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Mascher M, Jayakodi M, Shim H, Stein N. Promises and challenges of crop translational genomics. Nature 2024:10.1038/s41586-024-07713-5. [PMID: 39313530 DOI: 10.1038/s41586-024-07713-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/13/2024] [Indexed: 09/25/2024]
Abstract
Crop translational genomics applies breeding techniques based on genomic datasets to improve crops. Technological breakthroughs in the past ten years have made it possible to sequence the genomes of increasing numbers of crop varieties and have assisted in the genetic dissection of crop performance. However, translating research findings to breeding applications remains challenging. Here we review recent progress and future prospects for crop translational genomics in bringing results from the laboratory to the field. Genetic mapping, genomic selection and sequence-assisted characterization and deployment of plant genetic resources utilize rapid genotyping of large populations. These approaches have all had an impact on breeding for qualitative traits, where single genes with large phenotypic effects exert their influence. Characterization of the complex genetic architectures that underlie quantitative traits such as yield and flowering time, especially in newly domesticated crops, will require further basic research, including research into regulation and interactions of genes and the integration of genomic approaches and high-throughput phenotyping, before targeted interventions can be designed. Future priorities for translation include supporting genomics-assisted breeding in low-income countries and adaptation of crops to changing environments.
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Affiliation(s)
- Martin Mascher
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
| | - Murukarthick Jayakodi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Hyeonah Shim
- Department of Agriculture, Forestry and Bioresources, Plant Genomics and Breeding Institute, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, Seoul, Korea
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
- Martin Luther University Halle-Wittenberg, Halle, Germany.
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2
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Atanda SA, Bandillo N. Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program. PLANT METHODS 2024; 20:133. [PMID: 39218896 PMCID: PMC11367796 DOI: 10.1186/s13007-024-01258-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 08/05/2024] [Indexed: 09/04/2024]
Abstract
The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.
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Affiliation(s)
- Sikiru Adeniyi Atanda
- Agricultural Data Analytics Unit, North Dakota State University, Fargo, ND, 58105-6050, USA.
| | - Nonoy Bandillo
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108-6050, USA.
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3
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Montesinos-López OA, Mosqueda-González BA, Salinas-Ruiz J, Montesinos-López A, Crossa J. Sparse multi-trait genomic prediction under balanced incomplete block design. THE PLANT GENOME 2023:e20305. [PMID: 36815225 DOI: 10.1002/tpg2.20305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/05/2023] [Indexed: 06/18/2023]
Abstract
Sparse testing is essential to increase the efficiency of the genomic selection methodology, as the same efficiency (in this case prediction power) can be obtained while using less genotypes evaluated in the fields. For this reason, it is important to evaluate the existing methods for performing the allocation of lines to environments. With this goal, four methods (M1-M4) to allocate lines to environments were evaluated under the context of a multi-trait genomic prediction problem: M1 denotes the allocation of a fraction (subset) of lines in all locations, M2 denotes the allocation of a fraction of lines with some shared lines in locations but not arranged based on the balanced incomplete block design (BIBD) principle, M3 denotes the random allocation of a subset of lines to locations, and M4 denotes the allocation of a subset of lines to locations using the BIBD principle. The evaluation was done using seven real multi-environment data sets common in plant breeding programs. We found that the best method was M4 and the worst was M1, while no important differences were found between M3 and M4. We concluded that M4 and M3 are efficient in the context of sparse testing for multi-trait prediction.
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Affiliation(s)
| | | | | | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Edo. de México, Mexico
- Colegio de Postgraduados, Montecillos, Mexico
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4
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Atanda SA, Steffes J, Lan Y, Al Bari MA, Kim JH, Morales M, Johnson JP, Saludares R, Worral H, Piche L, Ross A, Grusak M, Coyne C, McGee R, Rao J, Bandillo N. Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea. THE PLANT GENOME 2022; 15:e20260. [PMID: 36193571 DOI: 10.1002/tpg2.20260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 08/10/2022] [Indexed: 06/16/2023]
Abstract
Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection. The proposed strategy departed from the previous reports that weak genetic correlation is a limitation to the advantage of MT-GS over UNI genomic selection, which was evident in the partially balanced phenotyping-enabled MT-GS. Our results point to heritability and genetic correlation between traits as possible metrics to optimize and further improve the estimation of model parameters, and ultimately, prediction performance. Overall, our study offers a new approach to optimize the prediction performance using the MT-GS and further highlight strategy to maximize the efficiency of GS in a plant breeding program. The sparse-testing-aided MT-GS proposed in this study can be further extended to multi-environment, multi-trait GS to improve prediction performance and further reduce the cost of phenotyping and time-consuming data collection process.
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Affiliation(s)
| | - Jenna Steffes
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Yang Lan
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Md Abdullah Al Bari
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Jeong-Hwa Kim
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Mario Morales
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Josephine P Johnson
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Rica Saludares
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Hannah Worral
- North Central Research Extension Center, NDSU, 5400 Hwy. 83, South Minot, ND, 58701, USA
| | - Lisa Piche
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Andrew Ross
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Mike Grusak
- Edward T. Schafer Agricultural Research Center, USDA-ARS, 1616 Albrecht Blvd. N, Fargo, ND, 58102-2765, USA
| | - Clarice Coyne
- USDA-ARS Plant Germplasm Introduction and Testing, Washington State Univ., Pullman, WA, 99164, USA
| | - Rebecca McGee
- USDA-ARS, Grain Legume Genetics and Physiology Research, Pullman, WA, 99164, USA
- Dep. of Horticulture, Washington State Univ., Pullman, WA, 99164, USA
| | - Jiajia Rao
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Nonoy Bandillo
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
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5
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Williamson HF, Leonelli S. Accelerating agriculture: Data-intensive plant breeding and the use of genetic gain as an indicator for agricultural research and development. STUDIES IN HISTORY AND PHILOSOPHY OF SCIENCE 2022; 95:167-176. [PMID: 36058040 DOI: 10.1016/j.shpsa.2022.08.006] [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: 10/06/2021] [Revised: 05/03/2022] [Accepted: 08/05/2022] [Indexed: 06/15/2023]
Abstract
Accelerating the rate of genetic gain has in recent years become a key objective in plant breeding for the Global South, building on the availability of new data technologies and bridging biological interest in crop improvement with economic interest in enhancing the cost efficiency of breeding programs. This paper explains the concept of genetic gain, the conditions for its emerging status as an indicator of agricultural development and the broader implications of this move, with particular emphasis on the changing knowledge-control regimes of plant breeding, the social and political consequences for smallholder farmers and climate-adaptive agriculture. We analyse how prioritising the variables used to derive the indicator when deciding on agricultural policies affects the relationship between development goals and practice. We conclude that genetic gain should not be considered as a primary indicator of agricultural development in the absence of information on other key areas (including agrobiodiversity, seed systems and the differential impact of climate change on soil, crops and communities), as well as tools to evaluate the pros and cons of the acceleration in seed selection, management and evaluation fostered by the adoption of genetic gain as a key indicator.
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Affiliation(s)
- Hugh F Williamson
- Exeter Centre for the Study of the Life Sciences, Department of Sociology, Philosophy and Anthropology, University of Exeter, Byrne House, St. German's Road, Exeter EX4 4PJ, UK.
| | - Sabina Leonelli
- Exeter Centre for the Study of the Life Sciences, Department of Sociology, Philosophy and Anthropology, University of Exeter, Byrne House, St. German's Road, Exeter EX4 4PJ, UK
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6
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Cerioli T, Hernandez CO, Angira B, McCouch SR, Robbins KR, Famoso AN. Development and validation of an optimized marker set for genomic selection in southern U.S. rice breeding programs. THE PLANT GENOME 2022; 15:e20219. [PMID: 35611838 DOI: 10.1002/tpg2.20219] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 03/28/2022] [Indexed: 06/15/2023]
Abstract
The potential of genomic selection (GS) to increase the efficiency of breeding programs has been clearly demonstrated; however, the implementation of GS in rice (Oryza sativa L.) breeding programs has been limited. In recent years, efforts have begun to work toward implementing GS into the Louisiana State University (LSU) Agricultural Center rice breeding program. One of the first steps for successful GS implementation is to establish a suitable marker set for the target germplasm and a reliable, cost-effective genotyping platform capable of providing informative marker data with an adequate turnaround time. The objective of this study was to develop a marker set for routine GS and demonstrate its effectiveness in southern U.S. rice germplasm. The utility of the resulting marker set, the LSU500, for GS applications was demonstrated using four years of breeding data across 7,607 experimental lines and four elite biparental populations. The predictive ability of GS ranged from 0.13 to 0.78 for key traits across different market classes and yield trials. Comparisons between phenotypic selection and GS within biparental populations demonstrates similar performance of GS compared with phenotypic selection in predicting future performance. The prediction accuracies obtained with the LSU500 marker set demonstrates the utility of this marker set for cost-effective GS applications in southern U.S. rice breeding programs. The LSU500 marker set has been established through the genotyping service provider Agriplex Genomics, and in the future, it will undergo improvements to reduce the cost and increase the accuracy of GS.
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Affiliation(s)
- Tommaso Cerioli
- H. Rouse Caffey Rice Research Station, Louisiana State Univ. Agricultural Center, Rayne, LA, 70578, USA
| | - Christopher O Hernandez
- H. Rouse Caffey Rice Research Station, Louisiana State Univ. Agricultural Center, Rayne, LA, 70578, USA
| | - Brijesh Angira
- H. Rouse Caffey Rice Research Station, Louisiana State Univ. Agricultural Center, Rayne, LA, 70578, USA
| | - Susan R McCouch
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell Univ., Ithaca, NY, 14850, USA
- Cornell Institute for Digital Agriculture, Cornell Univ., Ithaca, NY, 14850, USA
| | - Kelly R Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell Univ., Ithaca, NY, 14850, USA
| | - Adam N Famoso
- H. Rouse Caffey Rice Research Station, Louisiana State Univ. Agricultural Center, Rayne, LA, 70578, USA
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7
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Atanda SA, Govindan V, Singh R, Robbins KR, Crossa J, Bentley AR. Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1939-1950. [PMID: 35348821 PMCID: PMC9205816 DOI: 10.1007/s00122-022-04085-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/16/2022] [Indexed: 06/08/2023]
Abstract
Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1-9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder's advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.
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Affiliation(s)
| | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kelly R Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, USA
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
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8
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Martins Oliveira IC, Bernardeli A, Soler Guilhen JH, Pastina MM. Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids. Methods Mol Biol 2022; 2467:543-567. [PMID: 35451790 DOI: 10.1007/978-1-0716-2205-6_20] [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
For many plant and animal species, commercial products are hybrids between individuals from different genetic groups. For allogamous plant species such as maize, the breeding objective is to produce single-cross hybrid varieties from two inbred lines each selected in complementary groups. Efficient hybrid breeding requires methods that (1) quickly generate homozygous and homogeneous parental lines with high combining abilities, (2) efficiently choose among the large number of available parental lines the most promising ones, and (3) predict the performances of sets of non-phenotyped single-cross hybrids, or hybrids phenotyped in a limited number of environments, based on their relationship with another set of hybrids with known performances. The maize breeding community has been developing model-based prediction of hybrid performances well before the genomic era. This chapter (1) provides a reminder of the maize breeding scheme before the genomic era; (2) describes how genomic data were incorporated in the prediction models involved in different steps of genomic-based single-cross maize hybrid breeding; and (3) reviews factors affecting the accuracy of genomic prediction, approaches for optimizing GP-based single-cross maize hybrid breeding schemes, and ensuring the long-term sustainability of genomic selection.
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Affiliation(s)
| | - Arthur Bernardeli
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa-MG, Brazil
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9
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Joo KA, Muszynski MG, Kantar MB, Wang ML, He X, Del Valle Echevarria AR. Utilizing CRISPR-Cas in Tropical Crop Improvement: A Decision Process for Fitting Genome Engineering to Your Species. Front Genet 2021; 12:786140. [PMID: 34868276 PMCID: PMC8633396 DOI: 10.3389/fgene.2021.786140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 10/29/2021] [Indexed: 11/13/2022] Open
Abstract
Adopting modern gene-editing technologies for trait improvement in agriculture requires important workflow developments, yet these developments are not often discussed. Using tropical crop systems as a case study, we describe a workflow broken down into discrete processes with specific steps and decision points that allow for the practical application of the CRISPR-Cas gene editing platform in a crop of interest. While we present the steps of developing genome-edited plants as sequential, in practice parts can be done in parallel, which are discussed in this perspective. The main processes include 1) understanding the genetic basis of the trait along with having the crop’s genome sequence, 2) testing and optimization of the editing reagents, development of efficient 3) tissue culture and 4) transformation methods, and 5) screening methods to identify edited events with commercial potential. Our goal in this perspective is to help any lab that wishes to implement this powerful, easy-to-use tool in their pipeline, thus aiming to democratize the technology.
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Affiliation(s)
- Kathleen A Joo
- Department of Tropical Plant and Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Michael G Muszynski
- Department of Tropical Plant and Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Michael B Kantar
- Department of Tropical Plant and Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Ming-Li Wang
- Hawaii Agriculture Research Center, Waipahu, HI, United States
| | - Xiaoling He
- Hawaii Agriculture Research Center, Waipahu, HI, United States
| | - Angel R Del Valle Echevarria
- Department of Tropical Plant and Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, United States.,Hawaii Agriculture Research Center, Waipahu, HI, United States
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Saradadevi GP, Das D, Mangrauthia SK, Mohapatra S, Chikkaputtaiah C, Roorkiwal M, Solanki M, Sundaram RM, Chirravuri NN, Sakhare AS, Kota S, Varshney RK, Mohannath G. Genetic, Epigenetic, Genomic and Microbial Approaches to Enhance Salt Tolerance of Plants: A Comprehensive Review. BIOLOGY 2021; 10:biology10121255. [PMID: 34943170 PMCID: PMC8698797 DOI: 10.3390/biology10121255] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 12/17/2022]
Abstract
Simple Summary Globally, soil salinity, which refers to salt-affected soils, is increasing due to various environmental factors and human activities. Soil salinity poses one of the most serious challenges in the field of agriculture as it significantly reduces the growth and yield of crop plants, both quantitatively and qualitatively. Over the last few decades, several studies have been carried out to understand plant biology in response to soil salinity stress with a major emphasis on genetic and other hereditary components. Based on the outcome of these studies, several approaches are being followed to enhance plants’ ability to tolerate salt stress while still maintaining reasonable levels of crop yields. In this manuscript, we comprehensively list and discuss various biological approaches being followed and, based on the recent advances in the field of molecular biology, we propose some new approaches to improve salinity tolerance of crop plants. The global scientific community can make use of this information for the betterment of crop plants. This review also highlights the importance of maintaining global soil health to prevent several crop plant losses. Abstract Globally, soil salinity has been on the rise owing to various factors that are both human and environmental. The abiotic stress caused by soil salinity has become one of the most damaging abiotic stresses faced by crop plants, resulting in significant yield losses. Salt stress induces physiological and morphological modifications in plants as a result of significant changes in gene expression patterns and signal transduction cascades. In this comprehensive review, with a major focus on recent advances in the field of plant molecular biology, we discuss several approaches to enhance salinity tolerance in plants comprising various classical and advanced genetic and genetic engineering approaches, genomics and genome editing technologies, and plant growth-promoting rhizobacteria (PGPR)-based approaches. Furthermore, based on recent advances in the field of epigenetics, we propose novel approaches to create and exploit heritable genome-wide epigenetic variation in crop plants to enhance salinity tolerance. Specifically, we describe the concepts and the underlying principles of epigenetic recombinant inbred lines (epiRILs) and other epigenetic variants and methods to generate them. The proposed epigenetic approaches also have the potential to create additional genetic variation by modulating meiotic crossover frequency.
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Affiliation(s)
- Gargi Prasad Saradadevi
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Debajit Das
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Satendra K. Mangrauthia
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Sridev Mohapatra
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Channakeshavaiah Chikkaputtaiah
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Manish Roorkiwal
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Manish Solanki
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Raman Meenakshi Sundaram
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Neeraja N. Chirravuri
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Akshay S. Sakhare
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Suneetha Kota
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA 6150, Australia
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Gireesha Mohannath
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
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11
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Rembe M, Reif JC, Ebmeyer E, Thorwarth P, Korzun V, Schacht J, Boeven PHG, Varenne P, Kazman E, Philipp N, Kollers S, Pfeiffer N, Longin CFH, Hartwig N, Gils M, Zhao Y. Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions. FRONTIERS IN PLANT SCIENCE 2021; 12:703419. [PMID: 34630453 PMCID: PMC8498042 DOI: 10.3389/fpls.2021.703419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/02/2021] [Indexed: 06/13/2023]
Abstract
Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F2 (60 out of 629 plants) and the F5 stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha-1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.
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Affiliation(s)
- Maximilian Rembe
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | | | | | | | - Viktor Korzun
- KWS SAAT SE & Co. KGaA, Einbeck, Germany
- Federal State Budgetary Institution of Science Federal Research Center “Kazan Scientific Center of Russian Academy of Sciences”, Kazan, Russia
| | - Johannes Schacht
- Limagrain Europe, Ferme de l'Etang – BP3−77390, Verneuil-l'Ètang, France
| | | | - Pierrick Varenne
- Limagrain Europe, Ferme de l'Etang – BP3−77390, Verneuil-l'Ètang, France
| | | | | | | | | | | | | | - Mario Gils
- Nordsaat Saatzucht GmbH, Langenstein, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
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Jung S, Lee T, Gasic K, Campbell BT, Yu J, Humann J, Ru S, Edge-Garza D, Hough H, Main D. The Breeding Information Management System (BIMS): an online resource for crop breeding. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6355633. [PMID: 34415997 PMCID: PMC8378516 DOI: 10.1093/database/baab054] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/14/2021] [Accepted: 08/09/2021] [Indexed: 11/17/2022]
Abstract
In this era of big data, breeding programs are producing ever larger amounts of data. This necessitates access to efficient management systems to keep track of cross, performance, pedigree, geographical and image-based data, as well as genotyping data. In this article, we report the progress on the Breeding Information Management System (BIMS), a free, secure and online breeding management system that allows breeders to store, manage, archive and analyze their private breeding data. BIMS is the first publicly available database system that enables individual breeders to integrate their private phenotypic and genotypic data with public data and, at the same time, have complete control of their own breeding data along with access to tools such as data import/export, data analysis and data archiving. The integration of breeding data with publicly available genomic and genetic data enhances genetic understanding of important traits and maximizes the marker-assisted breeding utility for breeders and allied scientists. BIMS incorporates the use of the Android App Field Book, open-source phenotype data collection software for phones and tablets that allows breeders to replace hard copy field books, thus alleviating the possibility of transcription errors while providing faster access to the collected data. BIMS comes with training materials and support for individual or small group training and is currently implemented in the Genome Database for Rosaceae, CottonGEN, the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium. Database URLs: (https://www.rosaceae.org/), (https://www.cottongen.org/), (https://www.citrusgenomedb.org/), (https://www.pulsedb.org/) and (https://www.vaccinium.org/)
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Affiliation(s)
- Sook Jung
- Department of Horticulture, Washington State University, 45 Johnson Hall, Pullman, WA 99164, USA
| | - Taein Lee
- Department of Horticulture, Washington State University, 45 Johnson Hall, Pullman, WA 99164, USA
| | - Ksenija Gasic
- Plant and Environmental Sciences Department, 171 Poole Agricultural Center, Clemson University, Clemson, SC 29634, USA
| | - B Todd Campbell
- Coastal Plains Soil, Water, and Plant Research Center, USDA-ARS, 2611 West Lucas St., Florence, SC 29501-1242, USA
| | - Jing Yu
- Department of Horticulture, Washington State University, 45 Johnson Hall, Pullman, WA 99164, USA
| | - Jodi Humann
- Department of Horticulture, Washington State University, 45 Johnson Hall, Pullman, WA 99164, USA
| | - Sushan Ru
- Department of Horticulture, University of Auburn, 287 CASIC Building/120 Funchess, Auburn, AL 36849, USA
| | - Daniel Edge-Garza
- Centre for Horticultural Science, The University of Queensland, Brisbane St Lucia, Brisbane, QLD 4072, Australia
| | - Heidi Hough
- Department of Horticulture, Washington State University, 45 Johnson Hall, Pullman, WA 99164, USA
| | - Dorrie Main
- Department of Horticulture, Washington State University, 45 Johnson Hall, Pullman, WA 99164, USA
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13
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Cao S, Song J, Yuan Y, Zhang A, Ren J, Liu Y, Qu J, Hu G, Zhang J, Wang C, Cao J, Olsen M, Prasanna BM, San Vicente F, Zhang X. Genomic Prediction of Resistance to Tar Spot Complex of Maize in Multiple Populations Using Genotyping-by-Sequencing SNPs. FRONTIERS IN PLANT SCIENCE 2021; 12:672525. [PMID: 34335648 PMCID: PMC8322742 DOI: 10.3389/fpls.2021.672525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 06/21/2021] [Indexed: 06/13/2023]
Abstract
Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.
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Affiliation(s)
- Shiliang Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Junqiao Song
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- Maize Research Institute, Anyang Academy of Agricultural Sciences, Anyang, China
| | - Yibing Yuan
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Ao Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Jiaojiao Ren
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Agronomy, Xinjiang Agricultural University, Urumqi, China
| | - Yubo Liu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Jingtao Qu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guanghui Hu
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Jianguo Zhang
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Chunping Wang
- Maize Research Institute, Anyang Academy of Agricultural Sciences, Anyang, China
| | - Jingsheng Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
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14
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Beyene Y, Gowda M, Pérez-Rodríguez P, Olsen M, Robbins KR, Burgueño J, Prasanna BM, Crossa J. Application of Genomic Selection at the Early Stage of Breeding Pipeline in Tropical Maize. FRONTIERS IN PLANT SCIENCE 2021; 12:685488. [PMID: 34262585 PMCID: PMC8274566 DOI: 10.3389/fpls.2021.685488] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/31/2021] [Indexed: 06/13/2023]
Abstract
In maize, doubled haploid (DH) line production capacity of large-sized maize breeding programs often exceeds the capacity to phenotypically evaluate the complete set of testcross candidates in multi-location trials. The ability to partially select DH lines based on genotypic data while maintaining or improving genetic gains for key traits using phenotypic selection can result in significant resource savings. The present study aimed to evaluate genomic selection (GS) prediction scenarios for grain yield and agronomic traits of one of the tropical maize breeding pipelines of CIMMYT in eastern Africa, based on multi-year empirical data for designing a GS-based strategy at the early stages of the pipeline. We used field data from 3,068 tropical maize DH lines genotyped using rAmpSeq markers and evaluated as test crosses in well-watered (WW) and water-stress (WS) environments in Kenya from 2017 to 2019. Three prediction schemes were compared: (1) 1 year of performance data to predict a second year; (2) 2 years of pooled data to predict performance in the third year, and (3) using individual or pooled data plus converting a certain proportion of individuals from the testing set (TST) to the training set (TRN) to predict the next year's data. Employing five-fold cross-validation, the mean prediction accuracies for grain yield (GY) varied from 0.19 to 0.29 under WW and 0.22 to 0.31 under WS, when the 1-year datasets were used training set to predict a second year's data as a testing set. The mean prediction accuracies increased to 0.32 under WW and 0.31 under WS when the 2-year datasets were used as a training set to predict the third-year data set. In a forward prediction scenario, good predictive abilities (0.53 to 0.71) were found when the training set consisted of the previous year's breeding data and converting 30% of the next year's data from the testing set to the training set. The prediction accuracy for anthesis date and plant height across WW and WS environments obtained using 1-year data and integrating 10, 30, 50, 70, and 90% of the TST set to TRN set was much higher than those trained in individual years. We demonstrate that by increasing the TRN set to include genotypic and phenotypic data from the previous year and combining only 10-30% of the lines from the year of testing, the predicting accuracy can be increased, which in turn could be used to replace the first stage of field-based screening partially, thus saving significant costs associated with the testcross formation and multi-location testcross evaluation.
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Affiliation(s)
- Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Kelly R. Robbins
- School of Integrative Plant Science-Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
| | - Juan Burgueño
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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15
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Atanda SA, Olsen M, Crossa J, Burgueño J, Rincent R, Dzidzienyo D, Beyene Y, Gowda M, Dreher K, Boddupalli PM, Tongoona P, Danquah EY, Olaoye G, Robbins KR. Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage. FRONTIERS IN PLANT SCIENCE 2021; 12:658978. [PMID: 34239521 PMCID: PMC8259603 DOI: 10.3389/fpls.2021.658978] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/25/2021] [Indexed: 06/08/2023]
Abstract
To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.
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Affiliation(s)
- Sikiru Adeniyi Atanda
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Juan Burgueño
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Renaud Rincent
- French National Institute for Agriculture, Food, and Environment (INRAE), Paris, France
| | - Daniel Dzidzienyo
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Kate Dreher
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Pangirayi Tongoona
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
| | | | - Gbadebo Olaoye
- Agronomy Department, University of Ilorin, Ilorin, Nigeria
| | - Kelly R. Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
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16
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Prasanna BM, Cairns JE, Zaidi PH, Beyene Y, Makumbi D, Gowda M, Magorokosho C, Zaman-Allah M, Olsen M, Das A, Worku M, Gethi J, Vivek BS, Nair SK, Rashid Z, Vinayan MT, Issa AB, San Vicente F, Dhliwayo T, Zhang X. Beat the stress: breeding for climate resilience in maize for the tropical rainfed environments. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1729-1752. [PMID: 33594449 PMCID: PMC7885763 DOI: 10.1007/s00122-021-03773-7] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 01/09/2021] [Indexed: 05/03/2023]
Abstract
Intensive public sector breeding efforts and public-private partnerships have led to the increase in genetic gains, and deployment of elite climate-resilient maize cultivars for the stress-prone environments in the tropics. Maize (Zea mays L.) plays a critical role in ensuring food and nutritional security, and livelihoods of millions of resource-constrained smallholders. However, maize yields in the tropical rainfed environments are now increasingly vulnerable to various climate-induced stresses, especially drought, heat, waterlogging, salinity, cold, diseases, and insect pests, which often come in combinations to severely impact maize crops. The International Maize and Wheat Improvement Center (CIMMYT), in partnership with several public and private sector institutions, has been intensively engaged over the last four decades in breeding elite tropical maize germplasm with tolerance to key abiotic and biotic stresses, using an extensive managed stress screening network and on-farm testing system. This has led to the successful development and deployment of an array of elite stress-tolerant maize cultivars across sub-Saharan Africa, Asia, and Latin America. Further increasing genetic gains in the tropical maize breeding programs demands judicious integration of doubled haploidy, high-throughput and precise phenotyping, genomics-assisted breeding, breeding data management, and more effective decision support tools. Multi-institutional efforts, especially public-private alliances, are key to ensure that the improved maize varieties effectively reach the climate-vulnerable farming communities in the tropics, including accelerated replacement of old/obsolete varieties.
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Affiliation(s)
- Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya.
| | | | - P H Zaidi
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | | | | | - Mike Olsen
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Aparna Das
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Mosisa Worku
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | | | - B S Vivek
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - Sudha K Nair
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - Zerka Rashid
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - M T Vinayan
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
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17
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Anders S, Cowling W, Pareek A, Gupta KJ, Singla-Pareek SL, Foyer CH. Gaining Acceptance of Novel Plant Breeding Technologies. TRENDS IN PLANT SCIENCE 2021; 26:575-587. [PMID: 33893048 DOI: 10.1016/j.tplants.2021.03.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 02/10/2021] [Accepted: 03/11/2021] [Indexed: 05/28/2023]
Abstract
Ensuring the sustainability of agriculture under climate change has led to a surge in alternative strategies for crop improvement. Advances in integrated crop breeding, social acceptance, and farm-level adoption are crucial to address future challenges to food security. Societal acceptance can be slow when consumers do not see the need for innovation or immediate benefits. We consider how best to address the issue of social licence and harmonised governance for novel gene technologies in plant breeding. In addition, we highlight optimised breeding strategies that will enable long-term genetic gains to be achieved. Promoted by harmonised global policy change, innovative plant breeding can realise high and sustainable productivity together with enhanced nutritional traits.
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Affiliation(s)
- Sven Anders
- Department of Resource Economics and Environmental Sociology, University of Alberta, Edmonton, AB T6G 2H1, Canada
| | - Wallace Cowling
- The UWA Institute of Agriculture and UWA School of Agriculture and Environment, The University of Western Australia, Perth, Western Australia 6009, Australia
| | - Ashwani Pareek
- The UWA Institute of Agriculture and UWA School of Agriculture and Environment, The University of Western Australia, Perth, Western Australia 6009, Australia; Stress Physiology and Molecular Biology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | | | - Sneh L Singla-Pareek
- Plant Stress Biology, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Christine H Foyer
- School of Biosciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, UK.
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18
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Cortés AJ, López-Hernández F. Harnessing Crop Wild Diversity for Climate Change Adaptation. Genes (Basel) 2021; 12:783. [PMID: 34065368 PMCID: PMC8161384 DOI: 10.3390/genes12050783] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/28/2021] [Accepted: 05/19/2021] [Indexed: 12/20/2022] Open
Abstract
Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent 'big data' developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these 'big data' approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.
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Affiliation(s)
- Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 Vía Rionegro, Las Palmas, Rionegro 054048, Colombia;
- Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Medellín, Medellín 050034, Colombia
| | - Felipe López-Hernández
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 Vía Rionegro, Las Palmas, Rionegro 054048, Colombia;
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19
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Simeão RM, Resende MDV, Alves RS, Pessoa-Filho M, Azevedo ALS, Jones CS, Pereira JF, Machado JC. Genomic Selection in Tropical Forage Grasses: Current Status and Future Applications. FRONTIERS IN PLANT SCIENCE 2021; 12:665195. [PMID: 33995461 PMCID: PMC8120112 DOI: 10.3389/fpls.2021.665195] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 04/06/2021] [Indexed: 05/06/2023]
Abstract
The world population is expected to be larger and wealthier over the next few decades and will require more animal products, such as milk and beef. Tropical regions have great potential to meet this growing global demand, where pasturelands play a major role in supporting increased animal production. Better forage is required in consonance with improved sustainability as the planted area should not increase and larger areas cultivated with one or a few forage species should be avoided. Although, conventional tropical forage breeding has successfully released well-adapted and high-yielding cultivars over the last few decades, genetic gains from these programs have been low in view of the growing food demand worldwide. To guarantee their future impact on livestock production, breeding programs should leverage genotyping, phenotyping, and envirotyping strategies to increase genetic gains. Genomic selection (GS) and genome-wide association studies play a primary role in this process, with the advantage of increasing genetic gain due to greater selection accuracy, reduced cycle time, and increased number of individuals that can be evaluated. This strategy provides solutions to bottlenecks faced by conventional breeding methods, including long breeding cycles and difficulties to evaluate complex traits. Initial results from implementing GS in tropical forage grasses (TFGs) are promising with notable improvements over phenotypic selection alone. However, the practical impact of GS in TFG breeding programs remains unclear. The development of appropriately sized training populations is essential for the evaluation and validation of selection markers based on estimated breeding values. Large panels of single-nucleotide polymorphism markers in different tropical forage species are required for multiple application targets at a reduced cost. In this context, this review highlights the current challenges, achievements, availability, and development of genomic resources and statistical methods for the implementation of GS in TFGs. Additionally, the prediction accuracies from recent experiments and the potential to harness diversity from genebanks are discussed. Although, GS in TFGs is still incipient, the advances in genomic tools and statistical models will speed up its implementation in the foreseeable future. All TFG breeding programs should be prepared for these changes.
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Affiliation(s)
| | | | - Rodrigo S. Alves
- Instituto Nacional de Ciência e Tecnologia do Café, Universidade Federal de Viçosa, Viçosa, Brazil
| | | | | | - Chris S. Jones
- International Livestock Research Institute, Nairobi, Kenya
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20
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Mbanjo EGN, Rabbi IY, Ferguson ME, Kayondo SI, Eng NH, Tripathi L, Kulakow P, Egesi C. Technological Innovations for Improving Cassava Production in Sub-Saharan Africa. Front Genet 2021; 11:623736. [PMID: 33552138 PMCID: PMC7859516 DOI: 10.3389/fgene.2020.623736] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/23/2020] [Indexed: 11/17/2022] Open
Abstract
Cassava is crucial for food security of millions of people in sub-Saharan Africa. The crop has great potential to contribute to African development and is increasing its income-earning potential for small-scale farmers and related value chains on the continent. Therefore, it is critical to increase cassava production, as well as its quality attributes. Technological innovations offer great potential to drive this envisioned change. This paper highlights genomic tools and resources available in cassava. The paper also provides a glimpse of how these resources have been used to screen and understand the pattern of cassava genetic diversity on the continent. Here, we reviewed the approaches currently used for phenotyping cassava traits, highlighting the methodologies used to link genotypic and phenotypic information, dissect the genetics architecture of key cassava traits, and identify quantitative trait loci/markers significantly associated with those traits. Additionally, we examined how knowledge acquired is utilized to contribute to crop improvement. We explored major approaches applied in the field of molecular breeding for cassava, their promises, and limitations. We also examined the role of national agricultural research systems as key partners for sustainable cassava production.
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Affiliation(s)
| | | | | | | | - Ng Hwa Eng
- CGIAR Excellence in Breeding Platform, El Batan, Mexico
| | - Leena Tripathi
- International Institute of Tropical Agriculture, Nairobi, Kenya
| | - Peter Kulakow
- International Institute of Tropical Agriculture, Ibadan, Nigeria
| | - Chiedozie Egesi
- International Institute of Tropical Agriculture, Ibadan, Nigeria
- National Root Crops Research Institute, Umudike, Nigeria
- Department of Global Development, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
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21
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Atanda SA, Olsen M, Burgueño J, Crossa J, Dzidzienyo D, Beyene Y, Gowda M, Dreher K, Zhang X, Prasanna BM, Tongoona P, Danquah EY, Olaoye G, Robbins KR. Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:279-294. [PMID: 33037897 PMCID: PMC7813723 DOI: 10.1007/s00122-020-03696-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 09/23/2020] [Indexed: 06/01/2023]
Abstract
Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a "test-half-predict-half approach." Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT's maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or "test-half-predict-half" can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.
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Affiliation(s)
- Sikiru Adeniyi Atanda
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, USA
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya.
| | - Juan Burgueño
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Daniel Dzidzienyo
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Kate Dreher
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Pangirayi Tongoona
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
| | | | - Gbadebo Olaoye
- Agronomy Department, University of Ilorin, Ilorin, Nigeria
| | - Kelly R Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, USA.
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22
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Bohra A, Chand Jha U, Godwin ID, Kumar Varshney R. Genomic interventions for sustainable agriculture. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:2388-2405. [PMID: 32875704 PMCID: PMC7680532 DOI: 10.1111/pbi.13472] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/21/2020] [Accepted: 08/16/2020] [Indexed: 05/05/2023]
Abstract
Agricultural production faces a Herculean challenge to feed the increasing global population. Food production systems need to deliver more with finite land and water resources while exerting the least negative influence on the ecosystem. The unpredictability of climate change and consequent changes in pests/pathogens dynamics aggravate the enormity of the challenge. Crop improvement has made significant contributions towards food security, and breeding climate-smart cultivars are considered the most sustainable way to accelerate food production. However, a fundamental change is needed in the conventional breeding framework in order to respond adequately to the growing food demands. Progress in genomics has provided new concepts and tools that hold promise to make plant breeding procedures more precise and efficient. For instance, reference genome assemblies in combination with germplasm sequencing delineate breeding targets that could contribute to securing future food supply. In this review, we highlight key breakthroughs in plant genome sequencing and explain how the presence of these genome resources in combination with gene editing techniques has revolutionized the procedures of trait discovery and manipulation. Adoption of new approaches such as speed breeding, genomic selection and haplotype-based breeding could overcome several limitations of conventional breeding. We advocate that strengthening varietal release and seed distribution systems will play a more determining role in delivering genetic gains at farmer's field. A holistic approach outlined here would be crucial to deliver steady stream of climate-smart crop cultivars for sustainable agriculture.
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Affiliation(s)
- Abhishek Bohra
- ICAR‐Indian Institute of Pulses Research (IIPR)KanpurIndia
| | - Uday Chand Jha
- ICAR‐Indian Institute of Pulses Research (IIPR)KanpurIndia
| | - Ian D. Godwin
- Centre for Crop ScienceQueensland Alliance for Agriculture and Food Innovation (QAAFI)The University of QueenslandBrisbaneQldAustralia
| | - Rajeev Kumar Varshney
- International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
- The UWA Institute of AgricultureThe University of Western AustraliaPerthAustralia
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23
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McCouch S, Navabi ZK, Abberton M, Anglin NL, Barbieri RL, Baum M, Bett K, Booker H, Brown GL, Bryan GJ, Cattivelli L, Charest D, Eversole K, Freitas M, Ghamkhar K, Grattapaglia D, Henry R, Valadares Inglis MC, Islam T, Kehel Z, Kersey PJ, King GJ, Kresovich S, Marden E, Mayes S, Ndjiondjop MN, Nguyen HT, Paiva SR, Papa R, Phillips PWB, Rasheed A, Richards C, Rouard M, Amstalden Sampaio MJ, Scholz U, Shaw PD, Sherman B, Staton SE, Stein N, Svensson J, Tester M, Montenegro Valls JF, Varshney R, Visscher S, von Wettberg E, Waugh R, Wenzl P, Rieseberg LH. Mobilizing Crop Biodiversity. MOLECULAR PLANT 2020; 13:1341-1344. [PMID: 32835887 DOI: 10.1016/j.molp.2020.08.011] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 08/19/2020] [Accepted: 08/19/2020] [Indexed: 05/10/2023]
Affiliation(s)
- Susan McCouch
- Plant Breeding and Genetics, School of Integrated Plant Sciences, Cornell University, Ithaca, NY, 14853, USA
| | - Zahra Katy Navabi
- DivSeek, Global Institute for Food Security, 110 Gymnasium Place, University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada; Global Institute for Food Security, 110 Gymnasium Place, University of Saskatchewan, Saskatoon, SK, S7N 4J8, Canada
| | - Michael Abberton
- International Institute of Tropical Agriculture (IITA), PMB 5320, Oyo Rd, Ibadan, Nigeria
| | - Noelle L Anglin
- International Potato Center (CIP) 1895 Avenida La Molina, Lima Peru 12, Lima 15023, Peru
| | - Rosa Lia Barbieri
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Michael Baum
- International Center for Agricultural Research in the Dry Areas (ICARDA), Station Exp. INRA-Quich. Rue Hafiane Cherkaoui. Agdal. Rabat - Instituts, 10111, Rabat, Morocco
| | - Kirstin Bett
- Department of Plant Sciences, University of Saskatchewan, 51 Campus Dr., Saskatoon, SK S7N 5A8, Canada
| | - Helen Booker
- Department of Plant Agriculture, University of Guelph, Rm 316, Crop Science Bldg, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
| | - Gerald L Brown
- Genome Prairie, 111 Research Drive, Suite 101, Saskatoon, SK, S7N 3R2, Canada
| | - Glenn J Bryan
- The James Hutton Institute, Errol Road, Invergowrie, Dundee, DD2 5DA, UK
| | - Luigi Cattivelli
- CREA, Research Centre for Genomics and Bioinformatics, via San Protaso 302, Fiorenzuola d'Arda, 29017, Italy
| | - David Charest
- Genome British Columbia, 400-575 West 8th Avenue, Vancouver, BC, V5Z 0C4, Canada
| | - Kellye Eversole
- International Wheat Genome Sequencing Consortium, 2841 NE Marywood Ct, Lee's Summit, MO, 64086, USA
| | - Marcelo Freitas
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Kioumars Ghamkhar
- Forage Science, Grasslands Research Centre, AgResearch, Palmerston North, 4410, New Zealand
| | - Dario Grattapaglia
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Robert Henry
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072, Australia
| | - Maria Cleria Valadares Inglis
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Tofazzal Islam
- Institute of Biotechnology and Genetic Engineering (IBGE), Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
| | - Zakaria Kehel
- International Center for Agricultural Research in the Dry Areas (ICARDA), Station Exp. INRA-Quich. Rue Hafiane Cherkaoui. Agdal. Rabat - Instituts, 10111, Rabat, Morocco
| | - Paul J Kersey
- Royal Botanic Gardens, Kew, Richmond, Surrey, TW9 3AE, UK
| | - Graham J King
- Southern Cross University, PO Box 157, Lismore, NSW 2480, Australia
| | - Stephen Kresovich
- Feed the Future Innovation Lab for Crop Improvement, 431 Weill Hall, Cornell University, Ithaca, NY, 14853, USA
| | - Emily Marden
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC V6R 2A5, Canada
| | - Sean Mayes
- Crops For the Future (UK) CIC 76-80 Baddow Road, Chelmsford, Essex, CM2 7PJ, UK
| | - Marie Noelle Ndjiondjop
- Africa Rice Center (AfricaRice), Mbe Research Station, Bouaké, 01 BP 2511 Bouaké, Côte d'Ivoire
| | - Henry T Nguyen
- University of Missouri, Division of Plant Sciences, 25 Agriculture Lab Bldg, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, MO 65211, USA
| | - Samuel Rezende Paiva
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Roberto Papa
- Università Politecnica delle Marche, D3A-Dipartimento di Scienze Agrarie, Alimentari e Ambientali, Via Brecce Bianche, 60131, Ancona, Italy
| | - Peter W B Phillips
- Johnson Shoyama Graduate School of Public Policy, University of Saskatchewan, 101 Diefenbaker Place, Saskatoon, S7N 5B8, Canada
| | - Awais Rasheed
- CIMMYT-China office, Beijing 100081, Beijing, P.R. China
| | - Christopher Richards
- USDA-ARS National Laboratory for Genetic Resources Preservation, 1111 South Mason St, Fort Collins, CO, 80521, USA
| | - Mathieu Rouard
- Bioversity International, Parc Scientifique Agropolis II, 34397, Montpellier, Cedex 5, France
| | - Maria Jose Amstalden Sampaio
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany
| | - Paul D Shaw
- The James Hutton Institute, Errol Road, Invergowrie, Dundee, DD2 5DA, UK
| | - Brad Sherman
- Law School, University of Queensland, St Lucia, QLD, 4072, Australia
| | - S Evan Staton
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC V6R 2A5, Canada
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; CiBreed - Center for Integrated Breeding Research, Department of Crop Sciences, Georg-August University Göttingen, Von Siebold Straße 8, D-37075 Göttingen, Germany
| | | | - Mark Tester
- King Abdullah University of Science & Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Jose Francisco Montenegro Valls
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Rajeev Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru - 502 324, Telangana State, India
| | - Stephen Visscher
- Global Institute for Food Security, 110 Gymnasium Place, University of Saskatchewan, Saskatoon, SK, S7N 4J8, Canada
| | - Eric von Wettberg
- University of Vermont, 63 Carrigan Drive, Jeffords Hall, Burlington, VT, 05405, USA
| | - Robbie Waugh
- The James Hutton Institute, Errol Road, Invergowrie, Dundee, DD2 5DA, UK; School of Agriculture and Wine & Waite Research Institute, University of Adelaide, Waite Campus, Glen Osmond, SA, 5064, Australia
| | - Peter Wenzl
- Centro Internacional de Agricultura Tropical (CIAT), Km 17 Recta Cali-Palmira, 763537 Cali, Colombia
| | - Loren H Rieseberg
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC V6R 2A5, Canada.
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