1
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Al-Mamun HA, Danilevicz MF, Marsh JI, Gondro C, Edwards D. Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large-scale soybean dataset. THE PLANT GENOME 2024:e20503. [PMID: 39253773 DOI: 10.1002/tpg2.20503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 07/15/2024] [Accepted: 07/15/2024] [Indexed: 09/11/2024]
Abstract
The surge in high-throughput technologies has empowered the acquisition of vast genomic datasets, prompting the search for genetic markers and biomarkers relevant to complex traits. However, grappling with the inherent complexities of high dimensionality and sparsity within these datasets poses formidable hurdles. The immense number of features and their potential redundancy demand efficient strategies for extracting pertinent information and identifying significant markers. Feature selection is important in large genomic data as it helps in enhancing interpretability and computational efficiency. This study focuses on addressing these challenges through a comprehensive investigation into genomic feature selection methodologies, employing a rich soybean (Glycine max L. Merr.) dataset comprising 966 lines with over 5.5 million single nucleotide polymorphisms. Emphasizing the "small n large p" dilemma prevalent in contemporary genomic studies, we compared the efficacy of traditional genome-wide association studies (GWAS) with two prominent machine learning tools, random forest and extreme gradient boosting, in pinpointing predictive features. Utilizing the expansive soybean dataset, we assessed the performance of these methodologies in selecting features that optimize predictive modeling for various phenotypes. By constructing predictive models based on the selected features, we ascertain the comparative prediction accuracies, thereby illuminating the strengths and limitations of these feature selection methodologies in the realm of genomic data analysis.
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Affiliation(s)
- Hawlader A Al-Mamun
- Centre for Applied Bioinformatics, and School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Monica F Danilevicz
- Centre for Applied Bioinformatics, and School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Jacob I Marsh
- Department of Biology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Cedric Gondro
- Department of Animal Science, Michigan State University, East Lansing, Michigan, USA
| | - David Edwards
- Centre for Applied Bioinformatics, and School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
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2
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Mishra S, Srivastava AK, Khan AW, Tran LSP, Nguyen HT. The era of panomics-driven gene discovery in plants. TRENDS IN PLANT SCIENCE 2024; 29:995-1005. [PMID: 38658292 DOI: 10.1016/j.tplants.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: 12/06/2023] [Revised: 03/01/2024] [Accepted: 03/08/2024] [Indexed: 04/26/2024]
Abstract
Panomics is an approach to integrate multiple 'omics' datasets, generated using different individuals or natural variations. Considering their diverse phenotypic spectrum, the phenome is inherently associated with panomics-based science, which is further combined with genomics, transcriptomics, metabolomics, and other omics techniques, either independently or collectively. Panomics has been accelerated through recent technological advancements in the field of genomics that enable the detection of population-wide structural variations (SVs) and hence offer unprecedented insights into the genetic variations contributing to important agronomic traits. The present review provides the recent trends of panomics-driven gene discovery toward various traits related to plant development, stress tolerance, accumulation of specialized metabolites, and domestication/dedomestication. In addition, the success stories are highlighted in the broader context of enhancing crop productivity.
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Affiliation(s)
- Shefali Mishra
- Nuclear Agriculture and Biotechnology Division, Bhabha Atomic Research Centre, Mumbai, Maharashtra 400085, India
| | - Ashish Kumar Srivastava
- Nuclear Agriculture and Biotechnology Division, Bhabha Atomic Research Centre, Mumbai, Maharashtra 400085, India; Homi Bhabha National Institute, Mumbai 400094, India.
| | - Aamir W Khan
- Division of Plant Science and Technology and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211, USA
| | - Lam-Son Phan Tran
- Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, USA
| | - Henry T Nguyen
- Division of Plant Science and Technology and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211, USA.
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3
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Li S, Wang W, Sun L, Zhu H, Hou R, Zhang H, Tang X, Clark CB, Swarm SA, Nelson RL, Ma J. Artificial selection of mutations in two nearby genes gave rise to shattering resistance in soybean. Nat Commun 2024; 15:7588. [PMID: 39217192 PMCID: PMC11365945 DOI: 10.1038/s41467-024-52044-8] [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: 08/28/2023] [Accepted: 08/26/2024] [Indexed: 09/04/2024] Open
Abstract
Resistance to pod shattering is a key domestication-related trait selected for seed production in many crops. Here, we show that the transition from shattering in wild soybeans to shattering resistance in cultivated soybeans resulted from selection of mutations within the coding sequences of two nearby genes - Sh1 and Pdh1. Sh1 encodes a C2H2-like zinc finger transcription factor that promotes shattering by repressing SHAT1-5 expression, thereby reducing the secondary wall thickness of fiber cap cells in the abscission layers of pod sutures, while Pdh1 encodes a dirigent protein that orchestrates asymmetric lignin distribution in inner sclerenchyma, creating torsion in pod walls that facilitates shattering. Integration analyses of quantitative trait locus mapping, genome-wide association studies, and allele distribution in representative soybean germplasm suggest that these two genes are primary modulators underlying this domestication trait. Our study thus provides comprehensive understanding regarding the genetic, molecular, and cellular bases of shattering resistance in soybeans.
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Affiliation(s)
- Shuai Li
- Department of Agronomy, Purdue University, West Lafayette, IN, USA.
- College of Life Sciences, Qingdao Agricultural University, Qingdao, China.
| | - Weidong Wang
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
- Center for Plant Biology, Purdue University, West Lafayette, IN, USA
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Lianjun Sun
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Hong Zhu
- College of Agronomy, Qingdao Agricultural University, Qingdao, China
| | - Rui Hou
- College of Life Sciences, Qingdao Agricultural University, Qingdao, China
| | - Huiying Zhang
- College of Life Sciences, Qingdao Agricultural University, Qingdao, China
| | - Xuemin Tang
- College of Life Sciences, Qingdao Agricultural University, Qingdao, China
| | - Chancelor B Clark
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
- Center for Plant Biology, Purdue University, West Lafayette, IN, USA
| | - Stephen A Swarm
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
- Beck's Hybrids, Atlanta, IN, USA
| | - Randall L Nelson
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | - Jianxin Ma
- Department of Agronomy, Purdue University, West Lafayette, IN, USA.
- Center for Plant Biology, Purdue University, West Lafayette, IN, USA.
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4
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Zhu X, Wang H, Li Y, Rao D, Wang F, Gao Y, Zhong W, Zhao Y, Wu S, Chen X, Qiu H, Zhang W, Xia Z. A Novel 10-Base Pair Deletion in the First Exon of GmHY2a Promotes Hypocotyl Elongation, Induces Early Maturation, and Impairs Photosynthetic Performance in Soybean. Int J Mol Sci 2024; 25:6483. [PMID: 38928189 PMCID: PMC11203641 DOI: 10.3390/ijms25126483] [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: 04/24/2024] [Revised: 06/03/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
Plants photoreceptors perceive changes in light quality and intensity and thereby regulate plant vegetative growth and reproductive development. By screening a γ irradiation-induced mutant library of the soybean (Glycine max) cultivar "Dongsheng 7", we identified Gmeny, a mutant with elongated nodes, yellowed leaves, decreased chlorophyll contents, altered photosynthetic performance, and early maturation. An analysis of bulked DNA and RNA data sampled from a population segregating for Gmeny, using the BVF-IGV pipeline established in our laboratory, identified a 10 bp deletion in the first exon of the candidate gene Glyma.02G304700. The causative mutation was verified by a variation analysis of over 500 genes in the candidate gene region and an association analysis, performed using two populations segregating for Gmeny. Glyma.02G304700 (GmHY2a) is a homolog of AtHY2a in Arabidopsis thaliana, which encodes a PΦB synthase involved in the biosynthesis of phytochrome. A transcriptome analysis of Gmeny using the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed changes in multiple functional pathways, including photosynthesis, gibberellic acid (GA) signaling, and flowering time, which may explain the observed mutant phenotypes. Further studies on the function of GmHY2a and its homologs will help us to understand its profound regulatory effects on photosynthesis, photomorphogenesis, and flowering time.
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Affiliation(s)
- Xiaobin Zhu
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China; (X.Z.); (H.W.); (Y.L.); (F.W.); (Y.G.); (W.Z.); (Y.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyan Wang
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China; (X.Z.); (H.W.); (Y.L.); (F.W.); (Y.G.); (W.Z.); (Y.Z.)
| | - Yuzhuo Li
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China; (X.Z.); (H.W.); (Y.L.); (F.W.); (Y.G.); (W.Z.); (Y.Z.)
| | - Demin Rao
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun 132102, China; (D.R.); (H.Q.); (W.Z.)
| | - Feifei Wang
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China; (X.Z.); (H.W.); (Y.L.); (F.W.); (Y.G.); (W.Z.); (Y.Z.)
| | - Yi Gao
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China; (X.Z.); (H.W.); (Y.L.); (F.W.); (Y.G.); (W.Z.); (Y.Z.)
| | - Weiyu Zhong
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China; (X.Z.); (H.W.); (Y.L.); (F.W.); (Y.G.); (W.Z.); (Y.Z.)
| | - Yujing Zhao
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China; (X.Z.); (H.W.); (Y.L.); (F.W.); (Y.G.); (W.Z.); (Y.Z.)
| | - Shihao Wu
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China; (S.W.); (X.C.)
| | - Xin Chen
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China; (S.W.); (X.C.)
| | - Hongmei Qiu
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun 132102, China; (D.R.); (H.Q.); (W.Z.)
| | - Wei Zhang
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun 132102, China; (D.R.); (H.Q.); (W.Z.)
| | - Zhengjun Xia
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China; (X.Z.); (H.W.); (Y.L.); (F.W.); (Y.G.); (W.Z.); (Y.Z.)
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5
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Khan AW, Garg V, Sun S, Gupta S, Dudchenko O, Roorkiwal M, Chitikineni A, Bayer PE, Shi C, Upadhyaya HD, Bohra A, Bharadwaj C, Mir RR, Baruch K, Yang B, Coyne CJ, Bansal KC, Nguyen HT, Ronen G, Aiden EL, Veneklaas E, Siddique KHM, Liu X, Edwards D, Varshney RK. Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea. Nat Genet 2024; 56:1225-1234. [PMID: 38783120 DOI: 10.1038/s41588-024-01760-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
Chickpea (Cicer arietinum L.)-an important legume crop cultivated in arid and semiarid regions-has limited genetic diversity. Efforts are being undertaken to broaden its diversity by utilizing its wild relatives, which remain largely unexplored. Here, we present the Cicer super-pangenome based on the de novo genome assemblies of eight annual Cicer wild species. We identified 24,827 gene families, including 14,748 core, 2,958 softcore, 6,212 dispensable and 909 species-specific gene families. The dispensable genome was enriched for genes related to key agronomic traits. Structural variations between cultivated and wild genomes were used to construct a graph-based genome, revealing variations in genes affecting traits such as flowering time, vernalization and disease resistance. These variations will facilitate the transfer of valuable traits from wild Cicer species into elite chickpea varieties through marker-assisted selection or gene-editing. This study offers valuable insights into the genetic diversity and potential avenues for crop improvement in chickpea.
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Affiliation(s)
- Aamir W Khan
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, USA
| | - Vanika Garg
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
- Centre for Crop and Food Innovation, WA State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia
| | | | - Saurabh Gupta
- Curtin Health Innovation Research Institute (CHIRI), Curtin Medical School, Curtin University, Perth, Western Australia, Australia
| | - Olga Dudchenko
- Department of Molecular and Human Genetics, Center for Genome Architecture, Baylor College of Medicine, Houston, TX, USA
| | - Manish Roorkiwal
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
- Khalifa Center for Genetic Engineering and Biotechnology, United Arab Emirates University, Al-Ain, United Arab Emirates
| | - Annapurna Chitikineni
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
- Centre for Crop and Food Innovation, WA State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia
| | - Philipp E Bayer
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | | | - Hari D Upadhyaya
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
- Plant Genome Mapping Laboratory, The University of Georgia, Athens, GA, USA
| | - Abhishek Bohra
- Centre for Crop and Food Innovation, WA State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia
| | | | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture (FoA), SKUAST-Kashmir,Wadura Campus, Kashmir, India
| | | | | | - Clarice J Coyne
- USDA-ARS Plant Germplasm Introduction and Testing, Washington State University, Pullman, WA, USA
| | - Kailash C Bansal
- National Academy of Agricultural Sciences (NAAS), NASC Complex, New Delhi, India
| | - Henry T Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, USA
| | - Gil Ronen
- NRGene Ltd, Park HaMada, Ness Ziona, Israel
| | - Erez Lieberman Aiden
- Department of Molecular and Human Genetics, Center for Genome Architecture, Baylor College of Medicine, Houston, TX, USA
| | - Erik Veneklaas
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Kadambot H M Siddique
- UWA Institute of Agriculture, and School of Agriculture and Environment, University of Western Australia, Perth, Western Australia, Australia
| | - Xin Liu
- BGI Research, Qingdao, China.
- BGI Research, Shenzhen, China.
| | - David Edwards
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia.
| | - Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India.
- Centre for Crop and Food Innovation, WA State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
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6
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Hu H, Li R, Zhao J, Batley J, Edwards D. Technological Development and Advances for Constructing and Analyzing Plant Pangenomes. Genome Biol Evol 2024; 16:evae081. [PMID: 38669452 PMCID: PMC11058698 DOI: 10.1093/gbe/evae081] [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: 11/06/2023] [Revised: 04/09/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
A pangenome captures the genomic diversity for a species, derived from a collection of genetic sequences of diverse populations. Advances in sequencing technologies have given rise to three primary methods for pangenome construction and analysis: de novo assembly and comparison, reference genome-based iterative assembly, and graph-based pangenome construction. Each method presents advantages and challenges in processing varying amounts and structures of DNA sequencing data. With the emergence of high-quality genome assemblies and advanced bioinformatic tools, the graph-based pangenome is emerging as an advanced reference for exploring the biological and functional implications of genetic variations.
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Affiliation(s)
- Haifei Hu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
| | - Risheng Li
- Rice Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- College of Agriculture, South China Agricultural University, Guangzhou, Guangdong 510642, China
| | - Junliang Zhao
- Rice Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
| | - Jacqueline Batley
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia
- Centre for Applied Bioinformatics, University of Western Australia, Perth, WA 6009, Australia
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7
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Hu H, Scheben A, Wang J, Li F, Li C, Edwards D, Zhao J. Unravelling inversions: Technological advances, challenges, and potential impact on crop breeding. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:544-554. [PMID: 37961986 PMCID: PMC10893937 DOI: 10.1111/pbi.14224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 10/11/2023] [Accepted: 10/22/2023] [Indexed: 11/15/2023]
Abstract
Inversions, a type of chromosomal structural variation, significantly influence plant adaptation and gene functions by impacting gene expression and recombination rates. However, compared with other structural variations, their roles in functional biology and crop improvement remain largely unexplored. In this review, we highlight technological and methodological advancements that have allowed a comprehensive understanding of inversion variants through the pangenome framework and machine learning algorithms. Genome editing is an efficient method for inducing or reversing inversion mutations in plants, providing an effective mechanism to modify local recombination rates. Given the potential of inversions in crop breeding, we anticipate increasing attention on inversions from the scientific community in future research and breeding applications.
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Affiliation(s)
- Haifei Hu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co‐construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering LaboratoryGuangzhouChina
| | - Armin Scheben
- Simons Center for Quantitative Biology, Cold Spring Harbor LaboratoryCold Spring HarborNew YorkUSA
| | - Jian Wang
- Rice Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co‐construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering LaboratoryGuangzhouChina
| | - Fangping Li
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, State Key Laboratory for Conservation and Utilization of Subtropical Agro‐BioresourcesSouth China Agricultural UniversityGuangzhouChina
| | - Chengdao Li
- Western Crop Genetics Alliance, Centre for Crop & Food Innovation, Food Futures Institute, College of Science, Health, Engineering and EducationMurdoch UniversityMurdochWestern AustraliaAustralia
| | - David Edwards
- School of Biological SciencesUniversity of Western AustraliaPerthWestern AustraliaAustralia
- Australia & Centre for Applied BioinformaticsUniversity of Western AustraliaPerthWestern AustraliaAustralia
| | - Junliang Zhao
- Rice Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co‐construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering LaboratoryGuangzhouChina
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8
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Souza R, Rouf Mian MA, Vaughn JN, Li Z. Introgression of a Danbaekkong high-protein allele across different genetic backgrounds in soybean. FRONTIERS IN PLANT SCIENCE 2023; 14:1308731. [PMID: 38173927 PMCID: PMC10761420 DOI: 10.3389/fpls.2023.1308731] [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: 10/07/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024]
Abstract
Soybean meal is a major component of livestock feed due to its high content and quality of protein. Understanding the genetic control of protein is essential to develop new cultivars with improved meal protein. Previously, a genomic region on chromosome 20 significantly associated with elevated protein content was identified in the cultivar Danbaekkong. The present research aimed to introgress the Danbaekkong high-protein allele into elite lines with different genetic backgrounds by developing and deploying robust DNA markers. A multiparent population consisting of 10 F5-derived populations with a total of 1,115 recombinant inbred lines (RILs) was developed using "Benning HP" as the donor parent of the Danbaekkong high-protein allele. A new functional marker targeting the 321-bp insertion in the gene Glyma.20g085100 was developed and used to track the Danbaekkong high-protein allele across the different populations and enable assessment of its effect and stability. Across all populations, the high-protein allele consistently increased the content, with an increase of 3.3% in seed protein. A total of 103 RILs were selected from the multiparent population for yield testing in five environments to assess the impact of the high-protein allele on yield and to enable the selection of new breeding lines with high protein and high yield. The results indicated that the high-protein allele impacts yield negatively in general; however, it is possible to select high-yielding lines with high protein content. An analysis of inheritance of the Chr 20 high-protein allele in Danbaekkong indicated that it originated from a Glycine soja line (PI 163453) and is the same as other G. soja lines studied. A survey of the distribution of the allele across 79 G. soja accessions and 35 Glycine max ancestors of North American soybean cultivars showed that the high-protein allele is present in all G. soja lines evaluated but not in any of the 35 North American soybean ancestors. These results demonstrate that G. soja accessions are a valuable source of favorable alleles for improvement of protein composition.
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Affiliation(s)
- Renan Souza
- Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
| | - M. A. Rouf Mian
- Soybean and Nitrogen Fixation Research Unit, United States Department of Agriculture - Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Justin N. Vaughn
- Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
- Genomics and Bioinformatics Research Unit, United States Department of Agriculture - Agricultural Research Service (USDA-ARS), Athens, GA, United States
| | - Zenglu Li
- Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
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9
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Garg V, Khan AW, Fengler K, Llaca V, Yuan Y, Vuong TD, Harris C, Chan TF, Lam HM, Varshney RK, Nguyen HT. Near-gapless genome assemblies of Williams 82 and Lee cultivars for accelerating global soybean research. THE PLANT GENOME 2023; 16:e20382. [PMID: 37749941 DOI: 10.1002/tpg2.20382] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/01/2023] [Accepted: 08/03/2023] [Indexed: 09/27/2023]
Abstract
Complete, gapless telomere-to-telomere chromosome assemblies are a prerequisite for comprehensively investigating the architecture of complex regions, like centromeres or telomeres and removing uncertainties in the order, spacing, and orientation of genes. Using complementary genomics technologies and assembly algorithms, we developed highly contiguous, nearly gapless, genome assemblies for two economically important soybean [Glycine max (L.) Merr] cultivars (Williams 82 and Lee). The centromeres were distinctly annotated on all the chromosomes of both assemblies. We further found that the canonical telomeric repeats were present at the telomeres of all chromosomes of both Williams 82 and Lee genomes. A total of 10 chromosomes in Williams 82 and eight in Lee were entirely reconstructed in single contigs without any gap. Using the combination of ab initio prediction, protein homology, and transcriptome evidence, we identified 58,287 and 56,725 protein-coding genes in Williams 82 and Lee, respectively. The genome assemblies and annotations will serve as a valuable resource for studying soybean genomics and genetics and accelerating soybean improvement.
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Affiliation(s)
- Vanika Garg
- Murdoch's Centre for Crop & Food Innovation, WA State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia
| | - Aamir W Khan
- Division of Plant Sciences and Technology, University of Missouri, Columbia, Missouri, USA
| | - Kevin Fengler
- Research and Development, Corteva Agriscience, Johnston, Iowa, USA
| | - Victor Llaca
- Research and Development, Corteva Agriscience, Johnston, Iowa, USA
| | - Yuxuan Yuan
- School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Tri D Vuong
- Division of Plant Sciences and Technology, University of Missouri, Columbia, Missouri, USA
| | - Charlotte Harris
- Research and Development, Corteva Agriscience, Johnston, Iowa, USA
| | - Ting-Fung Chan
- School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Hon Ming Lam
- School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Rajeev K Varshney
- Murdoch's Centre for Crop & Food Innovation, WA State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia
| | - Henry T Nguyen
- Division of Plant Sciences and Technology, University of Missouri, Columbia, Missouri, USA
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10
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Vuong TD, Florez-Palacios L, Mozzoni L, Clubb M, Quigley C, Song Q, Kadam S, Yuan Y, Chan TF, Mian MAR, Nguyen HT. Genomic analysis and characterization of new loci associated with seed protein and oil content in soybeans. THE PLANT GENOME 2023; 16:e20400. [PMID: 37940622 DOI: 10.1002/tpg2.20400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/30/2023] [Accepted: 10/02/2023] [Indexed: 11/10/2023]
Abstract
Breeding for increased protein without a reduction in oil content in soybeans [Glycine max (L.) Merr.] is a challenge for soybean breeders but an expected goal. Many efforts have been made to develop new soybean varieties with high yield in combination with desirable protein and/or oil traits. An elite line, R05-1415, was reported to be high yielding, high protein, and low oil. Several significant quantitative trait loci (QTL) for protein and oil were reported in this line, but many of them were unstable across environments or genetic backgrounds. Thus, a new study under multiple field environments using the Infinium BARCSoySNP6K BeadChips was conducted to detect and confirm stable genomic loci for these traits. Genetic analyses consistently detected a single major genomic locus conveying these two traits with remarkably high phenotypic variation explained (R2 ), varying between 24.2% and 43.5%. This new genomic locus is located between 25.0 and 26.7 Mb, distant from the previously reported QTL and did not overlap with other commonly reported QTL and the recently cloned gene Glyma.20G085100. Homolog analysis indicated that this QTL did not result from the paracentric chromosome inversion with an adjacent genomic fragment that harbors the reported QTL. The pleiotropic effect of this QTL could be a challenge for improving protein and oil simultaneously; however, a further study of four candidate genes with significant expressions in the seed developmental stages coupled with haplotype analysis may be able to pinpoint causative genes. The functionality and roles of these genes can be determined and characterized, which lay a solid foundation for the improvement of protein and oil content in soybeans.
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Affiliation(s)
- Tri D Vuong
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | | | - Leandro Mozzoni
- Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, Arkansas, USA
| | - Michael Clubb
- Division of Plant Science and Technology, the Fisher Delta Research, Extension and Education Center (FDREEC), University of Missouri, Portageville, Missouri, USA
| | - Chuck Quigley
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, Maryland, USA
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, Maryland, USA
| | - Shaila Kadam
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | - Yuxuan Yuan
- School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Ting Fung Chan
- School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | | | - Henry T Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
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11
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Lemay MA, de Ronne M, Bélanger R, Belzile F. k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean. THE PLANT GENOME 2023; 16:e20374. [PMID: 37596724 DOI: 10.1002/tpg2.20374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/19/2023] [Indexed: 08/20/2023]
Abstract
Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a given trait in crop species. Assessing genetic variants other than single-nucleotide polymorphisms (SNPs) could alleviate this problem. In this study, we tested the potential of structural variant (SV)- and k-mer-based GWAS in soybean by applying these methods as well as conventional SNP/indel-based GWAS to 13 traits. We assessed the performance of each GWAS approach based on loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources invested. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Our results suggest that coupling k-mer- and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.
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Affiliation(s)
- Marc-André Lemay
- Département de phytologie, Université Laval, Québec, QC, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche et d'innovation sur les végétaux, Université Laval, Québec, QC, Canada
| | - Maxime de Ronne
- Département de phytologie, Université Laval, Québec, QC, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche et d'innovation sur les végétaux, Université Laval, Québec, QC, Canada
| | - Richard Bélanger
- Département de phytologie, Université Laval, Québec, QC, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche et d'innovation sur les végétaux, Université Laval, Québec, QC, Canada
| | - François Belzile
- Département de phytologie, Université Laval, Québec, QC, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche et d'innovation sur les végétaux, Université Laval, Québec, QC, Canada
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12
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Li X, Zhu T, Wang X, Zhu M. Genome-wide identification of glutamate receptor-like gene family in soybean. Heliyon 2023; 9:e21655. [PMID: 38027661 PMCID: PMC10651524 DOI: 10.1016/j.heliyon.2023.e21655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 10/02/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Glutamate receptor-like genes (GLRs) are essential in the growth and development of plants and many physiological and biochemical processes; however, related information in soybean is lacking. In this study, 105 GLRs, including 67 Glycine soja and 38 Glycine max GLRs, were identified and divided into two clades (Clades II and III) according to their phylogenetic relationships. GLR members in the same branch had a relatively conservative motif composition and genetic structure. Furthermore, the soybean GLR family mainly experienced purification selection during evolution. Cis-acting element analysis, gene ontology, and Kyoto Encyclopedia of Genes and Genomic annotations indicated the complexity of the gene regulation and functional diversity of the soybean GLR. Moreover, transcriptome data analysis showed that these GLRs had different expression profiles in different tissues, and Clade III members had higher and more common expression patterns. Additionally, the expression profiles under jasmonic acid treatment and salt stress indicate that the GLR participated in the jasmonic acid signaling pathway and plays a role in salt treatment. This study provides information for a comprehensive understanding of the soybean GLR family and a reference for further functional research and genetic improvement.
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Affiliation(s)
- Xinran Li
- School of Biological Science and Technology, Liupanshui Normal University, Liupanshui, China
| | - Tianhao Zhu
- College of Mathematical Sciences, Harbin Engineering University, Harbin, China
| | - Xuying Wang
- School of Biological Science and Technology, Liupanshui Normal University, Liupanshui, China
| | - Miao Zhu
- School of Biological Science and Technology, Liupanshui Normal University, Liupanshui, China
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13
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Potapova NA, Zlobin AS, Perfil’ev RN, Vasiliev GV, Salina EA, Tsepilov YA. Population Structure and Genetic Diversity of the 175 Soybean Breeding Lines and Varieties Cultivated in West Siberia and Other Regions of Russia. PLANTS (BASEL, SWITZERLAND) 2023; 12:3490. [PMID: 37836230 PMCID: PMC10575349 DOI: 10.3390/plants12193490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/23/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023]
Abstract
Soybean is a leguminous plant cultivated in many countries and is considered important in the food industry due to the high levels of oil and protein content in the beans. The high demand for soybeans and its products in the industry requires the expansion of cultivation areas. Despite climatic restrictions, West Siberia is gradually expanding its area of soybean cultivation. In this study, we present the first analysis of the population structure and genetic diversity of the 175 soybean Glycine max breeding lines and varieties cultivated in West Siberia (103 accessions) and other regions of Russia (72 accessions), and we compare them with the cultivated soybean varieties from other geographical locations. Principal component analysis revealed several genetic clusters with different levels of genetic heterogeneity. Studied accessions are genetically similar to varieties from China, Japan, and the USA and are genetically distant to varieties from South Korea. Admixture analysis revealed four ancestry groups based on genetic ancestry and geographical origin, which are consistent with the regions of cultivation and origin of accessions and correspond to the principal component analysis result. Population statistics, including nucleotide diversity, Tajima's D, and linkage disequilibrium, are comparatively similar to those observed for studied accessions of a different origin. This study provides essential population and genetic information about the unique collection of breeding lines and varieties cultivated in West Siberia and other Russian regions to foster further evolutionary, genome-wide associations and functional breeding studies.
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Affiliation(s)
- Nadezhda A. Potapova
- Kurchatov Genomic Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Institute for Information Transmission Problems (Kharkevich Institute) of the Russian Academy of Sciences, 127051 Moscow, Russia
| | - Alexander S. Zlobin
- Kurchatov Genomic Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Federal Research Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Roman N. Perfil’ev
- Federal Research Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Gennady V. Vasiliev
- Kurchatov Genomic Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Federal Research Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Elena A. Salina
- Kurchatov Genomic Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Federal Research Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Yakov A. Tsepilov
- Kurchatov Genomic Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Federal Research Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
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14
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Gao L, Kantar MB, Moxley D, Ortiz-Barrientos D, Rieseberg LH. Crop adaptation to climate change: An evolutionary perspective. MOLECULAR PLANT 2023; 16:1518-1546. [PMID: 37515323 DOI: 10.1016/j.molp.2023.07.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 07/30/2023]
Abstract
The disciplines of evolutionary biology and plant and animal breeding have been intertwined throughout their development, with responses to artificial selection yielding insights into the action of natural selection and evolutionary biology providing statistical and conceptual guidance for modern breeding. Here we offer an evolutionary perspective on a grand challenge of the 21st century: feeding humanity in the face of climate change. We first highlight promising strategies currently under way to adapt crops to current and future climate change. These include methods to match crop varieties with current and predicted environments and to optimize breeding goals, management practices, and crop microbiomes to enhance yield and sustainable production. We also describe the promise of crop wild relatives and recent technological innovations such as speed breeding, genomic selection, and genome editing for improving environmental resilience of existing crop varieties or for developing new crops. Next, we discuss how methods and theory from evolutionary biology can enhance these existing strategies and suggest novel approaches. We focus initially on methods for reconstructing the evolutionary history of crops and their pests and symbionts, because such historical information provides an overall framework for crop-improvement efforts. We then describe how evolutionary approaches can be used to detect and mitigate the accumulation of deleterious mutations in crop genomes, identify alleles and mutations that underlie adaptation (and maladaptation) to agricultural environments, mitigate evolutionary trade-offs, and improve critical proteins. Continuing feedback between the evolution and crop biology communities will ensure optimal design of strategies for adapting crops to climate change.
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Affiliation(s)
- Lexuan Gao
- CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Michael B Kantar
- Department of Tropical Plant & Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Dylan Moxley
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - Daniel Ortiz-Barrientos
- School of Biological Sciences and Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Loren H Rieseberg
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada.
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15
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Chandnani R, Qin T, Ye H, Hu H, Panjvani K, Tokizawa M, Macias JM, Medina AA, Bernardino K, Pradier PL, Banik P, Mooney A, V Magalhaes J, T Nguyen H, Kochian LV. Application of an Improved 2-Dimensional High-Throughput Soybean Root Phenotyping Platform to Identify Novel Genetic Variants Regulating Root Architecture Traits. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0097. [PMID: 37780968 PMCID: PMC10538525 DOI: 10.34133/plantphenomics.0097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 09/05/2023] [Indexed: 10/03/2023]
Abstract
Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots.
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Affiliation(s)
- Rahul Chandnani
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
- NRGene Canada, 110 Research Dr Suite 101, Saskatoon, SK, Canada
| | - Tongfei Qin
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Heng Ye
- Division of Plant Sciences and Technology, University of Missouri, Columbia, MO 65211, USA
| | - Haifei Hu
- School of Biological Sciences, The University of Western Australia, Crawley, WA 6009, Australia
- Rice Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China(Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong, China
| | - Karim Panjvani
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Mutsutomo Tokizawa
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Javier Mora Macias
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Alma Armenta Medina
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | | | - Pierre-Luc Pradier
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Pankaj Banik
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ashlyn Mooney
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | | | - Henry T Nguyen
- Division of Plant Sciences and Technology, University of Missouri, Columbia, MO 65211, USA
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Leon V Kochian
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
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16
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Yin Z, Yang Q, Shen D, Liu J, Huang W, Dou D. Online data resource for exploring transposon insertion polymorphisms in public soybean germplasm accessions. PLANT PHYSIOLOGY 2023; 193:1036-1044. [PMID: 37399251 DOI: 10.1093/plphys/kiad386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 05/30/2023] [Accepted: 06/11/2023] [Indexed: 07/05/2023]
Abstract
Soybean (Glycine max L. Merrill) is one of the most important economical crops. A large number of whole-genome resequencing datasets have been generated and are increasingly expanded for exploring genetic diversity and mining important quantitative trait loci. Most genome-wide association studies have focused on single-nucleotide polymorphisms, short insertions, and deletions. Nevertheless, structure variants mainly caused by transposon element mobilization are not fully considered. To fill this gap, we uniformly processed the publicly available whole-genome resequencing data from 5,521 soybean germplasm accessions and built an online soybean transposon insertion polymorphisms database named Soybean Transposon Insertion Polymorphisms Database (SoyTIPdb) (https://biotec.njau.edu.cn/soytipdb). The collected germplasm accessions derived from more than 45 countries and 160 regions representing the most comprehensive genetic diversity of soybean. SoyTIPdb implements easy-to-use query, analysis, and browse functions to help understand and find meaningful structural variations from TE insertions. In conclusion, SoyTIPdb is a valuable data resource and will help soybean breeders/researchers take advantage of the whole-genome sequencing datasets available in the public depositories.
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Affiliation(s)
- Zhiyuan Yin
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Qingjie Yang
- Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Danyu Shen
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Jinding Liu
- Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA
| | - Wen Huang
- Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA
| | - Daolong Dou
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
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17
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Song B, Ning W, Wei D, Jiang M, Zhu K, Wang X, Edwards D, Odeny DA, Cheng S. Plant genome resequencing and population genomics: Current status and future prospects. MOLECULAR PLANT 2023; 16:1252-1268. [PMID: 37501370 DOI: 10.1016/j.molp.2023.07.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 05/30/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
Advances in DNA sequencing technology have sparked a genomics revolution, driving breakthroughs in plant genetics and crop breeding. Recently, the focus has shifted from cataloging genetic diversity in plants to exploring their functional significance and delivering beneficial alleles for crop improvement. This transformation has been facilitated by the increasing adoption of whole-genome resequencing. In this review, we summarize the current progress of population-based genome resequencing studies and how these studies affect crop breeding. A total of 187 land plants from 163 countries have been resequenced, comprising 54 413 accessions. As part of resequencing efforts 367 traits have been surveyed and 86 genome-wide association studies have been conducted. Economically important crops, particularly cereals, vegetables, and legumes, have dominated the resequencing efforts, leaving a gap in 49 orders, including Lycopodiales, Liliales, Acorales, Austrobaileyales, and Commelinales. The resequenced germplasm is distributed across diverse geographic locations, providing a global perspective on plant genomics. We highlight genes that have been selected during domestication, or associated with agronomic traits, and form a repository of candidate genes for future research and application. Despite the opportunities for cross-species comparative genomics, many population genomic datasets are not accessible, impeding secondary analyses. We call for a more open and collaborative approach to population genomics that promotes data sharing and encourages contribution-based credit policy. The number of plant genome resequencing studies will continue to rise with the decreasing DNA sequencing costs, coupled with advances in analysis and computational technologies. This expansion, in terms of both scale and quality, holds promise for deeper insights into plant trait genetics and breeding design.
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Affiliation(s)
- Bo Song
- 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
| | - Weidong Ning
- 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; Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Wuhan, Hubei, China
| | - Di Wei
- Biotechnology Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 53007, China
| | - Mengyun Jiang
- 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; State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China; Shenzhen Research Institute of Henan University, Shenzhen 518000, China
| | - Kun Zhu
- 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; State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China; Shenzhen Research Institute of Henan University, Shenzhen 518000, China
| | - Xingwei Wang
- 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; State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China; Shenzhen Research Institute of Henan University, Shenzhen 518000, China
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Damaris A Odeny
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) - Eastern and Southern Africa, Nairobi, Kenya
| | - Shifeng Cheng
- 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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18
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Li D, Zhang Z, Gao X, Zhang H, Bai D, Wang Q, Zheng T, Li YH, Qiu LJ. The elite variations in germplasms for soybean breeding. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:37. [PMID: 37312749 PMCID: PMC10248635 DOI: 10.1007/s11032-023-01378-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/03/2023] [Indexed: 06/15/2023]
Abstract
The genetic base of soybean cultivars (Glycine max (L.) Merr.) has been narrowed through selective domestication and specific breeding improvement, similar to other crops. This presents challenges in breeding new cultivars with improved yield and quality, reduced adaptability to climate change, and increased susceptibility to diseases. On the other hand, the vast collection of soybean germplasms offers a potential source of genetic variations to address those challenges, but it has yet to be fully leveraged. In recent decades, rapidly improved high-throughput genotyping technologies have accelerated the harness of elite variations in soybean germplasm and provided the important information for solving the problem of a narrowed genetic base in breeding. In this review, we will overview the situation of maintenance and utilization of soybean germplasms, various solutions provided for different needs in terms of the number of molecular markers, and the omics-based high-throughput strategies that have been used or can be used to identify elite alleles. We will also provide an overall genetic information generated from soybean germplasms in yield, quality traits, and pest resistance for molecular breeding.
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Affiliation(s)
- Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Zhengwei Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Xinyue Gao
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Hao Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Dong Bai
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Qi Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
- College of Agriculture, Northeast Agricultural University, Harbin, 150030 China
| | - Tianqing Zheng
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Ying-Hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Li-Juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
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19
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Bisht A, Saini DK, Kaur B, Batra R, Kaur S, Kaur I, Jindal S, Malik P, Sandhu PK, Kaur A, Gill BS, Wani SH, Kaur B, Mir RR, Sandhu KS, Siddique KHM. Multi-omics assisted breeding for biotic stress resistance in soybean. Mol Biol Rep 2023; 50:3787-3814. [PMID: 36692674 DOI: 10.1007/s11033-023-08260-4] [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: 09/01/2022] [Accepted: 01/09/2023] [Indexed: 01/25/2023]
Abstract
Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.
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Affiliation(s)
- Ashita Bisht
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India
- CSK Himachal Pradesh Krishi Vishvavidyalaya, Highland Agricultural Research and Extension Centre, 175142, Kukumseri, Lahaul and Spiti, India
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India.
| | - Baljeet Kaur
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India
| | - Ritu Batra
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, 25004, Meerut, India
| | - Sandeep Kaur
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India
| | - Ishveen Kaur
- Agriculture, Environmental and Sustainability Sciences, College of sciences, University of Texas Rio Grande Valley, 78539, Edinburg, TX, USA
| | - Suruchi Jindal
- Division of Molecular Biology and Genetic Engineering, School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India
| | - Palvi Malik
- , Gurdev Singh Khush Institute of Genetics, Plant Breeding and Biotechnology, Punjab Agricultural University,, 141004, Ludhiana, India
| | - Pawanjit Kaur Sandhu
- Department of Chemistry, University of British Columbia, V1V 1V7, Okanagan, Kelowna, Canada
| | - Amandeep Kaur
- Division of Molecular Biology and Genetic Engineering, School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India
| | - Balwinder Singh Gill
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India
| | - Shabir Hussain Wani
- MRCFC Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir, Shalimar, India
| | - Balwinder Kaur
- Department of Entomology, UF/IFAS Research and Education Center, 33430, Belle Glade, Florida, USA
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture, SKUAST-Kashmir, 193201, India
| | - Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, 99163, Pullman, WA, USA.
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, 6001, Perth, WA, Australia.
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20
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Neik TX, Siddique KHM, Mayes S, Edwards D, Batley J, Mabhaudhi T, Song BK, Massawe F. Diversifying agrifood systems to ensure global food security following the Russia–Ukraine crisis. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2023. [DOI: 10.3389/fsufs.2023.1124640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
Abstract
The recent Russia–Ukraine conflict has raised significant concerns about global food security, leaving many countries with restricted access to imported staple food crops, particularly wheat and sunflower oil, sending food prices soaring with other adverse consequences in the food supply chain. This detrimental effect is particularly prominent for low-income countries relying on grain imports, with record-high food prices and inflation affecting their livelihoods. This review discusses the role of Russia and Ukraine in the global food system and the impact of the Russia–Ukraine conflict on food security. It also highlights how diversifying four areas of agrifood systems—markets, production, crops, and technology can contribute to achieving food supply chain resilience for future food security and sustainability.
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21
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Genome-wide association study reveals novel loci and a candidate gene for resistance to frogeye leaf spot (Cercospora sojina) in soybean. Mol Genet Genomics 2023; 298:441-454. [PMID: 36602595 DOI: 10.1007/s00438-022-01986-z] [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: 08/06/2022] [Accepted: 12/16/2022] [Indexed: 01/06/2023]
Abstract
Frogeye leaf spot, caused by the fungus Cercospora sojina, is a threat to soybeans in the southeastern and midwestern United States that can be controlled by crop genetic resistance. Limited genetic resistance to the disease has been reported, and only three sources of resistance have been used in modern soybean breeding. To discover novel sources and identify the genomic locations of resistance that could be used in soybean breeding, a GWAS was conducted using a panel of 329 soybean accessions selected to maximize genetic diversity. Accessions were phenotyped using a 1-5 visual rating and by using image analysis to count lesion number and measure the percent of leaf area diseased. Eight novel loci on eight chromosomes were identified for three traits utilizing the FarmCPU or BLINK models, of which a locus on chromosome 11 was highly significant across all model-trait combinations. KASP markers were designed using the SoySNP50K Beadchip and variant information from 65 of the accessions that have been sequenced to target SNPs in the gene model Glyma.11g230400, a LEUCINE-RICH REPEAT RECEPTOR-LIKE PROTEIN KINASE. The association of a KASP marker, GSM990, designed to detect a missense mutation in the gene was the most significant with all three traits in a genome-wide association, and the marker may be useful to select for resistance to frogeye leaf spot in soybean breeding.
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22
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Gui S, Martinez-Rivas FJ, Wen W, Meng M, Yan J, Usadel B, Fernie AR. Going broad and deep: sequencing-driven insights into plant physiology, evolution, and crop domestication. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:446-459. [PMID: 36534120 DOI: 10.1111/tpj.16070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
Deep sequencing is a term that has become embedded in the plant genomic literature in recent years and with good reason. A torrent of (largely) high-quality genomic and transcriptomic data has been collected and most of this has been publicly released. Indeed, almost 1000 plant genomes have been reported (www.plabipd.de) and the 2000 Plant Transcriptomes Project has long been completed. The EarthBioGenome project will dwarf even these milestones. That said, massive progress in understanding plant physiology, evolution, and crop domestication has been made by sequencing broadly (across a species) as well as deeply (within a single individual). We will outline the current state of the art in genome and transcriptome sequencing before we briefly review the most visible of these broad approaches, namely genome-wide association and transcriptome-wide association studies, as well as the compilation of pangenomes. This will include both (i) the most commonly used methods reliant on single nucleotide polymorphisms and short InDels and (ii) more recent examples which consider structural variants. We will subsequently present case studies exemplifying how their application has brought insight into either plant physiology or evolution and crop domestication. Finally, we will provide conclusions and an outlook as to the perspective for the extension of such approaches to different species, tissues, and biological processes.
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Affiliation(s)
- Songtao Gui
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | | | - Weiwei Wen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Minghui Meng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Björn Usadel
- IBG-4 Bioinformatics, Forschungszentrum Jülich, Wilhelm Johnen Str, BioSc, 52428, Jülich, Germany
- Institute for Biological Data Science, CEPLAS, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm, 14476, Germany
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23
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Wang J, Yang W, Zhang S, Hu H, Yuan Y, Dong J, Chen L, Ma Y, Yang T, Zhou L, Chen J, Liu B, Li C, Edwards D, Zhao J. A pangenome analysis pipeline provides insights into functional gene identification in rice. Genome Biol 2023; 24:19. [PMID: 36703158 PMCID: PMC9878884 DOI: 10.1186/s13059-023-02861-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 01/18/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND A pangenome aims to capture the complete genetic diversity within a species and reduce bias in genetic analysis inherent in using a single reference genome. However, the current linear format of most plant pangenomes limits the presentation of position information for novel sequences. Graph pangenomes have been developed to overcome this limitation. However, bioinformatics analysis tools for graph format genomes are lacking. RESULTS To overcome this problem, we develop a novel strategy for pangenome construction and a downstream pangenome analysis pipeline (PSVCP) that captures genetic variants' position information while maintaining a linearized layout. Using PSVCP, we construct a high-quality rice pangenome using 12 representative rice genomes and analyze an international rice panel with 413 diverse accessions using the pangenome as the reference. We show that PSVCP successfully identifies causal structural variations for rice grain weight and plant height. Our results provide insights into rice population structure and genomic diversity. We characterize a new locus (qPH8-1) associated with plant height on chromosome 8 undetected by the SNP-based genome-wide association study (GWAS). CONCLUSIONS Our results demonstrate that the pangenome constructed by our pipeline combined with a presence and absence variation-based GWAS can provide additional power for genomic and genetic analysis. The pangenome constructed in this study and the associated genome sequence and genetic variants data provide valuable genomic resources for rice genomics research and improvement in future.
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Affiliation(s)
- Jian Wang
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Wu Yang
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Shaohong Zhang
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Haifei Hu
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China.
- Western Crop Genetics Alliance, Murdoch University, Murdoch, Western Australia, 6150, Australia.
| | - Yuxuan Yuan
- School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Jingfang Dong
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Luo Chen
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Yamei Ma
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Tifeng Yang
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Lian Zhou
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Jiansong Chen
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Bin Liu
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Chengdao Li
- Western Crop Genetics Alliance, Murdoch University, Murdoch, Western Australia, 6150, Australia.
| | - David Edwards
- School of Biological Sciences and Centre for Applied Bioinformatics, University of Western Australia, Perth, WA, Australia.
| | - Junliang Zhao
- Rice Research Institute & Guangdong Key Laboratory of New Technology in Rice Breeding & Guangdong Rice Engineering Laboratory, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China.
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24
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Bayer PE, Edwards D. Investigating Pangenome Graphs Using Wheat Panache. Methods Mol Biol 2023; 2703:23-29. [PMID: 37646934 DOI: 10.1007/978-1-0716-3389-2_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Pangenome graphs quickly become the central data structure representing the diversity of variation we see across related genomes. Pangenome graphs have been published for some species, including plants of agronomic interest. However, visualizing these graphs is not easy as the graphs are large, and variants within these graphs are complex. Tools are needed to visualize graph data structures. Here, we present a workflow to search and visualize a wheat pangenome graph using Wheat Panache. The approach presented assists researchers interested in wheat genomics.
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Affiliation(s)
- Philipp E Bayer
- Centre for Applied Bioinformatics and School of Biological Sciences, The University of Western Australia, Perth, WA, Australia
| | - David Edwards
- Centre for Applied Bioinformatics and School of Biological Sciences, The University of Western Australia, Perth, WA, Australia.
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25
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Quach T, Nguyen H, Meyer O, Sato SJ, Clemente TE, Guo M. Introduction of Genome Editing Reagents and Genotyping of Derived Edited Alleles in Soybean (Glycine max (L.) Merr.). Methods Mol Biol 2023; 2653:273-285. [PMID: 36995632 DOI: 10.1007/978-1-0716-3131-7_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Cas9-based genome editing is a powerful genetic tool for loci specifically targeted for genome modification. This chapter describes up-to-date protocols using Cas9-based genome editing technology, including vector construction with GoldenBraid assembly, Agrobacterium-mediated soybean transformation, and identification of editing in the genome.
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Affiliation(s)
- Truyen Quach
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Hanh Nguyen
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Olivia Meyer
- Department of Agronomy & Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Shirley J Sato
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Tom Elmo Clemente
- Department of Agronomy & Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Ming Guo
- Department of Agronomy & Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
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26
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Tirnaz S, Zandberg J, Thomas WJW, Marsh J, Edwards D, Batley J. Application of crop wild relatives in modern breeding: An overview of resources, experimental and computational methodologies. FRONTIERS IN PLANT SCIENCE 2022; 13:1008904. [PMID: 36466237 PMCID: PMC9712971 DOI: 10.3389/fpls.2022.1008904] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/25/2022] [Indexed: 06/01/2023]
Abstract
Global agricultural industries are under pressure to meet the future food demand; however, the existing crop genetic diversity might not be sufficient to meet this expectation. Advances in genome sequencing technologies and availability of reference genomes for over 300 plant species reveals the hidden genetic diversity in crop wild relatives (CWRs), which could have significant impacts in crop improvement. There are many ex-situ and in-situ resources around the world holding rare and valuable wild species, of which many carry agronomically important traits and it is crucial for users to be aware of their availability. Here we aim to explore the available ex-/in- situ resources such as genebanks, botanical gardens, national parks, conservation hotspots and inventories holding CWR accessions. In addition we highlight the advances in availability and use of CWR genomic resources, such as their contribution in pangenome construction and introducing novel genes into crops. We also discuss the potential and challenges of modern breeding experimental approaches (e.g. de novo domestication, genome editing and speed breeding) used in CWRs and the use of computational (e.g. machine learning) approaches that could speed up utilization of CWR species in breeding programs towards crop adaptability and yield improvement.
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27
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Jha UC, Nayyar H, von Wettberg EJB, Naik YD, Thudi M, Siddique KHM. Legume Pangenome: Status and Scope for Crop Improvement. PLANTS (BASEL, SWITZERLAND) 2022; 11:3041. [PMID: 36432770 PMCID: PMC9696634 DOI: 10.3390/plants11223041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/25/2022] [Accepted: 11/04/2022] [Indexed: 05/31/2023]
Abstract
In the last decade, legume genomics research has seen a paradigm shift due to advances in genome sequencing technologies, assembly algorithms, and computational genomics that enabled the construction of high-quality reference genome assemblies of major legume crops. These advances have certainly facilitated the identification of novel genetic variants underlying the traits of agronomic importance in many legume crops. Furthermore, these robust sequencing technologies have allowed us to study structural variations across the whole genome in multiple individuals and at the species level using 'pangenome analysis.' This review updates the progress of constructing pangenome assemblies for various legume crops and discusses the prospects for these pangenomes and how to harness the information to improve various traits of economic importance through molecular breeding to increase genetic gain in legumes and tackle the increasing global food crisis.
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Affiliation(s)
- Uday Chand Jha
- Indian Institute of Pulses Research, Kanpur 208024, India
| | - Harsh Nayyar
- Department of Botany, Panjab University, Chandigarh 160014, India
| | - Eric J. B. von Wettberg
- Department and Plant and Soil Science, Gund Institute for the Environment, The University of Vermont, Burlington, VT 05405, USA
| | - Yogesh Dashrath Naik
- Department of Agricultural Biotechnology and Molecular Biology, Dr. Rajendra Prasad Central Agricultural University, Pusa 848125, India
| | - Mahendar Thudi
- Department of Agricultural Biotechnology and Molecular Biology, Dr. Rajendra Prasad Central Agricultural University, Pusa 848125, India
- Shandong Academy of Agricultural Sciences, Jinan 250100, China
- Department of Agricultural Biotechnology and Molecular Biology, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Kadambot H. M. Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001, Australia
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28
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Agyenim-Boateng KG, Zhang S, Islam MS, Gu Y, Li B, Azam M, Abdelghany AM, Qi J, Ghosh S, Shaibu AS, Gebregziabher BS, Feng Y, Li J, Li Y, Zhang C, Qiu L, Liu Z, Liang Q, Sun J. Profiling of naturally occurring folates in a diverse soybean germplasm by HPLC-MS/MS. Food Chem 2022; 384:132520. [PMID: 35217465 DOI: 10.1016/j.foodchem.2022.132520] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/27/2022] [Accepted: 02/17/2022] [Indexed: 11/04/2022]
Abstract
Soybean is a rich source of folates. We optimised the extraction and detection of folates from soybean seeds by HPLC-MS/MS and analysed the folate content and composition of 1074 accessions. Total folate content ranged from 64.51 to 691.24 μg/100 g fresh weight, with 10-fold variation, and 60 elite accessions with over 400 μg/100 g of total folate were identified. The most abundant component was 5-CHO-H4folate, which accounted for an average of 60% of total folate content. Seed-coat colour, seed weight, ecoregion, and accession type significantly affected soybean folate content. Furthermore, 5-CH3-H4folate correlated positively with seed protein (r = 0.24***) and negatively with oil (r = -0.26***). The geographical distribution of folate according to accession origin revealed that accessions from Northeast China contain higher amounts of total folate and 5-CHO-H4folate. This study provides comprehensive and novel insights into the folate profile of soybean, which will benefit soybean breeding for folate enhancement.
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Affiliation(s)
- Kwadwo Gyapong Agyenim-Boateng
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shengrui Zhang
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Md Shariful Islam
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yongzhe Gu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Germplasm and Biotechnology (MARA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Bin Li
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Muhammad Azam
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Ahmed M Abdelghany
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Crop Science Department, Faculty of Agriculture, Damanhour University, Damanhour 22516, Egypt
| | - Jie Qi
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Suprio Ghosh
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
| | - Abdulwahab S Shaibu
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Department of Agronomy, Bayero University, Kano 700001, Nigeria
| | - Berhane Sibhatu Gebregziabher
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Crop Sciences Research Department, Mehoni Agricultural Research Center, Maichew 7020, Ethiopia
| | - Yue Feng
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jing Li
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yinghui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Germplasm and Biotechnology (MARA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Chunyi Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Lijuan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Germplasm and Biotechnology (MARA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhangxiong Liu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Germplasm and Biotechnology (MARA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Qiuju Liang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Junming Sun
- The National Engineering Research Center of Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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29
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Petereit J, Bayer PE, Thomas WJW, Tay Fernandez CG, Amas J, Zhang Y, Batley J, Edwards D. Pangenomics and Crop Genome Adaptation in a Changing Climate. PLANTS (BASEL, SWITZERLAND) 2022; 11:1949. [PMID: 35956427 PMCID: PMC9370458 DOI: 10.3390/plants11151949] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 12/15/2022]
Abstract
During crop domestication and breeding, wild plant species have been shaped into modern high-yield crops and adapted to the main agro-ecological regions. However, climate change will impact crop productivity in these regions, and agriculture needs to adapt to support future food production. On a global scale, crop wild relatives grow in more diverse environments than crop species, and so may host genes that could support the adaptation of crops to new and variable environments. Through identification of individuals with increased climate resilience we may gain a greater understanding of the genomic basis for this resilience and transfer this to crops. Pangenome analysis can help to identify the genes underlying stress responses in individuals harbouring untapped genomic diversity in crop wild relatives. The information gained from the analysis of these pangenomes can then be applied towards breeding climate resilience into existing crops or to re-domesticating crops, combining environmental adaptation traits with crop productivity.
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Affiliation(s)
| | | | | | | | | | | | | | - David Edwards
- School of Biological Sciences, The University of Western Australia, Perth 6009, Australia; (J.P.); (P.E.B.); (W.J.W.T.); (C.G.T.F.); (J.A.); (Y.Z.); (J.B.)
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30
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Mullins E, Bresson J, Dalmay T, Dewhurst IC, Epstein MM, Firbank LG, Guerche P, Hejatko J, Moreno FJ, Naegeli H, Nogué F, Rostoks N, Sánchez Serrano JJ, Savoini G, Veromann E, Veronesi F, Goumperis T, Raffaello T. Risk assessment of a new bioinformatics evaluation of the insertion sites of genetically modified soybean event 40-3-2. EFSA J 2022; 20:e07412. [PMID: 35898294 PMCID: PMC9305392 DOI: 10.2903/j.efsa.2022.7412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Genetically modified (GM) soybean 40-3-2 expresses a 5-enolpyruvylshikimate-3-phosphate synthase protein from Agrobacterium sp. strain CP4 (CP4 EPSPS), which confers tolerance to glyphosate. This event was previously assessed by the GMO Panel as a single event and as part of a two-event stack and was found to be as safe as its conventional counterparts and other appropriate comparators with respect to potential effects on human and animal health and the environment. On September 2021, the European Commission requested EFSA to evaluate a new bioinformatics study which revealed predicted genomic deletions at the insertion sites using the available soybean reference genome. Considering the variability of the soybean genome, with a number of structural variants such as presence/absence variants and copy number variants including genic regions, as well as the fact that a number of genes are present only in particular varieties, the GMO Panel concludes that comparing only to the reference genome does not allow to conclude that the transformation event resulted in gene loss. In support of this, the transcriptomic analysis did not show major differences in gene expression when comparing the soybean 40-3-2 with the most closely related conventional variety, indicating that the genetic redundancy may compensate for the potential gene loss. Moreover, the composition, phenotypic and agronomic analyses already assessed by the GMO Panel in previous opinions did not show differences between soybean 40-3-2 and its comparators suggesting that the potential gene loss may not have a significant phenotypic effect in soybean 40-3-2. For these reasons, the EFSA GMO Panel concludes that the new information provided by the applicant on soybean 40-3-2 does not alter EFSA's previous conclusions.
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Petereit J, Marsh JI, Bayer PE, Danilevicz MF, Thomas WJW, Batley J, Edwards D. Genetic and Genomic Resources for Soybean Breeding Research. PLANTS (BASEL, SWITZERLAND) 2022; 11:1181. [PMID: 35567182 PMCID: PMC9101001 DOI: 10.3390/plants11091181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 11/17/2022]
Abstract
Soybean (Glycine max) is a legume species of significant economic and nutritional value. The yield of soybean continues to increase with the breeding of improved varieties, and this is likely to continue with the application of advanced genetic and genomic approaches for breeding. Genome technologies continue to advance rapidly, with an increasing number of high-quality genome assemblies becoming available. With accumulating data from marker arrays and whole-genome resequencing, studying variations between individuals and populations is becoming increasingly accessible. Furthermore, the recent development of soybean pangenomes has highlighted the significant structural variation between individuals, together with knowledge of what has been selected for or lost during domestication and breeding, information that can be applied for the breeding of improved cultivars. Because of this, resources such as genome assemblies, SNP datasets, pangenomes and associated databases are becoming increasingly important for research underlying soybean crop improvement.
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Affiliation(s)
| | - Jacob I. Marsh
- School of Biological Sciences, The University of Western Australia, Perth, WA 6009, Australia; (J.P.); (J.I.M.); (P.E.B.); (M.F.D.); (W.J.W.T.); (J.B.)
| | | | | | | | | | - David Edwards
- School of Biological Sciences, The University of Western Australia, Perth, WA 6009, Australia; (J.P.); (J.I.M.); (P.E.B.); (M.F.D.); (W.J.W.T.); (J.B.)
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Belzile F, Jean M, Torkamaneh D, Tardivel A, Lemay MA, Boudhrioua C, Arsenault-Labrecque G, Dussault-Benoit C, Lebreton A, de Ronne M, Tremblay V, Labbé C, O’Donoughue L, St-Amour VTB, Copley T, Fortier E, Ste-Croix DT, Mimee B, Cober E, Rajcan I, Warkentin T, Gagnon É, Legay S, Auclair J, Bélanger R. The SoyaGen Project: Putting Genomics to Work for Soybean Breeders. FRONTIERS IN PLANT SCIENCE 2022; 13:887553. [PMID: 35557742 PMCID: PMC9087807 DOI: 10.3389/fpls.2022.887553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 03/24/2022] [Indexed: 06/15/2023]
Abstract
The SoyaGen project was a collaborative endeavor involving Canadian soybean researchers and breeders from academia and the private sector as well as international collaborators. Its aims were to develop genomics-derived solutions to real-world challenges faced by breeders. Based on the needs expressed by the stakeholders, the research efforts were focused on maximizing realized yield through optimization of maturity and improved disease resistance. The main deliverables related to molecular breeding in soybean will be reviewed here. These include: (1) SNP datasets capturing the genetic diversity within cultivated soybean (both within a worldwide collection of > 1,000 soybean accessions and a subset of 102 short-season accessions (MG0 and earlier) directly relevant to this group); (2) SNP markers for selecting favorable alleles at key maturity genes as well as loci associated with increased resistance to key pathogens and pests (Phytophthora sojae, Heterodera glycines, Sclerotinia sclerotiorum); (3) diagnostic tools to facilitate the identification and mapping of specific pathotypes of P. sojae; and (4) a genomic prediction approach to identify the most promising combinations of parents. As a result of this fruitful collaboration, breeders have gained new tools and approaches to implement molecular, genomics-informed breeding strategies. We believe these tools and approaches are broadly applicable to soybean breeding efforts around the world.
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Affiliation(s)
- François Belzile
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Martine Jean
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Aurélie Tardivel
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | - Marc-André Lemay
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Chiheb Boudhrioua
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | | | | | - Amandine Lebreton
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Maxime de Ronne
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Vanessa Tremblay
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Caroline Labbé
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
| | - Louise O’Donoughue
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | - Vincent-Thomas Boucher St-Amour
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | - Tanya Copley
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | - Eric Fortier
- Centre de Recherche sur les Grains (CEROM), Saint-Mathieu-de-Beloeil, QC, Canada
| | | | - Benjamin Mimee
- Agriculture and Agri-Food Canada, St-Jean-sur-Richelieu, QC, Canada
| | - Elroy Cober
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Tom Warkentin
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Éric Gagnon
- Semences Prograin Inc., Saint-Césaire, QC, Canada
- Sevita Genetics, Inkerman, ON, Canada
| | | | | | - Richard Bélanger
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
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Gill M, Anderson R, Hu H, Bennamoun M, Petereit J, Valliyodan B, Nguyen HT, Batley J, Bayer PE, Edwards D. Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction. BMC PLANT BIOLOGY 2022; 22:180. [PMID: 35395721 PMCID: PMC8991976 DOI: 10.1186/s12870-022-03559-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/21/2022] [Indexed: 05/26/2023]
Abstract
Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.
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Affiliation(s)
- Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Robyn Anderson
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Haifei Hu
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA, Australia
| | - Jakob Petereit
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Babu Valliyodan
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, Columbia, MO, 65211, USA
- Department of Agriculture and Environmental Sciences, Lincoln University, Jefferson City, MO, 65101, USA
| | - Henry T Nguyen
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, Columbia, MO, 65211, USA
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Philipp E Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia.
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Marsh JI, Hu H, Petereit J, Bayer PE, Valliyodan B, Batley J, Nguyen HT, Edwards D. Haplotype mapping uncovers unexplored variation in wild and domesticated soybean at the major protein locus cqProt-003. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1443-1455. [PMID: 35141762 PMCID: PMC9033719 DOI: 10.1007/s00122-022-04045-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 01/22/2022] [Indexed: 05/04/2023]
Abstract
KEY MESSAGE The major soy protein QTL, cqProt-003, was analysed for haplotype diversity and global distribution, and results indicate 304 bp deletion and variable tandem repeats in protein coding regions are likely causal candidates. Here, we present association and linkage analysis of 985 wild, landrace and cultivar soybean accessions in a pan genomic dataset to characterize the major high-protein/low-oil associated locus cqProt-003 located on chromosome 20. A significant trait-associated region within a 173 kb linkage block was identified, and variants in the region were characterized, identifying 34 high confidence SNPs, 4 insertions, 1 deletion and a larger 304 bp structural variant in the high-protein haplotype. Trinucleotide tandem repeats of variable length present in the second exon of gene Glyma.20G085100 are strongly correlated with the high-protein phenotype and likely represent causal variation. Structural variation has previously been found in the same gene, for which we report the global distribution of the 304 bp deletion and have identified additional nested variation present in high-protein individuals. Mapping variation at the cqProt-003 locus across demographic groups suggests that the high-protein haplotype is common in wild accessions (94.7%), rare in landraces (10.6%) and near absent in cultivated breeding pools (4.1%), suggesting its decrease in frequency primarily correlates with domestication and continued during subsequent improvement. However, the variation that has persisted in under-utilized wild and landrace populations holds high breeding potential for breeders willing to forego seed oil to maximize protein content. The results of this study include the identification of distinct haplotype structures within the high-protein population, and a broad characterization of the genomic context and linkage patterns of cqProt-003 across global populations, supporting future functional characterization and modification.
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Affiliation(s)
- Jacob I Marsh
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, 6009, Australia
| | - Haifei Hu
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, 6009, Australia
| | - Jakob Petereit
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, 6009, Australia
| | - Philipp E Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, 6009, Australia
| | - Babu Valliyodan
- Department of Agriculture and Environmental Sciences, Lincoln University, Jefferson City, MO, 65101, USA
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, 6009, Australia
| | - Henry T Nguyen
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, Columbia, MO, 65211, USA
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, 6009, Australia.
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Zhang H, Jiang H, Hu Z, Song Q, An YQC. Development of a versatile resource for post-genomic research through consolidating and characterizing 1500 diverse wild and cultivated soybean genomes. BMC Genomics 2022; 23:250. [PMID: 35361112 PMCID: PMC8973893 DOI: 10.1186/s12864-022-08326-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 01/20/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND With advances in next-generation sequencing technologies, an unprecedented amount of soybean accessions has been sequenced by many individual studies and made available as raw sequencing reads for post-genomic research. RESULTS To develop a consolidated and user-friendly genomic resource for post-genomic research, we consolidated the raw resequencing data of 1465 soybean genomes available in the public and 91 highly diverse wild soybean genomes newly sequenced. These altogether provided a collection of 1556 sequenced genomes of 1501 diverse accessions (1.5 K). The collection comprises of wild, landraces and elite cultivars of soybean that were grown in East Asia or major soybean cultivating areas around the world. Our extensive sequence analysis discovered 32 million single nucleotide polymorphisms (32mSNPs) and revealed a SNP density of 30 SNPs/kb and 12 non-synonymous SNPs/gene reflecting a high structural and functional genomic diversity of the new collection. Each SNP was annotated with 30 categories of structural and/or functional information. We further identified paired accessions between the 1.5 K and 20,087 (20 K) accessions in US collection as genomic "equivalent" accessions sharing the highest genomic identity for minimizing the barriers in soybean germplasm exchange between countries. We also exemplified the utility of 32mSNPs in enhancing post-genomics research through in-silico genotyping, high-resolution GWAS, discovering and/or characterizing genes and alleles/mutations, identifying germplasms containing beneficial alleles that are potentially experiencing artificial selection. CONCLUSION The comprehensive analysis of publicly available large-scale genome sequencing data of diverse cultivated accessions and the newly in-house sequenced wild accessions greatly increased the soybean genome-wide variation resolution. This could facilitate a variety of genetic and molecular-level analyses in soybean. The 32mSNPs and 1.5 K accessions with their comprehensive annotation have been made available at the SoyBase and Ag Data Commons. The dataset could further serve as a versatile and expandable core resource for exploring the exponentially increasing genome sequencing data for a variety of post-genomic research.
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Affiliation(s)
- Hengyou Zhang
- Donald Danforth Plant Science Center, St Louis, MO 63132, USA
| | - He Jiang
- Donald Danforth Plant Science Center, St Louis, MO 63132, USA
| | - Zhenbin Hu
- Donald Danforth Plant Science Center, St Louis, MO 63132, USA
| | - Qijian Song
- US Department of Agriculture, Agricultural Research Service, Soybean Genomics and Improvement Laboratory, Beltsville, MD 20705, USA
| | - Yong-Qiang Charles An
- Donald Danforth Plant Science Center, St Louis, MO 63132, USA.
- US Department of Agriculture, Agricultural Research Service, Midwest Area, Plant Genetics Research Unit, 975 N Warson Rd, St. Louis, MO 63132, USA.
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de Ronne M, Santhanam P, Cinget B, Labbé C, Lebreton A, Ye H, Vuong TD, Hu H, Valliyodan B, Edwards D, Nguyen HT, Belzile F, Bélanger R. Mapping of partial resistance to Phytophthora sojae in soybean PIs using whole-genome sequencing reveals a major QTL. THE PLANT GENOME 2022; 15:e20184. [PMID: 34964282 DOI: 10.1002/tpg2.20184] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 11/09/2021] [Indexed: 06/14/2023]
Abstract
In the last decade, more than 70 quantitative trait loci (QTL) related to soybean [Glycine max (L.) Merr.] partial resistance (PR) against Phytophthora sojae have been identified by genome-wide association studies (GWAS). However, most of them have either a minor effect on the resistance level or are specific to a single phenotypic variable or one isolate, thereby limiting their use in breeding programs. In this study, we have used an analytical approach combining (a) the phenotypic characterization of a diverse panel of 357 soybean accessions for resistance to P. sojae captured through a single variable, corrected dry weight; (b) a new hydroponic assay allowing the inoculation of a combination of P. sojae isolates covering the spectrum of commercially relevant Rps genes; and (c) exhaustive genotyping through whole-genome resequencing (WGS). This led to the identification of a novel P. sojae resistance QTL with a relatively major effect compared with the previously reported QTL. The QTL interval, spanning ∼500 kb on chromosome (Chr) 15, does not colocalize with previously reported QTL for P. sojae resistance. Plants carrying the favorable allele at this QTL were 60% more resistant. Eight genes were found to reside in the linkage disequilibrium (LD) block containing the peak single-nucleotide polymorphism (SNP) including Glyma.15G217100, which encodes a major latex protein (MLP)-like protein, with a functional annotation related to pathogen resistance. Expression analysis of Glyma.15G217100 indicated that it was nearly eight times more highly expressed in a group of plant introductions (PIs) carrying the resistant (R) allele compared with those carrying the susceptible (S) allele within a short period after inoculation. These results offer new and valuable options to develop improved soybean cultivars with broad resistance to P. sojae through marker-assisted selection.
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Affiliation(s)
| | | | | | | | | | - Heng Ye
- Division of Plant Sciences and National Center for Soybean Biotechnology, Univ. of Missouri, Columbia, MO, 65211, USA
| | - Tri D Vuong
- Division of Plant Sciences and National Center for Soybean Biotechnology, Univ. of Missouri, Columbia, MO, 65211, USA
| | - Haifei Hu
- School of Biological Sciences and Institute of Agriculture, Univ. of Western Australia, Perth, Western Australia, Australia
| | - Babu Valliyodan
- Division of Plant Sciences and National Center for Soybean Biotechnology, Univ. of Missouri, Columbia, MO, 65211, USA
- Dep. of Agriculture and Environmental Sciences, Lincoln Univ., Jefferson City, MO, 65101, USA
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, Univ. of Western Australia, Perth, Western Australia, Australia
| | - Henry T Nguyen
- Division of Plant Sciences and National Center for Soybean Biotechnology, Univ. of Missouri, Columbia, MO, 65211, USA
| | - François Belzile
- Dép. de phytologie, Univ. Laval, Québec, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Univ. Laval, Québec, Canada
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Tay Fernandez CG, Nestor BJ, Danilevicz MF, Gill M, Petereit J, Bayer PE, Finnegan PM, Batley J, Edwards D. Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species. Int J Mol Sci 2022; 23:2671. [PMID: 35269811 PMCID: PMC8910360 DOI: 10.3390/ijms23052671] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/21/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023] Open
Abstract
Pangenomes are a rich resource to examine the genomic variation observed within a species or genera, supporting population genetics studies, with applications for the improvement of crop traits. Major crop species such as maize (Zea mays), rice (Oryza sativa), Brassica (Brassica spp.), and soybean (Glycine max) have had pangenomes constructed and released, and this has led to the discovery of valuable genes associated with disease resistance and yield components. However, pangenome data are not available for many less prominent crop species that are currently under-utilised. Despite many under-utilised species being important food sources in regional populations, the scarcity of genomic data for these species hinders their improvement. Here, we assess several under-utilised crops and review the pangenome approaches that could be used to build resources for their improvement. Many of these under-utilised crops are cultivated in arid or semi-arid environments, suggesting that novel genes related to drought tolerance may be identified and used for introgression into related major crop species. In addition, we discuss how previously collected data could be used to enrich pangenome functional analysis in genome-wide association studies (GWAS) based on studies in major crops. Considering the technological advances in genome sequencing, pangenome references for under-utilised species are becoming more obtainable, offering the opportunity to identify novel genes related to agro-morphological traits in these species.
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Affiliation(s)
| | | | | | | | | | | | | | | | - David Edwards
- School of Biological Sciences, The University of Western Australia, Perth, WA 6009, Australia; (C.G.T.F.); (B.J.N.); (M.F.D.); (M.G.); (J.P.); (P.E.B.); (P.M.F.); (J.B.)
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Lemay MA, Sibbesen JA, Torkamaneh D, Hamel J, Levesque RC, Belzile F. Combined use of Oxford Nanopore and Illumina sequencing yields insights into soybean structural variation biology. BMC Biol 2022; 20:53. [PMID: 35197050 PMCID: PMC8867729 DOI: 10.1186/s12915-022-01255-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/16/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Structural variants (SVs), including deletions, insertions, duplications, and inversions, are relatively long genomic variations implicated in a diverse range of processes from human disease to ecology and evolution. Given their complex signatures, tendency to occur in repeated regions, and large size, discovering SVs based on short reads is challenging compared to single-nucleotide variants. The increasing availability of long-read technologies has greatly facilitated SV discovery; however, these technologies remain too costly to apply routinely to population-level studies. Here, we combined short-read and long-read sequencing technologies to provide a comprehensive population-scale assessment of structural variation in a panel of Canadian soybean cultivars. RESULTS We used Oxford Nanopore long-read sequencing data (~12× mean coverage) for 17 samples to both benchmark SV calls made from Illumina short-read data and predict SVs that were subsequently genotyped in a population of 102 samples using Illumina data. Benchmarking results show that variants discovered using Oxford Nanopore can be accurately genotyped from the Illumina data. We first use the genotyped deletions and insertions for population genetics analyses and show that results are comparable to those based on single-nucleotide variants. We observe that the population frequency and distribution within the genome of deletions and insertions are constrained by the location of genes. Gene Ontology and PFAM domain enrichment analyses also confirm previous reports that genes harboring high-frequency deletions and insertions are enriched for functions in defense response. Finally, we discover polymorphic transposable elements from the deletions and insertions and report evidence of the recent activity of a Stowaway MITE. CONCLUSIONS We show that structural variants discovered using Oxford Nanopore data can be genotyped with high accuracy from Illumina data. Our results demonstrate that long-read and short-read sequencing technologies can be efficiently combined to enhance SV analysis in large populations, providing a reusable framework for their study in a wider range of samples and non-model species.
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Affiliation(s)
- Marc-André Lemay
- Département de phytologie, Université Laval, Quebec, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Quebec, Canada
| | | | - Davoud Torkamaneh
- Département de phytologie, Université Laval, Quebec, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Quebec, Canada
| | - Jérémie Hamel
- Institut de biologie intégrative et des systèmes, Université Laval, Quebec, Canada
- Département de microbiologie-infectiologie et d’immunologie, Université Laval, Quebec, Canada
| | - Roger C. Levesque
- Institut de biologie intégrative et des systèmes, Université Laval, Quebec, Canada
- Département de microbiologie-infectiologie et d’immunologie, Université Laval, Quebec, Canada
| | - François Belzile
- Département de phytologie, Université Laval, Quebec, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Quebec, Canada
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Tay Fernandez CG, Nestor BJ, Danilevicz MF, Marsh JI, Petereit J, Bayer PE, Batley J, Edwards D. Expanding Gene-Editing Potential in Crop Improvement with Pangenomes. Int J Mol Sci 2022; 23:ijms23042276. [PMID: 35216392 PMCID: PMC8879065 DOI: 10.3390/ijms23042276] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 02/01/2023] Open
Abstract
Pangenomes aim to represent the complete repertoire of the genome diversity present within a species or cohort of species, capturing the genomic structural variance between individuals. This genomic information coupled with phenotypic data can be applied to identify genes and alleles involved with abiotic stress tolerance, disease resistance, and other desirable traits. The characterisation of novel structural variants from pangenomes can support genome editing approaches such as Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR associated protein Cas (CRISPR-Cas), providing functional information on gene sequences and new target sites in variant-specific genes with increased efficiency. This review discusses the application of pangenomes in genome editing and crop improvement, focusing on the potential of pangenomes to accurately identify target genes for CRISPR-Cas editing of plant genomes while avoiding adverse off-target effects. We consider the limitations of applying CRISPR-Cas editing with pangenome references and potential solutions to overcome these limitations.
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40
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Zuo JF, Ikram M, Liu JY, Han CY, Niu Y, Dunwell JM, Zhang YM. Domestication and improvement genes reveal the differences of seed size- and oil-related traits in soybean domestication and improvement. Comput Struct Biotechnol J 2022; 20:2951-2964. [PMID: 35782726 PMCID: PMC9213226 DOI: 10.1016/j.csbj.2022.06.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 12/01/2022] Open
Abstract
Due to reduced diversity, it is essential to map domesticated and improved genes. 13 known and 442 candidate genes were mined for seed size- and oil-related traits. All the genes were used to explain trait changes in domestication and improvement. 56 domesticated and 15 improved genes may be valuable for future soybean breeding. This study provides useful gene resources for future breeding and biology research.
To address domestication and improvement studies of soybean seed size- and oil-related traits, a series of domesticated and improved regions, loci, and candidate genes were identified in 286 soybean accessions using domestication and improvement analyses, genome-wide association studies, quantitative trait locus (QTL) mapping and bulked segregant analyses in this study. As a result, 534 candidate domestication regions (CDRs) and 458 candidate improvement regions (CIRs) were identified in this study and integrated with those in five and three previous studies, respectively, to obtain 952 CDRs and 538 CIRs; 1469 loci for soybean seed size- and oil-related traits were identified in this study and integrated with those in Soybase to obtain 433 QTL clusters. The two results were intersected to obtain 245 domestication and 221 improvement loci for the above traits. Around these trait-related domestication and improvement loci, 7 domestication and 7 improvement genes were found to be truly associated with these traits, and 372 candidate domestication and 87 candidate improvement genes were identified using gene expression, SNP variants in genome, miRNA binding, KEGG pathway, DNA methylation, and haplotype analysis. These genes were used to explain the trait changes in domestication and improvement. As a result, the trait changes can be explained by their frequencies of elite haplotypes, base mutations in coding region, and three factors affecting their expression levels. In addition, 56 domestication and 15 improvement genes may be valuable for future soybean breeding. This study can provide useful gene resources for future soybean breeding and molecular biology research.
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Affiliation(s)
- Jian-Fang Zuo
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Muhammad Ikram
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Jin-Yang Liu
- Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China
| | - Chun-Yu Han
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yuan Niu
- School of Life Sciences and Food Engineering, Huaiyin Institute of Technology, Huaian, China
| | - Jim M. Dunwell
- School of Agriculture, Policy and Development, University of Reading, Reading, United Kingdom
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- Corresponding author.
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Savadi S, Mangalassery S, Sandesh MS. Advances in genomics and genome editing for breeding next generation of fruit and nut crops. Genomics 2021; 113:3718-3734. [PMID: 34517092 DOI: 10.1016/j.ygeno.2021.09.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/21/2021] [Accepted: 09/02/2021] [Indexed: 12/18/2022]
Abstract
Fruit tree crops are an essential part of the food production systems and are key to achieve food and nutrition security. Genetic improvement of fruit trees by conventional breeding has been slow due to the long juvenile phase. Advancements in genomics and molecular biology have paved the way for devising novel genetic improvement tools like genome editing, which can accelerate the breeding of these perennial crops to a great extent. In this article, advancements in genomics of fruit trees covering genome sequencing, transcriptome sequencing, genome editing technologies (GET), CRISPR-Cas system based genome editing, potential applications of CRISPR-Cas9 in fruit tree crops improvement, the factors influencing the CRISPR-Cas editing efficiency and the challenges for CRISPR-Cas9 applications in fruit tree crops improvement are reviewed. Besides, base editing, a recently emerging more precise editing system, and the future perspectives of genome editing in the improvement of fruit and nut crops are covered.
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Affiliation(s)
- Siddanna Savadi
- ICAR- Directorate of Cashew Research (DCR), Puttur 574 202, Dakshina Kannada, Karnataka, India.
| | | | - M S Sandesh
- ICAR- Directorate of Cashew Research (DCR), Puttur 574 202, Dakshina Kannada, Karnataka, India
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