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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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2
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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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3
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Montesinos-López OA, Crespo-Herrera L, Pierre CS, Cano-Paez B, Huerta-Prado GI, Mosqueda-González BA, Ramos-Pulido S, Gerard G, Alnowibet K, Fritsche-Neto R, Montesinos-López A, Crossa J. Feature engineering of environmental covariates improves plant genomic-enabled prediction. FRONTIERS IN PLANT SCIENCE 2024; 15:1349569. [PMID: 38812738 PMCID: PMC11135473 DOI: 10.3389/fpls.2024.1349569] [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: 12/04/2023] [Accepted: 04/11/2024] [Indexed: 05/31/2024]
Abstract
Introduction Because Genomic selection (GS) is a predictive methodology, it needs to guarantee high-prediction accuracies for practical implementations. However, since many factors affect the prediction performance of this methodology, its practical implementation still needs to be improved in many breeding programs. For this reason, many strategies have been explored to improve the prediction performance of this methodology. Methods When environmental covariates are incorporated as inputs in the genomic prediction models, this information only sometimes helps increase prediction performance. For this reason, this investigation explores the use of feature engineering on the environmental covariates to enhance the prediction performance of genomic prediction models. Results and discussion We found that across data sets, feature engineering helps reduce prediction error regarding only the inclusion of the environmental covariates without feature engineering by 761.625% across predictors. These results are very promising regarding the potential of feature engineering to enhance prediction accuracy. However, since a significant gain in prediction accuracy was observed in only some data sets, further research is required to guarantee a robust feature engineering strategy to incorporate the environmental covariates.
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Affiliation(s)
| | | | - Carolina Saint Pierre
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Edo. de Mexico, Mexico
| | - Bernabe Cano-Paez
- Facultad de Ciencias, Universidad Nacioanl Autónoma de México (UNAM), México City, Mexico
| | | | | | - Sofia Ramos-Pulido
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - Guillermo Gerard
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Edo. de Mexico, Mexico
| | - Khalid Alnowibet
- Department of Statistics and Operations Research, King Saud University, Riyah, Saudi Arabia
| | | | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Edo. de Mexico, Mexico
- Louisiana State University, Baton Rouge, LA, United States
- Distinguished Scientist Fellowship Program, King Saud University, Riyah, Saudi Arabia
- Instituto de Socieconomia, Estadistica e Informatica, Colegio de Postgraduados, Montecillos, Edo. de México, Texcoco, Mexico
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4
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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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5
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Montesinos-López OA, Crespo-Herrera L, Saint Pierre C, Bentley AR, de la Rosa-Santamaria R, Ascencio-Laguna JA, Agbona A, Gerard GS, Montesinos-López A, Crossa J. Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy? Front Genet 2023; 14:1209275. [PMID: 37554404 PMCID: PMC10405933 DOI: 10.3389/fgene.2023.1209275] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/03/2023] [Indexed: 08/10/2023] Open
Abstract
Genomic selection (GS) is transforming plant and animal breeding, but its practical implementation for complex traits and multi-environmental trials remains challenging. To address this issue, this study investigates the integration of environmental information with genotypic information in GS. The study proposes the use of two feature selection methods (Pearson's correlation and Boruta) for the integration of environmental information. Results indicate that the simple incorporation of environmental covariates may increase or decrease prediction accuracy depending on the case. However, optimal incorporation of environmental covariates using feature selection significantly improves prediction accuracy in four out of six datasets between 14.25% and 218.71% under a leave one environment out cross validation scenario in terms of Normalized Root Mean Squared Error, but not relevant gain was observed in terms of Pearson´s correlation. In two datasets where environmental covariates are unrelated to the response variable, feature selection is unable to enhance prediction accuracy. Therefore, the study provides empirical evidence supporting the use of feature selection to improve the prediction power of GS.
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Affiliation(s)
| | | | | | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), El Battan, Mexico
| | | | | | - Afolabi Agbona
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
- Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX, United States
| | - Guillermo S. Gerard
- International Maize and Wheat Improvement Center (CIMMYT), El Battan, Mexico
| | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, JA, Mexico
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), El Battan, Mexico
- Colegio de Postgraduados, Campus Montecillos, Montecillos, Mexico
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Montesinos-López OA, Bentley AR, Pierre CS, Crespo-Herrera L, Rebollar-Ruellas L, Valladares-Celis PE, Lillemo M, Montesinos-López A, Crossa J. Efficacy of plant breeding using genomic information. THE PLANT GENOME 2023:e20346. [PMID: 37139645 DOI: 10.1002/tpg2.20346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 04/05/2023] [Accepted: 04/07/2023] [Indexed: 05/05/2023]
Abstract
Genomic selection (GS) proposed by Meuwissen et al. more than 20 years ago, is revolutionizing plant and animal breeding. Although GS has been widely accepted and applied to plant and animal breeding, there are many factors affecting its efficacy. We studied 14 real datasets to respond to the practical question of whether the accuracy of genomic prediction increases when considering genomic as compared with not using genomic. We found across traits, environments, datasets, and metrics, that the average gain in prediction accuracy when genomic information is considered was 26.31%, while only in terms of Pearson's correlation the gain was of 46.1%, while only in terms of normalized root mean squared error the gain was of 6.6%. If the quality of the makers and relatedness of the individuals increase, major gains in prediction accuracy can be obtained, but if these two factors decrease, a lower increase is possible. Finally, our findings reinforce genomic is vital for improving the prediction accuracy and, therefore, the realized genetic gain in genomic assisted plant breeding programs.
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Affiliation(s)
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Estado de México, México
| | | | | | | | | | - Morten Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences, Ås, Norway
| | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Estado de México, México
- Colegio de Postgraduados, Montecillos, Estado de México, México
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7
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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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Filho CCF, Andrade MHML, Nunes JAR, Jarquin DH, Rios EF. Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics. THE PLANT GENOME 2023:e20306. [PMID: 36815221 DOI: 10.1002/tpg2.20306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/17/2022] [Indexed: 06/18/2023]
Abstract
Breeding for dry matter yield and persistence in alfalfa (Medicago sativa L.) can take several years as these traits must be evaluated under multiple harvests. Therefore, genotype-by-harvest interaction should be incorporated into genomic prediction models to explore genotypes' adaptability and stability. In this study, we investigated how enviromics could help to predict the genotypic performance under multiharvest alfalfa breeding trials by evaluating 177 families across 11 harvests under four cross-validation scenarios. All scenarios were analyzed using six models in a Bayesian mixed model framework. Our results demonstrate that models accounting to the enviromics information led to an increase of genetic variance and a decrease in the error variance, indicating better biological explanation when the enviromic information was incorporated. Furthermore, models that accounted for enviromic data led to higher predictive ability (PA) in a reduced number of harvests used in the training data set. The best enviromic models (M2 and M3) outperformed the base model (GBLUP model-M0) for predicting adaptability and persistence across all cross-validation scenarios. Incorporating environmental covariates also provided higher PA for persistence compared with the base model, as predictions increased from 0 to 0.16, 0.20, 0.56, and 0.46 for CV00, CV1, CV0, and CV2. The results also demonstrate that GBLUP without enviromics term has low power to predict persistence, thus the adoption of enviromics is a cheap and efficient alternative to increase accuracy and biological meaning.
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Affiliation(s)
| | | | - José Airton Rodrigues Nunes
- Departamento de Biologia, Instituto de Ciências Naturais, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil
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Meng G, Rasmussen SK, Christensen CSL, Fan W, Torp AM. Molecular breeding of barley for quality traits and resilience to climate change. Front Genet 2023; 13:1039996. [PMID: 36685930 PMCID: PMC9851277 DOI: 10.3389/fgene.2022.1039996] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
Barley grains are a rich source of compounds, such as resistant starch, beta-glucans and anthocyanins, that can be explored in order to develop various products to support human health, while lignocellulose in straw can be optimised for feed in husbandry, bioconversion into bioethanol or as a starting material for new compounds. Existing natural variations of these compounds can be used to breed improved cultivars or integrated with a large number of mutant lines. The technical demands can be in opposition depending on barley's end use as feed or food or as a source of biofuel. For example beta-glucans are beneficial in human diets but can lead to issues in brewing and poultry feed. Barley breeders have taken action to integrate new technologies, such as induced mutations, transgenics, marker-assisted selection, genomic selection, site-directed mutagenesis and lastly machine learning, in order to improve quality traits. Although only a limited number of cultivars with new quality traits have so far reached the market, research has provided valuable knowledge and inspiration for future design and a combination of methodologies to achieve the desired traits. The changes in climate is expected to affect the quality of the harvested grain and it is already a challenge to mitigate the unpredictable seasonal and annual variations in temperature and precipitation under elevated [CO2] by breeding. This paper presents the mutants and encoded proteins, with a particular focus on anthocyanins and lignocellulose, that have been identified and characterised in detail and can provide inspiration for continued breeding to achieve desired grain and straw qualities.
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Affiliation(s)
- Geng Meng
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark,College of Horticulture, Henan Agricultural University, Zhengzhou, China
| | - Søren K. Rasmussen
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark,*Correspondence: Søren K. Rasmussen,
| | | | - Weiyao Fan
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark
| | - Anna Maria Torp
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark
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10
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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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11
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Murad Leite Andrade MH, Acharya JP, Benevenuto J, de Bem Oliveira I, Lopez Y, Munoz P, Resende MFR, Rios EF. Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks. THE PLANT GENOME 2022; 15:e20235. [PMID: 35818699 DOI: 10.1002/tpg2.20235] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 04/30/2022] [Indexed: 06/15/2023]
Abstract
Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high-quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full-sib and 35 half-sib families was bulk-genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single-nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks.
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Affiliation(s)
| | - Janam P Acharya
- Agronomy Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Juliana Benevenuto
- Horticultural Sciences Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | | | - Yolanda Lopez
- Agronomy Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Patricio Munoz
- Horticultural Sciences Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Marcio F R Resende
- Horticultural Sciences Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Esteban F Rios
- Agronomy Dep., Univ. of Florida, Gainesville, FL, 32611, USA
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