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Antwi-Boasiako A, Jia S, Liu J, Guo N, Chen C, Karikari B, Feng J, Zhao T. Identification and Genetic Dissection of Resistance to Red Crown Rot Disease in a Diverse Soybean Germplasm Population. PLANTS (BASEL, SWITZERLAND) 2024; 13:940. [PMID: 38611470 PMCID: PMC11013609 DOI: 10.3390/plants13070940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024]
Abstract
Red crown rot (RCR) disease caused by Calonectria ilicicola negatively impacts soybean yield and quality. Unfortunately, the knowledge of the genetic architecture of RCR resistance in soybeans is limited. In this study, 299 diverse soybean accessions were used to explore their genetic diversity and resistance to RCR, and to mine for candidate genes via emergence rate (ER), survival rate (SR), and disease severity (DS) by a multi-locus random-SNP-effect mixed linear model of GWAS. All accessions had brown necrotic lesions on the primary root, with five genotypes identified as resistant. Nine single-nucleotide polymorphism (SNP) markers were detected to underlie RCR response (ER, SR, and DS). Two SNPs colocalized with at least two traits to form a haplotype block which possessed nine genes. Based on their annotation and the qRT-PCR, three genes, namely Glyma.08G074600, Glyma.08G074700, and Glyma.12G043600, are suggested to modulate soybean resistance to RCR. The findings from this study could serve as the foundation for breeding RCR-tolerant soybean varieties, and the candidate genes could be validated to deepen our understanding of soybean response to RCR.
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Affiliation(s)
- Augustine Antwi-Boasiako
- Key Laboratory of Biology and Genetics Improvement of Soybean, Ministry of Agriculture, Zhongshan Biological Breeding Laboratory (ZSBBL), National Innovation Platform for Soybean Breeding and Industry-Education Integration, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (A.A.-B.); (S.J.); (J.L.); (N.G.)
- Council for Scientific and Industrial Research-Crops Research Institute (CSIR-CRI), Fumesua, Kumasi P.O. Box 3785, Ghana
| | - Shihao Jia
- Key Laboratory of Biology and Genetics Improvement of Soybean, Ministry of Agriculture, Zhongshan Biological Breeding Laboratory (ZSBBL), National Innovation Platform for Soybean Breeding and Industry-Education Integration, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (A.A.-B.); (S.J.); (J.L.); (N.G.)
| | - Jiale Liu
- Key Laboratory of Biology and Genetics Improvement of Soybean, Ministry of Agriculture, Zhongshan Biological Breeding Laboratory (ZSBBL), National Innovation Platform for Soybean Breeding and Industry-Education Integration, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (A.A.-B.); (S.J.); (J.L.); (N.G.)
| | - Na Guo
- Key Laboratory of Biology and Genetics Improvement of Soybean, Ministry of Agriculture, Zhongshan Biological Breeding Laboratory (ZSBBL), National Innovation Platform for Soybean Breeding and Industry-Education Integration, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (A.A.-B.); (S.J.); (J.L.); (N.G.)
| | - Changjun Chen
- College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China;
| | - Benjamin Karikari
- Department of Agricultural Biotechnology, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Tamale P.O. Box TL 1882, Ghana;
- Département de Phytologie, Université Laval, Québec, QC G1V 0A6, Canada
| | - Jianying Feng
- Key Laboratory of Biology and Genetics Improvement of Soybean, Ministry of Agriculture, Zhongshan Biological Breeding Laboratory (ZSBBL), National Innovation Platform for Soybean Breeding and Industry-Education Integration, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (A.A.-B.); (S.J.); (J.L.); (N.G.)
| | - Tuanjie Zhao
- Key Laboratory of Biology and Genetics Improvement of Soybean, Ministry of Agriculture, Zhongshan Biological Breeding Laboratory (ZSBBL), National Innovation Platform for Soybean Breeding and Industry-Education Integration, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (A.A.-B.); (S.J.); (J.L.); (N.G.)
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Gao W, Ma R, Li X, Liu J, Jiang A, Tan P, Xiong G, Du C, Zhang J, Zhang X, Fang X, Yi Z, Zhang J. Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean ( Glycine max L.). Int J Mol Sci 2024; 25:2857. [PMID: 38474104 DOI: 10.3390/ijms25052857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean.
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Affiliation(s)
- Weiran Gao
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Ronghan Ma
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Xi Li
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Jiaqi Liu
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Aohua Jiang
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Pingting Tan
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Guoxi Xiong
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Chengzhang Du
- Institute of Specialty Crop, Chongqing Academy of Agricultural Sciences, Chongqing 402160, China
| | - Jijun Zhang
- Institute of Specialty Crop, Chongqing Academy of Agricultural Sciences, Chongqing 402160, China
| | - Xiaochun Zhang
- Institute of Specialty Crop, Chongqing Academy of Agricultural Sciences, Chongqing 402160, China
| | - Xiaomei Fang
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Zelin Yi
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Jian Zhang
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
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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:ijms24043167. [PMID: 36834578 PMCID: PMC9966586 DOI: 10.3390/ijms24043167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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Cai Z, Xian P, Cheng Y, Zhong Y, Yang Y, Zhou Q, Lian T, Ma Q, Nian H, Ge L. MOTHER-OF-FT-AND-TFL1 regulates the seed oil and protein content in soybean. THE NEW PHYTOLOGIST 2023. [PMID: 36740575 DOI: 10.1111/nph.18792] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Soybean is a major crop that produces valuable seed oil and protein for global consumption. Seed oil and protein are regulated by complex quantitative trait loci (QTLs) and have undergone intensive selections during the domestication of soybean. It is essential to identify the major genetic components and understand their mechanism behind seed oil and protein in soybean. We report that MOTHER-OF-FT-AND-TFL1 (GmMFT) is the gene of a classical QTL that has been reported to regulate seed oil and protein content in many studies. Mutation of MFT decreased seeds oil content and weight in both Arabidopsis and soybean, whereas increased expression of GmMFT enhanced seeds oil content and weight. Haplotype analysis showed that GmMFT has undergone selection, which resulted in the extended haplotype homozygosity in the cultivated soybean and the enriching of the oil-favorable allele in modern soybean cultivars. This work unraveled the GmMFT-mediated mechanism regulating seed oil and protein content and seed weight, and revealed a previously unknown function of MFT that provides new insights into targeted soybean improvement and breeding.
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Affiliation(s)
- Zhandong Cai
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- Department of Grassland Science, College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, Guangdong, China
| | - Peiqi Xian
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Yanbo Cheng
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Yiwang Zhong
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Yuan Yang
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Qianghua Zhou
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Tengxiang Lian
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Qibin Ma
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Hai Nian
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
| | - Liangfa Ge
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- Department of Grassland Science, College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
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Wang Z, Yu D, Morota G, Dhakal K, Singer W, Lord N, Huang H, Chen P, Mozzoni L, Li S, Zhang B. Genome-wide association analysis of sucrose and alanine contents in edamame beans. FRONTIERS IN PLANT SCIENCE 2023; 13:1086007. [PMID: 36816489 PMCID: PMC9935843 DOI: 10.3389/fpls.2022.1086007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/29/2022] [Indexed: 06/18/2023]
Abstract
The sucrose and Alanine (Ala) content in edamame beans significantly impacts the sweetness flavor of edamame-derived products as an important attribute to consumers' acceptance. Unlike grain-type soybeans, edamame beans are harvested as fresh beans at the R6 to R7 growth stages when beans are filled 80-90% of the pod capacity. The genetic basis of sucrose and Ala contents in fresh edamame beans may differ from those in dry seeds. To date, there is no report on the genetic basis of sucrose and Ala contents in the edamame beans. In this study, a genome-wide association study was conducted to identify single nucleotide polymorphisms (SNPs) related to sucrose and Ala levels in edamame beans using an association mapping panel of 189 edamame accessions genotyped with a SoySNP50K BeadChip. A total of 43 and 25 SNPs was associated with sucrose content and Ala content in the edamame beans, respectively. Four genes (Glyma.10g270800, Glyma.08g137500, Glyma.10g268500, and Glyma.18g193600) with known effects on the process of sucrose biosynthesis and 37 novel sucrose-related genes were characterized. Three genes (Gm17g070500, Glyma.14g201100 and Glyma.18g269600) with likely relevant effects in regulating Ala content and 22 novel Ala-related genes were identified. In addition, by summarizing the phenotypic data of edamame beans from three locations in two years, three PI accessions (PI 532469, PI 243551, and PI 407748) were selected as the high sucrose and high Ala parental lines for the perspective breeding of sweet edamame varieties. Thus, the beneficial alleles, candidate genes, and selected PI accessions identified in this study will be fundamental to develop edamame varieties with improved consumers' acceptance, and eventually promote edamame production as a specialty crop in the United States.
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Affiliation(s)
- Zhibo Wang
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Dajun Yu
- Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States
| | - Gota Morota
- School of Animal Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Kshitiz Dhakal
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - William Singer
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Nilanka Lord
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Haibo Huang
- Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States
| | - Pengyin Chen
- Fisher Delta Research Center, University of Missouri, Portageville, MO, United States
| | - Leandro Mozzoni
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Song Li
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Bo Zhang
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
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Zuo JF, Chen Y, Ge C, Liu JY, Zhang YM. Identification of QTN-by-environment interactions and their candidate genes for soybean seed oil-related traits using 3VmrMLM. FRONTIERS IN PLANT SCIENCE 2022; 13:1096457. [PMID: 36578334 PMCID: PMC9792120 DOI: 10.3389/fpls.2022.1096457] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Although seed oil content and its fatty acid compositions in soybean were affected by environment, QTN-by-environment (QEIs) and gene-by-environment interactions (GEIs) were rarely reported in genome-wide association studies. METHODS The 3VmrMLM method was used to associate the trait phenotypes, measured in five to seven environments, of 286 soybean accessions with 106,013 SNPs for detecting QTNs and QEIs. RESULTS Seven oil metabolism genes (GmSACPD-A, GmSACPD-B, GmbZIP123, GmSWEET39, GmFATB1A, GmDGAT2D, and GmDGAT1B) around 598 QTNs and one oil metabolism gene GmFATB2B around 54 QEIs were verified in previous studies; 76 candidate genes and 66 candidate GEIs were predicted to be associated with these traits, in which 5 genes around QEIs were verified in other species to participate in oil metabolism, and had differential expression across environments. These genes were found to be related to soybean seed oil content in haplotype analysis. In addition, most candidate GEIs were co-expressed with drought response genes in co-expression network, and three KEGG pathways which respond to drought were enriched under drought stress rather than control condition; six candidate genes were hub genes in the co-expression networks under drought stress. DISCUSSION The above results indicated that GEIs, together with drought response genes in co-expression network, may respond to drought, and play important roles in regulating seed oil-related traits together with oil metabolism genes. These results provide important information for genetic basis, molecular mechanisms, and soybean breeding for seed oil-related traits.
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Affiliation(s)
- Jian-Fang Zuo
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Ying Chen
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Chao Ge
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Jin-Yang Liu
- Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China
| | - Yuan-Ming Zhang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
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Xue Y, Gao H, Liu X, Tang X, Cao D, Luan X, Zhao L, Qiu L. QTL Mapping of Palmitic Acid Content Using Specific-Locus Amplified Fragment Sequencing (SLAF-Seq) Genotyping in Soybeans (Glycine max L.). Int J Mol Sci 2022; 23:ijms231911273. [PMID: 36232577 PMCID: PMC9569734 DOI: 10.3390/ijms231911273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/12/2022] [Accepted: 09/20/2022] [Indexed: 10/27/2022] Open
Abstract
Soybeans are essential crops that supply protein and oil. The composition and contents of soybean fatty acids are relevant to human health and have a significant relationship with soybean oil processing and applications. Identifying quantitative trait locus (QTL) genes related to palmitic acid could facilitate the development of a range of nutritive soybean cultivars using molecular marker-assisted selection. In this study, we used a cultivar with higher palmitic acid content, ‘Dongnong42’, and a lower palmitic acid content cultivar, ‘Hobbit’, to establish F2:6 recombinant inbred lines. A high-density genetic map containing 9980 SLAF markers was constructed and distributed across 20 soybean chromosomes. The genetic map contained a total genetic distance of 2602.58 cM and an average genetic distance of 0.39 cM between adjacent markers. Two QTLs related to palmitic acid content were mapped using inclusive composite interval mapping, explaining 4.2–10.1% of the phenotypic variance in three different years and environments, including the QTL included in seed palmitic 7-3, which was validated by developing SSR markers. Based on the SNP/Indel and significant differential expression analyses of Dongnong42 and Hobbit, two genes, Glyma.15g119700 and Glyma.15g119800, were selected as candidate genes. The high-density genetic map, QTLs, and molecular markers will be helpful for the map-based cloning of palmitic acid content genes. These could be used to accelerate breeding for high nutritive value cultivars via molecular marker-assisted breeding.
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Affiliation(s)
- Yongguo Xue
- Institute of Soybean Research, Heilongjiang Provincial Academy of Agricultural Sciences, Harbin 150086, China
- Key Laboratory of Soybean Biology of Ministry of Education China, Northeast Agricultural University, Harbin 150030, China
| | - Huawei Gao
- National Key Facility for Crop Gene Resources and Genetic Improvemen, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xinlei Liu
- Institute of Soybean Research, Heilongjiang Provincial Academy of Agricultural Sciences, Harbin 150086, China
| | - Xiaofei Tang
- Institute of Soybean Research, Heilongjiang Provincial Academy of Agricultural Sciences, Harbin 150086, China
| | - Dan Cao
- Institute of Soybean Research, Heilongjiang Provincial Academy of Agricultural Sciences, Harbin 150086, China
| | - Xiaoyan Luan
- Institute of Soybean Research, Heilongjiang Provincial Academy of Agricultural Sciences, Harbin 150086, China
| | - Lin Zhao
- Key Laboratory of Soybean Biology of Ministry of Education China, Northeast Agricultural University, Harbin 150030, China
- Correspondence: (L.Z.); (L.Q.)
| | - Lijuan Qiu
- Key Laboratory of Soybean Biology of Ministry of Education China, Northeast Agricultural University, Harbin 150030, China
- National Key Facility for Crop Gene Resources and Genetic Improvemen, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Correspondence: (L.Z.); (L.Q.)
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Kim DG, Lyu JI, Kim JM, Seo JS, Choi HI, Jo YD, Kim SH, Eom SH, Ahn JW, Bae CH, Kwon SJ. Identification of Loci Governing Agronomic Traits and Mutation Hotspots via a GBS-Based Genome-Wide Association Study in a Soybean Mutant Diversity Pool. Int J Mol Sci 2022; 23:ijms231810441. [PMID: 36142354 PMCID: PMC9499481 DOI: 10.3390/ijms231810441] [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: 07/21/2022] [Revised: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 11/25/2022] Open
Abstract
In this study, we performed a genotyping-by-sequencing analysis and a genome-wide association study of a soybean mutant diversity pool previously constructed by gamma irradiation. A GWAS was conducted to detect significant associations between 37,249 SNPs, 11 agronomic traits, and 6 phytochemical traits. In the merged data set, 66 SNPs on 13 chromosomes were highly associated (FDR p < 0.05) with the following 4 agronomic traits: days of flowering (33 SNPs), flower color (16 SNPs), node number (6 SNPs), and seed coat color (11 SNPs). These results are consistent with the findings of earlier studies on other genetic features (e.g., natural accessions and recombinant inbred lines). Therefore, our observations suggest that the genomic changes in the mutants generated by gamma irradiation occurred at the same loci as the mutations in the natural soybean population. These findings are indicative of the existence of mutation hotspots, or the acceleration of genome evolution in response to high doses of radiation. Moreover, this study demonstrated that the integration of GBS and GWAS to investigate a mutant population derived from gamma irradiation is suitable for dissecting the molecular basis of complex traits in soybeans.
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Affiliation(s)
- Dong-Gun Kim
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongup 56212, Korea
| | - Jae Il Lyu
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongup 56212, Korea
- Research Center of Crop Breeding for Omics and Artificial Intelligence, Kongju National University, Yesan 32439, Korea
| | - Jung Min Kim
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongup 56212, Korea
| | - Ji Su Seo
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongup 56212, Korea
| | - Hong-Il Choi
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongup 56212, Korea
| | - Yeong Deuk Jo
- Department of Horticultural Science, Chungnam National University, Daejeon 34134, Korea
| | - Sang Hoon Kim
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongup 56212, Korea
| | - Seok Hyun Eom
- Department of Horticultural Biotechnology, Institute of Life Sciences & Resources, Kyung Hee University, Yongin 17104, Korea
| | - Joon-Woo Ahn
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongup 56212, Korea
| | - Chang-Hyu Bae
- Department of Life Resources, Graduate School, Sunchon National University, Suncheon 57922, Korea
- Correspondence: (C.-H.B.); (S.-J.K.)
| | - Soon-Jae Kwon
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongup 56212, Korea
- Correspondence: (C.-H.B.); (S.-J.K.)
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Lu W, Sui M, Zhao X, Jia H, Han D, Yan X, Han Y. Genome-Wide Identification of Candidate Genes Underlying Soluble Sugar Content in Vegetable Soybean ( Glycine max L.) via Association and Expression Analysis. FRONTIERS IN PLANT SCIENCE 2022; 13:930639. [PMID: 35991392 PMCID: PMC9387354 DOI: 10.3389/fpls.2022.930639] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/18/2022] [Indexed: 05/11/2023]
Abstract
Soluble sugar is a major indicator of the intrinsic quality of vegetable soybean [Glycine max (L.) Merr. ]. The improvement of soluble sugar content in soybean is very important due to its healthcare functions for humans. The genetic mechanism of soluble sugar in soybean is unclear. In this study, 278 diverse soybean accessions were utilized to identify the quantitative trait nucleotides (QTNs) for total soluble sugar content in soybean seeds based on a genome-wide association study (GWAS). A total of 25,921 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected for GWAS. Totally, thirteen QTNs associated with total soluble sugar content were identified, which were distributed on ten chromosomes. One hundred and fifteen genes near the 200-kb flanking region of these identified QTNs were considered candidate genes associated with total soluble sugar content in soybean seed. Gene-based association analysis and haplotype analysis were utilized to further identify the effect of candidate genes on total soluble sugar content. Totally, 84 SNPs from seventeen genes across four chromosomes were significantly associated with the total soluble sugar content. Among them, three SNPs from Glyma.02G292900 were identified at two locations, and other eighty-one SNPs from sixteen genes were detected at three locations. Furthermore, expression level analysis of candidate genes revealed that Glyma.02G293200 and Glyma.02G294900 were significantly positively associated with soluble sugar content and Glyma.02G294000 was significantly negatively associated with soluble sugar content. Six genes (i.e., Glyma.02G292600, Glyma.02G292700, Glyma.02G294000, Glyma.02G294300, Glyma.02G294400, and Glyma.15G264200) identified by GWAS were also detected by the analysis of differential expression genes based on soybean germplasms with higher and lower soluble sugar content. Among them, Glyma.02G294000 is the only gene that was identified by gene-based association analysis with total soluble sugar content and was considered an important candidate gene for soluble sugar content. These candidate genes and beneficial alleles would be useful for improving the soluble sugar content of soybean.
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Affiliation(s)
- Wencheng Lu
- Heihe Branch of Heilongjiang Academy of Agricultural Sciences, Heihe, China
| | - Meinan Sui
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Xunchao Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Hongchang Jia
- Heihe Branch of Heilongjiang Academy of Agricultural Sciences, Heihe, China
| | - Dezhi Han
- Heihe Branch of Heilongjiang Academy of Agricultural Sciences, Heihe, China
| | - Xiaofei Yan
- Heihe Branch of Heilongjiang Academy of Agricultural Sciences, Heihe, China
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
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10
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Hong H, Najafabadi MY, Torkamaneh D, Rajcan I. Identification of quantitative trait loci associated with seed quality traits between Canadian and Ukrainian mega-environments using genome-wide association study. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2515-2530. [PMID: 35716202 DOI: 10.1007/s00122-022-04134-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
KEY MESSAGE Identifying QTL associated with soybean seed quality traits from a diverse GWAS panel cultivated in Canadian and Ukrainian mega-environments may facilitate future cultivar development for foreign markets. Understanding the complex genetic basis of seed quality traits for soybean in the mega-environments (MEs) is critical for developing a marker-assisted selection program that will lead to breeding superior cultivars adapted to specific regions. This study aimed to analyze the accumulation of 14 soybean seed quality traits in Canadian ME and two seed quality traits in Ukrainian ME and identify associated ME specific quantitative trait loci (QTLSP) and ME universal QTL (QTLU) for protein and oil using a genome-wide association study (GWAS) panel consisting of 184 soybean genotypes. The panel was planted in three locations in Canada and two locations in Ukraine in 2018 and 2019. Genotype plus genotype-by-environment biplot analysis was conducted to assess the accumulation of individual seed compounds across different locations. The protein accumulation was high in the Canadian ME and low in the Ukrainian ME, whereas the oil concentration showed the opposite trends between the two MEs. No QTLU were identified across the MEs for protein and oil concentrations. In contrast, nine Canadian QTLSP for protein were identified on various chromosomes, which were co-located with QTL controlling other traits identified in the Canadian ME. The lack of common QTLU for protein and oil suggests that it may be necessary to use QTLSP associated with these traits separately for the Canadian and Ukrainian ME. Additional Ukrainian data for seed compounds other than oil and protein are required to identify novel QTLSP and QTLU for such traits for the individual or combined Canadian and Ukrainian MEs.
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Affiliation(s)
- Huilin Hong
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | | | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC, G1V 0A6, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada.
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11
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Yoosefzadeh-Najafabadi M, Eskandari M, Torabi S, Torkamaneh D, Tulpan D, Rajcan I. Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. Int J Mol Sci 2022; 23:5538. [PMID: 35628351 PMCID: PMC9141736 DOI: 10.3390/ijms23105538] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 12/14/2022] Open
Abstract
A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.
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Affiliation(s)
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC G1V 0A6, Canada;
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
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12
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Turquetti-Moraes DK, Moharana KC, Almeida-Silva F, Pedrosa-Silva F, Venancio TM. Integrating omics approaches to discover and prioritize candidate genes involved in oil biosynthesis in soybean. Gene 2022; 808:145976. [PMID: 34592351 DOI: 10.1016/j.gene.2021.145976] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/15/2022]
Abstract
Soybean is a major source of edible protein and oil. Oil content is a quantitative trait that is significantly determined by genetic and environmental factors. Over the past 30 years, a large volume of soybean genetic, genomic, and transcriptomic data have been accumulated. Nevertheless, integrative analyses of such data remain scarce, in spite of their importance for crop improvement. We hypothesized that the co-occurrence of genomic regions for oil-related traits in different studies may reveal more stable regions encompassing important genetic determinants of oil content and quality in soybean. We integrated publicly available data, obtained with distinct techniques, to discover and prioritize candidate genes involved in oil biosynthesis and regulation in soybean. We detected key fatty acid biosynthesis genes (e.g., BCCP2 and ACCase, FADs, KAS family proteins) and several transcription factors, which are likely regulators of oil biosynthesis. In addition, we identified new candidates for seed oil accumulation and quality, such as Glyma.03G213300 and Glyma.19G160700, which encode a translocator protein homolog and a histone acetyltransferase, respectively. Further, oil and protein genomic hotspots are strongly associated with breeding and not with domestication, suggesting that soybean domestication prioritized other traits. The genes identified here are promising targets for breeding programs and for the development of soybean lines with increased oil content and quality.
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Affiliation(s)
- Dayana K Turquetti-Moraes
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, Brazil
| | - Kanhu C Moharana
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, Brazil
| | - Fabricio Almeida-Silva
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, Brazil
| | - Francisnei Pedrosa-Silva
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, Brazil
| | - Thiago M Venancio
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, Brazil.
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13
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Yoosefzadeh-Najafabadi M, Torabi S, Tulpan D, Rajcan I, Eskandari M. Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods. FRONTIERS IN PLANT SCIENCE 2021; 12:777028. [PMID: 34880894 PMCID: PMC8647880 DOI: 10.3389/fpls.2021.777028] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 10/18/2021] [Indexed: 05/12/2023]
Abstract
In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.
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Affiliation(s)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
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14
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Yang Y, Zhu X, Cui R, Wang R, Li H, Wang J, Chen H, Zhang D. Identification of soybean phosphorous efficiency QTLs and genes using chlorophyll fluorescence parameters through GWAS and RNA-seq. PLANTA 2021; 254:110. [PMID: 34716824 DOI: 10.1007/s00425-021-03760-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 10/14/2021] [Indexed: 06/13/2023]
Abstract
MAIN CONCLUSION Soybean phosphorous efficiency QTLs were identified and candidate genes were predicted using chlorophyll fluorescence parameters through GWAS and RNA-seq. Phosphorus (P) is an essential nutrient element for crop growth and development, lack of P uptake seriously affects yield in various crops. Photosynthesis is the basis of crop production, while it is very sensitive to P deficiency. It is of great importance to study the genetic relationship between photosynthesis and P efficiency to provide genetic insight for soybean improvement. In this study, a genome-wide association study (GWAS) was performed using 292,035 SNPs and the ratios of four main chlorophyll fluorescence parameters of 219 diverse soybean accessions under P deficiency and normal P across three experiments. In total, 52 SNPs in 12 genomic regions were detected in association with the four main chlorophyll fluorescence parameters under sufficient or deficient P levels. Combined it with RNA-seq analysis, we predicted three candidate genes for the significant genomic regions. For example, the expression level of the candidate gene (Glyma.18g092900) in P deficiency tolerant accession was three times higher than that of P deficiency sensitive one under phosphorous deficiency condition. This study provides insight into genetic links between photosynthetic and phosphorous efficiency and further functional analysis will provide valuable information for understanding the underlying genetic mechanism to facilitate marker-assisted breeding in soybean.
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Affiliation(s)
- Yuming Yang
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Xiuhua Zhu
- Henan Xuke Seed Industry Co., Ltd, Xuchang, China
| | - Ruifan Cui
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Ruiyang Wang
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Hongyan Li
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Jinshe Wang
- Zhengzhou National Subcenter for Soybean Improvement, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Huatao Chen
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
| | - Dan Zhang
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China.
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15
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Jianan Z, Li W, Zhang Y, Song W, Jiang H, Zhao J, Zhan Y, Teng W, Qiu L, Zhao X, Han Y. Identification of glutathione transferase gene associated with partial resistance to Sclerotinia stem rot of soybean using genome-wide association and linkage mapping. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2699-2709. [PMID: 34057551 DOI: 10.1007/s00122-021-03855-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 05/06/2021] [Indexed: 06/12/2023]
Abstract
KEY MESSAGE Association and linkage mapping techniques were used to identify and verify single nucleotide polymorphisms (SNPs) associated with Sclerotinia sclerotiorum resistance. A novel resistant gene, GmGST , was cloned and shown to be involved in soybean resistance to SSR. Sclerotinia stem rot (SSR), caused by the fungus Sclerotinia sclerotiorum, is one of the most devastating diseases in soybean (Glycine max (Linn.) Merr.) However, the genetic architecture underlying soybean resistance to SSR is poorly understood, despite several mapping and gene mining studies. In the present study, the identification of quantitative trait loci (QTLs) involved in the resistance to S. sclerotiorum was conducted in two segregating populations: an association population that consisted of 261 diverse soybean germplasms, and the MH population, derived from a cross between a partially resistant cultivar (Maple arrow) and a susceptible cultivar (Hefeng25). Three and five genomic regions affecting resistance were detected by genome-wide association study to control the lesion length of stems (LLS) and the death rate of seedling (DRS), respectively. Four QTLs were detected to underlie LLS, and one QTL controlled DRS after SSR infection. A major locus on chromosome (Chr.) 13 (qDRS13-1), which affected both DRS and LLS, was detected in both the natural population and the MH population. GmGST, encoding a glutathione S-transferase, was cloned as a candidate gene in qDRS13-1. GmGST was upregulated by the induction of the partially resistant cultivar Maple arrow. Transgenic experiments showed that the overexpression of GmGST in soybean increased resistance to S. sclerotiorum and the content of soluble pigment in stems of soybean. The results increase our understanding of the genetic architecture of soybean resistance to SSR and provide a framework for the future marker-assisted breeding of resistant soybean cultivars.
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Affiliation(s)
- Zou Jianan
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry, Northeast Agricultural University, Harbin, 150030, China
| | - Wenjing Li
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry, Northeast Agricultural University, Harbin, 150030, China
| | - Yuting Zhang
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry, Northeast Agricultural University, Harbin, 150030, China
| | - Wei Song
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry, Northeast Agricultural University, Harbin, 150030, China
| | - Haipeng Jiang
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry, Northeast Agricultural University, Harbin, 150030, China
| | - Jingyun Zhao
- Zhumadian Academy of Agricultural Sciences, Zhumadian, 463000, China
| | - Yuhang Zhan
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry, Northeast Agricultural University, Harbin, 150030, China
| | - Weili Teng
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry, Northeast Agricultural University, Harbin, 150030, China
| | - Lijuan Qiu
- Institute of Crop Science, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI) Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xue Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry, Northeast Agricultural University, Harbin, 150030, China.
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry, Northeast Agricultural University, Harbin, 150030, China.
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16
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Li Y, Wang Y, Wu X, Wang J, Wu X, Wang B, Lu Z, Li G. Novel Genomic Regions of Fusarium Wilt Resistance in Bottle Gourd [ Lagenaria siceraria (Mol.) Standl.] Discovered in Genome-Wide Association Study. FRONTIERS IN PLANT SCIENCE 2021; 12:650157. [PMID: 34025697 PMCID: PMC8137845 DOI: 10.3389/fpls.2021.650157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 03/30/2021] [Indexed: 06/12/2023]
Abstract
Fusarium wilt (FW) is a typical soil-borne disease that seriously affects the yield and fruit quality of bottle gourd. Thus, to improve resistance to FW in bottle gourd, the genetic mechanism underlying FW resistance needs to be explored. In this study, we performed a genome-wide association study (GWAS) based on 5,330 single-nucleotide polymorphisms (SNPs) and 89 bottle gourd accessions. The GWAS results revealed a total of 10 SNPs (P ≤ 0.01, -log10 P ≥ 2.0) significantly associated with FW resistance that were detected in at least two environments (2019DI, 2020DI, and the average across the 2 years); these SNPs were located on chromosomes 1, 2, 3, 4, 8, and 9. Linkage disequilibrium (LD) block structure analysis predicted three potential candidate genes for FW resistance. Genes HG_GLEAN_10001030 and HG_GLEAN_10001042 were within the range of the mean LD block of the marker BGReSe_14202; gene HG_GLEAN_10011803 was 280 kb upstream of the marker BGReSe_00818. Real-time quantitative PCR (qRT-PCR) analysis showed that HG_GLEAN_10011803 was significantly up-regulated in FW-infected plants of YD-4, Yin-10, and Hanbi; HG_GLEAN_10001030 and HG_GLEAN_10001042 were specifically up-regulated in FW-infected plants of YD-4. Therefore, gene HG_GLEAN_10011803 is likely the major effect candidate gene for resistance against FW in bottle gourd. This work provides scientific evidence for the exploration of candidate gene and development of functional markers in FW-resistant bottle gourd breeding programs.
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17
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Jing Y, Teng W, Qiu L, Zheng H, Li W, Han Y, Zhao X. Genetic dissection of soybean partial resistance to sclerotinia stem rot through genome wide association study and high throughout single nucleotide polymorphisms. Genomics 2021; 113:1262-1271. [PMID: 33689785 DOI: 10.1016/j.ygeno.2020.10.042] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 10/08/2020] [Accepted: 10/30/2020] [Indexed: 10/22/2022]
Abstract
Sclerotinia stem rot (SSR) is a disease of soybean [Glycine max (L.) Merr] that causes severe yield losses. We studied 185 representative soybean accessions to evaluate partial SSR resistance and sequenced these by the specific-locus amplified fragment sequencing method. In total, 22,048 single-nucleotide polymorphisms (SNPs), with minor allele frequencies (MAF) ≥5% and missing data <3%, were developed and applied to genome-wide association study of SSR responsiveness and assess linkage disequilibrium (LD) level for candidate gene selection. We identified 18 association signals related to SSR partial resistance. Among them, six overlapped the regions of previous quantitative trait loci, and twelve were novel. We identified 243 candidate genes located in the 200 kb genomic region of these peak SNPs. Based on quantitative real-time polymerase chain reaction and haplotype analysis, Glyma.03G196000 and Glyma.20G095100, encoding pentatricopeptide repeat proteins, might be important factors in the resistance response of soybean to SSR.
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Affiliation(s)
- Yan Jing
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030. Harbin, China
| | - Weili Teng
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030. Harbin, China
| | - Lijuan Qiu
- Institute of Crop Science, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI) Chinese Academy of Agricultural Sciences, 100081 Beijing, China
| | - Hongkun Zheng
- Bioinformatics Division, Biomarker Technologies Corporation, 101300, Beijing, China
| | - Wenbin Li
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030. Harbin, China
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030. Harbin, China.
| | - Xue Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030. Harbin, China.
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18
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Sung M, Van K, Lee S, Nelson R, LaMantia J, Taliercio E, McHale LK, Mian MAR. Identification of SNP markers associated with soybean fatty acids contents by genome-wide association analyses. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:27. [PMID: 37309353 PMCID: PMC10236115 DOI: 10.1007/s11032-021-01216-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 02/15/2021] [Indexed: 06/14/2023]
Abstract
Composition of fatty acids (FAs) in soybean seed is important for the quality and uses of soybean oil. Using gas chromatography, we have measured soybean FAs profiles of 621 soybean accessions (maturity groups I through IV) grown in five different environments; Columbus, OH (2015), Wooster, OH (2014 and 2015), Plymouth, NC (2015), and Urbana, IL (2015). Using publicly available SoySNP50K genotypic data and the FA profiles from this study, a genome-wide association analysis was completed with a compressed mixed linear model to identify 43 genomic regions significantly associated with a fatty acid at a genome wide significance threshold of 5%. Among these regions, one and three novel genomic regions associated with palmitic acid and stearic acid, respectively, were identified across all five environments. Additionally, nine novel environment-specific FA-related genomic regions were discovered providing new insights into the genetics of soybean FAs. Previously reported FA-related loci, such as FATB1a, SACPD-C, and KASIII, were also confirmed in this study. Our results will be useful for future functional studies and marker-assisted breeding for soybean FAs. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01216-1.
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Affiliation(s)
- Mikyung Sung
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695 USA
| | - Kyujung Van
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH 43210 USA
| | - Sungwoo Lee
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695 USA
- Department of Crop Science, Chungnam National University, Daejeon, 34134 South Korea
| | - Randall Nelson
- Department of Crop Sciences, University of Illinois and USDA-ARS (retired), Urbana, IL 61801 USA
| | - Jonathan LaMantia
- Corn, Soybean, Wheat Quality Research Unit, USDA-ARS, Wooster, OH 44691 USA
- Germplasm Analytics, TerViva Bioenergy Inc., Fort Pierce, FL 34951 USA
| | - Earl Taliercio
- Soybean and Nitrogen Fixation Unit, USDA-ARS, Raleigh, NC 27607 USA
| | - Leah K. McHale
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH 43210 USA
- Center for Soybean Research and Center of Applied Plant Sciences, The Ohio State University, Columbus, OH 43210 USA
| | - M. A. Rouf Mian
- Soybean and Nitrogen Fixation Unit, USDA-ARS, Raleigh, NC 27607 USA
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Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits. Cell Syst 2021; 12:5-22. [PMID: 33476553 DOI: 10.1016/j.cels.2020.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/15/2020] [Accepted: 10/07/2020] [Indexed: 02/08/2023]
Abstract
Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.
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Sui M, Wang Y, Bao Y, Wang X, Li R, Lv Y, Yan M, Quan C, Li C, Teng W, Li W, Zhao X, Han Y. Genome-wide association analysis of sucrose concentration in soybean (Glycine max L.) seed based on high-throughput sequencing. THE PLANT GENOME 2020; 13:e20059. [PMID: 33058418 DOI: 10.1002/tpg2.20059] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 08/31/2020] [Accepted: 09/02/2020] [Indexed: 06/11/2023]
Abstract
The sucrose concentration in soybean seed significantly affects the flavor of soybean-derived products. In this study, an association panel of 178 elite accessions and 33,149 single-nucleotide polymorphisms (SNPs) was utilized to identify quantitative trait nucleotides (QTNs) of sucrose concentration in soybean seeds by genome-wide association study (GWAS). Five QTNs (rs2688589, rs29026218, rs5926884, rs6886889, and rs10299216) distributed across five genomic regions in five chromosomes were identified in two or more locations by GWAS. A total of 60 candidate genes near the 200-kb flanking region of these five identified loci were identified. Three of these genes (Glyma.04G032600, Glyma.04G034600, and Glyma.11G092100) have been reported to be involved in the process of sugar biosynthesis. Based on gene-based association and haplotype analyses, a total of 35 SNPs from 10 genes associated with sucrose concentration were identified. Of them, Glyma.04G032600 was the only gene that has been reported to be related to sucrose content; the other nine genes were novel and may be associated with sucrose content. These beneficial alleles and candidate genes may be of great value in improving sucrose content in soybean seeds.
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Affiliation(s)
- Meinan Sui
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Yue Wang
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Yuyue Bao
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Xi Wang
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Ruiqiong Li
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Yan Lv
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Ming Yan
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Chao Quan
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Chunxia Li
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Weili Teng
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Wenbin Li
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Xue Zhao
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
| | - Yingpeng Han
- Key Laboratory of Soybean Biology and Breeding/Genetics, Chinese Ministry of Education, Northeast Agricultural University, Harbin, 150030, China
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Correa SM, Fernie AR, Nikoloski Z, Brotman Y. Towards model-driven characterization and manipulation of plant lipid metabolism. Prog Lipid Res 2020; 80:101051. [PMID: 32640289 DOI: 10.1016/j.plipres.2020.101051] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 06/20/2020] [Accepted: 06/21/2020] [Indexed: 01/09/2023]
Abstract
Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.
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Affiliation(s)
- Sandra M Correa
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel; Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín 050010, Colombia.
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Zoran Nikoloski
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modelling Group, Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm 14476, Germany.
| | - Yariv Brotman
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
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Sun M, Jing Y, Zhao X, Teng W, Qiu L, Zheng H, Li W, Han Y. Genome-wide association study of partial resistance to sclerotinia stem rot of cultivated soybean based on the detached leaf method. PLoS One 2020; 15:e0233366. [PMID: 32421759 PMCID: PMC7233537 DOI: 10.1371/journal.pone.0233366] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 05/04/2020] [Indexed: 12/12/2022] Open
Abstract
Sclerotinia stem rot (SSR) is a devastating fungal disease that causes severe yield losses of soybean worldwide. In the present study, a representative population of 185 soybean accessions was selected and utilized to identify the quantitative trait nucleotide (QTN) of partial resistance to soybean SSR via a genome-wide association study (GWAS). A total of 22,048 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) > 5% and missing data < 3% were used to assess linkage disequilibrium (LD) levels. Association signals associated with SSR partial resistance were identified by two models, including compressed mixed linear model (CMLM) and multi-locus random-SNP-effect mixed linear model (mrMLM). Finally, seven QTNs with major effects (a known locus and six novel loci) via CMLM and nine novel QTNs with minor effects via mrMLM were detected in relation to partial resistance to SSR, respectively. One of all the novel loci (Gm05:14834789 on Chr.05), which was co-located by these two methods, might be a stable one that showed high significance in SSR partial resistance. Additionally, a total of 71 major and 85 minor candidate genes located in the 200-kb genomic region of each peak SNP detected by CMLM and mrMLM were found, respectively. By using a gene-based association, a total of six SNPs from three major effects genes and eight SNPs from four minor effects genes were identified. Of them, Glyma.18G012200 has been characterized as a significant element in controlling fungal disease in plants.
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Affiliation(s)
- Mingming Sun
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
- Heilongjiang Journal Press of Agricultural Science and Technology, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Yan Jing
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Xue Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Weili Teng
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Lijuan Qiu
- Institute of Crop Science, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI) Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongkun Zheng
- Bioinformatics Division, Biomarker Technologies Corporation, Beijing, China
| | - Wenbin Li
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
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Yao Y, You Q, Duan G, Ren J, Chu S, Zhao J, Li X, Zhou X, Jiao Y. Quantitative trait loci analysis of seed oil content and composition of wild and cultivated soybean. BMC PLANT BIOLOGY 2020; 20:51. [PMID: 32005156 PMCID: PMC6995124 DOI: 10.1186/s12870-019-2199-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 12/12/2019] [Indexed: 05/26/2023]
Abstract
BACKGROUND Soybean oil is a major source of edible oil, and the domestication of wild soybean has resulted in significant changes in oil content and composition. Extensive efforts have been made to identify genetic loci that are related to soybean oil traits. The objective of this study was to identify quantitative trait loci (QTLs) related to soybean seed oil and compare the fatty acid composition between wild and cultivated soybean. RESULTS Using the specific-locus amplified fragment sequencing (SLAF-seq) method, a total of 181 recombinant inbred lines (RILs) derived from a cross between wild soybean ZYD00463 (Glycine soja) and cultivated soybean WDD01514 (Glycine max) were genotyped. Finally, a high-density genetic linkage map comprising 11,398 single-nucleotide polymorphism (SNP) markers on 20 linkage groups (LGs) was constructed. Twenty-four stable QTLs for seed oil content and composition were identified by model-based composite interval mapping (CIM) across multiple environments. Among these QTLs, 23 overlapped with or were adjacent to previously reported QTLs. One QTL, qPA10_1 (5.94-9.98 Mb) on Chr. Ten is a novel locus for palmitic acid. In the intervals of stable QTLs, some interesting genes involved in lipid metabolism were detected. CONCLUSIONS We developed 181 RILs from a cross between wild soybean ZYD00463 and cultivated soybean WDD01514 and constructed a high-density genetic map using the SLAF-seq method. We identified 24 stable QTLs for seed oil content and compositions, which includes qPA10_1 on Chr. 10, a novel locus for palmitic acid. Some interesting genes in the QTL regions were also detected. Our study will provide useful information for scientists to learn about genetic variations in lipid metabolism between wild and cultivated soybean.
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Affiliation(s)
- Yanjie Yao
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan, 430062, China
- Graduate School of Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- State Key Laboratory of Agricultural Microbiology, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, China
| | - Qingbo You
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan, 430062, China
| | - Guozhan Duan
- Soybean Research Laboratory, Xuchang Research Institute of Agricultural Sciences, Xuchang, 461000, China
| | - Jianjun Ren
- Soybean Research Laboratory, Xuchang Research Institute of Agricultural Sciences, Xuchang, 461000, China
| | - Shanshan Chu
- Collaborative Innovation Center of Henan Grain Crops /College of Agronomy, Henan Agricultural University, Zhengzhou, 450002, China
| | - Junqing Zhao
- Soybean Research Laboratory, Xuchang Research Institute of Agricultural Sciences, Xuchang, 461000, China
| | - Xia Li
- State Key Laboratory of Agricultural Microbiology, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, China.
| | - Xinan Zhou
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan, 430062, China.
| | - Yongqing Jiao
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan, 430062, China.
- Collaborative Innovation Center of Henan Grain Crops /College of Agronomy, Henan Agricultural University, Zhengzhou, 450002, China.
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Luo T, Xia W, Gong S, Mason AS, Li Z, Liu R, Dou Y, Tang W, Fan H, Zhang C, Xiao Y. Identifying Vitamin E Biosynthesis Genes in Elaeis guineensis by Genome-Wide Association Study. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2020; 68:678-685. [PMID: 31858793 DOI: 10.1021/acs.jafc.9b03832] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Elaeis guineensis is a tropical oil crop and has the highest oil yield per unit area. Palm oil has high palmitic acid content and is also rich in vitamins, including vitamin E. We conducted genome-wide association studies in a diversity panel of 161 E. guineensis accessions to identify single-nucleotide polymorphisms (SNPs) linked with vitamin E and validated candidate genes in these marker-associated intervals. Based on the SNPs reported in our previous research, 47 SNP markers were detected to be significantly associated with the variation of tocopherol and tocotrienol content at a cutoff P value of 6.3 × 10-7. A total of 656 candidate genes in the flanking regions of the 47 SNPs were identified, followed by pathway enrichment analysis. Of these candidate genes, EgHGGT (homogentisate geranylgeranyl transferase) involved in the biosynthesis of tocotrienols had a higher expression level in the mesocarp compared to other tissues. Expression of the EgHGGT gene was positively correlated with the variation in α-tocotrienol content. Induced overexpression of the gene in Arabidopsis caused a significant increase in vitamin E content and production of α-tocotrienols compared to wild Arabidopsis.
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Affiliation(s)
- Tingting Luo
- National Research Center of Rapeseed Engineering and Technology, College of Plant Science and Technology , Huazhong Agricultural University , Wuhan 430070 , China
| | - Wei Xia
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops , Hainan University , Haikou 570228 , P.R China
| | - Shufang Gong
- Coconut Research Institute , Chinese Academy of Tropical Agricultural Sciences , Wenchang 571339 , P.R. China
| | - Annaliese S Mason
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition , Justus Liebig University Giessen , Heinrich-Buff-Ring 26-32 , Giessen 35392i , Germany
| | - Zhiying Li
- Coconut Research Institute , Chinese Academy of Tropical Agricultural Sciences , Wenchang 571339 , P.R. China
- Hainan Key Laboratory for Biosafe Monitoring and Molecular Breeding in Off-Season Reproduction Region , Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences , Haikou 571101 , China
| | - Rui Liu
- Coconut Research Institute , Chinese Academy of Tropical Agricultural Sciences , Wenchang 571339 , P.R. China
| | - Yajing Dou
- Coconut Research Institute , Chinese Academy of Tropical Agricultural Sciences , Wenchang 571339 , P.R. China
| | - Wenqi Tang
- Coconut Research Institute , Chinese Academy of Tropical Agricultural Sciences , Wenchang 571339 , P.R. China
| | - Haikuo Fan
- Coconut Research Institute , Chinese Academy of Tropical Agricultural Sciences , Wenchang 571339 , P.R. China
| | - Chunyu Zhang
- National Research Center of Rapeseed Engineering and Technology, College of Plant Science and Technology , Huazhong Agricultural University , Wuhan 430070 , China
| | - Yong Xiao
- Coconut Research Institute , Chinese Academy of Tropical Agricultural Sciences , Wenchang 571339 , P.R. China
- Hainan Key Laboratory for Biosafe Monitoring and Molecular Breeding in Off-Season Reproduction Region , Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences , Haikou 571101 , China
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Natural Variation in Fatty Acid Composition of Diverse World Soybean Germplasms Grown in China. AGRONOMY-BASEL 2019. [DOI: 10.3390/agronomy10010024] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Soybean (Glycine max L. Merr.) is one of the most important crops in the world. Its major content of vegetable oil made it widely used for human consumption and several food industries. To investigate the variation in seed fatty acid composition of soybeans from different origins, a set of 633 soybean accessions originated from four diverse germplasm collections—including China, United States of America (USA), Japan, and Russia—were grown in three locations, Beijing, Anhui, and Hainan for two years. The results showed significant differences (P < 0.001) among the four germplasm origins for all fatty acid contents investigated. Higher levels, on average, of palmitic acid (PA) and linolenic acid (LNA) were observed in Russian germplasm (12.31% and 8.15%, respectively), whereas higher levels of stearic acid (SA) and oleic acid (OA) were observed in Chinese germplasm (3.95% and 21.95%, respectively). The highest level of linoleic acid (LA) was noticed in the USA germplasm accessions (56.34%). The largest variation in fatty acid composition was found in LNA, while a large variation was observed between Chinese and USA germplasms for LA level. Maturity group (MG) significantly (P < 0.0001) affected all fatty acids and higher levels of PA, SA, and OA were observed in early maturing accessions, while higher levels of LA and LNA were observed in late maturing accessions. The trends of fatty acids concentrations with different MG in this study further provide an evidence of the importance of MG in breeding for such soybean seed components. Collectively, the unique accessions identified in this study can be used to strengthen the soybean breeding programs for meeting various human nutrition patterns around the globe.
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Wang J, Zhao X, Wang W, Qu Y, Teng W, Qiu L, Zheng H, Han Y, Li W. Genome-wide association study of inflorescence length of cultivated soybean based on the high-throughout single-nucleotide markers. Mol Genet Genomics 2019; 294:607-620. [PMID: 30739204 DOI: 10.1007/s00438-019-01533-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 01/31/2019] [Indexed: 11/25/2022]
Abstract
As an important and complex trait, inflorescence length (IL) of soybean [Glycine max (L.) Merr.] significantly affected seed yields. Therefore, elucidating molecular basis of inflorescence architecture, especially for IL, was important for improving soybean yield potentials. Longer IL meaned to have more pod and seed in soybean. Hence, increasing IL and improving yield are targets for soybean breeding. In this study, a association panel, comprising 283 diverse samples, was used to dissect the genetic basis of IL based on genome-wide association analysis (GWAS) and haplotype analysis. GWAS and haplotype analysis were conducted through high-throughout single-nucleotide polymorphisms (SNP) developed by SLAF-seq methodology. A total of 39, 057 SNPs (minor allele frequency ≥ 0.2 and missing data ≤ 10%) were utilized to evaluate linkage disequilibrium (LD) level in the tested association panel. A total of 30 association signals were identified to be associated with IL via GWAS. Among them, 13 SNPs were novel, and another 17 SNPs were overlapped or located near the linked regions of known quantitative trait nucleotide (QTN) with soybean seed yield or yield component. The functional genes, located in the 200-kb genomic region of each peak SNP, were considered as candidate genes, such as the cell division/ elongation, specific enzymes, and signaling or transport of specific proteins. These genes have been reported to participant in the regulation of IL. Ten typical long-IL lines and ten typical short-IL lines were re-sequencing, and then, six SNPs from five genes were obtained based on candidate gene-based association. In addition, 42 haplotypes were defined based on haplotype analysis. Of them, 11 haplotypes were found to regulate long IL (> 14 mm) in soybean. The identified 30 QTN with beneficial alleles and their candidate genes might be valuable for dissecting the molecular mechanisms of IL and further improving the yield potential of soybean.
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Affiliation(s)
- Jinyang Wang
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Xue Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Wei Wang
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Yingfan Qu
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Weili Teng
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Lijuan Qiu
- Institute of Crop Science, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI) Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Hongkun Zheng
- Bioinformatics Division, Biomarker Technologies Corporation, Beijing, 101300, China
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China.
| | - Wenbin Li
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China.
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Zhao X, Jiang H, Feng L, Qu Y, Teng W, Qiu L, Zheng H, Han Y, Li W. Genome-wide association and transcriptional studies reveal novel genes for unsaturated fatty acid synthesis in a panel of soybean accessions. BMC Genomics 2019; 20:68. [PMID: 30665360 PMCID: PMC6341525 DOI: 10.1186/s12864-019-5449-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 01/11/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The nutritional value of soybean oil is largely influenced by the proportions of unsaturated fatty acids (FAs), including oleic acid (OA, 18:1), linoleic acid (LLA, 18:2), and linolenic acid (LNA, 18:3). Genome-wide association (GWAS) studies along with gene expression studies in soybean [Glycine max (L.) Merr.] were leveraged to dissect the genetics of unsaturated FAs. RESULTS A association panel of 194 diverse soybean accessions were phenotyped in 2013, 2014 and 2015 to identify Single Nucleotide Polymorphisms (SNPs) associated with OA, LLA, and LNA content, and determine putative candidate genes responsible for regulating unsaturated FAs composition. 149 SNPs that represented 73 genomic regions were found to be associated with the unsaturated FA contents in soybean seeds according to the results of GWAS. Twelve novel genes were predicted to be involved in unsaturated FA synthesis in soybean. The relationship between expression pattern of the candidate genes and the accumulation of unsaturated FAs revealed that multiple genes might be involved in unsaturated FAs regulation simultaneously but work in very different ways: Glyma.07G046200 and Glyma.20G245500 promote the OA accumulation in soybean seed in all the tested accessions; Glyma.13G68600 and Glyma.16G200200 promote the OA accumulation only in high OA germplasms; Glyma.07G151300 promotes OA accumulation in higher OA germplasms and suppresses that in lower OA germplasms; Glyma.16G003500 has the effect of increasing LLA accumulation in higher LA germplasms; Glyma.07G254500 suppresses the accumulation of LNA in lower OA germplasms; Glyma.14G194300 might be involved in the accumulation of LNA content in lower LNA germplasms. CONCLUSIONS The beneficial alleles and candidate genes identified might be valuable for improving marker-assisted breeding efficiency and exploring the molecular mechanisms underlying unsaturated fatty acid of soybean.
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Affiliation(s)
- Xue Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030, Harbin, China
| | - Haipeng Jiang
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030, Harbin, China
| | - Lei Feng
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030, Harbin, China
| | - Yingfan Qu
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030, Harbin, China
| | - Weili Teng
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030, Harbin, China
| | - Lijuan Qiu
- Institute of Crop Science, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI) Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Hongkun Zheng
- Bioinformatics Division, Biomarker Technologies Corporation, Beijing, 101300 China
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030, Harbin, China
| | - Wenbin Li
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, 150030, Harbin, China
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28
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Jing Y, Zhao X, Wang J, Teng W, Qiu L, Han Y, Li W. Identification of the Genomic Region Underlying Seed Weight per Plant in Soybean ( Glycine max L. Merr.) via High-Throughput Single-Nucleotide Polymorphisms and a Genome-Wide Association Study. FRONTIERS IN PLANT SCIENCE 2018; 9:1392. [PMID: 30369935 PMCID: PMC6194254 DOI: 10.3389/fpls.2018.01392] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 09/03/2018] [Indexed: 05/30/2023]
Abstract
Seed weight per plant (SWPP) of soybean (Glycine max (L.) Merr.), a complicated quantitative trait controlled by multiple genes, was positively associated with soybean seed yields. In the present study, a natural soybean population containing 185 diverse accessions primarily from China was used to analyze the genetic basis of SWPP via genome-wide association analysis (GWAS) based on high-throughput single-nucleotide polymorphisms (SNPs) generated by the Specific Locus Amplified Fragment Sequencing (SLAF-seq) method. A total of 33,149 SNPs were finally identified with minor allele frequencies (MAF) > 5% which were present in 97% of all the genotypes. Twenty association signals associated with SWPP were detected via GWAS. Among these signals, eight SNPs were novel loci, and the other twelve SNPs were overlapped or located in the linked genomic regions of the reported QTL from SoyBase database. Several genes belonging to the categories of hormone pathways, RNA regulation of transcription in plant development, ubiquitin, transporting systems, and other metabolisms were considered as candidate genes associated with SWPP. Furthermore, nine genes from the flanking region of Gm07:19488264, Gm08:15768591, Gm08:15768603, or Gm18:23052511 were significantly associated with SWPP and were stable among multiple environments. Nine out of 18 haplotypes from nine genes showed the effect of increasing SWPP. The identified loci along with the beneficial alleles and candidate genes could be of great value for studying the molecular mechanisms underlying SWPP and for improving the potential seed yield of soybean in the future.
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Affiliation(s)
- Yan Jing
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Xue Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Jinyang Wang
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Weili Teng
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Lijuan Qiu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Wenbin Li
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
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Zhang Y, He J, Wang H, Meng S, Xing G, Li Y, Yang S, Zhao J, Zhao T, Gai J. Detecting the QTL-Allele System of Seed Oil Traits Using Multi-Locus Genome-Wide Association Analysis for Population Characterization and Optimal Cross Prediction in Soybean. FRONTIERS IN PLANT SCIENCE 2018; 9:1793. [PMID: 30568668 PMCID: PMC6290252 DOI: 10.3389/fpls.2018.01793] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 11/19/2018] [Indexed: 05/18/2023]
Abstract
Soybean is one of the world's major vegetative oil sources, while oleic acid and linolenic acid content are the major quality traits of soybean oil. The restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS), characterized with error and false-positive control, has provided a potential approach for a relatively thorough detection of whole-genome QTL-alleles. The Chinese soybean landrace population (CSLRP) composed of 366 accessions was tested under four environments to identify the QTL-allele constitution of seed oil, oleic acid and linolenic acid content (SOC, OAC, and LAC). Using RTM-GWAS with 29,119 SNPLDBs (SNP linkage disequilibrium blocks) as genomic markers, 50, 98, and 50 QTLs with 136, 283, and 154 alleles (2-9 per locus) were detected, with their contribution 82.52, 90.31, and 83.86% to phenotypic variance, corresponding to their heritability 91.29, 90.97, and 90.24% for SOC, OAC, and LAC, respectively. The RTM-GWAS was shown to be more powerful and efficient than previous single-locus model GWAS procedures. For each trait, the detected QTL-alleles were organized into a QTL-allele matrix as the population genetic constitution. From which the genetic differentiation among 6 eco-populations was characterized as significant allele frequency differentiation on 28, 56, and 30 loci for the three traits, respectively. The QTL-allele matrices were also used for genomic selection for optimal crosses, which predicted transgressive potential up to 24.76, 40.30, and 2.37% for the respective traits, respectively. From the detected major QTLs, 38, 27, and 25 candidate genes were annotated for the respective traits, and two common QTL covering eight genes were identified for further study.
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Affiliation(s)
- Yinghu Zhang
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
- Jiangsu Coastal Institute of Agricultural Sciences, Yancheng, China
| | - Jianbo He
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
- National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, China
- Key Laboratory of Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Hongwei Wang
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
| | - Shan Meng
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
| | - Guangnan Xing
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
- National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, China
- Key Laboratory of Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Yan Li
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
- National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, China
- Key Laboratory of Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Shouping Yang
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
- National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, China
- Key Laboratory of Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jinming Zhao
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
- National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, China
- Key Laboratory of Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Tuanjie Zhao
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
- National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, China
- Key Laboratory of Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Junyi Gai
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
- National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, China
- Key Laboratory of Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- *Correspondence: Junyi Gai
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Li YH, Reif JC, Hong HL, Li HH, Liu ZX, Ma YS, Li J, Tian Y, Li YF, Li WB, Qiu LJ. Genome-wide association mapping of QTL underlying seed oil and protein contents of a diverse panel of soybean accessions. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2018; 266:95-101. [PMID: 29241572 DOI: 10.1016/j.plantsci.2017.04.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Revised: 04/24/2017] [Accepted: 04/26/2017] [Indexed: 05/08/2023]
Abstract
To investigate the genetic basis of variation in oil and protein contents in soybean seeds, a diverse collection of 421 mainly Chinese soybean cultivars was genotyped using 1536 SNPs, mostly from candidate genes related to acyl-lipid metabolism and from regions harboring known QTL. Six significant associations were identified for each of seed oil and protein contents which individually explained 2.7-5.9% of the phenotypic variance. Six associations occurred in or near known QTL and the remaining are putative novel QTL. Ten significant associations influenced the oil content without decreasing protein content, and vice versa. One SNP was pleiotropic, with opposite effects on oil and protein contents. The genetic region covering Map-6076 and-6077 was shown to be involved in controlling oil content in soybean by integrating the results of association mapping with information on known QTL and tissue-specific expression data. This region was subject to strong selection during the genetic improvement of soybean. Our results not only confirm and refine the map positions of known QTL but also contribute to a further elucidation of the genetic architecture of protein and oil contents in soybean seeds by identifying new associations exhibiting pleiotropic effects on seed protein and oil contents.
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Affiliation(s)
- Ying-Hui Li
- The National Key Facility for Crop, Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081 Beijing, China.
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
| | - Hui-Long Hong
- The National Key Facility for Crop, Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081 Beijing, China.
| | - Hui-Hui Li
- The National Key Facility for Crop, Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081 Beijing, China.
| | - Zhang-Xiong Liu
- The National Key Facility for Crop, Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081 Beijing, China.
| | - Yan-Song Ma
- Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, 150086 Harbin, China.
| | - Jun Li
- College of agriculture, Guangxi University, 530004 Nanning, China
| | - Yun Tian
- The National Key Facility for Crop, Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081 Beijing, China.
| | - Yan-Fei Li
- The National Key Facility for Crop, Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081 Beijing, China.
| | - Wen-Bin Li
- Key Laboratory of Soybean Biology in Chinese Education Ministry, Northeast Agricultural University, 150030 Harbin, China.
| | - Li-Juan Qiu
- The National Key Facility for Crop, Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081 Beijing, China.
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Fang C, Ma Y, Wu S, Liu Z, Wang Z, Yang R, Hu G, Zhou Z, Yu H, Zhang M, Pan Y, Zhou G, Ren H, Du W, Yan H, Wang Y, Han D, Shen Y, Liu S, Liu T, Zhang J, Qin H, Yuan J, Yuan X, Kong F, Liu B, Li J, Zhang Z, Wang G, Zhu B, Tian Z. Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biol 2017; 18:161. [PMID: 28838319 PMCID: PMC5571659 DOI: 10.1186/s13059-017-1289-9] [Citation(s) in RCA: 215] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 07/25/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Soybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding. RESULTS To understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits. CONCLUSIONS This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.
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Affiliation(s)
- Chao Fang
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100039, China
| | - Yanming Ma
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shiwen Wu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Zhi Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100039, China
| | - Zheng Wang
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Rui Yang
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Guanghui Hu
- Institute of maize research, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Zhengkui Zhou
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Hong Yu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Min Zhang
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yi Pan
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Guoan Zhou
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Haixiang Ren
- Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, Mudanjiang, 157041, China
| | - Weiguang Du
- Institute of Soybean Research, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Hongrui Yan
- Heihe Branch of Heilongjiang Academy of Agricultural Sciences, Heihe, 164300, China
| | - Yanping Wang
- Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, Mudanjiang, 157041, China
| | - Dezhi Han
- Institute of Soybean Research, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Yanting Shen
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100039, China
| | - Shulin Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100039, China
| | - Tengfei Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100039, China
| | - Jixiang Zhang
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100039, China
| | - Hao Qin
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jia Yuan
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xiaohui Yuan
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070, China
| | - Fanjiang Kong
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, 130102, China
| | - Baohui Liu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, 130102, China
| | - Jiayang Li
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA.
| | - Guodong Wang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100039, China.
| | - Baoge Zhu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100039, China.
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Li B, Fan S, Yu F, Chen Y, Zhang S, Han F, Yan S, Wang L, Sun J. High-resolution mapping of QTL for fatty acid composition in soybean using specific-locus amplified fragment sequencing. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017; 130:1467-1479. [PMID: 28389769 PMCID: PMC5487593 DOI: 10.1007/s00122-017-2902-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 03/30/2017] [Indexed: 05/18/2023]
Abstract
KEY MESSAGE We constructed a high-density linkage map comprising 3541 markers developed by specific-locus amplified fragment sequencing, and identified 26 stable QTL including nine novel loci, for fatty acid composition in soybean. Soybean oil quality and stability are mainly determined by the fatty acid composition of the seed. In the present study, we constructed a high-density genetic linkage map using 200 recombinant inbred lines derived from a cross between cultivated soybean varieties Luheidou2 and Nanhuizao, and SNP markers developed by specific-locus amplified fragment sequencing (SLAF-seq). This map comprises 3541 markers on 20 linkage groups and spans a genetic distance of 2534.42 cM, with an average distance of 0.72 cM between adjacent markers. Inclusive composite interval mapping revealed 26 stable QTL for five fatty acids, explaining 0.4-37.0% of the phenotypic variance for individual fatty acids across environments. Of these QTL, nine are novel loci (qLA1, qLNA2_1, qPA4_1, qLA4_1, qPA6_1, qSA12_1, qPA16_1, qOA18_1, and qFA19_1). These stable QTL harbor three fatty acid biosynthesis genes (GmFabG, GmACP, and GmFAD8), and 66 genes encoding lipid-related transcription factors. These stable QTL and tightly linked SNP markers can be used for marker-assisted selection in soybean breeding programs.
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Affiliation(s)
- Bin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, MOA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Shengxü Fan
- The National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, MOA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Fukuan Yu
- The National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, MOA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Ying Chen
- The National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, MOA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Shengrui Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, MOA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Fenxia Han
- The National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, MOA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Shurong Yan
- The National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, MOA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Lianzheng Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, MOA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Junming Sun
- The National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, MOA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Beijing, 100081, China.
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Jia C, Wu X, Chen M, Wang Y, Liu X, Gong P, Xu Q, Wang X, Gao H, Wang Z. Identification of genetic loci associated with crude protein and mineral concentrations in alfalfa (Medicago sativa) using association mapping. BMC PLANT BIOLOGY 2017; 17:97. [PMID: 28583066 PMCID: PMC5460482 DOI: 10.1186/s12870-017-1047-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 05/25/2017] [Indexed: 05/04/2023]
Abstract
BACKGROUND Alfalfa (Medicago sativa) is one of the most important legume forage species in China and many other countries of the world. It provides a quality source of proteins and minerals to animals. Genetic underpinnings for these important traits, however, are elusive. An alfalfa (M. sativa) association mapping study for six traits, namely crude protein (CP), rumen undegraded protein (RUP), and four mineral elements (Ca, K, Mg and P), was conducted in three consecutive years using a large collection encompassing 336 genotypes genotyped with 85 simple sequence repeat (SSR) markers. RESULTS All the traits were significantly influenced by genotype, environment, and genotype × environment interaction. Eight-five significant associations (P < 0.005) were identified. Among these, five associations with Ca were repeatedly observed and six co-localized associations were identified. CONCLUSIONS The identified marker alleles significantly associated with the traits provided important information for understanding genetic controls of alfalfa quality. The markers could be used in assisting selection for the individual traits in breeding populations for developing new alfalfa cultivars.
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Affiliation(s)
- Congjun Jia
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Xinming Wu
- Institute of Animal Husbandry and Veterinary Science, Shanxi Academy of Agricultural Sciences, Taiyuan, 030032 China
| | - Min Chen
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Yunqi Wang
- Institute of Animal Husbandry and Veterinary Science, Shanxi Academy of Agricultural Sciences, Taiyuan, 030032 China
| | - Xiqiang Liu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Pan Gong
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Qingfang Xu
- College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Taigu, 030801 China
| | - Xuemin Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Hongwen Gao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Zan Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
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Vargas-Bello-Pérez E, Toro-Mujica P, Enriquez-Hidalgo D, Fellenberg MA, Gómez-Cortés P. Short communication: Discrimination between retail bovine milks with different fat contents using chemometrics and fatty acid profiling. J Dairy Sci 2017; 100:4253-4257. [PMID: 28434733 DOI: 10.3168/jds.2016-12393] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 02/26/2017] [Indexed: 12/13/2022]
Abstract
We used a multivariate chemometric approach to differentiate or associate retail bovine milks with different fat contents and non-dairy beverages, using fatty acid profiles and statistical analysis. We collected samples of bovine milk (whole, semi-skim, and skim; n = 62) and non-dairy beverages (n = 27), and we analyzed them using gas-liquid chromatography. Principal component analysis of the fatty acid data yielded 3 significant principal components, which accounted for 72% of the total variance in the data set. Principal component 1 was related to saturated fatty acids (C4:0, C6:0, C8:0, C12:0, C14:0, C17:0, and C18:0) and monounsaturated fatty acids (C14:1 cis-9, C16:1 cis-9, C17:1 cis-9, and C18:1 trans-11); whole milk samples were clearly differentiated from the rest using this principal component. Principal component 2 differentiated semi-skim milk samples by n-3 fatty acid content (C20:3n-3, C20:5n-3, and C22:6n-3). Principal component 3 was related to C18:2 trans-9,trans-12 and C20:4n-6, and its lower scores were observed in skim milk and non-dairy beverages. A cluster analysis yielded 3 groups: group 1 consisted of only whole milk samples, group 2 was represented mainly by semi-skim milks, and group 3 included skim milk and non-dairy beverages. Overall, the present study showed that a multivariate chemometric approach is a useful tool for differentiating or associating retail bovine milks and non-dairy beverages using their fatty acid profile.
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Affiliation(s)
- Einar Vargas-Bello-Pérez
- Departamento de Ciencias Animales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago, Casilla 306, C.P. 6904411, Chile.
| | - Paula Toro-Mujica
- Departamento de Ciencias Animales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago, Casilla 306, C.P. 6904411, Chile
| | - Daniel Enriquez-Hidalgo
- Departamento de Ciencias Animales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago, Casilla 306, C.P. 6904411, Chile
| | - María Angélica Fellenberg
- Departamento de Ciencias Animales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago, Casilla 306, C.P. 6904411, Chile
| | - Pilar Gómez-Cortés
- Instituto de Investigación en Ciencias de la Alimentación (CSIC-UAM), Universidad Autónoma de Madrid, Nicolás Cabrera 9, Madrid, 28049, Spain
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Leamy LJ, Zhang H, Li C, Chen CY, Song BH. A genome-wide association study of seed composition traits in wild soybean (Glycine soja). BMC Genomics 2017; 18:18. [PMID: 28056769 PMCID: PMC5217241 DOI: 10.1186/s12864-016-3397-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Accepted: 12/07/2016] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Cultivated soybean (Glycine max) is a major agricultural crop that provides a crucial source of edible protein and oil. Decreased amounts of saturated palmitic acid and increased amounts of unsaturated oleic acid in soybean oil are considered optimal for human cardiovascular health and therefore there has considerable interest by breeders in discovering genes affecting the relative concentrations of these fatty acids. Using a genome-wide association (GWA) approach with nearly 30,000 single nucleotide polymorphisms (SNPs), we investigated the genetic basis of protein, oil and all five fatty acid levels in seeds from a sample of 570 wild soybeans (Glycine soja), the progenitor of domesticated soybean, to identify quantitative trait loci (QTLs) affecting these seed composition traits. RESULTS We discovered 29 SNPs located on ten different chromosomes that are significantly associated with the seven seed composition traits in our wild soybean sample. Eight SNPs co-localized with QTLs previously uncovered in linkage or association mapping studies conducted with cultivated soybean samples, while the remaining SNPs appeared to be in novel locations. Twenty-four of the SNPs significantly associated with fatty acid variation, with the majority located on chromosomes 14 (6 SNPs) and seven (8 SNPs). Two SNPs were common for two or more fatty acids, suggesting loci with pleiotropic effects. We also identified some candidate genes that are involved in fatty acid metabolism and regulation. For each of the seven traits, most of the SNPs produced differences between the average phenotypic values of the two homozygotes of about one-half standard deviation and contributed over 3% of their total variability. CONCLUSIONS This is the first GWA study conducted on seed composition traits solely in wild soybean populations, and a number of QTLs were found that have not been previously discovered. Some of these may be useful to breeders who select for increased protein/oil content or altered fatty acid ratios in the seeds. The results also provide additional insight into the genetic architecture of these traits in a large sample of wild soybean, and suggest some new candidate genes whose molecular effects on these traits need to be further studied.
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Affiliation(s)
- Larry J Leamy
- Department of Biological Sciences, the University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - Hengyou Zhang
- Department of Biological Sciences, the University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - Changbao Li
- Double Haploid Optimization Group, Monsanto Company, Chesterfield, MO, 63017, USA
| | - Charles Y Chen
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, 36849, USA.
| | - Bao-Hua Song
- Department of Biological Sciences, the University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.
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