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Li S, Guo C, Feng X, Wang J, Pan W, Xu C, Wei S, Han X, Yang M, Chen Q, Wang J, Hu L, Qi Z. Development and Validation of Kompetitive Allele-Specific Polymerase Chain Reaction Markers for Seed Protein Content in Soybean. PLANTS (BASEL, SWITZERLAND) 2024; 13:3485. [PMID: 39771183 PMCID: PMC11728539 DOI: 10.3390/plants13243485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Revised: 12/07/2024] [Accepted: 12/09/2024] [Indexed: 01/16/2025]
Abstract
Seed protein content is a critical trait in soybean breeding, as it provides a primary source of high-quality protein for both human consumption and animal feed. This study aimed to enhance molecular marker-assisted selection for high-protein soybean varieties by developing Kompetitive Allele-Specific Polymerase Chain Reaction (KASP) markers targeted at loci associated with seed protein content. Nineteen markers with high genotyping efficacy were identified through screening. Utilizing SN76 (a high-protein line) as the male parent and SN49 and DS1 (both low-protein lines) as female parents, 484 F6 generation individuals from these hybrid combinations were selected to validate the predictive accuracy of the 19 KASP markers. Notably, KASP-Pro-1, KASP-Pro-2, and KASP-Pro-3 effectively distinguished genotypes associated with high and low protein content, with prediction accuracies of 68.4%, 75.0%, and 83.3%, respectively. These results underscore the reliability and practical utility of the selected molecular markers, which are located within the genes Glyma.03G219900, Glyma.14G119000, and Glyma.17G074400, respectively. Haplotype analysis and gene pyramiding indicate that these three genes may influence seed protein content. Consequently, these KASP markers can be effectively integrated into genetic and genomic research on soybean seed protein content as well as into marker-assisted breeding.
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Affiliation(s)
- Shuangzhe Li
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Chenyijun Guo
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Xuezhen Feng
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Jing Wang
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Wenjing Pan
- Suihua Branch of Heilongjiang Academy of Agricultural Sciences, Suihua 152052, China; (W.P.); (J.W.)
| | - Chang Xu
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Siming Wei
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Xue Han
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Mingliang Yang
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Qingshan Chen
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Jinxing Wang
- Suihua Branch of Heilongjiang Academy of Agricultural Sciences, Suihua 152052, China; (W.P.); (J.W.)
| | - Limin Hu
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
| | - Zhaoming Qi
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.L.); (C.G.); (X.F.); (J.W.); (C.X.); (S.W.); (X.H.); (M.Y.); (Q.C.)
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Hu L, Wang X, Zhang J, Florez-Palacios L, Song Q, Jiang GL. Genome-Wide Detection of Quantitative Trait Loci and Prediction of Candidate Genes for Seed Sugar Composition in Early Mature Soybean. Int J Mol Sci 2023; 24:3167. [PMID: 36834578 PMCID: PMC9966586 DOI: 10.3390/ijms24043167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 01/28/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
Seed sugar composition, mainly including fructose, glucose, sucrose, raffinose, and stachyose, is an important indicator of soybean [Glycine max (L.) Merr.] seed quality. However, research on soybean sugar composition is limited. To better understand the genetic architecture underlying the sugar composition in soybean seeds, we conducted a genome-wide association study (GWAS) using a population of 323 soybean germplasm accessions which were grown and evaluated under three different environments. A total of 31,245 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected and used in the GWAS. The analysis identified 72 quantitative trait loci (QTLs) associated with individual sugars and 14 with total sugar. Ten candidate genes within the 100 Kb flanking regions of the lead SNPs across six chromosomes were significantly associated with sugar contents. According to GO and KEGG classification, eight genes were involved in the sugar metabolism in soybean and showed similar functions in Arabidopsis. The other two, located in known QTL regions associated with sugar composition, may play a role in sugar metabolism in soybean. This study advances our understanding of the genetic basis of soybean sugar composition and facilitates the identification of genes controlling this trait. The identified candidate genes will help improve seed sugar composition in soybean.
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Affiliation(s)
- Li Hu
- School of Agriculture, Yunnan University, Kunming 650091, China
| | - Xianzhi Wang
- School of Agriculture, Yunnan University, Kunming 650091, China
| | - Jiaoping Zhang
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Liliana Florez-Palacios
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Qijian Song
- USDA-ARS Beltsville Agricultural Research Center, Beltsville, MD 20705, USA
| | - Guo-Liang Jiang
- Agricultural Research Station, College of Agriculture, Virginia State University, Petersburg, VA 23806, USA
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Yuan W, Huang J, Li H, Ma Y, Gui C, Huang F, Feng X, Yu D, Wang H, Kan G. Genetic dissection reveals the complex architecture of amino acid composition in soybean seeds. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:17. [PMID: 36670242 DOI: 10.1007/s00122-023-04280-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
Five loci related to soybean protein and amino acid contents were colocated by performing linkage mapping and GWAS. The haplotype analysis showed that Glyma.08G109100 may be useful to improve the soybean seed composition. Soybean (Glycine max (L.) Merr.) seeds are good protein sources. Although genetic variation is abundant, natural variation in seed amino acids and their derived traits is lacking across soybean accessions. Here, we determined the contents of protein and 17 amino acids, obtained 36 derived traits based on the protein and total amino acid contents, and derived 34 traits based on seven amino acid family groups. Furthermore, we performed a linkage analysis of the contents of 17 amino acids and 73 amino acid-derived traits based on the recombinant inbred line (RIL)-derived Kefeng No. 1 × Nannong 1138-2. Six hundred thirty-nine quantitative trait loci (QTLs) were identified, explaining 6.07-39.00% of the phenotypic variation. Among these loci, five were detected in diverse soybean accessions using a genome-wide association study. A network analysis revealed that some loci that were significantly associated with multiple amino acids were tightly linked on chromosome 8 based on linkage disequilibrium values, which also further confirmed the results of the correlation analysis among amino acid traits. Through a combination of a genome-wide association study, linkage analysis, qRT-PCR, and genomic polymorphism comparison, Glyma.08G109100 on chromosome 8, which may affect amino acid contents, was selected. The haplotype analysis showed that Hap-T of Glyma.08G109100 may be useful to improve the contents of protein and 16 amino acids in soybean. This study provides new insights into the genetic basis of the amino acid composition in soybean seeds and may facilitate marker-based breeding of soybean with improved nutritional value.
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Affiliation(s)
- Wenjie Yuan
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Jie Huang
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Haiyang Li
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- School of Life Sciences, Guangzhou University, Guangzhou, China
| | - Yujie Ma
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, China
| | - Chunju Gui
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Fang Huang
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Xianzhong Feng
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Deyue Yu
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Hui Wang
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
| | - Guizhen Kan
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
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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: 4] [Impact Index Per Article: 1.3] [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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