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Jamison DR, Chen P, Hettiarachchy NS, Miller DM, Shakiba E. Identification of Quantitative Trait Loci (QTL) for Sucrose and Protein Content in Soybean Seed. PLANTS (BASEL, SWITZERLAND) 2024; 13:650. [PMID: 38475496 DOI: 10.3390/plants13050650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/11/2024] [Accepted: 02/16/2024] [Indexed: 03/14/2024]
Abstract
Protein and sugar content are important seed quality traits in soybean because they improve the value and sustainability of soy food and feed products. Thus, identifying Quantitative Trait Loci (QTL) for soybean seed protein and sugar content can benefit plant breeders and the soybean market by accelerating the breeding process via marker-assisted selection. For this study, a population of recombinant inbred lines (RILs) was developed from a cross between R08-3221 (high protein and low sucrose) and R07-2000 (high sucrose and low protein). Phenotypic data for protein content were taken from the F2:4 and F2:5 generations. The DA7250 NIR analyzer and HPLC instruments were used to analyze total seed protein and sucrose content. Genotypic data were generated using analysis via the SoySNP6k chip. A total of four QTLs were identified in this study. Two QTLs for protein content were located on chromosomes 11 and 20, and two QTLs associated with sucrose content were located on chromosomes 14 and. 11, the latter of which co-localized with detected QTLs for protein, explaining 10% of the phenotypic variation for protein and sucrose content in soybean seed within the study population. Soybean breeding programs can use the results to improve soybean seed quality.
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Affiliation(s)
- Daniel R Jamison
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Pengyin Chen
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | | | - David M Miller
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Ehsan Shakiba
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
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2
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Feng W, Fu L, Fu M, Sang Z, Wang Y, Wang L, Ren H, Du W, Hao X, Sun L, Zhang J, Wang W, Xing G, He J, Gai J. Transgressive Potential Prediction and Optimal Cross Design of Seed Protein Content in the Northeast China Soybean Population Based on Full Exploration of the QTL-Allele System. FRONTIERS IN PLANT SCIENCE 2022; 13:896549. [PMID: 35903228 PMCID: PMC9317943 DOI: 10.3389/fpls.2022.896549] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 06/09/2022] [Indexed: 06/12/2023]
Abstract
Northeast China is a major soybean production region in China. A representative sample of the Northeast China soybean germplasm population (NECSGP) composed of 361 accessions was evaluated for their seed protein content (SPC) in Tieling, Northeast China. This SPC varied greatly, with a mean SPC of 40.77%, ranging from 36.60 to 46.07%, but it was lower than that of the Chinese soybean landrace population (43.10%, ranging from 37.51 to 50.46%). The SPC increased slightly from 40.32-40.97% in the old maturity groups (MG, MGIII + II + I) to 40.93-41.58% in the new MGs (MG0 + 00 + 000). The restricted two-stage multi-locus genome-wide association study (RTM-GWAS) with 15,501 SNP linkage-disequilibrium block (SNPLDB) markers identified 73 SPC quantitative trait loci (QTLs) with 273 alleles, explaining 71.70% of the phenotypic variation, wherein 28 QTLs were new ones. The evolutionary changes of QTL-allele structures from old MGs to new MGs were analyzed, and 97.79% of the alleles in new MGs were inherited from the old MGs and 2.21% were new. The small amount of new positive allele emergence and possible recombination between alleles might explain the slight SPC increase in the new MGs. The prediction of recombination potentials in the SPC of all the possible crosses indicated that the mean of SPC overall crosses was 43.29% (+2.52%) and the maximum was 50.00% (+9.23%) in the SPC, and the maximum transgressive potential was 3.93%, suggesting that SPC breeding potentials do exist in the NECSGP. A total of 120 candidate genes were annotated and functionally classified into 13 categories, indicating that SPC is a complex trait conferred by a gene network.
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Affiliation(s)
- Weidan Feng
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Lianshun Fu
- Tieling Academy of Agricultural Sciences, Tieling, China
| | - Mengmeng Fu
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
| | - Ziqian Sang
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
| | - Yanping Wang
- Mudanjiang Research and Development Center for Soybean/Mudanjiang Experiment Station of the National Center for Soybean Improvement, Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, Mudanjiang, China
| | - Lei Wang
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Haixiang Ren
- Mudanjiang Research and Development Center for Soybean/Mudanjiang Experiment Station of the National Center for Soybean Improvement, Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, Mudanjiang, China
| | - Weiguang Du
- Mudanjiang Research and Development Center for Soybean/Mudanjiang Experiment Station of the National Center for Soybean Improvement, Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, Mudanjiang, China
| | - Xiaoshuai Hao
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Lei Sun
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jiaoping Zhang
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Wubin Wang
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Guangnan Xing
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jianbo He
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Junyi Gai
- Soybean Research Institute/MARA National Center for Soybean Improvement/MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, 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
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3
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Chen L, Yang S, Araya S, Quigley C, Taliercio E, Mian R, Specht JE, Diers BW, Song Q. Genotype imputation for soybean nested association mapping population to improve precision of QTL detection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1797-1810. [PMID: 35275252 PMCID: PMC9110473 DOI: 10.1007/s00122-022-04070-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
KEY MESSAGE Software for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification. Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.
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Affiliation(s)
- Linfeng Chen
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Soybean Research Institute, Nanjing Agricultural University, Nanjing, 210095, China
| | - Shouping Yang
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Soybean Research Institute, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Susan Araya
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA
| | - Charles Quigley
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA
| | - Earl Taliercio
- Soybean and Nitrogen Fixation Research, USDA-ARS, Raleigh, NC, 27607, USA
| | - Rouf Mian
- Soybean and Nitrogen Fixation Research, USDA-ARS, Raleigh, NC, 27607, USA
| | - James E Specht
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, 68583, USA
| | - Brian W Diers
- Department of Crop Sciences, National Soybean Research Center, University of Illinois, 1101 West Peabody Drive, Urbana, IL, 61801, USA
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA.
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4
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Qin J, Wang F, Zhao Q, Shi A, Zhao T, Song Q, Ravelombola W, An H, Yan L, Yang C, Zhang M. Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline. FRONTIERS IN PLANT SCIENCE 2022; 13:882732. [PMID: 35783963 PMCID: PMC9244705 DOI: 10.3389/fpls.2022.882732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/16/2022] [Indexed: 05/13/2023]
Abstract
Soybean is a primary meal protein for human consumption, poultry, and livestock feed. In this study, quantitative trait locus (QTL) controlling protein content was explored via genome-wide association studies (GWAS) and linkage mapping approaches based on 284 soybean accessions and 180 recombinant inbred lines (RILs), respectively, which were evaluated for protein content for 4 years. A total of 22 single nucleotide polymorphisms (SNPs) associated with protein content were detected using mixed linear model (MLM) and general linear model (GLM) methods in Tassel and 5 QTLs using Bayesian interval mapping (IM), single-trait multiple interval mapping (SMIM), single-trait composite interval mapping maximum likelihood estimation (SMLE), and single marker regression (SMR) models in Q-Gene and IciMapping. Major QTLs were detected on chromosomes 6 and 20 in both populations. The new QTL genomic region on chromosome 6 (Chr6_18844283-19315351) included 7 candidate genes and the Hap.X AA at the Chr6_19172961 position was associated with high protein content. Genomic selection (GS) of protein content was performed using Bayesian Lasso (BL) and ridge regression best linear unbiased prediction (rrBULP) based on all the SNPs and the SNPs significantly associated with protein content resulted from GWAS. The results showed that BL and rrBLUP performed similarly; GS accuracy was dependent on the SNP set and training population size. GS efficiency was higher for the SNPs derived from GWAS than random SNPs and reached a plateau when the number of markers was >2,000. The SNP markers identified in this study and other information were essential in establishing an efficient marker-assisted selection (MAS) and GS pipelines for improving soybean protein content.
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Affiliation(s)
- Jun Qin
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Hebei Laboratory of Crop Genetics and Breeding, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
| | - Fengmin Wang
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Hebei Laboratory of Crop Genetics and Breeding, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
| | - Qingsong Zhao
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Hebei Laboratory of Crop Genetics and Breeding, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
| | - Ainong Shi
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
- *Correspondence: Ainong Shi,
| | - Tiantian Zhao
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Hebei Laboratory of Crop Genetics and Breeding, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
| | - Qijian Song
- Soybean Genomics and Improvement Lab, United States Department of Agriculture - Agricultural Research Service (USDA-ARS), Beltsville, MD, United States
| | - Waltram Ravelombola
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Hongzhou An
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Hebei Laboratory of Crop Genetics and Breeding, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
| | - Long Yan
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Hebei Laboratory of Crop Genetics and Breeding, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
| | - Chunyan Yang
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Hebei Laboratory of Crop Genetics and Breeding, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
- Chunyan Yang,
| | - Mengchen Zhang
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Hebei Laboratory of Crop Genetics and Breeding, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
- Mengchen Zhang,
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5
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Li SN, Cheng P, Bai YQ, Shi Y, Yu JY, Li RC, Zhou RN, Zhang ZG, Wu XX, Chen QS. Analysis of Soybean Somatic Embryogenesis Using Chromosome Segment Substitution Lines and Transcriptome Sequencing. Genes (Basel) 2019; 10:E943. [PMID: 31752416 PMCID: PMC6896167 DOI: 10.3390/genes10110943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 11/05/2019] [Accepted: 11/18/2019] [Indexed: 12/15/2022] Open
Abstract
Soybean is an important cash crop that is widely used as a source of vegetable protein and edible oil. The regeneration ability of soybean directly affects the application of biotechnology. In this study, we used the exogenous hormone 2,4-D to treat immature embryos. Different levels of somatic incidence were selected from the chromosome segment substitution lines (CSSLs) constructed by SN14 and ZYD00006. Transcriptome sequencing of extreme materials was performed, and 2666 differentially expressed genes were obtained. At the same time, a difference table was generated by combining the data on CSSL rearrangement. In the extreme materials, a total of 93 differentially expressed genes were predicted and were then analyzed by cluster analysis and Gene Ontology (GO) annotation. After screening and annotating the target genes, three differentially expressed genes with hormone pathways were identified. The expression patterns of the target genes were verified by real-time quantitative PCR (qRT-PCR). Haplotype polymorphism detection and linkage disequilibrium analysis were performed on the candidate gene Glyma.09g248200. This study provided more information on the regulation network of soybean somatic embryogenesis and regeneration processes, and further identified important genes in the soybean regeneration process and provided a theoretical basis for accelerating the application of biotechnology to soybean for improving its breeding efficiency.
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Affiliation(s)
| | | | | | | | | | | | | | - Zhan-Guo Zhang
- College of Agriculture, Northeast Agricultural University, Harbin 150030, Heilongjiang, China; (S.-N.L.); (P.C.); (Y.-Q.B.); (Y.S.); (J.-Y.Y.); (R.-C.L.); (R.-N.Z.)
| | - Xiao-Xia Wu
- College of Agriculture, Northeast Agricultural University, Harbin 150030, Heilongjiang, China; (S.-N.L.); (P.C.); (Y.-Q.B.); (Y.S.); (J.-Y.Y.); (R.-C.L.); (R.-N.Z.)
| | - Qing-Shan Chen
- College of Agriculture, Northeast Agricultural University, Harbin 150030, Heilongjiang, China; (S.-N.L.); (P.C.); (Y.-Q.B.); (Y.S.); (J.-Y.Y.); (R.-C.L.); (R.-N.Z.)
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6
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Prenger EM, Ostezan A, Mian MAR, Stupar RM, Glenn T, Li Z. Identification and characterization of a fast-neutron-induced mutant with elevated seed protein content in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:2965-2983. [PMID: 31324928 DOI: 10.1007/s00122-019-03399-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Accepted: 07/11/2019] [Indexed: 06/10/2023]
Abstract
KEY MESSAGE Protein content of soybean is critical for utility of soybean meal. A fast-neutron-induced deletion on chromosome 12 was found to be associated with increased protein content. Soybean seed composition affects the utility of soybean, and improving seed composition is an essential breeding goal. Fast neutron radiation introduces genomic mutations resulting in novel variation for traits of interest. Two elite soybean lines were irradiated with fast neutrons and screened for altered seed composition. Twenty-three lines with altered protein, oil, or sucrose content were selected based on near-infrared spectroscopy data from five environments and yield tested at five locations. Mutants with significantly increased protein averaged 19.1-36.8 g kg-1 more protein than the parents across 10 environments. Comparative genomic hybridization (CGH) identified putative mutations in a mutant, G15FN-12, that has 36.8 g kg-1 higher protein than the parent genotype, and whole genome sequencing (WGS) of the mutant has confirmed these mutations. An F2:3 population was developed from G15FN-12 to determine association between genomic changes and increased protein content. Bulked segregant analysis of the population using the SoySNP50K BeadChip identified a CGH- and WGS-confirmed deletion on chromosome 12 to be responsible for elevated protein content. The population was genotyped using a KASP marker designed at the mutation region, and significant association (P < 0.0001) between the deletion on chromosome 12 and elevated protein content was observed and confirmed in the F3:4 generation. The F2 segregants homozygous for the deletion averaged 27 g kg-1 higher seed protein and 8 g kg-1 lower oil than homozygous wild-type segregants. Mutants with altered seed composition are a new resource for gene function studies and provide elite materials for genetic improvement of seed composition.
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Affiliation(s)
- Elizabeth M Prenger
- Department of Crop and Soil Sciences and Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Athens, GA, 30602, USA
| | - Alexandra Ostezan
- Department of Crop and Soil Sciences and Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Athens, GA, 30602, USA
| | - M A Rouf Mian
- Soybean and Nitrogen Fixation Research Unit, USDA-ARS, Raleigh, NC, 27607, USA
| | - Robert M Stupar
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Travis Glenn
- Deparment of Genetics, University of Georgia, Athens, GA, 30602, USA
| | - Zenglu Li
- Department of Crop and Soil Sciences and Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Athens, GA, 30602, USA.
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7
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Zhu S, Zhou L, Zhang C, Bao Y, Wu B, Chu H, Yu Y, He Y, Feng L. Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4065. [PMID: 31547118 PMCID: PMC6807262 DOI: 10.3390/s19194065] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 09/08/2019] [Accepted: 09/19/2019] [Indexed: 11/16/2022]
Abstract
Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise spectra were extracted and preprocessed, and average spectra were also obtained. Convolutional neural networks (CNN) using the average spectra and pixel-wise spectra of different numbers of soybeans were built. Pixel-wise CNN models obtained good performance predicting pixel-wise spectra and average spectra. With the increase of soybean numbers, performances were improved, with the classification accuracy of each variety over 90%. Traditionally, the number of samples used for modeling is large. It is time-consuming and requires labor to obtain hyperspectral data from large batches of samples. To explore the possibility of achieving decent identification results with few samples, a majority vote was also applied to the pixel-wise CNN models to identify a single soybean variety. Prediction maps were obtained to present the classification results intuitively. Models using pixel-wise spectra of 60 soybeans showed equivalent performance to those using the average spectra of 810 soybeans, illustrating the possibility of discriminating soybean varieties using few samples by acquiring pixel-wise spectra.
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Affiliation(s)
- Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| | - Lei Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| | - Yidan Bao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| | - Baohua Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| | - Hangjian Chu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| | - Yue Yu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| | - Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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8
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Zhang X, Hina A, Song S, Kong J, Bhat JA, Zhao T. Whole-genome mapping identified novel "QTL hotspots regions" for seed storability in soybean (Glycine max L.). BMC Genomics 2019; 20:499. [PMID: 31208334 PMCID: PMC6580613 DOI: 10.1186/s12864-019-5897-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 06/11/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Seed aging in soybean is a serious challenge for agronomic production and germplasm preservation. However, its genetic basis remains largely unclear in soybean. Unraveling the genetic mechanism involved in seed aging, and enhancing seed storability is an imperative goal for soybean breeding. The aim of this study is to identify quantitative trait loci (QTLs) using high-density genetic linkage maps of soybean for seed storability. In this regard, two recombinant inbred line (RIL) populations derived from Zhengyanghuangdou × Meng 8206 (ZM6) and Linhefenqingdou × Meng 8206 (LM6) crosses were evaluated for three seed-germination related traits viz., germination rate (GR), normal seedling length (SL) and normal seedling fresh weight (FW) under natural and artificial aging conditions to map QTLs for seed storability. RESULTS A total of 34 QTLs, including 13 QTLs for GR, 11 QTLs for SL and 10 QTLs for FW, were identified on 11 chromosomes with the phenotypic variation ranged from 7.30 to 23.16% under both aging conditions. All these QTLs were novel, and 21 of these QTLs were clustered in five QTL-rich regions on four different chromosomes viz., Chr3, Chr5, Chr17 &Chr18, among them the highest concentration of seven and six QTLs were found in "QTL hotspot A" (Chr17) and "QTL hotspot B" (Chr5), respectively. Furthermore, QTLs within all the five QTL clusters are linked to at least two studied traits, which is also supported by highly significant correlation between the three germination-related traits. QTLs for seed-germination related traits in "QTL hotspot B" were found in both RIL populations and aging conditions, and also QTLs underlying "QTL hotspot A" are identified in both RIL populations under artificial aging condition. These are the stable genomic regions governing the inheritance of seed storability in soybean, and will be the main focus for soybean breeders. CONCLUSION This study uncovers the genetic basis of seed storability in soybean. The newly identified QTLs provides valuable information, and will be main targets for fine mapping, candidate gene identification and marker-assisted breeding. Hence, the present study is the first report for the comprehensive and detailed investigation of genetic architecture of seed storability in soybean.
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Affiliation(s)
- Xi Zhang
- Soybean Research Institution, National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China
| | - Aiman Hina
- Soybean Research Institution, National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China
| | - Shiyu Song
- Soybean Research Institution, National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China
| | - Jiejie Kong
- Soybean Research Institution, National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China
| | - Javaid Akhter Bhat
- Soybean Research Institution, National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China
| | - Tuanjie Zhao
- Soybean Research Institution, National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China
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9
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Karikari B, Li S, Bhat JA, Cao Y, Kong J, Yang J, Gai J, Zhao T. Genome-Wide Detection of Major and Epistatic Effect QTLs for Seed Protein and Oil Content in Soybean Under Multiple Environments Using High-Density Bin Map. Int J Mol Sci 2019; 20:E979. [PMID: 30813455 PMCID: PMC6412760 DOI: 10.3390/ijms20040979] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 02/01/2019] [Accepted: 02/19/2019] [Indexed: 01/25/2023] Open
Abstract
Seed protein and oil content are the two important traits determining the quality and value of soybean. Development of improved cultivars requires detailed understanding of the genetic basis underlying the trait of interest. However, it is prerequisite to have a high-density linkage map for precisely mapping genomic regions, and therefore the present study used high-density genetic map containing 2267 recombination bin markers distributed on 20 chromosomes and spanned 2453.79 cM with an average distance of 1.08 cM between markers using restriction-site-associated DNA sequencing (RAD-seq) approach. A recombinant inbred line (RIL) population of 104 lines derived from a cross between Linhefenqingdou and Meng 8206 cultivars was evaluated in six different environments to identify main- and epistatic-effect quantitative trait loci (QTLs)as well as their interaction with environments. A total of 44 main-effect QTLs for protein and oil content were found to be distributed on 17 chromosomes, and 15 novel QTL were identified for the first time. Out of these QTLs, four were major and stable QTLs, viz., qPro-7-1, qOil-8-3, qOil-10-2 and qOil-10-4, detected in at least two environments plus combined environment with R² values >10%. Within the physical intervals of these four QTLs, 111 candidate genes were screened for their direct or indirect involvement in seed protein and oil biosynthesis/metabolism processes based on gene ontology and annotation information. Based on RNA sequencing (RNA-seq) data analysis, 15 of the 111 genes were highly expressed during seed development stage and root nodules that might be considered as the potential candidate genes. Seven QTLs associated with protein and oil content exhibited significant additive and additive × environment interaction effects, and environment-independent QTLs revealed higher additive effects. Moreover, three digenic epistatic QTLs pairs were identified, and no main-effect QTLs showed epistasis. In conclusion, the use of a high-density map identified closely linked flanking markers, provided better understanding of genetic architecture and candidate gene information, and revealed the scope available for improvement of soybean quality through marker assisted selection (MAS).
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Affiliation(s)
- Benjamin Karikari
- Key Laboratory of Biology and Genetics and Breeding for Soybean, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Soybean Research Institution, National Center for Soybean Improvement, Ministry of Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
| | - Shuguang Li
- Key Laboratory of Biology and Genetics and Breeding for Soybean, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Soybean Research Institution, National Center for Soybean Improvement, Ministry of Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
- Huaiyin Institute of Agricultural Sciences of Xuhuai Region in Jiangsu, Huai'an 223001, China.
| | - Javaid Akhter Bhat
- Key Laboratory of Biology and Genetics and Breeding for Soybean, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Soybean Research Institution, National Center for Soybean Improvement, Ministry of Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
| | - Yongce Cao
- Key Laboratory of Biology and Genetics and Breeding for Soybean, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Soybean Research Institution, National Center for Soybean Improvement, Ministry of Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
- College of Life Science, Yan'an University, Yan'an 716000, China.
| | - Jiejie Kong
- Key Laboratory of Biology and Genetics and Breeding for Soybean, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Soybean Research Institution, National Center for Soybean Improvement, Ministry of Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
| | - Jiayin Yang
- Huaiyin Institute of Agricultural Sciences of Xuhuai Region in Jiangsu, Huai'an 223001, China.
| | - Junyi Gai
- Key Laboratory of Biology and Genetics and Breeding for Soybean, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Soybean Research Institution, National Center for Soybean Improvement, Ministry of Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
| | - Tuanjie Zhao
- Key Laboratory of Biology and Genetics and Breeding for Soybean, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Soybean Research Institution, National Center for Soybean Improvement, Ministry of Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
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10
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Zhou W, Chen F, Zhao S, Yang C, Meng Y, Shuai H, Luo X, Dai Y, Yin H, Du J, Liu J, Fan G, Liu W, Yang W, Shu K. DA-6 promotes germination and seedling establishment from aged soybean seeds by mediating fatty acid metabolism and glycometabolism. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:101-114. [PMID: 29982626 PMCID: PMC6305204 DOI: 10.1093/jxb/ery247] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 06/27/2018] [Indexed: 05/17/2023]
Abstract
Soybean seeds contain higher concentrations of oil (triacylglycerol) and fatty acids than do cereal crop seeds, and the oxidation of these biomolecules during seed storage significantly shortens seed longevity and decreases germination ability. Here, we report that diethyl aminoethyl hexanoate (DA-6), a plant growth regulator, increases germination and seedling establishment from aged soybean seeds by increasing fatty acid metabolism and glycometabolism. Phenotypic analysis showed that DA-6 treatment markedly promoted germination and seedling establishment from naturally and artificially aged soybean seeds. Further analysis revealed that DA-6 increased the concentrations of soluble sugars during imbibition of aged soybean seeds. Consistently, the concentrations of several different fatty acids in DA-6-treated aged seeds were higher than those in untreated aged seeds. Subsequently, quantitative PCR analysis indicated that DA-6 induced the transcription of several key genes involved in the hydrolysis of triacylglycerol to sugars in aged soybean seeds. Furthermore, the activity of invertase in aged seeds, which catalyzes the hydrolysis of sucrose to form fructose and glucose, increased following DA-6 treatment. Taken together, DA-6 promotes germination and seedling establishment from aged soybean seeds by enhancing the hydrolysis of triacylglycerol and the conversion of fatty acids to sugars.
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Affiliation(s)
- Wenguan Zhou
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Feng Chen
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Sihua Zhao
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Caiqiong Yang
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Yongjie Meng
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Haiwei Shuai
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Xiaofeng Luo
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Yujia Dai
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Han Yin
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Junbo Du
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Jiang Liu
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Gaoqiong Fan
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Weiguo Liu
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Wenyu Yang
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
| | - Kai Shu
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest China, Institute of Ecological Agriculture, Sichuan Agricultural University, Chengdu, China
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