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Nowak B, Tomkowiak A, Sobiech A, Bocianowski J, Kowalczewski PŁ, Spychała J, Jamruszka T. Identification and Analysis of Candidate Genes Associated with Yield Structure Traits and Maize Yield Using Next-Generation Sequencing Technology. Genes (Basel) 2023; 15:56. [PMID: 38254946 PMCID: PMC10815399 DOI: 10.3390/genes15010056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
The main challenge of agriculture in the 21st century is the continuous increase in food production. In addition to ensuring food security, the goal of modern agriculture is the continued development and production of plant-derived biomaterials. Conventional plant breeding methods do not allow breeders to achieve satisfactory results in obtaining new varieties in a short time. Currently, advanced molecular biology tools play a significant role worldwide, markedly contributing to biological progress. The aim of this study was to identify new markers linked to candidate genes determining grain yield. Next-generation sequencing, gene association, and physical mapping were used to identify markers. An additional goal was to also optimize diagnostic procedures to identify molecular markers on reference materials. As a result of the conducted research, 19 SNP markers significantly associated with yield structure traits in maize were identified. Five of these markers (28629, 28625, 28640, 28649, and 29294) are located within genes that can be considered candidate genes associated with yield traits. For two markers (28639 and 29294), different amplification products were obtained on the electrophorograms. For marker 28629, a specific product of 189 bp was observed for genotypes 1, 4, and 10. For marker 29294, a specific product of 189 bp was observed for genotypes 1 and 10. Both markers can be used for the preliminary selection of well-yielding genotypes.
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Affiliation(s)
- Bartosz Nowak
- Smolice Plant Breeding Ltd., IHAR Group, Smolice 146, 63-740 Kobylin, Poland;
| | - Agnieszka Tomkowiak
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland; (A.S.); (J.S.); (T.J.)
| | - Aleksandra Sobiech
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland; (A.S.); (J.S.); (T.J.)
| | - Jan Bocianowski
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland;
| | - Przemysław Łukasz Kowalczewski
- Department of Food Technology of Plant Origin, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, Poland;
| | - Julia Spychała
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland; (A.S.); (J.S.); (T.J.)
| | - Tomasz Jamruszka
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland; (A.S.); (J.S.); (T.J.)
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van Voorn GAK, Boer MP, Truong SH, Friedenberg NA, Gugushvili S, McCormick R, Bustos Korts D, Messina CD, van Eeuwijk FA. A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library. FRONTIERS IN PLANT SCIENCE 2023; 14:1172359. [PMID: 37389290 PMCID: PMC10303120 DOI: 10.3389/fpls.2023.1172359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023]
Abstract
Introduction Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies. Methods Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values. Results We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation. Discussion A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.
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Affiliation(s)
- George A. K. van Voorn
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | - Martin P. Boer
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | | | | | - Shota Gugushvili
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | - Ryan McCormick
- Research & Development, Corteva Agriscience, Johnston, IA, United States
- Gro Intelligence, New York, NY, United States
| | - Daniela Bustos Korts
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
- Institute of Plant Production and Protection, Faculty of Agricultural Sciences, Universidad Austral de Chile, Valdivia, Chile
| | - Carlos D. Messina
- Research & Development, Corteva Agriscience, Johnston, IA, United States
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Fred A. van Eeuwijk
- Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
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Bayesian multi-level calibration of a process-based maize phenology model. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Onogi A. Integration of Crop Growth Models and Genomic Prediction. Methods Mol Biol 2022; 2467:359-396. [PMID: 35451783 DOI: 10.1007/978-1-0716-2205-6_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.
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Affiliation(s)
- Akio Onogi
- Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, Otsu, Shiga, Japan.
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Powell OM, Voss-Fels KP, Jordan DR, Hammer G, Cooper M. Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction. FRONTIERS IN PLANT SCIENCE 2021; 12:663565. [PMID: 34149761 PMCID: PMC8211918 DOI: 10.3389/fpls.2021.663565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/13/2021] [Indexed: 05/26/2023]
Abstract
Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.
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Affiliation(s)
- Owen M. Powell
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Kai P. Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
| | - David R. Jordan
- Queensland Alliance for Agriculture and Food Innovation, Hermitage Research Facility, The University of Queensland, Warwick, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
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Wang DR, Venturas MD, Mackay DS, Hunsaker DJ, Thorp KR, Gore MA, Pauli D. Use of hydraulic traits for modeling genotype-specific acclimation in cotton under drought. THE NEW PHYTOLOGIST 2020; 228:898-909. [PMID: 32557592 PMCID: PMC7586954 DOI: 10.1111/nph.16751] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 06/07/2020] [Indexed: 06/11/2023]
Abstract
Understanding the genetic and physiological basis of abiotic stress tolerance under field conditions is key to varietal crop improvement in the face of climate variability. Here, we investigate dynamic physiological responses to water stress in silico and their relationships to genotypic variation in hydraulic traits of cotton (Gossypium hirsutum), an economically important species for renewable textile fiber production. In conjunction with an ecophysiological process-based model, heterogeneous data (plant hydraulic traits, spatially-distributed soil texture, soil water content and canopy temperature) were used to examine hydraulic characteristics of cotton, evaluate their consequences on whole plant performance under drought, and explore potential genotype × environment effects. Cotton was found to have R-shaped hydraulic vulnerability curves (VCs), which were consistent under drought stress initiated at flowering. Stem VCs, expressed as percent loss of conductivity, differed across genotypes, whereas root VCs did not. Simulation results demonstrated how plant physiological stress can depend on the interaction between soil properties and irrigation management, which in turn affect genotypic rankings of transpiration in a time-dependent manner. Our study shows how a process-based modeling framework can be used to link genotypic variation in hydraulic traits to differential acclimating behaviors under drought.
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Affiliation(s)
- Diane R. Wang
- Department of GeographyUniversity at BuffaloBuffaloNY14261USA
- Present address:
Department of AgronomyPurdue UniversityWest LafayetteIN47907USA
| | | | - D. Scott Mackay
- Department of GeographyUniversity at BuffaloBuffaloNY14261USA
| | | | - Kelly R. Thorp
- US Arid‐Land Agricultural Research CenterMaricopaAZ37860USA
| | - Michael A. Gore
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNY14853USA
| | - Duke Pauli
- School of Plant SciencesUniversity of ArizonaTucsonAZ85721USA
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Adnan AA, Diels J, Jibrin JM, Kamara AY, Craufurd P, Shaibu AS, Mohammed IB, Tonnang ZEH. Options for calibrating CERES-maize genotype specific parameters under data-scarce environments. PLoS One 2019; 14:e0200118. [PMID: 30779756 PMCID: PMC6380597 DOI: 10.1371/journal.pone.0200118] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 01/31/2019] [Indexed: 11/18/2022] Open
Abstract
Most crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data were also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4-year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha-1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.88-0.94 and coefficient of determination (d-index) between 0.93-0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.58-0.88) and d-index (0.56-0.86) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. It is concluded that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.
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Affiliation(s)
- A A Adnan
- Department of Agronomy, Bayero University Kano, Kano, Nigeria.,Department of Earth and Environmental Sciences, Division of Soil and Water Management, KU Leuven, Leuven, Belgium.,Centre for Dryland Agriculture (CDA), Bayero University Kano, Kano, Nigeria
| | - J Diels
- Department of Earth and Environmental Sciences, Division of Soil and Water Management, KU Leuven, Leuven, Belgium
| | - J M Jibrin
- Centre for Dryland Agriculture (CDA), Bayero University Kano, Kano, Nigeria
| | - A Y Kamara
- International Institute of Tropical Agriculture, Ibadan, Nigeria, Carolyn House, Croydon, United Kingdom
| | - P Craufurd
- International Maize and Wheat Improvement Center (CIMMYT) World Agroforestry Centre (ICRAF) House United Nations Avenue, Nairobi, Kenya
| | - A S Shaibu
- Department of Agronomy, Bayero University Kano, Kano, Nigeria.,Centre for Dryland Agriculture (CDA), Bayero University Kano, Kano, Nigeria
| | - I B Mohammed
- International Institute of Tropical Agriculture, Ibadan, Nigeria, Carolyn House, Croydon, United Kingdom
| | - Z E H Tonnang
- International Institute of Tropical Agriculture, Ibadan, Nigeria, Carolyn House, Croydon, United Kingdom.,International Maize and Wheat Improvement Center (CIMMYT) World Agroforestry Centre (ICRAF) House United Nations Avenue, Nairobi, Kenya
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