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Zeffa DM, Júnior LP, de Assis R, Delfini J, Marcos AW, Koltun A, Baba VY, Constantino LV, Uhdre RS, Nogueira AF, Moda-Cirino V, Scapim CA, Gonçalves LSA. Multi-locus genome-wide association study for phosphorus use efficiency in a tropical maize germplasm. FRONTIERS IN PLANT SCIENCE 2024; 15:1366173. [PMID: 39246817 PMCID: PMC11380136 DOI: 10.3389/fpls.2024.1366173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 07/10/2024] [Indexed: 09/10/2024]
Abstract
Phosphorus (P) is an essential macronutrient for maize (Zea mays L.) growth and development. Therefore, generating cultivars with upgraded P use efficiency (PUE) represents one of the main strategies to reduce the global agriculture dependence on phosphate fertilizers. In this work, genome-wide association studies (GWAS) were performed to detect quantitative trait nucleotide (QTN) and potential PUE-related candidate genes and associated traits in greenhouse and field trials under contrasting P conditions. The PUE and other agronomy traits of 132 maize inbred lines were assessed in low and normal P supply through the greenhouse and field experiments and Multi-locus GWAS was used to map the associated QTNs. Wide genetic variability was observed among the maize inbred lines under low and normal P supply. In addition, we confirm the complex and quantitative nature of PUE. A total of 306 QTNs were associated with the 24 traits evaluated using different multi-locus GWAS methods. A total of 186 potential candidate genes were identified, mainly involved with transcription regulator, transporter, and transference activity. Further studies are still needed to elucidate the functions and relevance of these genes regarding PUE. Nevertheless, pyramiding the favorable alleles pinpointed in the present study can be considered an efficient strategy for molecular improvement to increase maize PUE.
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Affiliation(s)
- Douglas Mariani Zeffa
- Departamento de Agronomia, Universidade Estadual de Maringá, Maringá, Paraná, Brazil
| | - Luiz Perini Júnior
- Departamento de Agronomia, Universidade Estadual de Londrina, Londrina, Paraná, Brazil
| | - Rafael de Assis
- Departamento de Biologia, Universidade Estadual de Londrina, Londrina, Paraná, Brazil
| | - Jéssica Delfini
- Departamento de Agronomia, Universidade Estadual de Londrina, Londrina, Paraná, Brazil
| | - Antoni Wallace Marcos
- Departamento de Agronomia, Universidade Estadual de Londrina, Londrina, Paraná, Brazil
| | - Alessandra Koltun
- Departamento de Agronomia, Universidade Estadual de Maringá, Maringá, Paraná, Brazil
| | - Viviane Yumi Baba
- Departamento de Agronomia, Universidade Estadual de Londrina, Londrina, Paraná, Brazil
| | | | - Renan Santos Uhdre
- Departamento de Agronomia, Universidade Estadual de Maringá, Maringá, Paraná, Brazil
| | | | - Vania Moda-Cirino
- Área de Melhoramento Genético e Propagação Vegetal, Instituto de Desenvolvimento Rural do Paraná, Londrina, Paraná, Brazil
| | - Carlos Alberto Scapim
- Departamento de Agronomia, Universidade Estadual de Maringá, Maringá, Paraná, Brazil
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Kaur N, Lozada DN, Bhatta M, Barchenger DW, Khokhar ES, Nourbakhsh SS, Sanogo S. Insights into the genetic architecture of Phytophthora capsici root rot resistance in chile pepper (Capsicum spp.) from multi-locus genome-wide association study. BMC PLANT BIOLOGY 2024; 24:416. [PMID: 38760676 PMCID: PMC11100198 DOI: 10.1186/s12870-024-05097-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Phytophthora root rot, a major constraint in chile pepper production worldwide, is caused by the soil-borne oomycete, Phytophthora capsici. This study aimed to detect significant regions in the Capsicum genome linked to Phytophthora root rot resistance using a panel consisting of 157 Capsicum spp. genotypes. Multi-locus genome wide association study (GWAS) was conducted using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing (GBS). Individual plants were separately inoculated with P. capsici isolates, 'PWB-185', 'PWB-186', and '6347', at the 4-8 leaf stage and were scored for disease symptoms up to 14-days post-inoculation. Disease scores were used to calculate disease parameters including disease severity index percentage, percent of resistant plants, area under disease progress curve, and estimated marginal means for each genotype. RESULTS Most of the genotypes displayed root rot symptoms, whereas five accessions were completely resistant to all the isolates and displayed no symptoms of infection. A total of 55,117 SNP markers derived from GBS were used to perform multi-locus GWAS which identified 330 significant SNP markers associated with disease resistance. Of these, 56 SNP markers distributed across all the 12 chromosomes were common across the isolates, indicating association with more durable resistance. Candidate genes including nucleotide-binding site leucine-rich repeat (NBS-LRR), systemic acquired resistance (SAR8.2), and receptor-like kinase (RLKs), were identified within 0.5 Mb of the associated markers. CONCLUSIONS Results will be used to improve resistance to Phytophthora root rot in chile pepper by the development of Kompetitive allele-specific markers (KASP®) for marker validation, genomewide selection, and marker-assisted breeding.
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Affiliation(s)
- Navdeep Kaur
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
- Current address: Department of Horticultural Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Dennis N Lozada
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA.
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, 88003, USA.
| | | | | | - Ehtisham S Khokhar
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
| | - Seyed Shahabeddin Nourbakhsh
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
- Department of Extension Plant Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
| | - Soum Sanogo
- Department of Entomology, Plant Pathology and Weed Science, New Mexico State University, Las Cruces, NM, 88003, USA
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Singer WM, Lee YC, Shea Z, Vieira CC, Lee D, Li X, Cunicelli M, Kadam SS, Khan MAW, Shannon G, Mian MAR, Nguyen HT, Zhang B. Soybean genetics, genomics, and breeding for improving nutritional value and reducing antinutritional traits in food and feed. THE PLANT GENOME 2023; 16:e20415. [PMID: 38084377 DOI: 10.1002/tpg2.20415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 12/22/2023]
Abstract
Soybean [Glycine max (L.) Merr.] is a globally important crop due to its valuable seed composition, versatile feed, food, and industrial end-uses, and consistent genetic gain. Successful genetic gain in soybean has led to widespread adaptation and increased value for producers, processors, and consumers. Specific focus on the nutritional quality of soybean seed composition for food and feed has further elucidated genetic knowledge and bolstered breeding progress. Seed components are historical and current targets for soybean breeders seeking to improve nutritional quality of soybean. This article reviews genetic and genomic foundations for improvement of nutritionally important traits, such as protein and amino acids, oil and fatty acids, carbohydrates, and specific food-grade considerations; discusses the application of advanced breeding technology such as CRISPR/Cas9 in creating seed composition variations; and provides future directions and breeding recommendations regarding soybean seed composition traits.
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Affiliation(s)
- William M Singer
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Yi-Chen Lee
- Department of Agriculture, Fort Hays State University, Hays, Kansas, USA
| | - Zachary Shea
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Caio Canella Vieira
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, Arkansas, USA
| | - Dongho Lee
- Fisher Delta Research, Extension, and Education Center, University of Missouri, Portageville, Missouri, USA
| | - Xiaoying Li
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Mia Cunicelli
- Soybean and Nitrogen Fixation Research Unit, USDA-ARS, Raleigh, North Carolina, USA
| | - Shaila S Kadam
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | | | - Grover Shannon
- Fisher Delta Research, Extension, and Education Center, University of Missouri, Portageville, Missouri, USA
| | - M A Rouf Mian
- Soybean and Nitrogen Fixation Research Unit, USDA-ARS, Raleigh, North Carolina, USA
| | - Henry T Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | - Bo Zhang
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
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Zhang Q, Sun T, Wang J, Fei J, Liu Y, Liu L, Wang P. Genome-wide association study and high-quality gene mining related to soybean protein and fat. BMC Genomics 2023; 24:596. [PMID: 37805454 PMCID: PMC10559447 DOI: 10.1186/s12864-023-09687-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 09/20/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Soybean is one of the most important oil crops in the world, and its protein and fat are the primary sources of edible oil and vegetable protein. The effective components in soybean protein and fat have positive effects on improving human immunity, anti-tumor, and regulating blood lipids and metabolism. Therefore, increasing the contents of protein and fat in soybeans is essential for improving the quality of soybeans. RESULTS This study selected 292 soybean lines from different regions as experimental materials, based on SLAF-seq sequencing technology, and performed genome-wide association study (GWAS) on the phenotype data from 2019-2021 Planted at the experimental base of Jilin Agricultural University, such as the contents of protein and fat of soybeans. Through the GLM model and MLM model, four SNP sites (Gm09_39012959, Gm12_35492373, Gm16_9297124, and Gm20_24678362) that were significantly related to soybean fat content were associated for three consecutive years, and two SNP sites (Gm09_39012959 and Gm20_24678362) that were significantly related to soybean protein content were associated. By the annotation and enrichment of genes within the 100 Kb region of SNP loci flanking, two genes (Glyma.09G158100 and Glyma.09G158200) related to soybean protein synthesis and one gene (Glyma.12G180200) related to lipid metabolism were selected. By the preliminary verification of expression levels of genes with qPCR, it is found that during the periods of R6 and R7 of the accumulation of soybean protein and fat, Glyma.09G158100 and Glyma.09G158200 are positive regulatory genes that promote protein synthesis and accumulation, while Glyma.12G180200 is the negative regulatory gene that inhibits fat accumulation. CONCLUSIONS These results lay the basis for further verifying the gene function and studying the molecular mechanisms regulating the accumulation of protein and fat in soybean seeds.
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Affiliation(s)
- Qi Zhang
- Jilin Agricultural University, Changchun, China
| | | | - Jiabao Wang
- Jilin Agricultural University, Changchun, China
| | - JianBo Fei
- JiLin Agricultural Science and Technology University, Jilin, China
| | - Yufu Liu
- Jilin Provincial Seed Management Station, Jilin, China
| | - Lu Liu
- Jilin Agricultural University, Changchun, China
| | - Peiwu Wang
- Jilin Agricultural University, Changchun, China.
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Taranto F, Esposito S, Fania F, Sica R, Marzario S, Logozzo G, Gioia T, De Vita P. Breeding effects on durum wheat traits detected using GWAS and haplotype block analysis. FRONTIERS IN PLANT SCIENCE 2023; 14:1206517. [PMID: 37794940 PMCID: PMC10546023 DOI: 10.3389/fpls.2023.1206517] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 08/08/2023] [Indexed: 10/06/2023]
Abstract
Introduction The recent boosting of genomic data in durum wheat (Triticum turgidum subsp. durum) offers the opportunity to better understand the effects of breeding on the genetic structures that regulate the expression of traits of agronomic interest. Furthermore, the identification of DNA markers useful for marker-assisted selection could also improve the reliability of technical protocols used for variety protection and registration. Methods Within this motivation context, 123 durum wheat accessions, classified into three groups: landraces (LR), ancient (OC) and modern cultivars (MC), were evaluated in two locations, for 34 agronomic traits, including UPOV descriptors, to assess the impact of changes that occurred during modern breeding. Results The association mapping analysis, performed with 4,241 SNP markers and six multi-locus-GWAS models, revealed 28 reliable Quantitative Trait Nucleotides (QTNs) related to plant morphology and kernel-related traits. Some important genes controlling flowering time and plant height were in linkage disequilibrium (LD) decay with QTNs identified in this study. A strong association for yellow berry was found on chromosome 6A (Q.Yb-6A) in a region containing the nadh-ubiquinone oxidoreductase subunit, a gene involved in starch metabolism. The Q.Kcp-2A harbored the PPO locus, with the associated marker (Ku_c13700_1196) in LD decay with Ppo-A1 and Ppo-A2. Interestingly, the Q.FGSGls-2B.1, identified by RAC875_c34512_685 for flag leaf glaucosity, mapped less than 1 Mb from the Epistatic inhibitors of glaucousness (Iw1), thus representing a good candidate for supporting the morphological DUS traits also with molecular markers. LD haplotype block approach revealed a higher diversity, richness and length of haploblocks in MC than OC and LR (580 in LR, 585 in OC and 612 in MC), suggesting a possible effect exerted by breeding programs on genomic regions associated with the agronomic traits. Discussion Our findings pave new ways to support the phenotypic characterization necessary for variety registration by using a panel of cost-effectiveness SNP markers associated also to the UPOV descriptors. Moreover, the panel of associated SNPs might represent a reservoir of favourable alleles to use in durum wheat breeding and genetics.
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Affiliation(s)
- F. Taranto
- Italian National Council of Research (CNR), Institute of Biosciences and Bioresources (IBBR), Bari, Italy
| | - S. Esposito
- Council for Agricultural Research and Economics (CREA), Research Centre for Cereal and Industrial Crops (CREA-CI), Foggia, Italy
| | - F. Fania
- Council for Agricultural Research and Economics (CREA), Research Centre for Cereal and Industrial Crops (CREA-CI), Foggia, Italy
- Department of Agriculture, Food, Natural Resources, and Engineering (DAFNE) - University of Foggia, Foggia, Italy
| | - R. Sica
- School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy
| | - S. Marzario
- School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy
| | - G. Logozzo
- School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy
| | - T. Gioia
- School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy
| | - P. De Vita
- Council for Agricultural Research and Economics (CREA), Research Centre for Cereal and Industrial Crops (CREA-CI), Foggia, Italy
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Zhang C, Gong R, Zhong H, Dai C, Zhang R, Dong J, Li Y, Liu S, Hu J. Integrated multi-locus genome-wide association studies and transcriptome analysis for seed yield and yield-related traits in Brassica napus. FRONTIERS IN PLANT SCIENCE 2023; 14:1153000. [PMID: 37123841 PMCID: PMC10140536 DOI: 10.3389/fpls.2023.1153000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 03/21/2023] [Indexed: 05/03/2023]
Abstract
Rapeseed (Brassica napus L.), the third largest oil crop, is an important source of vegetable oil and biofuel for the world. Although the breeding and yield has been improved, rapeseed still has the lowest yield compared with other major crops. Thus, increasing rapeseed yield is essential for the high demand of vegetable oil and high-quality protein for live stocks. Silique number per plant (SN), seed per pod (SP), and 1000-seed weight (SW) are the three important factors for seed yield in rapeseed. Some yield-related traits, including plant height (PH), flowering time (FT), primary branch number (BN) and silique number per inflorescence (SI) also affect the yield per plant (YP). Using six multi-locus genome-wide association study (ML-GWAS) approaches, a total of 908 yield-related quantitative trait nucleotides (QTNs) were identified in a panel consisting of 403 rapeseed core accessions based on whole-genome sequencing. Integration of ML-GWAS with transcriptome analysis, 79 candidate genes, including BnaA09g39790D (RNA helicase), BnaA09g39950D (Lipase) and BnaC09g25980D (SWEET7), were further identified and twelve genes were validated by qRT-PCRs to affect the SW or SP in rapeseed. The distribution of superior alleles from nineteen stable QTNs in 20 elite rapeseed accessions suggested that the high-yielding accessions contained more superior alleles. These results would contribute to a further understanding of the genetic basis of yield-related traits and could be used for crop improvement in B. napus.
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Affiliation(s)
- Cuiping Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Ruolin Gong
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Hua Zhong
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Chunyan Dai
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Ru Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Jungang Dong
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Yangsheng Li
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, China
| | - Shuai Liu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii at Manoa, Honolulu, HI, United States
- *Correspondence: Jihong Hu, ; Shuai Liu,
| | - Jihong Hu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
- *Correspondence: Jihong Hu, ; Shuai Liu,
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Jha UC, Nayyar H, Parida SK, Deshmukh R, von Wettberg EJB, Siddique KHM. Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools. Int J Mol Sci 2022; 23:7710. [PMID: 35887057 PMCID: PMC9325250 DOI: 10.3390/ijms23147710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based 'dietary protein' biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.
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Affiliation(s)
- Uday C. Jha
- ICAR—Indian Institute of Pulses Research (IIPR), Kanpur 208024, India
| | - Harsh Nayyar
- Department of Botany, Panjab University, Chandigarh 160014, India;
| | - Swarup K. Parida
- National Institute of Plant Genome Research, New Delhi 110067, India;
| | - Rupesh Deshmukh
- National Agri-Food Biotechnology Institute, Punjab 140308, India;
| | | | - Kadambot H. M. Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001, Australia
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Khan SU, Saeed S, Khan MHU, Fan C, Ahmar S, Arriagada O, Shahzad R, Branca F, Mora-Poblete F. Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed. Biomolecules 2021; 11:1516. [PMID: 34680149 PMCID: PMC8533950 DOI: 10.3390/biom11101516] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/07/2021] [Accepted: 10/11/2021] [Indexed: 12/15/2022] Open
Abstract
Yield is one of the most important agronomic traits for the breeding of rapeseed (Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits.
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Affiliation(s)
- Shahid Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Sumbul Saeed
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Muhammad Hafeez Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Chuchuan Fan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3465548, Chile;
| | - Osvin Arriagada
- Departamento de Ciencias Vegetales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile;
| | - Raheel Shahzad
- Department of Biotechnology, Faculty of Science & Technology, Universitas Muhammadiyah Bandung, Bandung 40614, Indonesia;
| | - Ferdinando Branca
- Department of Agriculture, Food and Environment (Di3A), University of Catania, 95123 Catania, Italy;
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3465548, Chile;
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Delfini J, Moda-Cirino V, Dos Santos Neto J, Zeffa DM, Nogueira AF, Ribeiro LAB, Ruas PM, Gepts P, Gonçalves LSA. Genome-wide association study for grain mineral content in a Brazilian common bean diversity panel. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2795-2811. [PMID: 34027567 DOI: 10.1007/s00122-021-03859-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/10/2021] [Indexed: 06/12/2023]
Abstract
QTNs significantly associated to nine mineral content in grains of common bean were identified. The accumulation of favorable alleles was associated with a gradually increasing nutrient content in the grain. Biofortification is one of the strategies developed to address malnutrition in developing countries, the aim of which is to improve the nutritional content of crops. The common bean (Phaseolus vulgaris L.), a staple food in several African and Latin American countries, has excellent nutritional attributes and is considered a strong candidate for biofortification. The objective of this study was to identify genomic regions associated with nutritional content in common bean grains using 178 Mesoamerican accessions belonging to a Brazilian Diversity Panel (BDP) and 25,011 good-quality single nucleotide polymorphisms. The BDP was phenotyped in three environments for nine nutrients (phosphorus, potassium, calcium, magnesium, copper, manganese, sulfur, zinc, and iron) using four genome-wide association multi-locus methods. To obtain more accurate results, only quantitative trait nucleotides (QTNs) that showed repeatability (i.e., those detected at least twice using different methods or environments) were considered. Forty-eight QTNs detected for the nine minerals showed repeatability and were considered reliable. Pleiotropic QTNs and overlapping genomic regions surrounding the QTNs were identified, demonstrating the possible association between the deposition mechanisms of different nutrients in grains. The accumulation of favorable alleles in the same accession was associated with a gradually increasing nutrient content in the grain. The BDP proved to be a valuable source for association studies. The investigation of different methods and environments showed the reliability of markers associated with minerals. The loci identified in this study will potentially contribute to the improvement of Mesoamerican common beans, particularly carioca and black beans, the main groups consumed in Brazil.
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Affiliation(s)
- Jessica Delfini
- Plant Breeding, Instituto de Desenvolvimento Rural do Paraná-IDR-Paraná-Emater (IDR-Paraná), Londrina, Brazil
- Agronomy Department, Universidade Estadual de Londrina (UEL), Londrina, Brazil
| | - Vânia Moda-Cirino
- Plant Breeding, Instituto de Desenvolvimento Rural do Paraná-IDR-Paraná-Emater (IDR-Paraná), Londrina, Brazil
| | - José Dos Santos Neto
- Plant Breeding, Instituto de Desenvolvimento Rural do Paraná-IDR-Paraná-Emater (IDR-Paraná), Londrina, Brazil
- Agronomy Department, Universidade Estadual de Londrina (UEL), Londrina, Brazil
| | - Douglas Mariani Zeffa
- Plant Breeding, Instituto de Desenvolvimento Rural do Paraná-IDR-Paraná-Emater (IDR-Paraná), Londrina, Brazil
- Agronomy Department, Universidade Estadual de Maringá, Maringá, Paraná, Brazil
| | - Alison Fernando Nogueira
- Plant Breeding, Instituto de Desenvolvimento Rural do Paraná-IDR-Paraná-Emater (IDR-Paraná), Londrina, Brazil
- Agronomy Department, Universidade Estadual de Londrina (UEL), Londrina, Brazil
| | - Luriam Aparecida Brandão Ribeiro
- Plant Breeding, Instituto de Desenvolvimento Rural do Paraná-IDR-Paraná-Emater (IDR-Paraná), Londrina, Brazil
- Agronomy Department, Universidade Estadual de Londrina (UEL), Londrina, Brazil
| | - Paulo Maurício Ruas
- Biology Department, Universidade Estadual de Londrina (UEL), Londrina, Brazil
| | - Paul Gepts
- Department of Plant Sciences, Section of Crop and Ecosystem Sciences, University of California, Davis, CA, USA
| | - Leandro Simões Azeredo Gonçalves
- Agronomy Department, Universidade Estadual de Londrina (UEL), Londrina, Brazil.
- Agronomy Department, Universidade Estadual de Maringá, Maringá, Paraná, Brazil.
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10
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Li WX, Wang P, Zhao H, Sun X, Yang T, Li H, Hou Y, Liu C, Siyal M, Raja veesar R, Hu B, Ning H. QTL for Main Stem Node Number and Its Response to Plant Densities in 144 Soybean FW-RILs. FRONTIERS IN PLANT SCIENCE 2021; 12:666796. [PMID: 34489989 PMCID: PMC8417731 DOI: 10.3389/fpls.2021.666796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
Although the main stem node number of soybean [Glycine max (L.) Merr. ] is an important yield-related trait, there have been limited studies on the effect of plant density on the identification of quantitative trait loci (QTL) for main stem node number (MSNN). To address this issue, here, 144 four-way recombinant inbred lines (FW-RILs) derived from Kenfeng 14, Kenfeng 15, Heinong 48, and Kenfeng 19 were used to identify QTL for MSNN with densities of 2.2 × 105 (D1) and 3 × 105 (D2) plants/ha in five environments by linkage and association studies. As a result, the linkage and association studies identified 40 and 28 QTL in D1 and D2, respectively, indicating the difference in QTL in various densities. Among these QTL, five were common in the two densities; 36 were singly identified for response to density; 12 were repeatedly identified by both response to density and phenotype of two densities. Thirty-one were repeatedly detected across various methods, densities, and environments in the linkage and association studies. Among the 24 common QTL in the linkage and association studies, 15 explained a phenotypic variation of more than 10%. Finally, Glyma.06G094400, Glyma.06G147600, Glyma.19G160800.1, and Glyma.19G161100 were predicted to be associated with MSNN. These findings will help to elucidate the genetic basis of MSNN and improve molecular assistant selection in high-yield soybean breeding.
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Affiliation(s)
- Wen-Xia Li
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Ping Wang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
- High Education Institute, Huaiyin Institute of Technology, Huai'an, China
| | - Hengxing Zhao
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Xu Sun
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Tao Yang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Haoran Li
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Yongqin Hou
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Cuiqiao Liu
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Mahfishan Siyal
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Rameez Raja veesar
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Bo Hu
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Hailong Ning
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
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11
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Li X, Wang P, Zhang K, Liu S, Qi Z, Fang Y, Wang Y, Tian X, Song J, Wang J, Yang C, Sun X, Tian Z, Li WX, Ning H. Fine mapping QTL and mining genes for protein content in soybean by the combination of linkage and association analysis. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1095-1122. [PMID: 33420806 DOI: 10.1007/s00122-020-03756-0] [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: 03/06/2020] [Accepted: 12/19/2020] [Indexed: 05/29/2023]
Abstract
Soybean is one main source of dietary protein; therefore, improving protein content is an important objective in breeding programs. There is a significant negative correlation between protein and oil content, which influenced mapping quantitative trait locus (QTL) and quantitative trait nucleotides for these two traits. In this study, a linkage map was created with 2232 single-nucleotide polymorphism markers for the four-way recombinant inbred line (FW-RIL) population derived from the cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19), and then conditional and unconditional QTL analyses were carried out by inclusive complete interval mapping based on the phenotypic data of protein and oil content collected in 10 different environments. As shown in the results of linkage analysis, a total of 85 QTL have been detected. We have performed association analysis using 109,676 markers after quality filtering for FW-RIL, and the results have shown that a total of 60 QTNs were detected. We have performed association analysis using 63,306 markers after quality filtering for resource population, and the results have shown that a total of 123 QTNs were detected. We have combined linkage and association analysis, and there are six QTNs verified by FW-RIL and resource population. We have performed pathway analysis on the genes in these six QTN attenuation regions, and the result shows that a total of four candidate genes are related to the synthesis or metabolism of soybean protein. These findings will facilitate marker-assisted selection and molecular breeding of soybean.
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Affiliation(s)
- Xiyu Li
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Ping Wang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
- Huaiyin Institute of Technology, Huaian, China
| | - Kaixin Zhang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Shulin Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Zhongying Qi
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Yanlong Fang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Yue Wang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Xiaocui Tian
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Jie Song
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Jiajing Wang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Chang Yang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Xu Sun
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Wen-Xia Li
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China.
| | - Hailong Ning
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, China.
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12
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Wang P, Sun X, Zhang K, Fang Y, Wang J, Yang C, Li WX, Ning H. Mapping QTL/QTN and mining candidate genes for plant height and its response to planting densities in soybean [ Glycine max (L.) Merr.] through a FW-RIL population. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:12. [PMID: 37309477 PMCID: PMC10236039 DOI: 10.1007/s11032-021-01209-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 01/26/2021] [Indexed: 06/13/2023]
Abstract
Plant height (PH) determines the morphology and seed yield of soybean, so it is an important breeding target, which is controlled by multiple genes and affected by plant density. In this research, it was used about a four-way recombinant inbred lines (FW-RIL) with 144 families constructed by double cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19) as experimental materials, with the purpose to map QTL/QTN associated with PH under densities of 2.2×105 plant/ha (D1) and 3×105 plant/ha (D2) in five environments. The results showed that response of PH to densities varied in accordance to genotypes among environments. A total of 26 QTLs and 13 QTNs were identified specifically in D1; 20 QTLs and 21 QTNs were identified specifically in D2. Nine QTLs and one QTN were discovered commonly in two densities. Fifteen QTLs and 9 QTNs were repeatedly detected by multiple statistical methods, densities, or environments, which could be considered stable. Eighteen QTLs were detected, as well as 7 QTNs underlying responses of PH to density increment. Six QTNs, co-located in the interval of QTL, were detected in more than two environments or methods with a longer genome length over 3000 kb. Based on gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, five genes were predicted as candidates, which were likely to be involved in growth and development of PH. The results will help elucidate the genetic basis and improve molecular assistant selection of PH. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01209-0.
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Affiliation(s)
- Ping Wang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
- Huaiyin Institute of Technology, Huai’an, China
| | - Xu Sun
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Kaixin Zhang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Yanlong Fang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jiajing Wang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Chang Yang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Wen-Xia Li
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Hailong Ning
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
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13
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Karikari B, Wang Z, Zhou Y, Yan W, Feng J, Zhao T. Identification of quantitative trait nucleotides and candidate genes for soybean seed weight by multiple models of genome-wide association study. BMC PLANT BIOLOGY 2020; 20:404. [PMID: 32873245 PMCID: PMC7466808 DOI: 10.1186/s12870-020-02604-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 08/16/2020] [Indexed: 05/15/2023]
Abstract
BACKGROUND Seed weight is a complex yield-related trait with a lot of quantitative trait loci (QTL) reported through linkage mapping studies. Integration of QTL from linkage mapping into breeding program is challenging due to numerous limitations, therefore, Genome-wide association study (GWAS) provides more precise location of QTL due to higher resolution and diverse genetic diversity in un-related individuals. RESULTS The present study utilized 573 breeding lines population with 61,166 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and candidate genes for seed weight in Chinese summer-sowing soybean. GWAS was conducted with two single-locus models (SLMs) and six multi-locus models (MLMs). Thirty-nine SNPs were detected by the two SLMs while 209 SNPs were detected by the six MLMs. In all, two hundred and thirty-one QTNs were found to be associated with seed weight in YHSBLP with various effects. Out of these, seventy SNPs were concurrently detected by both SLMs and MLMs on 8 chromosomes. Ninety-four QTNs co-localized with previously reported QTL/QTN by linkage/association mapping studies. A total of 36 candidate genes were predicted. Out of these candidate genes, four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 and Glyma19g28070) were identified by the integration of co-expression network. Among them, three were relatively expressed higher in the high HSW genotypes at R5 stage compared with low HSW genotypes except Glyma12g33280. Our results show that using more models especially MLMs are effective to find important QTNs, and the identified HSW QTNs/genes could be utilized in molecular breeding work for soybean seed weight and yield. CONCLUSION Application of two single-locus plus six multi-locus models of GWAS identified 231 QTNs. Four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 & Glyma19g28070) detected via integration of co-expression network among the predicted candidate genes.
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Affiliation(s)
- Benjamin Karikari
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
| | - Zili Wang
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
| | - Yilan Zhou
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
| | - Wenliang Yan
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China
| | - Jianying Feng
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China.
| | - Tuanjie Zhao
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China.
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14
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Muhammad A, Hu W, Li Z, Li J, Xie G, Wang J, Wang L. Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS. Int J Mol Sci 2020; 21:ijms21165649. [PMID: 32781752 PMCID: PMC7460857 DOI: 10.3390/ijms21165649] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/02/2020] [Accepted: 08/05/2020] [Indexed: 12/14/2022] Open
Abstract
Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs (RAC875_29540_391, Kukri_07961_503, tplb0034e07_1581, BS00074341_51, and BobWhite_049_3064) were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat.
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Affiliation(s)
- Ali Muhammad
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
| | - Weicheng Hu
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
| | - Zhaoyang Li
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
| | - Jianguo Li
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, 100 Daxue Rd., Nanning 530004, China
| | - Guosheng Xie
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
| | - Jibin Wang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, 100 Daxue Rd., Nanning 530004, China
- Correspondence: (J.W.); (L.W.)
| | - Lingqiang Wang
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, 100 Daxue Rd., Nanning 530004, China
- Correspondence: (J.W.); (L.W.)
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15
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Tian X, Zhang K, Liu S, Sun X, Li X, Song J, Qi Z, Wang Y, Fang Y, Wang J, Jiang S, Yang C, Tian Z, Li WX, Ning H. Quantitative Trait Locus Analysis of Protein and Oil Content in Response to Planting Density in Soybean ( Glycine max [L.] Merri.) Seeds Based on SNP Linkage Mapping. Front Genet 2020; 11:563. [PMID: 32670348 PMCID: PMC7330087 DOI: 10.3389/fgene.2020.00563] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 05/11/2020] [Indexed: 12/31/2022] Open
Abstract
Soybean varieties suitable for high planting density allow greater yields. However, the seed protein and oil contents, which determine the value of this crop, can be influenced by planting density. Thus, it is important to understand the genetic basis of the responses of different soybean genotypes to planting density. In this study, we quantified the protein and oil contents in a four-way recombinant inbred line (FW-RIL) soybean population under two planting densities and the response to density. We performed quantitative trait locus (QTL) mapping using a single nucleotide polymorphism (SNP) linkage map generated by inclusive composite interval mapping. We identified 14 QTLs for protein content and 17 for oil content at a planting density of 2.15 × 105 plant/ha (D1) and 14 QTLs for protein content and 20 for oil content at a planting density 3.0 × 105 plant/ha (D2). Among the QTLs detected, two oil-content QTLs was detected at both plant densities. In addition, we identified 38 QTLs for the responses of protein and oil contents to planting density. Of the QTLs detected, 70 were identified in previous studies, while 33 were newly identified. Fourty-five QTLs accounted for over 10% of the phenotypic variation of the corresponding trait, based on 23 QTLs at a marker interval distance of ~600 kb detected under different densities and with the responses to density difference. Pathway analysis revealed four candidate genes involved in protein and oil biosynthesis/metabolism. These results improve our understanding of the genetic underpinnings of protein and oil biosynthesis in soybean, laying the foundation for enhancing protein and oil contents and increasing yields in soybean.
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Affiliation(s)
- Xiaocui Tian
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Kaixin Zhang
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Shulin Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Xu Sun
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Xiyu Li
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Jie Song
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Zhongying Qi
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Yue Wang
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Yanlong Fang
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Jiajing Wang
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Sitong Jiang
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Chang Yang
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Wen-Xia Li
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
| | - Hailong Ning
- Key Laboratory of Soybean Biology, Ministry of Education, Northeast Agricultural University, Harbin, China.,Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China.,Soybean Research Institute, Northeast Agricultural University, Harbin, China
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16
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Fan YL, Zhang XH, Zhong LJ, Wang XY, Jin LS, Lyu SH. One-step generation of composite soybean plants with transgenic roots by Agrobacterium rhizogenes-mediated transformation. BMC PLANT BIOLOGY 2020; 20:208. [PMID: 32397958 PMCID: PMC7333419 DOI: 10.1186/s12870-020-02421-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 04/29/2020] [Indexed: 05/22/2023]
Abstract
BACKGROUND Agrobacterium rhizogenes-mediated (ARM) transformation is a highly efficient technique for generating composite plants composed of transgenic roots and wild-type shoot, providing a powerful tool for studying root biology. The ARM transformation has been established in many plant species, including soybean. However, traditional transformation of soybean, transformation efficiency is low. Additionally, the hairy roots were induced in a medium, and then the generated composite plants were transplanted into another medium for growth. This two-step operation is not only time-consuming, but aggravates contamination risk in the study of plant-microbe interactions. RESULTS Here, we report a one-step ARM transformation method with higher transformation efficiency for generating composite soybean plants. Both the induction of hairy roots and continuous growth of the composite plants were conducted in a single growth medium. The primary root of a 7-day-old seedling was decapitated with a slanted cut, the residual hypocotyl (maintained 0.7-1 cm apical portion) was inoculated with A. rhizogenes harboring the gene construct of interest. Subsequently, the infected seedling was planted into a pot with wet sterile vermiculite. Almost 100% of the infected seedlings could produce transgenic positive roots 16 days post-inoculation in 7 tested genotypes. Importantly, the transgenic hairy roots in each composite plant are about three times more than those of the traditional ARM transformation, indicating that the one-step method is simpler in operation and higher efficiency in transformation. The reliability of the one-step method was verified by CRISPR/Cas9 system to knockout the soybean Rfg1, which restricts nodulation in Williams 82 (Nod-) by Sinorhizobium fredii USDA193. Furthermore, we applied this method to analyze the function of Arabidopsis YAO promoter in soybean. The activity of YAO promoter was detected in whole roots and stronger in the root tips. We also extended the protocol to tomato. CONCLUSIONS We established a one-step ARM transformation method, which is more convenient in operation and higher efficiency (almost 100%) in transformation for generating composite soybean plants. This method has been validated in promoter functional analysis and rhizobia-legume interactions. We anticipate a broad application of this method to analyze root-related events in tomato and other plant species besides soybean.
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Affiliation(s)
- Ying-lun Fan
- College of Agriculture, Liaocheng University, Liaocheng, 252000 China
| | - Xing-hui Zhang
- College of Agriculture, Liaocheng University, Liaocheng, 252000 China
| | - Li-jing Zhong
- College of Agriculture, Liaocheng University, Liaocheng, 252000 China
| | - Xiu-yuan Wang
- College of Agriculture, Liaocheng University, Liaocheng, 252000 China
| | - Liang-shen Jin
- College of Agriculture, Liaocheng University, Liaocheng, 252000 China
| | - Shan-hua Lyu
- College of Agriculture, Liaocheng University, Liaocheng, 252000 China
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Fang Y, Liu S, Dong Q, Zhang K, Tian Z, Li X, Li W, Qi Z, Wang Y, Tian X, Song J, Wang J, Yang C, Jiang S, Li WX, Ning H. Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height. FRONTIERS IN PLANT SCIENCE 2020; 11:9. [PMID: 32117360 PMCID: PMC7033546 DOI: 10.3389/fpls.2020.00009] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 01/07/2020] [Indexed: 05/05/2023]
Abstract
Plant height is an important target for soybean breeding. It is a typical quantitative trait controlled by multiple genes and is susceptible to environmental influences. Here, we carried out phenotypic analysis of 156 recombinant inbred lines derived from "Dongnong L13" and "Henong 60" in nine environments at four locations over 6 years using interval mapping and inclusive composite interval mapping methods. We performed quantitative trait locus (QTL) analysis by applying pre-built simple-sequence repeat maps. We detected 48 QTLs, including nine significant QTLs detected by multiple methods and in multiple environments. Meanwhile, genotyping of all lines using the SoySNP660k BeadChip produced 54,836 non-redundant single-nucleotide polymorphism (SNP) genotypes. We used five multi-locus genome-wide association analysis methods to locate 10 quantitative trait nucleotides (QTNs), four of which overlap with previously located QTLs. Five candidate genes related to plant height are predicted to lie within 200 kb of these four QTNs. We identified 19 homologous genes in Arabidopsis, two of which may be associated with plant height. These findings further our understanding of the multi-gene regulatory network and genetic determinants of soybean plant height, which will be important for breeding high-yielding soybean.
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Affiliation(s)
- Yanlong Fang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Shulin Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Quanzhong Dong
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Kaixin Zhang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Xiyu Li
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Wenbin Li
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Zhongying Qi
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Yue Wang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Xiaocui Tian
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jie Song
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jiajing Wang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Chang Yang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Sitong Jiang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Wen-Xia Li
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Hailong Ning
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
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Su J, Wang C, Hao F, Ma Q, Wang J, Li J, Ning X. Genetic Detection of Lint Percentage Applying Single-Locus and Multi-Locus Genome-Wide Association Studies in Chinese Early-Maturity Upland Cotton. FRONTIERS IN PLANT SCIENCE 2019; 10:964. [PMID: 31428110 PMCID: PMC6688134 DOI: 10.3389/fpls.2019.00964] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 07/10/2019] [Indexed: 05/28/2023]
Abstract
Upland cotton (Gossypium hirsutum L.) is the most important source of natural fiber in the world. Early-maturity upland cotton varieties are commonly planted in China. Nevertheless, lint yield of early-maturity upland cotton varieties is strikingly lower than that of middle- and late-maturity ones. How to effectively improve lint yield of early maturing cotton, becomes a focus of cotton research. Here, based on 72,792 high-quality single nucleotide polymorphisms of 160 early-maturing upland cotton accessions, we performed genome-wide association studies (GWASs) for lint percentage (LP), one of the most lint-yield component traits, applying one single-locus method and six multi-locus methods. A total of 4 and 45 significant quantitative trait nucleotides (QTNs) were respectively identified to be associated with LP. Interestingly, in two of four planting environments, two of these QTNs (A02_74713290 and A02_75551547) were simultaneously detected via both one single-locus and three or more multi-locus GWAS methods. Among the 42 genes within a genomic region (A02: 74.31-75.95 Mbp) containing the above two peak QTNs, Gh_A02G1269, Gh_A02G1280, and Gh_A02G1295 had the highest expression levels in ovules during seed development from 20 to 25 days post anthesis, whereas Gh_A02G1278 was preferentially expressed in the fibers rather than other organs. These results imply that the four potential candidate genes might be closely related to cotton LP by regulating the proportion of seed weight and fiber yield. The QTNs and potential candidate genes for LP, identified in this study, provide valuable resource for cultivating novel cotton varieties with earliness and high lint yield in the future.
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Affiliation(s)
- Junji Su
- Gansu Provincial Key Laboratory of Aridland Crop Science, College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Caixiang Wang
- Gansu Provincial Key Laboratory of Aridland Crop Science, College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Fushun Hao
- State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Science, Henan University, Kaifeng, China
| | - Qi Ma
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Ji Wang
- State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Science, Henan University, Kaifeng, China
| | - Jilian Li
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Xinzhu Ning
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
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Li S, Xu H, Yang J, Zhao T. Dissecting the Genetic Architecture of Seed Protein and Oil Content in Soybean from the Yangtze and Huaihe River Valleys Using Multi-Locus Genome-Wide Association Studies. Int J Mol Sci 2019; 20:E3041. [PMID: 31234445 PMCID: PMC6628128 DOI: 10.3390/ijms20123041] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/14/2019] [Accepted: 06/18/2019] [Indexed: 12/19/2022] Open
Abstract
Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein and oil content are two valuable quality traits controlled by multiple genes in soybean. In this study, the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) procedure was performed to dissect the genetic architecture of seed protein and oil content in a diverse panel of 279 soybean accessions from the Yangtze and Huaihe River Valleys in China. We identified 26 quantitative trait loci (QTLs) for seed protein content and 23 for seed oil content, including five associated with both traits. Among these, 39 QTLs corresponded to previously reported QTLs, whereas 10 loci were novel. As reported previously, the QTL on chromosome 20 was associated with both seed protein and oil content. This QTL exhibited opposing effects on these traits and contributed the most to phenotype variation. From the detected QTLs, 55 and 51 candidate genes were identified for seed protein and oil content, respectively. Among these genes, eight may be promising candidate genes for improving soybean nutritional quality. These results will facilitate marker-assisted selective breeding for soybean protein and oil content traits.
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Affiliation(s)
- Shuguang Li
- Huaiyin Institute of Agricultural Sciences of Xuhuai Region in Jiangsu/Huai'an Key Laboratory for Agricultural Biotechnology, Huai'an 223001, China.
| | - Haifeng Xu
- Huaiyin Institute of Agricultural Sciences of Xuhuai Region in Jiangsu/Huai'an Key Laboratory for Agricultural Biotechnology, Huai'an 223001, China.
| | - Jiayin Yang
- Huaiyin Institute of Agricultural Sciences of Xuhuai Region in Jiangsu/Huai'an Key Laboratory for Agricultural Biotechnology, Huai'an 223001, China.
| | - Tuanjie Zhao
- Soybean research institution, National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China.
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