Application of image-based phenotyping tools to identify QTL for in-field winter survival of winter wheat (Triticum aestivum L.)

Chen, Yi; Sidhu, Harwinder Singh; Kaviani, Mina; McElroy, Michel S; Pozniak, Curtis; Navabi, Alireza

doi:10.1007/s00122-019-03373-6

Cited by 17 publications

(12 citation statements)

References 71 publications

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“…We believe that both of the software is very useful for high-throughput phenotyping using UAVs because it is user-friendly and keeping updated. As alternative SfM/MVS software, Pix4D is known to create orthomosaic images for investigating phenotypes of crops (Zhang et al, 2018;Chen et al, 2019;Hassan et al, 2019;Li et al, 2019, Marcial-Pablo et al, 2019.…”

Section: Discussionmentioning

confidence: 99%

“…Image data analysis can now be performed even by consumergrade personal computers, and UAVs can be equipped with various sensors, such as digital red-green-blue (RGB), thermal, multispectral, and hyperspectral sensors (Gracia-Romero et al, 2019). From the image data derived from the sensors, specific phenotypes, such as vegetation fraction (VF), plant height, architecture, drought adaptability, and disease severity, have been evaluated (Gracia-Romero et al, 2017;Madec et al, 2017;Condorelli et al, 2018;Zhang et al, 2018;Chen et al, 2019;Hassan et al, 2019;Li et al, 2019;Marcial-Pablo et al, 2019;Ogawa et al, 2019;Wang et al, 2019b) and used for estimation of biomass and yield (Yue et al, 2017;Gong et al, 2018;Di Gennaro et al, 2019;Duan et al, 2019;Wang et al, 2019a).…”

Section: Introductionmentioning

confidence: 99%

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Remote-Sensing-Combined Haplotype Analysis Using Multi-Parental Advanced Generation Inter-Cross Lines Reveals Phenology QTLs for Canopy Height in Rice

et al. 2021

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High-throughput phenotyping systems with unmanned aerial vehicles (UAVs) enable observation of crop lines in the field. In this study, we show the ability of time-course monitoring of canopy height (CH) to identify quantitative trait loci (QTLs) and to characterise their pleiotropic effect on various traits. We generated a digital surface model from low-altitude UAV-captured colour digital images and investigated CH data of rice multi-parental advanced generation inter-cross (MAGIC) lines from tillering and heading to maturation. Genome-wide association studies (GWASs) using the CH data and haplotype information of the MAGIC lines revealed 11 QTLs for CH. Each QTL showed haplotype effects on different features of CH such as stage-specificity and constancy. Haplotype analysis revealed relationships at the QTL level between CH and, vegetation fraction and leaf colour [derived from UAV red–green–blue (RGB) data], and CH and yield-related traits. Noticeably, haplotypes with canopy lowering effects at qCH1-4, qCH2, and qCH10-2 increased the ratio of panicle weight to leaf and stem weight, suggesting biomass allocation to grain yield or others through growth regulation of CH. Allele mining using gene information with eight founders of the MAGIC lines revealed the possibility that qCH1-4 contains multiple alleles of semi-dwarf 1 (sd1), the IR-8 allele of which significantly contributed to the “green revolution” in rice. This use of remote-sensing-derived phenotyping data into genetics using the MAGIC lines gives insight into how rice plants grow, develop, and produce grains in phenology and provides information on effective haplotypes for breeding with ideal plant architecture and grain yield.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote-Sensing-Combined Haplotype Analysis Using Multi-Parental Advanced Generation Inter-Cross Lines Reveals Phenology QTLs for Canopy Height in Rice

et al. 2021

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“…Over the last few decades, breeding high yield varieties has been one of the most important approaches for yield increase, as most agronomic traits show high heritability and are controlled by genes. For tracking those agronomic traits, quantitative trait loci (QTL) mapping and genome wide association studies (GWAS) have become major strategies to identify trait related molecular markers [ 3 , 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…The QTL of other agronomic traits, such as TGW, GW, GN, spikelet number per spike, biomass, spike length, GPC, and seed parameters have been identified in almost all 21 chromosomes in wheat [ 4 , 5 , 6 , 7 , 11 , 55 ]. A few yield-related genes have been cloned.…”

Section: Introductionmentioning

confidence: 99%

Yield-Related QTL Clusters and the Potential Candidate Genes in Two Wheat DH Populations

Zhang

She

Yang

et al. 2021

IJMS

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In the present study, four large-scale field trials using two doubled haploid wheat populations were conducted in different environments for two years. Grain protein content (GPC) and 21 other yield-related traits were investigated. A total of 227 QTL were mapped on 18 chromosomes, which formed 35 QTL clusters. The potential candidate genes underlying the QTL clusters were suggested. Furthermore, adding to the significant correlations between yield and its related traits, correlation variations were clearly shown within the QTL clusters. The QTL clusters with consistently positive correlations were suggested to be directly utilized in wheat breeding, including 1B.2, 2A.2, 2B (4.9–16.5 Mb), 2B.3, 3B (68.9–214.5 Mb), 4A.2, 4B.2, 4D, 5A.1, 5A.2, 5B.1, and 5D. The QTL clusters with negative alignments between traits may also have potential value for yield or GPC improvement in specific environments, including 1A.1, 2B.1, 1B.3, 5A.3, 5B.2 (612.1–613.6 Mb), 7A.1, 7A.2, 7B.1, and 7B.2. One GPC QTL (5B.2: 671.3–672.9 Mb) contributed by cultivar Spitfire was positively associated with nitrogen use efficiency or grain protein yield and is highly recommended for breeding use. Another GPC QTL without negatively pleiotropic effects on 2A (50.0–56.3 Mb), 2D, 4D, and 6B is suggested for quality wheat breeding.

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“…For varieties to manifest spring growth, the dominant allele, Vrn, must be present in the genome, and dominant alleles are inhibitors of the vernalization requirement. In winter varieties, all three vrn loci occur in recessive form [10]. Vernalization requirements are strong, but not identical in all sensitive genotypes.…”

Section: Introductionmentioning

confidence: 96%

Effect of Gibberellic Acid on Growing-Point Development of Non-Vernalized Wheat Plants under Long-Day Conditions

Skalický

Kubeš

Vachová

et al. 2020

Plants

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The goal of this study was to determine whether the application of gibberellic acid (GA3) to seeds of common wheat varieties with different vernalization and photoperiod requirements affects the transition from vegetative to generative stage. Three varieties of wheat with different photoperiod sensitivities and vernalization were selected for the experiment—the winter varieties, Mironovskaya and Bezostaya, and the spring variety, Sirael. Seeds were treated with different concentrations of GA3 and plants were grown under long-day conditions with monitoring of their photosynthetic activity (Fv/Fm, Pn, E, gs). We monitored the activity of the photosynthetic apparatus by checking the plants to see if they were growing properly. The phenological stages of the wheat species were checked for indications of a transition from the vegetative to the generative stage. Selected concentrations of GA3 had no effect on the compensation of the vernalization process (transition to the generative phase). Chlorophyll fluorescence was one of the factors for monitoring stress. The variety, Bezostaya, is similar to the spring variety, Sirael, in its trends and values. The growth conditions of Bezostaya and Sirael were not affected by the activity of the photosynthetic apparatus. The development of growing points in winter varieties occurred at the prolonged single ridge stage. The spring variety reached the stage of head emergence after sixty days of growth (changes to the flowering phase did not appear in winter wheat). Application of GA3 to the seeds had no effect on the transition of the growing point to the double-ridge generative stage. The present study highlights the priming effect of GA3 on seeds of common wheat varieties with different vernalization and photoperiod requirements as it affected the transition from vegetative to generative stage.

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Application of image-based phenotyping tools to identify QTL for in-field winter survival of winter wheat (Triticum aestivum L.)

Cited by 17 publications

References 71 publications

Remote-Sensing-Combined Haplotype Analysis Using Multi-Parental Advanced Generation Inter-Cross Lines Reveals Phenology QTLs for Canopy Height in Rice

Remote-Sensing-Combined Haplotype Analysis Using Multi-Parental Advanced Generation Inter-Cross Lines Reveals Phenology QTLs for Canopy Height in Rice

Yield-Related QTL Clusters and the Potential Candidate Genes in Two Wheat DH Populations

Effect of Gibberellic Acid on Growing-Point Development of Non-Vernalized Wheat Plants under Long-Day Conditions

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