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Li Y, Zhao W, Tang J, Yue X, Gu J, Zhao B, Li C, Chen Y, Yuan J, Lin Y, Li Y, Kong F, He J, Wang D, Zhao TJ, Wang ZY. Identification of the domestication gene GmCYP82C4 underlying the major quantitative trait locus for the seed weight in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:62. [PMID: 38418640 DOI: 10.1007/s00122-024-04571-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
KEY MESSAGE A major quantitative trait locus (QTL) for the hundred-seed weight (HSW) was identified and confirmed in the two distinct soybean populations, and the target gene GmCYP82C4 underlying this locus was identified that significantly associated with soybean seed weight, and it was selected during the soybean domestication and improvement process. Soybean is a major oil crop for human beings and the seed weight is a crucial goal of soybean breeding. However, only a limited number of target genes underlying the quantitative trait loci (QTLs) controlling seed weight in soybean are known so far. In the present study, six loci associated with hundred-seed weight (HSW) were detected in the first population of 573 soybean breeding lines by genome-wide association study (GWAS), and 64 gene models were predicted in these candidate QTL regions. The QTL qHSW_1 exhibits continuous association signals on chromosome four and was also validated by region association study (RAS) in the second soybean population (409 accessions) with wild, landrace, and cultivar soybean accessions. There were seven genes in qHSW_1 candidate region by linkage disequilibrium (LD) block analysis, and only Glyma.04G035500 (GmCYP82C4) showed specifically higher expression in flowers, pods, and seeds, indicating its crucial role in the soybean seed development. Significant differences in HSW trait were detected when the association panels are genotyped by single-nucleotide polymorphisms (SNPs) in putative GmCYP82C4 promoter region. Eight haplotypes were generated by six SNPs in GmCYP82C4 in the second soybean population, and two superior haplotypes (Hap2 and Hap4) of GmCYP82C4 were detected with average HSW of 18.27 g and 18.38 g, respectively. The genetic diversity of GmCYP82C4 was analyzed in the second soybean population, and GmCYP82C4 was most likely selected during the soybean domestication and improvement process, leading to the highest proportion of Hap2 of GmCYP82C4 both in landrace and cultivar subpopulations. The QTLs and GmCYP82C4 identified in this study provide novel genetic resources for soybean seed weight trait, and the GmCYP82C4 could be used for soybean molecular breeding to develop desirable seed weight in the future.
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Affiliation(s)
- Yang Li
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Wenqian Zhao
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Jiajun Tang
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Xiuli Yue
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Jinbao Gu
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Biyao Zhao
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Cong Li
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Yanhang Chen
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Jianbo Yuan
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Yan Lin
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Yan Li
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Fanjiang Kong
- Innovative Center of Molecular Genetics and Evolution, School of Life Sciences, Guangzhou University, Guangzhou, China
| | - Jin He
- College of Agriculture, Guizhou University, Guiyang, China
| | - Dong Wang
- Key Laboratory of Molecular Biology and Gene Engineering in Jiangxi Province, College of Life Science, Nanchang University, Nanchang, China
| | - Tuan-Jie Zhao
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
| | - Zhen-Yu Wang
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China.
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Tayade R, Imran M, Ghimire A, Khan W, Nabi RBS, Kim Y. Molecular, genetic, and genomic basis of seed size and yield characteristics in soybean. FRONTIERS IN PLANT SCIENCE 2023; 14:1195210. [PMID: 38034572 PMCID: PMC10684784 DOI: 10.3389/fpls.2023.1195210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
Soybean (Glycine max L. Merr.) is a crucial oilseed cash crop grown worldwide and consumed as oil, protein, and food by humans and feed by animals. Comparatively, soybean seed yield is lower than cereal crops, such as maize, rice, and wheat, and the demand for soybean production does not keep up with the increasing consumption level. Therefore, increasing soybean yield per unit area is the most crucial breeding objective and is challenging for the scientific community. Moreover, yield and associated traits are extensively researched in cereal crops, but little is known about soybeans' genetics, genomics, and molecular regulation of yield traits. Soybean seed yield is a complex quantitative trait governed by multiple genes. Understanding the genetic and molecular processes governing closely related attributes to seed yield is crucial to increasing soybean yield. Advances in sequencing technologies have made it possible to conduct functional genomic research to understand yield traits' genetic and molecular underpinnings. Here, we provide an overview of recent progress in the genetic regulation of seed size in soybean, molecular, genetics, and genomic bases of yield, and related key seed yield traits. In addition, phytohormones, such as auxin, gibberellins, cytokinins, and abscisic acid, regulate seed size and yield. Hence, we also highlight the implications of these factors, challenges in soybean yield, and seed trait improvement. The information reviewed in this study will help expand the knowledge base and may provide the way forward for developing high-yielding soybean cultivars for future food demands.
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Affiliation(s)
- Rupesh Tayade
- Upland Field Machinery Research Center, Kyungpook National University, Daegu, Republic of Korea
| | - Muhammad Imran
- Division of Biosafety, National Institute of Agriculture Science, Rural Development Administration, Jeonju, Jeollabul-do, Republic of Korea
| | - Amit Ghimire
- Department of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea
- Department of Integrative Biology, Kyungpook National University, Daegu, Republic of Korea
| | - Waleed Khan
- Department of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea
- Department of Integrative Biology, Kyungpook National University, Daegu, Republic of Korea
| | - Rizwana Begum Syed Nabi
- Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration, Miryang, Republic of Korea
| | - Yoonha Kim
- Upland Field Machinery Research Center, Kyungpook National University, Daegu, Republic of Korea
- Department of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea
- Department of Integrative Biology, Kyungpook National University, Daegu, Republic of Korea
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Agyenim-Boateng KG, Zhang S, Gu R, Zhang S, Qi J, Azam M, Ma C, Li Y, Feng Y, Liu Y, Li J, Li B, Qiu L, Sun J. Identification of quantitative trait loci and candidate genes for seed folate content in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:149. [PMID: 37294438 DOI: 10.1007/s00122-023-04396-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/29/2023] [Indexed: 06/10/2023]
Abstract
KEY MESSAGE From 61 QTL mapped, a stable QTL cluster of 992 kb was discovered on chromosome 5 for folate content and a putative candidate gene, Glyma.05G237500, was identified. Folate (vitamin B9) is one of the most essential micronutrients whose deficiencies lead to various health defects in humans. Herein, we mapped the quantitative trait loci (QTL) underlying seed folate content in soybean using recombinant inbred lines developed from cultivars, ZH35 and ZH13, across four environments. We identified 61 QTL on 12 chromosomes through composite interval mapping, with phenotypic variance values ranging from 1.68 to 24.68%. A major-effect QTL cluster (qFo-05) was found on chromosome 5, spanning 992 kb and containing 134 genes. Through gene annotation and single-locus haplotyping analysis of qFo-05 in a natural soybean population, we identified seven candidate genes significantly associated with 5MTHF and total folate content in multiple environments. RNA-seq analysis showed a unique expression pattern of a hemerythrin RING zinc finger gene, Glyma.05G237500, between both parental cultivars during seed development, which suggest the gene might regulate folate content in soybean. This is the first study to investigate QTL underlying folate content in soybean and provides new insight for molecular breeding to improve folate content in soybean.
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Affiliation(s)
- Kwadwo Gyapong Agyenim-Boateng
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Shengrui Zhang
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Rongzhe Gu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/ Key Laboratory of Germplasm and Biotechnology (MARA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Shibi Zhang
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jie Qi
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Muhammad Azam
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Caiyou Ma
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yecheng Li
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yue Feng
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yitian Liu
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jing Li
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Bin Li
- MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Lijuan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/ Key Laboratory of Germplasm and Biotechnology (MARA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Junming Sun
- The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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Duan Z, Li Q, Wang H, He X, Zhang M. Genetic regulatory networks of soybean seed size, oil and protein contents. FRONTIERS IN PLANT SCIENCE 2023; 14:1160418. [PMID: 36959925 PMCID: PMC10028097 DOI: 10.3389/fpls.2023.1160418] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
As a leading oilseed crop that supplies plant oil and protein for daily human life, increasing yield and improving nutritional quality (high oil or protein) are the top two fundamental goals of soybean breeding. Seed size is one of the most critical factors determining soybean yield. Seed size, oil and protein contents are complex quantitative traits governed by genetic and environmental factors during seed development. The composition and quantity of seed storage reserves directly affect seed size. In general, oil and protein make up almost 60% of the total storage of soybean seed. Therefore, soybean's seed size, oil, or protein content are highly correlated agronomical traits. Increasing seed size helps increase soybean yield and probably improves seed quality. Similarly, rising oil and protein contents improves the soybean's nutritional quality and will likely increase soybean yield. Due to the importance of these three seed traits in soybean breeding, extensive studies have been conducted on their underlying quantitative trait locus (QTLs) or genes and the dissection of their molecular regulatory pathways. This review summarized the progress in functional genome controlling soybean seed size, oil and protein contents in recent decades, and presented the challenges and prospects for developing high-yield soybean cultivars with high oil or protein content. In the end, we hope this review will be helpful to the improvement of soybean yield and quality in the future breeding process.
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Affiliation(s)
- Zongbiao Duan
- Hainan Yazhou Bay Seed Laboratory, Sanya, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Qing Li
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China
| | - Hong Wang
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China
| | - Xuemei He
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Min Zhang
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
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Luo S, Jia J, Liu R, Wei R, Guo Z, Cai Z, Chen B, Liang F, Xia Q, Nian H, Cheng Y. Identification of major QTLs for soybean seed size and seed weight traits using a RIL population in different environments. FRONTIERS IN PLANT SCIENCE 2023; 13:1094112. [PMID: 36714756 PMCID: PMC9874164 DOI: 10.3389/fpls.2022.1094112] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/15/2022] [Indexed: 06/18/2023]
Abstract
INTRODUCTION The seed weight of soybean [Glycine max (L.) Merr.] is one of the major traits that determine soybean yield and is closely related to seed size. However, the genetic basis of the synergistic regulation of traits related to soybean yield is unclear. METHODS To understand the molecular genetic basis for the formation of soybean yield traits, the present study focused on QTLs mapping for seed size and weight traits in different environments and target genes mining. RESULTS A total of 85 QTLs associated with seed size and weight traits were identified using a recombinant inbred line (RIL) population developed from Guizao1×B13 (GB13). We also detected 18 environmentally stable QTLs. Of these, qSL-3-1 was a novel QTL with a stable main effect associated with seed length. It was detected in all environments, three of which explained more than 10% of phenotypic variance (PV), with a maximum of 15.91%. In addition, qSW-20-3 was a novel QTL with a stable main effect associated with seed width, which was identified in four environments. And the amount of phenotypic variance explained (PVE) varied from 9.22 to 21.93%. Five QTL clusters associated with both seed size and seed weight were summarized by QTL cluster identification. Fifteen candidate genes that may be involved in regulating soybean seed size and weight were also screened based on gene function annotation and GO enrichment analysis. DISCUSSION The results provide a biologically basic reference for understanding the formation of soybean seed size and weight traits.
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Affiliation(s)
- Shilin Luo
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Jia Jia
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Riqian Liu
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Ruqian Wei
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Zhibin Guo
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Zhandong Cai
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Bo Chen
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Fuwei Liang
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Qiuju Xia
- Rice Molecular Breeding Institute, Granlux Associated Grains, Shenzhen, Guangdong, China
| | - Hai Nian
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Yanbo Cheng
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
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Reyes VP, Kitony JK, Nishiuchi S, Makihara D, Doi K. Utilization of Genotyping-by-Sequencing (GBS) for Rice Pre-Breeding and Improvement: A Review. Life (Basel) 2022; 12:1752. [PMID: 36362909 PMCID: PMC9694628 DOI: 10.3390/life12111752] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 09/29/2023] Open
Abstract
Molecular markers play a crucial role in the improvement of rice. To benefit from these markers, genotyping is carried out to identify the differences at a specific position in the genome of individuals. The advances in sequencing technologies have led to the development of different genotyping techniques such as genotyping-by-sequencing. Unlike PCR-fragment-based genotyping, genotyping-by-sequencing has enabled the parallel sequencing and genotyping of hundreds of samples in a single run, making it more cost-effective. Currently, GBS is being used in several pre-breeding programs of rice to identify beneficial genes and QTL from different rice genetic resources. In this review, we present the current advances in the utilization of genotyping-by-sequencing for the development of rice pre-breeding materials and the improvement of existing rice cultivars. The challenges and perspectives of using this approach are also highlighted.
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Affiliation(s)
- Vincent Pamugas Reyes
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
| | | | - Shunsaku Nishiuchi
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
| | - Daigo Makihara
- International Center for Research and Education in Agriculture, Nagoya University, Nagoya 464-8601, Japan
| | - Kazuyuki Doi
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
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Seki K, Toda Y. QTL mapping for seed morphology using the instance segmentation neural network in Lactuca spp. FRONTIERS IN PLANT SCIENCE 2022; 13:949470. [PMID: 36311127 PMCID: PMC9606697 DOI: 10.3389/fpls.2022.949470] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Wild species of lettuce (Lactuca sp.) are thought to have first been domesticated for oilseed contents to provide seed oil for human consumption. Although seed morphology is an important trait contributing to oilseed in lettuce, the underlying genetic mechanisms remain elusive. Since lettuce seeds are small, a manual phenotypic determination required for a genetic dissection of such traits is challenging. In this study, we built and applied an instance segmentation-based seed morphology quantification pipeline to measure traits in seeds generated from a cross between the domesticated oilseed type cultivar 'Oilseed' and the wild species 'UenoyamaMaruba' in an automated manner. Quantitative trait locus (QTL) mapping following ddRAD-seq revealed 11 QTLs linked to 7 seed traits (area, width, length, length-to-width ratio, eccentricity, perimeter length, and circularity). Remarkably, the three QTLs with the highest LOD scores, qLWR-3.1, qECC-3.1, and qCIR-3.1, for length-to-width ratio, eccentricity, and circularity, respectively, mapped to linkage group 3 (LG3) around 161.5 to 214.6 Mb, a region previously reported to be associated with domestication traits from wild species. These results suggest that the oilseed cultivar harbors genes acquired during domestication to control seed shape in this genomic region. This study also provides genetic evidence that domestication arose, at least in part, by selection for the oilseed type from wild species and demonstrates the effectiveness of image-based phenotyping to accelerate discoveries of the genetic basis for small morphological features such as seed size and shape.
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Affiliation(s)
- Kousuke Seki
- Nagano Vegetable and Ornamental Crops Experiment Station, Shiojiri, Japan
| | - Yosuke Toda
- Phytometrics Co., Ltd., Shizuoka, Japan
- Bioscience and Biotechnology Center, Nagoya University, Nagoya, Japan
- Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Nagoya, Japan
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Shao Z, Shao J, Huo X, Li W, Kong Y, Du H, Li X, Zhang C. Identification of closely associated SNPs and candidate genes with seed size and shape via deep re-sequencing GWAS in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2341-2351. [PMID: 35588015 DOI: 10.1007/s00122-022-04116-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
KEY MESSAGE A soybean natural population was genotyped by deep re-sequencing and phenotyped for six seed size- and shape-related traits under six environments to identify closely associated SNPs and candidate genes. Seed size and shape are important determining factors for soybean yield formation, while their genetic basis and molecular mechanism are still largely unknown, which seriously constrains the increasing of soybean yield at present. In view of this, a natural population was genotyped via the deep re-sequencing technique (~ 20 ×) and phenotyped for six related traits under six environments. In total, 154 SNPs were closely associated with seed length across diverse environments, and 323, 483, 565, 394 and 2038 SNPs were closely associated with seed width, seed diameter, seed circumference, seed area and ratio of length to width under multiple environments. Moreover, 98.70%, 96.28%, 48.24%, 85.13%, 97.21% and 98.58% of them were further demonstrated by the BLUP and mean values of the related traits. Furthermore, 218 genes flanking the associated SNPs on chromosomes 6 and 10 were analyzed for DNA mutations and RNA expressions through SNP alleles and transcriptome data, simultaneously. The candidate genes, Glyma.10G035200 (Sn1-specific diacylglycerol lipase), Glyma.10G035400 (transcription factor) and Glyma.10G058200 (phenylalanine ammonia-lyase), were discovered to relate with the seed size and shape for their different DNA sequences or differential RNA expressions among soybean varieties at five seed developmental stages. Thus, these closely associated SNPs and related genes provide novel insights and useful information for the seed size and shape genetic basis dissection and breeding improvement in soybean.
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Affiliation(s)
- Zhenqi Shao
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Lekai South Street 2596, Baoding City, 071001, Hebei Province, People's Republic of China
| | - Jiabiao Shao
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Lekai South Street 2596, Baoding City, 071001, Hebei Province, People's Republic of China
| | - Xiaobo Huo
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Lekai South Street 2596, Baoding City, 071001, Hebei Province, People's Republic of China
| | - Wenlong Li
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Lekai South Street 2596, Baoding City, 071001, Hebei Province, People's Republic of China
| | - Youbin Kong
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Lekai South Street 2596, Baoding City, 071001, Hebei Province, People's Republic of China
| | - Hui Du
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Lekai South Street 2596, Baoding City, 071001, Hebei Province, People's Republic of China
| | - Xihuan Li
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Lekai South Street 2596, Baoding City, 071001, Hebei Province, People's Republic of China.
| | - Caiying Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Lekai South Street 2596, Baoding City, 071001, Hebei Province, People's Republic of China.
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Cao Y, Jia S, Chen L, Zeng S, Zhao T, Karikari B. Identification of major genomic regions for soybean seed weight by genome-wide association study. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:38. [PMID: 37313505 PMCID: PMC10248628 DOI: 10.1007/s11032-022-01310-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/12/2022] [Indexed: 06/15/2023]
Abstract
The hundred-seed weight (HSW) is an important yield component and one of the principal breeding traits in soybean. More than 250 quantitative trait loci (QTL) for soybean HSW have been identified. However, most of them have a large genomic region or are environmentally sensitive, which provide limited information for improving the phenotype in marker-assisted selection (MAS) and identifying the candidate genes. Here, we utilized 281 soybean accessions with 58,112 single nucleotide polymorphisms (SNPs) to dissect the genetic basis of HSW in across years in the northern Shaanxi province of China through one single-locus (SL) and three multi-locus (ML) genome-wide association study (GWAS) models. As a result, one hundred and fifty-four SNPs were detected to be significantly associated with HSW in at least one environment via SL-GWAS model, and 27 of these 154 SNPs were detected in all (three) environments and located within 7 linkage disequilibrium (LD) block regions with the distance of each block ranged from 40 to 610 Kb. A total of 15 quantitative trait nucleotides (QTNs) were identified by three ML-GWAS models. Combined with the results of different GWAS models, the 7 LD block regions associated with HSW detected by SL-GWAS model could be verified directly or indirectly by the results of ML-GWAS models. Eleven candidate genes underlying the stable loci that may regulate seed weight in soybean were predicted. The significantly associated SNPs and the stable loci as well as predicted candidate genes may be of great importance for marker-assisted breeding, polymerization breeding, and gene discovery for HSW in soybean. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01310-y.
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Affiliation(s)
- Yongce Cao
- Shaanxi Key Laboratory of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shaanxi, 716000 China
| | - Shihao Jia
- Shaanxi Key Laboratory of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shaanxi, 716000 China
| | - Liuxing Chen
- Shaanxi Key Laboratory of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shaanxi, 716000 China
| | - Shunan Zeng
- Shaanxi Key Laboratory of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shaanxi, 716000 China
| | - Tuanjie Zhao
- Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, National Center for Soybean Improvement, National Key Laboratory for Crop Genetics and Germplasm Enhancement, Soybean Research Institute of Nanjing Agricultural University, Nanjing, 210095 Jiangsu China
| | - Benjamin Karikari
- Department of Crop Science, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, 00233 Tamale, Ghana
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Nishimura K, Motoki K, Yamazaki A, Takisawa R, Yasui Y, Kawai T, Ushijima K, Nakano R, Nakazaki T. MIG-seq is an effective method for high-throughput genotyping in wheat ( Triticum spp.). DNA Res 2022; 29:6567359. [PMID: 35412600 PMCID: PMC9035812 DOI: 10.1093/dnares/dsac011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 04/08/2022] [Indexed: 12/04/2022] Open
Abstract
MIG-seq (Multiplexed inter-simple sequence repeats genotyping by sequencing) has been developed as a low cost genotyping technology, although the number of polymorphisms obtained is assumed to be minimal, resulting in the low application of this technique to analyses of agricultural plants. We applied MIG-seq to 12 plant species that include various crops and investigated the relationship between genome size and the number of bases that can be stably sequenced. The genome size and the number of loci, which can be sequenced by MIG-seq, are positively correlated. This is due to the linkage between genome size and the number of simple sequence repeats (SSRs) through the genome. The applicability of MIG-seq to population structure analysis, linkage mapping, and quantitative trait loci (QTL) analysis in wheat, which has a relatively large genome, was further evaluated. The results of population structure analysis for tetraploid wheat showed the differences among collection sites and subspecies, which agreed with previous findings. Additionally, in wheat biparental mapping populations, over 3,000 SNPs/indels with low deficiency were detected using MIG-seq, and the QTL analysis was able to detect recognized flowering-related genes. These results revealed the effectiveness of MIG-seq for genomic analysis of agricultural plants with large genomes, including wheat.
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Affiliation(s)
- Kazusa Nishimura
- Graduate School of Agriculture, Kyoto University, Kizugawa City, Kyoto Prefecture 619-0218, Japan
| | - Ko Motoki
- Graduate School of Agriculture, Kyoto University, Kizugawa City, Kyoto Prefecture 619-0218, Japan
| | - Akira Yamazaki
- Graduate School of Agriculture, Kyoto University, Kizugawa City, Kyoto Prefecture 619-0218, Japan
- Faculty of Agriculture, Kindai University, Nara City, Nara Prefecture 631-8505, Japan
| | - Rihito Takisawa
- Faculty of Agriculture, Ryukoku University, Otsu City, Shiga Prefecture 520-2194, Japan
| | - Yasuo Yasui
- Graduate School of Agriculture, Kyoto University, Kizugawa City, Kyoto Prefecture 619-0218, Japan
| | - Takashi Kawai
- Graduate School of Environmental and Life Science, Okayama University, Okayama City, Okayama Prefecture 700-8530, Japan
| | - Koichiro Ushijima
- Graduate School of Environmental and Life Science, Okayama University, Okayama City, Okayama Prefecture 700-8530, Japan
| | - Ryohei Nakano
- Graduate School of Agriculture, Kyoto University, Kizugawa City, Kyoto Prefecture 619-0218, Japan
| | - Tetsuya Nakazaki
- Graduate School of Agriculture, Kyoto University, Kizugawa City, Kyoto Prefecture 619-0218, Japan
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