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Li D, Wang Q, Tian Y, Lyv X, Zhang H, Hong H, Gao H, Li YF, Zhao C, Wang J, Wang R, Yang J, Liu B, Schnable PS, Schnable JC, Li YH, Qiu LJ. TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean. PLANT COMMUNICATIONS 2024:101010. [PMID: 38918950 DOI: 10.1016/j.xplc.2024.101010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/15/2024] [Accepted: 06/23/2024] [Indexed: 06/27/2024]
Abstract
A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.
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Affiliation(s)
- Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, 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 Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; College of Agriculture, Northeast Agricultural University, Harbin 150030, China
| | - Yu Tian
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiangguang Lyv
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, 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 Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Huilong Hong
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Huawei Gao
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
| | - Yan-Fei Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Chaosen Zhao
- Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
| | - Jiajun Wang
- Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
| | - Ruizhen Wang
- Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Bin Liu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | | | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
| | - Ying-Hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, 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 Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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Yoosefzadeh-Najafabadi M, Torabi S, Tulpan D, Rajcan I, Eskandari M. Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits. PLANTS (BASEL, SWITZERLAND) 2023; 12:2659. [PMID: 37514272 PMCID: PMC10383196 DOI: 10.3390/plants12142659] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/12/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.
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Affiliation(s)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
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Nissan N, Hooker J, Arezza E, Dick K, Golshani A, Mimee B, Cober E, Green J, Samanfar B. Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode. FRONTIERS IN BIOINFORMATICS 2023; 3:1199675. [PMID: 37409347 PMCID: PMC10319130 DOI: 10.3389/fbinf.2023.1199675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/31/2023] [Indexed: 07/07/2023] Open
Abstract
The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein-protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein-protein interaction predictors, the Protein-protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a "guilt by association" in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean.
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Affiliation(s)
- Nour Nissan
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON, Canada
- Department of Biology and Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON, Canada
| | - Julia Hooker
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON, Canada
- Department of Biology and Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON, Canada
| | - Eric Arezza
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - Kevin Dick
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - Ashkan Golshani
- Department of Biology and Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON, Canada
| | - Benjamin Mimee
- Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu Research and Development Centre, Saint-Jeansur-Richelieu, QC, Canada
| | - Elroy Cober
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON, Canada
| | - James Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - Bahram Samanfar
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON, Canada
- Department of Biology and Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON, Canada
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Tian Y, Li D, Wang X, Zhang H, Wang J, Yu L, Guo C, Luan X, Liu X, Li H, Reif JC, Li YH, Qiu LJ. Deciphering the genetic basis of resistance to soybean cyst nematode combining IBD and association mapping. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:50. [PMID: 36912956 PMCID: PMC10011322 DOI: 10.1007/s00122-023-04268-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 12/07/2022] [Indexed: 06/18/2023]
Abstract
IBD analysis clarified the dynamics of chromosomal recombination during the ZP pedigree breeding process and identified ten genomic regions resistant to SCN race3 combining association mapping. Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is one of the most devastating pathogens for soybean production worldwide. The cultivar Zhongpin03-5373 (ZP), derived from SCN-resistant progenitor parents, Peking, PI 437654 and Huipizhi Heidou, is an elite line with high resistance to SCN race3. In the current study, a pedigree variation map was generated for ZP and its ten progenitors using 3,025,264 high-quality SNPs identified from an average of 16.2 × re-sequencing for each genome. Through identity by decent (IBD) tracking, we showed the dynamic change of genome and detected important IBD fragments, which revealed the comprehensively artificial selection of important traits during ZP breeding process. A total of 2,353 IBD fragments related to SCN resistance including SCN-resistant genes rhg1, rhg4 and NSFRAN07 were identified based on the resistant-related genetic paths. Moreover, 23 genomic regions underlying resistance to SCN race3 were identified by genome-wide association study (GWAS) in 481 re-sequenced cultivated soybeans. Ten common loci were found by both IBD tracking and GWAS analysis. Haplotype analysis of 16 potential candidate genes suggested a causative SNP (C/T, - 1065) located in the promoter of Glyma.08G096500 and encoding a predicted TIFY5b-related protein on chr8 was highly correlated with SCN race3 resistance. Our results more thoroughly elucidated the dynamics of genomic fragments during ZP pedigree breeding and the genetic basis of SCN resistance, which will provide useful information for gene cloning and the development of resistant soybean cultivars using a marker-assisted selection approach.
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Affiliation(s)
- Yu Tian
- The National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, 100081, People's Republic of China
| | - Delin Li
- The National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, 100081, People's Republic of China
| | - Xueqing Wang
- The National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, 100081, People's Republic of China
| | - Hao Zhang
- The National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, 100081, People's Republic of China
| | - Jiajun Wang
- Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Lijie Yu
- Key Laboratory of Molecular Cytogenetics and Genetic Breeding, College of Life Science and Technology, Harbin Normal University, Heilongjiang Province, Harbin, China
| | - Changhong Guo
- Key Laboratory of Molecular Cytogenetics and Genetic Breeding, College of Life Science and Technology, Harbin Normal University, Heilongjiang Province, Harbin, China
| | - Xiaoyan Luan
- Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Xinlei Liu
- Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Hongjie Li
- The National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, 100081, People's Republic of China
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Ying-Hui Li
- The National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, 100081, People's Republic of China.
| | - Li-Juan Qiu
- The National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, 100081, People's Republic of China.
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Lin F, Chhapekar SS, Vieira CC, Da Silva MP, Rojas A, Lee D, Liu N, Pardo EM, Lee YC, Dong Z, Pinheiro JB, Ploper LD, Rupe J, Chen P, Wang D, Nguyen HT. Breeding for disease resistance in soybean: a global perspective. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3773-3872. [PMID: 35790543 PMCID: PMC9729162 DOI: 10.1007/s00122-022-04101-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 04/11/2022] [Indexed: 05/29/2023]
Abstract
KEY MESSAGE This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.
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Affiliation(s)
- Feng Lin
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824 USA
| | - Sushil Satish Chhapekar
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
| | - Caio Canella Vieira
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
- Fisher Delta Research Center, University of Missouri, Portageville, MO 63873 USA
| | - Marcos Paulo Da Silva
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701 USA
| | - Alejandro Rojas
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701 USA
| | - Dongho Lee
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
- Fisher Delta Research Center, University of Missouri, Portageville, MO 63873 USA
| | - Nianxi Liu
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun,, 130033 Jilin China
| | - Esteban Mariano Pardo
- Instituto de Tecnología Agroindustrial del Noroeste Argentino (ITANOA) [Estación Experimental Agroindustrial Obispo Colombres (EEAOC) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)], Av. William Cross 3150, C.P. T4101XAC, Las Talitas, Tucumán, Argentina
| | - Yi-Chen Lee
- Fisher Delta Research Center, University of Missouri, Portageville, MO 63873 USA
| | - Zhimin Dong
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun,, 130033 Jilin China
| | - Jose Baldin Pinheiro
- Departamento de Genética, Escola Superior de Agricultura “Luiz de Queiroz” (ESALQ/USP), PO Box 9, Piracicaba, SP 13418-900 Brazil
| | - Leonardo Daniel Ploper
- Instituto de Tecnología Agroindustrial del Noroeste Argentino (ITANOA) [Estación Experimental Agroindustrial Obispo Colombres (EEAOC) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)], Av. William Cross 3150, C.P. T4101XAC, Las Talitas, Tucumán, Argentina
| | - John Rupe
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701 USA
| | - Pengyin Chen
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
- Fisher Delta Research Center, University of Missouri, Portageville, MO 63873 USA
| | - Dechun Wang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824 USA
| | - Henry T. Nguyen
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
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Shaibu AS, Zhang S, Ma J, Feng Y, Huai Y, Qi J, Li J, Abdelghany AM, Azam M, Htway HTP, Sun J, Li B. The GmSNAP11 Contributes to Resistance to Soybean Cyst Nematode Race 4 in Glycine max. FRONTIERS IN PLANT SCIENCE 2022; 13:939763. [PMID: 35860531 PMCID: PMC9289622 DOI: 10.3389/fpls.2022.939763] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Soybean cyst nematode (SCN) has devastating effects on soybean production, making it crucial to identify genes conferring SCN resistance. Here we employed next-generation sequencing-based bulked segregant analysis (BSA) to discover genomic regions, candidate genes, and diagnostic markers for resistance to SCN race 4 (SCN4) in soybean. Phenotypic analysis revealed highly significant differences among the reactions of 145 recombinant inbred lines (RILs) to SCN4. In combination with euclidean distance (ED) and Δsingle-nucleotide polymorphism (SNP)-index analyses, we identified a genomic region on Gm11 (designated as rhg1-paralog) associated with SCN4 resistance. Overexpression and RNA interference analyzes of the two candidate genes identified in this region (GmPLAC8 and GmSNAP11) revealed that only GmSNAP11 significantly contributes to SCN4 resistance. We developed a diagnostic marker for GmSNAP11. Using this marker, together with previously developed markers for SCN-resistant loci, rhg1 and Rhg4, we evaluated the relationship between genotypes and SCN4 resistance in 145 RILs and 30 soybean accessions. The results showed that all the SCN4-resistant lines harbored all the three loci, however, some lines harboring the three loci were still susceptible to SCN4. This suggests that these three loci are necessary for the resistance to SCN4, but they alone cannot confer full resistance. The GmSNAP11 and the diagnostic markers developed could be used in genomic-assisted breeding to develop soybean varieties with increased resistance to SCN4.
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Affiliation(s)
- Abdulwahab S. Shaibu
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- Department of Agronomy, Bayero University Kano, Kano, Nigeria
| | - Shengrui Zhang
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Junkui Ma
- Institute of Industrial Crop Research, Shanxi Academy of Agricultural Sciences, Fenyang, China
| | - Yue Feng
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuanyuan Huai
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jie Qi
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jing Li
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ahmed M. Abdelghany
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Azam
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Honey Thet Paing Htway
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Junming Sun
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bin Li
- The National Engineering Research Center for Crop Molecular Breeding, MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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7
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Yoosefzadeh-Najafabadi M, Eskandari M, Torabi S, Torkamaneh D, Tulpan D, Rajcan I. Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. Int J Mol Sci 2022; 23:5538. [PMID: 35628351 PMCID: PMC9141736 DOI: 10.3390/ijms23105538] [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: 03/05/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 12/14/2022] Open
Abstract
A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.
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Affiliation(s)
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC G1V 0A6, Canada;
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
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Wei H, Lian Y, Li J, Li H, Song Q, Wu Y, Lei C, Wang S, Zhang H, Wang J, Lu W. Identification of Candidate Genes Controlling Soybean Cyst Nematode Resistance in "Handou 10" Based on Genome and Transcriptome Analyzes. FRONTIERS IN PLANT SCIENCE 2022; 13:860034. [PMID: 35371127 PMCID: PMC8965568 DOI: 10.3389/fpls.2022.860034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Soybean cyst nematode (SCN; Heterodera glycines Ichinohe) is a highly destructive pathogen for soybean production worldwide. The use of resistant varieties is the most effective way of preventing yield loss. Handou 10 is a commercial soybean variety with desirable agronomic traits and SCN resistance, however genes underlying the SCN resistance in the variety are unknown. An F2:8 recombinant inbred line (RIL) population derived from a cross between Zheng 9525 (susceptible) and Handou 10 was developed and its resistance to SCN HG type 2.5.7 (race 1) and 1.2.5.7 (race 2) was identified. We identified seven quantitative trait loci (QTLs) with additive effects. Among these, three QTLs on Chromosomes 7, 8, and 18 were resistant to both races. These QTLs could explain 1.91-7.73% of the phenotypic variation of SCN's female index. The QTLs on chromosomes 8 and 18 have already been reported and were most likely overlapped with rhg1 and Rhg4 loci, respectively. However, the QTL on chromosome 7 was novel. Candidate genes for the three QTLs were predicted through genes functional analysis and transcriptome analysis of infected roots of Handou 10 vs. Zheng 9525. Transcriptome analysis performed also indicated that the plant-pathogen interaction played an important role in the SCN resistance for Handou 10. The information will facilitate SCN-resistant gene cloning, and the novel resistant gene will be a source for improving soybeans' resistance to SCN.
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Affiliation(s)
- He Wei
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
| | - Yun Lian
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
| | - Jinying Li
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
| | - Haichao Li
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, United States
| | - Yongkang Wu
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
| | - Chenfang Lei
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
| | - Shiwei Wang
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
| | - Hui Zhang
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
| | - Jinshe Wang
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
| | - Weiguo Lu
- Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/National Centre for Plant Breeding/Zhengzhou Subcenter of National Soybean Improvement Center/Key Laboratory of Oil Crops in Huanghuaihai Plains of Ministry of Agriculture, Zhengzhou, China
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9
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Li YF, Li YH, Su SS, Reif JC, Qi ZM, Wang XB, Wang X, Tian Y, Li DL, Sun RJ, Liu ZX, Xu ZJ, Fu GH, Ji YL, Chen QS, Liu JQ, Qiu LJ. SoySNP618K array: A high-resolution single nucleotide polymorphism platform as a valuable genomic resource for soybean genetics and breeding. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2022; 64:632-648. [PMID: 34914170 DOI: 10.1111/jipb.13202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/05/2021] [Indexed: 05/13/2023]
Abstract
Innovations in genomics have enabled the development of low-cost, high-resolution, single nucleotide polymorphism (SNP) genotyping arrays that accelerate breeding progress and support basic research in crop science. Here, we developed and validated the SoySNP618K array (618,888 SNPs) for the important crop soybean. The SNPs were selected from whole-genome resequencing data containing 2,214 diverse soybean accessions; 29.34% of the SNPs mapped to genic regions representing 86.85% of the 56,044 annotated high-confidence genes. Identity-by-state analyses of 318 soybeans revealed 17 redundant accessions, highlighting the potential of the SoySNP618K array in supporting gene bank management. The patterns of population stratification and genomic regions enriched through domestication were highly consistent with previous findings based on resequencing data, suggesting that the ascertainment bias in the SoySNP618K array was largely compensated for. Genome-wide association mapping in combination with reported quantitative trait loci enabled fine-mapping of genes known to influence flowering time, E2 and GmPRR3b, and of a new candidate gene, GmVIP5. Moreover, genomic prediction of flowering and maturity time in 502 recombinant inbred lines was highly accurate (>0.65). Thus, the SoySNP618K array is a valuable genomic tool that can be used to address many questions in applied breeding, germplasm management, and basic crop research.
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Affiliation(s)
- Yan-Fei Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), 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 Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Shan-Shan Su
- Beijing Compass Biotechnology Co. Ltd, Beijing, 102206, China
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, 06466, Germany
| | - Zhao-Ming Qi
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Xiao-Bo Wang
- School of Agronomy, Anhui Agricultural University, Hefei, 230036, China
| | - Xing Wang
- Xuzhou Institute of Agricultural Sciences of Xu-huai Region of Jiangsu, Xuzhou, 221131, China
| | - Yu Tian
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - De-Lin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, China
| | - Ru-Jian Sun
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
- Hulun Buir Institution of Agricultural Sciences, Zhalantun, Inner Mongolia, 021000, China
| | - Zhang-Xiong Liu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Ze-Jun Xu
- Xuzhou Institute of Agricultural Sciences of Xu-huai Region of Jiangsu, Xuzhou, 221131, China
| | - Guang-Hui Fu
- Suzhou Academy of Agricultural Sciences, Suzhou, 234000, China
| | - Ya-Liang Ji
- Beijing Compass Biotechnology Co. Ltd, Beijing, 102206, China
| | - Qing-Shan Chen
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Ji-Qiang Liu
- Beijing Compass Biotechnology Co. Ltd, Beijing, 102206, China
| | - Li-Juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
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Yoosefzadeh-Najafabadi M, Torabi S, Tulpan D, Rajcan I, Eskandari M. Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods. FRONTIERS IN PLANT SCIENCE 2021; 12:777028. [PMID: 34880894 PMCID: PMC8647880 DOI: 10.3389/fpls.2021.777028] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 10/18/2021] [Indexed: 05/12/2023]
Abstract
In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.
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Affiliation(s)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
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11
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Zhang S, Du H, Ma Y, Li H, Kan G, Yu D. Linkage and association study discovered loci and candidate genes for glycinin and β-conglycinin in soybean (Glycine max L. Merr.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1201-1215. [PMID: 33464377 DOI: 10.1007/s00122-021-03766-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 01/06/2021] [Indexed: 06/12/2023]
Abstract
KEY MESSAGE Linkage mapping and GWAS identified 67 QTLs related to soybean glycinin, β-conglycinin and relevant traits. Polymorphisms of the candidate gene Gy1 promoter were associated with the glycinin content in soybean. The major components of storage proteins in soybean seeds are glycinin and β-conglycinin, which play important roles in determining protein nutrition and soy food processing properties. Increasing the protein content while improving the ratio of glycinin to β-conglycinin is substantially important for soybean protein improvement. To investigate the genetic mechanism of storage protein subunits, 184 recombinant inbred lines (RILs) derived from a cross of Kefeng No. 1 and Nannong 1138-2 and 211 diverse soybean cultivars were used to detect loci related to glycinin (11S), β-conglycinin (7S), the sum of glycinin and β-conglycinin (SGC), and the ratio of glycinin to β-conglycinin (RGC). Sixty-seven QTLs and 11 hot genomic regions were identified as affecting the four traits. One genetic region (q10-1) on chromosome 10 was associated with multiple traits by both linkage and association analysis. Eight genes in 11 hot genomic regions might be related to soybean protein subunit. The candidate gene analysis showed that polymorphisms in Gy1 promoters were significantly correlated with the 11S content. The QTLs and candidate genes identified in the present study allow for further understanding the genetic basis of 11S and 7S regulation and provide useful information for marker-assisted selection (MAS) in soybean quality improvement.
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Affiliation(s)
- Shanshan Zhang
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
| | - Hongyang Du
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yujie Ma
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
| | - Haiyang Li
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
- School of Life Sciences, Guangzhou University, Guangzhou, 510006, China
| | - Guizhen Kan
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Deyue Yu
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China.
- School of Life Sciences, Guangzhou University, Guangzhou, 510006, China.
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12
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Ravelombola WS, Qin J, Shi A, Nice L, Bao Y, Lorenz A, Orf JH, Young ND, Chen S. Genome-wide association study and genomic selection for tolerance of soybean biomass to soybean cyst nematode infestation. PLoS One 2020; 15:e0235089. [PMID: 32673346 PMCID: PMC7365597 DOI: 10.1371/journal.pone.0235089] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/08/2020] [Indexed: 02/07/2023] Open
Abstract
Soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is one of the most devastating pathogens affecting soybean production in the U.S. and worldwide. The use of SCN-resistant soybean cultivars is one of the most affordable strategies to cope with SCN infestation. Because of the limited sources of SCN resistance and changes in SCN virulence phenotypes, host resistance in current cultivars has increasingly been overcome by the pathogen. Host tolerance has been recognized as an additional tool to manage the SCN. The objectives of this study were to conduct a genome-wide association study (GWAS), to identify single nucleotide polymorphism (SNP) markers, and to perform a genomic selection (GS) study for SCN tolerance in soybean based on reduction in biomass. A total of 234 soybean genotypes (lines) were evaluated for their tolerance to SCN in greenhouse using four replicates. The tolerance index (TI = 100 × Biomass of a line in SCN infested / Biomass of the line without SCN) was used as phenotypic data of SCN tolerance. GWAS was conducted using a total of 3,782 high quality SNPs. GS was performed based upon the whole set of SNPs and the GWAS-derived SNPs, respectively. Results showed that (1) a large variation in soybean TI to SCN infection among the soybean genotypes was identified; (2) a total of 35, 21, and 6 SNPs were found to be associated with SCN tolerance using the models SMR, GLM (PCA), and MLM (PCA+K) with 6 SNPs overlapping between models; (3) GS accuracy was SNP set-, model-, and training population size-dependent; and (4) genes around Glyma.06G134900, Glyma.15G097500.1, Glyma.15G100900.3, Glyma.15G105400, Glyma.15G107200, and Glyma.19G121200.1 (Table 4). Glyma.06G134900, Glyma.15G097500.1, Glyma.15G100900.3, Glyma.15G105400, and Glyma.19G121200.1 are best candidates. To the best of our knowledge, this is the first report highlighting SNP markers associated with tolerance index based on biomass reduction under SCN infestation in soybean. This research opens a new approach to use SCN tolerance in soybean breeding and the SNP markers will provide a tool for breeders to select for SCN tolerance.
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Affiliation(s)
| | - Jun Qin
- Department of Horticulture, PTSC316, University of Arkansas, Fayetteville, AR, United States of America
- Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Ainong Shi
- Department of Horticulture, PTSC316, University of Arkansas, Fayetteville, AR, United States of America
| | - Liana Nice
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
| | - Yong Bao
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
| | - Aaron Lorenz
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
| | - James H Orf
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
| | - Nevin D Young
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States of America
| | - Senyu Chen
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
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13
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Tian Y, Liu B, Shi X, Reif JC, Guan R, Li YH, Qiu LJ. Deep genotyping of the gene GmSNAP facilitates pyramiding resistance to cyst nematode in soybean. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.cj.2019.04.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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14
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Neupane S, Purintun JM, Mathew FM, Varenhorst AJ, Nepal MP. Molecular Basis of Soybean Resistance to Soybean Aphids and Soybean Cyst Nematodes. PLANTS 2019; 8:plants8100374. [PMID: 31561499 PMCID: PMC6843664 DOI: 10.3390/plants8100374] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 09/05/2019] [Accepted: 09/17/2019] [Indexed: 01/25/2023]
Abstract
Soybean aphid (SBA; Aphis glycines Matsumura) and soybean cyst nematode (SCN; Heterodera glycines Ichninohe) are major pests of the soybean (Glycine max [L.] Merr.). Substantial progress has been made in identifying the genetic basis of limiting these pests in both model and non-model plant systems. Classical linkage mapping and genome-wide association studies (GWAS) have identified major and minor quantitative trait loci (QTLs) in soybean. Studies on interactions of SBA and SCN effectors with host proteins have identified molecular cues in various signaling pathways, including those involved in plant disease resistance and phytohormone regulations. In this paper, we review the molecular basis of soybean resistance to SBA and SCN, and we provide a synthesis of recent studies of soybean QTLs/genes that could mitigate the effects of virulent SBA and SCN populations. We also review relevant studies of aphid–nematode interactions, particularly in the soybean–SBA–SCN system.
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Affiliation(s)
- Surendra Neupane
- Department of Biology and Microbiology, South Dakota State University, Brookings, SD 57007, USA.
| | - Jordan M Purintun
- Department of Biology and Microbiology, South Dakota State University, Brookings, SD 57007, USA.
| | - Febina M Mathew
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD 57007, USA.
| | - Adam J Varenhorst
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD 57007, USA.
| | - Madhav P Nepal
- Department of Biology and Microbiology, South Dakota State University, Brookings, SD 57007, USA.
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15
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Tran DT, Steketee CJ, Boehm JD, Noe J, Li Z. Genome-Wide Association Analysis Pinpoints Additional Major Genomic Regions Conferring Resistance to Soybean Cyst Nematode ( Heterodera glycines Ichinohe). FRONTIERS IN PLANT SCIENCE 2019; 10:401. [PMID: 31031779 PMCID: PMC6470319 DOI: 10.3389/fpls.2019.00401] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 03/18/2019] [Indexed: 05/31/2023]
Abstract
Soybean cyst nematode (Heterodera glycines Ichinohe) (SCN) is the most destructive pest affecting soybeans [Glycine max (L.) Merr.] in the U.S. To date, only two major SCN resistance alleles, rhg1 and Rhg4, identified in PI 88788 (rhg1) and Peking (rhg1/Rhg4), residing on chromosomes (Chr) 18 and 8, respectively, have been widely used to develop SCN resistant cultivars in the U.S. Thus, some SCN populations have evolved to overcome the PI 88788 and Peking derived resistance, making it a priority for breeders to identify new alleles and sources of SCN resistance. Toward that end, 461 soybean accessions from various origins were screened using a greenhouse SCN bioassay and genotyped with Illumina SoySNP50K iSelect BeadChips and three KASP SNP markers developed at the Rhg1 and Rhg4 loci to perform a genome-wide association study (GWAS) and a haplotype analysis at the Rhg1 and Rhg4 loci. In total, 35,820 SNPs were used for GWAS, which identified 12 SNPs at four genomic regions on Chrs 7, 8, 10, and 18 that were significantly associated with SCN resistance (P < 0.001). Of those, three SNPs were located at Rhg1 and Rhg4, and 24 predicted genes were found near the significant SNPs on Chrs 7 and 10. KASP SNP genotyping results of the 462 accessions at the Rhg1 and Rhg4 loci identified 30 that carried PI 88788-type resistance, 50 that carried Peking-type resistance, and 58 that carried neither the Peking-type nor the PI 88788-type resistance alleles, indicating they may possess novel SCN resistance alleles. By using two subsets of SNPs near the Rhg1 and Rhg4 loci obtained from SoySNP iSelect BeadChips, a haplotype analysis of 461 accessions grouped those 58 accessions differently from the accessions carrying Peking or PI 88788 derived resistance, thereby validating the genotyping results at Rhg1 and Rhg4. The significant SNPs, candidate genes, and newly characterized SCN resistant accessions will be beneficial for the development of DNA markers to be used for marker-assisted breeding and developing soybean cultivars carrying novel sources of SCN resistance.
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Affiliation(s)
- Dung T. Tran
- Institute of Plant Breeding, Genetics and Genomics and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
| | - Clinton J. Steketee
- Institute of Plant Breeding, Genetics and Genomics and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
| | - Jeffrey D. Boehm
- Institute of Plant Breeding, Genetics and Genomics and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
| | - James Noe
- Department of Plant Pathology, University of Georgia, Athens, GA, United States
| | - Zenglu Li
- Institute of Plant Breeding, Genetics and Genomics and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
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16
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Jain S, Poromarto S, Osorno JM, McClean PE, Nelson BD. Genome wide association study discovers genomic regions involved in resistance to soybean cyst nematode (Heterodera glycines) in common bean. PLoS One 2019; 14:e0212140. [PMID: 30730982 PMCID: PMC6366866 DOI: 10.1371/journal.pone.0212140] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 01/28/2019] [Indexed: 12/25/2022] Open
Abstract
Common bean (Phaseolus vulgaris L.) is an important high protein crop grown worldwide. North Dakota and Minnesota are the largest producers of common beans in the USA, but crop production is threatened by soybean cyst nematode (SCN; Heterodera glycines Ichinohe) because most current cultivars are susceptible. Greenhouse screening data using SCN HG type 0 from 317 plant introductions (PI's) from the USDA core collection was used to conduct a genome wide association study (GWAS). These lines were divided into two subpopulations based on principal component analysis (Middle American vs. Andean). Phenotypic results based on the female index showed that accessions could be classified as highly resistant (21% and 27%), moderately resistant (51% and 48%), moderately susceptible (27% and 22%) and highly susceptible (1% and 3%) for Middle American and Andean gene pools, respectively. Mixed models with two principal components (PCs) and kinship matrix for Middle American genotypes and Andean genotypes were used in the GWAS analysis using 3,985 and 4,811 single nucleotide polymorphic (SNP) markers, respectively which were evenly distributed across all 11 chromosomes. Significant peaks on Pv07, and Pv11 in Middle American and on Pv07, Pv08, Pv09 and Pv11 in Andean group were found to be associated with SCN resistance. Homologs of soybean rhg1, a locus which confers resistance to SCN in soybean, were identified on chromosomes Pv01 and Pv08 in the Middle American and Andean gene pools, respectively. These genomic regions may be the key to develop SCN-resistant common bean cultivars.
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Affiliation(s)
- Shalu Jain
- Department of Plant Pathology, North Dakota State University, Fargo, North Dakota, United States of America
| | - Susilo Poromarto
- Department of Agrotechnology, Sebelas Maret University, Surakarta, Jawa Tengah, Indonesia
| | - Juan M. Osorno
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, United States of America
| | - Phillip E. McClean
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, United States of America
| | - Berlin D. Nelson
- Department of Plant Pathology, North Dakota State University, Fargo, North Dakota, United States of America
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Cao S, Loladze A, Yuan Y, Wu Y, Zhang A, Chen J, Huestis G, Cao J, Chaikam V, Olsen M, Prasanna BM, San Vicente F, Zhang X. Genome-Wide Analysis of Tar Spot Complex Resistance in Maize Using Genotyping-by-Sequencing SNPs and Whole-Genome Prediction. THE PLANT GENOME 2017; 10. [PMID: 28724072 DOI: 10.3835/plantgenome2016.10.0099] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Tar spot complex (TSC) is one of the most destructive foliar diseases of maize ( L.) in tropical and subtropical areas of Central and South America, causing significant grain yield losses when weather conditions are conducive. To dissect the genetic architecture of TSC resistance in maize, association mapping, in conjunction with linkage mapping, was conducted on an association-mapping panel and three biparental doubled-haploid (DH) populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Association mapping revealed four quantitative trait loci (QTL) on chromosome 2, 3, 7, and 8. All the QTL, except for the one on chromosome 3, were further validated by linkage mapping in different genetic backgrounds. Additional QTL were identified by linkage mapping alone. A major QTL located on bin 8.03 was consistently detected with the largest phenotypic explained variation: 13% in association-mapping analysis and 13.18 to 43.31% in linkage-mapping analysis. These results indicated that TSC resistance in maize was controlled by a major QTL located on bin 8.03 and several minor QTL with smaller effects on other chromosomes. Genomic prediction results showed moderate-to-high prediction accuracies in different populations using various training population sizes and marker densities. Prediction accuracy of TSC resistance was >0.50 when half of the population was included into the training set and 500 to 1,000 SNPs were used for prediction. Information obtained from this study can be used for developing functional molecular markers for marker-assisted selection (MAS) and for implementing genomic selection (GS) to improve TSC resistance in tropical maize.
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