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Petrasch S, Mesquida-Pesci SD, Pincot DDA, Feldmann MJ, López CM, Famula R, Hardigan MA, Cole GS, Knapp SJ, Blanco-Ulate B. Genomic prediction of strawberry resistance to postharvest fruit decay caused by the fungal pathogen Botrytis cinerea. G3 (BETHESDA, MD.) 2022; 12:6427547. [PMID: 34791166 PMCID: PMC8728004 DOI: 10.1093/g3journal/jkab378] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/08/2021] [Indexed: 12/18/2022]
Abstract
Gray mold, a disease of strawberry (Fragaria × ananassa) caused by the ubiquitous necrotroph Botrytis cinerea, renders fruit unmarketable and causes economic losses in the postharvest supply chain. To explore the feasibility of selecting for increased resistance to gray mold, we undertook genetic and genomic prediction studies in strawberry populations segregating for fruit quality and shelf life traits hypothesized to pleiotropically affect susceptibility. As predicted, resistance to gray mold was heritable but quantitative and genetically complex. While every individual was susceptible, the speed of symptom progression and severity differed. Narrow-sense heritability ranged from 0.38 to 0.71 for lesion diameter (LD) and 0.39 to 0.44 for speed of emergence of external mycelium (EM). Even though significant additive genetic variation was observed for LD and EM, the phenotypic ranges were comparatively narrow and genome-wide analyses did not identify any large-effect loci. Genomic selection (GS) accuracy ranged from 0.28 to 0.59 for LD and 0.37 to 0.47 for EM. Additive genetic correlations between fruit quality and gray mold resistance traits were consistent with prevailing hypotheses: LD decreased as titratable acidity increased, whereas EM increased as soluble solid content decreased and firmness increased. We concluded that phenotypic and GS could be effective for reducing LD and increasing EM, especially in long shelf life populations, but that a significant fraction of the genetic variation for resistance to gray mold was caused by the pleiotropic effects of fruit quality traits that differ among market and shelf life classes.
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Affiliation(s)
- Stefan Petrasch
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | | | - Dominique D A Pincot
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Mitchell J Feldmann
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Cindy M López
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Randi Famula
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Michael A Hardigan
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Steven J Knapp
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Barbara Blanco-Ulate
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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Varona L, Legarra A, Toro MA, Vitezica ZG. Genomic Prediction Methods Accounting for Nonadditive Genetic Effects. Methods Mol Biol 2022; 2467:219-243. [PMID: 35451778 DOI: 10.1007/978-1-0716-2205-6_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The use of genomic information for prediction of future phenotypes or breeding values for the candidates to selection has become a standard over the last decade. However, most procedures for genomic prediction only consider the additive (or substitution) effects associated with polymorphic markers. Nevertheless, the implementation of models that consider nonadditive genetic variation may be interesting because they (1) may increase the ability of prediction, (2) can be used to define mate allocation procedures in plant and animal breeding schemes, and (3) can be used to benefit from nonadditive genetic variation in crossbreeding or purebred breeding schemes. This study reviews the available methods for incorporating nonadditive effects into genomic prediction procedures and their potential applications in predicting future phenotypic performance, mate allocation, and crossbred and purebred selection. Finally, a brief outline of some future research lines is also proposed.
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Affiliation(s)
- Luis Varona
- Departamento de Anatomía, Embriología y Genética Animal, Universidad de Zaragoza, Zaragoza, Spain.
- Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain.
| | | | - Miguel A Toro
- Dpto. Producción Agraria, ETS Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
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Moeskjær S, Skovbjerg CK, Tausen M, Wind R, Roulund N, Janss L, Andersen SU. Major effect loci for plant size before onset of nitrogen fixation allow accurate prediction of yield in white clover. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:125-143. [PMID: 34628514 DOI: 10.1007/s00122-021-03955-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/24/2021] [Indexed: 06/13/2023]
Abstract
Accurate genomic prediction of yield within and across generations was achieved by estimating the genetic merit of individual white clover genotypes based on extensive genetic replication using cloned material. White clover is an agriculturally important forage legume grown throughout temperate regions as a mixed clover-grass crop. It is typically cultivated with low nitrogen input, making yield dependent on nitrogen fixation by rhizobia in root nodules. Here, we investigate the effects of clover and rhizobium genetic variation by monitoring plant growth and quantifying dry matter yield of 704 combinations of 145 clover genotypes and 170 rhizobium inocula. We find no significant effect of rhizobium variation. In contrast, we can predict yield based on a few white clover markers strongly associated with plant size prior to nitrogen fixation, and the prediction accuracy for polycross offspring yield is remarkably high. Several of the markers are located near a homolog of Arabidopsis thaliana GIGANTUS 1, which regulates growth rate and biomass accumulation. Our work provides fundamental insight into the genetics of white clover yield and identifies specific candidate genes as breeding targets.
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Affiliation(s)
- Sara Moeskjær
- Department of Molecular Biology and Genetics, Aarhus University, 8000, Aarhus C, Denmark
| | | | - Marni Tausen
- Bioinformatics Research Centre, Aarhus University, 8000, Aarhus C, Denmark
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus C, Denmark
| | - Rune Wind
- Department of Molecular Biology and Genetics, Aarhus University, 8000, Aarhus C, Denmark
| | | | - Luc Janss
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus C, Denmark
| | - Stig U Andersen
- Department of Molecular Biology and Genetics, Aarhus University, 8000, Aarhus C, Denmark.
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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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Rogers AR, Holland JB. Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data. G3 (BETHESDA, MD.) 2021; 12:6486423. [PMID: 35100364 PMCID: PMC9245610 DOI: 10.1093/g3journal/jkab440] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/06/2021] [Indexed: 12/30/2022]
Abstract
Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.
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Affiliation(s)
- Anna R Rogers
- Program in Genetics, North Carolina State University, Raleigh, NC
27695, USA
| | - James B Holland
- Program in Genetics, North Carolina State University, Raleigh, NC
27695, USA,USDA-ARS Plant Science Research Unit, North Carolina State
University, Raleigh, NC 27695, USA,Department of Crop and Soil Sciences, North Carolina State
University, Raleigh, NC 27695, USA,Corresponding author: Department of Agriculture—Agriculture
Research Service, Box 7620 North Carolina State University, Raleigh, NC 27695-7620, USA.
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Cazenave X, Petit B, Lateur M, Nybom H, Sedlak J, Tartarini S, Laurens F, Durel CE, Muranty H. Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple. G3 (BETHESDA, MD.) 2021; 12:6459174. [PMID: 34893831 PMCID: PMC9210277 DOI: 10.1093/g3journal/jkab420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/29/2021] [Indexed: 11/12/2022]
Abstract
Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e., genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.
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Affiliation(s)
- Xabi Cazenave
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Bernard Petit
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Marc Lateur
- Plant Breeding and Biodiversity, Centre Wallon de Recherches Agronomiques, Gembloux, Belgium
| | - Hilde Nybom
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Kristianstad, Sweden
| | - Jiri Sedlak
- Výzkumný a Šlechtitelský ústav Ovocnářský Holovousy s.r.o, Holovousy, Czech Republic
| | - Stefano Tartarini
- Department of Agricultural Sciences, University of Bologna, Bologna, Italy
| | - François Laurens
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Charles-Eric Durel
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Hélène Muranty
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France,Corresponding author:
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Zenda T, Liu S, Dong A, Li J, Wang Y, Liu X, Wang N, Duan H. Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value. FRONTIERS IN PLANT SCIENCE 2021; 12:774994. [PMID: 34925418 PMCID: PMC8672198 DOI: 10.3389/fpls.2021.774994] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/08/2021] [Indexed: 05/17/2023]
Abstract
Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the "omics" technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.
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Affiliation(s)
- Tinashe Zenda
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
- Department of Crop Science, Faculty of Agriculture and Environmental Science, Bindura University of Science Education, Bindura, Zimbabwe
| | - Songtao Liu
- Academy of Agriculture and Forestry Sciences, Hebei North University, Zhangjiakou, China
| | - Anyi Dong
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Jiao Li
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Yafei Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Xinyue Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Nan Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Huijun Duan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
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Guo Z, Yang Q, Huang F, Zheng H, Sang Z, Xu Y, Zhang C, Wu K, Tao J, Prasanna BM, Olsen MS, Wang Y, Zhang J, Xu Y. Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip. PLANT COMMUNICATIONS 2021; 2:100230. [PMID: 34778746 PMCID: PMC8577115 DOI: 10.1016/j.xplc.2021.100230] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 05/26/2023]
Abstract
Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers (40K mSNPs, 251K SNPs, and 690K haplotypes), and generated multiple panels with various marker densities (1K-40K mSNPs) by sequencing at different depths. Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs. Compared with the one-amplicon-one-SNP system, mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection, linkage disequilibrium decay analysis, and genome-wide association studies. The technologies, protocols, and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals, plants, and microorganisms.
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Affiliation(s)
- Zifeng Guo
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Quannv Yang
- School of Food Science and Engineering, Foshan University/CIMMYT-China Tropical Maize Research Center, Foshan 528225, Guangdong, China
| | - Feifei Huang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang 050035, China
| | - Hongjian Zheng
- Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences/CIMMYT-China Specialty Maize Research Center, Shanghai 201403, China
| | - Zhiqin Sang
- Xinjiang Academy of Agricultural Reclamation, Shihezi 832000, Xinjiang, China
| | - Yanfen Xu
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang 050035, China
| | - Cong Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang 050035, China
| | - Kunsheng Wu
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang 050035, China
| | - Jiajun Tao
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang 050035, China
| | - Boddupalli M. Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Michael S. Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Yunbo Wang
- School of Food Science and Engineering, Foshan University/CIMMYT-China Tropical Maize Research Center, Foshan 528225, Guangdong, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang 050035, China
- National Foxtail Millet Improvement Center, Minor Cereal Crops Laboratory of Hebei Province, Institute of Millet Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China
| | - Yunbi Xu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- School of Food Science and Engineering, Foshan University/CIMMYT-China Tropical Maize Research Center, Foshan 528225, Guangdong, China
- Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences/CIMMYT-China Specialty Maize Research Center, Shanghai 201403, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan Texcoco 56130, Mexico
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60
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Demenkov PS, Oshchepkova ЕА, Demenkov PS, Ivanisenko TV, Ivanisenko VA. Prioritization of biological processes based on the reconstruction and analysis of associative gene networks describing the response of plants to adverse environmental factors. Vavilovskii Zhurnal Genet Selektsii 2021; 25:580-592. [PMID: 34723066 PMCID: PMC8543060 DOI: 10.18699/vj21.065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 06/21/2021] [Accepted: 06/21/2021] [Indexed: 11/23/2022] Open
Abstract
Methods for prioritizing or ranking candidate genes according to their importance based on specif ic criteria
via the analysis of gene networks are widely used in biomedicine to search for genes associated with diseases and to
predict biomarkers, pharmacological targets and other clinically relevant molecules. These methods have also been
used in other f ields, particularly in crop production. This is largely due to the development of technologies to solve
problems in marker-oriented and genomic selection, which requires knowledge of the molecular genetic mechanisms
underlying the formation of agriculturally valuable traits. A new direction for the study of molecular genetic mechanisms
is the prioritization of biological processes based on the analysis of associative gene networks. Associative gene
networks are heterogeneous networks whose vertices can depict both molecular genetic objects (genes, proteins, metabolites,
etc.) and the higher-level factors (biological processes, diseases, external environmental factors, etc.) related
to regulatory, physicochemical or associative interactions. Using a previously developed method, biological processes
involved in plant responses to increased cadmium content, saline stress and drought conditions were prioritized according
to their degree of connection with the gene networks in the SOLANUM TUBEROSUM knowledge base. The
prioritization results indicate that fundamental processes, such as gene expression, post-translational modif ications,
protein degradation, programmed cell death, photosynthesis, signal transmission and stress response play important
roles in the common molecular genetic mechanisms for plant response to various adverse factors. On the other hand, a
group of processes related to the development of seeds (“seeding development”) was revealed to be drought specif ic,
while processes associated with ion transport (“ion transport”) were included in the list of responses specif ic to salt
stress and processes associated with the metabolism of lipids were found to be involved specif ically in the response to
cadmium.
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Affiliation(s)
- P S Demenkov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - Е А Oshchepkova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - P S Demenkov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - T V Ivanisenko
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - V A Ivanisenko
- Novosibirsk State University, Novosibirsk, Russiavosibirsk, Russia Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
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Mollandin F, Rau A, Croiseau P. An evaluation of the predictive performance and mapping power of the BayesR model for genomic prediction. G3 GENES|GENOMES|GENETICS 2021; 11:6317672. [PMID: 34849780 PMCID: PMC8527474 DOI: 10.1093/g3journal/jkab225] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/27/2021] [Indexed: 12/02/2022]
Abstract
Technological advances and decreasing costs have led to the rise of increasingly dense genotyping data, making feasible the identification of potential causal markers. Custom genotyping chips, which combine medium-density genotypes with a custom genotype panel, can capitalize on these candidates to potentially yield improved accuracy and interpretability in genomic prediction. A particularly promising model to this end is BayesR, which divides markers into four effect size classes. BayesR has been shown to yield accurate predictions and promise for quantitative trait loci (QTL) mapping in real data applications, but an extensive benchmarking in simulated data is currently lacking. Based on a set of real genotypes, we generated simulated data under a variety of genetic architectures and phenotype heritabilities, and we evaluated the impact of excluding or including causal markers among the genotypes. We define several statistical criteria for QTL mapping, including several based on sliding windows to account for linkage disequilibrium (LD). We compare and contrast these statistics and their ability to accurately prioritize known causal markers. Overall, we confirm the strong predictive performance for BayesR in moderately to highly heritable traits, particularly for 50k custom data. In cases of low heritability or weak LD with the causal marker in 50k genotypes, QTL mapping is a challenge, regardless of the criterion used. BayesR is a promising approach to simultaneously obtain accurate predictions and interpretable classifications of SNPs into effect size classes. We illustrated the performance of BayesR in a variety of simulation scenarios, and compared the advantages and limitations of each.
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Affiliation(s)
- Fanny Mollandin
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Jouy-en-Josas 78350, France
| | - Andrea Rau
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Jouy-en-Josas 78350, France
- BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université de Picardie Jules Verne, Peronne 80203, France
| | - Pascal Croiseau
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Jouy-en-Josas 78350, France
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Bohra A, Kilian B, Sivasankar S, Caccamo M, Mba C, McCouch SR, Varshney RK. Reap the crop wild relatives for breeding future crops. Trends Biotechnol 2021; 40:412-431. [PMID: 34629170 DOI: 10.1016/j.tibtech.2021.08.009] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 02/07/2023]
Abstract
Crop wild relatives (CWRs) have provided breeders with several 'game-changing' traits or genes that have boosted crop resilience and global agricultural production. Advances in breeding and genomics have accelerated the identification of valuable CWRs for use in crop improvement. The enhanced genetic diversity of breeding pools carrying optimum combinations of favorable alleles for targeted crop-growing regions is crucial to sustain genetic gain. In parallel, growing sequence information on wild genomes in combination with precise gene-editing tools provide a fast-track route to transform CWRs into ideal future crops. Data-informed germplasm collection and management strategies together with adequate policy support will be equally important to improve access to CWRs and their sustainable use to meet food and nutrition security targets.
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Affiliation(s)
- Abhishek Bohra
- ICAR-Indian Institute of Pulses Research (IIPR), 208024 Kanpur, India
| | | | - Shoba Sivasankar
- International Atomic Energy Agency (IAEA), Vienna International Centre, 1400 Vienna, Austria
| | | | - Chikelu Mba
- Food and Agriculture Organization of the United Nations (FAO), Rome 00153, Italy
| | - Susan R McCouch
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14850, USA.
| | - Rajeev K Varshney
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Murdoch, WA 6150, Australia.
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He S, Jiang Y, Thistlethwaite R, Hayden MJ, Trethowan R, Daetwyler HD. Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping. FRONTIERS IN PLANT SCIENCE 2021; 12:735285. [PMID: 34691111 PMCID: PMC8526887 DOI: 10.3389/fpls.2021.735285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/14/2021] [Indexed: 06/08/2023]
Abstract
Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders' main interest - response to selection - was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.
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Affiliation(s)
- Sang He
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yong Jiang
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Rebecca Thistlethwaite
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
| | - Matthew J. Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Richard Trethowan
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Cobbitty, NSW, Australia
| | - Hans D. Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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Bharadwaj R, Noceda C, Mohanapriya G, Kumar SR, Thiers KLL, Costa JH, Macedo ES, Kumari A, Gupta KJ, Srivastava S, Adholeya A, Oliveira M, Velada I, Sircar D, Sathishkumar R, Arnholdt-Schmitt B. Adaptive Reprogramming During Early Seed Germination Requires Temporarily Enhanced Fermentation-A Critical Role for Alternative Oxidase Regulation That Concerns Also Microbiota Effectiveness. FRONTIERS IN PLANT SCIENCE 2021; 12:686274. [PMID: 34659277 PMCID: PMC8518632 DOI: 10.3389/fpls.2021.686274] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/10/2021] [Indexed: 05/05/2023]
Abstract
Plants respond to environmental cues via adaptive cell reprogramming that can affect whole plant and ecosystem functionality. Microbiota constitutes part of the inner and outer environment of the plant. This Umwelt underlies steady dynamics, due to complex local and global biotic and abiotic changes. Hence, adaptive plant holobiont responses are crucial for continuous metabolic adjustment at the systems level. Plants require oxygen-dependent respiration for energy-dependent adaptive morphology, such as germination, root and shoot growth, and formation of adventitious, clonal, and reproductive organs, fruits, and seeds. Fermentative paths can help in acclimation and, to our view, the role of alternative oxidase (AOX) in coordinating complex metabolic and physiological adjustments is underestimated. Cellular levels of sucrose are an important sensor of environmental stress. We explored the role of exogenous sucrose and its interplay with AOX during early seed germination. We found that sucrose-dependent initiation of fermentation during the first 12 h after imbibition (HAI) was beneficial to germination. However, parallel upregulated AOX expression was essential to control negative effects by prolonged sucrose treatment. Early downregulated AOX activity until 12 HAI improved germination efficiency in the absence of sucrose but suppressed early germination in its presence. The results also suggest that seeds inoculated with arbuscular mycorrhizal fungi (AMF) can buffer sucrose stress during germination to restore normal respiration more efficiently. Following this approach, we propose a simple method to identify organic seeds and low-cost on-farm perspectives for early identifying disease tolerance, predicting plant holobiont behavior, and improving germination. Furthermore, the research strengthens the view that AOX can serve as a powerful functional marker source for seed hologenomes.
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Affiliation(s)
- Revuru Bharadwaj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
| | - Carlos Noceda
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- Cell and Molecular Biology of Plants (BIOCEMP)/Industrial Biotechnology and Bioproducts, Departamento de Ciencias de la Vida y de la Agricultura, Universidad de las Fuerzas Armadas-ESPE, Sangolquí, Ecuador
| | - Gunasekharan Mohanapriya
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
| | - Sarma Rajeev Kumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
| | - Karine Leitão Lima Thiers
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- Functional Genomics and Bioinformatics Group, Department of Biochemistry and Molecular Biology, Federal University of Ceará, Fortaleza, Brazil
| | - José Hélio Costa
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- Functional Genomics and Bioinformatics Group, Department of Biochemistry and Molecular Biology, Federal University of Ceará, Fortaleza, Brazil
| | - Elisete Santos Macedo
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
| | - Aprajita Kumari
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- National Institute of Plant Genome Research, New Delhi, India
| | - Kapuganti Jagadis Gupta
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- National Institute of Plant Genome Research, New Delhi, India
| | - Shivani Srivastava
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- Centre for Mycorrhizal Research, Sustainable Agriculture Division, The Energy and Resources Institute (TERI), TERI Gram, Gurugram, India
| | - Alok Adholeya
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- Centre for Mycorrhizal Research, Sustainable Agriculture Division, The Energy and Resources Institute (TERI), TERI Gram, Gurugram, India
| | - Manuela Oliveira
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- Department of Mathematics and CIMA - Center for Research on Mathematics and Its Applications, Universidade de Évora, Évora, Portugal
| | - Isabel Velada
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- MED—Mediterranean Institute for Agriculture, Environment and Development, Instituto de Investigação e Formação Avançada, Universidade de Évora, Évora, Portugal
| | - Debabrata Sircar
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- Department of Biotechnology, Indian Institute of Technology Roorkee, Roorkee, India
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
| | - Birgit Arnholdt-Schmitt
- Non-Institutional Competence Focus (NICFocus) ‘Functional Cell Reprogramming and Organism Plasticity’ (FunCROP), coordinated from Foros de Vale de Figueira, Alentejo, Portugal
- Functional Genomics and Bioinformatics Group, Department of Biochemistry and Molecular Biology, Federal University of Ceará, Fortaleza, Brazil
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Sharma S, Schulthess AW, Bassi FM, Badaeva ED, Neumann K, Graner A, Özkan H, Werner P, Knüpffer H, Kilian B. Introducing Beneficial Alleles from Plant Genetic Resources into the Wheat Germplasm. BIOLOGY 2021; 10:982. [PMID: 34681081 PMCID: PMC8533267 DOI: 10.3390/biology10100982] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/02/2022]
Abstract
Wheat (Triticum sp.) is one of the world's most important crops, and constantly increasing its productivity is crucial to the livelihoods of millions of people. However, more than a century of intensive breeding and selection processes have eroded genetic diversity in the elite genepool, making new genetic gains difficult. Therefore, the need to introduce novel genetic diversity into modern wheat has become increasingly important. This review provides an overview of the plant genetic resources (PGR) available for wheat. We describe the most important taxonomic and phylogenetic relationships of these PGR to guide their use in wheat breeding. In addition, we present the status of the use of some of these resources in wheat breeding programs. We propose several introgression schemes that allow the transfer of qualitative and quantitative alleles from PGR into elite germplasm. With this in mind, we propose the use of a stage-gate approach to align the pre-breeding with main breeding programs to meet the needs of breeders, farmers, and end-users. Overall, this review provides a clear starting point to guide the introgression of useful alleles over the next decade.
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Affiliation(s)
- Shivali Sharma
- Global Crop Diversity Trust, Platz der Vereinten Nationen 7, D-53113 Bonn, Germany; (S.S.); (P.W.)
| | - Albert W. Schulthess
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; (A.W.S.); (K.N.); (A.G.); (H.K.)
| | - Filippo M. Bassi
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat 10112, Morocco;
| | - Ekaterina D. Badaeva
- N.I. Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, Russia;
- The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), 630090 Novosibirsk, Russia
| | - Kerstin Neumann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; (A.W.S.); (K.N.); (A.G.); (H.K.)
| | - Andreas Graner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; (A.W.S.); (K.N.); (A.G.); (H.K.)
| | - Hakan Özkan
- Department of Field Crops, Faculty of Agriculture, University of Çukurova, Adana 01330, Turkey;
| | - Peter Werner
- Global Crop Diversity Trust, Platz der Vereinten Nationen 7, D-53113 Bonn, Germany; (S.S.); (P.W.)
| | - Helmut Knüpffer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; (A.W.S.); (K.N.); (A.G.); (H.K.)
| | - Benjamin Kilian
- Global Crop Diversity Trust, Platz der Vereinten Nationen 7, D-53113 Bonn, Germany; (S.S.); (P.W.)
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Rios EF, Andrade MHML, Resende MFR, Kirst M, de Resende MDV, de Almeida Filho JE, Gezan SA, Munoz P. Genomic prediction in family bulks using different traits and cross-validations in pine. G3-GENES GENOMES GENETICS 2021; 11:6321952. [PMID: 34544139 PMCID: PMC8496210 DOI: 10.1093/g3journal/jkab249] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 07/02/2021] [Indexed: 11/13/2022]
Abstract
Genomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5–20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations.
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Affiliation(s)
- Esteban F Rios
- Agronomy Department, University of Florida, Gainesville, FL 32611, USA
| | | | - Marcio F R Resende
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Matias Kirst
- School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA
| | - Marcos D V de Resende
- EMBRAPA Café/Department of Statistics, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa 36570-000, Brazil
| | | | | | - Patricio Munoz
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
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Zhang W, Boyle K, Brule-Babel A, Fedak G, Gao P, Djama ZR, Polley B, Cuthbert R, Randhawa H, Graf R, Jiang F, Eudes F, Fobert PR. Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population. BIOLOGY 2021; 10:biology10080756. [PMID: 34439988 PMCID: PMC8389552 DOI: 10.3390/biology10080756] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/01/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022]
Abstract
Simple Summary Genomic selection is a promising approach to select superior wheat lines with better resistance to Fusarium head blight. The accuracy of genomic selection is determined by many factors. In this study, we found a training population with large size, genomic selection models incorporating biological information, and multi-environment modelling led to considerably better predictabilities. A training population designed by the coefficient of determination (CDmean) could increase accuracy of prediction. Relatedness between training population (TP) and testing population is the key for accuracies of genomic selection across populations. Abstract Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.
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Affiliation(s)
- Wentao Zhang
- Aquatic and Crop Resources Development, National Research Council of Canada, Saskatoon, SK S7N 0W9, Canada; (K.B.); (P.G.); (B.P.)
- Correspondence: (W.Z.); (P.R.F.)
| | - Kerry Boyle
- Aquatic and Crop Resources Development, National Research Council of Canada, Saskatoon, SK S7N 0W9, Canada; (K.B.); (P.G.); (B.P.)
| | - Anita Brule-Babel
- Department of Plant Science, Agriculture Building, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
| | - George Fedak
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (G.F.); (Z.R.D.)
| | - Peng Gao
- Aquatic and Crop Resources Development, National Research Council of Canada, Saskatoon, SK S7N 0W9, Canada; (K.B.); (P.G.); (B.P.)
| | - Zeinab Robleh Djama
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (G.F.); (Z.R.D.)
| | - Brittany Polley
- Aquatic and Crop Resources Development, National Research Council of Canada, Saskatoon, SK S7N 0W9, Canada; (K.B.); (P.G.); (B.P.)
| | - Richard Cuthbert
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK S9H 3X2, Canada;
| | - Harpinder Randhawa
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB T1J 4B1, Canada; (H.R.); (R.G.); (F.J.); (F.E.)
| | - Robert Graf
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB T1J 4B1, Canada; (H.R.); (R.G.); (F.J.); (F.E.)
| | - Fengying Jiang
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB T1J 4B1, Canada; (H.R.); (R.G.); (F.J.); (F.E.)
| | - Francois Eudes
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB T1J 4B1, Canada; (H.R.); (R.G.); (F.J.); (F.E.)
| | - Pierre R. Fobert
- Aquatic and Crop Resources Development, National Research Council of Canada, Ottawa, ON K1A 0R6, Canada
- Correspondence: (W.Z.); (P.R.F.)
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Razzaq A, Kaur P, Akhter N, Wani SH, Saleem F. Next-Generation Breeding Strategies for Climate-Ready Crops. FRONTIERS IN PLANT SCIENCE 2021; 12:620420. [PMID: 34367194 PMCID: PMC8336580 DOI: 10.3389/fpls.2021.620420] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 06/14/2021] [Indexed: 05/17/2023]
Abstract
Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.
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Affiliation(s)
- Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
| | - Parwinder Kaur
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia
| | - Naheed Akhter
- College of Allied Health Professional, Faculty of Medical Sciences, Government College University Faisalabad, Faisalabad, Pakistan
| | - Shabir Hussain Wani
- Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India
| | - Fozia Saleem
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
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Chamarthi SK, Kaler AS, Abdel-Haleem H, Fritschi FB, Gillman JD, Ray JD, Smith JR, Dhanapal AP, King CA, Purcell LC. Identification and Confirmation of Loci Associated With Canopy Wilting in Soybean Using Genome-Wide Association Mapping. FRONTIERS IN PLANT SCIENCE 2021; 12:698116. [PMID: 34335664 PMCID: PMC8317169 DOI: 10.3389/fpls.2021.698116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/14/2021] [Indexed: 05/25/2023]
Abstract
Drought causes significant soybean [Glycine max (L.) Merr.] yield losses each year in rain-fed production systems of many regions. Genetic improvement of soybean for drought tolerance is a cost-effective approach to stabilize yield under rain-fed management. The objectives of this study were to confirm previously reported soybean loci and to identify novel loci associated with canopy wilting (CW) using a panel of 200 diverse maturity group (MG) IV accessions. These 200 accessions along with six checks were planted at six site-years using an augmented incomplete block design with three replications under irrigated and rain-fed treatments. Association mapping, using 34,680 single nucleotide polymorphisms (SNPs), identified 188 significant SNPs associated with CW that likely tagged 152 loci. This includes 87 SNPs coincident with previous studies that likely tagged 68 loci and 101 novel SNPs that likely tagged 84 loci. We also determined the ability of genomic estimated breeding values (GEBVs) from previous research studies to predict CW in different genotypes and environments. A positive relationship (P ≤ 0.05;0.37 ≤ r ≤ 0.5) was found between observed CW and GEBVs. In the vicinity of 188 significant SNPs, 183 candidate genes were identified for both coincident SNPs and novel SNPs. Among these 183 candidate genes, 57 SNPs were present within genes coding for proteins with biological functions involved in plant stress responses. These genes may be directly or indirectly associated with transpiration or water conservation. The confirmed genomic regions may be an important resource for pyramiding favorable alleles and, as candidates for genomic selection, enhancing soybean drought tolerance.
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Affiliation(s)
- Siva K. Chamarthi
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Avjinder S. Kaler
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Hussein Abdel-Haleem
- USDA-ARS, U.S. Arid Land Agricultural Research Center, Maricopa, AZ, United States
| | - Felix B. Fritschi
- Division of Plant Sciences, University of Missouri, Columbia, MO, United States
| | - Jason D. Gillman
- Plant Genetic Research Unit, USDA-ARS, University of Missouri, Columbia, MO, United States
| | - Jeffery D. Ray
- Crop Genetics Research Unit, USDA-ARS, Stoneville, MS, United States
| | - James R. Smith
- Crop Genetics Research Unit, USDA-ARS, Stoneville, MS, United States
| | - Arun P. Dhanapal
- Division of Plant Sciences, University of Missouri, Columbia, MO, United States
| | - Charles A. King
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Larry C. Purcell
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
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Reynolds MP, Lewis JM, Ammar K, Basnet BR, Crespo-Herrera L, Crossa J, Dhugga KS, Dreisigacker S, Juliana P, Karwat H, Kishii M, Krause MR, Langridge P, Lashkari A, Mondal S, Payne T, Pequeno D, Pinto F, Sansaloni C, Schulthess U, Singh RP, Sonder K, Sukumaran S, Xiong W, Braun HJ. Harnessing translational research in wheat for climate resilience. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5134-5157. [PMID: 34139769 PMCID: PMC8272565 DOI: 10.1093/jxb/erab256] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 06/14/2021] [Indexed: 05/24/2023]
Abstract
Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.
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Affiliation(s)
- Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Janet M Lewis
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Karim Ammar
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Bhoja R Basnet
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kanwarpal S Dhugga
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hannes Karwat
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Masahiro Kishii
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Margaret R Krause
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Peter Langridge
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB1, Glen Osmond SA 5064, Australia
- Wheat Initiative, Julius Kühn-Institute, Königin-Luise-Str. 19, 14195 Berlin, Germany
| | - Azam Lashkari
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Suchismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Thomas Payne
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Diego Pequeno
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Carolina Sansaloni
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Urs Schulthess
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kai Sonder
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Wei Xiong
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Hans J Braun
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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71
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Wolfe MD, Jannink JL, Kantar MB, Santantonio N. Multi-Species Genomics-Enabled Selection for Improving Agroecosystems Across Space and Time. FRONTIERS IN PLANT SCIENCE 2021; 12:665349. [PMID: 34249037 PMCID: PMC8261054 DOI: 10.3389/fpls.2021.665349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/12/2021] [Indexed: 05/27/2023]
Abstract
Plant breeding has been central to global increases in crop yields. Breeding deserves praise for helping to establish better food security, but also shares the responsibility of unintended consequences. Much work has been done describing alternative agricultural systems that seek to alleviate these externalities, however, breeding methods and breeding programs have largely not focused on these systems. Here we explore breeding and selection strategies that better align with these more diverse spatial and temporal agricultural systems.
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Affiliation(s)
- Marnin D. Wolfe
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- United States Department of Agriculture – Agriculture Research Service, Ithaca, NY, United States
| | - Michael B. Kantar
- Department of Tropical Plant and Soil Science, University of Hawai‘i at Mānoa, Honolulu, HI, United States
| | - Nicholas Santantonio
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
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72
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Jocković M, Jocić S, Cvejić S, Marjanović-Jeromela A, Jocković J, Radanović A, Miladinović D. Genetic Improvement in Sunflower Breeding—Integrated Omics Approach. PLANTS 2021; 10:plants10061150. [PMID: 34200113 PMCID: PMC8228292 DOI: 10.3390/plants10061150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/31/2021] [Accepted: 06/01/2021] [Indexed: 01/23/2023]
Abstract
Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.
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Affiliation(s)
- Milan Jocković
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Serbia; (S.J.); (S.C.); (A.M.-J.); (A.R.); (D.M.)
- Correspondence:
| | - Siniša Jocić
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Serbia; (S.J.); (S.C.); (A.M.-J.); (A.R.); (D.M.)
| | - Sandra Cvejić
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Serbia; (S.J.); (S.C.); (A.M.-J.); (A.R.); (D.M.)
| | - Ana Marjanović-Jeromela
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Serbia; (S.J.); (S.C.); (A.M.-J.); (A.R.); (D.M.)
| | - Jelena Jocković
- Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Dositeja Obradovića 3, 21000 Novi Sad, Serbia;
| | - Aleksandra Radanović
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Serbia; (S.J.); (S.C.); (A.M.-J.); (A.R.); (D.M.)
| | - Dragana Miladinović
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Serbia; (S.J.); (S.C.); (A.M.-J.); (A.R.); (D.M.)
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73
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Cooper M, Voss-Fels KP, Messina CD, Tang T, Hammer GL. Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1625-1644. [PMID: 33738512 PMCID: PMC8206060 DOI: 10.1007/s00122-021-03812-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 03/05/2021] [Indexed: 05/05/2023]
Abstract
KEY MESSAGE Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is "How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?" Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype-Management (G-M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G-M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G-M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G-M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Carlos D Messina
- Corteva Agriscience, Research and Development, Johnston, IA, 50131, USA
| | - Tom Tang
- Corteva Agriscience, Research and Development, Johnston, IA, 50131, USA
| | - Graeme L Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
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74
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Zhao Y, Thorwarth P, Jiang Y, Philipp N, Schulthess AW, Gils M, Boeven PHG, Longin CFH, Schacht J, Ebmeyer E, Korzun V, Mirdita V, Dörnte J, Avenhaus U, Horbach R, Cöster H, Holzapfel J, Ramgraber L, Kühnle S, Varenne P, Starke A, Schürmann F, Beier S, Scholz U, Liu F, Schmidt RH, Reif JC. Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat. SCIENCE ADVANCES 2021; 7:7/24/eabf9106. [PMID: 34117061 PMCID: PMC8195483 DOI: 10.1126/sciadv.abf9106] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 04/28/2021] [Indexed: 05/07/2023]
Abstract
The potential of big data to support businesses has been demonstrated in financial services, manufacturing, and telecommunications. Here, we report on efforts to enter a new data era in plant breeding by collecting genomic and phenotypic information from 12,858 wheat genotypes representing 6575 single-cross hybrids and 6283 inbred lines that were evaluated in six experimental series for yield in field trials encompassing ~125,000 plots. Integrating data resulted in twofold higher prediction ability compared with cases in which hybrid performance was predicted across individual experimental series. Our results suggest that combining data across breeding programs is a particularly appropriate strategy to exploit the potential of big data for predictive plant breeding. This paradigm shift can contribute to increasing yield and resilience, which is needed to feed the growing world population.
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Affiliation(s)
- Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Patrick Thorwarth
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70593 Stuttgart, Germany
| | - Yong Jiang
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Norman Philipp
- Syngenta Seeds GmbH, Kroppenstedterstr. 4, 39398 Hadmersleben, Germany
| | - Albert W Schulthess
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Mario Gils
- Nordsaat Saatzucht GmbH, , Böhnshauserstr. 1, 38895 Langenstein, Germany
| | | | - C Friedrich H Longin
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70593 Stuttgart, Germany
| | | | - Erhard Ebmeyer
- KWS LOCHOW GmbH, Ferdinand-von-Lochow-Str. 5, 29303 Bergen, Germany
| | - Viktor Korzun
- KWS SAAT SE & Co. KGaA, Grimsehlstr. 31, 37574 Einbeck, Germany
- Federal State Budgetary Institution of Science Federal Research Center, "Kazan Scientific Center of Russian Academy of Sciences," ul. Lobachevskogo, 2/31, Kazan, 420111 Tatarstan, Russian Federation
| | - Vilson Mirdita
- BASF Agricultural Solutions Seed GmbH, OT Gatersleben, Am Schwabeplan 8, 06466 Seeland, Germany
| | - Jost Dörnte
- Deutsche Saatveredelung AG, Leutewitz 26, 01665 Käbschütztal, Germany
| | - Ulrike Avenhaus
- W. von Borries-Eckendorf GmbH & Co. KG, Hovedisserstr. 92, 33818 Leopoldshöhe, Germany
| | - Ralf Horbach
- Saatzucht Bauer GmbH & Co. KG, Hofmarkstr.1, 93083 Niederträubling, Germany
| | | | - Josef Holzapfel
- Secobra Saatzucht GmbH, Feldkirchen 3, 85368 Moosburg, Germany
| | - Ludwig Ramgraber
- Saatzucht Josef Breun GmbH & Co. KG, Amselweg 1, 91074 Herzogenaurach, Germany
| | - Simon Kühnle
- Pflanzenzucht Oberlimpurg, Oberlimpurg 2, 74523 Schwäbisch Hall, Germany
| | - Pierrick Varenne
- Limagrain Europe, Ferme de l'Etang BP3, 77390 Verneuil l'Etang, France
| | - Anne Starke
- Limagrain GmbH, Salderstr. 4, 31226 Peine-Rosenthal, Germany
| | | | - Sebastian Beier
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Fang Liu
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Renate H Schmidt
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Jochen C Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany.
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Reynolds M, Atkin OK, Bennett M, Cooper M, Dodd IC, Foulkes MJ, Frohberg C, Hammer G, Henderson IR, Huang B, Korzun V, McCouch SR, Messina CD, Pogson BJ, Slafer GA, Taylor NL, Wittich PE. Addressing Research Bottlenecks to Crop Productivity. TRENDS IN PLANT SCIENCE 2021; 26:607-630. [PMID: 33893046 DOI: 10.1016/j.tplants.2021.03.011] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 05/22/2023]
Abstract
Asymmetry of investment in crop research leads to knowledge gaps and lost opportunities to accelerate genetic gain through identifying new sources and combinations of traits and alleles. On the basis of consultation with scientists from most major seed companies, we identified several research areas with three common features: (i) relatively underrepresented in the literature; (ii) high probability of boosting productivity in a wide range of crops and environments; and (iii) could be researched in 'precompetitive' space, leveraging previous knowledge, and thereby improving models that guide crop breeding and management decisions. Areas identified included research into hormones, recombination, respiration, roots, and source-sink, which, along with new opportunities in phenomics, genomics, and bioinformatics, make it more feasible to explore crop genetic resources and improve breeding strategies.
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Affiliation(s)
- Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico, El Batan, Texcoco, Mexico.
| | - Owen K Atkin
- Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, The Australian National University Canberra, Acton, ACT 2601, Australia.
| | - Malcolm Bennett
- Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, LE12 5RD, UK.
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Ian C Dodd
- The Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
| | - M John Foulkes
- Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, LE12 5RD, UK
| | - Claus Frohberg
- BASF BBC-Innovation Center Gent, Technologiepark-Zwijnaarde 101, 9052 Gent, Belgium
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Ian R Henderson
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, UK
| | - Bingru Huang
- Department of Plant Biology and Pathology, Rutgers University, 59 Dudley Road, New Brunswick, NJ 08901, USA.
| | | | - Susan R McCouch
- Plant Breeding & Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14850, USA.
| | - Carlos D Messina
- Corteva Agriscience, 7250 NW 62nd Avenue, Johnston, IA 50310, USA.
| | - Barry J Pogson
- Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, The Australian National University Canberra, Acton, ACT 2601, Australia
| | - Gustavo A Slafer
- Department of Crop and Forest Sciences, University of Lleida, AGROTECNIO, CERCA Center, Av. R. Roure 191, 25198 Lleida, Spain; ICREA, Catalonian Institution for Research and Advanced Studies, Barcelona, Spain.
| | - Nicolas L Taylor
- ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences and Institute of Agriculture, The University of Western Australia, Crawley, WA, Australia
| | - Peter E Wittich
- Syngenta Seeds B.V., Westeinde 62, 1601 BK, Enkhuizen, The Netherlands.
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Smith DT, Potgieter AB, Chapman SC. Scaling up high-throughput phenotyping for abiotic stress selection in the field. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1845-1866. [PMID: 34076731 DOI: 10.1007/s00122-021-03864-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/13/2021] [Indexed: 05/18/2023]
Abstract
High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.
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Affiliation(s)
- Daniel T Smith
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Andries B Potgieter
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Scott C Chapman
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
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77
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Kamenya SN, Mikwa EO, Song B, Odeny DA. Genetics and breeding for climate change in Orphan crops. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1787-1815. [PMID: 33486565 PMCID: PMC8205878 DOI: 10.1007/s00122-020-03755-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 12/16/2020] [Indexed: 05/17/2023]
Abstract
Climate change is rapidly changing how we live, what we eat and produce, the crops we breed and the target traits. Previously underutilized orphan crops that are climate resilient are receiving much attention from the crops research community, as they are often the only crops left in the field after periods of extreme weather conditions. There are several orphan crops with incredible resilience to biotic and abiotic stresses. Some are nutritious, while others provide good sources of biofuel, medicine and other industrial raw materials. Despite these benefits, orphan crops are still lacking in important genetic and genomic resources that could be used to fast track their improvement and make their production profitable. Progress has been made in generating draft genomes of at least 28 orphan crops over the last decade, thanks to the reducing cost of sequencing. The implementation of a structured breeding program that takes advantage of additional modern crop improvement tools such as genomic selection, speed breeding, genome editing, high throughput phenotyping and breeding digitization would make rapid improvement of these orphan crops possible, but would require coordinated research investment. Other production challenges such as lack of adequate germplasm conservation, poor/non-existent seed systems and agricultural extension services, as well as poor marketing channels will also need to be improved if orphan crops were to be profitable. We review the importance of breeding orphan crops under the increasing effects of climate change, highlight existing gaps that need to be addressed and share some lessons to be learned from major crops.
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Affiliation(s)
- Sandra Ndagire Kamenya
- African Center of Excellence in Agroecology and Livelihood Systems, Uganda Martyrs University, Kampala, Uganda
| | - Erick Owuor Mikwa
- The International Crops Research Institute for the Semi-Arid Tropics - Eastern and Southern Africa, Nairobi, Kenya
| | - Bo Song
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute At Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518060, People's Republic of China.
| | - Damaris Achieng Odeny
- The International Crops Research Institute for the Semi-Arid Tropics - Eastern and Southern Africa, Nairobi, Kenya.
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78
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Massel K, Lam Y, Wong ACS, Hickey LT, Borrell AK, Godwin ID. Hotter, drier, CRISPR: the latest edit on climate change. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1691-1709. [PMID: 33420514 DOI: 10.1007/s00122-020-03764-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/30/2020] [Indexed: 05/23/2023]
Abstract
Integrating CRISPR/Cas9 genome editing into modern breeding programs for crop improvement in cereals. Global climate trends in many agricultural regions have been rapidly changing over the past decades, and major advances in global food systems are required to ensure food security in the face of these emerging challenges. With increasing climate instability due to warmer temperatures and rising CO2 levels, the productivity of global agriculture will continue to be negatively impacted. To combat these growing concerns, creative approaches will be required, utilising all the tools available to produce more robust and tolerant crops with increased quality and yields under more extreme conditions. The integration of genome editing and transgenics into current breeding strategies is one promising solution to accelerate genetic gains through targeted genetic modifications, producing crops that can overcome the shifting climate realities. This review focuses on how revolutionary genome editing tools can be directly implemented into breeding programs for cereal crop improvement to rapidly counteract many of the issues affecting agriculture production in the years to come.
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Affiliation(s)
- Karen Massel
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Yasmine Lam
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Albert C S Wong
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Lee T Hickey
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Andrew K Borrell
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ian D Godwin
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
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Zenda T, Liu S, Dong A, Duan H. Advances in Cereal Crop Genomics for Resilience under Climate Change. Life (Basel) 2021; 11:502. [PMID: 34072447 PMCID: PMC8228855 DOI: 10.3390/life11060502] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/21/2021] [Accepted: 05/25/2021] [Indexed: 12/12/2022] Open
Abstract
Adapting to climate change, providing sufficient human food and nutritional needs, and securing sufficient energy supplies will call for a radical transformation from the current conventional adaptation approaches to more broad-based and transformative alternatives. This entails diversifying the agricultural system and boosting productivity of major cereal crops through development of climate-resilient cultivars that can sustainably maintain higher yields under climate change conditions, expanding our focus to crop wild relatives, and better exploitation of underutilized crop species. This is facilitated by the recent developments in plant genomics, such as advances in genome sequencing, assembly, and annotation, as well as gene editing technologies, which have increased the availability of high-quality reference genomes for various model and non-model plant species. This has necessitated genomics-assisted breeding of crops, including underutilized species, consequently broadening genetic variation of the available germplasm; improving the discovery of novel alleles controlling important agronomic traits; and enhancing creation of new crop cultivars with improved tolerance to biotic and abiotic stresses and superior nutritive quality. Here, therefore, we summarize these recent developments in plant genomics and their application, with particular reference to cereal crops (including underutilized species). Particularly, we discuss genome sequencing approaches, quantitative trait loci (QTL) mapping and genome-wide association (GWAS) studies, directed mutagenesis, plant non-coding RNAs, precise gene editing technologies such as CRISPR-Cas9, and complementation of crop genotyping by crop phenotyping. We then conclude by providing an outlook that, as we step into the future, high-throughput phenotyping, pan-genomics, transposable elements analysis, and machine learning hold much promise for crop improvements related to climate resilience and nutritional superiority.
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Affiliation(s)
- Tinashe Zenda
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Science, Faculty of Agriculture and Environmental Science, Bindura University of Science Education, Bindura P. Bag 1020, Zimbabwe
| | - Songtao Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
| | - Anyi Dong
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
| | - Huijun Duan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
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80
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Alemu A, Brazauskas G, Gaikpa DS, Henriksson T, Islamov B, Jørgensen LN, Koppel M, Koppel R, Liatukas Ž, Svensson JT, Chawade A. Genome-Wide Association Analysis and Genomic Prediction for Adult-Plant Resistance to Septoria Tritici Blotch and Powdery Mildew in Winter Wheat. Front Genet 2021; 12:661742. [PMID: 34054924 PMCID: PMC8149967 DOI: 10.3389/fgene.2021.661742] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 04/06/2021] [Indexed: 11/29/2022] Open
Abstract
Septoria tritici blotch (STB) caused by the fungal pathogen Zymoseptoria tritici and powdery mildew (PM) caused by Blumeria graminis f.sp tritici (Bgt) are among the forefront foliar diseases of wheat that lead to a significant loss of grain yield and quality. Resistance breeding aimed at developing varieties with inherent resistance to STB and PM diseases has been the most sustainable and environment-friendly approach. In this study, 175 winter wheat landraces and historical cultivars originated from the Nordic region were evaluated for adult-plant resistance (APR) to STB and PM in Denmark, Estonia, Lithuania, and Sweden. Genome-wide association study (GWAS) and genomic prediction (GP) were performed based on the adult-plant response to STB and PM in field conditions using 7,401 single-nucleotide polymorphism (SNP) markers generated by 20K SNP chip. Genotype-by-environment interaction was significant for both disease scores. GWAS detected stable and environment-specific quantitative trait locis (QTLs) on chromosomes 1A, 1B, 1D, 2B, 3B, 4A, 5A, 6A, and 6B for STB and 2A, 2D, 3A, 4B, 5A, 6B, 7A, and 7B for PM adult-plant disease resistance. GP accuracy was improved when assisted with QTL from GWAS as a fixed effect. The GWAS-assisted GP accuracy ranged within 0.53–0.75 and 0.36–0.83 for STB and PM, respectively, across the tested environments. This study highlights that landraces and historical cultivars are a valuable source of APR to STB and PM. Such germplasm could be used to identify and introgress novel resistance genes to modern breeding lines.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Gintaras Brazauskas
- Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (LAMMC), Kėdainiai, Lithuania
| | - David S Gaikpa
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | | | | | - Mati Koppel
- Estonian University of Life Sciences, Tartu, Estonia
| | - Reine Koppel
- Estonian Crop Research Institute, Jõgeva, Estonia
| | - Žilvinas Liatukas
- Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (LAMMC), Kėdainiai, Lithuania
| | | | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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81
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Technow F, Podlich D, Cooper M. Back to the future: Implications of genetic complexity for the structure of hybrid breeding programs. G3 (BETHESDA, MD.) 2021; 11:6265599. [PMID: 33950172 PMCID: PMC8495936 DOI: 10.1093/g3journal/jkab153] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/28/2021] [Indexed: 11/14/2022]
Abstract
Commercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate contribution of inbred lines reduce effective population size and constrain the accessible genetic space. Under these conditions, long-term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study, we therefore aim to propose a rationale for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this, we use simulations based on the NK model of genetic architecture. We indeed found that constraining genetic space through program decentralization and disproportionate contribution of parental inbred lines, is required to expose additive genetic variation and thus facilitate heritable genetic gains under high levels of genetic complexity. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for “why things worked” in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.
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Affiliation(s)
- Frank Technow
- Plant Breeding, Corteva Agriscience, Tavistock, ON, N0B 2R0, Canada
| | - Dean Podlich
- Systems and Innovation for Breeding and Seed Products, Corteva Agriscience, Johnston, IA, 50131, USA
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4067, Australia
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82
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Rice BR, Lipka AE. Diversifying maize genomic selection models. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:33. [PMID: 37309328 PMCID: PMC10236107 DOI: 10.1007/s11032-021-01221-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/07/2021] [Indexed: 06/14/2023]
Abstract
Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.
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Affiliation(s)
- Brian R. Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL USA
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83
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An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits. PLANTS 2021; 10:plants10040745. [PMID: 33920359 PMCID: PMC8069980 DOI: 10.3390/plants10040745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 11/17/2022]
Abstract
Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.
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84
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Liu X, Hu X, Li K, Liu Z, Wu Y, Feng G, Huang C, Wang H. Identifying quantitative trait loci for the general combining ability of yield-relevant traits in maize. BREEDING SCIENCE 2021; 71:217-228. [PMID: 34377070 PMCID: PMC8329886 DOI: 10.1270/jsbbs.20008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 12/14/2020] [Indexed: 06/13/2023]
Abstract
Maize is the most important staple crop worldwide. Many of its agronomic traits present with a high level of heterosis. Combining ability was proposed to exploit the rule of heterosis, and general combining ability (GCA) is a crucial measure of parental performance. In this study, a recombinant inbred line population was used to construct testcross populations by crossing with four testers based on North Carolina design II. Six yield-relevant traits were investigated as phenotypic data. GCA effects were estimated for three scenarios based on the heterotic group and the number of tester lines. These estimates were then used to identify quantitative trait loci (QTL) and dissect genetic basis of GCA. A higher heritability of GCA was obtained for each trait. Thus, testing in early generation of breeding may effectively select candidate lines with relatively superior GCA performance. The GCA QTL detected in each scenario was slightly different according to the linkage mapping. Most of the GCA-relevant loci were simultaneously detected in all three datasets. Therefore, the genetic basis of GCA was nearly constant although discrepant inbred lines were appointed as testers. In addition, favorable alleles corresponding to GCA could be pyramided via marker-assisted selection and made available for maize hybrid breeding.
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Affiliation(s)
- Xiaogang Liu
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiaojiao Hu
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Kun Li
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhifang Liu
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yujin Wu
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Guang Feng
- Liaoning Dandong Academy of Agricultural Sciences, Dandong 118109, China
| | - Changling Huang
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hongwu Wang
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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85
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Gonçalves MTV, Morota G, Costa PMDA, Vidigal PMP, Barbosa MHP, Peternelli LA. Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. PLoS One 2021; 16:e0236853. [PMID: 33661948 PMCID: PMC7932073 DOI: 10.1371/journal.pone.0236853] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 01/20/2021] [Indexed: 11/19/2022] Open
Abstract
The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.
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Affiliation(s)
| | - Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States of America
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86
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Zhao H, Li Y, Petkowski J, Kant S, Hayden MJ, Daetwyler HD. Genomic prediction and genomic heritability of grain yield and its related traits in a safflower genebank collection. THE PLANT GENOME 2021; 14:e20064. [PMID: 33140563 DOI: 10.1002/tpg2.20064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/11/2020] [Accepted: 09/13/2020] [Indexed: 05/28/2023]
Abstract
Safflower, a minor oilseed crop, is gaining increased attention for food and industrial uses. Safflower genebank collections are an important genetic resource for crop enhancement and future breeding programs. In this study, we investigated the population structure of a safflower collection sourced from the Australian Grain Genebank and assessed the potential of genomic prediction (GP) to evaluate grain yield and related traits using single and multi-site models. Prediction accuracies (PA) of genomic best linear unbiased prediction (GBLUP) from single site models ranged from 0.21 to 0.86 for all traits examined and were consistent with estimated genomic heritability (h2 ), which varied from low to moderate across traits. We generally observed a low level of genome × environment interactions (g × E). Multi-site g × E GBLUP models only improved PA for accessions with at least some phenotypes in the training set. We observed that relaxing quality filtering parameters for genotype-by-sequencing (GBS), such as missing genotype call rate, did not affect PA but upwardly biased h2 estimation. Our results indicate that GP is feasible in safflower evaluation and is potentially a cost-effective tool to facilitate fast introgression of desired safflower trait variation from genebank germplasm into breeding lines.
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Affiliation(s)
- Huanhuan Zhao
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC, 3400, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Joanna Petkowski
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC, 3400, Australia
- Centre for Agricultural Innovation, The University of Melbourne, Melbourne, VIC, Australia
| | - Matthew J Hayden
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Hans D Daetwyler
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
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87
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Labroo MR, Studer AJ, Rutkoski JE. Heterosis and Hybrid Crop Breeding: A Multidisciplinary Review. Front Genet 2021; 12:643761. [PMID: 33719351 PMCID: PMC7943638 DOI: 10.3389/fgene.2021.643761] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 02/08/2021] [Indexed: 11/24/2022] Open
Abstract
Although hybrid crop varieties are among the most popular agricultural innovations, the rationale for hybrid crop breeding is sometimes misunderstood. Hybrid breeding is slower and more resource-intensive than inbred breeding, but it allows systematic improvement of a population by recurrent selection and exploitation of heterosis simultaneously. Inbred parental lines can identically reproduce both themselves and their F1 progeny indefinitely, whereas outbred lines cannot, so uniform outbred lines must be bred indirectly through their inbred parents to harness heterosis. Heterosis is an expected consequence of whole-genome non-additive effects at the population level over evolutionary time. Understanding heterosis from the perspective of molecular genetic mechanisms alone may be elusive, because heterosis is likely an emergent property of populations. Hybrid breeding is a process of recurrent population improvement to maximize hybrid performance. Hybrid breeding is not maximization of heterosis per se, nor testing random combinations of individuals to find an exceptional hybrid, nor using heterosis in place of population improvement. Though there are methods to harness heterosis other than hybrid breeding, such as use of open-pollinated varieties or clonal propagation, they are not currently suitable for all crops or production environments. The use of genomic selection can decrease cycle time and costs in hybrid breeding, particularly by rapidly establishing heterotic pools, reducing testcrossing, and limiting the loss of genetic variance. Open questions in optimal use of genomic selection in hybrid crop breeding programs remain, such as how to choose founders of heterotic pools, the importance of dominance effects in genomic prediction, the necessary frequency of updating the training set with phenotypic information, and how to maintain genetic variance and prevent fixation of deleterious alleles.
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Affiliation(s)
| | | | - Jessica E. Rutkoski
- Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, IL, United States
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88
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Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G, Singh GP. Integrated genomic selection for rapid improvement of crops. Genomics 2021; 113:1070-1086. [PMID: 33610797 DOI: 10.1016/j.ygeno.2021.02.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/08/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022]
Abstract
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
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Affiliation(s)
| | | | | | | | | | - Satish Kumar
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hanif Khan
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | | | - Gyanendra Singh
- Indian Institute of Wheat and Barley Research, Karnal, India
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89
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Olanrewaju OS, Oyatomi O, Babalola OO, Abberton M. Genetic Diversity and Environmental Influence on Growth and Yield Parameters of Bambara Groundnut. FRONTIERS IN PLANT SCIENCE 2021; 12:796352. [PMID: 34987538 PMCID: PMC8721115 DOI: 10.3389/fpls.2021.796352] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 11/22/2021] [Indexed: 05/02/2023]
Abstract
Bambara groundnut (BGN) is a drought-tolerant crop majorly cultivated in sub-Saharan Africa. Due to a lack of extensive research, marginalization, lack of awareness, and lack of available fund among others, this crop's improvement has been limited. The development of this crop depends on evaluation and selection of unique and stable breeding lines in different environments. This study aims to estimate genetic diversity using morphological traits at different locations in 95 accessions of BGN collected from the Genebank of the International Institute of Tropical Agriculture (IITA), Ibadan. The experiment was carried out in three replicates at IITA experimental sites in two agroecological zones in Ibadan (7°40'19.62″ N, 3°91'73.13″ E) and Ikenne (6°51'00.873″ N, 3°41'48.528″ E) using a randomized complete block design. Ten vegetative growth traits and eight yield traits were scored. The data was subjected to ANOVA, PCA, correlation, and cluster analysis. Estimations of genetic parameters and broad sense heritability were carried out on the traits. ANOVA revealed significant variation in each trait except for days to emergence. Significant variation was also observed for accession and location interactions (genotype x environment interactions) for plant height, leaf length, leaf width, chlorophyll content, number of petioles, germination count, number of pods, number of seeds, seed length, seed width, and yield. PC1 and PC2 show 42.3% of the total variations observed by the PC, with seed thickness contributing more to PC1 and the number of seeds contributing more to PC2. Cluster analysis categorized the accessions into four distinct groups. The number of pods had the highest genotypic coefficient of variation of 32.55% and the phenotypic coefficient of variation of 97.61%, while seed length (0.63), seed width (0.54), and seed thickness (0.58) have high heritability values. The genetic advance was highest in yield (76.15%) and lowest in days to 50% germination (0.21%). This study can be used to predict appropriate agroecological zones for the planting of BGN while the knowledge of the diversity of the accessions based on the traits could serve a guide in selecting the best trait for the improvement of the crop.
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Affiliation(s)
- Oluwaseyi Samuel Olanrewaju
- Food Security and Safety Niche Area, Faculty of Natural and Agricultural Sciences, North-West University, Mafikeng, South Africa
- Genetic Resources Center, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Olaniyi Oyatomi
- Genetic Resources Center, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Olubukola Oluranti Babalola
- Food Security and Safety Niche Area, Faculty of Natural and Agricultural Sciences, North-West University, Mafikeng, South Africa
| | - Michael Abberton
- Genetic Resources Center, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
- *Correspondence: Michael Abberton
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90
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Cooper M, Messina CD. Can We Harness "Enviromics" to Accelerate Crop Improvement by Integrating Breeding and Agronomy? FRONTIERS IN PLANT SCIENCE 2021; 12:735143. [PMID: 34567047 PMCID: PMC8461239 DOI: 10.3389/fpls.2021.735143] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/16/2021] [Indexed: 05/02/2023]
Abstract
The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of "enviromics" as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate "enviromics" methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Mark Cooper,
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91
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Mohanta TK, Mishra AK, Mohanta YK, Al-Harrasi A. Space Breeding: The Next-Generation Crops. FRONTIERS IN PLANT SCIENCE 2021; 12:771985. [PMID: 34777452 PMCID: PMC8579881 DOI: 10.3389/fpls.2021.771985] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/04/2021] [Indexed: 05/03/2023]
Abstract
Since the beginning of space exploration, researchers have been exploring the role of microgravity, cosmic radiation, and other aspects of the space environment on plant growth and development. To create superior crop varieties and achieve noticeable success in the space environment, several types of research have been conducted thus far. Space-grown plants have been exposed to cosmic radiation and microgravity, which has led to the generation of crop varieties with diverse genotypes and phenotypes arising from different cellular, subcellular, genomic, chromosomal, and biochemical changes. DNA damage and chromosomal aberrations due to cosmic radiation are the major factors responsible for genetic polymorphism and the generation of crops with modified genetic combinations. These changes can be used to produce next-generation crop varieties capable of surviving diverse environmental conditions. This review aims to elucidate the detailed molecular mechanisms and genetic mutations found in plants used in recent space crop projects and how these can be applied in space breeding programmes in the future.
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Affiliation(s)
- Tapan Kumar Mohanta
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, Oman
- *Correspondence: Tapan Kumar Mohanta, ;
| | | | - Yugal Kishore Mohanta
- Department of Applied Biology, School of Biological Science, University of Science and Technology, Ri-Bhoi, India
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, Oman
- Ahmed Al-Harrasi,
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92
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Resende RT, Piepho HP, Rosa GJM, Silva-Junior OB, E Silva FF, de Resende MDV, Grattapaglia D. Enviromics in breeding: applications and perspectives on envirotypic-assisted selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:95-112. [PMID: 32964262 DOI: 10.1007/s00122-020-03684-z] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 09/10/2020] [Indexed: 05/18/2023]
Abstract
We propose the application of enviromics to breeding practice, by which the similarity among sites assessed on an "omics" scale of environmental attributes drives the prediction of unobserved genotype performances. Genotype by environment interaction (GEI) studies in plant breeding have focused mainly on estimating genetic parameters over a limited number of experimental trials. However, recent geographic information system (GIS) techniques have opened new frontiers for better understanding and dealing with GEI. These advances allow increasing selection accuracy across all sites of interest, including those where experimental trials have not yet been deployed. Here, we introduce the term enviromics, within an envirotypic-assisted breeding framework. In summary, likewise genotypes at DNA markers, any particular site is characterized by a set of "envirotypes" at multiple "enviromic" markers corresponding to environmental variables that may interact with the genetic background, thus providing informative breeding re-rankings for optimized decisions over different environments. Based on simulated data, we illustrate an index-based enviromics method (the "GIS-GEI") which, due to its higher granular resolution than standard methods, allows for: (1) accurate matching of sites to their most appropriate genotypes; (2) better definition of breeding areas that have high genetic correlation to ensure selection gains across environments; and (3) efficient determination of the best sites to carry out experiments for further analyses. Environmental scenarios can also be optimized for productivity improvement and genetic resources management, especially in the current outlook of dynamic climate change. Envirotyping provides a new class of markers for genetic studies, which are fairly inexpensive, increasingly available and transferable across species. We envision a promising future for the integration of enviromics approaches into plant breeding when coupled with next-generation genotyping/phenotyping and powerful statistical modeling of genetic diversity.
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Affiliation(s)
- Rafael T Resende
- School of Agronomy, University of Goiás (UFG), Goiânia, GO, 74690-900, Brazil.
| | - Hans-Peter Piepho
- Biostatistics Unit, University of Hohenheim, 70593, Stuttgart, Germany
| | - Guilherme J M Rosa
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, 53706, Madison, USA
| | | | - Fabyano F E Silva
- Department of Animal Science, University of Viçosa (UFV), Viçosa, MG, 36570-900, Brazil
| | - Marcos Deon V de Resende
- Department of Statistics, University of Viçosa (UFV), Viçosa, MG, 36570-900, Brazil
- EMBRAPA Coffee Research, Brasília, DF, 70770-901, Brazil
| | - Dario Grattapaglia
- EMBRAPA Genetic Resources and Biotechnology - EPqB, Brasília, DF, 70770-910, Brazil.
- Genomic Sciences and Biotechnology Program, Catholic University of Brasília, Brasília, DF, 70790-160, Brazil.
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93
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Badji A, Machida L, Kwemoi DB, Kumi F, Okii D, Mwila N, Agbahoungba S, Ibanda A, Bararyenya A, Nghituwamhata SN, Odong T, Wasswa P, Otim M, Ochwo-Ssemakula M, Talwana H, Asea G, Kyamanywa S, Rubaihayo P. Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils. PLANTS 2020; 10:plants10010029. [PMID: 33374402 PMCID: PMC7823878 DOI: 10.3390/plants10010029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/23/2022]
Abstract
Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.
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Affiliation(s)
- Arfang Badji
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Lewis Machida
- Alliance Bioversity-CIAT, Africa-Office, Kampala P.O. Box 24384, Uganda
| | | | - Frank Kumi
- Department of Crop Science, University of Cape Coast, Cape Coast PMB, Ghana
| | - Dennis Okii
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Natasha Mwila
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | | | - Angele Ibanda
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Astere Bararyenya
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | | | - Thomas Odong
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Peter Wasswa
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Michael Otim
- National Crops Resource Research Institute, Kampala P.O. Box 7084, Uganda
| | | | - Herbert Talwana
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Godfrey Asea
- National Crops Resource Research Institute, Kampala P.O. Box 7084, Uganda
| | - Samuel Kyamanywa
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Patrick Rubaihayo
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
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94
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Kim KD, Kang Y, Kim C. Application of Genomic Big Data in Plant Breeding:Past, Present, and Future. PLANTS (BASEL, SWITZERLAND) 2020; 9:E1454. [PMID: 33126607 PMCID: PMC7694055 DOI: 10.3390/plants9111454] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 10/26/2020] [Accepted: 10/26/2020] [Indexed: 01/11/2023]
Abstract
Plant breeding has a long history of developing new varieties that have ensured the food security of the human population. During this long journey together with humanity, plant breeders have successfully integrated the latest innovations in science and technologies to accelerate the increase in crop production and quality. For the past two decades, since the completion of human genome sequencing, genomic tools and sequencing technologies have advanced remarkably, and adopting these innovations has enabled us to cost down and/or speed up the plant breeding process. Currently, with the growing mass of genomic data and digitalized biological data, interdisciplinary approaches using new technologies could lead to a new paradigm of plant breeding. In this review, we summarize the overall history and advances of plant breeding, which have been aided by plant genomic research. We highlight the key advances in the field of plant genomics that have impacted plant breeding over the past decades and introduce the current status of innovative approaches such as genomic selection, which could overcome limitations of conventional breeding and enhance the rate of genetic gain.
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Affiliation(s)
- Kyung Do Kim
- Department of Bioscience and Bioinformatics, Myongji University, Yongin 17058, Korea;
| | - Yuna Kang
- Department of Crop Science, Chungnam National University, Daejeon 34134, Korea;
| | - Changsoo Kim
- Department of Crop Science, Chungnam National University, Daejeon 34134, Korea;
- Department of Smart Agriculture Systems, Chungnam National University, Daejeon 34134, Korea
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95
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He T, Li C. Harness the power of genomic selection and the potential of germplasm in crop breeding for global food security in the era with rapid climate change. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.cj.2020.04.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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96
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Cappetta E, Andolfo G, Di Matteo A, Barone A, Frusciante L, Ercolano MR. Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches. PLANTS (BASEL, SWITZERLAND) 2020; 9:E1236. [PMID: 32962095 PMCID: PMC7569914 DOI: 10.3390/plants9091236] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/13/2020] [Accepted: 09/15/2020] [Indexed: 01/16/2023]
Abstract
Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain per unit of time and reduce the generation interval by utilizing genome-wide markers in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effects. GS enables the prediction of the breeding value of candidate genotypes for selection. In this work, we address important issues related to GS and its implementation in the plant context with special emphasis on tomato breeding. Genomic constraints and critical parameters affecting the accuracy of prediction such as the number of markers, statistical model, phenotyping and complexity of trait, training population size and composition should be carefully evaluated. The comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding programs is also discussed. GS applied to tomato breeding has already been shown to be feasible. We illustrated how GS can improve the rate of gain in elite line selection, and descendent and backcross schemes. The GS schemes have begun to be delineated and computer science can provide support for future selection strategies. A new promising breeding framework is beginning to emerge for optimizing tomato improvement procedures.
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Affiliation(s)
| | | | | | | | | | - Maria Raffaella Ercolano
- Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Naples, Italy; (E.C.); (G.A.); (A.D.M.); (A.B.); (L.F.)
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97
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Juliana P, Singh RP, Braun HJ, Huerta-Espino J, Crespo-Herrera L, Govindan V, Mondal S, Poland J, Shrestha S. Genomic Selection for Grain Yield in the CIMMYT Wheat Breeding Program-Status and Perspectives. FRONTIERS IN PLANT SCIENCE 2020; 11:564183. [PMID: 33042185 PMCID: PMC7522222 DOI: 10.3389/fpls.2020.564183] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 08/27/2020] [Indexed: 06/11/2023]
Abstract
Genomic breeding technologies offer new opportunities for grain yield (GY) improvement in common wheat. In this study, we have evaluated the potential of genomic selection (GS) in breeding for GY in wheat by modeling a large dataset of 48,562 GY observations from the International Maize and Wheat Improvement Center (CIMMYT), including 36 yield trials evaluated between 2012 and 2019 in Obregón, Sonora, Mexico. Our key objective was to determine the value that GS can add to the current three-stage yield testing strategy at CIMMYT, and we draw inferences from predictive modeling of GY using 420 different populations, environments, cycles, and model combinations. First, we evaluated the potential of genomic predictions for minimizing the number of replications and lines tested within a site and year and obtained mean prediction accuracies (PAs) of 0.56, 0.5, and 0.42 in Stages 1, 2, and 3 of yield testing, respectively. However, these PAs were similar to the mean pedigree-based PAs indicating that genomic relationships added no value to pedigree relationships in the yield testing stages, characterized by small family-sizes. Second, we evaluated genomic predictions for minimizing GY testing across stages/years in Obregón and observed mean PAs of 0.41, 0.31, and 0.37, respectively when GY in the full irrigation bed planting (FI BP), drought stress (DS), and late-sown heat stress environments were predicted across years using genotype × environment (G × E) interaction models. Third, we evaluated genomic predictions for minimizing the number of yield testing environments and observed that in Stage 2, the FI BP, full irrigation flat planting and early-sown heat stress environments (mean PA of 0.37 ± 0.12) and the reduced irrigation and DS environments (mean PA of 0.45 ± 0.07) had moderate predictabilities among them. However, in both predictions across years and environments, the PAs were inconsistent across years and the G × E models had no advantage over the baseline model with environment and line effects. Overall, our results provide excellent insights into the predictability of a quantitative trait like GY and will have important implications on the future design of GS for GY in wheat breeding programs globally.
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Affiliation(s)
- Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Prakash Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hans-Joachim Braun
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Julio Huerta-Espino
- Campo Experimental Valle de Mexico, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP), Chapingo, Mexico
| | | | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Suchismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Sandesh Shrestha
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
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98
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Roth M, Muranty H, Di Guardo M, Guerra W, Patocchi A, Costa F. Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple. HORTICULTURE RESEARCH 2020; 7:148. [PMID: 32922820 PMCID: PMC7459338 DOI: 10.1038/s41438-020-00370-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 07/18/2020] [Accepted: 07/24/2020] [Indexed: 05/11/2023]
Abstract
Texture is a complex trait and a major component of fruit quality in apple. While the major effect of MdPG1, a gene controlling firmness, has already been exploited in elite cultivars, the genetic basis of crispness remains poorly understood. To further improve fruit texture, harnessing loci with minor effects via genomic selection is therefore necessary. In this study, we measured acoustic and mechanical features in 537 genotypes to dissect the firmness and crispness components of fruit texture. Predictions of across-year phenotypic values for these components were calculated using a model calibrated with 8,294 SNP markers. The best prediction accuracies following cross-validations within the training set of 259 genotypes were obtained for the acoustic linear distance (0.64). Predictions for biparental families using the entire training set varied from low to high accuracy, depending on the family considered. While adding siblings or half-siblings into the training set did not clearly improve predictions, we performed an optimization of the training set size and composition for each validation set. This allowed us to increase prediction accuracies by 0.17 on average, with a maximal accuracy of 0.81 when predicting firmness in the 'Gala' × 'Pink Lady' family. Our results therefore identified key genetic parameters to consider when deploying genomic selection for texture in apple. In particular, we advise to rely on a large training population, with high phenotypic variability from which a 'tailored training population' can be extracted using a priori information on genetic relatedness, in order to predict a specific target population.
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Affiliation(s)
- Morgane Roth
- Plant Breeding Research Division, Agroscope, Wädenswil, Zurich, Switzerland
- Present Address: GAFL, INRAE, 84140 Montfavet, France
| | - Hélène Muranty
- IRHS, INRAE, Agrocampus-Ouest, Université d’Angers, SFR 4207 QuaSaV, Beaucouzé, France
| | - Mario Di Guardo
- Department of Genomics and Biology of Fruit Crops, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all’Adige, Italy
- Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy
| | - Walter Guerra
- Research Centre Laimburg, Laimburg 6, 39040 Auer, Italy
| | - Andrea Patocchi
- Plant Breeding Research Division, Agroscope, Wädenswil, Zurich, Switzerland
| | - Fabrizio Costa
- Department of Genomics and Biology of Fruit Crops, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all’Adige, Italy
- Center Agriculture Food Environment, University of Trento, Via Mach 1, 38010 San Michele all’Adige, Italy
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99
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Costa-Neto G, Fritsche-Neto R, Crossa J. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity (Edinb) 2020; 126:92-106. [PMID: 32855544 PMCID: PMC7852533 DOI: 10.1038/s41437-020-00353-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 07/29/2020] [Accepted: 07/30/2020] [Indexed: 01/15/2023] Open
Abstract
Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational processing times, which do not always result in accuracy gains. We investigated the use of new kernel methods and modeling structures involving genomics and nongenomic sources of variation in two MET maize data sets. Five WGP models were considered, advancing in complexity from a main-effect additive model (A) to more complex structures, including dominance deviations (D), genotype × environment interaction (AE and DE), and the reaction-norm model using environmental covariables (W) and their interaction with A and D (AW + DW). A combination of those models built with three different kernel methods, Gaussian kernel (GK), Deep kernel (DK), and the benchmark genomic best linear-unbiased predictor (GBLUP/GB), was tested under three prediction scenarios: newly developed hybrids (CV1), sparse MET conditions (CV2), and new environments (CV0). GK and DK outperformed GB in prediction accuracy and reduction of computation time (~up to 20%) under all model-kernel scenarios. GK was more efficient in capturing the variation due to A + AE and D + DE effects and translated it into accuracy gains (~up to 85% compared with GB). DK provided more consistent predictions, even for more complex structures such as W + AW + DW. Our results suggest that DK and GK are more efficient in translating model complexity into accuracy, and more suitable for including dominance and reaction-norm effects in a biologically accurate and faster way.
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Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, "Luiz de Queiroz" Agriculture College, University of São Paulo, São Paulo, Brazil
| | - Roberto Fritsche-Neto
- Department of Genetics, "Luiz de Queiroz" Agriculture College, University of São Paulo, São Paulo, Brazil
| | - José Crossa
- Biometrics and Statistics Unit, Genetic Resources Program, and Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico.
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100
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Juliana P, Singh RP, Braun HJ, Huerta-Espino J, Crespo-Herrera L, Payne T, Poland J, Shrestha S, Kumar U, Joshi AK, Imtiaz M, Rahman MM, Toledo FH. Retrospective Quantitative Genetic Analysis and Genomic Prediction of Global Wheat Yields. FRONTIERS IN PLANT SCIENCE 2020; 11:580136. [PMID: 32973861 PMCID: PMC7481575 DOI: 10.3389/fpls.2020.580136] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 08/13/2020] [Indexed: 06/11/2023]
Abstract
Breeding for grain yield (GY) in bread wheat at the International Maize and Wheat Improvement Center (CIMMYT) involves three-stage testing at Obregon, Mexico in different selection environments (SEs). To understand the efficiency of selection in the SEs, we performed a large retrospective quantitative genetics study using CIMMYT's yield trials evaluated in the SEs (2013-2014 to 2017-2018), the South Asia Bread Wheat Genomic Prediction Yield Trials (SABWGPYTs) evaluated in India, Pakistan, and Bangladesh (2014-2015 to 2017-2018), and the Elite Spring Wheat Yield Trials (ESWYTs) evaluated in several sites globally (2003-2004 to 2016-2017). First, we compared the narrow-sense heritabilities in the Obregon SEs and target sites and observed that the mean heritability in the SEs was 44.2 and 92.3% higher than the mean heritabilities in the SABWGPYT and ESWYT sites, respectively. Second, we observed significant genetic correlations between a SE in Obregon and all the five SABWGPYT sites and 65.1% of the ESWYT sites. Third, we observed high ratios of response to indirect selection in the SEs of Obregon with a mean of 0.80 ± 0.21 and 2.6 ± 5.4 in the SABWGPYT and ESWYT sites, respectively. Furthermore, our results also indicated that for all the SABWGPYT sites and 82% of the ESWYT sites, a response greater than 0.5 can be achieved by indirect selection for GY in Obregon. We also performed genomic prediction for GY in the target sites using the performance of the same lines in the SEs of Obregon and observed moderate mean prediction accuracies of 0.24 ± 0.08 and 0.28 ± 0.08 in the SABWGPYT and ESWYT sites, respectively using the genotype x environment (GxE) model. However, we observed similar accuracies using the baseline model with environment and line effects and no advantage of modeling GxE interactions. Overall, this study provides important insights into the suitability of the Obregon SEs in breeding for GY, while the variable genomic predictabilities of GY and the high year-to-year GY fluctuations reported, highlight the importance of multi-environment testing across time and space to stave off GxE induced uncertainties in varietal yields.
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Affiliation(s)
- Philomin Juliana
- International Maize And Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Prakash Singh
- International Maize And Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hans-Joachim Braun
- International Maize And Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Julio Huerta-Espino
- Campo Experimental Valle de Mexico, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP), Chapingo, Mexico
| | | | - Thomas Payne
- International Maize And Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Sandesh Shrestha
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Uttam Kumar
- CIMMYT, New Delhi, India
- Borlaug Institute for South Asia (BISA), New Delhi, India
| | - Arun Kumar Joshi
- CIMMYT, New Delhi, India
- Borlaug Institute for South Asia (BISA), New Delhi, India
| | | | - Mohammad Mokhlesur Rahman
- Regional Agricultural Research Station, Bangladesh Agricultural Research Institute (BARI), Jamalpur, Bangladesh
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