both traditional and conditional QTL mapping using a doubled haploid (DH) population of 162 lines derived from Pelissier × Strongfield. The population was grown in the field over 5 years and GPC was measured. QTL contributing to GPC were detected on chromosome 1B, 2B, 3A, 5B, 7A, and 7B using traditional mapping. One major QTL on 3A (QGpc.spa-3A.3) was consistently detected over 3 years accounting for 9.4–18.1% of the phenotypic variance, with the favorable allele derived from Pelissier. Another major QTL on 7A (QGpc.spa-7A) detected in 3 years explained 6.9–14.8% of the phenotypic variance, with the beneficial allele derived from Strongfield. Comparison of the QTL described here with the results previously reported led to the identification of one novel major QTL on 3A (QGpc.spa-3A.3) and five novel minor QTL on 1B, 2B and 3A. Four QTL were common between traditional and conditional mapping, with QGpc.spa-3A.3 and QGpc.spa-7A detected in multiple environments. The QTL identified by conditional mapping were independent or partially independent of GY, making them of great importance for development of high GPC and high yielding durum.

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“…(Calayugan et al, 2020) were detected. In wheat, five QTLs for gluten strength (including QGlu.spa-1A and QGlu.spa-1B.1 on chr 1A and 1B, respectively) were identified (Ruan et al, 2020). Additionally, 16 wheat QTLs for grain Fe, Zn and protein contents, and 1000-kernel weight were identified, encompassing four Fe QTLs (QGFe.iari-2A, QGFe.iari-5A, QGFe.iari-7A and QGFe.iari-7B), five Zn QTLs (QGZn.iari-2A, QGZn.iari-4A, QGZn.iari-5A, QGZn.iari-7A and QGZn.iari-7B), two protein content QTLs (QGpc.iari-2A and QGpc.iari-3A), and five 1000-kernel weight QTLs (QTkw.iari-1A, QTkw.iari-2A, QTkw.iari-2B, QTkw.iari-5B and QTkw.iari-7A) (Krishnappa et al, 2017).…”

Section: Omics Facilitated Crop Improvement For Nutritive Traitsmentioning

confidence: 99%

Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value

et al. 2021

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Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the “omics” technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.

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

Cited by 16 publications

References 60 publications

Combining Protein Content and Grain Yield by Genetic Dissection in Bread Wheat under Low-Input Management

Combining Protein Content and Grain Yield by Genetic Dissection in Bread Wheat under Low-Input Management

Conditional Mapping Identified Quantitative Trait Loci for Grain Protein Concentration Expressing Independently of Grain Yield in Canadian Durum Wheat

Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value

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