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Nguyen HA, Martre P, Collet C, Draye X, Salon C, Jeudy C, Rincent R, Muller B. Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:2510-2526. [PMID: 38520390 DOI: 10.1093/jxb/erae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 03/21/2024] [Indexed: 03/25/2024]
Abstract
Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.
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Affiliation(s)
- Hong Anh Nguyen
- LEPSE, Université de Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France
| | - Pierre Martre
- LEPSE, Université de Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France
| | - Clothilde Collet
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Xavier Draye
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Christophe Salon
- Agroécologie, AgroSup Dijon, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Christian Jeudy
- Agroécologie, AgroSup Dijon, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Renaud Rincent
- GDEC, Université Clermont-Auvergne, INRAE, Clermont-Ferrand, France
| | - Bertrand Muller
- LEPSE, Université de Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France
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2
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Tessema BB, Raffo MA, Guo X, Svane SF, Krusell L, Jensen JD, Ruud AK, Malinowska M, Thorup-Kristensen K, Jensen J. Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments. PLANT METHODS 2024; 20:8. [PMID: 38216953 PMCID: PMC10785381 DOI: 10.1186/s13007-023-01121-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/04/2023] [Indexed: 01/14/2024]
Abstract
BACKGROUND In drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors. RESULTS The estimated heritabilities ([Formula: see text]) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation ([Formula: see text]) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest [Formula: see text] observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2. CONCLUSIONS The significant [Formula: see text] and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.
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Affiliation(s)
- Biructawit B Tessema
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, USA.
| | - Miguel A Raffo
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
| | - Xiangyu Guo
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
- Danish Pig Research Centre, Danish Agriculture & Food Council, Copenhagen, Denmark
| | - Simon F Svane
- Department of Plant and Environmental Science, University of Copenhagen, 1871, Frederiksberg, Denmark
| | - Lene Krusell
- Sejet Plant Breeding I/S, 8700, Horsens, Denmark
| | | | - Anja Karine Ruud
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
- Faculty of Biosciences, Department of Plant Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Marta Malinowska
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | | | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
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3
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Lärm L, Bauer FM, Hermes N, van der Kruk J, Vereecken H, Vanderborght J, Nguyen TH, Lopez G, Seidel SJ, Ewert F, Schnepf A, Klotzsche A. Multi-year belowground data of minirhizotron facilities in Selhausen. Sci Data 2023; 10:672. [PMID: 37789016 PMCID: PMC10547842 DOI: 10.1038/s41597-023-02570-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/14/2023] [Indexed: 10/05/2023] Open
Abstract
The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil-plant continuum and the root images to develop and compare image analysis methods.
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Affiliation(s)
- Lena Lärm
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany.
| | - Felix Maximilian Bauer
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany.
| | - Normen Hermes
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Jan van der Kruk
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Harry Vereecken
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Jan Vanderborght
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Thuy Huu Nguyen
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, 53115, Germany
| | - Gina Lopez
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, 53115, Germany
| | - Sabine Julia Seidel
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, 53115, Germany
| | - Frank Ewert
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, 53115, Germany
- Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, 15374, Germany
| | - Andrea Schnepf
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Anja Klotzsche
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany.
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4
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Smith AG, Han E, Petersen J, Olsen NAF, Giese C, Athmann M, Dresbøll DB, Thorup‐Kristensen K. RootPainter: deep learning segmentation of biological images with corrective annotation. THE NEW PHYTOLOGIST 2022; 236:774-791. [PMID: 35851958 PMCID: PMC9804377 DOI: 10.1111/nph.18387] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/30/2022] [Indexed: 05/27/2023]
Abstract
Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.
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Affiliation(s)
- Abraham George Smith
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
- Department of Computer ScienceUniversity of CopenhagenUniversitetsparken 12100CopenhagenDenmark
| | - Eusun Han
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
- CSIRO Agriculture and FoodPO Box 1700CanberraACT2601Australia
| | - Jens Petersen
- Department of Computer ScienceUniversity of CopenhagenUniversitetsparken 12100CopenhagenDenmark
| | - Niels Alvin Faircloth Olsen
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
| | - Christian Giese
- Department of Agroecology and Organic FarmingUniversity of BonnRegina‐Pacis‐Weg 353113BonnGermany
| | - Miriam Athmann
- Department of Organic Farming and Plant ProductionUniversity of KasselNordbahnhofstr. 1aD‐37213WitzenhausenGermany
| | - Dorte Bodin Dresbøll
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
| | - Kristian Thorup‐Kristensen
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
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5
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Roitsch T, Himanen K, Chawade A, Jaakola L, Nehe A, Alexandersson E. Functional phenomics for improved climate resilience in Nordic agriculture. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5111-5127. [PMID: 35727101 PMCID: PMC9440434 DOI: 10.1093/jxb/erac246] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 06/06/2022] [Indexed: 05/26/2023]
Abstract
The five Nordic countries span the most northern region for field cultivation in the world. This presents challenges per se, with short growing seasons, long days, and a need for frost tolerance. Climate change has additionally increased risks for micro-droughts and water logging, as well as pathogens and pests expanding northwards. Thus, Nordic agriculture demands crops that are adapted to the specific Nordic growth conditions and future climate scenarios. A focus on crop varieties and traits important to Nordic agriculture, including the unique resource of nutritious wild crops, can meet these needs. In fact, with a future longer growing season due to climate change, the region could contribute proportionally more to global agricultural production. This also applies to other northern regions, including the Arctic. To address current growth conditions, mitigate impacts of climate change, and meet market demands, the adaptive capacity of crops that both perform well in northern latitudes and are more climate resilient has to be increased, and better crop management systems need to be built. This requires functional phenomics approaches that integrate versatile high-throughput phenotyping, physiology, and bioinformatics. This review stresses key target traits, the opportunities of latitudinal studies, and infrastructure needs for phenotyping to support Nordic agriculture.
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Affiliation(s)
- Thomas Roitsch
- Department of Plant and Environmental Sciences, University of Copenhagen, Denmark
- Department of Adaptive Biotechnologies, Global Change Research Institute, Czech Academy of Sciences, Brno, Czechia
| | - Kristiina Himanen
- National Plant Phenotyping Infrastructure, HiLIFE, University of Helsinki, Finland
- Organismal and Evolutionary Biology Research Program, Viikki Plant Science Centre, Faculty of Biological and Environmental Sciences, University of Helsinki, Finland
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
| | - Laura Jaakola
- Climate laboratory Holt, Department of Arctic and Marine Biology, UiT the Arctic University of Norway, Tromsø, Norway
- NIBIO, Norwegian Institute of Bioeconomy Research, Ås, Norway
| | - Ajit Nehe
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
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6
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Hansen PB, Ruud AK, de los Campos G, Malinowska M, Nagy I, Svane SF, Thorup-Kristensen K, Jensen JD, Krusell L, Asp T. Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare. PLANTS 2022; 11:plants11172190. [PMID: 36079572 PMCID: PMC9459846 DOI: 10.3390/plants11172190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/19/2022] [Accepted: 08/21/2022] [Indexed: 11/30/2022]
Abstract
Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72–0.91) than with genomic models alone (0.55–0.86). The correlation between predictions and phenotypes varied from 0.17–0.28 for control plants and 0.23–0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.
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Affiliation(s)
- Pernille Bjarup Hansen
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
- Correspondence: (P.B.H.); (T.A.); Tel.: +45-87158243 (T.A.)
| | - Anja Karine Ruud
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
| | - Gustavo de los Campos
- Departments of Epidemiology & Biostatistics and Statistics & Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Marta Malinowska
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
| | - Istvan Nagy
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
| | - Simon Fiil Svane
- Section for Crop Sciences, Department of Plant and Environmental Sciences, Copenhagen University, 2630 Taastrup, Denmark
| | - Kristian Thorup-Kristensen
- Section for Crop Sciences, Department of Plant and Environmental Sciences, Copenhagen University, 2630 Taastrup, Denmark
| | | | - Lene Krusell
- Sejet Plant Breeding, Nørremarksvej 67, 8700 Horsens, Denmark
| | - Torben Asp
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
- Correspondence: (P.B.H.); (T.A.); Tel.: +45-87158243 (T.A.)
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7
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Bauer FM, Lärm L, Morandage S, Lobet G, Vanderborght J, Vereecken H, Schnepf A. Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline. PLANT PHENOMICS 2022; 2022:9758532. [PMID: 35693120 PMCID: PMC9168891 DOI: 10.34133/2022/9758532] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/05/2022] [Indexed: 11/28/2022]
Abstract
Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using “RootPainter.” Then, an automated feature extraction from the segments is carried out by “RhizoVision Explorer.” To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation (r = 0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.
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Affiliation(s)
- Felix Maximilian Bauer
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Lena Lärm
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Shehan Morandage
- Institute of Soil Science and Land Evaluation, University of Hohenheim, 70559 78 Stuttgart, Germany
| | - Guillaume Lobet
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Jan Vanderborght
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Harry Vereecken
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Andrea Schnepf
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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8
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Colombo M, Roumet P, Salon C, Jeudy C, Lamboeuf M, Lafarge S, Dumas AV, Dubreuil P, Ngo W, Derepas B, Beauchêne K, Allard V, Le Gouis J, Rincent R. Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits. FRONTIERS IN PLANT SCIENCE 2022; 13:853601. [PMID: 35401645 PMCID: PMC8992431 DOI: 10.3389/fpls.2022.853601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Roots are essential for water and nutrient uptake but are rarely the direct target of breeding efforts. To characterize the genetic variability of wheat root architecture, the root and shoot traits of 200 durum and 715 bread wheat varieties were measured at a young stage on a high-throughput phenotyping platform. Heritability of platform traits ranged from 0.40 for root biomass in durum wheat to 0.82 for the number of tillers. Field phenotyping data for yield components and SNP genotyping were already available for all the genotypes. Taking differences in earliness into account, several significant correlations between root traits and field agronomic performances were found, suggesting that plants investing more resources in roots in some stressed environments favored water and nutrient uptake, with improved wheat yield. We identified 100 quantitative trait locus (QTLs) of root traits in the bread wheat panels and 34 in the durum wheat panel. Most colocalized with QTLs of traits measured in field conditions, including yield components and earliness for bread wheat, but only in a few environments. Stress and climatic indicators explained the differential effect of some platform QTLs on yield, which was positive, null, or negative depending on the environmental conditions. Modern breeding has led to deeper rooting but fewer seminal roots in bread wheat. The number of tillers has been increased in bread wheat, but decreased in durum wheat, and while the root-shoot ratio for bread wheat has remained stable, for durum wheat it has been increased. Breeding for root traits or designing ideotypes might help to maintain current yield while adapting to specific drought scenarios.
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Affiliation(s)
- Michel Colombo
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Pierre Roumet
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Christophe Salon
- Univ. Bourgogne, Agroecol Lab, Univ. Bourgogne Franche Comte, AgroSup Dijon, INRAE, Dijon, France
| | - Christian Jeudy
- Univ. Bourgogne, Agroecol Lab, Univ. Bourgogne Franche Comte, AgroSup Dijon, INRAE, Dijon, France
| | - Mickael Lamboeuf
- Univ. Bourgogne, Agroecol Lab, Univ. Bourgogne Franche Comte, AgroSup Dijon, INRAE, Dijon, France
| | | | | | | | - Wa Ngo
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
| | - Brice Derepas
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
| | | | - Vincent Allard
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
| | - Jacques Le Gouis
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
| | - Renaud Rincent
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
- GQE-Le Moulon, INRAE, Univ. Paris-Sud, CNRS, AgroParisTech, Universite Paris-Saclay, Gif-sur-Yvette, France
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9
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Wacker TS, Popovic O, Olsen NAF, Markussen B, Smith AG, Svane SF, Thorup-Kristensen K. Semifield root phenotyping: Root traits for deep nitrate uptake. PLANT, CELL & ENVIRONMENT 2022; 45:823-836. [PMID: 34806183 DOI: 10.1111/pce.14227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 11/02/2021] [Accepted: 11/09/2021] [Indexed: 06/13/2023]
Abstract
Deep rooting winter wheat genotypes can reduce nitrate leaching losses and increase N uptake. We aimed to investigate which deep root traits are correlated to deep N uptake and to estimate genetic variation in root traits and deep 15 N tracer uptake. In 2 years, winter wheat genotypes were grown in RadiMax, a semifield root-screening facility. Minirhizotron root imaging was performed three times during the main growing season. At anthesis, 15 N was injected via subsurface drip irrigation at 1.8 m depth. Mature ears from above the injection area were analysed for 15 N content. From minirhizotron image-based root length data, 82 traits were constructed, describing root depth, density, distribution and growth aspects. Their ability to predict 15 N uptake was analysed with the least absolute shrinkage and selection operator (LASSO) regression. Root traits predicted 24% and 14% of tracer uptake variation in 2 years. Both root traits and genotype showed significant effects on tracer uptake. In 2018, genotype and the three LASSO-selected root traits predicted 41% of the variation in tracer uptake, in 2019 genotype and one root trait predicted 48%. In both years, one root trait significantly mediated the genotype effect on tracer uptake. Deep root traits from minirhizotron images can predict deep N uptake, indicating the potential to breed deep-N-uptake-genotypes.
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Affiliation(s)
- Tomke S Wacker
- Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Olga Popovic
- Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Niels A F Olsen
- Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Bo Markussen
- Data Science Laboratory, Department of Mathematical Sciences, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Abraham G Smith
- Department of Computer Science, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Simon F Svane
- Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Thorup-Kristensen
- Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
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Herms CH, Hennessy RC, Bak F, Dresbøll DB, Nicolaisen MH. Back to our roots: exploring the role of root morphology as a mediator of beneficial plant-microbe interactions. Environ Microbiol 2022; 24:3264-3272. [PMID: 35106901 PMCID: PMC9543362 DOI: 10.1111/1462-2920.15926] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/24/2022] [Accepted: 01/26/2022] [Indexed: 11/27/2022]
Abstract
Plant breeding for belowground traits that have a positive impact on the rhizosphere microbiome is a promising strategy to sustainably improve crop yields. Root architecture and morphology are understudied plant breeding targets despite their potential to significantly shape microbial community structure and function in the rhizosphere. In this review, we explore the relationship between various root architectural and morphological traits and rhizosphere interactions, focusing on the potential of root diameter to impact the rhizosphere microbiome structure and function while discussing the potential biological and ecological mechanisms underpinning this process. In addition, we propose three future research avenues to drive this research area in an effort to unravel the effect of belowground traits on rhizosphere microbiology. This knowledge will pave the way for new plant breeding strategies that can be exploited for sustainable and high‐yielding crop cultivars.
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Affiliation(s)
- Courtney Horn Herms
- Section for Microbial Ecology and Biotechnology, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg C, 1871, Denmark
| | - Rosanna Catherine Hennessy
- Section for Microbial Ecology and Biotechnology, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg C, 1871, Denmark
| | - Frederik Bak
- Section for Microbial Ecology and Biotechnology, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg C, 1871, Denmark
| | - Dorte Bodin Dresbøll
- Section for Crop Sciences, Department of Plant and Environmental Sciences, University of Copenhagen, Højbakkegård Allé 30, Taastrup, 2630, Denmark
| | - Mette Haubjerg Nicolaisen
- Section for Microbial Ecology and Biotechnology, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg C, 1871, Denmark
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11
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Ober ES, Alahmad S, Cockram J, Forestan C, Hickey LT, Kant J, Maccaferri M, Marr E, Milner M, Pinto F, Rambla C, Reynolds M, Salvi S, Sciara G, Snowdon RJ, Thomelin P, Tuberosa R, Uauy C, Voss-Fels KP, Wallington E, Watt M. Wheat root systems as a breeding target for climate resilience. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1645-1662. [PMID: 33900415 PMCID: PMC8206059 DOI: 10.1007/s00122-021-03819-w] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/18/2021] [Indexed: 05/08/2023]
Abstract
In the coming decades, larger genetic gains in yield will be necessary to meet projected demand, and this must be achieved despite the destabilizing impacts of climate change on crop production. The root systems of crops capture the water and nutrients needed to support crop growth, and improved root systems tailored to the challenges of specific agricultural environments could improve climate resiliency. Each component of root initiation, growth and development is controlled genetically and responds to the environment, which translates to a complex quantitative system to navigate for the breeder, but also a world of opportunity given the right tools. In this review, we argue that it is important to know more about the 'hidden half' of crop plants and hypothesize that crop improvement could be further enhanced using approaches that directly target selection for root system architecture. To explore these issues, we focus predominantly on bread wheat (Triticum aestivum L.), a staple crop that plays a major role in underpinning global food security. We review the tools available for root phenotyping under controlled and field conditions and the use of these platforms alongside modern genetics and genomics resources to dissect the genetic architecture controlling the wheat root system. To contextualize these advances for applied wheat breeding, we explore questions surrounding which root system architectures should be selected for, which agricultural environments and genetic trait configurations of breeding populations are these best suited to, and how might direct selection for these root ideotypes be implemented in practice.
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Affiliation(s)
- Eric S Ober
- NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.
| | - Samir Alahmad
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - James Cockram
- NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Cristian Forestan
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - Lee T Hickey
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Josefine Kant
- Forschungszentrum Jülich, IBG-2, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
| | - Marco Maccaferri
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - Emily Marr
- NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | | | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Estado de Mexico, Mexico
| | - Charlotte Rambla
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Matthew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Estado de Mexico, Mexico
| | - Silvio Salvi
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - Giuseppe Sciara
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | | | - Roberto Tuberosa
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - Cristobal Uauy
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, NR4 7UH, UK
| | - Kai P Voss-Fels
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | | | - Michelle Watt
- School of BioSciences, University of Melbourne, Parkville, VIC, 3010, Australia
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12
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Dowd T, McInturf S, Li M, Topp CN. Rated-M for mesocosm: allowing the multimodal analysis of mature root systems in 3D. Emerg Top Life Sci 2021; 5:249-260. [PMID: 33555320 PMCID: PMC8166344 DOI: 10.1042/etls20200278] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/12/2021] [Accepted: 01/18/2021] [Indexed: 11/17/2022]
Abstract
A plants' water and nutrients are primarily absorbed through roots, which in a natural setting is highly dependent on the 3-dimensional configuration of the root system, collectively known as root system architecture (RSA). RSA is difficult to study due to a variety of factors, accordingly, an arsenal of methods have been developed to address the challenges of both growing root systems for imaging, and the imaging methods themselves, although there is no 'best' method as each has its own spectrum of trade-offs. Here, we describe several methods for plant growth or imaging. Then, we introduce the adaptation and integration of three complementary methods, root mesocosms, photogrammetry, and electrical resistance tomography (ERT). Mesocosms can allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. The recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Lastly, we demonstrate how 3-dimensional water availability can be measured using ERT inside of root mesocosms.
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Affiliation(s)
- Tyler Dowd
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
| | - Samuel McInturf
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
| | - Mao Li
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
| | - Christopher N Topp
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
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13
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Malinowska M, Nagy I, Wagemaker CAM, Ruud AK, Svane SF, Thorup-Kristensen K, Jensen CS, Eriksen B, Krusell L, Jahoor A, Jensen J, Eriksen LB, Asp T. The cytosine methylation landscape of spring barley revealed by a new reduced representation bisulfite sequencing pipeline, WellMeth. THE PLANT GENOME 2020; 13:e20049. [PMID: 33217208 DOI: 10.1002/tpg2.20049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 05/14/2020] [Accepted: 06/10/2020] [Indexed: 06/11/2023]
Abstract
Patterns and level of cytosine methylation vary widely among plant species and are associated with genome size as well as the proportion of transposons and other repetitive elements in the genome. We explored epigenetic patterns and diversity in a representative proportion of the spring barley (Hordeum vulgare L.) genome across several commercial and historical cultivars. This study adapted a genotyping-by-sequencing (GBS) approach for the detection of methylated cytosines in genomic DNA. To analyze the data, we developed WellMeth, a complete pipeline for analysis of reduced representation bisulfite sequencing. WellMeth enabled quantification of context-specific DNA methylation at the single-base resolution as well as identification of differentially methylated sites (DMCs) and regions (DMRs). On average, DNA methylation levels were significantly higher than what is commonly observed in many plants species, reaching over 10-fold higher levels than those in Arabidopsis thaliana (L.) Heynh. in the CHH methylation. Preferential methylation was observed within and at the edges of long-terminal repeats (LTR) retrotransposons Gypsy and Copia. From a pairwise comparison of cultivars, numerous DMRs could be identified of which more than 5,000 were conserved within the analyzed set of barley cultivars. The subset of regions overlapping with genes showed enrichment in gene ontology (GO) categories associated with chromatin and cellular structure and organization. A significant correlation between genetic and epigenetic distances suggests that a considerable portion of methylated regions is under strict genetic control in barley. The data presented herein represents the first step in efforts toward a better understanding of genome-level structural and functional aspects of methylation in barley.
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Affiliation(s)
- Marta Malinowska
- Department of Molecular Biology and Genetics, Aarhus University, Slagelse, Denmark
- Center for Quantitative Genetics & Genomics, Aarhus University, Slagelse, Denmark
| | - Istvan Nagy
- Department of Molecular Biology and Genetics, Aarhus University, Slagelse, Denmark
- Center for Quantitative Genetics & Genomics, Aarhus University, Slagelse, Denmark
| | | | - Anja K Ruud
- Department of Molecular Biology and Genetics, Aarhus University, Slagelse, Denmark
- Center for Quantitative Genetics & Genomics, Aarhus University, Slagelse, Denmark
| | - Simon F Svane
- Department of Plant and Environmental Science, University of Copenhagen, Frederiksberg, Denmark
| | | | | | | | | | | | | | - Lars Bonde Eriksen
- Landbrug & Fødevarer, SEGES, Aarhus, Denmark
- LIMAGRAIN A/S, Horsens, Denmark
| | - Torben Asp
- Department of Molecular Biology and Genetics, Aarhus University, Slagelse, Denmark
- Center for Quantitative Genetics & Genomics, Aarhus University, Slagelse, Denmark
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14
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Thorup-Kristensen K, Halberg N, Nicolaisen M, Olesen JE, Crews TE, Hinsinger P, Kirkegaard J, Pierret A, Dresbøll DB. Digging Deeper for Agricultural Resources, the Value of Deep Rooting. TRENDS IN PLANT SCIENCE 2020; 25:406-417. [PMID: 31964602 DOI: 10.1016/j.tplants.2019.12.007] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/25/2019] [Accepted: 12/09/2019] [Indexed: 05/25/2023]
Abstract
In the quest for sustainable intensification of crop production, we discuss the option of extending the root depth of crops to increase the volume of soil exploited by their root systems. We discuss the evidence that deeper rooting can be obtained by appropriate choice of crop species, by plant breeding, or crop management and its potential contributions to production and sustainable development goals. Many studies highlight the potentials of deeper rooting, but we evaluate its contributions to sustainable intensification of crop production, the causes of the limited research into deep rooting of crops, and the research priorities to fill the knowledge gaps.
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Affiliation(s)
- Kristian Thorup-Kristensen
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark.
| | - Niels Halberg
- DCA - Danish Centre for Food and Agriculture, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| | - Mette Nicolaisen
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
| | - Jørgen Eivind Olesen
- Department of Agroecology, Aarhus Universitet, Blichers Allé 20, 8830 Tjele, Denmark
| | - Timothy E Crews
- The Land Institute, 2440E Water Well Rd. Salina, KS 67401, USA
| | - Philippe Hinsinger
- Eco&Sols, University of Montpellier, CIRAD, INRAE, IRD, Montpellier SupAgro, Montpellier, France
| | - John Kirkegaard
- CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Alain Pierret
- Institut d'Écologie et des Sciences de l'Environnement de Paris (iEES-Paris), Sorbonne Université, CNRS, INRAE, IRD, Université de Paris, Université Paris Est Creteil, Paris, France; Department of Agricultural Land Management (DALaM), Ministry of Agriculture and Forestry, Vientiane, Lao PDR
| | - Dorte Bodin Dresbøll
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
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15
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Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. MOLECULAR PLANT 2020; 13:187-214. [PMID: 31981735 DOI: 10.1016/j.molp.2020.01.008] [Citation(s) in RCA: 239] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/06/2020] [Accepted: 01/10/2020] [Indexed: 05/18/2023]
Abstract
Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
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Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crops Science/College of Agronomy, Henan Agricultural University, Zhengzhou 450002, P.R. China
| | - Jian Zhang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - John H Doonan
- The National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | | | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
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16
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Guo X, Svane SF, Füchtbauer WS, Andersen JR, Jensen J, Thorup-Kristensen K. Genomic prediction of yield and root development in wheat under changing water availability. PLANT METHODS 2020; 16:90. [PMID: 32625241 PMCID: PMC7329460 DOI: 10.1186/s13007-020-00634-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 06/24/2020] [Indexed: 05/16/2023]
Abstract
BACKGROUND Deeper roots help plants take up available resources in deep soil ensuring better growth and higher yields under conditions of drought. A large-scale semi-field root phenotyping facility was developed to allow a water availability gradient and detect potential interaction of genotype by water availability gradient. Genotyped winter wheat lines were grown as rows in four beds of this facility, where indirect genetic effects from neighbors could be important to trait variation. The objective was to explore the possibility of genomic prediction for grain-related traits and deep root traits collected via images taken in a minirhizotron tube under each row of winter wheat measured. RESULTS The analysis comprised four grain-related traits: grain yield, thousand-kernel weight, protein concentration, and total nitrogen content measured on each half row that were harvested separately. Two root traits, total root length between 1.2 and 2 m depth and root length in four intervals on each tube were also analyzed. Two sets of models with or without the effects of neighbors from both sides of each row were applied. No interaction between genotypes and changing water availability were detected for any trait. Estimated genomic heritabilities ranged from 0.263 to 0.680 for grain-related traits and from 0.030 to 0.055 for root traits. The coefficients of genetic variation were similar for grain-related and root traits. The prediction accuracy of breeding values ranged from 0.440 to 0.598 for grain-related traits and from 0.264 to 0.334 for root traits. Including neighbor effects in the model generally increased the estimated genomic heritabilities and accuracy of predicted breeding values for grain yield and nitrogen content. CONCLUSIONS Similar relative amounts of additive genetic variance were found for both yield traits and root traits but no interaction between genotypes and water availability were detected. It is possible to obtain accurate genomic prediction of breeding values for grain-related traits and reasonably accurate predicted breeding values for deep root traits using records from the semi-field facility. Including neighbor effects increased the estimated additive genetic variance of grain-related traits and accuracy of predicting breeding values. High prediction accuracy can be obtained although heritability is low.
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Affiliation(s)
- Xiangyu Guo
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Simon F. Svane
- Department of Plant and Environmental Science, University of Copenhagen, 1871 Frederiksberg, Denmark
| | | | | | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
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17
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Chen S, Svane SF, Thorup-Kristensen K. Testing deep placement of an 15N tracer as a method for in situ deep root phenotyping of wheat, barley and ryegrass. PLANT METHODS 2019; 15:148. [PMID: 31827580 PMCID: PMC6900857 DOI: 10.1186/s13007-019-0533-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 11/23/2019] [Indexed: 05/09/2023]
Abstract
BACKGROUND Deep rooting is one of the most promising plant traits for improving crop yield under water-limited conditions. Most root phenotyping methods are designed for laboratory-grown plants, typically measuring very young plants not grown in soil and not allowing full development of the root system. RESULTS This study introduced the 15N tracer method to detect genotypic variations of deep rooting and N uptake, and to support the minirhizotron method. The method was tested in a new semifield phenotyping facility on two genotypes of winter wheat, seven genotypes of spring barley and four genotypes of ryegrass grown along a drought stress gradient in four individual experiments. The 15N labeled fertilizer was applied at increasing soil depths from 0.4 to 1.8 m or from 0.7 to 2.8 m through a subsurface tracer supply system, and sampling of aboveground biomass was conducted to measure the 15N uptake. The results confirm that the 15N labeling system could identify the approximate extension of the root system. The results of 15N labeling as well as root measurements made by minirhizotrons showed rather high variation. However, in the spring barley experiment, we did find correlations between root observations and 15N uptake from the deepest part of the root zone. The labeled crop rows mostly had significantly higher 15N enrichment than their neighbor rows. CONCLUSION We concluded that the 15N tracer method is promising as a future method for deep root phenotyping because the method will be used for phenotyping for deep root function rather than deep root growth. With some modifications to the injection principle and sampling process to reduce measurement variability, we suggest that the 15N tracer method may be a useful tool for deep root phenotyping. The results demonstrated that the minirhizotrons observed roots of the tested rows rather than their neighboring rows.
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Affiliation(s)
- Si Chen
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058 China
| | - Simon Fiil Svane
- Department of Plant and Environmental Science, University of Copenhagen, Højbakkegårds Alle, 13, 2630 Taastrup, Denmark
| | - Kristian Thorup-Kristensen
- Department of Plant and Environmental Science, University of Copenhagen, Højbakkegårds Alle, 13, 2630 Taastrup, Denmark
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