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Kitony JK, Colt K, Abramson BW, Hartwick NT, Petrus S, Konozy EHE, Karimi N, Yant L, Michael TP. Chromosome-level baobab genome illuminates its evolutionary trajectory and environmental adaptation. Nat Commun 2024; 15:8833. [PMID: 39396056 PMCID: PMC11470940 DOI: 10.1038/s41467-024-53157-w] [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: 04/14/2024] [Accepted: 10/03/2024] [Indexed: 10/14/2024] Open
Abstract
Baobab (Adansonia digitata) is a long-lived tree endemic to Africa with economic, ecological, and cultural importance, yet its genomic features are underexplored. Here, we report a chromosome-level reference genome anchored to 42 chromosomes for A. digitata, alongside draft assemblies for a sibling tree, two trees from distinct locations in Africa, and A. za from Madagascar. The baobab genome is uniquely rich in DNA transposons, which make up 33%, while LTR retrotransposons account for 10%. A. digitata experienced whole genome multiplication (WGM) around 30 million years ago (MYA), followed by a second WGM event 3-11 MYA, likely linked to autotetraploidy. Resequencing of 25 trees identify three subpopulations, with gene flow across West Africa distinct from East Africa. Gene enrichment and fixation index (Fst) analyses show baobab retained multiple circadian, flowering, and light-responsive genes, which likely support longevity through the UV RESISTANCE LOCUS 8 (UVR8) pathway. In sum, we provide genomic resources and insights for baobab breeding and conservation.
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Affiliation(s)
- Justine K Kitony
- Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Kelly Colt
- Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bradley W Abramson
- Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Noblis, Inc., Washington, DC, USA
| | - Nolan T Hartwick
- Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Semar Petrus
- Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Cepheid, Sunnyvale, CA, USA
| | - Emadeldin H E Konozy
- Biomedical and Clinical Research Centre (BCRC), College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Nisa Karimi
- Missouri Botanical Garden, Science and Conservation Division, St. Louis, MO, USA
- Department of Botany, University of Wisconsin - Madison, Madison, WI, USA
| | - Levi Yant
- School of Life Sciences, University of Nottingham, Nottingham, UK
- Department of Botany, Faculty of Science, Charles University, Prague, Czech Republic
| | - Todd P Michael
- Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
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2
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Farooq MA, Gao S, Hassan MA, Huang Z, Rasheed A, Hearne S, Prasanna B, Li X, Li H. Artificial intelligence in plant breeding. Trends Genet 2024; 40:891-908. [PMID: 39117482 DOI: 10.1016/j.tig.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 08/10/2024]
Abstract
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.
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Affiliation(s)
- Muhammad Amjad Farooq
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Shang Gao
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Muhammad Adeel Hassan
- Adaptive Cropping Systems Laboratory, Beltsville Agricultural Research Center, US Department of Agriculture, Beltsville, MD 20705, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Zhangping Huang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Sarah Hearne
- CIMMYT, KM 45 Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Boddupalli Prasanna
- CIMMYT, International Centre for Research in Agroforestry (ICRAF) House, Nairobi 00100, Kenya
| | - Xinhai Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China
| | - Huihui Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China.
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3
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Khalilisamani N, Li Z, Pettolino FA, Moncuquet P, Reverter A, MacMillan CP. Leveraging transcriptomics-based approaches to enhance genomic prediction: integrating SNPs and gene networks for cotton fibre quality improvement. FRONTIERS IN PLANT SCIENCE 2024; 15:1420837. [PMID: 39372856 PMCID: PMC11450228 DOI: 10.3389/fpls.2024.1420837] [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: 04/21/2024] [Accepted: 08/19/2024] [Indexed: 10/08/2024]
Abstract
Cultivated cotton plants are the world's largest source of natural fibre, where yield and quality are key traits for this renewable and biodegradable commodity. The Gossypium hirsutum cotton genome contains ~80K protein-coding genes, making precision breeding of complex traits a challenge. This study tested approaches to improving the genomic prediction (GP) accuracy of valuable cotton fibre traits to help accelerate precision breeding. With a biology-informed basis, a novel approach was tested for improving GP for key cotton fibre traits with transcriptomics of key time points during fibre development, namely, fibre cells undergoing primary, transition, and secondary wall development. Three test approaches included weighting of SNPs in DE genes overall, in target DE gene lists informed by gene annotation, and in a novel approach of gene co-expression network (GCN) clusters created with partial correlation and information theory (PCIT) as the prior information in GP models. The GCN clusters were nucleated with known genes for fibre biomechanics, i.e., fasciclin-like arabinogalactan proteins, and cluster size effects were evaluated. The most promising improvements in GP accuracy were achieved by using GCN clusters for cotton fibre elongation by 4.6%, and strength by 4.7%, where cluster sizes of two and three neighbours proved most effective. Furthermore, the improvements in GP were due to only a small number of SNPs, in the order of 30 per trait using the GCN cluster approach. Non-trait-specific biological time points, and genes, were found to have neutral effects, or even reduced GP accuracy for certain traits. As the GCN clusters were generated based on known genes for fibre biomechanics, additional candidate genes were identified for fibre elongation and strength. These results demonstrate that GCN clusters make a specific and unique contribution in improving the GP of cotton fibre traits. The findings also indicate that there is room for incorporating biology-based GCNs into GP models of genomic selection pipelines for cotton breeding to help improve precision breeding of target traits. The PCIT-GCN cluster approach may also hold potential application in other crops and trees for enhancing breeding of complex traits.
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Affiliation(s)
- Nima Khalilisamani
- Cotton Biotechnology, Agriculture and Food, CSIRO, Canberra, ACT, Australia
| | - Zitong Li
- Cotton Biotechnology, Agriculture and Food, CSIRO, Canberra, ACT, Australia
| | | | - Philippe Moncuquet
- Cotton Biotechnology, Agriculture and Food, CSIRO, Canberra, ACT, Australia
| | - Antonio Reverter
- Livestock and Aquatic Genomics, Agriculture and Food, CSIRO, St Lucia, QLD, Australia
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4
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Washburn JD, Varela JI, Xavier A, Chen Q, Ertl D, Gage JL, Holland JB, Lima DC, Romay MC, Lopez-Cruz M, de los Campos G, Barber W, Zimmer C, Silva IT, Rocha F, Rincent R, Ali B, Hu H, Runcie DE, Gusev K, Slabodkin A, Bax P, Aubert J, Gangloff H, Mary-Huard T, Vanrenterghem T, Quesada-Traver C, Yates S, Ariza-Suárez D, Ulrich A, Wyler M, Kick DR, Bellis ES, Causey JL, Chavez ES, Wang Y, Piyush V, Fernando GD, Hu RK, Kumar R, Timon AJ, Venkatesh R, Abá KS, Chen H, Ranaweera T, Shiu SH, Wang P, Gordon MJ, Amos BK, Busato S, Perondi D, Gogna A, Psaroudakis D, Chen CPJ, Al-Mamun HA, Danilevicz MF, Upadhyaya SR, Edwards D, de Leon N. Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.13.612969. [PMID: 39345633 PMCID: PMC11429743 DOI: 10.1101/2024.09.13.612969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.
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Affiliation(s)
- Jacob D. Washburn
- USDA-ARS-MWA-PGRU, 302-A Curtis Hall, U. of MO., Columbia, MO, 65211, USA
| | - José Ignacio Varela
- Department of Plant and Agroecosystem Sciences, University of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
| | - Alencar Xavier
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
- Department of Agronomy, Purdue University, 915 Mitch Daniels Blvd, West Lafayette, IN 47907, United States
| | - Qiuyue Chen
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, 50131, USA
| | - Joseph L. Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - James B. Holland
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
- USDA-ARS Plant Science Research Unit, Raleigh, NC, 27695, USA
| | - Dayane Cristina Lima
- Department of Plant and Agroecosystem Sciences, University of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Marco Lopez-Cruz
- Departments of Epidemiology & Biostatistics and Statistics & Probability, and Institute for Quantitative Health Science and Engineering, Michigan State University, 775 Woodlot Dr., East Lansing, MI, 48823, USA
| | - Gustavo de los Campos
- Departments of Epidemiology & Biostatistics and Statistics & Probability, and Institute for Quantitative Health Science and Engineering, Michigan State University, 775 Woodlot Dr., East Lansing, MI, 48823, USA
| | - Wesley Barber
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
| | - Cristiano Zimmer
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
| | | | - Fabiani Rocha
- Corteva Agrisciences, 8305 NW 62nd Ave, Johnston, IA, 50131, USA
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Baber Ali
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Haixiao Hu
- Department of Plant Sciences, University of California Davis, One Shield Drive, Davis, CA, 95616, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, One Shield Drive, Davis, CA, 95616, USA
| | - Kirill Gusev
- Smart Agri Labs, 2055 Limestone Rd STE 200-C, Wilmington, DE, 19808, USA
| | - Andrei Slabodkin
- Smart Agri Labs, 2055 Limestone Rd STE 200-C, Wilmington, DE, 19808, USA
| | - Phillip Bax
- Smart Agri Labs, 2055 Limestone Rd STE 200-C, Wilmington, DE, 19808, USA
| | - Julie Aubert
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Hugo Gangloff
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Theodore Vanrenterghem
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Carles Quesada-Traver
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, CH-8092 Zurich, Switzerland
| | - Steven Yates
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, CH-8092 Zurich, Switzerland
| | - Daniel Ariza-Suárez
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, CH-8092 Zurich, Switzerland
| | - Argeo Ulrich
- Puregene AG, Etzmatt 273, CH-4314 Zeiningen, Switzerland
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, CH-8092 Zürich, Switzerland
| | - Michele Wyler
- MWSchmid GmbH, Hauptstrasse 34, CH-8750 Glarus, Switzerland
| | - Daniel R. Kick
- USDA-ARS-MWA-PGRU, 302-A Curtis Hall, U. of MO., Columbia, MO, 65211, USA
| | - Emily S. Bellis
- Department of Computer Science, Arkansas State University, 2105 E. Aggie Rd., Jonesboro, AR, 72401, USA
| | - Jason L. Causey
- Department of Computer Science, Arkansas State University, 2105 E. Aggie Rd., Jonesboro, AR, 72401, USA
| | - Emilio Soriano Chavez
- Department of Computer Science, Arkansas State University, 2105 E. Aggie Rd., Jonesboro, AR, 72401, USA
| | - Yixing Wang
- Department of Computer Science, Arkansas State University, 2105 E. Aggie Rd., Jonesboro, AR, 72401, USA
| | - Ved Piyush
- Department of Statistics, University of Nebraska - Lincoln, 340 Hardin Hall North Wing, Lincoln, NE, 68583, USA
| | - Gayara D. Fernando
- Department of Statistics, University of Nebraska - Lincoln, 340 Hardin Hall North Wing, Lincoln, NE, 68583, USA
| | - Robert K Hu
- Genomics and Computational Biology, Perelman School of Medicine at the University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Rachit Kumar
- Genomics and Computational Biology, Perelman School of Medicine at the University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
- Medical Scientist Training Program, Perelman School of Medicine at the University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA, 19104, USA
| | - Annan J. Timon
- Genomics and Computational Biology, Perelman School of Medicine at the University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Rasika Venkatesh
- Genomics and Computational Biology, Perelman School of Medicine at the University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Kenia Segura Abá
- DOE Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI, 48824, USA
- Genetics and Genome Sciences Graduate Program, Michigan State University, East Lansing, MI, 48824, USA
| | - Huan Chen
- Genetics and Genome Sciences Graduate Program, Michigan State University, East Lansing, MI, 48824, USA
| | - Thilanka Ranaweera
- DOE Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI, 48824, USA
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Shin-Han Shiu
- DOE Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI, 48824, USA
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Peiran Wang
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - Max J. Gordon
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - B K. Amos
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - Sebastiano Busato
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - Daniel Perondi
- NC Plant Science Initiative, North Carolina State University, 840 Oval Drive, Raleigh, NC, 27606, USA
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, 27606, USA
| | - Abhishek Gogna
- Department of Breeding Research, Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung, Corrensstraße 3, Gatersleben, 6466, Germany
| | - Dennis Psaroudakis
- Department of Molecular Genetics, Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung, Corrensstraße 3, Gatersleben, 6466, Germany
| | - C. P. James Chen
- School of Animal Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Hawlader A. Al-Mamun
- School of Biological Sciences and Centre of Applied Bioinformatics, University of Western Australia, Perth, WA, Australia
| | - Monica F. Danilevicz
- School of Biological Sciences and Centre of Applied Bioinformatics, University of Western Australia, Perth, WA, Australia
| | - Shriprabha R. Upadhyaya
- School of Biological Sciences and Centre of Applied Bioinformatics, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Centre of Applied Bioinformatics, University of Western Australia, Perth, WA, Australia
| | - Natalia de Leon
- Department of Plant and Agroecosystem Sciences, University of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
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Lee AMJ, Foong MYM, Song BK, Chew FT. Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2024; 44:60. [PMID: 39267903 PMCID: PMC11391014 DOI: 10.1007/s11032-024-01497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 09/01/2024] [Indexed: 09/15/2024]
Abstract
To ensure the nutritional needs of an expanding global population, it is crucial to optimize the growing capabilities and breeding values of fruit and vegetable crops. While genomic selection, initially implemented in animal breeding, holds tremendous potential, its utilization in fruit and vegetable crops remains underexplored. In this systematic review, we reviewed 63 articles covering genomic selection and its applications across 25 different types of fruit and vegetable crops over the last decade. The traits examined were directly related to the edible parts of the crops and carried significant economic importance. Comparative analysis with WHO/FAO data identified potential economic drivers underlying the study focus of some crops and highlighted crops with potential for further genomic selection research and application. Factors affecting genomic selection accuracy in fruit and vegetable studies are discussed and suggestions made to assist in their implementation into plant breeding schemes. Genetic gain in fruits and vegetables can be improved by utilizing genomic selection to improve selection intensity, accuracy, and integration of genetic variation. However, the reduction of breeding cycle times may not be beneficial in crops with shorter life cycles such as leafy greens as compared to fruit trees. There is an urgent need to integrate genomic selection methods into ongoing breeding programs and assess the actual genomic estimated breeding values of progeny resulting from these breeding programs against the prediction models. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-024-01497-2.
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Affiliation(s)
- Adrian Ming Jern Lee
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543 Republic of Singapore
- NUS Agritech Centre, National University of Singapore, 85 Science Park Dr, #01-03, Singapore, 118258 Republic of Singapore
| | - Melissa Yuin Mern Foong
- School of Science, Monash University Malaysia, Bandar Sunway, 47500 Subang Jaya, Selangor Darul Ehsan Malaysia
| | - Beng Kah Song
- School of Science, Monash University Malaysia, Bandar Sunway, 47500 Subang Jaya, Selangor Darul Ehsan Malaysia
| | - Fook Tim Chew
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543 Republic of Singapore
- NUS Agritech Centre, National University of Singapore, 85 Science Park Dr, #01-03, Singapore, 118258 Republic of Singapore
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6
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Saludares RA, Atanda SA, Piche L, Worral H, Dariva F, McPhee K, Bandillo N. Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop. THE PLANT GENOME 2024:e20496. [PMID: 39099220 DOI: 10.1002/tpg2.20496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 07/02/2024] [Accepted: 07/07/2024] [Indexed: 08/06/2024]
Abstract
Phenotypic selection of complex traits such as seed yield and protein in the preliminary yield trial (PYT) is often constrained by limited seed availability, resulting in trials with few environments and minimal to no replications. Multi-trait multi-environment enabled genomic prediction (MTME-GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. In this study, we assessed the efficiency of MTME-GP for improving seed protein and seed yield in field pea, the top two breeding targets but highly antagonistic traits in pulse crop. We utilized a set of 300 selection candidates in the PYT that virtually represented all possible families of the North Dakota State University field pea breeding program. Selection candidates were evaluated in three diverse, contrasting environments, as indicated by a range of heritability. Using whole- and split-environment cross validation schemes, MTME-GP had higher predictive ability than a standard additive G-BLUP model. Integrating a range of overlapping genotypes in between environments showed improvement on the predictive ability of the MTME-GP model but tends to plateau at 50%-80% training set size. Regardless of the cross-validation scheme, accuracy was among the lowest in stressed environments, presumably due to low heritability for seed protein and yield. This study provided insights into the potential of MTME-GP in a public pulse crop breeding program. The MTME-GP framework can be further improved with more testing environments and integration of additional orthogonal information in the early stages of the breeding pipeline.
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Affiliation(s)
- Rica Amor Saludares
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA
| | - Sikiru Adeniyi Atanda
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA
| | - Lisa Piche
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA
| | - Hannah Worral
- North Central Research Extension Center, Minot, North Dakota, USA
| | - Francoise Dariva
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA
| | - Kevin McPhee
- Department of Plant Science and Plant Pathology, Montana State University, Bozeman, Montana, USA
| | - Nonoy Bandillo
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA
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7
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Bacelar E, Pinto T, Anjos R, Morais MC, Oliveira I, Vilela A, Cosme F. Impacts of Climate Change and Mitigation Strategies for Some Abiotic and Biotic Constraints Influencing Fruit Growth and Quality. PLANTS (BASEL, SWITZERLAND) 2024; 13:1942. [PMID: 39065469 PMCID: PMC11280748 DOI: 10.3390/plants13141942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 07/07/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024]
Abstract
Factors such as extreme temperatures, light radiation, and nutritional condition influence the physiological, biochemical, and molecular processes associated with fruit development and its quality. Besides abiotic stresses, biotic constraints can also affect fruit growth and quality. Moreover, there can be interactions between stressful conditions. However, it is challenging to predict and generalize the risks of climate change scenarios on seasonal patterns of growth, development, yield, and quality of fruit species because their responses are often highly complex and involve changes at multiple levels. Advancements in genetic editing technologies hold great potential for the agricultural sector, particularly in enhancing fruit crop traits. These improvements can be tailored to meet consumer preferences, which is crucial for commercial success. Canopy management and innovative training systems are also key factors that contribute to maximizing yield efficiency and improving fruit quality, which are essential for the competitiveness of orchards. Moreover, the creation of habitats that support pollinators is a critical aspect of sustainable agriculture, as they play a significant role in the production of many crops, including fruits. Incorporating these strategies allows fruit growers to adapt to changing climate conditions, which is increasingly important for the stability of food production. By investing in these areas, fruit growers can stay ahead of challenges and opportunities in the industry, ultimately leading to increased success and profitability. In this review, we aim to provide an updated overview of the current knowledge on this important topic. We also provide recommendations for future research.
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Affiliation(s)
- Eunice Bacelar
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), University of Trás-of-Montes and Alto Douro, Quinta de Prados, P-5000-801 Vila Real, Portugal; (T.P.); (R.A.); (M.C.M.); (I.O.)
| | - Teresa Pinto
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), University of Trás-of-Montes and Alto Douro, Quinta de Prados, P-5000-801 Vila Real, Portugal; (T.P.); (R.A.); (M.C.M.); (I.O.)
| | - Rosário Anjos
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), University of Trás-of-Montes and Alto Douro, Quinta de Prados, P-5000-801 Vila Real, Portugal; (T.P.); (R.A.); (M.C.M.); (I.O.)
| | - Maria Cristina Morais
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), University of Trás-of-Montes and Alto Douro, Quinta de Prados, P-5000-801 Vila Real, Portugal; (T.P.); (R.A.); (M.C.M.); (I.O.)
| | - Ivo Oliveira
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), University of Trás-of-Montes and Alto Douro, Quinta de Prados, P-5000-801 Vila Real, Portugal; (T.P.); (R.A.); (M.C.M.); (I.O.)
| | - Alice Vilela
- Chemistry Research Centre–Vila Real (CQ-VR), Department of Agronomy, School of Agrarian and Veterinary Sciences (ECAV), University of Trás-os-Montes and Alto Douro, P-5000-801 Vila Real, Portugal;
| | - Fernanda Cosme
- Chemistry Research Centre–Vila Real (CQ-VR), Department of Biology and Environment, School of Life Sciences and Environment, University of Trás-os-Montes and Alto Douro, P-5000-801 Vila Real, Portugal;
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8
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Kim S, Chu SH, Park YJ, Lee CY. Stacked generalization as a computational method for the genomic selection. Front Genet 2024; 15:1401470. [PMID: 39050246 PMCID: PMC11266134 DOI: 10.3389/fgene.2024.1401470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 05/30/2024] [Indexed: 07/27/2024] Open
Abstract
As genomic selection emerges as a promising breeding method for both plants and animals, numerous methods have been introduced and applied to various real and simulated data sets. Research suggests that no single method is universally better than others; rather, performance is highly dependent on the characteristics of the data and the nature of the prediction task. This implies that each method has its strengths and weaknesses. In this study, we exploit this notion and propose a different approach. Rather than comparing multiple methods to determine the best one for a particular study, we advocate combining multiple methods to achieve better performance than each method in isolation. In pursuit of this goal, we introduce and develop a computational method of the stacked generalization within ensemble methods. In this method, the meta-model merges predictions from multiple base models to achieve improved performance. We applied this method to plant and animal data and compared its performance with currently available methods using standard performance metrics. We found that the proposed method yielded a lower or comparable mean squared error in predicting phenotypes compared to the current methods. In addition, the proposed method showed greater resistance to overfitting compared to the current methods. Further analysis included statistical hypothesis testing, which showed that the proposed method outperformed or matched the current methods. In summary, the proposed stacked generalization integrates currently available methods to achieve stable and better performance. In this context, our study provides general recommendations for effective practices in genomic selection.
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Affiliation(s)
- Sunhee Kim
- The Department of Industrial Engineering, Kongju National University, Cheonan, Republic of Korea
| | - Sang-Ho Chu
- The Department of Plant Resources, Kongju National University, Yesan, Republic of Korea
| | - Yong-Jin Park
- The Department of Plant Resources, Kongju National University, Yesan, Republic of Korea
| | - Chang-Yong Lee
- The Department of Industrial Engineering, Kongju National University, Cheonan, Republic of Korea
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9
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Resende RT, Hickey L, Amaral CH, Peixoto LL, Marcatti GE, Xu Y. Satellite-enabled enviromics to enhance crop improvement. MOLECULAR PLANT 2024; 17:848-866. [PMID: 38637991 DOI: 10.1016/j.molp.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
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Affiliation(s)
- Rafael T Resende
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil; TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil.
| | - Lee Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Cibele H Amaral
- Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA; Environmental Data Science Innovation & Inclusion Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Lucas L Peixoto
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil
| | - Gustavo E Marcatti
- TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil; Universidade Federal de São João del-Rei, Forest Engineering Department, Campus Sete Lagoas, Sete Lagoas (MG) 35701-970, Brazil
| | - Yunbi Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China; BGI Bioverse, Shenzhen 518083, China.
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10
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Dieng I, Gardunia B, Covarrubias-Pazaran G, Gemenet DC, Trognitz B, Ofodile S, Fowobaje K, Ntukidem S, Shah T, Imoro S, Tripathi L, Mushoriwa H, Mbabazi R, Salvo S, Derera J. Q&A: Methods for estimating genetic gain in sub-Saharan Africa and achieving improved gains. THE PLANT GENOME 2024; 17:e20471. [PMID: 38923724 DOI: 10.1002/tpg2.20471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/17/2024] [Accepted: 04/19/2024] [Indexed: 06/28/2024]
Abstract
Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi-environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics-IITA/Estimating-Realized-Genetic-Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.
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Affiliation(s)
- Ibnou Dieng
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | | | | | - Dorcus C Gemenet
- EiB-CIMMYT c/o ICRAF House United Nations Avenue, Nairobi, Kenya
| | | | - Sam Ofodile
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Kayode Fowobaje
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Solomon Ntukidem
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Trushar Shah
- IITA c/o International Livestock Research Institute (ILRI), Nairobi, Kenya
| | - Simon Imoro
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Leena Tripathi
- IITA c/o International Livestock Research Institute (ILRI), Nairobi, Kenya
| | - Hapson Mushoriwa
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | | | | | - John Derera
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
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11
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Williams K, Subramani M, Lofton LW, Penney M, Todd A, Ozbay G. Tools and Techniques to Accelerate Crop Breeding. PLANTS (BASEL, SWITZERLAND) 2024; 13:1520. [PMID: 38891328 PMCID: PMC11174677 DOI: 10.3390/plants13111520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/25/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024]
Abstract
As climate changes and a growing global population continue to escalate the need for greater production capabilities of food crops, technological advances in agricultural and crop research will remain a necessity. While great advances in crop improvement over the past century have contributed to massive increases in yield, classic breeding schemes lack the rate of genetic gain needed to meet future demands. In the past decade, new breeding techniques and tools have been developed to aid in crop improvement. One such advancement is the use of speed breeding. Speed breeding is known as the application of methods that significantly reduce the time between crop generations, thereby streamlining breeding and research efforts. These rapid-generation advancement tactics help to accelerate the pace of crop improvement efforts to sustain food security and meet the food, feed, and fiber demands of the world's growing population. Speed breeding may be achieved through a variety of techniques, including environmental optimization, genomic selection, CRISPR-Cas9 technology, and epigenomic tools. This review aims to discuss these prominent advances in crop breeding technologies and techniques that have the potential to greatly improve plant breeders' ability to rapidly produce vital cultivars.
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Affiliation(s)
- Krystal Williams
- Molecular Genetics and Epigenomics Laboratory, Department of Agriculture and Natural Resources, College of Agriculture, Science, and Technology, Delaware State University, Dover, DE 19901, USA;
| | - Mayavan Subramani
- Molecular Genetics and Epigenomics Laboratory, Department of Agriculture and Natural Resources, College of Agriculture, Science, and Technology, Delaware State University, Dover, DE 19901, USA;
| | - Lily W. Lofton
- Department of Plant Pathology, University of Georgia, Athens, GA 30602, USA;
- Toxicology & Mycotoxin Research Unit, US National Poultry Research Center, USDA-ARS, Athens, GA 30602, USA
| | - Miranda Penney
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA;
| | - Antonette Todd
- Molecular Genetics and Epigenomics Laboratory, Department of Agriculture and Natural Resources, College of Agriculture, Science, and Technology, Delaware State University, Dover, DE 19901, USA;
| | - Gulnihal Ozbay
- One Health Laboratory, Department of Agriculture and Natural Resources, College of Agriculture, Science, and Technology, Delaware State University, Dover, DE 19901, USA
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12
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Montesinos-López OA, Crespo-Herrera L, Pierre CS, Cano-Paez B, Huerta-Prado GI, Mosqueda-González BA, Ramos-Pulido S, Gerard G, Alnowibet K, Fritsche-Neto R, Montesinos-López A, Crossa J. Feature engineering of environmental covariates improves plant genomic-enabled prediction. FRONTIERS IN PLANT SCIENCE 2024; 15:1349569. [PMID: 38812738 PMCID: PMC11135473 DOI: 10.3389/fpls.2024.1349569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/11/2024] [Indexed: 05/31/2024]
Abstract
Introduction Because Genomic selection (GS) is a predictive methodology, it needs to guarantee high-prediction accuracies for practical implementations. However, since many factors affect the prediction performance of this methodology, its practical implementation still needs to be improved in many breeding programs. For this reason, many strategies have been explored to improve the prediction performance of this methodology. Methods When environmental covariates are incorporated as inputs in the genomic prediction models, this information only sometimes helps increase prediction performance. For this reason, this investigation explores the use of feature engineering on the environmental covariates to enhance the prediction performance of genomic prediction models. Results and discussion We found that across data sets, feature engineering helps reduce prediction error regarding only the inclusion of the environmental covariates without feature engineering by 761.625% across predictors. These results are very promising regarding the potential of feature engineering to enhance prediction accuracy. However, since a significant gain in prediction accuracy was observed in only some data sets, further research is required to guarantee a robust feature engineering strategy to incorporate the environmental covariates.
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Affiliation(s)
| | | | - Carolina Saint Pierre
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Edo. de Mexico, Mexico
| | - Bernabe Cano-Paez
- Facultad de Ciencias, Universidad Nacioanl Autónoma de México (UNAM), México City, Mexico
| | | | | | - Sofia Ramos-Pulido
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - Guillermo Gerard
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Edo. de Mexico, Mexico
| | - Khalid Alnowibet
- Department of Statistics and Operations Research, King Saud University, Riyah, Saudi Arabia
| | | | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Edo. de Mexico, Mexico
- Louisiana State University, Baton Rouge, LA, United States
- Distinguished Scientist Fellowship Program, King Saud University, Riyah, Saudi Arabia
- Instituto de Socieconomia, Estadistica e Informatica, Colegio de Postgraduados, Montecillos, Edo. de México, Texcoco, Mexico
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13
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Agho CA, Śliwka J, Nassar H, Niinemets Ü, Runno-Paurson E. Machine Learning-Based Identification of Mating Type and Metalaxyl Response in Phytophthora infestans Using SSR Markers. Microorganisms 2024; 12:982. [PMID: 38792811 PMCID: PMC11124124 DOI: 10.3390/microorganisms12050982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Phytophthora infestans is the causal agent of late blight in potato. The occurrence of P. infestans with both A1 and A2 mating types in the field may result in sexual reproduction and the generation of recombinant strains. Such strains with new combinations of traits can be highly aggressive, resistant to fungicides, and can make the disease difficult to control in the field. Metalaxyl-resistant isolates are now more prevalent in potato fields. Understanding the genetic structure and rapid identification of mating types and metalaxyl response of P. infestans in the field is a prerequisite for effective late blight disease monitoring and management. Molecular and phenotypic assays involving molecular and phenotypic markers such as mating types and metalaxyl response are typically conducted separately in the studies of the genotypic and phenotypic diversity of P. infestans. As a result, there is a pressing need to reduce the experimental workload and more efficiently assess the aggressiveness of different strains. We think that employing genetic markers to not only estimate genotypic diversity but also to identify the mating type and fungicide response using machine learning techniques can guide and speed up the decision-making process in late blight disease management, especially when the mating type and metalaxyl resistance data are not available. This technique can also be applied to determine these phenotypic traits for dead isolates. In this study, over 600 P. infestans isolates from different populations-Estonia, Pskov region, and Poland-were classified for mating types and metalaxyl response using machine learning techniques based on simple sequence repeat (SSR) markers. For both traits, random forest and the support vector machine demonstrated good accuracy of over 70%, compared to the decision tree and artificial neural network models whose accuracy was lower. There were also associations (p < 0.05) between the traits and some of the alleles detected, but machine learning prediction techniques based on multilocus SSR genotypes offered better prediction accuracy.
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Affiliation(s)
- Collins A. Agho
- Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 1, 51006 Tartu, Estonia
| | - Jadwiga Śliwka
- Plant Breeding and Acclimatization Institute—National Research Institute in Radzików, Department of Potato Genetics and Parental Lines, Platanowa Str. 19, 05-831 Młochów, Poland
| | - Helina Nassar
- Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 1, 51006 Tartu, Estonia
| | - Ülo Niinemets
- Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 1, 51006 Tartu, Estonia
- Estonian Academy of Sciences, Kohtu 6, 10130 Tallinn, Estonia
| | - Eve Runno-Paurson
- Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 1, 51006 Tartu, Estonia
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14
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Chang-Brahim I, Koppensteiner LJ, Beltrame L, Bodner G, Saranti A, Salzinger J, Fanta-Jende P, Sulzbachner C, Bruckmüller F, Trognitz F, Samad-Zamini M, Zechner E, Holzinger A, Molin EM. Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding. FRONTIERS IN PLANT SCIENCE 2024; 15:1319938. [PMID: 38699541 PMCID: PMC11064034 DOI: 10.3389/fpls.2024.1319938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/13/2024] [Indexed: 05/05/2024]
Abstract
Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.
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Affiliation(s)
- Ignacio Chang-Brahim
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Lorenzo Beltrame
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Gernot Bodner
- Department of Crop Sciences, Institute of Agronomy, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
| | - Anna Saranti
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Jules Salzinger
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Phillipp Fanta-Jende
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Christoph Sulzbachner
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Felix Bruckmüller
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Friederike Trognitz
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Elisabeth Zechner
- Verein zur Förderung einer nachhaltigen und regionalen Pflanzenzüchtung, Zwettl, Austria
| | - Andreas Holzinger
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Eva M. Molin
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
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15
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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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16
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Montesinos-López OA, Solis-Camacho MA, Crespo-Herrera L, Saint Pierre C, Huerta Prado GI, Ramos-Pulido S, Al-Nowibet K, Fritsche-Neto R, Gerard G, Montesinos-López A, Crossa J. Data Augmentation Enhances Plant-Genomic-Enabled Predictions. Genes (Basel) 2024; 15:286. [PMID: 38540344 PMCID: PMC10969940 DOI: 10.3390/genes15030286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/16/2024] [Accepted: 02/21/2024] [Indexed: 06/14/2024] Open
Abstract
Genomic selection (GS) is revolutionizing plant breeding. However, its practical implementation is still challenging, since there are many factors that affect its accuracy. For this reason, this research explores data augmentation with the goal of improving its accuracy. Deep neural networks with data augmentation (DA) generate synthetic data from the original training set to increase the training set and to improve the prediction performance of any statistical or machine learning algorithm. There is much empirical evidence of their success in many computer vision applications. Due to this, DA was explored in the context of GS using 14 real datasets. We found empirical evidence that DA is a powerful tool to improve the prediction accuracy, since we improved the prediction accuracy of the top lines in the 14 datasets under study. On average, across datasets and traits, the gain in prediction performance of the DA approach regarding the Conventional method in the top 20% of lines in the testing set was 108.4% in terms of the NRMSE and 107.4% in terms of the MAAPE, but a worse performance was observed on the whole testing set. We encourage more empirical evaluations to support our findings.
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Affiliation(s)
- Osval A. Montesinos-López
- Facultad de Telemática, Universidad de Colima, Colima 28040, Colima, Mexico; (O.A.M.-L.); (M.A.S.-C.)
| | | | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, Texcoco 52640, Edo. de México, Mexico; (L.C.-H.); (C.S.P.); (G.G.)
| | - Carolina Saint Pierre
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, Texcoco 52640, Edo. de México, Mexico; (L.C.-H.); (C.S.P.); (G.G.)
| | | | - Sofia Ramos-Pulido
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara 44430, Jalisco, Mexico;
| | - Khalid Al-Nowibet
- Distinguish Scientist Fellowship Program and Department of Statistics and Operations Research, King Saud University, Riyah 11451, Saudi Arabia;
| | | | - Guillermo Gerard
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, Texcoco 52640, Edo. de México, Mexico; (L.C.-H.); (C.S.P.); (G.G.)
| | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara 44430, Jalisco, Mexico;
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, Texcoco 52640, Edo. de México, Mexico; (L.C.-H.); (C.S.P.); (G.G.)
- Distinguish Scientist Fellowship Program and Department of Statistics and Operations Research, King Saud University, Riyah 11451, Saudi Arabia;
- Louisiana State University, Baton Rouge, LA 70803, USA;
- Colegio de Postgraduados, Montecillo 56230, Edo. de México, Mexico
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17
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Martina M, De Rosa V, Magon G, Acquadro A, Barchi L, Barcaccia G, De Paoli E, Vannozzi A, Portis E. Revitalizing agriculture: next-generation genotyping and -omics technologies enabling molecular prediction of resilient traits in the Solanaceae family. FRONTIERS IN PLANT SCIENCE 2024; 15:1278760. [PMID: 38375087 PMCID: PMC10875072 DOI: 10.3389/fpls.2024.1278760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024]
Abstract
This review highlights -omics research in Solanaceae family, with a particular focus on resilient traits. Extensive research has enriched our understanding of Solanaceae genomics and genetics, with historical varietal development mainly focusing on disease resistance and cultivar improvement but shifting the emphasis towards unveiling resilience mechanisms in genebank-preserved germplasm is nowadays crucial. Collecting such information, might help researchers and breeders developing new experimental design, providing an overview of the state of the art of the most advanced approaches for the identification of the genetic elements laying behind resilience. Building this starting point, we aim at providing a useful tool for tackling the global agricultural resilience goals in these crops.
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Affiliation(s)
- Matteo Martina
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
| | - Valeria De Rosa
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Udine, Italy
| | - Gabriele Magon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padua, Legnaro, Italy
| | - Alberto Acquadro
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
| | - Lorenzo Barchi
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
| | - Gianni Barcaccia
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padua, Legnaro, Italy
| | - Emanuele De Paoli
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Udine, Italy
| | - Alessandro Vannozzi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padua, Legnaro, Italy
| | - Ezio Portis
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
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18
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Singh S, Singh R, Priyadarsini S, Ola AL. Genomics empowering conservation action and improvement of celery in the face of climate change. PLANTA 2024; 259:42. [PMID: 38270699 DOI: 10.1007/s00425-023-04321-x] [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: 09/28/2023] [Accepted: 12/23/2023] [Indexed: 01/26/2024]
Abstract
MAIN CONCLUSION Integration of genomic approaches like whole genome sequencing, functional genomics, evolutionary genomics, and CRISPR/Cas9-based genome editing has accelerated the improvement of crop plants including leafy vegetables like celery in the face of climate change. The anthropogenic climate change is a real peril to the existence of life forms on our planet, including human and plant life. Climate change is predicted to be a significant threat to biodiversity and food security in the coming decades and is rapidly transforming global farming systems. To avoid the ghastly future in the face of climate change, the elucidation of shifts in the geographical range of plant species, species adaptation, and evolution is necessary for plant scientists to develop climate-resilient strategies. In the post-genomics era, the increasing availability of genomic resources and integration of multifaceted genomics elements is empowering biodiversity conservation action, restoration efforts, and identification of genomic regions adaptive to climate change. Genomics has accelerated the true characterization of crop wild relatives, genomic variations, and the development of climate-resilient varieties to ensure food security for 10 billion people by 2050. In this review, we have summarized the applications of multifaceted genomic tools, like conservation genomics, whole genome sequencing, functional genomics, genome editing, pangenomics, in the conservation and adaptation of plant species with a focus on celery, an aromatic and medicinal Apiaceae vegetable. We focus on how conservation scientists can utilize genomics and genomic data in conservation and improvement.
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Affiliation(s)
- Saurabh Singh
- Department of Vegetable Science, Rani Lakshmi Bai Central Agricultural University, Jhansi, UP, 284003, India.
| | - Rajender Singh
- Division of Crop Improvement and Seed Technology, ICAR-Central Potato Research Institute (CPRI), Shimla, India
| | - Srija Priyadarsini
- Institute of Agricultural Sciences, SOA (Deemed to be University), Bhubaneswar, 751029, India
| | - Arjun Lal Ola
- Department of Vegetable Science, Rani Lakshmi Bai Central Agricultural University, Jhansi, UP, 284003, India
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19
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Kang HI, Kim IS, Shim D, Kang KS, Cheon KS. Genomic selection for growth characteristics in Korean red pine ( Pinus densiflora Seibold & Zucc.). FRONTIERS IN PLANT SCIENCE 2024; 15:1285094. [PMID: 38322820 PMCID: PMC10844423 DOI: 10.3389/fpls.2024.1285094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 01/05/2024] [Indexed: 02/08/2024]
Abstract
Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164-0.498 and a predictive ability of 0.018-0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86-5.10, and against the square root of heritability was 0.19-0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.
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Affiliation(s)
- Hye-In Kang
- Division of Tree Improvement and Biotechnology, Department of Forest Bio-resources, National Institute of Forest Science, Suwon, Republic of Korea
| | - In Sik Kim
- Division of Tree Improvement and Biotechnology, Department of Forest Bio-resources, National Institute of Forest Science, Suwon, Republic of Korea
| | - Donghwan Shim
- Department of Biological Sciences, Chungnam National University, Daejeon, Republic of Korea
| | - Kyu-Suk Kang
- Department of Agriculture, Forestry and Bioresources, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
| | - Kyeong-Seong Cheon
- Division of Tree Improvement and Biotechnology, Department of Forest Bio-resources, National Institute of Forest Science, Suwon, Republic of Korea
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20
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Park HY, Lim YJ, Jung M, Sathiyamoorthy S, Heo SH, Park B, Shin Y. Genome of Raphanus sativus L . Bakdal, an elite line of large cultivated Korean radish. Front Genet 2024; 15:1328050. [PMID: 38304338 PMCID: PMC10831357 DOI: 10.3389/fgene.2024.1328050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/04/2024] [Indexed: 02/03/2024] Open
Affiliation(s)
- Han Yong Park
- Department of Bioresource Engineering, Sejong University, Seoul, Republic of Korea
| | - Yu-jin Lim
- Research and Development Center, Insilicogen Inc., Yongin-si, Gyeonggi-do, Republic of Korea
| | - Myunghee Jung
- Research and Development Center, Insilicogen Inc., Yongin-si, Gyeonggi-do, Republic of Korea
| | | | - Seong Ho Heo
- Department of Bioresource Engineering, Sejong University, Seoul, Republic of Korea
- Institute of Breeding Research, DASAN Co., Ltd., Pyeongtaek, Republic of Korea
| | - Byeongjun Park
- Department of Bioresource Engineering, Sejong University, Seoul, Republic of Korea
- Institute of Breeding Research, DASAN Co., Ltd., Pyeongtaek, Republic of Korea
| | - Younhee Shin
- Research and Development Center, Insilicogen Inc., Yongin-si, Gyeonggi-do, Republic of Korea
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21
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Montesinos-López A, Gutiérrez-Pulido H, Ramos-Pulido S, Montesinos-López JC, Montesinos-López OA, Crossa J. Bayesian discrete lognormal regression model for genomic prediction. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:21. [PMID: 38221602 DOI: 10.1007/s00122-023-04526-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 12/11/2023] [Indexed: 01/16/2024]
Abstract
KEY MESSAGE Genomic prediction models for quantitative traits assume continuous and normally distributed phenotypes. In this research, we proposed a novel Bayesian discrete lognormal regression model. Genomic selection is a powerful tool in modern breeding programs that uses genomic information to predict the performance of individuals and select those with desirable traits. It has revolutionized animal and plant breeding, as it allows breeders to identify the best candidates without labor-intensive and time-consuming phenotypic evaluations. While several statistical models have been developed, most of them have been for quantitative continuous traits and only a few for count responses. In this paper, we propose a discrete lognormal regression model in the Bayesian context, that with a Gibbs sampler to explore the corresponding posterior distribution and make the predictions. Two datasets of resistance disease is used in the wheat crop and are then evaluated against the traditional Gaussian model and a lognormal model. The results indicate the proposed model is a competitive and natural model for predicting count genomic traits.
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Affiliation(s)
- Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, C. P. 44430, Guadalajara, Jalisco, México
| | - Humberto Gutiérrez-Pulido
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, C. P. 44430, Guadalajara, Jalisco, México
| | - Sofía Ramos-Pulido
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, C. P. 44430, Guadalajara, Jalisco, México
| | | | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz Km. 45, El Batán, C. P. 56237, Texcoco, Edo. de México, México.
- Colegio de Postgraduados, C. P. 56230, Montecillos, Edo. de México, México.
- Centre for Crop & Food Innovation, Food Futures Institute, Murdoch University, Murdoch, 6150, Australia.
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22
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Deng CH, Naithani S, Kumari S, Cobo-Simón I, Quezada-Rodríguez EH, Skrabisova M, Gladman N, Correll MJ, Sikiru AB, Afuwape OO, Marrano A, Rebollo I, Zhang W, Jung S. Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences. Database (Oxford) 2023; 2023:baad088. [PMID: 38079567 PMCID: PMC10712715 DOI: 10.1093/database/baad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.
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Affiliation(s)
- Cecilia H Deng
- Molecular and Digital Breeding, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, 120 Mt Albert Road, Auckland 1025, New Zealand
| | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Sunita Kumari
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
| | - Irene Cobo-Simón
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
- Institute of Forest Science (ICIFOR-INIA, CSIC), Madrid, Spain
| | - Elsa H Quezada-Rodríguez
- Departamento de Producción Agrícola y Animal, Universidad Autónoma Metropolitana-Xochimilco, Ciudad de México, México
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Maria Skrabisova
- Department of Biochemistry, Faculty of Science, Palacky University, Olomouc, Czech Republic
| | - Nick Gladman
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
- U.S. Department of Agriculture-Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA
| | - Melanie J Correll
- Agricultural and Biological Engineering Department, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA
| | | | | | - Annarita Marrano
- Phoenix Bioinformatics, 39899 Balentine Drive, Suite 200, Newark, CA 94560, USA
| | | | - Wentao Zhang
- National Research Council Canada, 110 Gymnasium Pl, Saskatoon, Saskatchewan S7N 0W9, Canada
| | - Sook Jung
- Department of Horticulture, Washington State University, 303c Plant Sciences Building, Pullman, WA 99164-6414, USA
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Bharati R, Sen MK, Severová L, Svoboda R, Fernández-Cusimamani E. Polyploidization and genomic selection integration for grapevine breeding: a perspective. FRONTIERS IN PLANT SCIENCE 2023; 14:1248978. [PMID: 38034577 PMCID: PMC10684766 DOI: 10.3389/fpls.2023.1248978] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
Grapevines are economically important woody perennial crops widely cultivated for their fruits that are used for making wine, grape juice, raisins, and table grapes. However, grapevine production is constantly facing challenges due to climate change and the prevalence of pests and diseases, causing yield reduction, lower fruit quality, and financial losses. To ease the burden, continuous crop improvement to develop superior grape genotypes with desirable traits is imperative. Polyploidization has emerged as a promising tool to generate genotypes with novel genetic combinations that can confer desirable traits such as enhanced organ size, improved fruit quality, and increased resistance to both biotic and abiotic stresses. While previous studies have shown high polyploid induction rates in Vitis spp., rigorous screening of genotypes among the produced polyploids to identify those exhibiting desired traits remains a major bottleneck. In this perspective, we propose the integration of the genomic selection approach with omics data to predict genotypes with desirable traits among the vast unique individuals generated through polyploidization. This integrated approach can be a powerful tool for accelerating the breeding of grapevines to develop novel and improved grapevine varieties.
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Affiliation(s)
- Rohit Bharati
- Department of Crop Sciences and Agroforestry, The Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Suchdol, Czechia
| | - Madhab Kumar Sen
- Department of Agroecology and Crop Production, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Suchdol, Czechia
| | - Lucie Severová
- Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czechia
| | - Roman Svoboda
- Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czechia
| | - Eloy Fernández-Cusimamani
- Department of Crop Sciences and Agroforestry, The Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Suchdol, Czechia
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24
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Zhang Y, Zhang M, Ye J, Xu Q, Feng Y, Xu S, Hu D, Wei X, Hu P, Yang Y. Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice ( Oryza sativa L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:81. [PMID: 37965378 PMCID: PMC10641074 DOI: 10.1007/s11032-023-01423-y] [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: 07/11/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023]
Abstract
Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01423-y.
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Affiliation(s)
- Yuanyuan Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Mengchen Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Junhua Ye
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Qun Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yue Feng
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Siliang Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Dongxiu Hu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Xinghua Wei
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Peisong Hu
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yaolong Yang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
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25
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Zhou G, Gao J, Zuo D, Li J, Li R. MSXFGP: combining improved sparrow search algorithm with XGBoost for enhanced genomic prediction. BMC Bioinformatics 2023; 24:384. [PMID: 37817077 PMCID: PMC10566073 DOI: 10.1186/s12859-023-05514-7] [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: 07/12/2023] [Accepted: 10/02/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND With the significant reduction in the cost of high-throughput sequencing technology, genomic selection technology has been rapidly developed in the field of plant breeding. Although numerous genomic selection methods have been proposed by researchers, the existing genomic selection methods still face the problem of poor prediction accuracy in practical applications. RESULTS This paper proposes a genome prediction method MSXFGP based on a multi-strategy improved sparrow search algorithm (SSA) to optimize XGBoost parameters and feature selection. Firstly, logistic chaos mapping, elite learning, adaptive parameter adjustment, Levy flight, and an early stop strategy are incorporated into the SSA. This integration serves to enhance the global and local search capabilities of the algorithm, thereby improving its convergence accuracy and stability. Subsequently, the improved SSA is utilized to concurrently optimize XGBoost parameters and feature selection, leading to the establishment of a new genomic selection method, MSXFGP. Utilizing both the coefficient of determination R2 and the Pearson correlation coefficient as evaluation metrics, MSXFGP was evaluated against six existing genomic selection models across six datasets. The findings reveal that MSXFGP prediction accuracy is comparable or better than existing widely used genomic selection methods, and it exhibits better accuracy when R2 is utilized as an assessment metric. Additionally, this research provides a user-friendly Python utility designed to aid breeders in the effective application of this innovative method. MSXFGP is accessible at https://github.com/DIBreeding/MSXFGP . CONCLUSIONS The experimental results show that the prediction accuracy of MSXFGP is comparable or better than existing genome selection methods, providing a new approach for plant genome selection.
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Affiliation(s)
- Ganghui Zhou
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot, 010011, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Zhaowuda Road No. 306, Hohhot, 010018, China
| | - Jing Gao
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot, 010011, China.
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Zhaowuda Road No. 306, Hohhot, 010018, China.
- Inner Mongolia Autonomous Region Big Data Center, Chilechuan Street No. 1, Hohhot, 010091, China.
| | - Dongshi Zuo
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot, 010011, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Zhaowuda Road No. 306, Hohhot, 010018, China
| | - Jin Li
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot, 010011, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Zhaowuda Road No. 306, Hohhot, 010018, China
| | - Rui Li
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot, 010011, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Zhaowuda Road No. 306, Hohhot, 010018, China
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26
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Wang X, Han L, Li J, Shang X, Liu Q, Li L, Zhang H. Next-generation bulked segregant analysis for Breeding 4.0. Cell Rep 2023; 42:113039. [PMID: 37651230 DOI: 10.1016/j.celrep.2023.113039] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/11/2023] [Accepted: 08/10/2023] [Indexed: 09/02/2023] Open
Abstract
Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.
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Affiliation(s)
- Xi Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Juan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Qian Liu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
| | - Hongwei Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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Sui Y, Che Y, Zhong Y, He L. Genome-Wide Association Studies Using 3VmrMLM Model Provide New Insights into Branched-Chain Amino Acid Contents in Rice Grains. PLANTS (BASEL, SWITZERLAND) 2023; 12:2970. [PMID: 37631180 PMCID: PMC10459631 DOI: 10.3390/plants12162970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Rice (Oryza sativa L.) is a globally important food source providing carbohydrates, amino acids, and dietary fiber for humans and livestock. The branched-chain amino acid (BCAA) level is a complex trait related to the nutrient quality of rice. However, the genetic mechanism underlying the BCAA (valine, leucine, and isoleucine) accumulation in rice grains remains largely unclear. In this study, the grain BCAA contents and 239,055 SNPs of a diverse panel containing 422 rice accessions were adopted to perform a genome-wide association study (GWAS) using a recently proposed 3VmrMLM model. A total of 357 BCAA-content-associated main-effect quantitative trait nucleotides (QTNs) were identified from 15 datasets (12 BCAA content datasets and 3 BLUP datasets of BCAA). Furthermore, the allelic variation of two novel candidate genes, LOC_Os01g52530 and LOC_Os06g15420, responsible for the isoleucine (Ile) content alteration were identified. To reveal the genetic basis of the potential interactions between the gene and environmental factor, 53 QTN-by-environment interactions (QEIs) were detected using the 3VmrMLM model. The LOC_Os03g24460, LOC_Os01g55590, and LOC_Os12g31820 were considered as the candidate genes potentially contributing to the valine (Val), leucine (Leu), and isoleucine (Ile) accumulations, respectively. Additionally, 10 QTN-by-QTN interactions (QQIs) were detected using the 3VmrMLM model, which were putative gene-by-gene interactions related to the Leu and Ile contents. Taken together, these findings suggest that the implementation of the 3VmrMLM model in a GWAS may provide new insights into the deeper understanding of BCAA accumulation in rice grains. The identified QTNs/QEIs/QQIs serve as potential targets for the genetic improvement of rice with high BCAA levels.
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Affiliation(s)
| | | | | | - Liqiang He
- School of Tropical Agriculture and Forestry, School of Tropical Crops, Hainan University, Haikou 570228, China
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Gebhardt C. A physical map of traits of agronomic importance based on potato and tomato genome sequences. Front Genet 2023; 14:1197206. [PMID: 37564870 PMCID: PMC10411547 DOI: 10.3389/fgene.2023.1197206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/30/2023] [Indexed: 08/12/2023] Open
Abstract
Potato, tomato, pepper, and eggplant are worldwide important crop and vegetable species of the Solanaceae family. Molecular linkage maps of these plants have been constructed and used to map qualitative and quantitative traits of agronomic importance. This research has been undertaken with the vision to identify the molecular basis of agronomic characters on the one hand, and on the other hand, to assist the selection of improved varieties in breeding programs by providing DNA-based markers that are diagnostic for specific agronomic characters. Since 2011, whole genome sequences of tomato and potato became available in public databases. They were used to combine the results of several hundred mapping and map-based cloning studies of phenotypic characters between 1988 and 2022 in physical maps of the twelve tomato and potato chromosomes. The traits evaluated were qualitative and quantitative resistance to pathogenic oomycetes, fungi, bacteria, viruses, nematodes, and insects. Furthermore, quantitative trait loci for yield and sugar content of tomato fruits and potato tubers and maturity or earliness were physically mapped. Cloned genes for pathogen resistance, a few genes underlying quantitative trait loci for yield, sugar content, and maturity, and several hundred candidate genes for these traits were included in the physical maps. The comparison between the physical chromosome maps revealed, in addition to known intrachromosomal inversions, several additional inversions and translocations between the otherwise highly collinear tomato and potato genomes. The integration of the positional information from independent mapping studies revealed the colocalization of qualitative and quantitative loci for resistance to different types of pathogens, called resistance hotspots, suggesting a similar molecular basis. Synteny between potato and tomato with respect to genomic positions of quantitative trait loci was frequently observed, indicating eventual similarity between the underlying genes.
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29
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Sinha D, Maurya AK, Abdi G, Majeed M, Agarwal R, Mukherjee R, Ganguly S, Aziz R, Bhatia M, Majgaonkar A, Seal S, Das M, Banerjee S, Chowdhury S, Adeyemi SB, Chen JT. Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes (Basel) 2023; 14:1484. [PMID: 37510388 PMCID: PMC10380062 DOI: 10.3390/genes14071484] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.
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Affiliation(s)
- Dwaipayan Sinha
- Department of Botany, Government General Degree College, Mohanpur 721436, India
| | - Arun Kumar Maurya
- Department of Botany, Multanimal Modi College, Modinagar, Ghaziabad 201204, India
| | - Gholamreza Abdi
- Department of Biotechnology, Persian Gulf Research Institute, Persian Gulf University, Bushehr 75169, Iran
| | - Muhammad Majeed
- Department of Botany, University of Gujrat, Punjab 50700, Pakistan
| | - Rachna Agarwal
- Applied Genomics Section, Bhabha Atomic Research Centre, Mumbai 400085, India
| | - Rashmi Mukherjee
- Research Center for Natural and Applied Sciences, Department of Botany (UG & PG), Raja Narendralal Khan Women's College, Gope Palace, Midnapur 721102, India
| | - Sharmistha Ganguly
- Department of Dravyaguna, Institute of Post Graduate Ayurvedic Education and Research, Kolkata 700009, India
| | - Robina Aziz
- Department of Botany, Government, College Women University, Sialkot 51310, Pakistan
| | - Manika Bhatia
- TERI School of Advanced Studies, New Delhi 110070, India
| | - Aqsa Majgaonkar
- Department of Botany, St. Xavier's College (Autonomous), Mumbai 400001, India
| | - Sanchita Seal
- Department of Botany, Polba Mahavidyalaya, Polba 712148, India
| | - Moumita Das
- V. Sivaram Research Foundation, Bangalore 560040, India
| | - Swastika Banerjee
- Department of Botany, Kairali College of +3 Science, Champua, Keonjhar 758041, India
| | - Shahana Chowdhury
- Department of Biotechnology, Faculty of Engineering Sciences, German University Bangladesh, TNT Road, Telipara, Chandona Chowrasta, Gazipur 1702, Bangladesh
| | - Sherif Babatunde Adeyemi
- Ethnobotany/Phytomedicine Laboratory, Department of Plant Biology, Faculty of Life Sciences, University of Ilorin, Ilorin P.M.B 1515, Nigeria
| | - Jen-Tsung Chen
- Department of Life Sciences, National University of Kaohsiung, Kaohsiung 811, Taiwan
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30
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Paliwal S, Tripathi MK, Tiwari S, Tripathi N, Payasi DK, Tiwari PN, Singh K, Yadav RK, Asati R, Chauhan S. Molecular Advances to Combat Different Biotic and Abiotic Stresses in Linseed ( Linum usitatissimum L.): A Comprehensive Review. Genes (Basel) 2023; 14:1461. [PMID: 37510365 PMCID: PMC10379177 DOI: 10.3390/genes14071461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/11/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
Flax, or linseed, is considered a "superfood", which means that it is a food with diverse health benefits and potentially useful bioactive ingredients. It is a multi-purpose crop that is prized for its seed oil, fibre, nutraceutical, and probiotic qualities. It is suited to various habitats and agro-ecological conditions. Numerous abiotic and biotic stressors that can either have a direct or indirect impact on plant health are experienced by flax plants as a result of changing environmental circumstances. Research on the impact of various stresses and their possible ameliorators is prompted by such expectations. By inducing the loss of specific alleles and using a limited number of selected varieties, modern breeding techniques have decreased the overall genetic variability required for climate-smart agriculture. However, gene banks have well-managed collectionns of landraces, wild linseed accessions, and auxiliary Linum species that serve as an important source of novel alleles. In the past, flax-breeding techniques were prioritised, preserving high yield with other essential traits. Applications of molecular markers in modern breeding have made it easy to identify quantitative trait loci (QTLs) for various agronomic characteristics. The genetic diversity of linseed species and the evaluation of their tolerance to abiotic stresses, including drought, salinity, heavy metal tolerance, and temperature, as well as resistance to biotic stress factors, viz., rust, wilt, powdery mildew, and alternaria blight, despite addressing various morphotypes and the value of linseed as a supplement, are the primary topics of this review.
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Affiliation(s)
- Shruti Paliwal
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Manoj Kumar Tripathi
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
- Department of Plant Molecular Biology and Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Sushma Tiwari
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
- Department of Plant Molecular Biology and Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Niraj Tripathi
- Directorate of Research Services, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur 482004, India
| | - Devendra K Payasi
- All India Coordinated Research Project on Linseed, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Regional Agricultural Research Station, Sagar 470001, India
| | - Prakash N Tiwari
- Department of Plant Molecular Biology and Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Kirti Singh
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Rakesh Kumar Yadav
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Ruchi Asati
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Shailja Chauhan
- Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
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31
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Bhat JA, Feng X, Mir ZA, Raina A, Siddique KHM. Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding. PHYSIOLOGIA PLANTARUM 2023; 175:e13969. [PMID: 37401892 DOI: 10.1111/ppl.13969] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/11/2023] [Accepted: 06/27/2023] [Indexed: 07/05/2023]
Abstract
Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.
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Affiliation(s)
| | - Xianzhong Feng
- Zhejiang Lab, Hangzhou, China
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Zahoor A Mir
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Aamir Raina
- Department of Botany, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, India
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, Western Australia, Australia
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32
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Hoang NV, Sogbohossou EOD, Xiong W, Simpson CJC, Singh P, Walden N, van den Bergh E, Becker FFM, Li Z, Zhu XG, Brautigam A, Weber APM, van Haarst JC, Schijlen EGWM, Hendre PS, Van Deynze A, Achigan-Dako EG, Hibberd JM, Schranz ME. The Gynandropsis gynandra genome provides insights into whole-genome duplications and the evolution of C4 photosynthesis in Cleomaceae. THE PLANT CELL 2023; 35:1334-1359. [PMID: 36691724 PMCID: PMC10118270 DOI: 10.1093/plcell/koad018] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/18/2023] [Indexed: 06/17/2023]
Abstract
Gynandropsis gynandra (Cleomaceae) is a cosmopolitan leafy vegetable and medicinal plant, which has also been used as a model to study C4 photosynthesis due to its evolutionary proximity to C3 Arabidopsis (Arabidopsis thaliana). Here, we present the genome sequence of G. gynandra, anchored onto 17 main pseudomolecules with a total length of 740 Mb, an N50 of 42 Mb and 30,933 well-supported gene models. The G. gynandra genome and previously released genomes of C3 relatives in the Cleomaceae and Brassicaceae make an excellent model for studying the role of genome evolution in the transition from C3 to C4 photosynthesis. Our analyses revealed that G. gynandra and its C3 relative Tarenaya hassleriana shared a whole-genome duplication event (Gg-α), then an addition of a third genome (Th-α, +1×) took place in T. hassleriana but not in G. gynandra. Analysis of syntenic copy number of C4 photosynthesis-related gene families indicates that G. gynandra generally retained more duplicated copies of these genes than C3T. hassleriana, and also that the G. gynandra C4 genes might have been under positive selection pressure. Both whole-genome and single-gene duplication were found to contribute to the expansion of the aforementioned gene families in G. gynandra. Collectively, this study enhances our understanding of the polyploidy history, gene duplication and retention, as well as their impact on the evolution of C4 photosynthesis in Cleomaceae.
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Affiliation(s)
| | | | - Wei Xiong
- Biosystematics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
| | - Conor J C Simpson
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
| | - Pallavi Singh
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
| | - Nora Walden
- Biosystematics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
| | - Erik van den Bergh
- Biosystematics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
| | - Frank F M Becker
- Laboratory of Genetics, Wageningen University and Research, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
| | - Zheng Li
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Xin-Guang Zhu
- State Key Laboratory for Plant Molecular Genetics, Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Andrea Brautigam
- Faculty of Biology, Bielefeld University, 33501 Bielefeld, Germany
| | - Andreas P M Weber
- Cluster of Excellence on Plant Science (CEPLAS), Institute of Plant Biochemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Jan C van Haarst
- Business Unit Bioscience, Wageningen University and Research, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
| | - Elio G W M Schijlen
- Business Unit Bioscience, Wageningen University and Research, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
| | - Prasad S Hendre
- African Orphan Crops Consortium (AOCC), World Agroforestry (ICRAF), Nairobi 00100, Kenya
| | - Allen Van Deynze
- African Orphan Crops Consortium (AOCC), World Agroforestry (ICRAF), Nairobi 00100, Kenya
- Seed Biotechnology Center, University of California, Davis, California 95616, USA
| | - Enoch G Achigan-Dako
- Laboratory of Genetics, Biotechnology and Seed Science (GbioS), Faculty of Agronomic Sciences, University of Abomey-Calavi, BP 2549 Abomey-Calavi, Republic of Benin
| | - Julian M Hibberd
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
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Plessis A. Abiotic stress experiments need a reality check to improve translation to the field. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:1741-1744. [PMID: 36626547 PMCID: PMC10049913 DOI: 10.1093/jxb/erac509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
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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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35
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Yan H, Guo H, Li T, Zhang H, Xu W, Xie J, Zhu X, Yu Y, Chen J, Zhao S, Xu J, Hu M, Jiang Y, Zhang H, Ma M, He Z. High-precision early warning system for rice cadmium accumulation risk assessment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160135. [PMID: 36375547 DOI: 10.1016/j.scitotenv.2022.160135] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/01/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Rapid global industrialization has resulted in widespread cadmium contamination in agricultural soils and products. A considerable proportion of rice consumers are exposed to Cd levels above the provisional safe intake limit, raising widespread environmental concerns on risk management. Therefore, a generalized approach is urgently needed to enable correct evaluation and early warning of cadmium contaminants in rice products. Combining big data and computer science together, this study developed a system named "SMART Cd Early Warning", which integrated 4 modules including genotype-to-phenotype (G2P) modelling, high-throughput sequencing, G2P prediction and rice Cd contamination risk assessment, for rice cadmium accumulation early warning. This system can rapidly assess the risk of rice cadmium accumulation by genotyping leaves at seeding stage. The parameters including statistical methods, population size, training population-testing population ratio, SNP density were assessed to ensure G2P model exhibited superior performance in terms of prediction precision (up to 0.76 ± 0.003) and computing efficiency (within 2 h). In field trials of cadmium-contaminated farmlands in Wenling and Fuyang city, Zhejiang Province, "SMART Cd Early Warning" exhibited superior capability for identification risk rice varieties, suggesting a potential of "SMART Cd Early-Warning system" in OsGCd risk assessment and early warning in the age of smart.
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Affiliation(s)
- Huili Yan
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Hanyao Guo
- Hebei Normal University, Shijiazhuang 050024, China
| | - Ting Li
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hezifan Zhang
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wenxiu Xu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Jianyin Xie
- Key Lab of Crop Heterosis and Utilization of Ministry of Education, Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Xiaoyang Zhu
- Key Lab of Crop Heterosis and Utilization of Ministry of Education, Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Yijun Yu
- Zhejiang Station for Management of Arable Land Quality and Fertilizer, Hangzhou 310020, China
| | - Jian Chen
- Plant Protection, Fertilizer and Rural Energy Agency of Wenling, Wenling 317500, China
| | - Shouqing Zhao
- Plant Protection, Fertilizer and Rural Energy Agency of Wenling, Wenling 317500, China
| | - Jun Xu
- Fuyang Agricultural Technology Extension Center, Fuyang 311400, China
| | - Minjun Hu
- Fuyang Agricultural Technology Extension Center, Fuyang 311400, China
| | - Yugen Jiang
- Fuyang Agricultural Technology Extension Center, Fuyang 311400, China
| | - Hongliang Zhang
- Key Lab of Crop Heterosis and Utilization of Ministry of Education, Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China; Sanya Institute of China Agricultural University, Sanya 572024, China
| | - Mi Ma
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Zhenyan He
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.
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Jeon D, Kang Y, Lee S, Choi S, Sung Y, Lee TH, Kim C. Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction. FRONTIERS IN PLANT SCIENCE 2023; 14:1092584. [PMID: 36743488 PMCID: PMC9892199 DOI: 10.3389/fpls.2023.1092584] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/05/2023] [Indexed: 06/18/2023]
Abstract
As the world's population grows and food needs diversification, the demand for cereals and horticultural crops with beneficial traits increases. In order to meet a variety of demands, suitable cultivars and innovative breeding methods need to be developed. Breeding methods have changed over time following the advance of genetics. With the advent of new sequencing technology in the early 21st century, predictive breeding, such as genomic selection (GS), emerged when large-scale genomic information became available. GS shows good predictive ability for the selection of individuals with traits of interest even for quantitative traits by using various types of the whole genome-scanning markers, breaking away from the limitations of marker-assisted selection (MAS). In the current review, we briefly describe the history of breeding techniques, each breeding method, various statistical models applied to GS and methods to increase the GS efficiency. Consequently, we intend to propose and define the term digital breeding through this review article. Digital breeding is to develop a predictive breeding methods such as GS at a higher level, aiming to minimize human intervention by automatically proceeding breeding design, propagating breeding populations, and to make selections in consideration of various environments, climates, and topography during the breeding process. We also classified the phases of digital breeding based on the technologies and methods applied to each phase. This review paper will provide an understanding and a direction for the final evolution of plant breeding in the future.
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Affiliation(s)
- Donghyun Jeon
- Plant Computational Genomics Laboratory, Department of Science in Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
| | - Yuna Kang
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Solji Lee
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Sehyun Choi
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Yeonjun Sung
- Plant Computational Genomics Laboratory, Department of Science in Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
| | - Tae-Ho Lee
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, Republic of Korea
| | - Changsoo Kim
- Plant Computational Genomics Laboratory, Department of Science in Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
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Sun B, Guo R, Liu Z, Shi X, Yang Q, Shi J, Zhang M, Yang C, Zhao S, Zhang J, He J, Zhang J, Su J, Song Q, Yan L. Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content. FRONTIERS IN PLANT SCIENCE 2022; 13:1064623. [PMID: 36582644 PMCID: PMC9793221 DOI: 10.3389/fpls.2022.1064623] [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: 10/10/2022] [Accepted: 11/10/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Genomic selection (GS) is a potential breeding approach for soybean improvement. METHODS In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. RESULTS Our results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log10(P) greater than 1 were included. DISCUSSION This information will help improve GS efficiency and facilitate the application of GS.
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Affiliation(s)
- Bo Sun
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
- College of Life Sciences, Hebei Agricultural University, Baoding, China
| | - Rui Guo
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Zhi Liu
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Xiaolei Shi
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Qing Yang
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Jiayao Shi
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Mengchen Zhang
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Chunyan Yang
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Shugang Zhao
- College of Life Sciences, Hebei Agricultural University, Baoding, China
| | - Jie Zhang
- College of Life Sciences, Hebei Agricultural University, Baoding, China
| | - Jianhan He
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing, China
- Key Laboratory for Biology and Genetic Improvement of Soybean, (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing, China
| | - Jianhui Su
- Agricultural-Regionalization Workstation of Shijiazhuang’s Gaocheng District, Shijiazhuang, China
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Long Yan
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
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Akohoue F, Miedaner T. Meta-analysis and co-expression analysis revealed stable QTL and candidate genes conferring resistances to Fusarium and Gibberella ear rots while reducing mycotoxin contamination in maize. FRONTIERS IN PLANT SCIENCE 2022; 13:1050891. [PMID: 36388551 PMCID: PMC9662303 DOI: 10.3389/fpls.2022.1050891] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Fusarium (FER) and Gibberella ear rots (GER) are the two most devastating diseases of maize (Zea mays L.) which reduce yield and affect grain quality worldwide, especially by contamination with mycotoxins. Genetic improvement of host resistance to effectively tackle FER and GER diseases requires the identification of stable quantitative trait loci (QTL) to facilitate the application of genomics-assisted breeding for improving selection efficiency in breeding programs. We applied improved meta-analysis algorithms to re-analyze 224 QTL identified in 15 studies based on dense genome-wide single nucleotide polymorphisms (SNP) in order to identify meta-QTL (MQTL) and colocalized genomic loci for fumonisin (FUM) and deoxynivalenol (DON) accumulation, silk (SR) and kernel (KR) resistances of both FER and GER, kernel dry-down rate (KDD) and husk coverage (HC). A high-resolution genetic consensus map with 36,243 loci was constructed and enabled the projection of 164 of the 224 collected QTL. Candidate genes (CG) mining was performed within the most refined MQTL, and identified CG were cross-validated using publicly available transcriptomic data of maize under Fusarium graminearum infection. The meta-analysis revealed 40 MQTL, of which 29 were associated each with 2-5 FER- and/or GER-related traits. Twenty-eight of the 40 MQTL were common to both FER and GER resistances and 19 MQTL were common to silk and kernel resistances. Fourteen most refined MQTL on chromosomes 1, 2, 3, 4, 7 and 9 harbored a total of 2,272 CG. Cross-validation identified 59 of these CG as responsive to FER and/or GER diseases. MQTL ZmMQTL2.2, ZmMQTL9.2 and ZmMQTL9.4 harbored promising resistance genes, of which GRMZM2G011151 and GRMZM2G093092 were specific to the resistant line for both diseases and encoded "terpene synthase21 (tps21)" and "flavonoid O-methyltransferase2 (fomt2)", respectively. Our findings revealed stable refined MQTL harboring promising candidate genes for use in breeding programs for improving FER and GER resistances with reduced mycotoxin accumulation. These candidate genes can be transferred into elite cultivars by integrating refined MQTL into genomics-assisted backcross breeding strategies.
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Yadav B, Kaur V, Narayan OP, Yadav SK, Kumar A, Wankhede DP. Integrated omics approaches for flax improvement under abiotic and biotic stress: Current status and future prospects. FRONTIERS IN PLANT SCIENCE 2022; 13:931275. [PMID: 35958216 PMCID: PMC9358615 DOI: 10.3389/fpls.2022.931275] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/27/2022] [Indexed: 05/03/2023]
Abstract
Flax (Linum usitatissimum L.) or linseed is one of the important industrial crops grown all over the world for seed oil and fiber. Besides oil and fiber, flax offers a wide range of nutritional and therapeutic applications as a feed and food source owing to high amount of α-linolenic acid (omega-3 fatty acid), lignans, protein, minerals, and vitamins. Periodic losses caused by unpredictable environmental stresses such as drought, heat, salinity-alkalinity, and diseases pose a threat to meet the rising market demand. Furthermore, these abiotic and biotic stressors have a negative impact on biological diversity and quality of oil/fiber. Therefore, understanding the interaction of genetic and environmental factors in stress tolerance mechanism and identification of underlying genes for economically important traits is critical for flax improvement and sustainability. In recent technological era, numerous omics techniques such as genomics, transcriptomics, metabolomics, proteomics, phenomics, and ionomics have evolved. The advancements in sequencing technologies accelerated development of genomic resources which facilitated finer genetic mapping, quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection in major cereal and oilseed crops including flax. Extensive studies in the area of genomics and transcriptomics have been conducted post flax genome sequencing. Interestingly, research has been focused more for abiotic stresses tolerance compared to disease resistance in flax through transcriptomics, while the other areas of omics such as metabolomics, proteomics, ionomics, and phenomics are in the initial stages in flax and several key questions remain unanswered. Little has been explored in the integration of omic-scale data to explain complex genetic, physiological and biochemical basis of stress tolerance in flax. In this review, the current status of various omics approaches for elucidation of molecular pathways underlying abiotic and biotic stress tolerance in flax have been presented and the importance of integrated omics technologies in future research and breeding have been emphasized to ensure sustainable yield in challenging environments.
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Affiliation(s)
- Bindu Yadav
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Vikender Kaur
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Om Prakash Narayan
- College of Arts and Sciences, University of Florida, Gainesville, FL, United States
| | - Shashank Kumar Yadav
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Ashok Kumar
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
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Kumar S, Jacob SR, Mir RR, Vikas VK, Kulwal P, Chandra T, Kaur S, Kumar U, Kumar S, Sharma S, Singh R, Prasad S, Singh AM, Singh AK, Kumari J, Saharan MS, Bhardwaj SC, Prasad M, Kalia S, Singh K. Indian Wheat Genomics Initiative for Harnessing the Potential of Wheat Germplasm Resources for Breeding Disease-Resistant, Nutrient-Dense, and Climate-Resilient Cultivars. Front Genet 2022; 13:834366. [PMID: 35846116 PMCID: PMC9277310 DOI: 10.3389/fgene.2022.834366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Wheat is one of the major staple cereal food crops in India. However, most of the wheat-growing areas experience several biotic and abiotic stresses, resulting in poor quality grains and reduced yield. To ensure food security for the growing population in India, there is a compelling need to explore the untapped genetic diversity available in gene banks for the development of stress-resistant/tolerant cultivars. The improvement of any crop lies in exploring and harnessing the genetic diversity available in its genetic resources in the form of cultivated varieties, landraces, wild relatives, and related genera. A huge collection of wheat genetic resources is conserved in various gene banks across the globe. Molecular and phenotypic characterization followed by documentation of conserved genetic resources is a prerequisite for germplasm utilization in crop improvement. The National Genebank of India has an extensive and diverse collection of wheat germplasm, comprising Indian wheat landraces, primitive cultivars, breeding lines, and collection from other countries. The conserved germplasm can contribute immensely to the development of wheat cultivars with high levels of biotic and abiotic stress tolerance. Breeding wheat varieties that can give high yields under different stress environments has not made much headway due to high genotypes and environmental interaction, non-availability of truly resistant/tolerant germplasm, and non-availability of reliable markers linked with the QTL having a significant impact on resistance/tolerance. The development of new breeding technologies like genomic selection (GS), which takes into account the G × E interaction, will facilitate crop improvement through enhanced climate resilience, by combining biotic and abiotic stress resistance/tolerance and maximizing yield potential. In this review article, we have summarized different constraints being faced by Indian wheat-breeding programs, challenges in addressing biotic and abiotic stresses, and improving quality and nutrition. Efforts have been made to highlight the wealth of Indian wheat genetic resources available in our National Genebank and their evaluation for the identification of trait-specific germplasm. Promising genotypes to develop varieties of important targeted traits and the development of different genomics resources have also been highlighted.
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Affiliation(s)
- Sundeep Kumar
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
- *Correspondence: Sundeep Kumar,
| | - Sherry R. Jacob
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-Kashmir), Jammu and Kashmir, India
| | - V. K. Vikas
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Pawan Kulwal
- State Level Biotechnology Centre, Mahatma Phule Krishi Vidyapeeth, Rahuri, India
| | - Tilak Chandra
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Satinder Kaur
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India
| | - Uttam Kumar
- Borlaug Institute for South Asia, Ludhiana, India
| | - Suneel Kumar
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Shailendra Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, Uttar Pradesh
| | - Ravinder Singh
- School of Biotechnology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu (SKUAST-Jammu), Jammu and Kashmir, India
| | - Sai Prasad
- Indian Agriculture Research Institute Regional Research Station, Indore, India
| | - Anju Mahendru Singh
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
| | - Amit Kumar Singh
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Jyoti Kumari
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - M. S. Saharan
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, India
| | | | - Manoj Prasad
- Laboratory of Plant Virology, National Institute of Plant Genome Research, New Delhi, India
| | - Sanjay Kalia
- Department of Biotechnology, Ministry of Science and Technology, New Delhi, India
| | - Kuldeep Singh
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
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Zenda T, Wang N, Dong A, Zhou Y, Duan H. Reproductive-Stage Heat Stress in Cereals: Impact, Plant Responses and Strategies for Tolerance Improvement. Int J Mol Sci 2022; 23:6929. [PMID: 35805930 PMCID: PMC9266455 DOI: 10.3390/ijms23136929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 06/18/2022] [Accepted: 06/20/2022] [Indexed: 02/04/2023] Open
Abstract
Reproductive-stage heat stress (RSHS) poses a major constraint to cereal crop production by damaging main plant reproductive structures and hampering reproductive processes, including pollen and stigma viability, pollination, fertilization, grain setting and grain filling. Despite this well-recognized fact, research on crop heat stress (HS) is relatively recent compared to other abiotic stresses, such as drought and salinity, and in particular, RSHS studies in cereals are considerably few in comparison with seedling-stage and vegetative-stage-centered studies. Meanwhile, climate change-exacerbated HS, independently or synergistically with drought, will have huge implications on crop performance and future global food security. Fortunately, due to their sedentary nature, crop plants have evolved complex and diverse transient and long-term mechanisms to perceive, transduce, respond and adapt to HS at the molecular, cell, physiological and whole plant levels. Therefore, uncovering the molecular and physiological mechanisms governing plant response and tolerance to RSHS facilitates the designing of effective strategies to improve HS tolerance in cereal crops. In this review, we update our understanding of several aspects of RSHS in cereals, particularly impacts on physiological processes and yield; HS signal perception and transduction; and transcriptional regulation by heat shock factors and heat stress-responsive genes. We also discuss the epigenetic, post-translational modification and HS memory mechanisms modulating plant HS tolerance. Moreover, we offer a critical set of strategies (encompassing genomics and plant breeding, transgenesis, omics and agronomy) that could accelerate the development of RSHS-resilient cereal crop cultivars. We underline that a judicious combination of all of these strategies offers the best foot forward in RSHS tolerance improvement in cereals. Further, we highlight critical shortcomings to RSHS tolerance investigations in cereals and propositions for their circumvention, as well as some knowledge gaps, which should guide future research priorities. Overall, our review furthers our understanding of HS tolerance in plants and supports the rational designing of RSHS-tolerant cereal crop cultivars for the warming climate.
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Affiliation(s)
- Tinashe Zenda
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (T.Z.); (N.W.); (A.D.)
- Department of Crop Genetics and Breeding, College o Agronomy, Hebei Agricultural University, Baoding 071001, China
| | - Nan Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (T.Z.); (N.W.); (A.D.)
- Department of Crop Genetics and Breeding, College o Agronomy, Hebei Agricultural University, Baoding 071001, China
| | - Anyi Dong
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (T.Z.); (N.W.); (A.D.)
- Department of Crop Genetics and Breeding, College o Agronomy, Hebei Agricultural University, Baoding 071001, China
| | - Yuzhi Zhou
- Library Department, Hebei Agricultural University, Baoding 071001, China;
| | - Huijun Duan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (T.Z.); (N.W.); (A.D.)
- Department of Crop Genetics and Breeding, College o Agronomy, Hebei Agricultural University, Baoding 071001, China
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Parihar AK, Kumar J, Gupta DS, Lamichaney A, Naik SJ S, Singh AK, Dixit GP, Gupta S, Toklu F. Genomics Enabled Breeding Strategies for Major Biotic Stresses in Pea ( Pisum sativum L.). FRONTIERS IN PLANT SCIENCE 2022; 13:861191. [PMID: 35665148 PMCID: PMC9158573 DOI: 10.3389/fpls.2022.861191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/28/2022] [Indexed: 06/15/2023]
Abstract
Pea (Pisum sativum L.) is one of the most important and productive cool season pulse crops grown throughout the world. Biotic stresses are the crucial constraints in harnessing the potential productivity of pea and warrant dedicated research and developmental efforts to utilize omics resources and advanced breeding techniques to assist rapid and timely development of high-yielding multiple stress-tolerant-resistant varieties. Recently, the pea researcher's community has made notable achievements in conventional and molecular breeding to accelerate its genetic gain. Several quantitative trait loci (QTLs) or markers associated with genes controlling resistance for fusarium wilt, fusarium root rot, powdery mildew, ascochyta blight, rust, common root rot, broomrape, pea enation, and pea seed borne mosaic virus are available for the marker-assisted breeding. The advanced genomic tools such as the availability of comprehensive genetic maps and linked reliable DNA markers hold great promise toward the introgression of resistance genes from different sources to speed up the genetic gain in pea. This review provides a brief account of the achievements made in the recent past regarding genetic and genomic resources' development, inheritance of genes controlling various biotic stress responses and genes controlling pathogenesis in disease causing organisms, genes/QTLs mapping, and transcriptomic and proteomic advances. Moreover, the emerging new breeding approaches such as transgenics, genome editing, genomic selection, epigenetic breeding, and speed breeding hold great promise to transform pea breeding. Overall, the judicious amalgamation of conventional and modern omics-enabled breeding strategies will augment the genetic gain and could hasten the development of biotic stress-resistant cultivars to sustain pea production under changing climate. The present review encompasses at one platform the research accomplishment made so far in pea improvement with respect to major biotic stresses and the way forward to enhance pea productivity through advanced genomic tools and technologies.
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Affiliation(s)
- Ashok Kumar Parihar
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Jitendra Kumar
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Debjyoti Sen Gupta
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Amrit Lamichaney
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Satheesh Naik SJ
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Anil K. Singh
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Girish P. Dixit
- All India Coordinated Research Project on Chickpea, ICAR-IIPR, Kanpur, India
| | - Sanjeev Gupta
- Indian Council of Agricultural Research, New Delhi, India
| | - Faruk Toklu
- Department of Field Crops, Faculty of Agricultural, Cukurova University, Adana, Turkey
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Nie WF, Xing E, Wang J, Mao Y, Ding X, Guo J. Emerging Strategies Mold Plasticity of Vegetable Plants in Response to High Temperature Stress. PLANTS (BASEL, SWITZERLAND) 2022; 11:959. [PMID: 35406939 PMCID: PMC9002854 DOI: 10.3390/plants11070959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 06/14/2023]
Abstract
As a result of energy consumption and human activities, a large amount of carbon dioxide emissions has led to global warming, which seriously affects the growth and development of plants. Vegetables are an indispensable part of people's diet. In the plant kingdom, a variety of vegetables are highly sensitive to climate change. For them, an increase of just a few degrees above their optimum temperature threshold can result in a loss of yield and quality. Emerging strategies such as practice management and breeding varieties in response to above-optimal temperatures are critical for abiotic stress resistance of vegetable crops. In this study, the function and application of multiple strategies, including breeding improvement, epigenetic modification directed generation of alleles, gene editing techniques, and accumulation of mutations in multigenerational adaptation to abiotic stress, were discussed in vegetable crops. It is believed to be meaningful for plants to build plasticity under high temperature stress, thus generating more genetic structures for heat resistant traits in vegetable products.
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Affiliation(s)
- Wen-Feng Nie
- Department of Horticulture, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou 225009, China; (E.X.); (J.W.); (Y.M.)
| | - Enjie Xing
- Department of Horticulture, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou 225009, China; (E.X.); (J.W.); (Y.M.)
| | - Jinyu Wang
- Department of Horticulture, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou 225009, China; (E.X.); (J.W.); (Y.M.)
| | - Yueying Mao
- Department of Horticulture, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou 225009, China; (E.X.); (J.W.); (Y.M.)
| | - Xiaotao Ding
- Shanghai Key Laboratory of Protected Horticultural Technology, Horticulture Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
| | - Jianfei Guo
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
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