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Han D, Zhao X, Zhang D, Wang Z, Zhu Z, Sun H, Qu Z, Wang L, Liu Z, Zhu X, Yuan M. Genome-wide association studies reveal novel QTLs for agronomic traits in soybean. FRONTIERS IN PLANT SCIENCE 2024; 15:1375646. [PMID: 38807775 PMCID: PMC11132100 DOI: 10.3389/fpls.2024.1375646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/15/2024] [Indexed: 05/30/2024]
Abstract
Introduction Soybean, as a globally significant crop, has garnered substantial attention due to its agricultural importance. The utilization of molecular approaches to enhance grain yield in soybean has gained popularity. Methods In this study, we conducted a genome-wide association study (GWAS) using 156 Chinese soybean accessions over a two-year period. We employed the general linear model (GLM) and the mixed linear model (MLM) to analyze three agronomic traits: pod number, grain number, and grain weight. Results Our findings revealed significant associations between qgPNpP-98, qgGNpP-89 and qgHGW-85 QTLs and pod number, grain number, and grain weight, respectively. These QTLs were identified on chromosome 16, a region spanning 413171bp exhibited associations with all three traits. Discussion These QTL markers identified in this study hold potential for improving yield and agronomic traits through marker-assisted selection and genomic selection in breeding programs.
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Affiliation(s)
- Dongwei Han
- Qiqihar Branch of Heilongjiang Academy of Agricultural Science, Qiqihar, Heilongjiang, China
- Heilongjiang Chinese Academy of Sciences Qiuying Zhang Soybean Scientist Studio, Qiqihar, Heilongjiang, China
| | - Xi Zhao
- Biotechnology Institute, Heilongjiang Academy of Agricultural Science, Harbin, Heilongjiang, China
| | - Di Zhang
- Qiqihar Branch of Heilongjiang Academy of Agricultural Science, Qiqihar, Heilongjiang, China
| | - Zhen Wang
- Qiqihar Branch of Heilongjiang Academy of Agricultural Science, Qiqihar, Heilongjiang, China
| | - Zhijia Zhu
- Qiqihar Branch of Heilongjiang Academy of Agricultural Science, Qiqihar, Heilongjiang, China
| | - Haoyue Sun
- Qiqihar Branch of Heilongjiang Academy of Agricultural Science, Qiqihar, Heilongjiang, China
| | - Zhongcheng Qu
- Qiqihar Branch of Heilongjiang Academy of Agricultural Science, Qiqihar, Heilongjiang, China
| | - Lianxia Wang
- Qiqihar Branch of Heilongjiang Academy of Agricultural Science, Qiqihar, Heilongjiang, China
| | - Zhangxiong Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xu Zhu
- Department of Research and Development, Ruibiotech Co., Ltd, Beijing, China
| | - Ming Yuan
- Qiqihar Branch of Heilongjiang Academy of Agricultural Science, Qiqihar, Heilongjiang, China
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Hu D, Zhao Y, Zhu L, Li X, Zhang J, Cui X, Li W, Hao D, Yang Z, Wu F, Dong S, Su X, Huang F, Yu D. Genetic dissection of ten photosynthesis-related traits based on InDel- and SNP-GWAS in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:96. [PMID: 38589730 DOI: 10.1007/s00122-024-04607-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/21/2024] [Indexed: 04/10/2024]
Abstract
KEY MESSAGE A total of 416 InDels and 112 SNPs were significantly associated with soybean photosynthesis-related traits. GmIWS1 and GmCDC48 might be related to chlorophyll fluorescence and gas-exchange parameters, respectively. Photosynthesis is one of the main factors determining crop yield. A better understanding of the genetic architecture for photosynthesis is of great significance for soybean yield improvement. Our previous studies identified 5,410,112 single nucleotide polymorphisms (SNPs) from the resequencing data of 219 natural soybean accessions. Here, we identified 634,106 insertions and deletions (InDels) from these 219 accessions and used these InDel variations to perform principal component and linkage disequilibrium analysis of this population. The genome-wide association study (GWAS) were conducted on six chlorophyll fluorescence parameters (chlorophyll content, light energy absorbed per reaction center, quantum yield for electron transport, probability that a trapped exciton moves an electron into the electron transport chain beyond primary quinone acceptor, maximum quantum yield of photosystem II primary photochemistry in the dark-adapted state, performance index on absorption basis) and four gas-exchange parameters (intercellular carbon dioxide concentration, stomatal conductance, net photosynthesis rate, transpiration rate) and revealed 416 significant InDels and 112 significant SNPs. Based on GWAS results, GmIWS1 (encoding a transcription elongation factor) and GmCDC48 (encoding a cell division cycle protein) with the highest expression in the mapping region were determined as the candidate genes responsible for chlorophyll fluorescence and gas-exchange parameters, respectively. Further identification of favorable haplotypes with higher photosynthesis, seed weight and seed yield were carried out for GmIWS1 and GmCDC48. Overall, this study revealed the natural variations and candidate genes underlying the photosynthesis-related traits based on abundant phenotypic and genetic data, providing valuable insights into the genetic mechanisms controlling photosynthesis and yield in soybean.
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Affiliation(s)
- Dezhou Hu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
- College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yajun Zhao
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Lixun Zhu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xiao Li
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Jinyu Zhang
- Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang, 453003, China
| | - Xuan Cui
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Wenlong Li
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Derong Hao
- Jiangsu Yanjiang Institute of Agricultural Sciences, Nantong, 226012, China
| | - Zhongyi Yang
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Fei Wu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Shupeng Dong
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xiaoyue Su
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Fang Huang
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Deyue Yu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, 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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Kiseleva AA, Leonova IN, Ageeva EV, Likhenko IE, Salina EA. Identification of genetic loci for early maturity in spring bread wheat using the association analysis and gene dissection. PeerJ 2023; 11:e16109. [PMID: 37842052 PMCID: PMC10569184 DOI: 10.7717/peerj.16109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/25/2023] [Indexed: 10/17/2023] Open
Abstract
Background Early maturity in spring bread wheat is highly desirable in the regions where it enables the plants to evade high temperatures and plant pathogens at the end of the growing season. Methods To reveal the genetic loci responsible for the maturity time association analysis was carried out based on phenotyping for an 11-year period and high-throughput SNP genotyping of a panel of the varieties contrasting for this trait. The expression of candidate genes was verified using qPCR. The association between the SNP markers and the trait was validated using the biparental F2:3 population. Results Our data showed that under long-day conditions, the period from seedling to maturity is mostly influenced by the time from heading to maturity, rather than the heading time. The QTLs associated with the trait were located on 2A, 3B, 4A, 5B, 7A and 7B chromosomes with the 7BL locus being the most significant and promising for its SNPs accelerated the maturity time by about 9 days. Gene dissection in this locus detected a number of candidates, the best being TraesCS7B02G391800 (bZIP9) and TraesCS7B02G412200 (photosystem II reaction center). The two genes are predominantly expressed in the flag leaf while flowering. The effect of the SNPs was verified in F2:3 population and confirmed the association of the 4A, 5B and 7BL loci with the maturity time.
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Affiliation(s)
- Antonina A. Kiseleva
- Laboratory of Plant Molecular Genetics and Cytogenetics, The Federal State Budgetary Institution of Science Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - Irina N. Leonova
- Laboratory of Plant Molecular Genetics and Cytogenetics, The Federal State Budgetary Institution of Science Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Elena V. Ageeva
- Laboratory of Field Crop Breeding and Seed Industry, Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Ivan E. Likhenko
- Laboratory of Field Crop Breeding and Seed Industry, Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Elena A. Salina
- Laboratory of Plant Molecular Genetics and Cytogenetics, The Federal State Budgetary Institution of Science Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
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Liyanage DK, Torkamaneh D, Belzile F, Balasubramanian P, Hill B, Thilakarathna MS. The Genotypic Variability among Short-Season Soybean Cultivars for Nitrogen Fixation under Drought Stress. PLANTS (BASEL, SWITZERLAND) 2023; 12:1004. [PMID: 36903865 PMCID: PMC10005650 DOI: 10.3390/plants12051004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Soybean fixes atmospheric nitrogen through the symbiotic rhizobia bacteria that inhabit root nodules. Drought stress negatively affect symbiotic nitrogen fixation (SNF) in soybean. The main objective of this study was to identify allelic variations associated with SNF in short-season Canadian soybean varieties under drought stress. A diversity panel of 103 early-maturity Canadian soybean varieties was evaluated under greenhouse conditions to determine SNF-related traits under drought stress. Drought was imposed after three weeks of plant growth, where plants were maintained at 30% field capacity (FC) (drought) and 80% FC (well-watered) until seed maturity. Under drought stress, soybean plants had lower seed yield, yield components, seed nitrogen content, % nitrogen derived from the atmosphere (%Ndfa), and total seed nitrogen fixed compared to those under well-watered conditions. Significant genotypic variability among soybean varieties was found for yield, yield parameters, and nitrogen fixation traits. A genome-wide association study (GWAS) was conducted using 2.16 M single nucleotide single nucleotide polymorphisms (SNPs) for different yield and nitrogen fixation related parameters for 30% FC and their relative performance (30% FC/80% FC). In total, five quantitative trait locus (QTL) regions, including candidate genes, were detected as significantly associated with %Ndfa under drought stress and relative performance. These genes can potentially aid in future breeding efforts to develop drought-resistant soybean varieties.
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Affiliation(s)
- Dilrukshi Kombala Liyanage
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC G1V 0A6, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC G1V 0A6, Canada
| | - François Belzile
- Département de Phytologie, Université Laval, Québec City, QC G1V 0A6, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC G1V 0A6, Canada
| | - Parthiba Balasubramanian
- Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, Lethbridge, AB T1J 4B1, Canada
| | - Brett Hill
- Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, Lethbridge, AB T1J 4B1, Canada
| | - Malinda S. Thilakarathna
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
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Liu H, Zou M, Zhang B, Yang X, Yuan P, Ding G, Xu F, Shi L. Genome-wide association study identifies candidate genes and favorable haplotypes for seed yield in Brassica napus. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:61. [PMID: 37313016 PMCID: PMC10248642 DOI: 10.1007/s11032-022-01332-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/19/2022] [Indexed: 06/15/2023]
Abstract
Oilseed rape (Brassica napus L.) is one of the most essential oil crops. Genetic improvement of seed yield (SY) is a major aim of B. napus breeding. Several studies have been reported on the genetic mechanisms of SY of B. napus. Here, a genome-wide association study (GWAS) of SY was conducted using a panel of 403 natural accessions of B. napus, with more than five million high-quality single-nucleotide polymorphisms (SNPs). A total of 1773 significant SNPs were detected associated with SY, and 783 significant SNPs were co-located with previously reported QTLs. The lead SNPs chrA01__8920351 and chrA02__4555979 were jointly detected in Trial 2_2 and Trial 2_mean value, and in Trial 1_2 and Trial 1_mean value, respectively. Subsequently, two candidate genes of BnaA01g17200D and BnaA02g08680D were identified through combining transcriptome, candidate gene association analysis, and haplotype analysis. BnaA09g10430D detected through lead SNP chrA09__5160639 was associated with SY of B. napus. Our results provide valuable information for studying the genetic control of seed yield in B. napus and valuable genes, haplotypes, and cultivars resources for the breeding of high seed yield B. napus cultivars. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01332-6.
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Affiliation(s)
- Haijiang Liu
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Maoyan Zou
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Bingbing Zhang
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Xinyu Yang
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Pan Yuan
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Guangda Ding
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Fangsen Xu
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Lei Shi
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
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Li X, Hu D, Cai L, Wang H, Liu X, Du H, Yang Z, Zhang H, Hu Z, Huang F, Kan G, Kong F, Liu B, Yu D, Wang H. CALCIUM-DEPENDENT PROTEIN KINASE38 regulates flowering time and common cutworm resistance in soybean. PLANT PHYSIOLOGY 2022; 190:480-499. [PMID: 35640995 PMCID: PMC9434205 DOI: 10.1093/plphys/kiac260] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/03/2022] [Indexed: 06/02/2023]
Abstract
Photoperiod-sensitive plants such as soybean (Glycine max) often face threats from herbivorous insects throughout their whole growth period and especially during flowering; however, little is known about the relationship between plant flowering and insect resistance. Here, we used gene editing, multiple omics, genetic diversity and evolutionary analyses to confirm that the calcium-dependent protein kinase GmCDPK38 plays a dual role in coordinating flowering time regulation and insect resistance of soybean. Haplotype 2 (Hap2)-containing soybeans flowered later and were more resistant to the common cutworm (Spodoptera litura Fabricius) than those of Hap3. gmcdpk38 mutants with Hap3 knocked out exhibited similar flowering and resistance phenotypes as Hap2. Knocking out GmCDPK38 altered numerous flowering- and resistance-related phosphorylated proteins, genes, and metabolites. For example, the S-adenosylmethionine synthase GmSAMS1 was post-translationally upregulated in the gmcdpk38 mutants. GmCDPK38 has abundant genetic diversity in wild soybeans and was likely selected during soybean domestication. We found that Hap2 was mostly distributed at low latitudes and had a higher frequency in cultivars than in wild soybeans, while Hap3 was widely selected at high latitudes. Overall, our results elucidated that the two distinct traits (flowering time and insect resistance) are mediated by GmCDPK38.
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Affiliation(s)
- Xiao Li
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Dezhou Hu
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Linyan Cai
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Huiqi Wang
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Xinyu Liu
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Haiping Du
- School of Life Sciences, Guangzhou University, Guangzhou 510006, China
| | - Zhongyi Yang
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Huairen Zhang
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhenbin Hu
- Department of Biology, Saint Louis University, St. Louis, Missouri 63103, USA
| | - Fang Huang
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Guizhen Kan
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Fanjiang Kong
- School of Life Sciences, Guangzhou University, Guangzhou 510006, China
| | - Baohui Liu
- School of Life Sciences, Guangzhou University, Guangzhou 510006, China
| | - Deyue Yu
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Hui Wang
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
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Bhat JA, Adeboye KA, Ganie SA, Barmukh R, Hu D, Varshney RK, Yu D. Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.). Front Genet 2022; 13:953833. [PMID: 36419833 PMCID: PMC9677453 DOI: 10.3389/fgene.2022.953833] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/22/2022] [Indexed: 11/09/2022] Open
Abstract
Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.
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Affiliation(s)
- Javaid Akhter Bhat
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- International Genome Center, Jiangsu University, Zhenjiang, China
- *Correspondence: Javaid Akhter Bhat, ; Rajeev K. Varshney, ; Deyue Yu,
| | | | - Showkat Ahmad Ganie
- Plant Molecular Science and Centre of Systems and Synthetic Biology, Department of Biological Sciences, Royal Holloway University of London, Surrey, United Kingdom
| | - Rutwik Barmukh
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Dezhou Hu
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Murdoch’s Centre for Crop & Food Innovation, State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Perth, WA, Australia
- *Correspondence: Javaid Akhter Bhat, ; Rajeev K. Varshney, ; Deyue Yu,
| | - Deyue Yu
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- *Correspondence: Javaid Akhter Bhat, ; Rajeev K. Varshney, ; Deyue Yu,
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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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10
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Hyten DL. Genotyping Platforms for Genome-Wide Association Studies: Options and Practical Considerations. Methods Mol Biol 2022; 2481:29-42. [PMID: 35641757 DOI: 10.1007/978-1-0716-2237-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Genome-wide association studies (GWAS) in crops requires genotyping platforms that are capable of producing accurate high density genotyping data on hundreds of plants in a cost-effective manner. Currently there are multiple commercial platforms available that are being effectively used across crops. These platforms include genotyping arrays such as the Illumina Infinium arrays and the Applied Biosystems Axiom Arrays along with a variety of resequencing methods. These methods are being used to genotype tens of thousands of markers up to millions of markers on GWAS panels. They are being used on crops with simple genomes to crops with very complex, large, polyploid genomes. Depending on the crop and the goal of the GWAS, there are several options and practical considerations to take into account when selecting a genotyping technology to ensure that the right coverage, accuracy, and cost for the study is achieved.
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Affiliation(s)
- David L Hyten
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
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11
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Yue Y, Sun S, Li J, Yu H, Wu H, Sun B, Li T, Han T, Jiang B. GmFULa improves soybean yield by enhancing carbon assimilation without altering flowering time or maturity. PLANT CELL REPORTS 2021; 40:1875-1888. [PMID: 34272585 PMCID: PMC8494661 DOI: 10.1007/s00299-021-02752-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/04/2021] [Indexed: 05/27/2023]
Abstract
KEY MESSAGE GmFULa improved soybean yield by enhancing carbon assimilation. Meanwhile, different from known yield-related genes, it did not alter flowering time or maturity. Soybean (Glycine max (L.) Merr.) is highly demanded by a continuously growing human population. However, increasing soybean yield is a major challenge. FRUITFULL (FUL), a MADS-box transcription factor, plays important roles in multiple developmental processes, especially fruit and pod development, which are crucial for soybean yield formation. However, the functions of its homologs in soybean are not clear. Here, through haplotype analysis, we found that one haplotype of the soybean homolog GmFULa (GmFULa-H02) is dominant in cultivated soybeans, suggesting that GmFULa-H02 was highly selected during domestication and varietal improvement of soybean. Interestingly, transgenic overexpression of GmFULa enhanced vegetative growth with more biomass accumulated and ultimately increased the yield but without affecting the plant height or changing the flowering time and maturity, indicating that it enhances the efficiency of dry matter accumulation. It also promoted the yield factors like branch number, pod number and 100-seed weight, which ultimately increased the yield. It increased the palisade tissue cell number and the chlorophyll content to promote photosynthesis and increase the soluble sugar content in leaves and fresh seeds. Furthermore, GmFULa were found to be sublocalized in the nucleus and positively regulate sucrose synthases (SUSs) and sucrose transporters (SUTs) by binding with the conserved CArG boxes in their promoters. Overall, these results showed GmFULa promotes the capacity of assimilation and the transport of the resultant assimilates to increase yield, and provided insights into the link between GmFULa and sucrose synthesis with transport-related molecular pathways that control seed yield.
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Affiliation(s)
- Yanlei Yue
- College of Life Sciences, Henan Agricultural University, Zhengzhou, 450002, China
| | - Shi Sun
- MARA Key Lab of Soybean Biology (Beijing), Institute of Crop Sciences, The Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jiawen Li
- College of Life Sciences, Henan Agricultural University, Zhengzhou, 450002, China
| | - Haidong Yu
- College of Life Sciences, Henan Agricultural University, Zhengzhou, 450002, China
| | - Hongxia Wu
- College of Life Sciences, Henan Agricultural University, Zhengzhou, 450002, China
| | - Baiquan Sun
- MARA Key Lab of Soybean Biology (Beijing), Institute of Crop Sciences, The Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Tao Li
- College of Life Sciences, Henan Agricultural University, Zhengzhou, 450002, China.
| | - Tianfu Han
- MARA Key Lab of Soybean Biology (Beijing), Institute of Crop Sciences, The Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Bingjun Jiang
- MARA Key Lab of Soybean Biology (Beijing), Institute of Crop Sciences, The Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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12
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Zhang W, Xu W, Zhang H, Liu X, Cui X, Li S, Song L, Zhu Y, Chen X, Chen H. Comparative selective signature analysis and high-resolution GWAS reveal a new candidate gene controlling seed weight in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1329-1341. [PMID: 33507340 DOI: 10.1007/s00122-021-03774-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/11/2021] [Indexed: 05/26/2023]
Abstract
KEY MESSAGE We detected a QTL qHSW-16 undergone strong selection associated with seed weight and identified a novel candidate gene controlling seed weight candidate gene for this major QTL by qRT-PCT. Soybean [Glycine max (L.) Merr.] provides more than half of the world's oilseed production. To expand its germplasm resources useful for breeding increased yield and oil quality cultivars, it is necessary to resolve the diversity and evolutionary history of this crop. In this work, we resequenced 283 soybean accessions from China and obtained a large number of high-quality SNPs for investigation of the population genetics that underpin variation in seed weight and other agronomic traits. Selective signature analysis detected 78 (~ 25.0 Mb) and 39 (~ 22.60 Mb) novel putative selective signals that were selected during soybean domestication and improvement, respectively. Genome-wide association study (GWAS) identified five loci associated with seed weight. Among these QTLs, qHSW-16, overlapped with the improvement-selective region on chromosome 16, suggesting that this QTL may be underwent strong selection during soybean improvement. Of the 18 candidate genes in qHSW-16, only SoyZH13_16G122400 showed higher expression levels in a large seed variety compared to a small seed variety during seed development. These results identify SoyZH13_16G122400 as a novel candidate gene controlling seed weight and provide foundational insights into the molecular targets for breeding improvement of seed weight and potential seed yield in soybean.
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Affiliation(s)
- Wei Zhang
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Wenjing Xu
- College of Horticulture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Hongmei Zhang
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Xiaoqing Liu
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Xiaoyan Cui
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Songsong Li
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
- College of Horticulture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Li Song
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, 225009, China
| | - Yuelin Zhu
- College of Horticulture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xin Chen
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China.
| | - Huatao Chen
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China.
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Ikram M, Han X, Zuo JF, Song J, Han CY, Zhang YW, Zhang YM. Identification of QTNs and Their Candidate Genes for 100-Seed Weight in Soybean (Glycine max L.) Using Multi-Locus Genome-Wide Association Studies. Genes (Basel) 2020; 11:E714. [PMID: 32604988 PMCID: PMC7397327 DOI: 10.3390/genes11070714] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/18/2020] [Accepted: 06/24/2020] [Indexed: 12/29/2022] Open
Abstract
100-seed weight (100-SW) in soybeans is a yield component trait and controlled by multiple genes with different effects, but limited information is available for its quantitative trait nucleotides (QTNs) and candidate genes. To better understand the genetic architecture underlying the trait and improve the precision of marker-assisted selection, a total of 43,834 single nucleotide polymorphisms (SNPs) in 250 soybean accessions were used to identify significant QTNs for 100-SW in four environments and their BLUP values using six multi-locus and one single-locus genome-wide association study methods. As a result, a total of 218 significant QTNs were detected using multi-locus methods, whereas eight QTNs were identified by a single-locus method. Among 43 QTNs or QTN clusters identified repeatedly across various environments and/or approaches, all of them exhibited significant trait differences between their corresponding alleles, 33 were found in the genomic region of previously reported QTLs, 10 were identified as new QTNs, and three (qHSW-4-1, qcHSW-7-3, and qcHSW-10-4) were detected in all the four environments. The number of seed weight (SW) increasing alleles for each accession ranged from 8 (18.6%) to 36 (83.72%), and three accessions (Yixingwuhuangdou, Nannong 95C-5, and Yafanzaodou) had more than 35 SW increasing alleles. Among 36 homologous seed-weight genes in Arabidopsis underlying the above 43 stable QTNs, more importantly, Glyma05g34120, GmCRY1, and GmCPK11 had known seed-size/weight-related genes in soybean, and Glyma07g07850, Glyma10g03440, and Glyma10g36070 were candidate genes identified in this study. These results provide useful information for genetic foundation, marker-assisted selection, genomic prediction, and functional genomics of 100-SW.
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Affiliation(s)
- Muhammad Ikram
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; (M.I.); (X.H.); (J.-F.Z.); (C.-Y.H.); (Y.-W.Z.)
| | - Xu Han
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; (M.I.); (X.H.); (J.-F.Z.); (C.-Y.H.); (Y.-W.Z.)
| | - Jian-Fang Zuo
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; (M.I.); (X.H.); (J.-F.Z.); (C.-Y.H.); (Y.-W.Z.)
| | - Jian Song
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China;
| | - Chun-Yu Han
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; (M.I.); (X.H.); (J.-F.Z.); (C.-Y.H.); (Y.-W.Z.)
| | - Ya-Wen Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; (M.I.); (X.H.); (J.-F.Z.); (C.-Y.H.); (Y.-W.Z.)
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; (M.I.); (X.H.); (J.-F.Z.); (C.-Y.H.); (Y.-W.Z.)
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