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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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2
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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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3
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Jighly A, Weeks A, Christy B, O'Leary GJ, Kant S, Aggarwal R, Hessel D, Forrest KL, Technow F, Tibbits JFG, Totir R, Spangenberg GC, Hayden MJ, Munkvold J, Daetwyler HD. Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4415-4426. [PMID: 37177829 DOI: 10.1093/jxb/erad162] [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: 12/06/2021] [Accepted: 05/12/2023] [Indexed: 05/15/2023]
Abstract
Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.
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Affiliation(s)
- Abdulqader Jighly
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Anna Weeks
- Agriculture Victoria, Rutherglen Centre, Rutherglen, VIC 3685, Australia
| | - Brendan Christy
- Agriculture Victoria, Rutherglen Centre, Rutherglen, VIC 3685, Australia
| | - Garry J O'Leary
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia
- Centre for Agricultural Innovation, The University of Melbourne, Parkville, VIC 3010Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | | | | | - Kerrie L Forrest
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | | | - Josquin F G Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | | | - German C Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Matthew J Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | | | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
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4
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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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5
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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6
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Genome-Enabled Prediction Methods Based on Machine Learning. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:189-218. [PMID: 35451777 DOI: 10.1007/978-1-0716-2205-6_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based prediction (GWP), many ML studies have been carried out. This chapter provides a description of main semiparametric and nonparametric algorithms used in GWP in animals and plants. Thirty-four ML comparative studies conducted in the last decade were used to develop a meta-analysis through a Thurstonian model, to evaluate algorithms with the best predictive qualities. It was found that some kernel, Bayesian, and ensemble methods displayed greater robustness and predictive ability. However, the type of study and data distribution must be considered in order to choose the most appropriate model for a given problem.
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7
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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8
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Onogi A. Integration of Crop Growth Models and Genomic Prediction. Methods Mol Biol 2022; 2467:359-396. [PMID: 35451783 DOI: 10.1007/978-1-0716-2205-6_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.
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Affiliation(s)
- Akio Onogi
- Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, Otsu, Shiga, Japan.
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9
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Toda Y, Kaga A, Kajiya-Kanegae H, Hattori T, Yamaoka S, Okamoto M, Tsujimoto H, Iwata H. Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters. THE PLANT GENOME 2021; 14:e20157. [PMID: 34595846 DOI: 10.1002/tpg2.20157] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 08/19/2021] [Indexed: 05/12/2023]
Abstract
The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because the inclusion of remote sensing data in multitrait models improved prediction accuracies of target traits. However, the present multitrait GP model cannot incorporate high-dimensional remote sensing data due to the difficulty in the estimation of a covariance matrix among the traits, which leads to failure in improving its prediction accuracy. In this study, we focused on growth models to express growth patterns using remote sensing data with a few parameters and investigated whether a multitrait GP model using these parameters could derive better prediction accuracy of soybean [Glycine max (L.) Merr.] biomass. A total of 198 genotypes of soybean germplasm were cultivated in experimental fields, and longitudinal changes of their canopy height and area were measured continuously via remote sensing with an unmanned aerial vehicle. Growth parameters were estimated by applying simple growth models and incorporated into the GP of biomass. By evaluating heritability and correlation, we showed that the estimated growth parameters appropriately represented the observed growth curves. Also, the use of these growth parameters in the multitrait GP model contributed to successful biomass prediction. We conclude that the growth models could describe the genetic variation of soybean growth curves based on several growth parameters. These dimension-reduction growth models will be indispensable for extracting useful information from remote sensing data and using this data in GP and plant breeding.
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Affiliation(s)
- Yusuke Toda
- Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Akito Kaga
- Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2 Kannondai, Tsukuba, Ibaraki, 305-8518, Japan
| | - Hiromi Kajiya-Kanegae
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, Kintetsu Kasumigaseki Building, 3-5-1 Kasumigaseki, Chiyoda, Tokyo, 100-0013, Japan
| | - Tomohiro Hattori
- Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Shuhei Yamaoka
- Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Masanori Okamoto
- Center for Bioscience Research and Education, Utsunomiya Univ., 350 Minecho, Utsunomiya, Tochigi, 321-8505, Japan
| | - Hisashi Tsujimoto
- Arid Land Research Center, Tottori Univ., 1390 Hamasaka, Tottori, 680-0001, Japan
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
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Powell OM, Voss-Fels KP, Jordan DR, Hammer G, Cooper M. Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction. FRONTIERS IN PLANT SCIENCE 2021; 12:663565. [PMID: 34149761 PMCID: PMC8211918 DOI: 10.3389/fpls.2021.663565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/13/2021] [Indexed: 05/26/2023]
Abstract
Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.
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Affiliation(s)
- Owen M. Powell
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Kai P. Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
| | - David R. Jordan
- Queensland Alliance for Agriculture and Food Innovation, Hermitage Research Facility, The University of Queensland, Warwick, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
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11
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Costa-Neto G, Crossa J, Fritsche-Neto R. Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize. FRONTIERS IN PLANT SCIENCE 2021; 12:717552. [PMID: 34691099 PMCID: PMC8529011 DOI: 10.3389/fpls.2021.717552] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/03/2021] [Indexed: 05/21/2023]
Abstract
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.
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Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States
- *Correspondence: Germano Costa-Neto
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
- Colegio de Posgraduado, Mexico City, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Breeding Analytics and Data Management Unit, International Rice Research Institute (IRRI), Los Baños, Philippines
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