1
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Mascher M, Jayakodi M, Shim H, Stein N. Promises and challenges of crop translational genomics. Nature 2024:10.1038/s41586-024-07713-5. [PMID: 39313530 DOI: 10.1038/s41586-024-07713-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/13/2024] [Indexed: 09/25/2024]
Abstract
Crop translational genomics applies breeding techniques based on genomic datasets to improve crops. Technological breakthroughs in the past ten years have made it possible to sequence the genomes of increasing numbers of crop varieties and have assisted in the genetic dissection of crop performance. However, translating research findings to breeding applications remains challenging. Here we review recent progress and future prospects for crop translational genomics in bringing results from the laboratory to the field. Genetic mapping, genomic selection and sequence-assisted characterization and deployment of plant genetic resources utilize rapid genotyping of large populations. These approaches have all had an impact on breeding for qualitative traits, where single genes with large phenotypic effects exert their influence. Characterization of the complex genetic architectures that underlie quantitative traits such as yield and flowering time, especially in newly domesticated crops, will require further basic research, including research into regulation and interactions of genes and the integration of genomic approaches and high-throughput phenotyping, before targeted interventions can be designed. Future priorities for translation include supporting genomics-assisted breeding in low-income countries and adaptation of crops to changing environments.
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Affiliation(s)
- Martin Mascher
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
| | - Murukarthick Jayakodi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Hyeonah Shim
- Department of Agriculture, Forestry and Bioresources, Plant Genomics and Breeding Institute, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, Seoul, Korea
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
- Martin Luther University Halle-Wittenberg, Halle, Germany.
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2
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Schreiber M, Jayakodi M, Stein N, Mascher M. Plant pangenomes for crop improvement, biodiversity and evolution. Nat Rev Genet 2024; 25:563-577. [PMID: 38378816 DOI: 10.1038/s41576-024-00691-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2023] [Indexed: 02/22/2024]
Abstract
Plant genome sequences catalogue genes and the genetic elements that regulate their expression. Such inventories further research aims as diverse as mapping the molecular basis of trait diversity in domesticated plants or inquiries into the origin of evolutionary innovations in flowering plants millions of years ago. The transformative technological progress of DNA sequencing in the past two decades has enabled researchers to sequence ever more genomes with greater ease. Pangenomes - complete sequences of multiple individuals of a species or higher taxonomic unit - have now entered the geneticists' toolkit. The genomes of crop plants and their wild relatives are being studied with translational applications in breeding in mind. But pangenomes are applicable also in ecological and evolutionary studies, as they help classify and monitor biodiversity across the tree of life, deepen our understanding of how plant species diverged and show how plants adapt to changing environments or new selection pressures exerted by human beings.
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Affiliation(s)
- Mona Schreiber
- Department of Biology, University of Marburg, Marburg, Germany
| | - Murukarthick Jayakodi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
- Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Martin Mascher
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
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3
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Roscher-Ehrig L, Weber SE, Abbadi A, Malenica M, Abel S, Hemker R, Snowdon RJ, Wittkop B, Stahl A. Phenomic Selection for Hybrid Rapeseed Breeding. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0215. [PMID: 39049840 PMCID: PMC11268845 DOI: 10.34133/plantphenomics.0215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/19/2024] [Indexed: 07/27/2024]
Abstract
Phenomic selection is a recent approach suggested as a low-cost, high-throughput alternative to genomic selection. Instead of using genetic markers, it employs spectral data to predict complex traits using equivalent statistical models. Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted. However, for hybrid breeding, the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation. Here, we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding. We performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models. We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height, even when spectral data was collected at single locations, while being less affected by population structure. Furthermore, we demonstrate that phenomic prediction across generations is feasible, and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection. We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.
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Affiliation(s)
| | - Sven E. Weber
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | | | | | | | | | - Rod J. Snowdon
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | - Benjamin Wittkop
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | - Andreas Stahl
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants,
Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
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4
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Mandal R, He X, Singh G, Kabir MR, Joshi AK, Singh PK. Screening of CIMMYT and South Asian Bread Wheat Germplasm Reveals Marker-Trait Associations for Seedling Resistance to Septoria Nodorum Blotch. Genes (Basel) 2024; 15:890. [PMID: 39062669 PMCID: PMC11276481 DOI: 10.3390/genes15070890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/02/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
Wheat (Triticum aestivum L.) production is adversely impacted by Septoria nodorum blotch (SNB), a fungal disease caused by Parastagonospora nodorum. Wheat breeders are constantly up against this biotic challenge as they try to create resistant cultivars. The genome-wide association study (GWAS) has become an efficient tool for identifying molecular markers linked with SNB resistance. This technique is used to acquire an understanding of the genetic basis of resistance and to facilitate marker-assisted selection. In the current study, a total of 174 bread wheat accessions from South Asia and CIMMYT were assessed for SNB reactions at the seedling stage in three greenhouse experiments at CIMMYT, Mexico. The results indicated that 129 genotypes were resistant to SNB, 39 were moderately resistant, and only 6 were moderately susceptible. The Genotyping Illumina Infinium 15K Bead Chip was used, and 11,184 SNP markers were utilized to identify marker-trait associations (MTAs) after filtering. Multiple tests confirmed the existence of significant MTAs on chromosomes 5B, 5A, and 3D, and the ones at Tsn1 on 5B were the most stable and conferred the highest phenotypic variation. The resistant genotypes identified in this study could be cultivated in South Asian countries as a preventative measure against the spread of SNB. This work also identified molecular markers of SNB resistance that could be used in future wheat breeding projects.
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Affiliation(s)
- Rupsanatan Mandal
- Visiting Scientist, International Maize and Wheat Improvement Center (CIMMYT), Texcoco 56237, Mexico;
- Department of Genetics and Plant Breeding, Uttar Banga Krishi Viswavidyalaya, Cooch Behar 736165, India
| | - Xinyao He
- International Maize and Wheat Improvement Centre, Texcoco 56237, Mexico;
| | - Gyanendra Singh
- ICAR-Indian Institute of Wheat and Barley Research, Karnal 132001, India;
| | | | - Arun Kumar Joshi
- International Maize and Wheat Improvement Center (CIMMYT)-India Office, New Delhi 110012, India;
- Borlaug Institute for South Asia, New Delhi 110012, India
| | - Pawan Kumar Singh
- International Maize and Wheat Improvement Centre, Texcoco 56237, Mexico;
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Garin V, Diallo C, Tékété ML, Théra K, Guitton B, Dagno K, Diallo AG, Kouressy M, Leiser W, Rattunde F, Sissoko I, Touré A, Nébié B, Samaké M, Kholovà J, Berger A, Frouin J, Pot D, Vaksmann M, Weltzien E, Témé N, Rami JF. Characterization of adaptation mechanisms in sorghum using a multireference back-cross nested association mapping design and envirotyping. Genetics 2024; 226:iyae003. [PMID: 38381593 PMCID: PMC10990433 DOI: 10.1093/genetics/iyae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/20/2023] [Indexed: 02/23/2024] Open
Abstract
Identifying the genetic factors impacting the adaptation of crops to environmental conditions is of key interest for conservation and selection purposes. It can be achieved using population genomics, and evolutionary or quantitative genetics. Here we present a sorghum multireference back-cross nested association mapping population composed of 3,901 lines produced by crossing 24 diverse parents to 3 elite parents from West and Central Africa-back-cross nested association mapping. The population was phenotyped in environments characterized by differences in photoperiod, rainfall pattern, temperature levels, and soil fertility. To integrate the multiparental and multi-environmental dimension of our data we proposed a new approach for quantitative trait loci (QTL) detection and parental effect estimation. We extended our model to estimate QTL effect sensitivity to environmental covariates, which facilitated the integration of envirotyping data. Our models allowed spatial projections of the QTL effects in agro-ecologies of interest. We utilized this strategy to analyze the genetic architecture of flowering time and plant height, which represents key adaptation mechanisms in environments like West Africa. Our results allowed a better characterization of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod-sensitive and the region of possible candidate gene Elf3 being photoperiod-insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology-dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the West and Central Africa-back-cross nested association mapping and the presented analytical approach constitute unique resources to better understand adaptation in sorghum with direct application to develop climate-smart varieties.
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Affiliation(s)
- Vincent Garin
- Crop Physiology Laboratory, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 502 324, India
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Chiaka Diallo
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
- Département d’Enseignement et de Recherche des Sciences et Techniques Agricoles, Institut polytechnique rural de formation et de recherche appliquée de Katibougou, Koulikoro, BP 06, Mali
| | - Mohamed Lamine Tékété
- Institut d’Economie Rurale, Bamako, BP 262, Mali
- Faculté des Sciences et Techniques, Université des Sciences des Techniques et des Technologies de Bamako, Bamako, BP E 3206, Mali
| | | | - Baptiste Guitton
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Karim Dagno
- Institut d’Economie Rurale, Bamako, BP 262, Mali
| | | | | | - Willmar Leiser
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Fred Rattunde
- Agronomy Department, University of Wisconsin, Madison, WI 53705, WI, USA
| | - Ibrahima Sissoko
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Aboubacar Touré
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Baloua Nébié
- Dryland Crops Program, International Maize and Wheat Improvement Center (CIMMYT-Senegal) U/C CERAAS, Thiès, Po Box 3320, Senegal
| | - Moussa Samaké
- Faculté des Sciences et Techniques, Université des Sciences des Techniques et des Technologies de Bamako, Bamako, BP E 3206, Mali
| | - Jana Kholovà
- Crop Physiology Laboratory, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 502 324, India
- Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences, Prague, 165 00, Czech Republic
| | - Angélique Berger
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Julien Frouin
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - David Pot
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Michel Vaksmann
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Eva Weltzien
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
- Agronomy Department, University of Wisconsin, Madison, WI 53705, WI, USA
| | - Niaba Témé
- Institut d’Economie Rurale, Bamako, BP 262, Mali
| | - Jean-François Rami
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
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6
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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7
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Knapp SJ, Cole GS, Pincot DDA, Dilla-Ermita CJ, Bjornson M, Famula RA, Gordon TR, Harshman JM, Henry PM, Feldmann MJ. Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance to Macrophomina in strawberry. HORTICULTURE RESEARCH 2024; 11:uhad289. [PMID: 38487295 PMCID: PMC10939388 DOI: 10.1093/hr/uhad289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/17/2023] [Indexed: 03/17/2024]
Abstract
Two decades have passed since the strawberry (Fragaria x ananassa) disease caused by Macrophomina phaseolina, a necrotrophic soilborne fungal pathogen, began surfacing in California, Florida, and elsewhere. This disease has since become one of the most common causes of plant death and yield losses in strawberry. The Macrophomina problem emerged and expanded in the wake of the global phase-out of soil fumigation with methyl bromide and appears to have been aggravated by an increase in climate change-associated abiotic stresses. Here we show that sources of resistance to this pathogen are rare in gene banks and that the favorable alleles they carry are phenotypically unobvious. The latter were exposed by transgressive segregation and selection in populations phenotyped for resistance to Macrophomina under heat and drought stress. The genetic gains were immediate and dramatic. The frequency of highly resistant individuals increased from 1% in selection cycle 0 to 74% in selection cycle 2. Using GWAS and survival analysis, we found that phenotypic selection had increased the frequencies of favorable alleles among 10 loci associated with resistance and that favorable alleles had to be accumulated among four or more of these loci for an individual to acquire resistance. An unexpectedly straightforward solution to the Macrophomina disease resistance breeding problem emerged from our studies, which showed that highly resistant cultivars can be developed by genomic selection per se or marker-assisted stacking of favorable alleles among a comparatively small number of large-effect loci.
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Affiliation(s)
- Steven J Knapp
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Dominique D A Pincot
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Christine Jade Dilla-Ermita
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
- Crop Improvement and Protection Research, USDA-ARS, 1636 E. Alisal Street, CA 93905, USA
| | - Marta Bjornson
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Randi A Famula
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Thomas R Gordon
- Department of Plant Pathology, University of California, One Shields Avenue, Davis, CA 95616, USA
| | - Julia M Harshman
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Peter M Henry
- Crop Improvement and Protection Research, USDA-ARS, 1636 E. Alisal Street, CA 93905, USA
| | - Mitchell J Feldmann
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
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8
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Sadeqi MB, Ballvora A, Dadshani S, Léon J. Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat. Int J Mol Sci 2023; 24:14275. [PMID: 37762585 PMCID: PMC10531695 DOI: 10.3390/ijms241814275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/07/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Estimation and prediction play a key role in breeding programs. Currently, phenotyping of complex traits such as nitrogen use efficiency (NUE) in wheat is still expensive, requires high-throughput technologies and is very time consuming compared to genotyping. Therefore, researchers are trying to predict phenotypes based on marker information. Genetic parameters such as population structure, genomic relationship matrix, marker density and sample size are major factors that increase the performance and accuracy of a model. However, they play an important role in adjusting the statistically significant false discovery rate (FDR) threshold in estimation. In parallel, there are many genetic hyper-parameters that are hidden and not represented in the given genomic selection (GS) model but have significant effects on the results, such as panel size, number of markers, minor allele frequency, number of call rates for each marker, number of cross validations and batch size in the training set of the genomic file. The main challenge is to ensure the reliability and accuracy of predicted breeding values (BVs) as results. Our study has confirmed the results of bias-variance tradeoff and adaptive prediction error for the ensemble-learning-based model STACK, which has the highest performance when estimating genetic parameters and hyper-parameters in a given GS model compared to other models.
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Affiliation(s)
- Mohammad Bahman Sadeqi
- INRES-Plant Breeding, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany; (M.B.S.); (J.L.)
| | - Agim Ballvora
- INRES-Plant Breeding, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany; (M.B.S.); (J.L.)
| | - Said Dadshani
- INRES-Plant Nutrition, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany;
| | - Jens Léon
- INRES-Plant Breeding, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany; (M.B.S.); (J.L.)
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9
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Atta K, Mondal S, Gorai S, Singh AP, Kumari A, Ghosh T, Roy A, Hembram S, Gaikwad DJ, Mondal S, Bhattacharya S, Jha UC, Jespersen D. Impacts of salinity stress on crop plants: improving salt tolerance through genetic and molecular dissection. FRONTIERS IN PLANT SCIENCE 2023; 14:1241736. [PMID: 37780527 PMCID: PMC10540871 DOI: 10.3389/fpls.2023.1241736] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/14/2023] [Indexed: 10/03/2023]
Abstract
Improper use of water resources in irrigation that contain a significant amount of salts, faulty agronomic practices such as improper fertilization, climate change etc. are gradually increasing soil salinity of arable lands across the globe. It is one of the major abiotic factors that inhibits overall plant growth through ionic imbalance, osmotic stress, oxidative stress, and reduced nutrient uptake. Plants have evolved with several adaptation strategies at morphological and molecular levels to withstand salinity stress. Among various approaches, harnessing the crop genetic variability across different genepools and developing salinity tolerant crop plants offer the most sustainable way of salt stress mitigation. Some important major genetic determinants controlling salinity tolerance have been uncovered using classical genetic approaches. However, its complex inheritance pattern makes breeding for salinity tolerance challenging. Subsequently, advances in sequence based breeding approaches and functional genomics have greatly assisted in underpinning novel genetic variants controlling salinity tolerance in plants at the whole genome level. This current review aims to shed light on physiological, biochemical, and molecular responses under salt stress, defense mechanisms of plants, underlying genetics of salt tolerance through bi-parental QTL mapping and Genome Wide Association Studies, and implication of Genomic Selection to breed salt tolerant lines.
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Affiliation(s)
- Kousik Atta
- ICAR-Indian Agricultural Research Institute, New Delhi, India
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | - Saptarshi Mondal
- Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
| | - Shouvik Gorai
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | - Aditya Pratap Singh
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
- School of Agriculture, GIET University, Gunupur, Rayagada, Odisha, India
| | - Amrita Kumari
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | - Tuhina Ghosh
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Arkaprava Roy
- ICAR-Indian Agricultural Research Institute, New Delhi, India
- ICAR- National Institute of Biotic Stress Management, Raipur, India
| | - Suryakant Hembram
- WBAS (Research), Government of West Bengal, Field Crop Research Station, Burdwan, India
| | | | - Subhasis Mondal
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | | | | | - David Jespersen
- Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
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10
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Feldmann MJ, Covarrubias-Pazaran G, Piepho HP. Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures. G3 (BETHESDA, MD.) 2023; 13:jkad148. [PMID: 37405459 PMCID: PMC10468314 DOI: 10.1093/g3journal/jkad148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/09/2023] [Accepted: 06/12/2023] [Indexed: 07/06/2023]
Abstract
Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction and its successor, genomic prediction, have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.
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Affiliation(s)
- Mitchell J Feldmann
- Department of Plant Sciences, University of California Davis, One Shields Ave, Davis, CA 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, El Batán, 56130 Texcoco, Edo. de México, México
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart 70599, Germany
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11
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Marla S, Felderhoff T, Hayes C, Perumal R, Wang X, Poland J, Morris GP. Genomics and phenomics enabled prebreeding improved early-season chilling tolerance in Sorghum. G3 (BETHESDA, MD.) 2023; 13:jkad116. [PMID: 37232400 PMCID: PMC10411554 DOI: 10.1093/g3journal/jkad116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting, and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index values from the chilling nested association mapping population detected CT quantitative trait locus (QTL) that colocalized with manual phenotyping CT QTL. Two of the 4 first-generation Kompetitive Allele Specific PCR (KASP) molecular markers, generated using the peak QTL single nucleotide polymorphisms (SNPs), failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic fixation index analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from 2 independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13-24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.
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Affiliation(s)
- Sandeep Marla
- Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
| | - Terry Felderhoff
- Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
| | - Chad Hayes
- USDA-ARS, Plant Stress & Germplasm Development Unit, Cropping Systems Research Laboratory, Lubbock, TX 79415, USA
| | - Ramasamy Perumal
- Western Kansas Agricultural Research Center, Kansas State University, Hays, KS 67601, USA
| | - Xu Wang
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
- Department of Agricultural and Biological Engineering, University of Florida, IFAS Gulf Coast Research and Education Center, Wimauma, FL 33598, USA
| | - Jesse Poland
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Geoffrey P Morris
- Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA
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12
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Adams J, de Vries M, van Eeuwijk F. Efficient Genomic Prediction of Yield and Dry Matter in Hybrid Potato. PLANTS (BASEL, SWITZERLAND) 2023; 12:2617. [PMID: 37514232 PMCID: PMC10385487 DOI: 10.3390/plants12142617] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/27/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023]
Abstract
There is an ongoing endeavor within the potato breeding sector to rapidly adapt potato from a clonal polyploid crop to a diploid hybrid potato crop. While hybrid breeding allows for the efficient generation and selection of parental lines, it also increases breeding program complexity and results in longer breeding cycles. Over the past two decades, genomic prediction has revolutionized hybrid crop breeding through shorter breeding cycles, lower phenotyping costs, and better population improvement, resulting in increased genetic gains for genetically complex traits. In order to accelerate the genetic gains in hybrid potato, the proper implementation of genomic prediction is a crucial milestone in the rapid improvement of this crop. The authors of this paper set out to test genomic prediction in hybrid potato using current genotyped material with two alternative models: one model that predicts the general combining ability effects (GCA) and another which predicts both the general and specific combining ability effects (GCA+SCA). Using a training set comprising 769 hybrids and 456 genotyped parental lines, we found that reasonable a prediction accuracy could be achieved for most phenotypes with both zero common parents (ρ=0.36-0.61) and one (ρ=0.50-0.68) common parent between the training and test sets. There was no benefit with the inclusion of non-additive genetic effects in the GCA+SCA model despite SCA variance contributing between 9% and 19% of the total genetic variance. Genotype-by-environment interactions, while present, did not appear to affect the prediction accuracy, though prediction errors did vary across the trial's targets. These results suggest that genomically estimated breeding values on parental lines are sufficient for hybrid yield prediction.
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Affiliation(s)
- James Adams
- Biometris, Mathematical and Statistical Methods, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
- Solynta, Dreijenlaan 2, 6703 HA Wageningen, The Netherlands
| | | | - Fred van Eeuwijk
- Biometris, Mathematical and Statistical Methods, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
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13
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Li Z, Gutierrez L. Editorial: Statistical methods for analyzing multiple environmental quantitative genomic data. Front Genet 2023; 14:1212804. [PMID: 37404327 PMCID: PMC10316013 DOI: 10.3389/fgene.2023.1212804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 06/09/2023] [Indexed: 07/06/2023] Open
Affiliation(s)
- Zitong Li
- CSIRO Agriculture and Food, Canberra, ACT, Australia
| | - Lucia Gutierrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, United States
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14
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Chizk TM, Clark JR, Johns C, Nelson L, Ashrafi H, Aryal R, Worthington ML. Genome-wide association identifies key loci controlling blackberry postharvest quality. FRONTIERS IN PLANT SCIENCE 2023; 14:1182790. [PMID: 37351206 PMCID: PMC10282842 DOI: 10.3389/fpls.2023.1182790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/27/2023] [Indexed: 06/24/2023]
Abstract
Introduction Blackberry (Rubus subgenus Rubus) is a soft-fruited specialty crop that often suffers economic losses due to degradation in the shipping process. During transportation, fresh-market blackberries commonly leak, decay, deform, or become discolored through a disorder known as red drupelet reversion (RDR). Over the past 50 years, breeding programs have achieved better fruit firmness and postharvest quality through traditional selection methods, but the underlying genetic variation is poorly understood. Methods We conducted a genome-wide association of fruit firmness and RDR measured in 300 tetraploid fresh-market blackberry genotypes from 2019-2021 with 65,995 SNPs concentrated in genic regions of the R. argutus reference genome. Results Fruit firmness and RDR had entry-mean broad sense heritabilities of 68% and 34%, respectively. Three variants on homologs of polygalacturonase (PG), pectin methylesterase (PME), and glucan endo-1,3-β-glucosidase explained 27% of variance in fruit firmness and were located on chromosomes Ra06, Ra01, and Ra02, respectively. Another PG homolog variant on chromosome Ra02 explained 8% of variance in RDR, but it was in strong linkage disequilibrium with 212 other RDR-associated SNPs across a 23 Mb region. A large cluster of six PME and PME inhibitor homologs was located near the fruit firmness quantitative trait locus (QTL) identified on Ra01. RDR and fruit firmness shared a significant negative correlation (r = -0.28) and overlapping QTL regions on Ra02 in this study. Discussion Our work demonstrates the complex nature of postharvest quality traits in blackberry, which are likely controlled by many small-effect QTLs. This study is the first large-scale effort to map the genetic control of quantitative traits in blackberry and provides a strong framework for future GWAS. Phenotypic and genotypic datasets may be used to train genomic selection models that target the improvement of postharvest quality.
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Affiliation(s)
- T. Mason Chizk
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - John R. Clark
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Carmen Johns
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Lacy Nelson
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Hamid Ashrafi
- Department of Horticultural Science, North Carolina State University, Raleigh, NC, United States
| | - Rishi Aryal
- Department of Horticultural Science, North Carolina State University, Raleigh, NC, United States
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15
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Alemu A, Batista L, Singh PK, Ceplitis A, Chawade A. Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:92. [PMID: 37009920 PMCID: PMC10068637 DOI: 10.1007/s00122-023-04352-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
Linkage disequilibrium (LD)-based haplotyping with subsequent SNP tagging improved the genomic prediction accuracy up to 0.07 and 0.092 for Fusarium head blight resistance and spike width, respectively, across six different models. Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r2 < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - Pawan K Singh
- International Maize and Wheat Improvement Center, Texcoco, Mexico
| | | | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
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Kumar M, Kumar S, Sandhu KS, Kumar N, Saripalli G, Prakash R, Nambardar A, Sharma H, Gautam T, Balyan HS, Gupta PK. GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:14. [PMID: 37313293 PMCID: PMC10248620 DOI: 10.1007/s11032-023-01357-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/26/2023] [Indexed: 06/15/2023]
Abstract
In wheat, a genome-wide association study (GWAS) and genomic prediction (GP) analysis were conducted for pre-harvest sprouting (PHS) tolerance and two of its related traits. For this purpose, an association panel of 190 accessions was phenotyped for PHS (using sprouting score), falling number, and grain color over two years and genotyped with 9904 DArTseq based SNP markers. GWAS for main-effect quantitative trait nucleotides (M-QTNs) using three different models (CMLM, SUPER, and FarmCPU) and epistatic QTNs (E-QTNs) using PLINK were performed. A total of 171 M-QTNs (CMLM, 47; SUPER, 70; FarmCPU, 54) for all three traits, and 15 E-QTNs involved in 20 first-order epistatic interactions were identified. Some of the above QTNs overlapped the previously reported QTLs, MTAs, and cloned genes, allowing delineating 26 PHS-responsive genomic regions that spread over 16 wheat chromosomes. As many as 20 definitive and stable QTNs were considered important for use in marker-assisted recurrent selection (MARS). The gene, TaPHS1, for PHS tolerance (PHST) associated with one of the QTNs was also validated using the KASP assay. Some of the M-QTNs were shown to have a key role in the abscisic acid pathway involved in PHST. Genomic prediction accuracies (based on the cross-validation approach) using three different models ranged from 0.41 to 0.55, which are comparable to the results of previous studies. In summary, the results of the present study improved our understanding of the genetic architecture of PHST and its related traits in wheat and provided novel genomic resources for wheat breeding based on MARS and GP. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01357-5.
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Affiliation(s)
- Manoj Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Sachin Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | | | - Neeraj Kumar
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC USA
| | - Gautam Saripalli
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD USA
| | - Ram Prakash
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Akash Nambardar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Hemant Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Tinku Gautam
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
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17
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Singh J, Chhabra B, Raza A, Yang SH, Sandhu KS. Important wheat diseases in the US and their management in the 21st century. FRONTIERS IN PLANT SCIENCE 2023; 13:1010191. [PMID: 36714765 PMCID: PMC9877539 DOI: 10.3389/fpls.2022.1010191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/28/2022] [Indexed: 05/27/2023]
Abstract
Wheat is a crop of historical significance, as it marks the turning point of human civilization 10,000 years ago with its domestication. Due to the rapid increase in population, wheat production needs to be increased by 50% by 2050 and this growth will be mainly based on yield increases, as there is strong competition for scarce productive arable land from other sectors. This increasing demand can be further achieved using sustainable approaches including integrated disease pest management, adaption to warmer climates, less use of water resources and increased frequency of abiotic stress tolerances. Out of 200 diseases of wheat, 50 cause economic losses and are widely distributed. Each year, about 20% of wheat is lost due to diseases. Some major wheat diseases are rusts, smut, tan spot, spot blotch, fusarium head blight, common root rot, septoria blotch, powdery mildew, blast, and several viral, nematode, and bacterial diseases. These diseases badly impact the yield and cause mortality of the plants. This review focuses on important diseases of the wheat present in the United States, with comprehensive information of causal organism, economic damage, symptoms and host range, favorable conditions, and disease management strategies. Furthermore, major genetic and breeding efforts to control and manage these diseases are discussed. A detailed description of all the QTLs, genes reported and cloned for these diseases are provided in this review. This study will be of utmost importance to wheat breeding programs throughout the world to breed for resistance under changing environmental conditions.
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Affiliation(s)
- Jagdeep Singh
- Department of Crop, Soil & Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Bhavit Chhabra
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, United States
| | - Ali Raza
- College of Agriculture, Oil Crops Research Institute, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Seung Hwan Yang
- Department of Integrative Biotechnology, Chonnam National University, Yeosu, Republic of Korea
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18
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Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, Queensland 4072, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
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19
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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20
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Schneider HM. Functional implications of multiseriate cortical sclerenchyma for soil resource capture and crop improvement. AOB PLANTS 2022; 14:plac050. [PMID: 36545297 PMCID: PMC9762723 DOI: 10.1093/aobpla/plac050] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/17/2022] [Indexed: 06/09/2023]
Abstract
Suboptimal nutrient and water availability are primary constraints to crop growth. Global agriculture requires crops with greater nutrient and water efficiency. Multiseriate cortical sclerenchyma (MCS), a root anatomical trait characterized by small cells with thick cell walls encrusted with lignin in the outer cortex, has been shown to be an important trait for adaptation in maize and wheat in mechanically impeded soils. However, MCS has the potential to improve edaphic stress tolerance in a number of different crop taxa and in a number of different environments. This review explores the functional implications of MCS as an adaptive trait for water and nutrient acquisition and discusses future research perspectives on this trait for incorporation into crop breeding programs. For example, MCS may influence water and nutrient uptake, resistance to pests, symbiotic interactions, microbial interactions in the rhizosphere and soil carbon deposition. Root anatomical phenotypes are underutilized; however, important breeding targets for the development of efficient, productive and resilient crops urgently needed in global agriculture.
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21
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Anilkumar C, Sunitha NC, Devate NB, Ramesh S. Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review. PLANTA 2022; 256:87. [PMID: 36149531 DOI: 10.1007/s00425-022-03996-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.
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Affiliation(s)
- C Anilkumar
- ICAR-National Rice Research Institute, Cuttack, India
| | - N C Sunitha
- University of Agricultural Sciences, Bangalore, India
| | | | - S Ramesh
- University of Agricultural Sciences, Bangalore, India.
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22
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Rasskazov D, Chadaeva I, Sharypova E, Zolotareva K, Khandaev B, Ponomarenko P, Podkolodnyy N, Tverdokhleb N, Vishnevsky O, Bogomolov A, Podkolodnaya O, Savinkova L, Zemlyanskaya E, Golubyatnikov V, Kolchanov N, Ponomarenko M. Plant_SNP_TATA_Z-Tester: A Web Service That Unequivocally Estimates the Impact of Proximal Promoter Mutations on Plant Gene Expression. Int J Mol Sci 2022; 23:ijms23158684. [PMID: 35955817 PMCID: PMC9369029 DOI: 10.3390/ijms23158684] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 11/16/2022] Open
Abstract
Synthetic targeted optimization of plant promoters is becoming a part of progress in mainstream postgenomic agriculture along with hybridization of cultivated plants with wild congeners, as well as marker-assisted breeding. Therefore, here, for the first time, we compiled all the experimental data—on mutational effects in plant proximal promoters on gene expression—that we could find in PubMed. Some of these datasets cast doubt on both the existence and the uniqueness of the sought solution, which could unequivocally estimate effects of proximal promoter mutation on gene expression when plants are grown under various environmental conditions during their development. This means that the inverse problem under study is ill-posed. Furthermore, we found experimental data on in vitro interchangeability of plant and human TATA-binding proteins allowing the application of Tikhonov’s regularization, making this problem well-posed. Within these frameworks, we created our Web service Plant_SNP_TATA_Z-tester and then determined the limits of its applicability using those data that cast doubt on both the existence and the uniqueness of the sought solution. We confirmed that the effects (of proximal promoter mutations on gene expression) predicted by Plant_SNP_TATA_Z-tester correlate statistically significantly with all the experimental data under study. Lastly, we exemplified an application of Plant_SNP_TATA_Z-tester to agriculturally valuable mutations in plant promoters.
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Affiliation(s)
| | - Irina Chadaeva
- Institute of Cytology and Genetics, 630090 Novosibirsk, Russia
| | | | | | - Bato Khandaev
- Institute of Cytology and Genetics, 630090 Novosibirsk, Russia
| | | | - Nikolay Podkolodnyy
- Institute of Cytology and Genetics, 630090 Novosibirsk, Russia
- Institute of Computational Mathematics and Mathematical Geophysics, 630090 Novosibirsk, Russia
| | | | - Oleg Vishnevsky
- Institute of Cytology and Genetics, 630090 Novosibirsk, Russia
| | - Anton Bogomolov
- Institute of Cytology and Genetics, 630090 Novosibirsk, Russia
| | | | | | | | | | | | - Mikhail Ponomarenko
- Institute of Cytology and Genetics, 630090 Novosibirsk, Russia
- Correspondence: ; Tel.: +7-(383)-363-4963 (ext. 1311)
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23
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Mohd Saad NS, Neik TX, Thomas WJW, Amas JC, Cantila AY, Craig RJ, Edwards D, Batley J. Advancing designer crops for climate resilience through an integrated genomics approach. CURRENT OPINION IN PLANT BIOLOGY 2022; 67:102220. [PMID: 35489163 DOI: 10.1016/j.pbi.2022.102220] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 03/15/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
Climate change and exponential population growth are exposing an immediate need for developing future crops that are highly resilient and adaptable to changing environments to maintain global food security in the next decade. Rigorous selection from long domestication history has rendered cultivated crops genetically disadvantaged, raising concerns in their ability to adapt to these new challenges and limiting their usefulness in breeding programmes. As a result, future crop improvement efforts must rely on integrating various genomic strategies ranging from high-throughput sequencing to machine learning, in order to exploit germplasm diversity and overcome bottlenecks created by domestication, expansive multi-dimensional phenotypes, arduous breeding processes, complex traits and big data.
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Affiliation(s)
- Nur Shuhadah Mohd Saad
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ting Xiang Neik
- Sunway College Kuala Lumpur, Bandar Sunway, 47500, Selangor, Malaysia
| | - William J W Thomas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Junrey C Amas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Aldrin Y Cantila
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ryan J Craig
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - David Edwards
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Jacqueline Batley
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia.
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24
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Roy J, Del Río Mendoza LE, Bandillo N, McClean PE, Rahman M. Genetic mapping and genomic prediction of sclerotinia stem rot resistance to rapeseed/canola (Brassica napus L.) at seedling stage. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2167-2184. [PMID: 35522263 DOI: 10.1007/s00122-022-04104-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 04/12/2022] [Indexed: 06/14/2023]
Abstract
GWAS detected ninety-eight significant SNPs associated with Sclerotinia sclerotiorum resistance. Six statistical models resulted in medium to high predictive ability, depending on trait, indicating potential of genomic prediction for disease resistance breeding. The lack of complete host resistance and a complex resistance inheritance nature between rapeseed/canola and Sclerotinia sclerotiorum often limits the development of functional molecular markers that enable breeding for sclerotinia stem rot (SSR) resistance. However, genomics-assisted selection has the potential to accelerate the breeding for SSR resistance. Therefore, genome-wide association (GWA) mapping and genomic prediction (GP) were performed using a diverse panel of 337 rapeseed/canola genotypes. Three-week-old seedlings were screened using the petiole inoculation technique (PIT). Days to wilt (DW) up to 2 weeks and lesion phenotypes (LP) at 3, 4, and 7 days post-inoculation (dpi) were recorded. A strong correlation (r = - 0.90) between DW and LP_4dpi implied that a single time point scoring at four days could be used as a proxy trait. GWA analyses using single-locus (SL) and multi-locus (ML) models identified a total of 41, and 208 significantly associated SNPs, respectively. Out of these, ninety-eight SNPs were identified by a combination of the SL model and any of the ML models, at least two ML models, or two traits. These SNPs explained 1.25-12.22% of the phenotypic variance and considered as significant, could be associated with SSR resistance. Eighty-three candidate genes with a function in disease resistance were associated with the significant SNPs. Six GP models resulted in moderate to high (0.42-0.67) predictive ability depending on SSR resistance traits. The resistant genotypes and significant SNPs will serve as valuable resources for future SSR resistance breeding. Our results also highlight the potential of genomic selection to improve rapeseed/canola breeding for SSR resistance.
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Affiliation(s)
- Jayanta Roy
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA
| | | | - Nonoy Bandillo
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA
| | - Phillip E McClean
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA
- Genomics, Phenomics, and Bioinformatics Program, North Dakota State University, Fargo, ND, 58108, USA
| | - Mukhlesur Rahman
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA.
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25
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Theeuwen TPJM, Logie LL, Harbinson J, Aarts MGM. Genetics as a key to improving crop photosynthesis. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3122-3137. [PMID: 35235648 PMCID: PMC9126732 DOI: 10.1093/jxb/erac076] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/23/2022] [Indexed: 05/02/2023]
Abstract
Since the basic biochemical mechanisms of photosynthesis are remarkably conserved among plant species, genetic modification approaches have so far been the main route to improve the photosynthetic performance of crops. Yet, phenotypic variation observed in wild species and between varieties of crop species implies there is standing natural genetic variation for photosynthesis, offering a largely unexplored resource to use for breeding crops with improved photosynthesis and higher yields. The reason this has not yet been explored is that the variation probably involves thousands of genes, each contributing only a little to photosynthesis, making them hard to identify without proper phenotyping and genetic tools. This is changing, though, and increasingly studies report on quantitative trait loci for photosynthetic phenotypes. So far, hardly any of these quantitative trait loci have been used in marker assisted breeding or genomic selection approaches to improve crop photosynthesis and yield, and hardly ever have the underlying causal genes been identified. We propose to take the genetics of photosynthesis to a higher level, and identify the genes and alleles nature has used for millions of years to tune photosynthesis to be in line with local environmental conditions. We will need to determine the physiological function of the genes and alleles, and design novel strategies to use this knowledge to improve crop photosynthesis through conventional plant breeding, based on readily available crop plant germplasm. In this work, we present and discuss the genetic methods needed to reveal natural genetic variation, and elaborate on how to apply this to improve crop photosynthesis.
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Affiliation(s)
- Tom P J M Theeuwen
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
- Correspondence:
| | - Louise L Logie
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
| | - Jeremy Harbinson
- Biophysics, Wageningen University & Research, Wageningen, The Netherlands
| | - Mark G M Aarts
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
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26
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Belzile F, Jean M, Torkamaneh D, Tardivel A, Lemay MA, Boudhrioua C, Arsenault-Labrecque G, Dussault-Benoit C, Lebreton A, de Ronne M, Tremblay V, Labbé C, O’Donoughue L, St-Amour VTB, Copley T, Fortier E, Ste-Croix DT, Mimee B, Cober E, Rajcan I, Warkentin T, Gagnon É, Legay S, Auclair J, Bélanger R. The SoyaGen Project: Putting Genomics to Work for Soybean Breeders. FRONTIERS IN PLANT SCIENCE 2022; 13:887553. [PMID: 35557742 PMCID: PMC9087807 DOI: 10.3389/fpls.2022.887553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 03/24/2022] [Indexed: 06/15/2023]
Abstract
The SoyaGen project was a collaborative endeavor involving Canadian soybean researchers and breeders from academia and the private sector as well as international collaborators. Its aims were to develop genomics-derived solutions to real-world challenges faced by breeders. Based on the needs expressed by the stakeholders, the research efforts were focused on maximizing realized yield through optimization of maturity and improved disease resistance. The main deliverables related to molecular breeding in soybean will be reviewed here. These include: (1) SNP datasets capturing the genetic diversity within cultivated soybean (both within a worldwide collection of > 1,000 soybean accessions and a subset of 102 short-season accessions (MG0 and earlier) directly relevant to this group); (2) SNP markers for selecting favorable alleles at key maturity genes as well as loci associated with increased resistance to key pathogens and pests (Phytophthora sojae, Heterodera glycines, Sclerotinia sclerotiorum); (3) diagnostic tools to facilitate the identification and mapping of specific pathotypes of P. sojae; and (4) a genomic prediction approach to identify the most promising combinations of parents. As a result of this fruitful collaboration, breeders have gained new tools and approaches to implement molecular, genomics-informed breeding strategies. We believe these tools and approaches are broadly applicable to soybean breeding efforts around the world.
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Affiliation(s)
- François Belzile
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Martine Jean
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Aurélie Tardivel
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | - Marc-André Lemay
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Chiheb Boudhrioua
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | | | | | - Amandine Lebreton
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Maxime de Ronne
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Vanessa Tremblay
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Caroline Labbé
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Louise O’Donoughue
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | - Vincent-Thomas Boucher St-Amour
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | - Tanya Copley
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | - Eric Fortier
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | | | - Benjamin Mimee
- Agriculture and Agri-Food Canada, St-Jean-sur-Richelieu, QC, Canada
| | - Elroy Cober
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Tom Warkentin
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Éric Gagnon
- Semences Prograin Inc., Saint-Césaire, QC, Canada
- Sevita Genetics, Inkerman, ON, Canada
| | | | | | - Richard Bélanger
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
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27
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Edger PP, Iorizzo M, Bassil NV, Benevenuto J, Ferrão LFV, Giongo L, Hummer K, Lawas LMF, Leisner CP, Li C, Munoz PR, Ashrafi H, Atucha A, Babiker EM, Canales E, Chagné D, DeVetter L, Ehlenfeldt M, Espley RV, Gallardo K, Günther CS, Hardigan M, Hulse-Kemp AM, Jacobs M, Lila MA, Luby C, Main D, Mengist MF, Owens GL, Perkins-Veazie P, Polashock J, Pottorff M, Rowland LJ, Sims CA, Song GQ, Spencer J, Vorsa N, Yocca AE, Zalapa J. There and back again; historical perspective and future directions for Vaccinium breeding and research studies. HORTICULTURE RESEARCH 2022; 9:uhac083. [PMID: 35611183 PMCID: PMC9123236 DOI: 10.1093/hr/uhac083] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/22/2022] [Indexed: 06/02/2023]
Abstract
The genus Vaccinium L. (Ericaceae) contains a wide diversity of culturally and economically important berry crop species. Consumer demand and scientific research in blueberry (Vaccinium spp.) and cranberry (Vaccinium macrocarpon) have increased worldwide over the crops' relatively short domestication history (~100 years). Other species, including bilberry (Vaccinium myrtillus), lingonberry (Vaccinium vitis-idaea), and ohelo berry (Vaccinium reticulatum) are largely still harvested from the wild but with crop improvement efforts underway. Here, we present a review article on these Vaccinium berry crops on topics that span taxonomy to genetics and genomics to breeding. We highlight the accomplishments made thus far for each of these crops, along their journey from the wild, and propose research areas and questions that will require investments by the community over the coming decades to guide future crop improvement efforts. New tools and resources are needed to underpin the development of superior cultivars that are not only more resilient to various environmental stresses and higher yielding, but also produce fruit that continue to meet a variety of consumer preferences, including fruit quality and health related traits.
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Affiliation(s)
- Patrick P Edger
- Department of Horticulture, Michigan State University, East Lansing, MI, 48824, USA
- MSU AgBioResearch, Michigan State University, East Lansing, MI, 48824, USA
| | - Massimo Iorizzo
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC USA
- Department of Horticultural Science, North Carolina State University, Raleigh, NC USA
| | - Nahla V Bassil
- USDA-ARS, National Clonal Germplasm Repository, Corvallis, OR 97333, USA
| | - Juliana Benevenuto
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Luis Felipe V Ferrão
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Lara Giongo
- Fondazione Edmund Mach - Research and Innovation CentreItaly
| | - Kim Hummer
- USDA-ARS, National Clonal Germplasm Repository, Corvallis, OR 97333, USA
| | - Lovely Mae F Lawas
- Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA
| | - Courtney P Leisner
- Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA
| | - Changying Li
- Phenomics and Plant Robotics Center, College of Engineering, University of Georgia, Athens, USA
| | - Patricio R Munoz
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Hamid Ashrafi
- Department of Horticultural Science, North Carolina State University, Raleigh, NC USA
| | - Amaya Atucha
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Ebrahiem M Babiker
- USDA-ARS Southern Horticultural Laboratory, Poplarville, MS 39470-0287, USA
| | - Elizabeth Canales
- Department of Agricultural Economics, Mississippi State University, Mississippi State, MS 39762, USA
| | - David Chagné
- The New Zealand Institute for Plant and Food Research Limited (PFR), Palmerston North, New Zealand
| | - Lisa DeVetter
- Department of Horticulture, Washington State University Northwestern Washington Research and Extension Center, Mount Vernon, WA, 98221, USA
| | - Mark Ehlenfeldt
- SEBS, Plant Biology, Rutgers University, New Brunswick NJ 01019 USA
| | - Richard V Espley
- The New Zealand Institute for Plant and Food Research Limited (PFR), Palmerston North, New Zealand
| | - Karina Gallardo
- School of Economic Sciences, Washington State University, Puyallup, WA 98371, USA
| | - Catrin S Günther
- The New Zealand Institute for Plant and Food Research Limited (PFR), Palmerston North, New Zealand
| | - Michael Hardigan
- USDA-ARS, Horticulture Crops Research Unit, Corvallis, OR 97333, USA
| | - Amanda M Hulse-Kemp
- USDA-ARS, Genomics and Bioinformatics Research Unit, Raleigh, NC 27695, USA
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - MacKenzie Jacobs
- Department of Horticulture, Michigan State University, East Lansing, MI, 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48823, USA
| | - Mary Ann Lila
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC USA
| | - Claire Luby
- USDA-ARS, Horticulture Crops Research Unit, Corvallis, OR 97333, USA
| | - Dorrie Main
- Department of Horticulture, Washington State University, Pullman, WA, 99163, USA
| | - Molla F Mengist
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC USA
- Department of Horticultural Science, North Carolina State University, Raleigh, NC USA
| | | | | | - James Polashock
- SEBS, Plant Biology, Rutgers University, New Brunswick NJ 01019 USA
| | - Marti Pottorff
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC USA
| | - Lisa J Rowland
- USDA-ARS, Genetic Improvement of Fruits and Vegetables Laboratory, Beltsville, MD 20705, USA
| | - Charles A Sims
- Food Science and Human Nutrition Department, University of Florida, Gainesville, FL 32611, USA
| | - Guo-qing Song
- Plant Biotechnology Resource and Outreach Center, Department of Horticulture, Michigan State University, East Lansing, MI 48824, USA
| | - Jessica Spencer
- Department of Horticultural Science, North Carolina State University, Raleigh, NC USA
| | - Nicholi Vorsa
- SEBS, Plant Biology, Rutgers University, New Brunswick NJ 01019 USA
| | - Alan E Yocca
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Juan Zalapa
- USDA-ARS, VCRU, Department of Horticulture, University of Wisconsin-Madison, Madison, WI 53706, USA
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28
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Huang M, Robbins KR, Li Y, Umanzor S, Marty-Rivera M, Bailey D, Yarish C, Lindell S, Jannink JL. Simulation of sugar kelp (Saccharina latissima) breeding guided by practices to accelerate genetic gains. G3 (BETHESDA, MD.) 2022; 12:jkac003. [PMID: 35088860 PMCID: PMC8895986 DOI: 10.1093/g3journal/jkac003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 12/17/2021] [Indexed: 11/18/2022]
Abstract
Though Saccharina japonica cultivation has been established for many decades in East Asian countries, the domestication process of sugar kelp (Saccharina latissima) in the Northeast United States is still at its infancy. In this study, by using data from our breeding experience, we will demonstrate how obstacles for accelerated genetic gain can be assessed using simulation approaches that inform resource allocation decisions. Thus far, we have used 140 wild sporophytes that were sampled in 2018 from the northern Gulf of Maine to southern New England. From these sporophytes, we sampled gametophytes and made and evaluated over 600 progeny sporophytes from crosses among the gametophytes in 2019 and 2020. The biphasic life cycle of kelp gives a great advantage in selective breeding as we can potentially select both on the sporophytes and gametophytes. However, several obstacles exist, such as the amount of time it takes to complete a breeding cycle, the number of gametophytes that can be maintained in the laboratory, and whether positive selection can be conducted on farm-tested sporophytes. Using the Gulf of Maine population characteristics for heritability and effective population size, we simulated a founder population of 1,000 individuals and evaluated the impact of overcoming these obstacles on rate of genetic gain. Our results showed that key factors to improve current genetic gain rely mainly on our ability to induce reproduction of the best farm-tested sporophytes, and to accelerate the clonal vegetative growth of released gametophytes so that enough gametophyte biomass is ready for making crosses by the next growing season. Overcoming these challenges could improve rates of genetic gain more than 2-fold. Future research should focus on conditions favorable for inducing spring reproduction, and on increasing the amount of gametophyte tissue available in time to make fall crosses in the same year.
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Affiliation(s)
- Mao Huang
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Kelly R Robbins
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Yaoguang Li
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315, USA
| | - Schery Umanzor
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315, USA
- College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Juneau, AK 99775, USA
| | - Michael Marty-Rivera
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315, USA
| | - David Bailey
- Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
| | - Charles Yarish
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315, USA
| | - Scott Lindell
- Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
| | - Jean-Luc Jannink
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853, USA
- United States Department of Agriculture—Agriculture Research Service, Ithaca, NY 14853, USA
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29
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Sandhu KS, Patil SS, Aoun M, Carter AH. Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat. Front Genet 2022; 13:831020. [PMID: 35173770 PMCID: PMC8841657 DOI: 10.3389/fgene.2022.831020] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait-based GS models. This study's main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait- and multi-trait-based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait-based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.
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Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Shruti Sunil Patil
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States1
| | - Meriem Aoun
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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30
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Ballén-Taborda C, Chu Y, Ozias-Akins P, Holbrook CC, Timper P, Jackson SA, Bertioli DJ, Leal-Bertioli SCM. Development and Genetic Characterization of Peanut Advanced Backcross Lines That Incorporate Root-Knot Nematode Resistance From Arachis stenosperma. FRONTIERS IN PLANT SCIENCE 2022; 12:785358. [PMID: 35111175 PMCID: PMC8801422 DOI: 10.3389/fpls.2021.785358] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/01/2021] [Indexed: 06/08/2023]
Abstract
Crop wild species are increasingly important for crop improvement. Peanut (Arachis hypogaea L.) wild relatives comprise a diverse genetic pool that is being used to broaden its narrow genetic base. Peanut is an allotetraploid species extremely susceptible to peanut root-knot nematode (PRKN) Meloidogyne arenaria. Current resistant cultivars rely on a single introgression for PRKN resistance incorporated from the wild relative Arachis cardenasii, which could be overcome as a result of the emergence of virulent nematode populations. Therefore, new sources of resistance may be needed. Near-immunity has been found in the peanut wild relative Arachis stenosperma. The two loci controlling the resistance, present on chromosomes A02 and A09, have been validated in tetraploid lines and have been shown to reduce nematode reproduction by up to 98%. To incorporate these new resistance QTL into cultivated peanut, we used a marker-assisted backcrossing approach, using PRKN A. stenosperma-derived resistant lines as donor parents. Four cycles of backcrossing were completed, and SNP assays linked to the QTL were used for foreground selection. In each backcross generation seed weight, length, and width were measured, and based on a statistical analysis we observed that only one generation of backcrossing was required to recover the elite peanut's seed size. A populating of 271 BC3F1 lines was genome-wide genotyped to characterize the introgressions across the genome. Phenotypic information for leaf spot incidence and domestication traits (seed size, fertility, plant architecture, and flower color) were recorded. Correlations between the wild introgressions in different chromosomes and the phenotypic data allowed us to identify candidate regions controlling these domestication traits. Finally, PRKN resistance was validated in BC3F3 lines. We observed that the QTL in A02 and/or large introgression in A09 are needed for resistance. This present work represents an important step toward the development of new high-yielding and nematode-resistant peanut cultivars.
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Affiliation(s)
- Carolina Ballén-Taborda
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, United States
| | - Ye Chu
- Department of Horticulture, University of Georgia, Tifton, GA, United States
| | - Peggy Ozias-Akins
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, United States
- Department of Horticulture, University of Georgia, Tifton, GA, United States
| | - C. Corley Holbrook
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Tifton, GA, United States
| | - Patricia Timper
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Tifton, GA, United States
| | - Scott A. Jackson
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, United States
| | - David J. Bertioli
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, United States
- Department of Crop and Soil Science, University of Georgia, Athens, GA, United States
| | - Soraya C. M. Leal-Bertioli
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, United States
- Department of Plant Pathology, University of Georgia, Athens, GA, United States
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Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:1. [PMID: 37309486 PMCID: PMC10248672 DOI: 10.1007/s11032-021-01272-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.
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Affiliation(s)
- Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| | - Anand Kumar
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 202002 India
| | - Sumandeep Bazzer
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
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Motto M, Sahay S. Energy plants (crops): potential natural and future designer plants. HANDBOOK OF BIOFUELS 2022:73-114. [DOI: 10.1016/b978-0-12-822810-4.00004-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Moore VM, Schlautman B, Fei SZ, Roberts LM, Wolfe M, Ryan MR, Wells S, Lorenz AJ. Plant Breeding for Intercropping in Temperate Field Crop Systems: A Review. FRONTIERS IN PLANT SCIENCE 2022; 13:843065. [PMID: 35432391 PMCID: PMC9009171 DOI: 10.3389/fpls.2022.843065] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 03/07/2022] [Indexed: 05/14/2023]
Abstract
Monoculture cropping systems currently dominate temperate agroecosystems. However, intercropping can provide valuable benefits, including greater yield stability, increased total productivity, and resilience in the face of pest and disease outbreaks. Plant breeding efforts in temperate field crops are largely focused on monoculture production, but as intercropping becomes more widespread, there is a need for cultivars adapted to these cropping systems. Cultivar development for intercropping systems requires a systems approach, from the decision to breed for intercropping systems through the final stages of variety testing and release. Design of a breeding scheme should include information about species variation for performance in intercropping, presence of genotype × management interaction, observation of key traits conferring success in intercropping systems, and the specificity of intercropping performance. Together this information can help to identify an optimal selection scheme. Agronomic and ecological knowledge are critical in the design of selection schemes in cropping systems with greater complexity, and interaction with other researchers and key stakeholders inform breeding decisions throughout the process. This review explores the above considerations through three case studies: (1) forage mixtures, (2) perennial groundcover systems (PGC), and (3) soybean-pennycress intercropping. We provide an overview of each cropping system, identify relevant considerations for plant breeding efforts, describe previous breeding focused on the cropping system, examine the extent to which proposed theoretical approaches have been implemented in breeding programs, and identify areas for future development.
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Affiliation(s)
- Virginia M. Moore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- *Correspondence: Virginia M. Moore,
| | | | - Shui-zhang Fei
- Department of Horticulture, Iowa State University, Ames, IA, United States
| | - Lucas M. Roberts
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States
| | - Marnin Wolfe
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- Department of Crop, Soil and Environmental Sciences, College of Agriculture, Auburn University, Auburn, AL, United States
| | - Matthew R. Ryan
- Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Samantha Wells
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States
| | - Aaron J. Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States
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López-Malvar A, Malvar RA, Butrón A, Revilla P, Jiménez-Galindo JC, Souto XC, Santiago R. Identification of single nucleotide polymorphisms (SNPs) for maize cell wall hydroxycinnamates using a multi-parent advanced generation intercross (MAGIC) population. PHYTOCHEMISTRY 2022; 193:113002. [PMID: 34768187 DOI: 10.1016/j.phytochem.2021.113002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 11/01/2021] [Accepted: 11/02/2021] [Indexed: 06/13/2023]
Abstract
Higher hydroxycinnamate content makes maize tissues more recalcitrant to damage by insects, less digestible by ruminants, and less suitable for biofuel production. In a Genome Wide Association Analysis (GWAS) study carried out in a maize MAGIC population, we identified 24 SNPs associated with esterified cell wall-bound hydroxycinnamates, that represented 15 Quantitative Traic Loci (QTL). We identified new genomic regions associated to cell wall bound hydroxycinnamates in maize stover that could have an impact on their content across different genetic backgrounds. The high resolution QTL described in this study could be valuable for addressing positional mapping of genes involved in hydroxycinnamate biosynthesis and could uncover genes implicated in the esterification of hydroxycinnamic acids to the arabinoxylan chains that are poorly understood. However, we found that genetic correlation coefficients between hydroxycinnamate content and economical important traits such as saccharification efficiency, animal digestibility andi pest resistance were low to moderate, so modify specific hydroxycinnamates to indirectly improve cultivar performance will be unsuitable.
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Affiliation(s)
- A López-Malvar
- Facultad de Biología, Departamento de Biología Vegetal y Ciencias del Suelo, Universidad de Vigo, As Lagoas Marcosende, Agrobiología Ambiental, Calidad de Suelos y Plantas (UVIGO), Unidad Asociada a la MBG (CSIC), Vigo, 36310, Spain.
| | - R A Malvar
- Misión Biológica de Galicia (CSIC), Pazo de Salcedo, Carballeira 8, 36143, Spain
| | - A Butrón
- Misión Biológica de Galicia (CSIC), Pazo de Salcedo, Carballeira 8, 36143, Spain
| | - P Revilla
- Misión Biológica de Galicia (CSIC), Pazo de Salcedo, Carballeira 8, 36143, Spain
| | - J C Jiménez-Galindo
- National Institute of Forestry Agriculture and Livestock Research (INIFAP), Ave. Hidalgo 1213, Cd. Cuauhtémoc, 31500, Chihuahua, Mexico
| | - X C Souto
- E.E. Forestales, Dpto. Ingenieria Recursos Naturales y Medio Ambiente, Universidad de Vigo, Pontevedra, 36005, Spain
| | - R Santiago
- Facultad de Biología, Departamento de Biología Vegetal y Ciencias del Suelo, Universidad de Vigo, As Lagoas Marcosende, Agrobiología Ambiental, Calidad de Suelos y Plantas (UVIGO), Unidad Asociada a la MBG (CSIC), Vigo, 36310, Spain
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35
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Gesteiro N, Cao A, Santiago R, Malvar RA, Butrón A. Genomics of maize resistance to kernel contamination with fumonisins using a multiparental advanced generation InterCross maize population (MAGIC). BMC PLANT BIOLOGY 2021; 21:596. [PMID: 34915847 PMCID: PMC8675497 DOI: 10.1186/s12870-021-03380-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/17/2021] [Indexed: 06/01/2023]
Abstract
Maize kernel is exposed to several fungal species, most notably Fusarium verticillioides, which can contaminate maize kernels with fumonisins. In an effort to increase genetic gains and avoid the laborious tasks of conventional breeding, the use of marker-assisted selection or genomic selection programs was proposed. To this end, in the present study a Genome Wide Association Study (GWAS) was performed on 339 RILs of a Multiparental Advanced Generation InterCross (MAGIC) population that had previously been used to locate Quantitative Trait Locus (QTL) for resistance to Fusarium Ear Rot (FER). Six QTLs for fumonisin content were detected in the bins 3.08, 4.07, 4.10, 7.03-7.04, 9.04-9.05 and 10.04-10.5. Five of the six QTLs collocate in regions where QTLs for FER were also found. However, the genetic variation for fumonisin content in kernel is conditioned by many other QTLs of small effect that could show QTL x environment interaction effects. Although a genomic selection approach to directly reduce fumonisin content in the kernel could be suitable, improving resistance to fumonisin content by genomic selection for FER would be more advisable.
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Affiliation(s)
- Noemi Gesteiro
- Misión Biológica de Galicia (CSIC), Box 28, 36080, Pontevedra, Spain
| | - Ana Cao
- Misión Biológica de Galicia (CSIC), Box 28, 36080, Pontevedra, Spain
| | - Rogelio Santiago
- Departamento Biología Vegetal y Ciencias del Suelo, Facultad de Biología, Universidad de Vigo, Unidad Asociada Agrobiología Ambiental, Calidad de Suelos y Plantas, As Lagoas Marcosende, 36310 Vigo, Spain
| | - Rosa Ana Malvar
- Misión Biológica de Galicia (CSIC), Box 28, 36080, Pontevedra, Spain
| | - Ana Butrón
- Misión Biológica de Galicia (CSIC), Box 28, 36080, Pontevedra, Spain
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Chettry U, Chrungoo NK. Beyond the Cereal Box: Breeding Buckwheat as a Strategic Crop for Human Nutrition. PLANT FOODS FOR HUMAN NUTRITION (DORDRECHT, NETHERLANDS) 2021; 76:399-409. [PMID: 34652552 DOI: 10.1007/s11130-021-00930-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/05/2021] [Indexed: 04/24/2023]
Abstract
While intensification of farming systems is essential for achieving the Millennium Development Goal of "Zero hunger", issues such as availability of nutritious foods would demand increased attention if any long-term form of food security is to be achieved. Since wheat, rice and maize have reached near to 80 percent of their yield potential and reliance on these crops alone would not be sufficient to close the gap between demand and supply, there is a need to bring other climate-resilient and nutritionally dense crops into agricultural portfolio. Buckwheat (Fagopyrum spp.) has attracted considerable interest amongst global scientific community due to its nutritional and pharmaceutical properties. The gluten free nature of buckwheat, nutritionally balanced amino acid composition of its grain protein, and high levels of anti-oxidants, such as rutin, makes buckwheat an important crop with immense nutraceutical benefits. However, a key challenge in buckwheat cultivation is the variation in yield between years, which impacts the entire value chain. Current information on buckwheat indicates existence of significant phenotypic variation for agronomic and nutritional traits. However, genetic bottlenecks in conventional breeding restrict effective utilization of the existing diversity in mainstreaming buckwheat cultivation. Availability of high density buckwheat genome map for both the cultivated species viz. F. esculentum and F. tataricum would add to our understanding of genetic basis of their agronomic traits. The review examines the potential of buckwheat as a strategic crop for human nutrition and prospects of effective exploitation genomic information of common and Tartary buckwheat for genome assisted breeding.
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Affiliation(s)
- Upasna Chettry
- Department of Botany, North-Eastern Hill University, Shillong, 793022, India
| | - Nikhil K Chrungoo
- Department of Botany, North-Eastern Hill University, Shillong, 793022, India.
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Xin Z, Wang M, Cuevas HE, Chen J, Harrison M, Pugh NA, Morris G. Sorghum genetic, genomic, and breeding resources. PLANTA 2021; 254:114. [PMID: 34739592 PMCID: PMC8571242 DOI: 10.1007/s00425-021-03742-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/28/2021] [Indexed: 05/24/2023]
Abstract
Sorghum research has entered an exciting and fruitful era due to the genetic, genomic, and breeding resources that are now available to researchers and plant breeders. As the world faces the challenges of a rising population and a changing global climate, new agricultural solutions will need to be developed to address the food and fiber needs of the future. To that end, sorghum will be an invaluable crop species as it is a stress-resistant C4 plant that is well adapted for semi-arid and arid regions. Sorghum has already remained as a staple food crop in many parts of Africa and Asia and is critically important for animal feed and niche culinary applications in other regions, such as the United States. In addition, sorghum has begun to be developed into a promising feedstock for forage and bioenergy production. Due to this increasing demand for sorghum and its potential to address these needs, the continuous development of powerful community resources is required. These resources include vast collections of sorghum germplasm, high-quality reference genome sequences, sorghum association panels for genome-wide association studies of traits involved in food and bioenergy production, mutant populations for rapid discovery of causative genes for phenotypes relevant to sorghum improvement, gene expression atlas, and online databases that integrate all resources and provide the sorghum community with tools that can be used in breeding and genomic studies. Used in tandem, these valuable resources will ensure that the rate, quality, and collaborative potential of ongoing sorghum improvement efforts is able to rival that of other major crops.
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Affiliation(s)
- Zhanguo Xin
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, TX, 79424, USA.
| | - Mingli Wang
- Plant Genetic Resources Conservation Unit, USDA-ARS, Griffin, GA, 30223, USA
| | - Hugo E Cuevas
- Tropical Agriculture Research Station, USDA-ARS, Mayagüez, 00680, Puerto Rico
| | - Junping Chen
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, TX, 79424, USA
| | - Melanie Harrison
- Plant Genetic Resources Conservation Unit, USDA-ARS, Griffin, GA, 30223, USA
| | - N Ace Pugh
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, TX, 79424, USA
| | - Geoffrey Morris
- Crop Quantitative Genomics, Soil and Crop Sciences, Colorado State University, Plant Sciences Building, Fort Collins, CO, 80523, USA
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Larkin DL, Mason RE, Moon DE, Holder AL, Ward BP, Brown-Guedira G. Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection. FRONTIERS IN PLANT SCIENCE 2021; 12:715314. [PMID: 34745156 PMCID: PMC8569947 DOI: 10.3389/fpls.2021.715314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/27/2021] [Indexed: 06/13/2023]
Abstract
Many studies have evaluated the effectiveness of genomic selection (GS) using cross-validation within training populations; however, few have looked at its performance for forward prediction within a breeding program. The objectives for this study were to compare the performance of naïve GS (NGS) models without covariates and multi-trait GS (MTGS) models by predicting two years of F4: 7 advanced breeding lines for three Fusarium head blight (FHB) resistance traits, deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), and severity (SEV) in soft red winter wheat and comparing predictions with phenotypic performance over two years of selection based on selection accuracy and response to selection. On average, for DON, the NGS model correctly selected 69.2% of elite genotypes, while the MTGS model correctly selected 70.1% of elite genotypes compared with 33.0% based on phenotypic selection from the advanced generation. During the 2018 breeding cycle, GS models had the greatest response to selection for DON, FDK, and SEV compared with phenotypic selection. The MTGS model performed better than NGS during the 2019 breeding cycle for all three traits, whereas NGS outperformed MTGS during the 2018 breeding cycle for all traits except for SEV. Overall, GS models were comparable, if not better than phenotypic selection for FHB resistance traits. This is particularly helpful when adverse environmental conditions prohibit accurate phenotyping. This study also shows that MTGS models can be effective for forward prediction when there are strong correlations between traits of interest and covariates in both training and validation populations.
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Affiliation(s)
- Dylan L. Larkin
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Richard Esten Mason
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - David E. Moon
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Amanda L. Holder
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Brian P. Ward
- USDA-ARS SEA, Plant Science Research, Raleigh, NC, United States
| | - Gina Brown-Guedira
- USDA-ARS SEA, Plant Science Research, Raleigh, NC, United States
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, United States
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Metabolomics for Crop Breeding: General Considerations. Genes (Basel) 2021; 12:genes12101602. [PMID: 34680996 PMCID: PMC8535592 DOI: 10.3390/genes12101602] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/05/2021] [Accepted: 10/08/2021] [Indexed: 12/16/2022] Open
Abstract
The development of new, more productive varieties of agricultural crops is becoming an increasingly difficult task. Modern approaches for the identification of beneficial alleles and their use in elite cultivars, such as quantitative trait loci (QTL) mapping and marker-assisted selection (MAS), are effective but insufficient for keeping pace with the improvement of wheat or other crops. Metabolomics is a powerful but underutilized approach that can assist crop breeding. In this review, basic methodological information is summarized, and the current strategies of applications of metabolomics related to crop breeding are explored using recent examples. We briefly describe classes of plant metabolites, cellular localization of metabolic pathways, and the strengths and weaknesses of the main metabolomics technique. Among the commercialized genetically modified crops, about 50 with altered metabolic enzyme activities have been identified in the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. These plants are reviewed as encouraging examples of the application of knowledge of biochemical pathways. Based on the recent examples of metabolomic studies, we discuss the performance of metabolic markers, the integration of metabolic and genomic data in metabolic QTLs (mQTLs) and metabolic genome-wide association studies (mGWAS). The elucidation of metabolic pathways and involved genes will help in crop breeding and the introgression of alleles of wild relatives in a more targeted manner.
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Kim M, Nguyen TTP, Ahn JH, Kim GJ, Sim SC. Genome-wide association study identifies QTL for eight fruit traits in cultivated tomato (Solanum lycopersicum L.). HORTICULTURE RESEARCH 2021; 8:203. [PMID: 34465758 PMCID: PMC8408251 DOI: 10.1038/s41438-021-00638-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 06/19/2021] [Accepted: 06/25/2021] [Indexed: 05/28/2023]
Abstract
Genome-wide association study (GWAS) is effective in identifying favorable alleles for traits of interest with high mapping resolution in crop species. In this study, we conducted GWAS to explore quantitative trait loci (QTL) for eight fruit traits using 162 tomato accessions with diverse genetic backgrounds. The eight traits included fruit weight, fruit width, fruit height, fruit shape index, pericarp thickness, locule number, fruit firmness, and brix. Phenotypic variations of these traits in the tomato collection were evaluated with three replicates in field trials over three years. We filtered 34,550 confident SNPs from the 51 K Axiom® tomato array based on < 10% of missing data and > 5% of minor allele frequency for association analysis. The 162 tomato accessions were divided into seven clusters and their membership coefficients were used to account for population structure along with a kinship matrix. To identify marker-trait associations (MTAs), four phenotypic data sets representing each of three years and combined were independently analyzed in the multilocus mixed model (MLMM). A total of 30 significant MTAs was detected over data sets for eight fruit traits at P < 0.0005. The number of MTA per trait ranged from one (brix) to seven (fruit weight and fruit width). Two SNP markers on chromosomes 1 and 2 were significantly associated with multiple traits, suggesting pleiotropic effects of QTL. Furthermore, 16 of 30 MTAs suggest potential novel QTL for eight fruit traits. These results facilitate genetic dissection of tomato fruit traits and provide a useful resource to develop molecular tools for improving fruit traits via marker-assisted selection and genomic selection in tomato breeding programs.
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Affiliation(s)
- Minkyung Kim
- Department of Bioresources Engineering, Sejong University, Seoul, Republic of Korea
| | | | | | - Gi-Jun Kim
- Asia Seed R&D center, Icheon, Republic of Korea
| | - Sung-Chur Sim
- Department of Bioresources Engineering, Sejong University, Seoul, Republic of Korea.
- Plant Engineering Research Institute, Sejong University, Seoul, Republic of Korea.
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Hu D, Jing J, Snowdon RJ, Mason AS, Shen J, Meng J, Zou J. Exploring the gene pool of Brassica napus by genomics-based approaches. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:1693-1712. [PMID: 34031989 PMCID: PMC8428838 DOI: 10.1111/pbi.13636] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/13/2021] [Accepted: 05/14/2021] [Indexed: 05/08/2023]
Abstract
De novo allopolyploidization in Brassica provides a very successful model for reconstructing polyploid genomes using progenitor species and relatives to broaden crop gene pools and understand genome evolution after polyploidy, interspecific hybridization and exotic introgression. B. napus (AACC), the major cultivated rapeseed species and the third largest oilseed crop in the world, is a young Brassica species with a limited genetic base resulting from its short history of domestication, cultivation, and intensive selection during breeding for target economic traits. However, the gene pool of B. napus has been significantly enriched in recent decades that has been benefit from worldwide effects by the successful introduction of abundant subgenomic variation and novel genomic variation via intraspecific, interspecific and intergeneric crosses. An important question in this respect is how to utilize such variation to breed crops adapted to the changing global climate. Here, we review the genetic diversity, genome structure, and population-level differentiation of the B. napus gene pool in relation to known exotic introgressions from various species of the Brassicaceae, especially those elucidated by recent genome-sequencing projects. We also summarize progress in gene cloning, trait-marker associations, gene editing, molecular marker-assisted selection and genome-wide prediction, and describe the challenges and opportunities of these techniques as molecular platforms to exploit novel genomic variation and their value in the rapeseed gene pool. Future progress will accelerate the creation and manipulation of genetic diversity with genomic-based improvement, as well as provide novel insights into the neo-domestication of polyploid crops with novel genetic diversity from reconstructed genomes.
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Affiliation(s)
- Dandan Hu
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Jinjie Jing
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Rod J. Snowdon
- Department of Plant BreedingIFZ Research Centre for Biosystems, Land Use and NutritionJustus Liebig UniversityGiessenGermany
| | - Annaliese S. Mason
- Department of Plant BreedingIFZ Research Centre for Biosystems, Land Use and NutritionJustus Liebig UniversityGiessenGermany
- Plant Breeding DepartmentINRESThe University of BonnBonnGermany
| | - Jinxiong Shen
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Jinling Meng
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Jun Zou
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
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Feldmann MJ, Piepho HP, Bridges WC, Knapp SJ. Average semivariance yields accurate estimates of the fraction of marker-associated genetic variance and heritability in complex trait analyses. PLoS Genet 2021; 17:e1009762. [PMID: 34437540 PMCID: PMC8425577 DOI: 10.1371/journal.pgen.1009762] [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: 03/31/2021] [Revised: 09/08/2021] [Accepted: 08/09/2021] [Indexed: 12/15/2022] Open
Abstract
The development of genome-informed methods for identifying quantitative trait loci (QTL) and studying the genetic basis of quantitative variation in natural and experimental populations has been driven by advances in high-throughput genotyping. For many complex traits, the underlying genetic variation is caused by the segregation of one or more ‘large-effect’ loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance (p) and heritability ( HM2) for large-effect loci underlying complex phenotypes. We found that commonly used statistical methods overestimate p and HM2. The source of the upward bias was traced to inequalities between the expected values of variance components in the numerators and denominators of these parameters. Algebraic solutions for bias-correcting estimates of p and HM2 were found that only depend on the degrees of freedom and are constant for a given study design. We discovered that average semivariance methods, which have heretofore not been used in complex trait analyses, yielded unbiased estimates of p and HM2, in addition to best linear unbiased predictors of the additive and dominance effects of the underlying loci. The cryptic bias problem described here is unrelated to selection bias, although both cause the overestimation of p and HM2. The solutions we described are predicted to more accurately describe the contributions of large-effect loci to the genetic variation underlying complex traits of medical, biological, and agricultural importance. The contributions of individual genes to the phenotypic variation observed for genetically complex traits has been an ongoing and important challenge in biology, medicine, and agriculture. While many genes have statistically undetectable effects, those with large effects often warrant in-depth study and can be important predictors of complex phenotypes such as disease risk in humans or disease resistance in domesticated plants and animals. The genes identified through associations with genetic markers in complex trait analyses typically account for a fraction of the heritable variation, a genetic parameter we called ‘marker heritability’. We discovered that textbook statistical methods systematically overestimate marker heritability and thus overestimate the contributions of specific genes to the phenotypic variation observed for complex traits in natural and experimental populations. We describe the source of the upward bias, validate our findings through computer simulation, describe methods for bias-correcting estimates of marker heritability, and illustrate their application through empirical examples. The statistical methods we describe supply investigators with more accurate estimates of the contributions of specific genes or networks of interacting genes to the heritable variation observed in complex trait studies.
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Affiliation(s)
- Mitchell J. Feldmann
- Department of Plant Sciences, University of California, Davis, California, United States of America
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany
| | - William C. Bridges
- Department of Mathematical Sciences, Clemson University, Clemson, South Carolina, United States of America
| | - Steven J. Knapp
- Department of Plant Sciences, University of California, Davis, California, United States of America
- * E-mail:
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McGaugh SE, Lorenz AJ, Flagel LE. The utility of genomic prediction models in evolutionary genetics. Proc Biol Sci 2021; 288:20210693. [PMID: 34344180 PMCID: PMC8334854 DOI: 10.1098/rspb.2021.0693] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 07/15/2021] [Indexed: 12/25/2022] Open
Abstract
Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.
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Affiliation(s)
- Suzanne E. McGaugh
- Ecology, Evolution, and Behavior, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA
| | - Aaron J. Lorenz
- Agronomy and Plant Genetics, University of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Circle, Saint Paul, MN 55108, USA
| | - Lex E. Flagel
- Plant and Microbial Biology, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA
- Bayer Crop Science, 700 W Chesterfield Parkway, Chesterfield, MO 63017, USA
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Tong C, Hill CB, Zhou G, Zhang XQ, Jia Y, Li C. Opportunities for Improving Waterlogging Tolerance in Cereal Crops-Physiological Traits and Genetic Mechanisms. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10081560. [PMID: 34451605 PMCID: PMC8401455 DOI: 10.3390/plants10081560] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/12/2021] [Accepted: 07/28/2021] [Indexed: 05/22/2023]
Abstract
Waterlogging occurs when soil is saturated with water, leading to anaerobic conditions in the root zone of plants. Climate change is increasing the frequency of waterlogging events, resulting in considerable crop losses. Plants respond to waterlogging stress by adventitious root growth, aerenchyma formation, energy metabolism, and phytohormone signalling. Genotypes differ in biomass reduction, photosynthesis rate, adventitious roots development, and aerenchyma formation in response to waterlogging. We reviewed the detrimental effects of waterlogging on physiological and genetic mechanisms in four major cereal crops (rice, maize, wheat, and barley). The review covers current knowledge on waterlogging tolerance mechanism, genes, and quantitative trait loci (QTL) associated with waterlogging tolerance-related traits, the conventional and modern breeding methods used in developing waterlogging tolerant germplasm. Lastly, we describe candidate genes controlling waterlogging tolerance identified in model plants Arabidopsis and rice to identify homologous genes in the less waterlogging-tolerant maize, wheat, and barley.
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Affiliation(s)
- Cen Tong
- Western Crop Genetic Alliance, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia; (C.T.); (C.B.H.); (G.Z.); (X.-Q.Z.); (Y.J.)
- Western Australian State Agricultural Biotechnology Centre, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
| | - Camilla Beate Hill
- Western Crop Genetic Alliance, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia; (C.T.); (C.B.H.); (G.Z.); (X.-Q.Z.); (Y.J.)
- Western Australian State Agricultural Biotechnology Centre, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
| | - Gaofeng Zhou
- Western Crop Genetic Alliance, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia; (C.T.); (C.B.H.); (G.Z.); (X.-Q.Z.); (Y.J.)
- Western Australian State Agricultural Biotechnology Centre, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
| | - Xiao-Qi Zhang
- Western Crop Genetic Alliance, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia; (C.T.); (C.B.H.); (G.Z.); (X.-Q.Z.); (Y.J.)
- Western Australian State Agricultural Biotechnology Centre, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
| | - Yong Jia
- Western Crop Genetic Alliance, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia; (C.T.); (C.B.H.); (G.Z.); (X.-Q.Z.); (Y.J.)
- Western Australian State Agricultural Biotechnology Centre, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
| | - Chengdao Li
- Western Crop Genetic Alliance, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia; (C.T.); (C.B.H.); (G.Z.); (X.-Q.Z.); (Y.J.)
- Western Australian State Agricultural Biotechnology Centre, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
- Department of Primary Industries and Regional Development, 3-Baron-Hay Court, South Perth, WA 6151, Australia
- Correspondence: ; Tel.: +61-893-607-519
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Oloka BM, da Silva Pereira G, Amankwaah VA, Mollinari M, Pecota KV, Yada B, Olukolu BA, Zeng ZB, Craig Yencho G. Discovery of a major QTL for root-knot nematode (Meloidogyne incognita) resistance in cultivated sweetpotato (Ipomoea batatas). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1945-1955. [PMID: 33813604 PMCID: PMC8263542 DOI: 10.1007/s00122-021-03797-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 02/19/2021] [Indexed: 05/27/2023]
Abstract
Utilizing a high-density integrated genetic linkage map of hexaploid sweetpotato, we discovered a major dominant QTL for root-knot nematode (RKN) resistance and modeled its effects. This discovery is useful for development of a modern sweetpotato breeding program that utilizes marker-assisted selection and genomic selection approaches for faster genetic gain of RKN resistance. The root-knot nematode [Meloidogyne incognita (Kofoid & White) Chitwood] (RKN) causes significant storage root quality reduction and yields losses in cultivated sweetpotato [Ipomoea batatas (L.) Lam.]. In this study, resistance to RKN was examined in a mapping population consisting of 244 progenies derived from a cross (TB) between 'Tanzania,' a predominant African landrace cultivar with resistance to RKN, and 'Beauregard,' an RKN susceptible major cultivar in the USA. We performed quantitative trait loci (QTL) analysis using a random-effect QTL mapping model on the TB genetic map. An RKN bioassay incorporating potted cuttings of each genotype was conducted in the greenhouse and replicated five times over a period of 10 weeks. For each replication, each genotype was inoculated with ca. 20,000 RKN eggs, and root-knot galls were counted ~62 days after inoculation. Resistance to RKN in the progeny was highly skewed toward the resistant parent, exhibiting medium to high levels of resistance. We identified one major QTL on linkage group 7, dominant in nature, which explained 58.3% of the phenotypic variation in RKN counts. This work represents a significant step forward in our understanding of the genetic architecture of RKN resistance and sets the stage for future utilization of genomics-assisted breeding in sweetpotato breeding programs.
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Affiliation(s)
- Bonny Michael Oloka
- Department of Horticultural Science, North Carolina State University, 214 Kilgore Hall, Box 7609, Raleigh, NC, 27695, USA
- National Agricultural Research Organisation (NARO), National Crops Resources Research Institute (NaCRRI), Namulonge, P.O. Box 7084, Kampala, Uganda
| | | | - Victor A Amankwaah
- Department of Horticultural Science, North Carolina State University, 214 Kilgore Hall, Box 7609, Raleigh, NC, 27695, USA
- CSIR-Crops Research Institute, Kumasi, Ghana
| | - Marcelo Mollinari
- Department of Horticultural Science, North Carolina State University, 214 Kilgore Hall, Box 7609, Raleigh, NC, 27695, USA
| | - Kenneth V Pecota
- Department of Horticultural Science, North Carolina State University, 214 Kilgore Hall, Box 7609, Raleigh, NC, 27695, USA
| | - Benard Yada
- National Agricultural Research Organisation (NARO), National Crops Resources Research Institute (NaCRRI), Namulonge, P.O. Box 7084, Kampala, Uganda
| | | | - Zhao-Bang Zeng
- Department of Horticultural Science, North Carolina State University, 214 Kilgore Hall, Box 7609, Raleigh, NC, 27695, USA
| | - G Craig Yencho
- Department of Horticultural Science, North Carolina State University, 214 Kilgore Hall, Box 7609, Raleigh, NC, 27695, USA.
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Kim YC, Kang Y, Yang EY, Cho MC, Schafleitner R, Lee JH, Jang S. Applications and Major Achievements of Genome Editing in Vegetable Crops: A Review. FRONTIERS IN PLANT SCIENCE 2021; 12:688980. [PMID: 34178006 PMCID: PMC8231707 DOI: 10.3389/fpls.2021.688980] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/18/2021] [Indexed: 05/04/2023]
Abstract
The emergence of genome-editing technology has allowed manipulation of DNA sequences in genomes to precisely remove or replace specific sequences in organisms resulting in targeted mutations. In plants, genome editing is an attractive method to alter gene functions to generate improved crop varieties. Genome editing is thought to be simple to use and has a lower risk of off-target effects compared to classical mutation breeding. Furthermore, genome-editing technology tools can also be applied directly to crops that contain complex genomes and/or are not easily bred using traditional methods. Currently, highly versatile genome-editing tools for precise and predictable editing of almost any locus in the plant genome make it possible to extend the range of application, including functional genomics research and molecular crop breeding. Vegetables are essential nutrient sources for humans and provide vitamins, minerals, and fiber to diets, thereby contributing to human health. In this review, we provide an overview of the brief history of genome-editing technologies and the components of genome-editing tool boxes, and illustrate basic modes of operation in representative systems. We describe the current and potential practical application of genome editing for the development of improved nutritious vegetables and present several case studies demonstrating the potential of the technology. Finally, we highlight future directions and challenges in applying genome-editing systems to vegetable crops for research and product development.
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Affiliation(s)
- Young-Cheon Kim
- Division of Life Sciences, Jeonbuk National University, Jeonju, South Korea
| | - Yeeun Kang
- World Vegetable Center Korea Office, Wanju-gun, South Korea
| | - Eun-Young Yang
- National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun, South Korea
| | - Myeong-Cheoul Cho
- National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Wanju-gun, South Korea
| | | | - Jeong Hwan Lee
- Division of Life Sciences, Jeonbuk National University, Jeonju, South Korea
| | - Seonghoe Jang
- World Vegetable Center Korea Office, Wanju-gun, South Korea
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Mores A, Borrelli GM, Laidò G, Petruzzino G, Pecchioni N, Amoroso LGM, Desiderio F, Mazzucotelli E, Mastrangelo AM, Marone D. Genomic Approaches to Identify Molecular Bases of Crop Resistance to Diseases and to Develop Future Breeding Strategies. Int J Mol Sci 2021; 22:5423. [PMID: 34063853 PMCID: PMC8196592 DOI: 10.3390/ijms22115423] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/30/2021] [Accepted: 05/15/2021] [Indexed: 12/16/2022] Open
Abstract
Plant diseases are responsible for substantial crop losses each year and affect food security and agricultural sustainability. The improvement of crop resistance to pathogens through breeding represents an environmentally sound method for managing disease and minimizing these losses. The challenge is to breed varieties with a stable and broad-spectrum resistance. Different approaches, from markers to recent genomic and 'post-genomic era' technologies, will be reviewed in order to contribute to a better understanding of the complexity of host-pathogen interactions and genes, including those with small phenotypic effects and mechanisms that underlie resistance. An efficient combination of these approaches is herein proposed as the basis to develop a successful breeding strategy to obtain resistant crop varieties that yield higher in increasing disease scenarios.
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Affiliation(s)
- Antonia Mores
- Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, S.S. 673, Km 25,200, 71122 Foggia, Italy; (A.M.); (G.M.B.); (G.L.); (G.P.); (N.P.); (A.M.M.)
| | - Grazia Maria Borrelli
- Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, S.S. 673, Km 25,200, 71122 Foggia, Italy; (A.M.); (G.M.B.); (G.L.); (G.P.); (N.P.); (A.M.M.)
| | - Giovanni Laidò
- Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, S.S. 673, Km 25,200, 71122 Foggia, Italy; (A.M.); (G.M.B.); (G.L.); (G.P.); (N.P.); (A.M.M.)
| | - Giuseppe Petruzzino
- Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, S.S. 673, Km 25,200, 71122 Foggia, Italy; (A.M.); (G.M.B.); (G.L.); (G.P.); (N.P.); (A.M.M.)
| | - Nicola Pecchioni
- Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, S.S. 673, Km 25,200, 71122 Foggia, Italy; (A.M.); (G.M.B.); (G.L.); (G.P.); (N.P.); (A.M.M.)
| | | | - Francesca Desiderio
- Council for Agricultural Research and Economics, Genomics and Bioinformatics Research Center, Via San Protaso 302, 29017 Fiorenzuola d’Arda, Italy; (F.D.); (E.M.)
| | - Elisabetta Mazzucotelli
- Council for Agricultural Research and Economics, Genomics and Bioinformatics Research Center, Via San Protaso 302, 29017 Fiorenzuola d’Arda, Italy; (F.D.); (E.M.)
| | - Anna Maria Mastrangelo
- Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, S.S. 673, Km 25,200, 71122 Foggia, Italy; (A.M.); (G.M.B.); (G.L.); (G.P.); (N.P.); (A.M.M.)
| | - Daniela Marone
- Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, S.S. 673, Km 25,200, 71122 Foggia, Italy; (A.M.); (G.M.B.); (G.L.); (G.P.); (N.P.); (A.M.M.)
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Scott MF, Fradgley N, Bentley AR, Brabbs T, Corke F, Gardner KA, Horsnell R, Howell P, Ladejobi O, Mackay IJ, Mott R, Cockram J. Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding. Genome Biol 2021; 22:137. [PMID: 33957956 PMCID: PMC8101041 DOI: 10.1186/s13059-021-02354-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 04/16/2021] [Indexed: 11/25/2022] Open
Abstract
Background Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination. Results We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders’ genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders’ genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling. Conclusions Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02354-7.
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Affiliation(s)
- Michael F Scott
- University College London (UCL) Genetics Institute, Gower St, London, WC1E 6BT, UK.,Current address: School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Nick Fradgley
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Alison R Bentley
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.,Current address: International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco, Mexico
| | | | - Fiona Corke
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE, UK
| | - Keith A Gardner
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard Horsnell
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Phil Howell
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | | | - Ian J Mackay
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.,Current address: SRUC, Peter Wilson Building King's Buildings, W Mains Rd, Edinburgh, EH9 3JG, UK
| | - Richard Mott
- University College London (UCL) Genetics Institute, Gower St, London, WC1E 6BT, UK.
| | - James Cockram
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.
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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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50
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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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