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Smith DT, Potgieter AB, Chapman SC. Scaling up high-throughput phenotyping for abiotic stress selection in the field. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1845-1866. [PMID: 34076731 DOI: 10.1007/s00122-021-03864-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/13/2021] [Indexed: 05/18/2023]
Abstract
High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.
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Affiliation(s)
- Daniel T Smith
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Andries B Potgieter
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Scott C Chapman
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
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Mehta S, Chakraborty A, Roy A, Singh IK, Singh A. Fight Hard or Die Trying: Current Status of Lipid Signaling during Plant-Pathogen Interaction. PLANTS (BASEL, SWITZERLAND) 2021; 10:1098. [PMID: 34070722 PMCID: PMC8228701 DOI: 10.3390/plants10061098] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/15/2021] [Accepted: 05/24/2021] [Indexed: 12/29/2022]
Abstract
Plant diseases pose a substantial threat to food availability, accessibility, and security as they account for economic losses of nearly $300 billion on a global scale. Although various strategies exist to reduce the impact of diseases, they can introduce harmful chemicals to the food chain and have an impact on the environment. Therefore, it is necessary to understand and exploit the plants' immune systems to control the spread of pathogens and enable sustainable agriculture. Recently, growing pieces of evidence suggest a functional myriad of lipids to be involved in providing structural integrity, intracellular and extracellular signal transduction mediators to substantial cross-kingdom cell signaling at the host-pathogen interface. Furthermore, some pathogens recognize or exchange plant lipid-derived signals to identify an appropriate host or development, whereas others activate defense-related gene expression. Typically, the membrane serves as a reservoir of lipids. The set of lipids involved in plant-pathogen interaction includes fatty acids, oxylipins, phospholipids, glycolipids, glycerolipids, sphingolipids, and sterols. Overall, lipid signals influence plant-pathogen interactions at various levels ranging from the communication of virulence factors to the activation and implementation of host plant immune defenses. The current review aims to summarize the progress made in recent years regarding the involvement of lipids in plant-pathogen interaction and their crucial role in signal transduction.
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Affiliation(s)
- Sahil Mehta
- International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India;
| | - Amrita Chakraborty
- EVA4.0 Unit, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Kamýcká 129, Suchdol, 165 21 Prague 6, Czech Republic; (A.C.); (A.R.)
| | - Amit Roy
- EVA4.0 Unit, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Kamýcká 129, Suchdol, 165 21 Prague 6, Czech Republic; (A.C.); (A.R.)
- Excelentní Tým pro Mitigaci (ETM), Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Kamýcká 129, Suchdol, 165 21 Prague 6, Czech Republic
| | - Indrakant K. Singh
- Molecular Biology Research Lab, Department of Zoology, Deshbandhu College, University of Delhi, Kalkaji, New Delhi 110019, India
| | - Archana Singh
- Department of Botany, Hansraj College, University of Delhi, New Delhi 110007, India
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Costa-Neto G, Galli G, Carvalho HF, Crossa J, Fritsche-Neto R. EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3 (BETHESDA, MD.) 2021; 11. [PMID: 33835165 DOI: 10.1101/2020.10.14.339705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 01/21/2021] [Indexed: 05/20/2023]
Abstract
Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.
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Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
| | - Giovanni Galli
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
| | - Humberto Fanelli Carvalho
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
| | - José Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera Mexico-Veracruz, El Batan Km. 45, CP 56237 Mexico; Colegio de Postgraduados, Montecillos, Edo. de Mexico, CP 56264, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
- Quantitative Genetics and Biometrics Cluster, International Rice Research Institute (IRRI), Los Baños, Philippines
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Pennacchi JP, Lira JMS, Rodrigues M, Garcia FHS, Mendonça AMDC, Barbosa JPRAD. A systemic approach to the quantification of the phenotypic plasticity of plant physiological traits: the multivariate plasticity index. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:1864-1878. [PMID: 33211856 DOI: 10.1093/jxb/eraa545] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 11/12/2020] [Indexed: 06/11/2023]
Abstract
The phenotype of an individual emerges from the interaction of its genotype with the environment in which it is located. Phenotypic plasticity (PP) is the ability of a specific genotype to present multiple phenotypes in response to the environment. Past and current methods for quantification of PP present limitations, mainly in what constitutes a systemic analysis of multiple traits. This research proposes an integrative index for quantifying and evaluating PP. The multivariate plasticity index (MVPi) was calculated based on the Euclidian distance between scores of a canonical variate analysis. It was evaluated for leaf physiological traits in two cases using Brazilian Cerrado species and sugarcane varieties, grown under diverse environmental conditions. The MVPi was sensitive to plant behaviour from simple to complex genotype-environment interactions and was able to inform coarse and fine changes in PP. It was correlated to biomass allocation, showing agreement between plant organizational levels. The new method proved to be elucidative of plant metabolic changes, mainly by explaining PP as an integrated process and emergent property. We recommend the MVPi method as a tool for analysis of phenotypic plasticity in the context of a systemic evaluation of plant phenotypic traits.
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Affiliation(s)
- João Paulo Pennacchi
- Universidade Federal de Lavras, Câmpus Universitário, Caixa Postal 3037, CEP, Lavras, MG, Brazil
| | - Jean Marcel Sousa Lira
- Universidade Federal de Lavras, Câmpus Universitário, Caixa Postal 3037, CEP, Lavras, MG, Brazil
| | - Marcelo Rodrigues
- Universidade Federal do Triângulo Mineiro, Av. Rio Paranaíba, 1229, CEP, Iturama, MG, Brazil
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Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G, Singh GP. Integrated genomic selection for rapid improvement of crops. Genomics 2021; 113:1070-1086. [PMID: 33610797 DOI: 10.1016/j.ygeno.2021.02.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/08/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022]
Abstract
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
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Affiliation(s)
| | | | | | | | | | - Satish Kumar
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hanif Khan
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | | | - Gyanendra Singh
- Indian Institute of Wheat and Barley Research, Karnal, India
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Palanivel H, Shah S. Unlocking the inherent potential of plant genetic resources: food security and climate adaptation strategy in Fiji and the Pacific. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2021; 23:14264-14323. [PMID: 33619427 PMCID: PMC7888530 DOI: 10.1007/s10668-021-01273-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 01/28/2021] [Indexed: 06/12/2023]
Abstract
Pacific Island Countries (PICs) are the center of origin and diversity for several root, fruit and nut crops, which are indispensable for food security, rural livelihoods, and cultural identity of local communities. However, declining genetic diversity of traditional food crops and high vulnerability to climate change are major impediments for maintaining agricultural productivity. Limited initiatives to achieve food self-sufficiency and utilization of Plant Genetic Resources (PGR) for enhancing resilience of agro-ecosystems are other serious constraints. This review focuses on the visible and anticipated impacts of climate ge, on major food and tree crops in agriculture and agroforestry systems in the PICs. We argue that crop improvement through plant breeding is a viable strategy to enhance food security and climatic resilience in the region. The exploitation of adaptive traits: abiotic and biotic stress tolerance, yield and nutritional efficiency, is imperative in a world threatened by climatic extremes. However, the insular constraints of Fiji and other small PICs are major limitations for the utilization of PGR through high throughput techniques which are also cost prohibitive. Crop Improvement programs should instead focus on the identification, conservation, documentation and dissemination of information on unique landraces, community seed banks, introduction of new resistant genotypes, and sustaining and enhancing allelic diversity.
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Affiliation(s)
- Hemalatha Palanivel
- Department of Biotechnology, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, PO Box 16417, Addis Ababa, Ethiopia
| | - Shipra Shah
- Department of Forestry, College of Agriculture, Fisheries and Forestry, Koronivia Campus, Fiji National University, PO Box 1544, Nausori, Republic of Fiji
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Cooper M, Messina CD. Can We Harness "Enviromics" to Accelerate Crop Improvement by Integrating Breeding and Agronomy? FRONTIERS IN PLANT SCIENCE 2021; 12:735143. [PMID: 34567047 PMCID: PMC8461239 DOI: 10.3389/fpls.2021.735143] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/16/2021] [Indexed: 05/02/2023]
Abstract
The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of "enviromics" as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate "enviromics" methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Mark Cooper,
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58
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Cursi DE, Gazaffi R, Hoffmann HP, Brasco TL, do Amaral LR, Dourado Neto D. Novel Tools for Adjusting Spatial Variability in the Early Sugarcane Breeding Stage. FRONTIERS IN PLANT SCIENCE 2021; 12:749533. [PMID: 34868135 PMCID: PMC8638809 DOI: 10.3389/fpls.2021.749533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 10/26/2021] [Indexed: 05/14/2023]
Abstract
The detection of spatial variability in field trials has great potential for accelerating plant breeding progress due to the possibility of better controlling non-genetic variation. Therefore, we aimed to evaluate a digital soil mapping approach and a high-density soil sampling procedure for identifying and adjusting spatial dependence in the early sugarcane breeding stage. Two experiments were conducted in regions with different soil classifications. High-density sampling of soil physical and chemical properties was performed in a regular grid to investigate the structure of spatial variability. Soil apparent electrical conductivity (ECa) was measured in both experimental areas with an EM38-MK2® sensor. In addition, principal component analysis (PCA) was employed to reduce the dimensionality of the physical and chemical soil data sets. After conducting the PCA and obtaining different thematic maps, we determined each experimental plot's exact position within the field. Tons of cane per hectare (TCH) data for each experiment were obtained and analyzed using mixed linear models. When environmental covariates were considered, a previous forward model selection step was applied to incorporate the variables. The PCA based on high-density soil sampling data captured part of the total variability in the data for Experimental Area 1 and was suggested to be an efficient index to be incorporated as a covariate in the statistical model, reducing the experimental error (residual variation coefficient, CVe). When incorporated into the different statistical models, the ECa information increased the selection accuracy of the experimental genotypes. Therefore, we demonstrate that the genetic parameter increased when both approaches (spatial analysis and environmental covariates) were employed.
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Affiliation(s)
- Danilo Eduardo Cursi
- Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Sugarcane Breeding Program of RIDESA/UFSCar, Araras, Brazil
- *Correspondence: Danilo Eduardo Cursi,
| | - Rodrigo Gazaffi
- Sugarcane Breeding Program of RIDESA/UFSCar, Araras, Brazil
- Department of Biotechnology, Vegetal and Animal Production, Federal University of São Carlos, Araras, Brazil
| | - Hermann Paulo Hoffmann
- Sugarcane Breeding Program of RIDESA/UFSCar, Araras, Brazil
- Department of Biotechnology, Vegetal and Animal Production, Federal University of São Carlos, Araras, Brazil
| | - Thiago Luis Brasco
- School of Agricultural Engineering, University of Campinas (FEAGRI/UNICAMP), Campinas, Brazil
| | - Lucas Rios do Amaral
- School of Agricultural Engineering, University of Campinas (FEAGRI/UNICAMP), Campinas, Brazil
| | - Durval Dourado Neto
- Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
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59
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Resende RT, Piepho HP, Rosa GJM, Silva-Junior OB, E Silva FF, de Resende MDV, Grattapaglia D. Enviromics in breeding: applications and perspectives on envirotypic-assisted selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:95-112. [PMID: 32964262 DOI: 10.1007/s00122-020-03684-z] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 09/10/2020] [Indexed: 05/18/2023]
Abstract
We propose the application of enviromics to breeding practice, by which the similarity among sites assessed on an "omics" scale of environmental attributes drives the prediction of unobserved genotype performances. Genotype by environment interaction (GEI) studies in plant breeding have focused mainly on estimating genetic parameters over a limited number of experimental trials. However, recent geographic information system (GIS) techniques have opened new frontiers for better understanding and dealing with GEI. These advances allow increasing selection accuracy across all sites of interest, including those where experimental trials have not yet been deployed. Here, we introduce the term enviromics, within an envirotypic-assisted breeding framework. In summary, likewise genotypes at DNA markers, any particular site is characterized by a set of "envirotypes" at multiple "enviromic" markers corresponding to environmental variables that may interact with the genetic background, thus providing informative breeding re-rankings for optimized decisions over different environments. Based on simulated data, we illustrate an index-based enviromics method (the "GIS-GEI") which, due to its higher granular resolution than standard methods, allows for: (1) accurate matching of sites to their most appropriate genotypes; (2) better definition of breeding areas that have high genetic correlation to ensure selection gains across environments; and (3) efficient determination of the best sites to carry out experiments for further analyses. Environmental scenarios can also be optimized for productivity improvement and genetic resources management, especially in the current outlook of dynamic climate change. Envirotyping provides a new class of markers for genetic studies, which are fairly inexpensive, increasingly available and transferable across species. We envision a promising future for the integration of enviromics approaches into plant breeding when coupled with next-generation genotyping/phenotyping and powerful statistical modeling of genetic diversity.
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Affiliation(s)
- Rafael T Resende
- School of Agronomy, University of Goiás (UFG), Goiânia, GO, 74690-900, Brazil.
| | - Hans-Peter Piepho
- Biostatistics Unit, University of Hohenheim, 70593, Stuttgart, Germany
| | - Guilherme J M Rosa
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, 53706, Madison, USA
| | | | - Fabyano F E Silva
- Department of Animal Science, University of Viçosa (UFV), Viçosa, MG, 36570-900, Brazil
| | - Marcos Deon V de Resende
- Department of Statistics, University of Viçosa (UFV), Viçosa, MG, 36570-900, Brazil
- EMBRAPA Coffee Research, Brasília, DF, 70770-901, Brazil
| | - Dario Grattapaglia
- EMBRAPA Genetic Resources and Biotechnology - EPqB, Brasília, DF, 70770-910, Brazil.
- Genomic Sciences and Biotechnology Program, Catholic University of Brasília, Brasília, DF, 70790-160, Brazil.
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Costa-Neto G, Crossa J, Fritsche-Neto R. Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize. FRONTIERS IN PLANT SCIENCE 2021; 12:717552. [PMID: 34691099 PMCID: PMC8529011 DOI: 10.3389/fpls.2021.717552] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/03/2021] [Indexed: 05/21/2023]
Abstract
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.
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Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States
- *Correspondence: Germano Costa-Neto
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
- Colegio de Posgraduado, Mexico City, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Breeding Analytics and Data Management Unit, International Rice Research Institute (IRRI), Los Baños, Philippines
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61
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Gaete A, Pulgar R, Hodar C, Maldonado J, Pavez L, Zamorano D, Pastenes C, González M, Franck N, Mandakovic D. Tomato Cultivars With Variable Tolerances to Water Deficit Differentially Modulate the Composition and Interaction Patterns of Their Rhizosphere Microbial Communities. FRONTIERS IN PLANT SCIENCE 2021; 12:688533. [PMID: 34326856 PMCID: PMC8313812 DOI: 10.3389/fpls.2021.688533] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/11/2021] [Indexed: 05/09/2023]
Abstract
Since drought is the leading environmental factor limiting crop productivity, and plants have a significant impact in defining the assembly of plant-specific microbial communities associated with roots, we aimed to determine the effect of thoroughly selected water deficit tolerant and susceptible Solanum lycopersicum cultivars on their rhizosphere microbiome and compared their response with plant-free soil microbial communities. We identified a total of 4,248 bacterial and 276 fungal different operational taxonomic units (OTUs) in soils by massive sequencing. We observed that tomato cultivars significantly affected the alpha and beta diversity of their bacterial rhizosphere communities but not their fungal communities compared with bulk soils (BSs), showing a plant effect exclusively on the bacterial soil community. Also, an increase in alpha diversity in response to water deficit of both bacteria and fungi was observed in the susceptible rhizosphere (SRz) but not in the tolerant rhizosphere (TRz) cultivar, implying a buffering effect of the tolerant cultivar on its rhizosphere microbial communities. Even though water deficit did not affect the microbial diversity of the tolerant cultivar, the interaction network analysis revealed that the TRz microbiota displayed the smallest and least complex soil network in response to water deficit with the least number of connected components, nodes, and edges. This reduction of the TRz network also correlated with a more efficient community, reflected in increased cooperation within kingdoms. Furthermore, we identified some specific bacteria and fungi in the TRz in response to water deficit, which, given that they belong to taxa with known beneficial characteristics for plants, could be contributing to the tolerant phenotype, highlighting the metabolic bidirectionality of the holobiont system. Future assays involving characterization of root exudates and exchange of rhizospheres between drought-tolerant and susceptible cultivars could determine the effect of specific metabolites on the microbiome community and may elucidate their functional contribution to the tolerance of plants to water deficit.
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Affiliation(s)
- Alexis Gaete
- Laboratorio de Bioinformática y Expresión Génica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
- Center for Genome Regulation, Santiago, Chile
- Programa de Doctorado en Ciencias Silvoagropecuarias y Veterinarias, Campus Sur Universidad de Chile, Santiago, Chile
| | - Rodrigo Pulgar
- Laboratorio de Genómica y Genética de Interacciones Biológicas (LGIB), Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Christian Hodar
- Laboratorio de Bioinformática y Expresión Génica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
- Center for Genome Regulation, Santiago, Chile
| | - Jonathan Maldonado
- Laboratorio de Bioinformática y Expresión Génica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
- Laboratorio de Biología de Sistemas de Plantas, Departamento Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Leonardo Pavez
- Instituto de Ciencias Naturales, Universidad de Las Américas, Santiago, Chile
- Departamento de Ciencias Químicas y Biológicas, Universidad Bernardo O’Higgins, Santiago, Chile
| | - Denisse Zamorano
- Programa de Doctorado en Ciencias Silvoagropecuarias y Veterinarias, Campus Sur Universidad de Chile, Santiago, Chile
- Centro de Estudios en Zonas Áridas (CEZA), Universidad de Chile, Coquimbo, Chile
| | - Claudio Pastenes
- Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago, Chile
| | - Mauricio González
- Laboratorio de Bioinformática y Expresión Génica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
- Center for Genome Regulation, Santiago, Chile
| | - Nicolás Franck
- Centro de Estudios en Zonas Áridas (CEZA), Universidad de Chile, Coquimbo, Chile
- Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago, Chile
| | - Dinka Mandakovic
- Laboratorio de Genómica y Genética de Interacciones Biológicas (LGIB), Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
- Centro de Estudios en Zonas Áridas (CEZA), Universidad de Chile, Coquimbo, Chile
- GEMA Center for Genomics, Ecology and Environment, Universidad Mayor, Santiago, Chile
- *Correspondence: Dinka Mandakovic,
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62
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Portraying Fungal Mechanisms in Stress Tolerance: Perspective for Sustainable Agriculture. Fungal Biol 2021. [DOI: 10.1007/978-3-030-60659-6_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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63
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Deery DM, Jones HG. Field Phenomics: Will It Enable Crop Improvement? PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9871989. [PMID: 34549194 PMCID: PMC8433881 DOI: 10.34133/2021/9871989] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 08/14/2021] [Indexed: 05/19/2023]
Abstract
Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders' needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders.
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Affiliation(s)
| | - Hamlyn G. Jones
- CSIRO Agriculture and Food, Canberra, ACT, Australia
- Division of Plant Sciences, University of Dundee, UK
- School of Agriculture and Environment, University of Western Australia, Australia
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Li Z, Tirado SB, Kadam DC, Coffey L, Miller ND, Spalding EP, Lorenz AJ, de Leon N, Kaeppler SM, Schnable PS, Springer NM, Hirsch CN. Characterizing introgression-by-environment interactions using maize near isogenic lines. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:2761-2773. [PMID: 32572549 DOI: 10.1007/s00122-020-03630-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 06/02/2020] [Indexed: 06/11/2023]
Abstract
Significant introgression-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome. Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to introgression-by-environment interactions. To study potential patterns of introgression-by-environment interactions, fifteen near-isogenic lines (NILs) with > 90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess introgression-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the introgression-by-environment interaction varies across traits. Some introgressed segments were more prone to introgression-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of introgression-by-environment interaction in NILs is useful in consideration of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.
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Affiliation(s)
- Zhi Li
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Sara B Tirado
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
- Department of Plant and Microbial Biology, University of Minnesota, 1479 Gortner Avenue, Saint Paul, MN, 55108, USA
| | - Dnyaneshwar C Kadam
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Lisa Coffey
- Department of Agronomy, Iowa State University, 1111 WOI Rd, Ames, IA, 50011, USA
| | - Nathan D Miller
- Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI, 53706, USA
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI, 53706, USA
| | - Aaron J Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, 1111 WOI Rd, Ames, IA, 50011, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, 1479 Gortner Avenue, Saint Paul, MN, 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA.
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65
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Gene co-expression network analysis to identify critical modules and candidate genes of drought-resistance in wheat. PLoS One 2020; 15:e0236186. [PMID: 32866164 PMCID: PMC7458298 DOI: 10.1371/journal.pone.0236186] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/30/2020] [Indexed: 12/17/2022] Open
Abstract
AIM To establish a gene co-expression network for identifying principal modules and hub genes that are associated with drought resistance mechanisms, analyzing their mechanisms, and exploring candidate genes. METHODS AND FINDINGS 42 data sets including PRJNA380841 and PRJNA369686 were used to construct the co-expression network through weighted gene co-expression network analysis (WGCNA). A total of 1,896,897,901 (284.30 Gb) clean reads and 35,021 differentially expressed genes (DEGs) were obtained from 42 samples. Functional enrichment analysis indicated that photosynthesis, DNA replication, glycolysis/gluconeogenesis, starch and sucrose metabolism, arginine and proline metabolism, and cell cycle were significantly influenced by drought stress. Furthermore, the DEGs with similar expression patterns, detected by K-means clustering, were grouped into 29 clusters. Genes involved in the modules, such as dark turquoise, yellow, and brown, were found to be appreciably linked with drought resistance. Twelve central, greatly correlated genes in stage-specific modules were subsequently confirmed and validated at the transcription levels, including TraesCS7D01G417600.1 (PP2C), TraesCS5B01G565300.1 (ERF), TraesCS4A01G068200.1 (HSP), TraesCS2D01G033200.1 (HSP90), TraesCS6B01G425300.1 (RBD), TraesCS7A01G499200.1 (P450), TraesCS4A01G118400.1 (MYB), TraesCS2B01G415500.1 (STK), TraesCS1A01G129300.1 (MYB), TraesCS2D01G326900.1 (ALDH), TraesCS3D01G227400.1 (WRKY), and TraesCS3B01G144800.1 (GT). CONCLUSIONS Analyzing the response of wheat to drought stress during different growth stages, we have detected three modules and 12 hub genes that are associated with drought resistance mechanisms, and five of those genes are newly identified for drought resistance. The references provided by these modules will promote the understanding of the drought-resistance mechanism. In addition, the candidate genes can be used as a basis of transgenic or molecular marker-assisted selection for improving the drought resistance and increasing the yields of wheat.
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66
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Costa-Neto G, Fritsche-Neto R, Crossa J. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity (Edinb) 2020; 126:92-106. [PMID: 32855544 PMCID: PMC7852533 DOI: 10.1038/s41437-020-00353-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 07/29/2020] [Accepted: 07/30/2020] [Indexed: 01/15/2023] Open
Abstract
Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational processing times, which do not always result in accuracy gains. We investigated the use of new kernel methods and modeling structures involving genomics and nongenomic sources of variation in two MET maize data sets. Five WGP models were considered, advancing in complexity from a main-effect additive model (A) to more complex structures, including dominance deviations (D), genotype × environment interaction (AE and DE), and the reaction-norm model using environmental covariables (W) and their interaction with A and D (AW + DW). A combination of those models built with three different kernel methods, Gaussian kernel (GK), Deep kernel (DK), and the benchmark genomic best linear-unbiased predictor (GBLUP/GB), was tested under three prediction scenarios: newly developed hybrids (CV1), sparse MET conditions (CV2), and new environments (CV0). GK and DK outperformed GB in prediction accuracy and reduction of computation time (~up to 20%) under all model-kernel scenarios. GK was more efficient in capturing the variation due to A + AE and D + DE effects and translated it into accuracy gains (~up to 85% compared with GB). DK provided more consistent predictions, even for more complex structures such as W + AW + DW. Our results suggest that DK and GK are more efficient in translating model complexity into accuracy, and more suitable for including dominance and reaction-norm effects in a biologically accurate and faster way.
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Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, "Luiz de Queiroz" Agriculture College, University of São Paulo, São Paulo, Brazil
| | - Roberto Fritsche-Neto
- Department of Genetics, "Luiz de Queiroz" Agriculture College, University of São Paulo, São Paulo, Brazil
| | - José Crossa
- Biometrics and Statistics Unit, Genetic Resources Program, and Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico.
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67
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Würbel H, Voelkl B, Altman NS, Forsman A, Forstmeier W, Gurevitch J, Jaric I, Karp NA, Kas MJ, Schielzeth H, Van de Casteele T. Reply to 'It is time for an empirically informed paradigm shift in animal research'. Nat Rev Neurosci 2020; 21:661-662. [PMID: 32826978 DOI: 10.1038/s41583-020-0370-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Hanno Würbel
- Animal Welfare Division, Vetsuisse, University of Bern, Bern, Switzerland.
| | - Bernhard Voelkl
- Animal Welfare Division, Vetsuisse, University of Bern, Bern, Switzerland
| | - Naomi S Altman
- Department of Statistics, The Pennsylvania State University, University Park, PA, USA
| | - Anders Forsman
- Department of Biology and Environmental Science, Linnaeus University, Kalmar, Sweden
| | - Wolfgang Forstmeier
- Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Jessica Gurevitch
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY, USA
| | - Ivana Jaric
- Animal Welfare Division, Vetsuisse, University of Bern, Bern, Switzerland
| | - Natasha A Karp
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Martien J Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands
| | - Holger Schielzeth
- Institute of Ecology and Evolution, Friedrich Schiller University Jena, Jena, Germany
| | - Tom Van de Casteele
- Statistics and Decision Sciences, Janssen R&D, Johnson & Johnson, Beerse, Belgium
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68
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Brown D, Van den Bergh I, de Bruin S, Machida L, van Etten J. Data synthesis for crop variety evaluation. A review. AGRONOMY FOR SUSTAINABLE DEVELOPMENT 2020; 40:25. [PMID: 32863892 PMCID: PMC7440334 DOI: 10.1007/s13593-020-00630-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/23/2020] [Indexed: 05/12/2023]
Abstract
Crop varieties should fulfill multiple requirements, including agronomic performance and product quality. Variety evaluations depend on data generated from field trials and sensory analyses, performed with different levels of participation from farmers and consumers. Such multi-faceted variety evaluation is expensive and time-consuming; hence, any use of these data should be optimized. Data synthesis can help to take advantage of existing and new data, combining data from different sources and combining it with expert knowledge to produce new information and understanding that supports decision-making. Data synthesis for crop variety evaluation can partly build on extant experiences and methods, but it also requires methodological innovation. We review the elements required to achieve data synthesis for crop variety evaluation, including (1) data types required for crop variety evaluation, (2) main challenges in data management and integration, (3) main global initiatives aiming to solve those challenges, (4) current statistical approaches to combine data for crop variety evaluation and (5) existing data synthesis methods used in evaluation of varieties to combine different datasets from multiple data sources. We conclude that currently available methods have the potential to overcome existing barriers to data synthesis and could set in motion a virtuous cycle that will encourage researchers to share data and collaborate on data-driven research.
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Affiliation(s)
- David Brown
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
- Bioversity International, Turrialba, 30501 Costa Rica
| | - Inge Van den Bergh
- Bioversity International, C/O KU Leuven, W. De Croylaan 42, P.O. Box 2455, 3001 Leuven, Belgium
| | - Sytze de Bruin
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
| | - Lewis Machida
- Bioversity International, C/O International Institute of Tropical Agriculture (IITA), Nelson Mandela African Institute of Science and Technology, P.O. Box 447, Arusha, Tanzania
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69
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Nguyen GN, Norton SL. Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm. PLANTS (BASEL, SWITZERLAND) 2020; 9:E817. [PMID: 32610615 PMCID: PMC7411623 DOI: 10.3390/plants9070817] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/25/2020] [Accepted: 06/26/2020] [Indexed: 02/07/2023]
Abstract
Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.
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Affiliation(s)
- Giao N. Nguyen
- Australian Grains Genebank, Agriculture Victoria, 110 Natimuk Road, Horsham 3400, Australia;
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Pieters O, De Swaef T, Lootens P, Stock M, Roldán-Ruiz I, wyffels F. Gloxinia-An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants. SENSORS 2020; 20:s20113055. [PMID: 32481619 PMCID: PMC7309107 DOI: 10.3390/s20113055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/20/2020] [Accepted: 05/26/2020] [Indexed: 11/28/2022]
Abstract
The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations.
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Affiliation(s)
- Olivier Pieters
- IDLab-AIRO—Ghent University—imec, Technologiepark-Zwijnaarde 126, 9052 Zwijnaarde, Belgium;
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Caritasstraat 39, 9090 Melle, Belgium; (T.D.S.); (P.L.); (I.R.-R.)
- Correspondence:
| | - Tom De Swaef
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Caritasstraat 39, 9090 Melle, Belgium; (T.D.S.); (P.L.); (I.R.-R.)
| | - Peter Lootens
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Caritasstraat 39, 9090 Melle, Belgium; (T.D.S.); (P.L.); (I.R.-R.)
| | - Michiel Stock
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure links 653, 9000 Ghent, Belgium;
| | - Isabel Roldán-Ruiz
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Caritasstraat 39, 9090 Melle, Belgium; (T.D.S.); (P.L.); (I.R.-R.)
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ledeganckstraat 35, 9000 Gent, Belgium
| | - Francis wyffels
- IDLab-AIRO—Ghent University—imec, Technologiepark-Zwijnaarde 126, 9052 Zwijnaarde, Belgium;
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Afreen T, Singh V, Yadav VK, Singh RP, Singh H. Impact of rainfall variability on the ecophysiology of Hyptis suaveolens: a study in the constructed tropical grassland. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:388. [PMID: 32440902 DOI: 10.1007/s10661-020-08340-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 05/04/2020] [Indexed: 06/11/2023]
Abstract
Hyptis suaveolens is considered one of the most potent invaders in the eastern part of Uttar Pradesh, India. Climate change especially precipitation variability along with invasion has enormous consequences. To understand how an invasive plant (H. suaveolens) performs and interacts with precipitation variability, particularly in tropical monsoon climate, is vital. To assess the above, three rainout shelters with simulated rainfall of 1600 mm (60% more rainfall than ambient), 1100 mm (average rainfall) and 800 mm (20% less rainfall than ambient) along with one unsheltered plot (open C) were established. Three invaded grassland (IG) and three uninvaded grasslands (NIG) patches of 1 × 1 m2 size were established randomly in each sheltered and unsheltered plot. Among the studied physiological properties and growth measurements, photosynthetic rate, height, diameter and biomass varied significantly with precipitation, in general, the maximum value of these in plots receiving maximum precipitation. Also, the aboveground biomass of H. suaveolens was found to be more sensitive towards precipitation treatment than belowground biomass. H. suaveolens biomass was linearly related to soil moisture (R2 = 0.73), and a linear combination of SM and soil pH increased the R2 value by 19%. The results indicate that H. suaveolens mediates certain soil properties especially related to N-mineralisation, to maintain a constant supply of nutrient, for faster growth under the favourable condition of enhanced precipitation. These findings suggest that the population of H. suaveolens has not evolved drought tolerance, so it is likely that H. suaveolens will not spread in the part of the world which is drier either naturally or due to climate change.
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Affiliation(s)
- Talat Afreen
- Ecosystems Analysis Laboratory, Department of Botany, Institute of Science, Banaras Hindu University, Varanasi, 221005, India
| | - Vartika Singh
- Ecosystems Analysis Laboratory, Department of Botany, Institute of Science, Banaras Hindu University, Varanasi, 221005, India
| | - Vinod Kumar Yadav
- Ecosystems Analysis Laboratory, Department of Botany, Institute of Science, Banaras Hindu University, Varanasi, 221005, India
| | - Rahul Prasad Singh
- Ecosystems Analysis Laboratory, Department of Botany, Institute of Science, Banaras Hindu University, Varanasi, 221005, India
| | - Hema Singh
- Ecosystems Analysis Laboratory, Department of Botany, Institute of Science, Banaras Hindu University, Varanasi, 221005, India.
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Jiang S, Cheng Q, Yan J, Fu R, Wang X. Genome optimization for improvement of maize breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:1491-1502. [PMID: 31811314 DOI: 10.1007/s00122-019-03493-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Accepted: 11/26/2019] [Indexed: 05/09/2023]
Abstract
We propose a new model to improve maize breeding that incorporates doubled haploid production, genomic selection, and genome optimization. Breeding 4.0 has been considered the next era of plant breeding. It is clear that the Breeding 4.0 era for maize will feature the integration of multi-disciplinary technologies including genomics and phenomics, gene editing and synthetic biology, and Big Data and artificial intelligence. The breeding approach of passively selecting ideal genotypes from designated genetic pools must soon evolve to virtual design of optimized genomes by pyramiding superior alleles using computational simulation. An optimized genome expressing optimal phenotypes, which may never actually be created, can function as a blueprint for breeding programs to use minimal materials and hybridizations to achieve maximum genetic gain. We propose a new breeding pipeline, "genomic design breeding," that incorporates doubled haploid production, genomic selection, and genome optimization and is facilitated by different scales of trait predictions and decision-making models. Successful implementation of the proposed model will facilitate the evolution of maize breeding from "art" to "science" and eventually to "intelligence," in the Breeding 4.0 era.
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Affiliation(s)
- Shuqin Jiang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100913, China
| | - Qian Cheng
- Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Shaanxi, China
| | - Jun Yan
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100913, China
| | - Ran Fu
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100913, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100913, China.
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Price EJ, Drapal M, Perez‐Fons L, Amah D, Bhattacharjee R, Heider B, Rouard M, Swennen R, Becerra Lopez‐Lavalle LA, Fraser PD. Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 101:1258-1268. [PMID: 31845400 PMCID: PMC7383867 DOI: 10.1111/tpj.14649] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 11/09/2019] [Accepted: 11/28/2019] [Indexed: 05/06/2023]
Abstract
Roots, tubers, and bananas (RTB) are vital staples for food security in the world's poorest nations. A major constraint to current RTB breeding programmes is limited knowledge on the available diversity due to lack of efficient germplasm characterization and structure. In recent years large-scale efforts have begun to elucidate the genetic and phenotypic diversity of germplasm collections and populations and, yet, biochemical measurements have often been overlooked despite metabolite composition being directly associated with agronomic and consumer traits. Here we present a compound database and concentration range for metabolites detected in the major RTB crops: banana (Musa spp.), cassava (Manihot esculenta), potato (Solanum tuberosum), sweet potato (Ipomoea batatas), and yam (Dioscorea spp.), following metabolomics-based diversity screening of global collections held within the CGIAR institutes. The dataset including 711 chemical features provides a valuable resource regarding the comparative biochemical composition of each RTB crop and highlights the potential diversity available for incorporation into crop improvement programmes. Particularly, the tropical crops cassava, sweet potato and banana displayed more complex compositional metabolite profiles with representations of up to 22 chemical classes (unknowns excluded) than that of potato, for which only metabolites from 10 chemical classes were detected. Additionally, over 20% of biochemical signatures remained unidentified for every crop analyzed. Integration of metabolomics with the on-going genomic and phenotypic studies will enhance 'omics-wide associations of molecular signatures with agronomic and consumer traits via easily quantifiable biochemical markers to aid gene discovery and functional characterization.
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Affiliation(s)
- Elliott J. Price
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
- Present address:
Masaryk UniversityBrno‐Bohunice625 00Czech Republic
| | - Margit Drapal
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
| | - Laura Perez‐Fons
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
| | - Delphine Amah
- International Institute of Tropical AgriculturePMB 5320IbadanNigeria
| | | | | | - Mathieu Rouard
- Bioversity InternationalParc Scientifique Agropolis II34397MontpellierFrance
| | - Rony Swennen
- Laboratory of Tropical Crop ImprovementDivision of Crop BiotechnicsKU LeuvenB‐3001LeuvenBelgium
- Bioversity InternationalWillem De Croylaan 42B‐3001LeuvenBelgium
- International Institute of Tropical Agriculture. C/0 The Nelson Mandela African Institution of Science and TechnologyP.O. Box 44ArushaTanzania
| | | | - Paul D. Fraser
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
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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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75
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Xu Y, Liu X, Fu J, Wang H, Wang J, Huang C, Prasanna BM, Olsen MS, Wang G, Zhang A. Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants. PLANT COMMUNICATIONS 2020; 1:100005. [PMID: 33404534 PMCID: PMC7747995 DOI: 10.1016/j.xplc.2019.100005] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- CIMMYT-China Tropical Maize Research Center, Foshan University, Foshan 528231, China
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Xiaogang Liu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Junjie Fu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hongwu Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jiankang Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Changling Huang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Boddupalli M. Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Michael S. Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Guoying Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Aimin Zhang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
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76
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Tiwari P, Bajpai M, Singh LK, Mishra S, Yadav AN. Phytohormones Producing Fungal Communities: Metabolic Engineering for Abiotic Stress Tolerance in Crops. Fungal Biol 2020. [DOI: 10.1007/978-3-030-45971-0_8] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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77
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Ewing PM, Runck BC, Kono TYJ, Kantar MB. The home field advantage of modern plant breeding. PLoS One 2019; 14:e0227079. [PMID: 31877180 PMCID: PMC6932805 DOI: 10.1371/journal.pone.0227079] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 12/10/2019] [Indexed: 12/03/2022] Open
Abstract
Since the mid-20th century, crop breeding has driven unprecedented yield gains. Breeders generally select for broadly- and reliably-performing varieties that display little genotype-by-environment interaction (GxE). In contrast, ecological theory predicts that across environments that vary spatially or temporally, the most productive population will be a mixture of narrowly adapted specialists. We quantified patterns of broad and narrow adaptation in modern, commercial maize (Zea mays L.) hybrids planted across 216 site-years, from 1999–2018, for the University of Illinois yield trials. We found that location was the dominant source of yield variation (44.5%), and yearly weather was the smallest (1.7%), which suggested a benefit for reliable performance in narrow biophysical environments. Varieties displayed a large “home field advantage” when growing in the location of best performance relative to other varieties. Home field advantage accounted for 19% of GxE and provided a yield increase of 1.01 ± 0.04 Mg ∙ ha-1 (7.6% relative to mean yield), yet was both smaller than predicted by a null model and unchanged across time. This counterfactual suggests that commercial breeding programs have missed an opportunity to further increase yields by leveraging local adaptation. Public breeding programs may pursue this opportunity by releasing specialist varieties that perform reliably in narrow environments. As seed sources are increasingly privatized and consolidated, this alternate strategy may compliment private breeding to support global food security.
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Affiliation(s)
- Patrick M. Ewing
- Department of Crop, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, United States of America
| | - Bryan C. Runck
- GEMS Agroinformatics Initiative, University of Minnesota, Minneapolis, MN, United States of America
| | - Thomas Y. J. Kono
- Minnesota Supercomputing Institute, Minneapolis, MN, United States of America
| | - Michael B. Kantar
- Department of Tropical Plant and Soil Science, University of Hawaii at Manoa, Honolulu, HI, United States of America
- * E-mail:
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78
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Prabha R, Singh DP, Gupta S, Gupta VK, El-Enshasy HA, Verma MK. Rhizosphere Metagenomics of Paspalum scrobiculatum L. (Kodo Millet) Reveals Rhizobiome Multifunctionalities. Microorganisms 2019; 7:microorganisms7120608. [PMID: 31771141 PMCID: PMC6956225 DOI: 10.3390/microorganisms7120608] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 10/15/2019] [Indexed: 12/23/2022] Open
Abstract
Multifunctionalities linked with the microbial communities associated with the millet crop rhizosphere has remained unexplored. In this study, we are analyzing microbial communities inhabiting rhizosphere of kodo millet and their associated functions and its impact over plant growth and survival. Metagenomics of Paspalum scrobiculatum L.(kodo millet) rhizopshere revealed taxonomic communities with functional capabilities linked to support growth and development of the plants under nutrient-deprived, semi-arid and dry biotic conditions. Among 65 taxonomically diverse phyla identified in the rhizobiome, Actinobacteria were the most abundant followed by the Proteobacteria. Functions identified for different genes/proteins led to revelations that multifunctional rhizobiome performs several metabolic functions including carbon fixation, nitrogen, phosphorus, sulfur, iron and aromatic compound metabolism, stress response, secondary metabolite synthesis and virulence, disease, and defense. Abundance of genes linked with N, P, S, Fe and aromatic compound metabolism and phytohormone synthesis—along with other prominent functions—clearly justifies growth, development, and survival of the plants under nutrient deprived dry environment conditions. The dominance of actinobacteria, the known antibiotic producing communities shows that the kodo rhizobiome possesses metabolic capabilities to defend themselves against biotic stresses. The study opens avenues to revisit multi-functionalities of the crop rhizosphere for establishing link between taxonomic abundance and targeted functions that help plant growth and development in stressed and nutrient deprived soil conditions. It further helps in understanding the role of rhizosphere microbiome in adaptation and survival of plants in harsh abiotic conditions.
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Affiliation(s)
- Ratna Prabha
- Chhattisgarh Swami Vivekananda Technical University, Bhilai, Chhattisgarh 491107, India; (R.P.); (M.K.V.)
| | - Dhananjaya P. Singh
- ICAR-National Bureau of Agriculturally Important Microorganisms, Indian Council of Agricultural Research, Kushmaur, Maunath Bhanjan 275101, UP, India
- Correspondence:
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock 18057, Germany;
| | - Vijai Kumar Gupta
- Department of Chemistry and Biotechnology, ERA Chair of Green Chemistry, Tallinn University of Technology, 12618 Tallinn, Estonia;
| | - Hesham A. El-Enshasy
- Institute of Bioproduct Development, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, Johor, Malaysia;
| | - Mukesh K. Verma
- Chhattisgarh Swami Vivekananda Technical University, Bhilai, Chhattisgarh 491107, India; (R.P.); (M.K.V.)
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79
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Sun TJ, Fan L, Yang J, Cao RZ, Yang CY, Zhang J, Wang DM. A Glycine max sodium/hydrogen exchanger enhances salt tolerance through maintaining higher Na + efflux rate and K +/Na + ratio in Arabidopsis. BMC PLANT BIOLOGY 2019; 19:469. [PMID: 31690290 PMCID: PMC6833268 DOI: 10.1186/s12870-019-2084-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 10/17/2019] [Indexed: 05/03/2023]
Abstract
BACKGROUND Soybean (Glycine max (L.)) is one the most important oil-yielding cash crops. However, the soybean production has been seriously restricted by salinization. It is therefore crucial to identify salt tolerance-related genes and reveal molecular mechanisms underlying salt tolerance in soybean crops. A better understanding of how plants resist salt stress provides insights in improving existing soybean varieties as well as cultivating novel salt tolerant varieties. In this study, the biological function of GmNHX1, a NHX-like gene, and the molecular basis underlying GmNHX1-mediated salt stress resistance have been revealed. RESULTS We found that the transcription level of GmNHX1 was up-regulated under salt stress condition in soybean, reaching its peak at 24 h after salt treatment. By employing the virus-induced gene silencing technique (VIGS), we also found that soybean plants became more susceptible to salt stress after silencing GmNHX1 than wild-type and more silenced plants wilted than wild-type under salt treatment. Furthermore, Arabidopsis thaliana expressing GmNHX1 grew taller and generated more rosette leaves under salt stress condition compared to wild-type. Exogenous expression of GmNHX1 resulted in an increase of Na+ transportation to leaves along with a reduction of Na+ absorption in roots, and the consequent maintenance of a high K+/Na+ ratio under salt stress condition. GmNHX1-GFP-transformed onion bulb endothelium cells showed fluorescent pattern in which GFP fluorescence signals enriched in vacuolar membranes. Using the non-invasive micro-test technique (NMT), we found that the Na+ efflux rate of both wild-type and transformed plants after salt treatment were significantly higher than that of before salt treatment. Additionally, the Na+ efflux rate of transformed plants after salt treatment were significantly higher than that of wild-type. Meanwhile, the transcription levels of three osmotic stress-related genes, SKOR, SOS1 and AKT1 were all up-regulated in GmNHX1-expressing plants under salt stress condition. CONCLUSION Vacuolar membrane-localized GmNHX1 enhances plant salt tolerance through maintaining a high K+/Na+ ratio along with inducing the expression of SKOR, SOS1 and AKT1. Our findings provide molecular insights on the roles of GmNHX1 and similar sodium/hydrogen exchangers in regulating salt tolerance.
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Affiliation(s)
- Tian-Jie Sun
- Hebei Key Laboratory of Plant Physiology and Molecular Pathology, Hebei Agricultural University, Baoding, 071000 Hebei China
| | - Long Fan
- Hebei Key Laboratory of Plant Physiology and Molecular Pathology, Hebei Agricultural University, Baoding, 071000 Hebei China
| | - Jun Yang
- Hebei Key Laboratory of Plant Physiology and Molecular Pathology, Hebei Agricultural University, Baoding, 071000 Hebei China
| | - Ren-Zhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447 USA
| | - Chun-Yan Yang
- Hebei Food and Oil Crops Institute, Shijiazhuang, 050031 Hebei China
| | - Jie Zhang
- Hebei Key Laboratory of Plant Physiology and Molecular Pathology, Hebei Agricultural University, Baoding, 071000 Hebei China
| | - Dong-Mei Wang
- Hebei Key Laboratory of Plant Physiology and Molecular Pathology, Hebei Agricultural University, Baoding, 071000 Hebei China
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80
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Mahender A, Ali J, Prahalada GD, Sevilla MAL, Balachiranjeevi CH, Md J, Maqsood U, Li Z. Genetic dissection of developmental responses of agro-morphological traits under different doses of nutrient fertilizers using high-density SNP markers. PLoS One 2019; 14:e0220066. [PMID: 31335882 PMCID: PMC6650078 DOI: 10.1371/journal.pone.0220066] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Accepted: 07/07/2019] [Indexed: 11/19/2022] Open
Abstract
The production and productivity of rice (Oryza sativa L.) are primarily influenced by the application of the critical nutrients nitrogen (N), phosphorus (P), and potassium (K). However, excessive application of these fertilizers is detrimental to the environment and increases the cost of production. Hence, there is a need to develop varieties that simultaneously increase yields under both optimal and suboptimal rates of fertilizer application by maximizing nutrient use efficiency (NuUE). To unravel the hidden genetic variation and understand the molecular and physiological mechanisms of NuUE, three different mapping populations (MPs; BC1F5) derived from three donors (Haoannong, Cheng-Hui 448, and Zhong 413) and recipient Weed Tolerant Rice 1 were developed. A total of three favorable agronomic traits (FATs) were considered as the measure of NuUE. Analysis of variance and descriptive statistics indicated the existence of genetic variation for NuUE and quantitative inheritance of FATs. The genotypic data from single-nucleotide polymorphism (SNP) markers from Tunable Genotyping-By-Sequencing (tGBS) and phenotypic values were used for locating the genomic regions conferring NuUE. A total of 19 quantitative trait loci (QTLs) were detected, out of which 11 QTLs were putative on eight chromosomes, which individually explained 17.02% to 34.85% of the phenotypic variation. Notably, qLC-II_1 and qLC-II_11 detected at zero fertilizer application showed higher performance for LC under zero percentage of NPK fertilizer. The remarkable findings of the present study are that the detected QTLs were associated in building tolerance to low/no nutrient application and six candidate genes on chromosomes 2 and 5 within these putative QTLs were found associated with low nutrient tolerance and related to several physiological and metabolic pathways involved in abiotic stress tolerance. The identified superior introgressed lines (ILs) and trait-associated genetic regions can be effectively used in marker-assisted selection (MAS) for NuUE breeding programs.
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Affiliation(s)
- Anumalla Mahender
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Manila, Philippines
| | - Jauhar Ali
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Manila, Philippines
- * E-mail:
| | - G. D. Prahalada
- Strategic Innovation Platform, International Rice Research Institute, Los Baños, Manila, Philippines
| | - Ma. Anna Lynn Sevilla
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Manila, Philippines
| | - C. H. Balachiranjeevi
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Manila, Philippines
| | - Jamaloddin Md
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Manila, Philippines
| | - Umer Maqsood
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering, Pakistan
| | - Zhikang Li
- Chinese Academy of Agricultural Sciences, Haidian District, P.R. China
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81
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Neill EM, Byrd MCR, Billman T, Brandizzi F, Stapleton AE. Plant growth regulators interact with elevated temperature to alter heat stress signaling via the Unfolded Protein Response in maize. Sci Rep 2019. [PMID: 31316112 DOI: 10.1101/532796] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023] Open
Abstract
Plants are increasingly exposed to high temperatures, which can cause accumulation of unfolded protein in the endoplasmic reticulum (ER). This condition, known as ER stress, evokes the unfolded protein response (UPR), a cytoprotective signaling pathway. One important branch of the UPR is regulated by splicing of bZIP60 mRNA by the IRE1 stress sensor. There is increasing evidence that commercial plant growth regulators may protect against abiotic stressors including heat stress and drought, but there is very little mechanistic information about these effects or about the regulatory pathways involved. We evaluated evidence in the B73 Zea mays inbred for differences in the activity of the UPR between permissive and elevated temperature in conjunction with plant growth regulator application. Treatment with elevated temperature and plant growth regulators increased UPR activation, as assessed by an increase in splicing of the mRNA of the IRE1 target bZIP60 following paclobutrazol treatment. We propose that plant growth regulator treatment induces bZIP60 mRNA splicing which 'primes' plants for rapid adaptive response to subsequent endoplasmic reticulum-stress inducing conditions.
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Affiliation(s)
- Elena M Neill
- University of North Carolina Wilmington, Department of Biology and Marine Biology, Department of Mathematics and Statistics, Wilmington, NC, USA
- North Carolina Department of Health and Human Services, State Laboratory of Public Health, Raleigh, NC, USA
| | - Michael C R Byrd
- University of North Carolina Wilmington, Department of Biology and Marine Biology, Department of Mathematics and Statistics, Wilmington, NC, USA
| | - Thomas Billman
- University of North Carolina Wilmington, Department of Biology and Marine Biology, Department of Mathematics and Statistics, Wilmington, NC, USA
- Blue Cross and Blue Shield of North Carolina, Underwriting Division, Durham, NC, USA
| | - Federica Brandizzi
- Michigan State University, Department of Plant Biology, East Lansing, MI, USA
| | - Ann E Stapleton
- University of North Carolina Wilmington, Department of Biology and Marine Biology, Department of Mathematics and Statistics, Wilmington, NC, USA.
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82
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Neill EM, Byrd MCR, Billman T, Brandizzi F, Stapleton AE. Plant growth regulators interact with elevated temperature to alter heat stress signaling via the Unfolded Protein Response in maize. Sci Rep 2019; 9:10392. [PMID: 31316112 PMCID: PMC6637120 DOI: 10.1038/s41598-019-46839-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 07/03/2019] [Indexed: 01/09/2023] Open
Abstract
Plants are increasingly exposed to high temperatures, which can cause accumulation of unfolded protein in the endoplasmic reticulum (ER). This condition, known as ER stress, evokes the unfolded protein response (UPR), a cytoprotective signaling pathway. One important branch of the UPR is regulated by splicing of bZIP60 mRNA by the IRE1 stress sensor. There is increasing evidence that commercial plant growth regulators may protect against abiotic stressors including heat stress and drought, but there is very little mechanistic information about these effects or about the regulatory pathways involved. We evaluated evidence in the B73 Zea mays inbred for differences in the activity of the UPR between permissive and elevated temperature in conjunction with plant growth regulator application. Treatment with elevated temperature and plant growth regulators increased UPR activation, as assessed by an increase in splicing of the mRNA of the IRE1 target bZIP60 following paclobutrazol treatment. We propose that plant growth regulator treatment induces bZIP60 mRNA splicing which 'primes' plants for rapid adaptive response to subsequent endoplasmic reticulum-stress inducing conditions.
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Affiliation(s)
- Elena M Neill
- University of North Carolina Wilmington, Department of Biology and Marine Biology, Department of Mathematics and Statistics, Wilmington, NC, USA
- North Carolina Department of Health and Human Services, State Laboratory of Public Health, Raleigh, NC, USA
| | - Michael C R Byrd
- University of North Carolina Wilmington, Department of Biology and Marine Biology, Department of Mathematics and Statistics, Wilmington, NC, USA
| | - Thomas Billman
- University of North Carolina Wilmington, Department of Biology and Marine Biology, Department of Mathematics and Statistics, Wilmington, NC, USA
- Blue Cross and Blue Shield of North Carolina, Underwriting Division, Durham, NC, USA
| | - Federica Brandizzi
- Michigan State University, Department of Plant Biology, East Lansing, MI, USA
| | - Ann E Stapleton
- University of North Carolina Wilmington, Department of Biology and Marine Biology, Department of Mathematics and Statistics, Wilmington, NC, USA.
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83
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Pour‐Aboughadareh A, Yousefian M, Moradkhani H, Moghaddam Vahed M, Poczai P, Siddique KHM. iPASTIC: An online toolkit to estimate plant abiotic stress indices. APPLICATIONS IN PLANT SCIENCES 2019; 7:e11278. [PMID: 31346510 PMCID: PMC6636621 DOI: 10.1002/aps3.11278] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 06/16/2019] [Indexed: 05/23/2023]
Abstract
PREMISE In crop breeding programs, breeders use yield performance in both optimal and stressful environments as a key indicator for screening the most tolerant genotypes. During the past four decades, several yield-based indices have been suggested for evaluating stress tolerance in crops. Despite the well-established use of these indices in agronomy and plant breeding, a user-friendly software that would provide access to these methods is still lacking. METHODS AND RESULTS The Plant Abiotic Stress Index Calculator (iPASTIC) is an online program based on JavaScript and R that calculates common stress tolerance and susceptibility indices for various crop traits including the tolerance index (TOL), relative stress index (RSI), mean productivity (MP), harmonic mean (HM), yield stability index (YSI), geometric mean productivity (GMP), stress susceptibility index (SSI), stress tolerance index (STI), and yield index (YI). Along with these indices, this easily accessible tool can also calculate their ranking patterns, estimate the relative frequency for each index, and create heat maps based on Pearson's and Spearman's rank-order correlation analyses. In addition, it can also render three-dimensional plots based on both yield performances and each index to separate entry genotypes into Fernandez's groups (A, B, C, and D), and perform principal component analysis. The accuracy of the results calculated from our software was tested using two different data sets obtained from previous experiments testing the salinity and drought stress in wheat genotypes, respectively. CONCLUSIONS iPASTIC can be widely used in agronomy and plant breeding programs as a user-friendly interface for agronomists and breeders dealing with large volumes of data. The software is available at https://mohsenyousefian.com/ipastic/.
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Affiliation(s)
- Alireza Pour‐Aboughadareh
- Seed and Plant Improvement InstituteAgricultural Research, Education and Extension Organization (AREEO)KarajIran
| | - Mohsen Yousefian
- Department of Computer ScienceUniversity of ManitobaWinnipegManitobaCanada
| | - Hoda Moradkhani
- Department of Plant BreedingKermanshah BranchIslamic Azad UniversityKermanshahIran
| | | | - Peter Poczai
- Botany UnitFinnish Museum of Natural HistoryUniversity of HelsinkiP.O. Box 7HelsinkiFI‐00014Finland
- Department of Molecular Plant PhysiologyInstitute for Water and Wetland ResearchRadboud University6500 GLNijmegenThe Netherlands
| | - Kadambot H. M. Siddique
- The UWA Institute of AgricultureThe University of Western AustraliaLB 5005PerthWestern Australia6001Australia
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84
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Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J. Crop Phenomics: Current Status and Perspectives. FRONTIERS IN PLANT SCIENCE 2019; 10:714. [PMID: 31214228 PMCID: PMC6557228 DOI: 10.3389/fpls.2019.00714] [Citation(s) in RCA: 132] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 05/14/2019] [Indexed: 05/19/2023]
Abstract
Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.
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85
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Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F, Lorence A, Chawade A, Khafif M, Noshita K, Mueller-Linow M, Zhou J, Tardieu F. What is cost-efficient phenotyping? Optimizing costs for different scenarios. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:14-22. [PMID: 31003607 DOI: 10.1016/j.plantsci.2018.06.015] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 05/17/2018] [Accepted: 06/13/2018] [Indexed: 05/22/2023]
Abstract
Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5-26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10-20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, "cost-effective" phenotyping may involve either low investment ("affordable phenotyping"), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs.
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Affiliation(s)
- Daniel Reynolds
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UH, UK
| | | | - Claude Welcker
- INRA Univ Montpellier, LEPSE, 2 place Viala 34060 Montpellier, France
| | - Aaron Bostrom
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Joshua Ball
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Francesco Cellini
- Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura, 75010, Metaponto, MT, Italy
| | - Argelia Lorence
- Phenomics Facility, Arkansas Biosciences Institute, Arkansas State University, Jonesboro, Arkansas, USA
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), P.O. Box 101, 230 53 Alnarp, Sweden
| | - Mehdi Khafif
- Université de Toulouse, INRA, CNRS, LIPM Castanet-Tolosan, France
| | - Koji Noshita
- Japan Science and Technology Agency (JST), Precursory Research for Embryonic Science and Technology (PRESTO), Graduate School of Agriculture and Life Science, The University of Tokyo, Japan
| | - Mark Mueller-Linow
- Institute of Bio- and Geosciences (IBG), IBG-2: Plant Sciences, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - Ji Zhou
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UH, UK; Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, 210095, China.
| | - François Tardieu
- INRA Univ Montpellier, LEPSE, 2 place Viala 34060 Montpellier, France.
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86
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Voss-Fels KP, Cooper M, Hayes BJ. Accelerating crop genetic gains with genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:669-686. [PMID: 30569365 DOI: 10.1007/s00122-018-3270-8] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 12/12/2018] [Indexed: 05/05/2023]
Abstract
Genomic prediction based on additive genetic effects can accelerate genetic gain. There are opportunities for further improvement by including non-additive effects that access untapped sources of genetic diversity. Several studies have reported a worrying gap between the projected global future demand for plant-based products and the current annual rates of production increase, indicating that enhancing the rate of genetic gain might be critical for future food security. Therefore, new breeding technologies and strategies are required to significantly boost genetic improvement of future crop cultivars. Genomic selection (GS) has delivered considerable genetic gain in animal breeding and is becoming an essential component of many modern plant breeding programmes as well. In this paper, we review the lessons learned from implementing GS in livestock and the impact of GS on crop breeding, and discuss important features for the success of GS under different breeding scenarios. We highlight major challenges associated with GS including rapid genotyping, phenotyping, genotype-by-environment interaction and non-additivity and give examples for opportunities to overcome these issues. Finally, the potential of combining GS with other modern technologies in order to maximise the rate of crop genetic improvement is discussed, including the potential of increasing prediction accuracy by integration of crop growth models in GS frameworks.
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Affiliation(s)
- Kai Peter Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Ben John Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
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87
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Andorf C, Beavis WD, Hufford M, Smith S, Suza WP, Wang K, Woodhouse M, Yu J, Lübberstedt T. Technological advances in maize breeding: past, present and future. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:817-849. [PMID: 30798332 DOI: 10.1007/s00122-019-03306-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 02/05/2019] [Indexed: 05/18/2023]
Abstract
Maize has for many decades been both one of the most important crops worldwide and one of the primary genetic model organisms. More recently, maize breeding has been impacted by rapid technological advances in sequencing and genotyping technology, transformation including genome editing, doubled haploid technology, parallelled by progress in data sciences and the development of novel breeding approaches utilizing genomic information. Herein, we report on past, current and future developments relevant for maize breeding with regard to (1) genome analysis, (2) germplasm diversity characterization and utilization, (3) manipulation of genetic diversity by transformation and genome editing, (4) inbred line development and hybrid seed production, (5) understanding and prediction of hybrid performance, (6) breeding methodology and (7) synthesis of opportunities and challenges for future maize breeding.
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Affiliation(s)
| | - William D Beavis
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | - Matthew Hufford
- Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA, 50011-1010, USA
| | - Stephen Smith
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | - Walter P Suza
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | - Kan Wang
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | | | - Jianming Yu
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | - Thomas Lübberstedt
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA.
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88
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Gosseau F, Blanchet N, Varès D, Burger P, Campergue D, Colombet C, Gody L, Liévin JF, Mangin B, Tison G, Vincourt P, Casadebaig P, Langlade N. Heliaphen, an Outdoor High-Throughput Phenotyping Platform for Genetic Studies and Crop Modeling. FRONTIERS IN PLANT SCIENCE 2019; 9:1908. [PMID: 30700989 PMCID: PMC6343525 DOI: 10.3389/fpls.2018.01908] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 12/10/2018] [Indexed: 05/17/2023]
Abstract
Heliaphen is an outdoor platform designed for high-throughput phenotyping. It allows the automated management of drought scenarios and monitoring of plants throughout their lifecycles. A robot moving between plants growing in 15-L pots monitors the plant water status and phenotypes the leaf or whole-plant morphology. From these measurements, we can compute more complex traits, such as leaf expansion (LE) or transpiration rate (TR) in response to water deficit. Here, we illustrate the capabilities of the platform with two practical cases in sunflower (Helianthus annuus): a genetic and genomic study of the response of yield-related traits to drought, and a modeling study using measured parameters as inputs for a crop simulation. For the genetic study, classical measurements of thousand-kernel weight (TKW) were performed on a biparental population under automatically managed drought stress and control conditions. These data were used for an association study, which identified five genetic markers of the TKW drought response. A complementary transcriptomic analysis identified candidate genes associated with these markers that were differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used a crop simulation model to predict the impact on crop yield of two traits measured on the platform (LE and TR) for a large number of environments. We conducted simulations in 42 contrasting locations across Europe using 21 years of climate data. We defined the pattern of abiotic stresses occurring at the continental scale and identified ideotypes (i.e., genotypes with specific trait values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can assist the identification of the genetic architecture controlling complex response traits and facilitate the estimation of ecophysiological model parameters to define ideotypes adapted to different environmental conditions.
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Affiliation(s)
- Florie Gosseau
- LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | - Nicolas Blanchet
- LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | - Didier Varès
- LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | - Philippe Burger
- AGIR, INRA, Université de Toulouse, Castanet-Tolosan, France
| | | | | | - Louise Gody
- LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | | | - Brigitte Mangin
- LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | | | - Patrick Vincourt
- LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | | | - Nicolas Langlade
- LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France
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89
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Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates. REMOTE SENSING 2018. [DOI: 10.3390/rs11010063] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.
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90
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Dungey HS, Dash JP, Pont D, Clinton PW, Watt MS, Telfer EJ. Phenotyping Whole Forests Will Help to Track Genetic Performance. TRENDS IN PLANT SCIENCE 2018; 23:854-864. [PMID: 30217472 DOI: 10.1016/j.tplants.2018.08.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 08/07/2018] [Accepted: 08/10/2018] [Indexed: 06/08/2023]
Abstract
Phenotyping is the accurate and precise physical description of organisms. Accurate and quantitative phenotyping underpins the delivery of benefits from genetic improvement programs in agriculture. In forest trees, phenotyping at an equivalent precision has been impossible because trees and forests are large, long-lived, and highly variable. These facts have restricted the delivery of genetic gains in forestry compared to other agricultural sectors. We describe a landscape-scale phenotyping platform that integrates remote sensing, spatial information systems, and genomics to facilitate the delivery of greater gains enabling forestry to catch up with other sectors. Combining remote sensing at a range of spatial and temporal scales with genomics will ultimately impact on tree breeding globally.
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Affiliation(s)
- Heidi S Dungey
- Scion, 49 Sala Street, Rotorua, 3020, New Zealand; www.scionresearch.com/about-us/about-scion/our-people/people/forest-science/heidi-dungey.
| | | | - David Pont
- Scion, 49 Sala Street, Rotorua, 3020, New Zealand
| | - Peter W Clinton
- Scion, 10 Kyle Street, Riccarton, Christchurch 8011, New Zealand
| | - Michael S Watt
- Scion, 10 Kyle Street, Riccarton, Christchurch 8011, New Zealand
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91
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Araus JL, Kefauver SC. Breeding to adapt agriculture to climate change: affordable phenotyping solutions. CURRENT OPINION IN PLANT BIOLOGY 2018; 45:237-247. [PMID: 29853283 DOI: 10.1016/j.pbi.2018.05.003] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 03/26/2018] [Accepted: 05/07/2018] [Indexed: 06/08/2023]
Abstract
Breeding is one of the central pillars of adaptation of crops to climate change. However, phenotyping is a key bottleneck that is limiting breeding efficiency. The awareness of phenotyping as a breeding limitation is not only sustained by the lack of adequate approaches, but also by the perception that phenotyping is an expensive activity. Phenotyping is not just dependent on the choice of appropriate traits and tools (e.g. sensors) but relies on how these tools are deployed on their carrying platforms, the speed and volume of data extraction and analysis (throughput), the handling of spatial variability and characterization of environmental conditions, and finally how all the information is integrated and processed. Affordable high throughput phenotyping aims to achieve reasonably priced solutions for all the components comprising the phenotyping pipeline. This mini-review will cover current and imminent solutions for all these components, from the increasing use of conventional digital RGB cameras, within the category of sensors, to open-access cloud-structured data processing and the use of smartphones. Emphasis will be placed on field phenotyping, which is really the main application for day-to-day phenotyping.
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Affiliation(s)
- José L Araus
- Section of Plant Physiology, Faculty of Biology, University of Barcelona, Spain.
| | - Shawn C Kefauver
- Section of Plant Physiology, Faculty of Biology, University of Barcelona, Spain
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92
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Seaver SMD, Lerma-Ortiz C, Conrad N, Mikaili A, Sreedasyam A, Hanson AD, Henry CS. PlantSEED enables automated annotation and reconstruction of plant primary metabolism with improved compartmentalization and comparative consistency. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2018; 95:1102-1113. [PMID: 29924895 DOI: 10.1111/tpj.14003] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 05/30/2018] [Accepted: 06/06/2018] [Indexed: 05/19/2023]
Abstract
Genome-scale metabolic reconstructions help us to understand and engineer metabolism. Next-generation sequencing technologies are delivering genomes and transcriptomes for an ever-widening range of plants. While such omic data can, in principle, be used to compare metabolic reconstructions in different species, organs and environmental conditions, these comparisons require a standardized framework for the reconstruction of metabolic networks from transcript data. We previously introduced PlantSEED as a framework covering primary metabolism for 10 species. We have now expanded PlantSEED to include 39 species and provide tools that enable automated annotation and metabolic reconstruction from transcriptome data. The algorithm for automated annotation in PlantSEED propagates annotations using a set of signature k-mers (short amino acid sequences characteristic of particular proteins) that identify metabolic enzymes with an accuracy of about 97%. PlantSEED reconstructions are built from a curated template that includes consistent compartmentalization for more than 100 primary metabolic subsystems. Together, the annotation and reconstruction algorithms produce reconstructions without gaps and with more accurate compartmentalization than existing resources. These tools are available via the PlantSEED web interface at http://modelseed.org, which enables users to upload, annotate and reconstruct from private transcript data and simulate metabolic activity under various conditions using flux balance analysis. We demonstrate the ability to compare these metabolic reconstructions with a case study involving growth on several nitrogen sources in roots of four species.
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Affiliation(s)
- Samuel M D Seaver
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA
- Computation Institute, The University of Chicago, Chicago, IL, 60637, USA
| | - Claudia Lerma-Ortiz
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA
| | - Neal Conrad
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA
| | - Arman Mikaili
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA
| | | | - Andrew D Hanson
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA
| | - Christopher S Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA
- Computation Institute, The University of Chicago, Chicago, IL, 60637, USA
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93
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Xu C, Zhang H, Sun J, Guo Z, Zou C, Li WX, Xie C, Huang C, Xu R, Liao H, Wang J, Xu X, Wang S, Xu Y. Genome-wide association study dissects yield components associated with low-phosphorus stress tolerance in maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2018; 131:1699-1714. [PMID: 29754325 DOI: 10.1007/s00122-018-3108-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 05/07/2018] [Indexed: 05/28/2023]
Abstract
Phosphorus deficiency in soil is a worldwide constraint threatening maize production. Through a genome-wide association study, we identified molecular markers and associated candidate genes and molecular pathways for low-phosphorus stress tolerance. Phosphorus deficiency in soils will severely affect maize (Zea mays L.) growth and development, thus decreasing the final yield. Deciphering the genetic basis of yield-related traits can benefit our understanding of maize tolerance to low-phosphorus stress. However, considering that yield-related traits should be evaluated under field condition with large populations rather than under hydroponic condition at a single-plant level, searching for appropriate field experimental sites and target traits for low-phosphorus stress tolerance is still very challenging. In this study, a genome-wide association analysis using two natural populations was performed to detect candidate genes in response to low-phosphorus stress at two experimental sites representative of different climate and soil types. In total, 259 candidate genes were identified and these candidate genes are mainly involved in four major pathways: transcriptional regulation, reactive oxygen scavenging, hormone regulation, and remodeling of cell wall. Among these candidate genes, 98 showed differential expression by transcriptome data. Based on a haplotype analysis of grain number under phosphorus deficiency condition, the positive haplotypes with favorable alleles across five loci increased grain number by 42% than those without favorable alleles. For further verifying the feasibility of genomic selection for improving maize low-phosphorus tolerance, we also validated the predictive ability of five genomic selection methods and suggested that moderate-density SNPs were sufficient to make accurate predictions for low-phosphorus tolerance traits. All these results will facilitate elucidating genetic basis of maize tolerance to low-phosphorus stress and improving marker-assisted selection efficiency in breeding process.
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Affiliation(s)
- Cheng Xu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China
| | - Hongwei Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China
| | - Jianhao Sun
- Institute of Soil Fertilizer and Water-saving Agriculture, Gansu Academy of Agricultural Sciences, Lanzhou, China
| | - Zifeng Guo
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China
| | - Cheng Zou
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China
| | - Wen-Xue Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China
| | - Chuanxiao Xie
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China
| | - Changling Huang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China
| | - Ruineng Xu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, China
- Root Biology Center, College of Natural Resources and Environment, South China Agricultural University, Guangzhou, China
| | - Hong Liao
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, China
- Root Biology Center, College of Natural Resources and Environment, South China Agricultural University, Guangzhou, China
| | - Jinxiang Wang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, China
- Root Biology Center, College of Natural Resources and Environment, South China Agricultural University, Guangzhou, China
| | - Xiaojie Xu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China
| | - Shanhong Wang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China
| | - Yunbi Xu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 South Zhongguancun Street, Beijing, 100081, China.
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, CP 56130, Mexico.
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94
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Curcic Z, Ciric M, Nagl N, Taski-Ajdukovic K. Effect of Sugar Beet Genotype, Planting and Harvesting Dates and Their Interaction on Sugar Yield. FRONTIERS IN PLANT SCIENCE 2018; 9:1041. [PMID: 30073010 PMCID: PMC6058597 DOI: 10.3389/fpls.2018.01041] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 06/26/2018] [Indexed: 05/27/2023]
Abstract
Climate changes are affecting the plant production, including sugar beet growing especially in the southern and central parts of the Europe. Modifying the sowing and harvesting dates are one of the most often used adaptations in sugar beet cultivation. The aim of this study was to assess the interactions between planting date and sugar beet genotypes for different harvest dates with recommendation for duration of vegetation period for specific hybrids in order to achieve the best performance and to evaluate influence of climatic factors on sugar yield. Three-way analysis of variance and AMMI (Additive main effect and multiple interactions) analysis were performed to investigate interaction between main factors. Analysis of variance revealed that genotypes (G), planting date (PD), harvest date (HD) and interaction G × PD significantly affected sugar yield in 2016. In 2017 genotypes, planting date, harvest date and G x PD interaction significantly affected sugar yield on probability level of 1%, while PD × HD interaction had significant effect on probability level of 5%. Results of AMMI analysis enabled discrimination of genotypes with the highest level of stability in certain planting dates. Hybrids with combined yield and sugar content (NZ type) should have the advantage in earlier planting dates compared to of sugar beet hybrids with higher sugar content (Z type). However, in shortened vegetation period Z type hybrids are more stable and with better sugar yield results. Results of our study suggest that delaying the harvest date decreases differences between sugar yields obtained from hybrids sown in different planting dates. Major factors in the study affecting sugar yield were growing degree days, insolation and number of days from planting to harvest.
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95
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Shahriari Z, Heidari B, Dadkhodaie A. Dissection of genotype × environment interactions for mucilage and seed yield in Plantago species: Application of AMMI and GGE biplot analyses. PLoS One 2018; 13:e0196095. [PMID: 29715274 PMCID: PMC5929508 DOI: 10.1371/journal.pone.0196095] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 04/08/2018] [Indexed: 11/18/2022] Open
Abstract
Genotype × environment interaction (GEI) is an important aspect of both plant breeding and the successful introduction of new cultivars. In the present study, additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects and genotype (G) × environment (E) interaction (GGE) biplot analyses were used to identify stable genotypes and to dissect GEI in Plantago. In total, 10 managed field trials were considered as environments to analyze GEI in thirty genotypes belonging to eight Plantago species. Genotypes were evaluated in a drought stress treatment and in normal irrigation conditions at two locations in Shiraz (Bajgah) for three years (2013-2014- 2015) and Kooshkak (Marvdasht, Fars, Iran) for two years (2014–2015). Three traits, seed yield and mucilage yield and content, were measured at each experimental site and in natural Plantago habitats. AMMI2 biplot analyses identified genotypes from several species with higher stability for seed yield and other genotypes with stable mucilage content and yield. P. lanceolata (G26), P. officinalis (G10), P. ovata (G14), P. ampleexcaulis (G11) and P. major (G4) had higher stability for seed yield. For mucilage yield, G21, G18 and G20 (P. psyllium), G1, G2 and G4 (P. major), G9 and G10 (P. officinalis) and P. lanceolata were identified as stable. G13 (P. ovata), G5 and G6 (P. major) and G30 (P. lagopus) had higher stability for mucilage content. No one genotype was found to have high levels of stability for more than one trait but some species had more than one genotype exhibiting stable trait performance. Based on trait variation, GGE biplot analysis identified two representative environments, one for seed yield and one for mucilage yield and content, with good discriminating ability. The identification of stable genotypes and representative environments should assist the breeding of new Plantago cultivars.
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Affiliation(s)
- Zolfaghar Shahriari
- Department of Crop Production and Plant Breeding, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Bahram Heidari
- Department of Crop Production and Plant Breeding, School of Agriculture, Shiraz University, Shiraz, Iran
- * E-mail:
| | - Ali Dadkhodaie
- Department of Crop Production and Plant Breeding, School of Agriculture, Shiraz University, Shiraz, Iran
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96
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Iwayama K, Aisaka Y, Kutsuna N, Nagano AJ. FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions. Bioinformatics 2018; 33:1672-1680. [PMID: 28158396 PMCID: PMC5447243 DOI: 10.1093/bioinformatics/btx049] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 01/28/2017] [Indexed: 12/31/2022] Open
Abstract
Motivation Considerable attention has been given to the quantification of environmental effects on organisms. In natural conditions, environmental factors are continuously changing in a complex manner. To reveal the effects of such environmental variations on organisms, transcriptome data in field environments have been collected and analyzed. Nagano et al. proposed a model that describes the relationship between transcriptomic variation and environmental conditions and demonstrated the capability to predict transcriptome variation in rice plants. However, the computational cost of parameter optimization has prevented its wide application. Results We propose a new statistical model and efficient parameter optimization based on the previous study. We developed and released FIT, an R package that offers functions for parameter optimization and transcriptome prediction. The proposed method achieves comparable or better prediction performance within a shorter computational time than the previous method. The package will facilitate the study of the environmental effects on transcriptomic variation in field conditions. Availability and Implementation Freely available from CRAN (https://cran.r-project.org/web/packages/FIT/). Supplementary information Supplementary data are available at Bioinformatics online
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Affiliation(s)
- Koji Iwayama
- Research Institute for Food and Agriculture, Ryukoku University, Otsu, Shiga, Japan
| | | | - Natsumaro Kutsuna
- LPixel Inc, Hongo, Bunkyo-ku, Tokyo, Japan.,Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-shi, Chiba, Japan
| | - Atsushi J Nagano
- Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, Otsu, Shiga, Japan.,Center for Ecological Research, Kyoto University, Otsu, Shiga, Japan.,JST PRESTO, Kawaguchi, Saitama, Japan
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97
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Xavier A, Jarquin D, Howard R, Ramasubramanian V, Specht JE, Graef GL, Beavis WD, Diers BW, Song Q, Cregan PB, Nelson R, Mian R, Shannon JG, McHale L, Wang D, Schapaugh W, Lorenz AJ, Xu S, Muir WM, Rainey KM. Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population. G3 (BETHESDA, MD.) 2018; 8:519-529. [PMID: 29217731 PMCID: PMC5919731 DOI: 10.1534/g3.117.300300] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 11/21/2017] [Indexed: 02/06/2023]
Abstract
Genetic improvement toward optimized and stable agronomic performance of soybean genotypes is desirable for food security. Understanding how genotypes perform in different environmental conditions helps breeders develop sustainable cultivars adapted to target regions. Complex traits of importance are known to be controlled by a large number of genomic regions with small effects whose magnitude and direction are modulated by environmental factors. Knowledge of the constraints and undesirable effects resulting from genotype by environmental interactions is a key objective in improving selection procedures in soybean breeding programs. In this study, the genetic basis of soybean grain yield responsiveness to environmental factors was examined in a large soybean nested association population. For this, a genome-wide association to performance stability estimates generated from a Finlay-Wilkinson analysis and the inclusion of the interaction between marker genotypes and environmental factors was implemented. Genomic footprints were investigated by analysis and meta-analysis using a recently published multiparent model. Results indicated that specific soybean genomic regions were associated with stability, and that multiplicative interactions were present between environments and genetic background. Seven genomic regions in six chromosomes were identified as being associated with genotype-by-environment interactions. This study provides insight into genomic assisted breeding aimed at achieving a more stable agronomic performance of soybean, and documented opportunities to exploit genomic regions that were specifically associated with interactions involving environments and subpopulations.
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Affiliation(s)
- Alencar Xavier
- Department of Agronomy, Purdue University, West Lafayette, Indiana 47907
| | - Diego Jarquin
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Nebraska 68583
| | - Reka Howard
- Department of Statistics, University of Nebraska-Lincoln, Nebraska 68583
| | | | - James E Specht
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Nebraska 68583
| | - George L Graef
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Nebraska 68583
| | | | - Brian W Diers
- Department of Crop Sciences, University of Illinois, Urbana, Illinois 61801
| | - Qijian Song
- United States Department of Agriculture (USDA)-Agricultural Research Service (ARS), Beltsville, Maryland 20705
| | - Perry B Cregan
- United States Department of Agriculture (USDA)-Agricultural Research Service (ARS), Beltsville, Maryland 20705
| | - Randall Nelson
- Department of Crop Sciences, University of Illinois, Urbana, Illinois 61801
- USDA-ARS, Urbana, Illinois 61801
| | - Rouf Mian
- USDA-ARS, Raleigh, North Carolina 27607
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina 27607
| | - J Grover Shannon
- Department of Plant Sciences, University of Missouri, Portageville, Missouri 63873
| | - Leah McHale
- Department of Horticulture and Crop Sciences, Ohio State University, Columbus, Ohio 43210
| | - Dechun Wang
- Department of Plant Sciences, Michigan State University, East Lansing, Michigan 48824
| | - William Schapaugh
- Department of Agronomy, Kansas State University, Manhattan, Kansas 66506
| | - Aaron J Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, Minnesota 55108
| | - Shizhong Xu
- Botany and Plant Sciences, University of California, Riverside, California 92521
| | - William M Muir
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana 47907
| | - Katy M Rainey
- Department of Agronomy, Purdue University, West Lafayette, Indiana 47907
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98
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Li X, Wu L, Geng X, Xia X, Wang X, Xu Z, Xu Q. Deciphering the Environmental Impacts on Rice Quality for Different Rice Cultivated Areas. RICE (NEW YORK, N.Y.) 2018; 11:7. [PMID: 29352429 PMCID: PMC5775188 DOI: 10.1186/s12284-018-0198-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 01/02/2018] [Indexed: 05/15/2023]
Abstract
BACKGROUND Rice (Oryza sativa L.) is cultivated in a wide range of climatic conditions, and is one of mankind's major staple foods. The interaction of environmental factors with genotype effects major agronomic traits such as yield, quality, and resistance in rice. However, studies on the environmental factors affecting agronomic traits are often difficult to conduct because most environmental factors are dynamic and constantly changing. RESULTS A series of recombinant inbred lines (RILs) derived from an indica/japonica cross were planted into four typical rice cultivated areas arranging from latitude N22° to N42°. The environmental data from the heading to mature (45 days) stages were recorded for each RIL in the four areas. We determined that light, temperature, and humidity significantly affected the milling quality and cooking quality overall the four areas. Within each area, these environmental factors mainly affected the head rice ratio, grain length, alkali consumption, and amylose and protein content. Moreover, the effect of these environmental factors dynamically changed from heading to mature stage. Compared to light and humidity, temperature was more stable and predictable, and night temperature showed a stronger correlation efficiency to cooking quality than day temperature, and the daily temperature range had contrary effects compared to day and night temperature on grain quality. CONCLUSIONS The present study evaluated the critical phase during the grain filling stage by calculating the dynamic changes of correlation efficiency between the quality traits and climate parameters. Our findings suggest that the sowing date could be adjusted to improve rice quality so as to adjust for environmental changes.
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Affiliation(s)
- Xiukun Li
- Rice Research Institute of Shenyang Agricultural University, Shenyang, 110866, China
| | - Lian Wu
- Rice Research Institute of Shenyang Agricultural University, Shenyang, 110866, China
| | - Xin Geng
- Rice Research Institute of Shenyang Agricultural University, Shenyang, 110866, China
| | - Xiuhong Xia
- Rice Research Institute of Shenyang Agricultural University, Shenyang, 110866, China
| | - Xuhong Wang
- Rice Research Institute of Shenyang Agricultural University, Shenyang, 110866, China
| | - Zhengjin Xu
- Rice Research Institute of Shenyang Agricultural University, Shenyang, 110866, China
| | - Quan Xu
- Rice Research Institute of Shenyang Agricultural University, Shenyang, 110866, China.
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99
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Rasheed A, Hao Y, Xia X, Khan A, Xu Y, Varshney RK, He Z. Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives. MOLECULAR PLANT 2017; 10:1047-1064. [PMID: 28669791 DOI: 10.1016/j.molp.2017.06.008] [Citation(s) in RCA: 219] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 05/29/2017] [Accepted: 06/19/2017] [Indexed: 05/18/2023]
Abstract
There is a rapidly rising trend in the development and application of molecular marker assays for gene mapping and discovery in field crops and trees. Thus far, more than 50 SNP arrays and 15 different types of genotyping-by-sequencing (GBS) platforms have been developed in over 25 crop species and perennial trees. However, much less effort has been made on developing ultra-high-throughput and cost-effective genotyping platforms for applied breeding programs. In this review, we discuss the scientific bottlenecks in existing SNP arrays and GBS technologies and the strategies to develop targeted platforms for crop molecular breeding. We propose that future practical breeding platforms should adopt automated genotyping technologies, either array or sequencing based, target functional polymorphisms underpinning economic traits, and provide desirable prediction accuracy for quantitative traits, with universal applications under wide genetic backgrounds in crops. The development of such platforms faces serious challenges at both the technological level due to cost ineffectiveness, and the knowledge level due to large genotype-phenotype gaps in crop plants. It is expected that such genotyping platforms will be achieved in the next ten years in major crops in consideration of (a) rapid development in gene discovery of important traits, (b) deepened understanding of quantitative traits through new analytical models and population designs, (c) integration of multi-layer -omics data leading to identification of genes and pathways responsible for important breeding traits, and (d) improvement in cost effectiveness of large-scale genotyping. Crop breeding chips and genotyping platforms will provide unprecedented opportunities to accelerate the development of cultivars with desired yield potential, quality, and enhanced adaptation to mitigate the effects of climate change.
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Affiliation(s)
- Awais Rasheed
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; International Maize and Wheat Improvement Center (CIMMYT), c/o CAAS, Beijing 100081, China
| | - Yuanfeng Hao
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China
| | - Xianchun Xia
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China
| | - Awais Khan
- Department of Plant Pathology and Plant-Microbe Biology, Cornell University, Geneva, NY, USA
| | - Yunbi Xu
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; International Maize and Wheat Improvement Center (CIMMYT), c/o CAAS, Beijing 100081, China
| | - Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502324, India
| | - Zhonghu He
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; International Maize and Wheat Improvement Center (CIMMYT), c/o CAAS, Beijing 100081, China.
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100
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Shakoor N, Lee S, Mockler TC. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. CURRENT OPINION IN PLANT BIOLOGY 2017; 38:184-192. [PMID: 28738313 DOI: 10.1016/j.pbi.2017.05.006] [Citation(s) in RCA: 130] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Accepted: 05/17/2017] [Indexed: 05/18/2023]
Abstract
Effective implementation of technology that facilitates accurate and high-throughput screening of thousands of field-grown lines is critical for accelerating crop improvement and breeding strategies for higher yield and disease tolerance. Progress in the development of field-based high throughput phenotyping methods has advanced considerably in the last 10 years through technological progress in sensor development and high-performance computing. Here, we review recent advances in high throughput field phenotyping technologies designed to inform the genetics of quantitative traits, including crop yield and disease tolerance. Successful application of phenotyping platforms to advance crop breeding and identify and monitor disease requires: (1) high resolution of imaging and environmental sensors; (2) quality data products that facilitate computer vision, machine learning and GIS; (3) capacity infrastructure for data management and analysis; and (4) automated environmental data collection. Accelerated breeding for agriculturally relevant crop traits is key to the development of improved varieties and is critically dependent on high-resolution, high-throughput field-scale phenotyping technologies that can efficiently discriminate better performing lines within a larger population and across multiple environments.
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Affiliation(s)
| | - Scott Lee
- Donald Danforth Plant Science Center, United States
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