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Wei J, Guo T, Mu Q, Alladassi BME, Mural RV, Boyles RE, Hoffmann L, Hayes CM, Sigmon B, Thompson AM, Salas-Fernandez MG, Rooney WL, Kresovich S, Schnable JC, Li X, Yu J. Genetic and Environmental Patterns Underlying Phenotypic Plasticity in Flowering Time and Plant Height in Sorghum. PLANT, CELL & ENVIRONMENT 2024. [PMID: 39415476 DOI: 10.1111/pce.15213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 09/17/2024] [Accepted: 10/03/2024] [Indexed: 10/18/2024]
Abstract
Phenotypic plasticity is the property of a genotype to produce different phenotypes under different environmental conditions. Understanding genetic and environmental factors behind phenotypic plasticity helps answer some longstanding biology questions and improve phenotype prediction. In this study, we investigated the phenotypic plasticity of flowering time and plant height with a set of diverse sorghum lines evaluated across 14 natural field environments. An environmental index was identified to quantitatively connect the environments. Reaction norms were then obtained with the identified indices for genetic dissection of phenotypic plasticity and performance prediction. Genome-wide association studies (GWAS) detected different sets of loci for reaction-norm parameters (intercept and slope), including 10 new genomic regions in addition to known maturity (Ma1) and dwarfing genes (Dw1, Dw2, Dw3, Dw4 and qHT7.1). Cross-validations under multiple scenarios showed promising results in predicting diverse germplasm in dynamic environments. Additional experiments conducted at four new environments, including one from a site outside of the geographical region of the initial environments, further validated the predictions. Our findings indicate that identifying the environmental index enriches our understanding of gene-environmental interplay underlying phenotypic plasticity, and that genomic prediction with the environmental dimension facilitates prediction-guided breeding for future environments.
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Affiliation(s)
- Jialu Wei
- Department of Agronomy, Iowa State University, Ames, Iowa, USA
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, Iowa, USA
| | - Qi Mu
- Department of Agronomy, Iowa State University, Ames, Iowa, USA
| | | | - Ravi V Mural
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Richard E Boyles
- Department of Plant and Environmental Sciences, Clemson University, Clemson, South Carolina, USA
| | - Leo Hoffmann
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - Chad M Hayes
- USDA-ARS, Plant Stress & Germplasm Development Unit, Lubbock, Texas, USA
| | - Brandi Sigmon
- Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Addie M Thompson
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
| | | | - William L Rooney
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - Stephen Kresovich
- Advanced Plant Technology Program, Clemson University, Clemson, South Carolina, USA
| | - James C Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Xianran Li
- USDA-ARS, Wheat Health, Genetics and Quality Research Unit, Pullman, Washington, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, Iowa, USA
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2
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Kusmec A, Yeh CT'E, Schnable PS. Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates. THE NEW PHYTOLOGIST 2024; 244:618-634. [PMID: 39183371 DOI: 10.1111/nph.19937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 05/20/2024] [Indexed: 08/27/2024]
Abstract
Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well-studied major crops. Using 727 maize (Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre- and postanthesis maximum temperature, mid-season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well-studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates.
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Affiliation(s)
- Aaron Kusmec
- Department of Agronomy, Iowa State University, Ames, IA, 50011-3650, USA
| | | | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA, 50011-3650, USA
- Plant Sciences Institute, Iowa State University, Ames, IA, 50011-3650, USA
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3
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Washburn JD, Varela JI, Xavier A, Chen Q, Ertl D, Gage JL, Holland JB, Lima DC, Romay MC, Lopez-Cruz M, de los Campos G, Barber W, Zimmer C, Silva IT, Rocha F, Rincent R, Ali B, Hu H, Runcie DE, Gusev K, Slabodkin A, Bax P, Aubert J, Gangloff H, Mary-Huard T, Vanrenterghem T, Quesada-Traver C, Yates S, Ariza-Suárez D, Ulrich A, Wyler M, Kick DR, Bellis ES, Causey JL, Chavez ES, Wang Y, Piyush V, Fernando GD, Hu RK, Kumar R, Timon AJ, Venkatesh R, Abá KS, Chen H, Ranaweera T, Shiu SH, Wang P, Gordon MJ, Amos BK, Busato S, Perondi D, Gogna A, Psaroudakis D, Chen CPJ, Al-Mamun HA, Danilevicz MF, Upadhyaya SR, Edwards D, de Leon N. Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.13.612969. [PMID: 39345633 PMCID: PMC11429743 DOI: 10.1101/2024.09.13.612969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.
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Affiliation(s)
- Jacob D. Washburn
- USDA-ARS-MWA-PGRU, 302-A Curtis Hall, U. of MO., Columbia, MO, 65211, USA
| | - José Ignacio Varela
- Department of Plant and Agroecosystem Sciences, University of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
| | - Alencar Xavier
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
- Department of Agronomy, Purdue University, 915 Mitch Daniels Blvd, West Lafayette, IN 47907, United States
| | - Qiuyue Chen
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, 50131, USA
| | - Joseph L. Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - James B. Holland
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
- USDA-ARS Plant Science Research Unit, Raleigh, NC, 27695, USA
| | - Dayane Cristina Lima
- Department of Plant and Agroecosystem Sciences, University of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Marco Lopez-Cruz
- Departments of Epidemiology & Biostatistics and Statistics & Probability, and Institute for Quantitative Health Science and Engineering, Michigan State University, 775 Woodlot Dr., East Lansing, MI, 48823, USA
| | - Gustavo de los Campos
- Departments of Epidemiology & Biostatistics and Statistics & Probability, and Institute for Quantitative Health Science and Engineering, Michigan State University, 775 Woodlot Dr., East Lansing, MI, 48823, USA
| | - Wesley Barber
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
| | - Cristiano Zimmer
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
| | | | - Fabiani Rocha
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Baber Ali
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Haixiao Hu
- Department of Plant Sciences, University of California Davis, One Shield Drive, Davis, CA, 95616, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, One Shield Drive, Davis, CA, 95616, USA
| | - Kirill Gusev
- Smart Agri Labs, 2055 Limestone Rd STE 200-C, Wilmington, DE, 19808, USA
| | - Andrei Slabodkin
- Smart Agri Labs, 2055 Limestone Rd STE 200-C, Wilmington, DE, 19808, USA
| | - Phillip Bax
- Smart Agri Labs, 2055 Limestone Rd STE 200-C, Wilmington, DE, 19808, USA
| | - Julie Aubert
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Hugo Gangloff
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Theodore Vanrenterghem
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Carles Quesada-Traver
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, CH-8092 Zurich, Switzerland
| | - Steven Yates
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, CH-8092 Zurich, Switzerland
| | - Daniel Ariza-Suárez
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, CH-8092 Zurich, Switzerland
| | - Argeo Ulrich
- Puregene AG, Etzmatt 273, CH-4314 Zeiningen, Switzerland
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, CH-8092 Zürich, Switzerland
| | - Michele Wyler
- MWSchmid GmbH, Hauptstrasse 34, CH-8750 Glarus, Switzerland
| | - Daniel R. Kick
- USDA-ARS-MWA-PGRU, 302-A Curtis Hall, U. of MO., Columbia, MO, 65211, USA
| | - Emily S. Bellis
- Department of Computer Science, Arkansas State University, 2105 E. Aggie Rd., Jonesboro, AR, 72401, USA
| | - Jason L. Causey
- Department of Computer Science, Arkansas State University, 2105 E. Aggie Rd., Jonesboro, AR, 72401, USA
| | - Emilio Soriano Chavez
- Department of Computer Science, Arkansas State University, 2105 E. Aggie Rd., Jonesboro, AR, 72401, USA
| | - Yixing Wang
- Department of Computer Science, Arkansas State University, 2105 E. Aggie Rd., Jonesboro, AR, 72401, USA
| | - Ved Piyush
- Department of Statistics, University of Nebraska - Lincoln, 340 Hardin Hall North Wing, Lincoln, NE, 68583, USA
| | - Gayara D. Fernando
- Department of Statistics, University of Nebraska - Lincoln, 340 Hardin Hall North Wing, Lincoln, NE, 68583, USA
| | - Robert K Hu
- Genomics and Computational Biology, Perelman School of Medicine at the University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Rachit Kumar
- Genomics and Computational Biology, Perelman School of Medicine at the University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
- Medical Scientist Training Program, Perelman School of Medicine at the University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA, 19104, USA
| | - Annan J. Timon
- Genomics and Computational Biology, Perelman School of Medicine at the University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Rasika Venkatesh
- Genomics and Computational Biology, Perelman School of Medicine at the University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Kenia Segura Abá
- DOE Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI, 48824, USA
- Genetics and Genome Sciences Graduate Program, Michigan State University, East Lansing, MI, 48824, USA
| | - Huan Chen
- Genetics and Genome Sciences Graduate Program, Michigan State University, East Lansing, MI, 48824, USA
| | - Thilanka Ranaweera
- DOE Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI, 48824, USA
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Shin-Han Shiu
- DOE Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI, 48824, USA
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Peiran Wang
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - Max J. Gordon
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - B K. Amos
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - Sebastiano Busato
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - Daniel Perondi
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - Abhishek Gogna
- Department of Breeding Research, Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung, Corrensstraße 3, Gatersleben, 6466, Germany
| | - Dennis Psaroudakis
- Department of Molecular Genetics, Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung, Corrensstraße 3, Gatersleben, 6466, Germany
| | - C. P. James Chen
- School of Animal Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Hawlader A. Al-Mamun
- School of Biological Sciences and Centre of Applied Bioinformatics, University of Western Australia, Perth, WA, Australia
| | - Monica F. Danilevicz
- School of Biological Sciences and Centre of Applied Bioinformatics, University of Western Australia, Perth, WA, Australia
| | - Shriprabha R. Upadhyaya
- School of Biological Sciences and Centre of Applied Bioinformatics, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Centre of Applied Bioinformatics, University of Western Australia, Perth, WA, Australia
| | - Natalia de Leon
- Department of Plant and Agroecosystem Sciences, University of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
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4
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Liu L, Zhan J, Yan J. Engineering the future cereal crops with big biological data: toward intelligence-driven breeding by design. J Genet Genomics 2024; 51:781-789. [PMID: 38531485 DOI: 10.1016/j.jgg.2024.03.005] [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: 10/30/2023] [Revised: 03/17/2024] [Accepted: 03/17/2024] [Indexed: 03/28/2024]
Abstract
How to feed 10 billion human populations is one of the challenges that need to be addressed in the following decades, especially under an unpredicted climate change. Crop breeding, initiating from the phenotype-based selection by local farmers and developing into current biotechnology-based breeding, has played a critical role in securing the global food supply. However, regarding the changing environment and ever-increasing human population, can we breed outstanding crop varieties fast enough to achieve high productivity, good quality, and widespread adaptability? This review outlines the recent achievements in understanding cereal crop breeding, including the current knowledge about crop agronomic traits, newly developed techniques, crop big biological data research, and the possibility of integrating them for intelligence-driven breeding by design, which ushers in a new era of crop breeding practice and shapes the novel architecture of future crops. This review focuses on the major cereal crops, including rice, maize, and wheat, to explain how intelligence-driven breeding by design is becoming a reality.
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Affiliation(s)
- Lei Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China.
| | - Jimin Zhan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
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5
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Li W, Boer MP, Joosen RVL, Zheng C, Percival-Alwyn L, Cockram J, Van Eeuwijk FA. Modeling QTL-by-environment interactions for multi-parent populations. FRONTIERS IN PLANT SCIENCE 2024; 15:1410851. [PMID: 39145196 PMCID: PMC11322070 DOI: 10.3389/fpls.2024.1410851] [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: 04/01/2024] [Accepted: 06/27/2024] [Indexed: 08/16/2024]
Abstract
Multi-parent populations (MPPs) are attractive for genetic and breeding studies because they combine genetic diversity with an easy-to-control population structure. Most methods for mapping QTLs in MPPs focus on the detection of QTLs in single environments. Little attention has been given to mapping QTLs in multienvironment trials (METs) and to detecting and modeling QTL-by-environment interactions (QEIs). We present mixed model approaches for the detection and modeling of consistent versus environment-dependent QTLs, i.e., QTL-by-environment interaction (QEI). QTL effects are assumed to be normally distributed with variances expressing consistency or dependence on environments and families. The entries of the corresponding design matrices are functions of identity-by-descent (IBD) probabilities between parents and offspring and follow from the parental origin of offspring DNA. A polygenic effect is added to the models to account for background genetic variation. We illustrate the wide applicability of our method by analyzing several public MPP datasets with observations from METs. The examples include diallel, nested association mapping (NAM), and multi-parent advanced inter-cross (MAGIC) populations. The results of our approach compare favorably with those of previous studies that used tailored methods.
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Affiliation(s)
- Wenhao Li
- Biometris, Wageningen University and Research Center, Wageningen, Netherlands
| | - Martin P. Boer
- Biometris, Wageningen University and Research Center, Wageningen, Netherlands
| | | | - Chaozhi Zheng
- Biometris, Wageningen University and Research Center, Wageningen, Netherlands
| | | | | | - Fred A. Van Eeuwijk
- Biometris, Wageningen University and Research Center, Wageningen, Netherlands
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6
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Ji Y, Hewavithana T, Sharpe AG, Jin L. Understanding grain development in the Poaceae family by comparing conserved and distinctive pathways through omics studies in wheat and maize. FRONTIERS IN PLANT SCIENCE 2024; 15:1393140. [PMID: 39100085 PMCID: PMC11295249 DOI: 10.3389/fpls.2024.1393140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 07/04/2024] [Indexed: 08/06/2024]
Abstract
The Poaceae family, commonly known as the grass family, encompasses a diverse group of crops that play an essential role in providing food, fodder, biofuels, environmental conservation, and cultural value for both human and environmental well-being. Crops in Poaceae family are deeply intertwined with human societies, economies, and ecosystems, making it one of the most significant plant families in the world. As the major reservoirs of essential nutrients, seed grain of these crops has garnered substantial attention from researchers. Understanding the molecular and genetic processes that controls seed formation, development and maturation can provide insights for improving crop yield, nutritional quality, and stress tolerance. The diversity in photosynthetic pathways between C3 and C4 plants introduces intriguing variations in their physiological and biochemical processes, potentially affecting seed development. In this review, we explore recent studies performed with omics technologies, such as genomics, transcriptomics, proteomics and metabolomics that shed light on the mechanisms underlying seed development in wheat and maize, as representatives of C3 and C4 plants respectively, providing insights into their unique adaptations and strategies for reproductive success.
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Affiliation(s)
- Yuanyuan Ji
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Thulani Hewavithana
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Andrew G. Sharpe
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Lingling Jin
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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7
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Hao H, Li P, Li K, Shan Y, Liu F, Hu N, Zhang B, Li M, Sang X, Xu X, Lv Y, Chen W, Jiao W. A novel prediction approach driven by graph representation learning for heavy metal concentrations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174713. [PMID: 38997020 DOI: 10.1016/j.scitotenv.2024.174713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/14/2024] [Accepted: 07/09/2024] [Indexed: 07/14/2024]
Abstract
The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental factors into consideration, which negatively affects the accuracy of the prediction model and the interpretability of intrinsic mechanisms. Graph representation learning (GraRL) can simultaneously learn the attribute relationships between environmental factors and graph structural information. Herein, we developed the GraRL-HM method to predict the HM concentrations in soil-rice systems. The method consists of two modules, which are PeTPG and GCN-HM. In PeTPG, a graphic structure was generated using graph representation and communitization technology to explore the correlations and transmission paths of different environmental factors. Subsequently, the GCN-HM model based on the graph convolutional neural network (GCN) was used to predict the HM concentrations. The GraRL-HM method was validated by 2295 sets of data covering 21 environmental factors. The results indicated that the PeTPG model simplified correlation paths between factor nodes from 396 to 184, reducing by 53.5 % graph scale by eliminating the invalid paths. The concise and efficient graph structure enhanced the learning efficiency and representation accuracy of downstream prediction models. The GCN-HM model was superior to the four benchmark models in predicting the HM concentration in the crop, improving R2 by 36.1 %. This study develops a novel approach to improve the prediction accuracy of pollutant accumulation and provides valuable insights into intelligent regulation and planting guidance for heavy metal pollution control.
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Affiliation(s)
- Huijuan Hao
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Panpan Li
- Information Centre, Strategic Support Force Medical Center, 9 Anxiang North Lane, Chaoyang District, Beijing 100101, PR China
| | - Ke Li
- Strategic Support Force Medical Center, Beijing 100101, PR China
| | - Yongping Shan
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Feng Liu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Naiwen Hu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Bo Zhang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Man Li
- Shandong Provincial Soil Pollution Prevention and Control Centre, Jinan 250012, PR China
| | - Xudong Sang
- Strategic Support Force Medical Center, Beijing 100101, PR China
| | - Xiaotong Xu
- Strategic Support Force Medical Center, Beijing 100101, PR China
| | - Yuntao Lv
- Risk Assessment Laboratory for Environmental Factors of Agro-product Quality Safety, Ministry of Agriculture and Villages, Changsha 410005, PR China
| | - Wanming Chen
- Risk Assessment Laboratory for Environmental Factors of Agro-product Quality Safety, Ministry of Agriculture and Villages, Changsha 410005, PR China
| | - Wentao Jiao
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 100085, PR China.
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8
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Ali B, Huguenin-Bizot B, Laurent M, Chaumont F, Maistriaux LC, Nicolas S, Duborjal H, Welcker C, Tardieu F, Mary-Huard T, Moreau L, Charcosset A, Runcie D, Rincent R. High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:175. [PMID: 38958724 DOI: 10.1007/s00122-024-04679-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 06/15/2024] [Indexed: 07/04/2024]
Abstract
KEY MESSAGE Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.
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Affiliation(s)
- Baber Ali
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Bertrand Huguenin-Bizot
- Laboratoire Reproduction Et Développement Des Plantes, CNRS, ENS de Lyon-46, Allée d'Italie, 69364, Lyon, France
| | - Maxime Laurent
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-La-Neuve, Belgium
| | - François Chaumont
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-La-Neuve, Belgium
| | - Laurie C Maistriaux
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-La-Neuve, Belgium
| | - Stéphane Nicolas
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Hervé Duborjal
- Limagrain, Limagrain Fields Seeds, Research Centre, 63720, Chappes, France
| | | | | | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Laurence Moreau
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Alain Charcosset
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Daniel Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, USA
| | - Renaud Rincent
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France.
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9
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Groli EL, Frascaroli E, Maccaferri M, Ammar K, Tuberosa R. Dissecting the effect of heat stress on durum wheat under field conditions. FRONTIERS IN PLANT SCIENCE 2024; 15:1393349. [PMID: 39006958 PMCID: PMC11239346 DOI: 10.3389/fpls.2024.1393349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 05/30/2024] [Indexed: 07/16/2024]
Abstract
Introduction Heat stress negatively affects wheat production in several ways, mainly by reducing growth rate, photosynthetic capacity and reducing spike fertility. Modeling stress response means analyzing simultaneous relationships among traits affecting the whole plant response and determinants of grain yield. The aim of this study was to dissect the diverse impacts of heat stress on key yield traits and to identify the most promising sources of alleles for heat tolerance. Methods We evaluated a diverse durum wheat panel of 183 cultivars and breeding lines from worldwide, for their response to long-term heat stress under field conditions (HS) with respect to non stress conditions (NS), considering phenological traits, grain yield (GY) and its components as a function of the timing of heat stress and climatic covariates. We investigated the relationships among plant and environmental variables by means of a structural equation model (SEM) and Genetic SEM (GSEM). Results Over two years of experiments at CENEB, CIMMYT, the effects of HS were particularly pronounced for the normalized difference vegetation index, NDVI (-51.3%), kernel weight per spike, KWS (-40.5%), grain filling period, GFP (-38.7%), and GY (-56.6%). Average temperatures around anthesis were negatively correlated with GY, thousand kernel weight TKW and test weight TWT, but also with spike density, a trait determined before heading/anthesis. Under HS, the correlation between the three major determinants of GY, i.e., fertile spike density, spike fertility and kernel size, were of noticeable magnitude. NDVI measured at medium milk-soft dough stage under HS was correlated with both spike fertility and grain weight while under NS it was less predictive of grain weight but still highly correlated with spike fertility. GSEM modeling suggested that the causal model of performance under HS directly involves genetic effects on GY, NDVI, KWS and HD. Discussion We identified consistently suitable sources of genetic resistance to heat stress to be used in different durum wheat pre-breeding programs. Among those, Desert Durums and CIMMYT'80 germplasm showed the highest degree of adaptation and capacity to yield under high temperatures and can be considered as a valuable source of alleles for adaptation to breed new HS resilient cultivars.
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Affiliation(s)
- Eder Licieri Groli
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
| | - Elisabetta Frascaroli
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
| | - Marco Maccaferri
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
| | - Karim Ammar
- International Maize and Wheat Improvement Center, CIMMYT, El Batán, Mexico
| | - Roberto Tuberosa
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
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10
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Fan Z, Lin S, Jiang J, Zeng Y, Meng Y, Ren J, Wu P. Dual-Model GWAS Analysis and Genomic Selection of Maize Flowering Time-Related Traits. Genes (Basel) 2024; 15:740. [PMID: 38927676 PMCID: PMC11203321 DOI: 10.3390/genes15060740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/16/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
An appropriate flowering period is an important selection criterion in maize breeding. It plays a crucial role in the ecological adaptability of maize varieties. To explore the genetic basis of flowering time, GWAS and GS analyses were conducted using an associating panel consisting of 379 multi-parent DH lines. The DH population was phenotyped for days to tasseling (DTT), days to pollen-shedding (DTP), and days to silking (DTS) in different environments. The heritability was 82.75%, 86.09%, and 85.26% for DTT, DTP, and DTS, respectively. The GWAS analysis with the FarmCPU model identified 10 single-nucleotide polymorphisms (SNPs) distributed on chromosomes 3, 8, 9, and 10 that were significantly associated with flowering time-related traits. The GWAS analysis with the BLINK model identified seven SNPs distributed on chromosomes 1, 3, 8, 9, and 10 that were significantly associated with flowering time-related traits. Three SNPs 3_198946071, 9_146646966, and 9_152140631 showed a pleiotropic effect, indicating a significant genetic correlation between DTT, DTP, and DTS. A total of 24 candidate genes were detected. A relatively high prediction accuracy was achieved with 100 significantly associated SNPs detected from GWAS, and the optimal training population size was 70%. This study provides a better understanding of the genetic architecture of flowering time-related traits and provides an optimal strategy for GS.
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Affiliation(s)
| | | | | | | | | | | | - Penghao Wu
- College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China; (Z.F.); (S.L.); (J.J.); (Y.Z.); (Y.M.); (J.R.)
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11
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Resende RT, Hickey L, Amaral CH, Peixoto LL, Marcatti GE, Xu Y. Satellite-enabled enviromics to enhance crop improvement. MOLECULAR PLANT 2024; 17:848-866. [PMID: 38637991 DOI: 10.1016/j.molp.2024.04.005] [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: 11/15/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
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Affiliation(s)
- Rafael T Resende
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil; TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil.
| | - Lee Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Cibele H Amaral
- Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA; Environmental Data Science Innovation & Inclusion Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Lucas L Peixoto
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil
| | - Gustavo E Marcatti
- TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil; Universidade Federal de São João del-Rei, Forest Engineering Department, Campus Sete Lagoas, Sete Lagoas (MG) 35701-970, Brazil
| | - Yunbi Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China; BGI Bioverse, Shenzhen 518083, China.
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12
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Roth L, Binder M, Kirchgessner N, Tschurr F, Yates S, Hund A, Kronenberg L, Walter A. From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0185. [PMID: 38827955 PMCID: PMC11142864 DOI: 10.34133/plantphenomics.0185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/08/2024] [Indexed: 06/05/2024]
Abstract
Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.
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Affiliation(s)
- Lukas Roth
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Martina Binder
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Norbert Kirchgessner
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Flavian Tschurr
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Steven Yates
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Andreas Hund
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | | | - Achim Walter
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
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13
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Montesinos-López OA, Herr AW, Crossa J, Montesinos-López A, Carter AH. Enhancing winter wheat prediction with genomics, phenomics and environmental data. BMC Genomics 2024; 25:544. [PMID: 38822262 PMCID: PMC11143639 DOI: 10.1186/s12864-024-10438-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 05/21/2024] [Indexed: 06/02/2024] Open
Abstract
In the realm of multi-environment prediction, when the goal is to predict a complete environment using the others as a training set, the efficiency of genomic selection (GS) falls short of expectations. Genotype by environment interaction poses a challenge in achieving high prediction accuracies. Consequently, current efforts are focused on enhancing efficiency by integrating various types of inputs, such as phenomics data, environmental information, and other omics data. In this study, we sought to evaluate the impact of incorporating environmental information into the modeling process, in addition to genomic and phenomics information. Our evaluation encompassed five data sets of soft white winter wheat, and the results revealed a significant improvement in prediction accuracy, as measured by the normalized root mean square error (NRMSE), through the integration of environmental information. Notably, there was an average gain in prediction accuracy of 49.19% in terms of NRMSE across the data sets. Moreover, the observed prediction accuracy ranged from 5.68% (data set 3) to 60.36% (data set 4), underscoring the substantial effect of integrating environmental information. By including genomic, phenomic, and environmental data in prediction models, plant breeding programs can improve selection efficiency across locations.
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Affiliation(s)
| | - Andrew W Herr
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México- Veracruz, Edo. de México, CP 52640, México
- Universidad de Guadalajara, Montecillos, Edo. de México, CP 56230, México
| | | | - Arron H Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA.
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14
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Lian JP, Yuan C, Feng YZ, Liu Q, Wang CY, Zhou YF, Huang QJ, Zhu QF, Zhang YC, Chen YQ, Yu Y. MicroRNA397 promotes rice flowering by regulating the photorespiration pathway. PLANT PHYSIOLOGY 2024; 194:2101-2116. [PMID: 37995372 DOI: 10.1093/plphys/kiad626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/19/2023] [Accepted: 10/29/2023] [Indexed: 11/25/2023]
Abstract
The precise timing of flowering plays a pivotal role in ensuring successful plant reproduction and seed production. This process is intricately governed by complex genetic networks that integrate internal and external signals. This study delved into the regulatory function of microRNA397 (miR397) and its target gene LACCASE-15 (OsLAC15) in modulating flowering traits in rice (Oryza sativa). Overexpression of miR397 led to earlier heading dates, decreased number of leaves on the main stem, and accelerated differentiation of the spikelet meristem. Conversely, overexpression of OsLAC15 resulted in delayed flowering and prolonged vegetative growth. Through biochemical and physiological assays, we uncovered that miR397-OsLAC15 had a profound impact on carbohydrate accumulation and photosynthetic assimilation, consequently enhancing the photosynthetic intensity in miR397-overexpressing rice plants. Notably, we identified that OsLAC15 is at least partially localized within the peroxisome organelle, where it regulates the photorespiration pathway. Moreover, we observed that a high CO2 concentration could rescue the late flowering phenotype in OsLAC15-overexpressing plants. These findings shed valuable insights into the regulatory mechanisms of miR397-OsLAC15 in rice flowering and provided potential strategies for developing crop varieties with early flowering and high-yield traits through genetic breeding.
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Affiliation(s)
- Jian-Ping Lian
- Guangdong Provincial Key Laboratory of Plant Resources, State Key Laboratory for Biocontrol, School of Life Science, Sun Yat-Sen University, Guangzhou 510275, PR China
| | - Chao Yuan
- Guangdong Provincial Key Laboratory of Plant Resources, State Key Laboratory for Biocontrol, School of Life Science, Sun Yat-Sen University, Guangzhou 510275, PR China
| | - Yan-Zhao Feng
- Guangdong Key Laboratory of Crop Germplasm Resources Preservation and Utilization, Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Qing Liu
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Cong-Ying Wang
- Guangdong Provincial Key Laboratory of High Technology for Plant Protection, Plant Protection Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Yan-Fei Zhou
- Guangdong Provincial Key Laboratory of Plant Resources, State Key Laboratory for Biocontrol, School of Life Science, Sun Yat-Sen University, Guangzhou 510275, PR China
| | - Qiao-Juan Huang
- Guangdong Provincial Key Laboratory of Plant Resources, State Key Laboratory for Biocontrol, School of Life Science, Sun Yat-Sen University, Guangzhou 510275, PR China
| | - Qing-Feng Zhu
- Guangdong Key Laboratory of Crop Germplasm Resources Preservation and Utilization, Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Yu-Chan Zhang
- Guangdong Provincial Key Laboratory of Plant Resources, State Key Laboratory for Biocontrol, School of Life Science, Sun Yat-Sen University, Guangzhou 510275, PR China
| | - Yue-Qin Chen
- Guangdong Provincial Key Laboratory of Plant Resources, State Key Laboratory for Biocontrol, School of Life Science, Sun Yat-Sen University, Guangzhou 510275, PR China
| | - Yang Yu
- Guangdong Key Laboratory of Crop Germplasm Resources Preservation and Utilization, Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
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15
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Yu H, Weng L, Wu S, He J, Yuan Y, Wang J, Xu X, Feng X. Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0158. [PMID: 38524738 PMCID: PMC10959008 DOI: 10.34133/plantphenomics.0158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 02/21/2024] [Indexed: 03/26/2024]
Abstract
The rate of soybean canopy establishment largely determines photoperiodic sensitivity, subsequently influencing yield potential. However, assessing the rate of soybean canopy development in large-scale field breeding trials is both laborious and time-consuming. High-throughput phenotyping methods based on unmanned aerial vehicle (UAV) systems can be used to monitor and quantitatively describe the development of soybean canopies for different genotypes. In this study, high-resolution and time-series raw data from field soybean populations were collected using UAVs. The RGB (red, green, and blue) and infrared images are used as inputs to construct the multimodal image segmentation model-the RGB & Infrared Feature Fusion Segmentation Network (RIFSeg-Net). Subsequently, the segment anything model was employed to extract complete individual leaves from the segmentation results obtained from RIFSeg-Net. These leaf aspect ratios facilitated the accurate categorization of soybean populations into 2 distinct varieties: oval leaf type variety and lanceolate leaf type variety. Finally, dynamic modeling was conducted to identify 5 phenotypic traits associated with the canopy development rate that differed substantially among the classified soybean varieties. The results showed that the developed multimodal image segmentation model RIFSeg-Net for extracting soybean canopy cover from UAV images outperformed traditional deep learning image segmentation networks (precision = 0.94, recall = 0.93, F1-score = 0.93). The proposed method has high practical value in the field of germplasm resource identification. This approach could lead to the use of a practical tool for further genotypic differentiation analysis and the selection of target genes.
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Affiliation(s)
- Hui Yu
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology,
Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
| | - Lin Weng
- Zhejiang Lab, Hangzhou 310012, China
| | | | | | | | - Jun Wang
- Zhejiang Lab, Hangzhou 310012, China
| | | | - Xianzhong Feng
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology,
Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
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16
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Araújo MS, Chaves SFS, Dias LAS, Ferreira FM, Pereira GR, Bezerra ARG, Alves RS, Heinemann AB, Breseghello F, Carneiro PCS, Krause MD, Costa-Neto G, Dias KOG. GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:80. [PMID: 38472532 DOI: 10.1007/s00122-024-04579-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 02/06/2024] [Indexed: 03/14/2024]
Abstract
KEY MESSAGE We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.
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Affiliation(s)
- Maurício S Araújo
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Saulo F S Chaves
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Luiz A S Dias
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Filipe M Ferreira
- Department of Crop Science - College of Agricultural Sciences, São Paulo State University, Botucatu, São Paulo, Brazil
| | - Guilherme R Pereira
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | - Rodrigo S Alves
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Alexandre B Heinemann
- Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, Goiás, Brazil
| | - Flávio Breseghello
- Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, Goiás, Brazil
| | - Pedro C S Carneiro
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | | | - Kaio O G Dias
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
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17
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Blancon J, Buet C, Dubreuil P, Tixier MH, Baret F, Praud S. Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:68. [PMID: 38441678 PMCID: PMC10914915 DOI: 10.1007/s00122-024-04572-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/03/2024] [Indexed: 03/07/2024]
Abstract
KEY MESSAGE Green Leaf Area Index dynamics is a promising secondary trait for grain yield and drought tolerance. Multivariate GWAS is particularly well suited to identify the genetic determinants of the green leaf area index dynamics. Improvement of maize grain yield is impeded by important genotype-environment interactions, especially under drought conditions. The use of secondary traits, that are correlated with yield, more heritable and less prone to genotype-environment interactions, can increase breeding efficiency. Here, we studied the genetic basis of a new secondary trait: the green leaf area index (GLAI) dynamics over the maize life cycle. For this, we used an unmanned aerial vehicle to characterize the GLAI dynamics of a diverse panel in well-watered and water-deficient trials in two years. From the dynamics, we derived 24 traits (slopes, durations, areas under the curve), and showed that six of them were heritable traits representative of the panel diversity. To identify the genetic determinants of GLAI, we compared two genome-wide association approaches: a univariate (single-trait) method and a multivariate (multi-trait) method combining GLAI traits, grain yield, and precocity. The explicit modeling of correlation structure between secondary traits and grain yield in the multivariate mixed model led to 2.5 times more associations detected. A total of 475 quantitative trait loci (QTLs) were detected. The genetic architecture of GLAI traits appears less complex than that of yield with stronger-effect QTLs that are more stable between environments. We also showed that a subset of GLAI QTLs explains nearly one fifth of yield variability across a larger environmental network of 11 water-deficient trials. GLAI dynamics is a promising grain yield secondary trait in optimal and drought conditions, and the detected QTLs could help to increase breeding efficiency through a marker-assisted approach.
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Affiliation(s)
- Justin Blancon
- UMR GDEC, INRAE, Université Clermont Auvergne, 63000, Clermont-Ferrand, France.
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France.
| | - Clément Buet
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
| | - Pierre Dubreuil
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
| | | | | | - Sébastien Praud
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
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18
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Guo T, Wei J, Li X, Yu J. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:1004-1015. [PMID: 37819624 PMCID: PMC10837014 DOI: 10.1093/jxb/erad398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 10/17/2023] [Indexed: 10/13/2023]
Abstract
Phenotypic plasticity is an important topic in biology and evolution. However, how to generate broadly applicable insights from individual studies remains a challenge. Here, with flowering time observed from a large geographical region for sorghum and rice genetic populations, we examine the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. Both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental mean or a large sample size resulted in genetic parameters consistent with the overall pattern. Furthermore, high accuracy through genomic prediction was obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under the subsets of environments with either a large range or a large sample size. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. Our findings have implications for the genetic architecture of complex traits, plant-environment interaction, and climate adaptation.
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Affiliation(s)
- Tingting Guo
- Hubei Hongshan Laboratory, Wuhan, Hubei, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Jialu Wei
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
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19
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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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20
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Lopez-Cruz M, Aguate FM, Washburn JD, de Leon N, Kaeppler SM, Lima DC, Tan R, Thompson A, De La Bretonne LW, de Los Campos G. Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America. Nat Commun 2023; 14:6904. [PMID: 37903778 PMCID: PMC10616096 DOI: 10.1038/s41467-023-42687-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/18/2023] [Indexed: 11/01/2023] Open
Abstract
Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.
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Affiliation(s)
- Marco Lopez-Cruz
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
| | - Fernando M Aguate
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Jacob D Washburn
- United States Department of Agriculture, Agricultural Research Service, University of Missouri, Columbia, MO, 65211, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
- Wisconsin Crop Innovation Center, University of Wisconsin, Middleton, WI, 53562, USA
| | | | - Ruijuan Tan
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
- Plant Resilience Institute, Michigan State University, East Lansing, MI, 48824, USA
| | | | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA.
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21
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Weber SE, Chawla HS, Ehrig L, Hickey LT, Frisch M, Snowdon RJ. Accurate prediction of quantitative traits with failed SNP calls in canola and maize. FRONTIERS IN PLANT SCIENCE 2023; 14:1221750. [PMID: 37936929 PMCID: PMC10627008 DOI: 10.3389/fpls.2023.1221750] [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: 05/12/2023] [Accepted: 10/05/2023] [Indexed: 11/09/2023]
Abstract
In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.
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Affiliation(s)
- Sven E. Weber
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | | | - Lennard Ehrig
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Lee T. Hickey
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Matthias Frisch
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
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22
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Bouidghaghen J, Moreau L, Beauchêne K, Chapuis R, Mangel N, Cabrera-Bosquet L, Welcker C, Bogard M, Tardieu F. Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field. Nat Commun 2023; 14:6603. [PMID: 37857601 PMCID: PMC10587076 DOI: 10.1038/s41467-023-42298-z] [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: 04/30/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023] Open
Abstract
Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.
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Affiliation(s)
- Jugurta Bouidghaghen
- LEPSE, Univ Montpellier, INRAE, Montpellier, France
- ARVALIS, Chemin de la côte vieille, Baziège, France
| | - Laurence Moreau
- GQE-Le Moulon, INRAE, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Katia Beauchêne
- ARVALIS, 45 Voie Romaine, Ouzouer-Le-Marché, Beauce La Romaine, France
| | | | - Nathalie Mangel
- ARVALIS, Station de recherche et d'expérimentation, Boigneville, France
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23
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Wu S, Liu X, Dong A, Gragnoli C, Griffin C, Wu J, Yau ST, Wu R. The metabolomic physics of complex diseases. Proc Natl Acad Sci U S A 2023; 120:e2308496120. [PMID: 37812720 PMCID: PMC10589719 DOI: 10.1073/pnas.2308496120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/15/2023] [Indexed: 10/11/2023] Open
Abstract
Human diseases involve metabolic alterations. Metabolomic profiles have served as a vital biomarker for the early identification of high-risk individuals and disease prevention. However, current approaches can only characterize individual key metabolites, without taking into account the reality that complex diseases are multifactorial, dynamic, heterogeneous, and interdependent. Here, we leverage a statistical physics model to combine all metabolites into bidirectional, signed, and weighted interaction networks and trace how the flow of information from one metabolite to the next causes changes in health state. Viewing a disease outcome as the consequence of complex interactions among its interconnected components (metabolites), we integrate concepts from ecosystem theory and evolutionary game theory to model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and through extrinsic influences from its conspecifics. We code intrinsic contributions as nodes and extrinsic contributions as edges into quantitative networks and implement GLMY homology theory to analyze and interpret the topological change of health state from symbiosis to dysbiosis and vice versa. The application of this model to real data allows us to identify several hub metabolites and their interaction webs, which play a part in the formation of inflammatory bowel diseases. The findings by our model could provide important information on drug design to treat these diseases and beyond.
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Affiliation(s)
- Shuang Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing100083, China
| | - Xiang Liu
- Chern Institute of Mathematics, Nankai University, Tianjin300071, China
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing101408, China
| | - Ang Dong
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing100083, China
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA17033
- Department of Medicine, Creighton University School of Medicine, Omaha, NE68124
- Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome00197, Italy
| | - Christopher Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA16802
| | - Jie Wu
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing101408, China
| | - Shing-Tung Yau
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing101408, China
- Yau Mathematical Sciences Center, Tsinghua University, Beijing100084, China
| | - Rongling Wu
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing101408, China
- Yau Mathematical Sciences Center, Tsinghua University, Beijing100084, China
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24
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Tschurr F, Kirchgessner N, Hund A, Kronenberg L, Anderegg J, Walter A, Roth L. Frost Damage Index: The Antipode of Growing Degree Days. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0104. [PMID: 37799632 PMCID: PMC10550053 DOI: 10.34133/plantphenomics.0104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 09/19/2023] [Indexed: 10/07/2023]
Abstract
Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed "frost damage index" (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.
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Affiliation(s)
- Flavian Tschurr
- Department of Environmental System Sciences,
Institute of Agricultural Sciences, ETH Zürich, Zürich, Switzerland
| | - Norbert Kirchgessner
- Department of Environmental System Sciences,
Institute of Agricultural Sciences, ETH Zürich, Zürich, Switzerland
| | - Andreas Hund
- Department of Environmental System Sciences,
Institute of Agricultural Sciences, ETH Zürich, Zürich, Switzerland
| | - Lukas Kronenberg
- Department of Environmental System Sciences,
Institute of Agricultural Sciences, ETH Zürich, Zürich, Switzerland
- Crop Genetics, John Innes Centre, Norwich, UK
| | - Jonas Anderegg
- Department of Environmental System Sciences,
Institute of Agricultural Sciences, ETH Zürich, Zürich, Switzerland
- Department of Environmental System Sciences,
Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland
| | - Achim Walter
- Department of Environmental System Sciences,
Institute of Agricultural Sciences, ETH Zürich, Zürich, Switzerland
| | - Lukas Roth
- Department of Environmental System Sciences,
Institute of Agricultural Sciences, ETH Zürich, Zürich, Switzerland
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25
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Sabir K, Rose T, Wittkop B, Stahl A, Snowdon RJ, Ballvora A, Friedt W, Kage H, Léon J, Ordon F, Stützel H, Zetzsche H, Chen TW. Stage-specific genotype-by-environment interactions determine yield components in wheat. NATURE PLANTS 2023; 9:1688-1696. [PMID: 37735253 DOI: 10.1038/s41477-023-01516-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/18/2023] [Indexed: 09/23/2023]
Abstract
In cereal crops, environmental fluctuations affect different physiological processes during various developmental phases associated with the formation of yield components. Because these effects are coupled with cultivar-specific phenology, studies investigating environmental responses in different cultivars can give contradictory results regarding key phases impacting yield performance. To dissect how genotype-by-environment interactions affect grain yield in winter wheat, we estimated the sensitivities of yield components to variation in global radiation, temperature and precipitation in 220 cultivars across 81 time-windows ranging from double ridge to seed desiccation. Environmental sensitivity responses were prominent in the short-term physiological subphases of spike and kernel development, causing phenologically dependent, stage-specific genotype-by-environment interactions. Here we reconcile contradicting findings from previous studies and show previously undetected effects; for example, the positive impact of global radiation on kernel weight during canopy senescence. This deep insight into the three-way interactions between phenology, yield formation and environmental fluctuations provides comprehensive new information for breeding and modelling cereal crops.
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Affiliation(s)
- Khadija Sabir
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany
| | - Till Rose
- Department of Agronomy and Crop Science, Christian Albrechts University of Kiel, Kiel, Germany
| | - Benjamin Wittkop
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Agim Ballvora
- Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
| | - Wolfgang Friedt
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Henning Kage
- Department of Agronomy and Crop Science, Christian Albrechts University of Kiel, Kiel, Germany
| | - Jens Léon
- Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
- Field Lab Campus Klein-Altendorf, University of Bonn, Rheinbach, Germany
| | - Frank Ordon
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Hartmut Stützel
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany
| | - Holger Zetzsche
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Tsu-Wei Chen
- Group of Intensive Plant Food Systems, Albrecht Daniel Thaer Institut of Agricultural and Horticultural Science, Humboldt University Berlin, Berlin, Germany.
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26
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Heuermann MC, Knoch D, Junker A, Altmann T. Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation. Nat Commun 2023; 14:5783. [PMID: 37723146 PMCID: PMC10507097 DOI: 10.1038/s41467-023-41332-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 08/31/2023] [Indexed: 09/20/2023] Open
Abstract
In plant science, the suboptimal match of growing conditions hampers the transfer of knowledge from controlled environments in glasshouses or climate chambers to field environments. Here we present the PhenoSphere, a plant cultivation infrastructure designed to simulate field-like environments in a reproducible manner. To benchmark the PhenoSphere, the effects on plant growth of weather conditions of a single maize growing season and of an averaged season over three years are compared to those of a standard glasshouse and of four years of field trials. The single season simulation proves superior to the glasshouse and the averaged season in the PhenoSphere: The simulated weather regime of the single season triggers plant growth and development progression very similar to that observed in the field. Hence, the PhenoSphere enables detailed analyses of performance-related trait expression and causal biological mechanisms in plant populations exposed to weather conditions of current and anticipated future climate scenarios.
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Affiliation(s)
- Marc C Heuermann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany.
| | - Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany
| | - Astrid Junker
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany
- Syngenta Seeds GmbH, Zum Knipkenbach 20, 32107, Bad Salzuflen, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany
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27
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Weber SE, Frisch M, Snowdon RJ, Voss-Fels KP. Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets. FRONTIERS IN PLANT SCIENCE 2023; 14:1217589. [PMID: 37731980 PMCID: PMC10507710 DOI: 10.3389/fpls.2023.1217589] [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: 05/05/2023] [Accepted: 08/21/2023] [Indexed: 09/22/2023]
Abstract
In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.
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Affiliation(s)
- Sven E. Weber
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Matthias Frisch
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Kai P. Voss-Fels
- Institute for Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
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Messina CD, Gho C, Hammer GL, Tang T, Cooper M. Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4847-4861. [PMID: 37354091 PMCID: PMC10474595 DOI: 10.1093/jxb/erad231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m-2 year-1 to 7.5 g m-2 year-1, closing the genetic gain gap with respect to the 8.6 g m-2 year-1 observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Δr) above current prediction approaches of ~30% (Δr=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.
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Affiliation(s)
- Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Carla Gho
- School of Agriculture & Food Sciences, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Graeme L Hammer
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Tom Tang
- Corteva Agrisciences, Johnston, IA, USA
| | - Mark Cooper
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
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Song B, Ning W, Wei D, Jiang M, Zhu K, Wang X, Edwards D, Odeny DA, Cheng S. Plant genome resequencing and population genomics: Current status and future prospects. MOLECULAR PLANT 2023; 16:1252-1268. [PMID: 37501370 DOI: 10.1016/j.molp.2023.07.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 05/30/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
Advances in DNA sequencing technology have sparked a genomics revolution, driving breakthroughs in plant genetics and crop breeding. Recently, the focus has shifted from cataloging genetic diversity in plants to exploring their functional significance and delivering beneficial alleles for crop improvement. This transformation has been facilitated by the increasing adoption of whole-genome resequencing. In this review, we summarize the current progress of population-based genome resequencing studies and how these studies affect crop breeding. A total of 187 land plants from 163 countries have been resequenced, comprising 54 413 accessions. As part of resequencing efforts 367 traits have been surveyed and 86 genome-wide association studies have been conducted. Economically important crops, particularly cereals, vegetables, and legumes, have dominated the resequencing efforts, leaving a gap in 49 orders, including Lycopodiales, Liliales, Acorales, Austrobaileyales, and Commelinales. The resequenced germplasm is distributed across diverse geographic locations, providing a global perspective on plant genomics. We highlight genes that have been selected during domestication, or associated with agronomic traits, and form a repository of candidate genes for future research and application. Despite the opportunities for cross-species comparative genomics, many population genomic datasets are not accessible, impeding secondary analyses. We call for a more open and collaborative approach to population genomics that promotes data sharing and encourages contribution-based credit policy. The number of plant genome resequencing studies will continue to rise with the decreasing DNA sequencing costs, coupled with advances in analysis and computational technologies. This expansion, in terms of both scale and quality, holds promise for deeper insights into plant trait genetics and breeding design.
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Affiliation(s)
- Bo Song
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Weidong Ning
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Wuhan, Hubei, China
| | - Di Wei
- Biotechnology Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 53007, China
| | - Mengyun Jiang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China; Shenzhen Research Institute of Henan University, Shenzhen 518000, China
| | - Kun Zhu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China; Shenzhen Research Institute of Henan University, Shenzhen 518000, China
| | - Xingwei Wang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China; Shenzhen Research Institute of Henan University, Shenzhen 518000, China
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Damaris A Odeny
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) - Eastern and Southern Africa, Nairobi, Kenya
| | - Shifeng Cheng
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China.
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Shimono H, Abe A, Kim CH, Sato C, Iwata H. Upcycling rice yield trial data using a weather-driven crop growth model. Commun Biol 2023; 6:764. [PMID: 37479731 PMCID: PMC10362053 DOI: 10.1038/s42003-023-05145-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 07/13/2023] [Indexed: 07/23/2023] Open
Abstract
Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Yp) calculated by a weather-driven crop growth model, called as the "YpCGM method". We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world's changing climate.
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Affiliation(s)
- Hiroyuki Shimono
- Faculty of Agriculture, Iwate University, Morioka, Iwate, 020-8550, Japan.
- Agri-Innovation Center, Iwate University, Morioka, Iwate, 020-8550, Japan.
| | - Akira Abe
- Iwate Biotechnology Research Center, Kitakami, Iwate, 024-0003, Japan
| | - Chyon Hae Kim
- Faculty of Science and Engineering, Iwate University, Morioka, Iwate, 020-8550, Japan
| | - Chikashi Sato
- Ifuu Rinrin, 77-9, Rikuzentakata, Iwate, 029-2205, Japan
| | - Hiroyoshi Iwata
- Laboratory of Biometry and Bioinformatics, University of Tokyo, Bunkyo-ku, Tokyo, 113-8657, Japan
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Roth L, Fossati D, Krähenbühl P, Walter A, Hund A. Image-based phenomic prediction can provide valuable decision support in wheat breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:162. [PMID: 37368140 PMCID: PMC10299972 DOI: 10.1007/s00122-023-04395-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 05/29/2023] [Indexed: 06/28/2023]
Abstract
KEY MESSAGE Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.
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Affiliation(s)
- Lukas Roth
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland.
| | | | - Patrick Krähenbühl
- Delley Samen und Pflanzen AG, Route de Portalban 40, 1567, Delley, Switzerland
| | - Achim Walter
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
| | - Andreas Hund
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
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Großkinsky DK, Faure JD, Gibon Y, Haslam RP, Usadel B, Zanetti F, Jonak C. The potential of integrative phenomics to harness underutilized crops for improving stress resilience. FRONTIERS IN PLANT SCIENCE 2023; 14:1216337. [PMID: 37409292 PMCID: PMC10318926 DOI: 10.3389/fpls.2023.1216337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023]
Affiliation(s)
- Dominik K. Großkinsky
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
| | - Jean-Denis Faure
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
| | - Yves Gibon
- INRAE, Univ. Bordeaux, UMR BFP, Villenave d’Ornon, France
- Bordeaux Metabolome, INRAE, Univ. Bordeaux, Villenave d’Ornon, France
| | | | - Björn Usadel
- IBG-4 Bioinformatics, CEPLAS, Forschungszentrum, Jülich, Germany
- Biological Data Science, Heinrich Heine University, Universitätsstrasse 1, Düsseldorf, Germany
| | - Federica Zanetti
- Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum - Università di Bologna, Bologna, Italy
| | - Claudia Jonak
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
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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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Plancade S, Marchadier E, Huet S, Ressayre A, Noûs C, Dillmann C. A successive time-to-event model of phyllochron dynamics for hypothesis testing: application to the analysis of genetic and environmental effects in maize. PLANT METHODS 2023; 19:54. [PMID: 37287031 PMCID: PMC10245529 DOI: 10.1186/s13007-023-01029-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND The time between the appearance of successive leaves, or phyllochron, characterizes the vegetative development of annual plants. Hypothesis testing models, which allow the comparison of phyllochrons between genetic groups and/or environmental conditions, are usually based on regression of thermal time on the number of leaves; most of the time a constant leaf appearance rate is assumed. However regression models ignore auto-correlation of the leaf number process and may lead to biased testing procedures. Moreover, the hypothesis of constant leaf appearance rate may be too restrictive. METHODS We propose a stochastic process model in which emergence of new leaves is considered to result from successive time-to-events. This model provides a flexible and more accurate modeling as well as unbiased testing procedures. It was applied to an original maize dataset collected in the field over three years on plants originating from two divergent selection experiments for flowering time in two maize inbred lines. RESULTS AND CONCLUSION We showed that the main differences in phyllochron were not observed between selection populations but rather between ancestral lines, years of experimentation and leaf ranks. Our results highlight a strong departure from the assumption of a constant leaf appearance rate over a season which could be related to climate variations, even if the impact of individual climate variables could not be clearly determined.
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Affiliation(s)
- Sandra Plancade
- UR MIAT, University of Toulouse, INRAE, 31320 Castanet-Tolosan, France
| | - Elodie Marchadier
- GQE - Le Moulon, Université Paris-Saclay, INRAE, CNRS, AgroParisTech, IDEEV, 12 route 128, 91190 Gif-sur-Yvette, France
| | - Sylvie Huet
- MaIAGE, Université Paris-Saclay, INRAE, 78350 Jouy-en-Josas, France
| | - Adrienne Ressayre
- GQE - Le Moulon, Université Paris-Saclay, INRAE, CNRS, AgroParisTech, IDEEV, 12 route 128, 91190 Gif-sur-Yvette, France
| | - Camille Noûs
- Cogitamus Laboratory, 31320 Castanet-Tolosan, France
| | - Christine Dillmann
- GQE - Le Moulon, Université Paris-Saclay, INRAE, CNRS, AgroParisTech, IDEEV, 12 route 128, 91190 Gif-sur-Yvette, France
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Waters DL, van der Werf JHJ, Robinson H, Hickey LT, Clark SA. Partitioning the forms of genotype-by-environment interaction in the reaction norm analysis of stability. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:99. [PMID: 37027025 PMCID: PMC10082108 DOI: 10.1007/s00122-023-04319-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/07/2023] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE The reaction norm analysis of stability can be enhanced by partitioning the contribution of different types of G × E to the variation in slope. The slope of regression in a reaction norm model, where the performance of a genotype is regressed over an environmental covariable, is often used as a measure of stability of genotype performance. This method could be developed further by partitioning variation in the slope of regression into the two sources of genotype-by-environment interaction (G × E) which cause it: scale-type G × E (heterogeneity of variance) and rank-type G × E (heterogeneity of correlation). Because the two types of G × E have very different properties, separating their effect would enable a clearer understanding of stability. The aim of this paper was to demonstrate two methods which seek to achieve this in reaction norm models. Reaction norm models were fit to yield data from a multi-environment trial in Barley (Hordeum vulgare), with the adjusted mean yield from each environment used as the environmental covariable. Stability estimated from factor-analytic models, which can disentangle the two types of G × E and estimate stability based on rank-type G × E, was used for comparison. Adjusting the reaction norm slope to account for scale-type G × E using a genetic regression more than tripled the correlation with factor-analytic estimates of stability (0.24-0.26 to 0.80-0.85), indicating that it removed variation in the reaction norm slope that originated from scale-type G × E. A standardisation procedure had a more modest increase (055-0.59) but could be useful when curvilinear reaction norms are required. Analyses which use reaction norms to explore the stability of genotypes could gain additional insight into the mechanisms of stability by applying the methods outlined in this study.
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Affiliation(s)
- Dominic L Waters
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.
| | - Julius H J van der Werf
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
| | - Hannah Robinson
- InterGrain Pty Ltd, Perth, WA, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Sam A Clark
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
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Gesesse CA, Nigir B, de Sousa K, Gianfranceschi L, Gallo GR, Poland J, Kidane YG, Abate Desta E, Fadda C, Pè ME, Dell’Acqua M. Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers' traditional knowledge. Proc Natl Acad Sci U S A 2023; 120:e2205774119. [PMID: 36972461 PMCID: PMC10083613 DOI: 10.1073/pnas.2205774119] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 11/14/2022] [Indexed: 03/29/2023] Open
Abstract
In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers' appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker-trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers' traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.
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Affiliation(s)
- Cherinet Alem Gesesse
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
- Amhara Regional Agricultural Research Institute, Bahir Dar6000, Ethiopia
| | - Bogale Nigir
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
| | - Kauê de Sousa
- Digital Inclusion, Bioversity International, Parc Scientifique Agropolis II, Montpellier34397, France
- Department of Agricultural Sciences, Inland Norway University of Applied Sciences, Hamar2322, Norway
| | | | | | - Jesse Poland
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal23955-6900, Saudi Arabia
| | - Yosef Gebrehawaryat Kidane
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
- Biodiversity for Food and Agriculture, Bioversity International, Addis Ababa1000, Ethiopia; and
| | - Ermias Abate Desta
- Amhara Regional Agricultural Research Institute, Bahir Dar6000, Ethiopia
| | - Carlo Fadda
- Biodiversity for Food and Agriculture, Bioversity International, Nairobi00621, Kenya
| | - Mario Enrico Pè
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
| | - Matteo Dell’Acqua
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
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Manthena V, Jarquín D, Howard R. Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification. Front Genet 2023; 13:1032691. [PMID: 37065625 PMCID: PMC10090538 DOI: 10.3389/fgene.2022.1032691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/22/2022] [Indexed: 04/18/2023] Open
Abstract
Modern plant breeding programs collect several data types such as weather, images, and secondary or associated traits besides the main trait (e.g., grain yield). Genomic data is high-dimensional and often over-crowds smaller data types when naively combined to explain the response variable. There is a need to develop methods able to effectively combine different data types of differing sizes to improve predictions. Additionally, in the face of changing climate conditions, there is a need to develop methods able to effectively combine weather information with genotype data to predict the performance of lines better. In this work, we develop a novel three-stage classifier to predict multi-class traits by combining three data types-genomic, weather, and secondary trait. The method addressed various challenges in this problem, such as confounding, differing sizes of data types, and threshold optimization. The method was examined in different settings, including binary and multi-class responses, various penalization schemes, and class balances. Then, our method was compared to standard machine learning methods such as random forests and support vector machines using various classification accuracy metrics and using model size to evaluate the sparsity of the model. The results showed that our method performed similarly to or better than machine learning methods across various settings. More importantly, the classifiers obtained were highly sparse, allowing for a straightforward interpretation of relationships between the response and the selected predictors.
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Affiliation(s)
- Vamsi Manthena
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Diego Jarquín
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Reka Howard
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, United States
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38
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Montesinos-López OA, Herr AW, Crossa J, Carter AH. Genomics combined with UAS data enhances prediction of grain yield in winter wheat. Front Genet 2023; 14:1124218. [PMID: 37065497 PMCID: PMC10090417 DOI: 10.3389/fgene.2023.1124218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/17/2023] [Indexed: 03/31/2023] Open
Abstract
With the human population continuing to increase worldwide, there is pressure to employ novel technologies to increase genetic gain in plant breeding programs that contribute to nutrition and food security. Genomic selection (GS) has the potential to increase genetic gain because it can accelerate the breeding cycle, increase the accuracy of estimated breeding values, and improve selection accuracy. However, with recent advances in high throughput phenotyping in plant breeding programs, the opportunity to integrate genomic and phenotypic data to increase prediction accuracy is present. In this paper, we applied GS to winter wheat data integrating two types of inputs: genomic and phenotypic. We observed the best accuracy of grain yield when combining both genomic and phenotypic inputs, while only using genomic information fared poorly. In general, the predictions with only phenotypic information were very competitive to using both sources of information, and in many cases using only phenotypic information provided the best accuracy. Our results are encouraging because it is clear we can enhance the prediction accuracy of GS by integrating high quality phenotypic inputs in the models.
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Affiliation(s)
| | - Andrew W. Herr
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Edo. de México, México
- Colegio de Postgraduados, Montecillos, Edo. de México, México
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
- *Correspondence: Arron H. Carter,
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Bocianowski J, Tomkowiak A, Bocianowska M, Sobiech A. The Use of DArTseq Technology to Identify Markers Related to the Heterosis Effects in Selected Traits in Maize. Curr Issues Mol Biol 2023; 45:2644-2660. [PMID: 37185697 PMCID: PMC10136425 DOI: 10.3390/cimb45040173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/18/2023] [Accepted: 03/21/2023] [Indexed: 05/17/2023] Open
Abstract
Spectacular scientific advances in the area of molecular biology and the development of modern biotechnological tools have had a significant impact on the development of maize heterosis breeding. One technology based on next-generation sequencing is DArTseq. The plant material used for the research consisted of 13 hybrids resulting from the crossing of inbred maize lines. A two-year field experiment was established at two Polish breeding stations: Smolice and Łagiewniki. Nine quantitative traits were observed: cob length, cob diameter, core length, core diameter, number of rows of grain, number of grains in a row, mass of grain from the cob, weight of one thousand grains, and yield. The isolated DNA was subjected to DArTseq genotyping. Association mapping was performed using a method based on the mixed linear model. A total of 81602 molecular markers (28571 SNPs and 53031 SilicoDArTs) were obtained as a result of next-generation sequencing. Out of 81602, 15409 (13850 SNPs and 1559 SilicoDArTs) were selected for association analysis. The 105 molecular markers (8 SNPs and 97 SilicoDArTs) were associated with the heterosis effect of at least one trait in at least one environment. A total of 186 effects were observed. The number of statistically significant relationships between the molecular marker and heterosis effect varied from 8 (for cob length) and 9 (for yield) to 42 (for the number of rows of grain). Of particular note were three markers (2490222, 2548691 and 7058267), which were significant in 17, 8 and 6 cases, respectively. Two of them (2490222 and 7058267) were associated with the heterosis effects of yield in three of the four environments.
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Affiliation(s)
- Jan Bocianowski
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznan, Poland
| | - Agnieszka Tomkowiak
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznan, Poland
| | - Marianna Bocianowska
- Faculty of Chemical Technology, Poznań University of Technology, Piotrowo 3A, 60-965 Poznan, Poland
| | - Aleksandra Sobiech
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznan, Poland
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Fan J, Li Y, Yu S, Gou W, Guo X, Zhao C. Application of Internet of Things to Agriculture-The LQ-FieldPheno Platform: A High-Throughput Platform for Obtaining Crop Phenotypes in Field. RESEARCH (WASHINGTON, D.C.) 2023; 6:0059. [PMID: 36951796 PMCID: PMC10027232 DOI: 10.34133/research.0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/07/2023] [Indexed: 01/22/2023]
Abstract
The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation. However, high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology. In view of this, this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno, which was developed by China National Agricultural Information Engineering Technology Research Centre. The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform, which introduces the Internet of Things (IoT) into agricultural breeding. The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment, including an automatic control system, upward field track, intelligent navigation vehicle, and environmental sensors. Furthermore, it combines an RGB camera, a 6-band multispectral camera, a thermal infrared camera, a 3-dimensional laser radar, and a deep camera. Special software is developed to control motions and sensors and to design run lines. Using wireless sensor networks and mobile communication wireless networks of IoT, the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput, automatic, and high time sequence. Moreover, the LQ-FieldPheno has the characteristics of multiple data acquisition, vital timeliness, remarkable expansibility, high-cost performance, and flexible customization. The LQ-FieldPheno has been operated in the 2020 maize growing season, and the collected point cloud data are used to estimate the maize plant height. Compared with the traditional crop phenotypic measurement technology, the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters. The proposed platform could contribute to the research of crop phenotype, remote sensing, agronomy, and related disciplines.
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Affiliation(s)
- Jiangchuan Fan
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Yinglun Li
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Shuan Yu
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Wenbo Gou
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Xinyu Guo
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Chunjiang Zhao
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
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Cooper M, Powell O, Gho C, Tang T, Messina C. Extending the breeder's equation to take aim at the target population of environments. FRONTIERS IN PLANT SCIENCE 2023; 14:1129591. [PMID: 36895882 PMCID: PMC9990092 DOI: 10.3389/fpls.2023.1129591] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realize these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realized trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have focused on improving prediction accuracy within MET training data sets, with less attention to quantifying the structure of the TPE and the MET-TPE relationship and their potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We extend the breeder's equation and use an example to demonstrate the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Owen Powell
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Carla Gho
- School of Agriculture & Food Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Tom Tang
- Corteva Agriscience, Johnston, IA, United States
| | - Carlos Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
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Nguyen VH, Morantte RIZ, Lopena V, Verdeprado H, Murori R, Ndayiragije A, Katiyar SK, Islam MR, Juma RU, Flandez-Galvez H, Glaszmann JC, Cobb JN, Bartholomé J. Multi-environment Genomic Selection in Rice Elite Breeding Lines. RICE (NEW YORK, N.Y.) 2023; 16:7. [PMID: 36752880 PMCID: PMC9908796 DOI: 10.1186/s12284-023-00623-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. RESULTS The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia's and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25-0.88 for plant height, and - 0.29-0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. CONCLUSION Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.
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Affiliation(s)
- Van Hieu Nguyen
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Rose Imee Zhella Morantte
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Vitaliano Lopena
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Holden Verdeprado
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Rosemary Murori
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Alexis Ndayiragije
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Sanjay Kumar Katiyar
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Md Rafiqul Islam
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Roselyne Uside Juma
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Hayde Flandez-Galvez
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Jean-Christophe Glaszmann
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Joshua N Cobb
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- RiceTec. Inc, PO Box 1305, Alvin, TX, 77512, USA
| | - Jérôme Bartholomé
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
- CIRAD, UMR AGAP Institut, Cali, Colombia.
- Alliance Bioversity-CIAT, Cali, Colombia.
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43
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Guo T, Li X. Machine learning for predicting phenotype from genotype and environment. Curr Opin Biotechnol 2023; 79:102853. [PMID: 36463837 DOI: 10.1016/j.copbio.2022.102853] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022]
Abstract
Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.
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Affiliation(s)
- Tingting Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA; Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
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Jubair S, Domaratzki M. Crop genomic selection with deep learning and environmental data: A survey. Front Artif Intell 2023; 5:1040295. [PMID: 36703955 PMCID: PMC9871498 DOI: 10.3389/frai.2022.1040295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.
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Affiliation(s)
- Sheikh Jubair
- Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada
| | - Mike Domaratzki
- Department of Computer Science, University of Western Ontario, London, ON, Canada
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45
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Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, Queensland 4072, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
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Costa-Neto G, Crespo-Herrera L, Fradgley N, Gardner K, Bentley AR, Dreisigacker S, Fritsche-Neto R, Montesinos-López OA, Crossa J. Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data. G3 (BETHESDA, MD.) 2022; 13:6861853. [PMID: 36454213 PMCID: PMC9911085 DOI: 10.1093/g3journal/jkac313] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 12/03/2022]
Abstract
Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G × E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.
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Affiliation(s)
- Germano Costa-Neto
- Institute for Genomics Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Nick Fradgley
- NIAB, 93 Lawrence Weaver Road, Cambridge CB3 0LE, UK
| | - Keith Gardner
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | | | - Osval A Montesinos-López
- Corresponding authors: Facultad de Telematica, Universidad de Colima, Mexico. ; and International Maize and Wheat Improvement Center (CIMMYT) and Colegio de Post-Graduados, Mexico.
| | - Jose Crossa
- Corresponding authors: Facultad de Telematica, Universidad de Colima, Mexico. ; and International Maize and Wheat Improvement Center (CIMMYT) and Colegio de Post-Graduados, Mexico.
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Wang K, Yang B, Li Q, Liu S. Systematic Evaluation of Genomic Prediction Algorithms for Genomic Prediction and Breeding of Aquatic Animals. Genes (Basel) 2022; 13:genes13122247. [PMID: 36553514 PMCID: PMC9778314 DOI: 10.3390/genes13122247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 12/04/2022] Open
Abstract
The extensive use of genomic selection (GS) in livestock and crops has led to a series of genomic-prediction (GP) algorithms despite the lack of a single algorithm that can suit all the species and traits. A systematic evaluation of available GP algorithms is thus necessary to identify the optimal GP algorithm for selective breeding in aquaculture species. In this study, a systematic comparison of ten GP algorithms, including both traditional and machine-learning algorithms, was conducted using publicly available genotype and phenotype data of eight traits, including weight and disease resistance traits, from five aquaculture species. The study aimed to provide insights into the optimal algorithm for GP in aquatic animals. Notably, no algorithm showed the best performance in all traits. However, reproducing kernel Hilbert space (RKHS) and support-vector machine (SVM) algorithms achieved relatively high prediction accuracies in most of the tested traits. Bayes A and random forest (RF) better prevented noise interference in the phenotypic data compared to the other algorithms. The prediction performances of GP algorithms in the Crassostrea gigas dataset were improved by using a genome-wide association study (GWAS) to select subsets of significant SNPs. An R package, "ASGS," which integrates the commonly used traditional and machine-learning algorithms for efficiently finding the optimal algorithm, was developed to assist the application of genomic selection breeding of aquaculture species. This work provides valuable information and a tool for optimizing algorithms for GP, aiding genetic breeding in aquaculture species.
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Affiliation(s)
- Kuiqin Wang
- Key Laboratory of Mariculture, Ministry of Education, College of Fisheries, Ocean University of China, Qingdao 266003, China
| | - Ben Yang
- Key Laboratory of Mariculture, Ministry of Education, College of Fisheries, Ocean University of China, Qingdao 266003, China
| | - Qi Li
- Key Laboratory of Mariculture, Ministry of Education, College of Fisheries, Ocean University of China, Qingdao 266003, China
- Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Shikai Liu
- Key Laboratory of Mariculture, Ministry of Education, College of Fisheries, Ocean University of China, Qingdao 266003, China
- Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
- Correspondence: ; Tel.: +86-0532-82032595
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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49
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Zuffo LT, DeLima RO, Lübberstedt T. Combining datasets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5460-5473. [PMID: 35608947 PMCID: PMC9467658 DOI: 10.1093/jxb/erac236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 06/06/2022] [Indexed: 05/13/2023]
Abstract
The identification of genomic regions associated with root traits and the genomic prediction of untested genotypes can increase the rate of genetic gain in maize breeding programs targeting roots traits. Here, we combined two maize association panels with different genetic backgrounds to identify single nucleotide polymorphisms (SNPs) associated with root traits, and used a genome-wide association study (GWAS) and to assess the potential of genomic prediction for these traits in maize. For this, we evaluated 377 lines from the Ames panel and 302 from the Backcrossed Germplasm Enhancement of Maize (BGEM) panel in a combined panel of 679 lines. The lines were genotyped with 232 460 SNPs, and four root traits were collected from 14-day-old seedlings. We identified 30 SNPs significantly associated with root traits in the combined panel, whereas only two and six SNPs were detected in the Ames and BGEM panels, respectively. Those 38 SNPs were in linkage disequilibrium with 35 candidate genes. In addition, we found higher prediction accuracy in the combined panel than in the Ames or BGEM panel. We conclude that combining association panels appears to be a useful strategy to identify candidate genes associated with root traits in maize and improve the efficiency of genomic prediction.
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Affiliation(s)
- Leandro Tonello Zuffo
- Corteva Agriscience, Rio Verde, GO, Brazil
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa, MG, Brazil
- Department of Agronomy, Iowa State University, Ames, IA, USA
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50
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Messina CD, Rotundo J, Hammer GL, Gho C, Reyes A, Fang Y, van Oosterom E, Borras L, Cooper M. Radiation use efficiency increased over a century of maize (Zea mays L.) breeding in the US corn belt. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5503-5513. [PMID: 35640591 DOI: 10.1093/jxb/erac212] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 05/23/2022] [Indexed: 05/26/2023]
Abstract
In the absence of stress, crop growth depends on the amount of light intercepted by the canopy and the conversion efficiency [radiation use efficiency (RUE)]. This study tested the hypothesis that long-term genetic gain for grain yield was partly due to improved RUE. The hypothesis was tested using 30 elite maize hybrids commercialized in the US corn belt between 1930 and 2017. Crops grown under irrigation showed that pre-flowering crop growth increased at a rate of 0.11 g m-2 year-1, while light interception remained constant. Therefore, RUE increased at a rate of 0.0049 g MJ-1 year-1, translating into an average of 3 g m-2 year-1 of grain yield over 100 years of maize breeding. Considering that the harvest index has not changed for crops grown at optimal density for the hybrid, the cumulative RUE increase over the history of commercial maize breeding in the USA can account for ~32% of the documented yield trend for maize grown in the central US corn belt. The remaining RUE gap between this study and theoretical maximum values suggests that a yield improvement of a similar magnitude could be achieved by further increasing RUE.
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Affiliation(s)
- Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Jose Rotundo
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Graeme L Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carla Gho
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Andres Reyes
- Corteva Agriscience, 18369 County Rd 96, Woodland, CA, USA
| | - Yinan Fang
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Erik van Oosterom
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Lucas Borras
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
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