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Mugabe D, Yoosefzadeh-Najafabadi M, Rajcan I. Genetic diversity and genome-wide association study of partial resistance to Sclerotinia stem rot in a Canadian soybean germplasm panel. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:201. [PMID: 39127987 DOI: 10.1007/s00122-024-04708-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 07/31/2024] [Indexed: 08/12/2024]
Abstract
KEY MESSAGE Developing genetically resistant soybean cultivars is key in controlling the destructive Sclerotinia Stem Rot (SSR) disease. Here, a GWAS study in Canadian soybeans identified potential marker-trait associations and candidate genes, paving the way for more efficient breeding methods for SSR. Sclerotinia stem rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is one of the most important diseases leading to significant soybean yield losses in Canada and worldwide. Developing soybean cultivars that are genetically resistant to the disease is the most inexpensive and reliable method to control the disease. However, breeding for resistance is hampered by the highly complex nature of genetic resistance to SSR in soybean. This study sought to understand the genetic basis underlying SSR resistance particularly in soybean grown in Canada. Consequently, a panel of 193 genotypes was assembled based on maturity group and genetic diversity as representative of Canadian soybean cultivars. Plants were inoculated and screened for SSR resistance in controlled environments, where variation for SSR phenotypic response was observed. The panel was also genotyped via genotyping-by-sequencing and the resulting genotypic data were imputed using BEAGLE v5 leading to a catalogue of 417 K SNPs. Through genome-wide association analyses (GWAS) using FarmCPU method with threshold of FDR-adjusted p-values < 0.1, we identified significant SNPs on chromosomes 2 and 9 with allele effects of 16.1 and 14.3, respectively. Further analysis identified three potential candidate genes linked to SSR disease resistance within a 100 Kb window surrounding each of the peak SNPs. Our results will be important in developing molecular markers that can speed up the breeding for SSR resistance in Canadian grown soybean.
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Affiliation(s)
- Deus Mugabe
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | | | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada.
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2
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Gangurde SS, Thompson E, Yaduru S, Wang H, Fountain JC, Chu Y, Ozias-Akins P, Isleib TG, Holbrook C, Dutta B, Culbreath AK, Pandey MK, Guo B. Linkage Mapping and Genome-Wide Association Study Identified Two Peanut Late Leaf Spot Resistance Loci, PLLSR-1 and PLLSR-2, Using Nested Association Mapping. PHYTOPATHOLOGY 2024; 114:1346-1355. [PMID: 38669464 DOI: 10.1094/phyto-04-23-0143-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
Identification of candidate genes and molecular markers for late leaf spot (LLS) disease resistance in peanut (Arachis hypogaea) has been a focus of molecular breeding for the U.S. industry-funded peanut genome project. Efforts have been hindered by limited mapping resolution due to low levels of genetic recombination and marker density available in traditional biparental mapping populations. To address this, a multi-parental nested association mapping population has been genotyped with the peanut 58K single-nucleotide polymorphism (SNP) array and phenotyped for LLS severity in the field for 3 years. Joint linkage-based quantitative trait locus (QTL) mapping identified nine QTLs for LLS resistance with significant phenotypic variance explained up to 47.7%. A genome-wide association study identified 13 SNPs consistently associated with LLS resistance. Two genomic regions harboring the consistent QTLs and SNPs were identified from 1,336 to 1,520 kb (184 kb) on chromosome B02 and from 1,026.9 to 1,793.2 kb (767 kb) on chromosome B03, designated as peanut LLS resistance loci, PLLSR-1 and PLLSR-2, respectively. PLLSR-1 contains 10 nucleotide-binding site leucine-rich repeat disease resistance genes. A nucleotide-binding site leucine-rich repeat disease resistance gene, Arahy.VKVT6A, was also identified on homoeologous chromosome A02. PLLSR-2 contains five significant SNPs associated with five different genes encoding callose synthase, pollen defective in guidance protein, pentatricopeptide repeat, acyl-activating enzyme, and C2 GRAM domains-containing protein. This study highlights the power of multi-parent populations such as nested association mapping for genetic mapping and marker-trait association studies in peanuts. Validation of these two LLS resistance loci will be needed for marker-assisted breeding.
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Affiliation(s)
- Sunil S Gangurde
- U.S. Department of Agriculture, Agricultural Research Service, Crop Genetics and Breeding Research Unit, Tifton, GA, U.S.A
- Department of Plant Pathology, University of Georgia, Tifton, GA, U.S.A
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India
| | - Ethan Thompson
- U.S. Department of Agriculture, Agricultural Research Service, Crop Genetics and Breeding Research Unit, Tifton, GA, U.S.A
- Department of Plant Pathology, University of Georgia, Tifton, GA, U.S.A
| | - Shasidhar Yaduru
- U.S. Department of Agriculture, Agricultural Research Service, Crop Genetics and Breeding Research Unit, Tifton, GA, U.S.A
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India
| | - Hui Wang
- U.S. Department of Agriculture, Agricultural Research Service, Crop Genetics and Breeding Research Unit, Tifton, GA, U.S.A
- Department of Plant Pathology, University of Georgia, Tifton, GA, U.S.A
| | - Jake C Fountain
- Department of Plant Pathology, University of Georgia, Griffin, GA, U.S.A
| | - Ye Chu
- Department of Horticulture, University of Georgia, Tifton, GA, U.S.A
| | - Peggy Ozias-Akins
- Department of Horticulture, University of Georgia, Tifton, GA, U.S.A
| | - Thomas G Isleib
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, U.S.A
| | - Corley Holbrook
- U.S. Department of Agriculture, Agricultural Research Service, Crop Genetics and Breeding Research Unit, Tifton, GA, U.S.A
| | - Bhabesh Dutta
- Department of Plant Pathology, University of Georgia, Tifton, GA, U.S.A
| | | | - Manish K Pandey
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India
| | - Baozhu Guo
- U.S. Department of Agriculture, Agricultural Research Service, Crop Genetics and Breeding Research Unit, Tifton, GA, U.S.A
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Zhu H, Lai R, Chen W, Lu C, Chachar Z, Lu S, Lin H, Fan L, Hu Y, An Y, Li X, Zhang X, Qi Y. Genetic dissection of maize (Zea maysL.) trace element traits using genome-wide association studies. BMC PLANT BIOLOGY 2023; 23:631. [PMID: 38062375 PMCID: PMC10704835 DOI: 10.1186/s12870-023-04643-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023]
Abstract
Maize (Zea mays L.) is an important food and feed crop worldwide and serves as a a vital source of biological trace elements, which are important breeding targets. In this study, 170 maize materials were used to detect QTNs related to the content of Mn, Fe and Mo in maize grains through two GWAS models, namely MLM_Q + K and MLM_PCA + K. The results identified 87 (Mn), 205 (Fe), and 310 (Mo) QTNs using both methods in the three environments. Considering comprehensive factors such as co-location across multiple environments, strict significance threshold, and phenotypic value in multiple environments, 8 QTNs related to Mn, 10 QTNs related to Fe, and 26 QTNs related to Mo were used to identify 44 superior alleles. Consequently, three cross combinations with higher Mn element, two combinations with higher Fe element, six combinations with higher Mo element, and two combinations with multiple element (Mn/Fe/Mo) were predicted to yield offspring with higher numbers of superior alleles, thereby increasing the likelihood of enriching the corresponding elements. Additionally, the candidate genes identified 100 kb downstream and upstream the QTNs featured function and pathways related to maize elemental transport and accumulation. These results are expected to facilitate the screening and development of high-quality maize varieties enriched with trace elements, establish an important theoretical foundation for molecular marker assisted breeding and contribute to a better understanding of the regulatory network governing trace elements in maize.
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Affiliation(s)
- Hang Zhu
- Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
- College of Agriculture, Yangtze University, Jingzhou, 434025, Hubei, China
| | - Ruiqiang Lai
- Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China
| | - Weiwei Chen
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
- Heyuan Provincial Academy of Sciences Research Institute, Guangdong Academy of Sciences, GDAS, Heyuan, 517001, Guangdong, China
| | - Chuanli Lu
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
- Heyuan Provincial Academy of Sciences Research Institute, Guangdong Academy of Sciences, GDAS, Heyuan, 517001, Guangdong, China
| | - Zaid Chachar
- Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China
| | - Siqi Lu
- Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
| | - Huanzhang Lin
- Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
| | - Lina Fan
- Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
| | - Yuanqiang Hu
- Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
| | - Yuxing An
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
- Heyuan Provincial Academy of Sciences Research Institute, Guangdong Academy of Sciences, GDAS, Heyuan, 517001, Guangdong, China
| | - Xuhui Li
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China.
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China.
- Heyuan Provincial Academy of Sciences Research Institute, Guangdong Academy of Sciences, GDAS, Heyuan, 517001, Guangdong, China.
| | - Xiangbo Zhang
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China.
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China.
- Heyuan Provincial Academy of Sciences Research Institute, Guangdong Academy of Sciences, GDAS, Heyuan, 517001, Guangdong, China.
| | - Yongwen Qi
- Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China.
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China.
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China.
- College of Agriculture, Yangtze University, Jingzhou, 434025, Hubei, China.
- Heyuan Provincial Academy of Sciences Research Institute, Guangdong Academy of Sciences, GDAS, Heyuan, 517001, Guangdong, China.
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Sang Y, Liu X, Yuan C, Yao T, Li Y, Wang D, Zhao H, Wang Y. Genome-wide association study on resistance of cultivated soybean to Fusarium oxysporum root rot in Northeast China. BMC PLANT BIOLOGY 2023; 23:625. [PMID: 38062401 PMCID: PMC10702129 DOI: 10.1186/s12870-023-04646-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Fusarium oxysporum is a prevalent fungal pathogen that diminishes soybean yield through seedling disease and root rot. Preventing Fusarium oxysporum root rot (FORR) damage entails on the identification of resistance genes and developing resistant cultivars. Therefore, conducting fine mapping and marker development for FORR resistance genes is of great significance for fostering the cultivation of resistant varieties. In this study, 350 soybean germplasm accessions, mainly from Northeast China, underwent genotyping using the SoySNP50K Illumina BeadChip, which includes 52,041 single nucleotide polymorphisms (SNPs). Their resistance to FORR was assessed in a greenhouse. Genome-wide association studies utilizing the general linear model, mixed linear model, compressed mixed linear model, and settlement of MLM under progressively exclusive relationship models were conducted to identify marker-trait associations while effectively controlling for population structure. RESULTS The results demonstrated that these models effectively managed population structure. Eight SNP loci significantly associated with FORR resistance in soybean were detected, primarily located on Chromosome 6. Notably, there was a strong linkage disequilibrium between the large-effect SNPs ss715595462 and ss715595463, contributing substantially to phenotypic variation. Within the genetic interval encompassing these loci, 28 genes were present, with one gene Glyma.06G088400 encoding a protein kinase family protein containing a leucine-rich repeat domain identified as a potential candidate gene in the reference genome of Williams82. Additionally, quantitative real-time reverse transcription polymerase chain reaction analysis evaluated the gene expression levels between highly resistant and susceptible accessions, focusing on primary root tissues collected at different time points after F. oxysporum inoculation. Among the examined genes, only this gene emerged as the strongest candidate associated with FORR resistance. CONCLUSIONS The identification of this candidate gene Glyma.06G088400 improves our understanding of soybean resistance to FORR and the markers strongly linked to resistance can be beneficial for molecular marker-assisted selection in breeding resistant soybean accessions against F. oxysporum.
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Affiliation(s)
- Yongsheng Sang
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China
- College of Agronomy, Jilin Agricultural University, Changchun, 130118, Jilin, PR China
| | - Xiaodong Liu
- Crop Germplasm Institute, Jilin Academy of Agricultural Sciences, Changchun, 130118, Jilin, China
| | - Cuiping Yuan
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China
| | - Tong Yao
- College of Agronomy, Jilin Agricultural University, Changchun, 130118, Jilin, PR China
| | - Yuqiu Li
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China
| | - Dechun Wang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, 1066 Bogue St., Rm. A384-E, East Lansing, MI, 48824, USA
| | - Hongkun Zhao
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China.
| | - Yumin Wang
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China.
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5
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Sang Y, Zhao H, Liu X, Yuan C, Qi G, Li Y, Dong L, Wang Y, Wang D, Wang Y, Dong Y. Genome-wide association study of powdery mildew resistance in cultivated soybean from Northeast China. FRONTIERS IN PLANT SCIENCE 2023; 14:1268706. [PMID: 38023859 PMCID: PMC10651740 DOI: 10.3389/fpls.2023.1268706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023]
Abstract
Powdery mildew (PMD), caused by the pathogen Microsphaera diffusa, leads to substantial yield decreases in susceptible soybean under favorable environmental conditions. Effective prevention of soybean PMD damage can be achieved by identifying resistance genes and developing resistant cultivars. In this study, we genotyped 331 soybean germplasm accessions, primarily from Northeast China, using the SoySNP50K BeadChip, and evaluated their resistance to PMD in a greenhouse setting. To identify marker-trait associations while effectively controlling for population structure, we conducted genome-wide association studies utilizing factored spectrally transformed linear mixed models, mixed linear models, efficient mixed-model association eXpedited, and compressed mixed linear models. The results revealed seven single nucleotide polymorphism (SNP) loci strongly associated with PMD resistance in soybean. Among these, one SNP was localized on chromosome (Chr) 14, and six SNPs with low linkage disequilibrium were localized near or in the region of previously mapped genes on Chr 16. In the reference genome of Williams82, we discovered 96 genes within the candidate region, including 17 resistance (R)-like genes, which were identified as potential candidate genes for PMD resistance. In addition, we performed quantitative real-time reverse transcription polymerase chain reaction analysis to evaluate the gene expression levels in highly resistant and susceptible genotypes, focusing on leaf tissues collected at different times after M. diffusa inoculation. Among the examined genes, three R-like genes, including Glyma.16G210800, Glyma.16G212300, and Glyma.16G213900, were identified as strong candidates associated with PMD resistance. This discovery can significantly enhance our understanding of soybean resistance to PMD. Furthermore, the significant SNPs strongly associated with resistance can serve as valuable markers for genetic improvement in breeding M. diffusa-resistant soybean cultivars.
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Affiliation(s)
- Yongsheng Sang
- College of Agronomy, Jilin Agricultural University, Changchun, Jilin, China
- Soybean Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
| | - Hongkun Zhao
- Soybean Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
| | - Xiaodong Liu
- Crop Germplasm Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
| | - Cuiping Yuan
- Soybean Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
| | - Guangxun Qi
- Soybean Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
| | - Yuqiu Li
- Soybean Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
| | - Lingchao Dong
- Soybean Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
| | - Yingnan Wang
- Soybean Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
| | - Dechun Wang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States
| | - Yumin Wang
- Soybean Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
| | - Yingshan Dong
- College of Agronomy, Jilin Agricultural University, Changchun, Jilin, China
- Soybean Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin, China
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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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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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Han X, Zhang YW, Liu JY, Zuo JF, Zhang ZC, Guo L, Zhang YM. 4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs. BIOTECHNOLOGY FOR BIOFUELS AND BIOPRODUCTS 2022; 15:92. [PMID: 36076247 PMCID: PMC9461130 DOI: 10.1186/s13068-022-02191-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 08/27/2022] [Indexed: 11/10/2022]
Abstract
Background The yield and quality of soybean oil are determined by seed oil-related traits, and metabolites/lipids act as bridges between genes and traits. Although there are many studies on the mode of inheritance of metabolites or traits, studies on multi-dimensional genetic network (MDGN) are limited. Results In this study, six seed oil-related traits, 59 metabolites, and 107 lipids in 398 recombinant inbred lines, along with their candidate genes and miRNAs, were used to construct an MDGN in soybean. Around 175 quantitative trait loci (QTLs), 36 QTL-by-environment interactions, and 302 metabolic QTL clusters, 70 and 181 candidate genes, including 46 and 70 known homologs, were previously reported to be associated with the traits and metabolites, respectively. Gene regulatory networks were constructed using co-expression, protein–protein interaction, and transcription factor binding site and miRNA target predictions between candidate genes and 26 key miRNAs. Using modern statistical methods, 463 metabolite–lipid, 62 trait–metabolite, and 89 trait–lipid associations were found to be significant. Integrating these associations into the above networks, an MDGN was constructed, and 128 sub-networks were extracted. Among these sub-networks, the gene–trait or gene–metabolite relationships in 38 sub-networks were in agreement with previous studies, e.g., oleic acid (trait)–GmSEI–GmDGAT1a–triacylglycerol (16:0/18:2/18:3), gene and metabolite in each of 64 sub-networks were predicted to be in the same pathway, e.g., oleic acid (trait)–GmPHS–d-glucose, and others were new, e.g., triacylglycerol (16:0/18:1/18:2)–GmbZIP123–GmHD-ZIPIII-10–miR166s–oil content. Conclusions This study showed the advantages of MGDN in dissecting the genetic relationships between complex traits and metabolites. Using sub-networks in MGDN, 3D genetic sub-networks including pyruvate/threonine/citric acid revealed genetic relationships between carbohydrates, oil, and protein content, and 4D genetic sub-networks including PLDs revealed the relationships between oil-related traits and phospholipid metabolism likely influenced by the environment. This study will be helpful in soybean quality improvement and molecular biological research. Supplementary Information The online version contains supplementary material available at 10.1186/s13068-022-02191-1.
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Molecular Breeding to Overcome Biotic Stresses in Soybean: Update. PLANTS 2022; 11:plants11151967. [PMID: 35956444 PMCID: PMC9370206 DOI: 10.3390/plants11151967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/16/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022]
Abstract
Soybean (Glycine max (L.) Merr.) is an important leguminous crop and biotic stresses are a global concern for soybean growers. In recent decades, significant development has been carried outtowards identification of the diseases caused by pathogens, sources of resistance and determination of loci conferring resistance to different diseases on linkage maps of soybean. Host-plant resistance is generally accepted as the bestsolution because of its role in the management of environmental and economic conditions of farmers owing to low input in terms of chemicals. The main objectives of soybean crop improvement are based on the identification of sources of resistance or tolerance against various biotic as well as abiotic stresses and utilization of these sources for further hybridization and transgenic processes for development of new cultivars for stress management. The focus of the present review is to summarize genetic aspects of various diseases caused by pathogens in soybean and molecular breeding research work conducted to date.
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Gu X, Huang S, Zhu Z, Ma Y, Yang X, Yao L, Gao X, Zhang M, Liu W, Qiu L, Zhao H, Wang Q, Li Z, Li Z, Meng Q, Yang S, Wang C, Hu X, Ding J. Genome-wide association of single nucleotide polymorphism loci and candidate genes for frogeye leaf spot (Cercospora sojina) resistance in soybean. BMC PLANT BIOLOGY 2021; 21:588. [PMID: 34895144 PMCID: PMC8665500 DOI: 10.1186/s12870-021-03366-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 11/25/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Frogeye leaf spot (FLS) is a destructive fungal disease that affects soybean production. The most economical and effective strategy to control FLS is the use of resistant cultivars. However, the use of a limited number of resistant loci in FLS management will be countered by the emergence of new high-virulence Cercospora sojina races. Therefore, we identified quantitative trait loci (QTL) that control resistance to FLS and identified novel resistant genes using a genome-wide association study (GWAS) on 234 Chinese soybean cultivars. RESULTS A total of 30,890 single nucleotide polymorphism (SNP) markers were used to estimate linkage disequilibrium (LD) and population structure. The GWAS results showed four loci (p < 0.0001) distributed over chromosomes (Chr.) 5 and 20, that are significantly associated with FLS resistance. No previous studies have reported resistance loci in these regions. Subsequently, 45 genes in the two resistance-related haplotype blocks were annotated. Among them, Glyma20g31630 encoding pyruvate dehydrogenase (PDH), Glyma05g28980, which encodes mitogen-activated protein kinase 7 (MPK7), and Glyma20g31510, Glyma20g31520 encoding calcium-dependent protein kinase 4 (CDPK4) in the haplotype blocks deserves special attention. CONCLUSIONS This study showed that GWAS can be employed as an effective strategy for identifying disease resistance traits in soybean and narrowing SNPs and candidate genes. The prediction of candidate genes in the haplotype blocks identified by disease resistance loci can provide a useful reference to study systemic disease resistance.
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Affiliation(s)
- Xin Gu
- Wuhu Institute of Technology, Wuhu, 241003, China
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Shanshan Huang
- Key Laboratory of Crop Biotechnology Breeding of the Ministry of Agriculture, Beidahuang Kenfeng Seed Co., Ltd., Harbin, 150030, China
| | - Zhiguo Zhu
- Wuhu Institute of Technology, Wuhu, 241003, China
| | - Yansong Ma
- Key Laboratory of Crop Biotechnology Breeding of the Ministry of Agriculture, Beidahuang Kenfeng Seed Co., Ltd., Harbin, 150030, China
| | - Xiaohe Yang
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Liangliang Yao
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Xuedong Gao
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Maoming Zhang
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Wei Liu
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Lei Qiu
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Haihong Zhao
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Qingsheng Wang
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Zengjie Li
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Zhimin Li
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Qingying Meng
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Shuai Yang
- Potato Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Chao Wang
- Key Laboratory of Crop Biotechnology Breeding of the Ministry of Agriculture, Beidahuang Kenfeng Seed Co., Ltd., Harbin, 150030, China
| | - Xiping Hu
- Key Laboratory of Crop Biotechnology Breeding of the Ministry of Agriculture, Beidahuang Kenfeng Seed Co., Ltd., Harbin, 150030, China.
| | - Junjie Ding
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China.
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Lai R, Ikram M, Li R, Xia Y, Yuan Q, Zhao W, Zhang Z, Siddique KHM, Guo P. Identification of Novel Quantitative Trait Nucleotides and Candidate Genes for Bacterial Wilt Resistance in Tobacco ( Nicotiana tabacum L.) Using Genotyping-by-Sequencing and Multi-Locus Genome-Wide Association Studies. FRONTIERS IN PLANT SCIENCE 2021; 12:744175. [PMID: 34745174 PMCID: PMC8566715 DOI: 10.3389/fpls.2021.744175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/22/2021] [Indexed: 05/17/2023]
Abstract
Tobacco bacterial wilt (TBW) is a devastating soil-borne disease threatening the yield and quality of tobacco. However, its genetic foundations are not fully understood. In this study, we identified 126,602 high-quality single-nucleotide polymorphisms (SNPs) in 94 tobacco accessions using genotyping-by-sequencing (GBS) and a 94.56 KB linkage disequilibrium (LD) decay rate for candidate gene selection. The population structure analysis revealed two subpopulations with 37 and 57 tobacco accessions. Four multi-locus genome-wide association study (ML-GWAS) approaches identified 142 quantitative trait nucleotides (QTNs) in E1-E4 and the best linear unbiased prediction (BLUP), explaining 0.49-22.52% phenotypic variance. Of these, 38 novel stable QTNs were identified across at least two environments/methods, and their alleles showed significant TBW-DI differences. The number of superior alleles associated with TBW resistance for each accession ranged from 4 to 24; eight accessions had more than 18 superior alleles. Based on TBW-resistant alleles, the five best cross combinations were predicted, including MC133 × Ruyuan No. 1 and CO258 × ROX28. We identified 52 candidate genes around 38 QTNs related to TBW resistance based on homologous functional annotation and KEGG enrichment analysis, e.g., CYCD3;2, BSK1, Nitab4.5_0000641g0050, Nitab4.5_0000929g0030. To the best of our knowledge, this is the first comprehensive study to identify QTNs, superior alleles, and their candidate genes for breeding TBW-resistant tobacco varieties. The results provide further insight into the genetic architecture, marker-assisted selection, and functional genomics of TBW resistance, improving future breeding efforts to increase crop productivity.
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Affiliation(s)
- Ruiqiang Lai
- International Crop Research Center for Stress Resistance, School of Life Sciences, Guangzhou University, Guangzhou, China
| | - Muhammad Ikram
- International Crop Research Center for Stress Resistance, School of Life Sciences, Guangzhou University, Guangzhou, China
| | - Ronghua Li
- International Crop Research Center for Stress Resistance, School of Life Sciences, Guangzhou University, Guangzhou, China
| | - Yanshi Xia
- International Crop Research Center for Stress Resistance, School of Life Sciences, Guangzhou University, Guangzhou, China
| | - Qinghua Yuan
- Crop Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Weicai Zhao
- Nanxiong Research Institute of Guangdong Tobacco Co., Ltd., Nanxiong, China
| | - Zhenchen Zhang
- Crop Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Kadambot H. M. Siddique
- The UWA Institute of Agriculture, UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia
| | - Peiguo Guo
- International Crop Research Center for Stress Resistance, School of Life Sciences, Guangzhou University, Guangzhou, China
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