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Li X, Wu ME, Zhang J, Xu J, Diao Y, Li Y. The OsCLV2s-OsCRN1 co-receptor regulates grain shape in rice. J Genet Genomics 2024:S1673-8527(24)00064-X. [PMID: 38575110 DOI: 10.1016/j.jgg.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/29/2024] [Accepted: 03/29/2024] [Indexed: 04/06/2024]
Abstract
The highly conserved CLV-WUS negative feedback pathway plays a decisive role in regulating stem cell maintenance in shoot and floral meristems in higher plants, including Arabidopsis, rice, maize, and tomato. Here, we report the discovery that CLV-like genes directly regulate grain shape in rice. We find significant natural variations in the OsCLV2c, OsCLV2d, and OsCRN1 loci in a genome-wide association study of grain shape in rice. OsCLV2a, OsCLV2c, OsCLV2d, and OsCRN1 negatively regulate grain length-width ratio and show distinctive geographical distribution, indica-japonica differentiation, and artificial selection signatures. Notably, OsCLV2a and OsCRN1 interact biochemically and genetically, suggesting that the two components function in a complex to regulate grain shape of rice. Furthermore, the genetic contributions of the haplotypes combining OsCLV2a, OsCLV2c, and OsCRN1 are significantly higher than those of each single gene alone in controlling key yield traits. These findings identify two groups of receptor-like kinases that may function as distinct co-receptors to control grain size in rice, thereby revealing a previously unrecognized role of the CLV class genes in regulating seed development and proposing a framework to understand the molecular mechanisms of the CLV-WUS pathway in rice and other crops.
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Affiliation(s)
- Xingxing Li
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China
| | - Meng-En Wu
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China
| | - Juncheng Zhang
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China
| | - Jingyue Xu
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China
| | - Yuanfei Diao
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China
| | - Yibo Li
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China.
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2
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Flay C, Symonds VV, Storey R, Davy M, Datson P. Mapping QTL associated with resistance to Pseudomonas syringae pv. actinidiae in kiwifruit ( Actinidia chinensis var. chinensis). FRONTIERS IN PLANT SCIENCE 2024; 14:1255506. [PMID: 38596713 PMCID: PMC11003357 DOI: 10.3389/fpls.2023.1255506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 10/25/2023] [Indexed: 04/11/2024]
Abstract
Pseudomonas syringae pv. actinidiae (Psa) is a bacterial pathogen of kiwifruit. This pathogen causes leaf-spotting, cane dieback, wilting, cankers (lesions), and in severe cases, plant death. Families of diploid A. chinensis seedlings grown in the field show a range of susceptibilities to the disease with up to 100% of seedlings in some families succumbing to Psa. But the effect of selection for field resistance to Psa on the alleles that remain in surviving seedlings has not been assessed. The objective of this work was to analyse, the effect of plant removal from Psa on the allele frequency of an incomplete-factorial-cross population. This population was founded using a range of genotypically distinct diploid A. chinensis var. chinensis parents to make 28 F1 families. However, because of the diversity of these families, low numbers of surviving individuals, and a lack of samples from dead individuals, standard QTL mapping approaches were unlikely to yield good results. Instead, a modified bulk segregant analysis (BSA) overcame these drawbacks while reducing the costs of sampling and sample processing, and the complexity of data analysis. Because the method was modified, part one of this work was used to determine the signal strength required for a QTL to be detected with BSA. Once QTL detection accuracy was known, part two of this work analysed the 28 families from the incomplete-factorial-cross population that had multiple individuals removed due to Psa infection. Each family was assigned to one of eight bulks based on a single parent that contributed to the families. DNA was extracted in bulk by grinding sampled leaf discs together before DNA extraction. Each sample bulk was compared against a bulk made up of WGS data from the parents contributing to the sample bulk. The deviation in allele frequency from the expected allele frequency within surviving populations using the modified BSA method was able to identify 11 QTLs for Psa that were present in at least two analyses. The identification of these Psa resistance QTL will enable marker development to selectively breed for resistance to Psa in future kiwifruit breeding programs.
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Affiliation(s)
- Casey Flay
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
- The New Zealand Institute for Plant and Food Research Limited, Te Puke, New Zealand
| | - V. Vaughan Symonds
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
| | - Roy Storey
- The New Zealand Institute for Plant and Food Research Limited, Te Puke, New Zealand
| | - Marcus Davy
- The New Zealand Institute for Plant and Food Research Limited, Te Puke, New Zealand
| | - Paul Datson
- The New Zealand Institute for Plant and Food Research Limited, Te Puke, New Zealand
- Kiwifruit Breeding Centre, Te Puke, New Zealand
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3
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Ludwig E, Sumner J, Berry J, Polydore S, Ficor T, Agnew E, Haines K, Greenham K, Fahlgren N, Mockler TC, Gehan MA. Natural variation in Brachypodium distachyon responses to combined abiotic stresses. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:1676-1701. [PMID: 37483133 DOI: 10.1111/tpj.16387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 06/26/2023] [Accepted: 07/05/2023] [Indexed: 07/25/2023]
Abstract
The demand for agricultural production is becoming more challenging as climate change increases global temperature and the frequency of extreme weather events. This study examines the phenotypic variation of 149 accessions of Brachypodium distachyon under drought, heat, and the combination of stresses. Heat alone causes the largest amounts of tissue damage while the combination of stresses causes the largest decrease in biomass compared to other treatments. Notably, Bd21-0, the reference line for B. distachyon, did not have robust growth under stress conditions, especially the heat and combined drought and heat treatments. The climate of origin was significantly associated with B. distachyon responses to the assessed stress conditions. Additionally, a GWAS found loci associated with changes in plant height and the amount of damaged tissue under stress. Some of these SNPs were closely located to genes known to be involved in responses to abiotic stresses and point to potential causative loci in plant stress response. However, SNPs found to be significantly associated with a response to heat or drought individually are not also significantly associated with the combination of stresses. This, with the phenotypic data, suggests that the effects of these abiotic stresses are not simply additive, and the responses to the combined stresses differ from drought and heat alone.
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Affiliation(s)
- Ella Ludwig
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Joshua Sumner
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Jeffrey Berry
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
- Bayer Crop Sciences, St. Louis, Missouri, 63017, USA
| | - Seth Polydore
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Tracy Ficor
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Erica Agnew
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Kristina Haines
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Kathleen Greenham
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
- University of Minnesota, St. Paul, Minnesota, 55108, USA
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Todd C Mockler
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Malia A Gehan
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
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Phosuwan S, Nounjan N, Theerakulpisut P, Siangliw M, Charoensawan V. Comparative quantitative trait loci analysis framework reveals relationships between salt stress responsive phenotypes and pathways. FRONTIERS IN PLANT SCIENCE 2024; 15:1264909. [PMID: 38463565 PMCID: PMC10920293 DOI: 10.3389/fpls.2024.1264909] [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: 07/21/2023] [Accepted: 02/07/2024] [Indexed: 03/12/2024]
Abstract
Soil salinity is a complex abiotic stress that involves several biological pathways. Hence, focusing on a specific or a few salt-tolerant phenotypes is unlikely to provide comprehensive insights into the intricate and interwinding mechanisms that regulate salt responsiveness. In this study, we develop a heuristic framework for systematically integrating and comprehensively evaluating quantitative trait loci (QTL) analyses from multiple stress-related traits obtained by different studies. Making use of a combined set of 46 salinity-related traits from three independent studies that were based on the same chromosome segment substitution line (CSSL) population of rice (Oryza sativa), we demonstrate how our approach can address technical biases and limitations from different QTL studies and calling methods. This allows us to compile a comprehensive list of trait-specific and multi-trait QTLs, as well as salinity-related candidate genes. In doing so, we discover several novel relationships between traits that demonstrate similar trends of phenotype scores across the CSSLs, as well as the similarities between genomic locations that the traits were mapped to. Finally, we experimentally validate our findings by expression analyses and functional validations of several selected candidate genes from multiple pathways in rice and Arabidopsis orthologous genes, including OsKS7 (ENT-KAURENE SYNTHASE 7), OsNUC1 (NUCLEOLIN 1) and OsFRO1 (FERRIC REDUCTASE OXIDASE 1) to name a few. This work not only introduces a novel approach for conducting comparative analyses of multiple QTLs, but also provides a list of candidate genes and testable hypotheses for salinity-related mechanisms across several biological pathways.
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Affiliation(s)
- Sunadda Phosuwan
- Doctor of Philosophy Program in Biochemistry (International Program), Faculty of Science, Mahidol University, Bangkok, Thailand
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Noppawan Nounjan
- Biodiversity and Environmental Management Division, International College, Khon Kaen University, Khon Kaen, Thailand
| | - Piyada Theerakulpisut
- Salt-tolerant Rice Research Group, Department of Biology, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand
| | - Meechai Siangliw
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Thailand
| | - Varodom Charoensawan
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom, Thailand
- Division of Medical Bioinformatics, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Genomics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- School of Chemistry, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, Thailand
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5
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Sahito JH, Zhang H, Gishkori ZGN, Ma C, Wang Z, Ding D, Zhang X, Tang J. Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize. Int J Mol Sci 2024; 25:1918. [PMID: 38339196 PMCID: PMC10855973 DOI: 10.3390/ijms25031918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/30/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024] Open
Abstract
Genome-wide association studies (GWAS) have emerged as a powerful tool for unraveling intricate genotype-phenotype association across various species. Maize (Zea mays L.), renowned for its extensive genetic diversity and rapid linkage disequilibrium (LD), stands as an exemplary candidate for GWAS. In maize, GWAS has made significant advancements by pinpointing numerous genetic loci and potential genes associated with complex traits, including responses to both abiotic and biotic stress. These discoveries hold the promise of enhancing adaptability and yield through effective breeding strategies. Nevertheless, the impact of environmental stress on crop growth and yield is evident in various agronomic traits. Therefore, understanding the complex genetic basis of these traits becomes paramount. This review delves into current and future prospectives aimed at yield, quality, and environmental stress resilience in maize and also addresses the challenges encountered during genomic selection and molecular breeding, all facilitated by the utilization of GWAS. Furthermore, the integration of omics, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics has enriched our understanding of intricate traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. Collectively, these insights not only advance our understanding of the genetic mechanism regulating complex traits but also propel the utilization of marker-assisted selection in maize molecular breeding programs, where GWAS plays a pivotal role. Therefore, GWAS provides robust support for delving into the genetic mechanism underlying complex traits in maize and enhancing breeding strategies.
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Affiliation(s)
- Javed Hussain Sahito
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Hao Zhang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Zeeshan Ghulam Nabi Gishkori
- Institute of Biotechnology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Chenhui Ma
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Zhihao Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Dong Ding
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Jihua Tang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
- The Shennong Laboratory, Zhengzhou 450002, China
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6
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Sun F, Deng Y, Ma X, Liu Y, Zhao L, Yu S, Zhang L. Structure-based prediction of protein-protein interaction network in rice. Genet Mol Biol 2024; 47:e20230068. [PMID: 38314883 PMCID: PMC10849033 DOI: 10.1590/1678-4685-gmb-2023-0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 10/02/2023] [Indexed: 02/07/2024] Open
Abstract
Comprehensive protein-protein interaction (PPI) maps are critical for understanding the functional organization of the proteome, but challenging to produce experimentally. Here, we developed a computational method for predicting PPIs based on protein docking. Evaluation of performance on benchmark sets demonstrated the ability of the docking-based method to accurately identify PPIs using predicted protein structures. By employing the docking-based method, we constructed a structurally resolved PPI network consisting of 24,653 interactions between 2,131 proteins, which greatly extends the current knowledge on the rice protein-protein interactome. Moreover, we mapped the trait-associated single nucleotide polymorphisms (SNPs) to the structural interactome, and computationally identified 14 SNPs that had significant consequences on PPI network. The protein structural interactome map provided a resource to facilitate functional investigation of PPI-perturbing alleles associated with agronomically important traits in rice.
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Affiliation(s)
- Fangnan Sun
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
| | - Yaxin Deng
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
| | - Xiaosong Ma
- Shanghai Academy of Agricultural Sciences, Shanghai Agrobiological Gene Center, Shanghai, China
| | - Yuan Liu
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
| | - Lingxia Zhao
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
| | - Shunwu Yu
- Shanghai Academy of Agricultural Sciences, Shanghai Agrobiological Gene Center, Shanghai, China
| | - Lida Zhang
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
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7
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Xing J, Zhang J, Wang Y, Wei X, Yin Z, Zhang Y, Pu A, Dong Z, Long Y, Wan X. Mining genic resources regulating nitrogen-use efficiency based on integrative biological analyses and their breeding applications in maize and other crops. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:1148-1164. [PMID: 37967146 DOI: 10.1111/tpj.16550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/08/2023] [Accepted: 11/05/2023] [Indexed: 11/17/2023]
Abstract
Nitrogen (N) is an essential factor for limiting crop yields, and cultivation of crops with low nitrogen-use efficiency (NUE) exhibits increasing environmental and ecological risks. Hence, it is crucial to mine valuable NUE improvement genes, which is very important to develop and breed new crop varieties with high NUE in sustainable agriculture system. Quantitative trait locus (QTL) and genome-wide association study (GWAS) analysis are the most common methods for dissecting genetic variations underlying complex traits. In addition, with the advancement of biotechnology, multi-omics technologies can be used to accelerate the process of exploring genetic variations. In this study, we integrate the substantial data of QTLs, quantitative trait nucleotides (QTNs) from GWAS, and multi-omics data including transcriptome, proteome, and metabolome and further analyze their interactions to predict some NUE-related candidate genes. We also provide the genic resources for NUE improvement among maize, rice, wheat, and sorghum by homologous alignment and collinearity analysis. Furthermore, we propose to utilize the knowledge gained from classical cases to provide the frameworks for improving NUE and breeding N-efficient varieties through integrated genomics, systems biology, and modern breeding technologies.
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Affiliation(s)
- Jiapeng Xing
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd., Beijing, 100192, China
| | - Juan Zhang
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd., Beijing, 100192, China
| | - Yanbo Wang
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xun Wei
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd., Beijing, 100192, China
| | - Zechao Yin
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yuqian Zhang
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
| | - Aqing Pu
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
| | - Zhenying Dong
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yan Long
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd., Beijing, 100192, China
| | - Xiangyuan Wan
- Research Institute of Biology and Agriculture, Shunde Innovation School, Zhongzhi International Institute of Agricultural Biosciences, University of Science and Technology Beijing, Beijing, 100083, China
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd., Beijing, 100192, China
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8
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Dai K, Wang X, Liu H, Qiao P, Wang J, Shi W, Guo J, Diao X. Efficient identification of QTL for agronomic traits in foxtail millet (Setaria italica) using RTM- and MLM-GWAS. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:18. [PMID: 38206376 DOI: 10.1007/s00122-023-04522-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/07/2023] [Indexed: 01/12/2024]
Abstract
KEY MESSAGE Eleven QTLs for agronomic traits were identified by RTM- and MLM-GWAS, putative candidate genes were predicted and two markers for grain weight were developed and validated. Foxtail millet (Setaria italica), the second most cultivated millet crop after pearl millet, is an important grain crop in arid regions. Seven agronomic traits of 408 diverse foxtail millet accessions from 15 provinces in China were evaluated in three environments. They were clustered into two divergent groups based on genotypic data using ADMIXTURE, which was highly consistent with their geographical distribution. Two models for genome-wide association studies (GWAS), namely restricted two-stage multi-locus multi-allele (RTM)-GWAS and mixed linear model (MLM)-GWAS, were used to dissect the genetic architecture of the agronomic traits based on 13,723 SNPs. Eleven quantitative trait loci (QTLs) for seven traits were identified using two models (RTM- and MLM-GWAS). Among them, five were considered stable QTLs that were identified in at least two environments using MLM-GWAS. One putative candidate gene (SETIT_006045mg, Chr4: 744,701-746,852) that can enhance grain weight per panicle was identified based on homologous gene comparison and gene expression analysis and was validated by haplotype analysis of 330 accessions with high-depth (10×) resequencing data (unpublished). In addition, homologous gene comparison and haplotype analysis identified one putative foxtail millet ortholog (SETIT_032906mg, Chr2: 5,020,600-5,029,771) with rice affecting the target traits. Two markers (cGWP6045 and kTGW2906) were developed and validated and can be used for marker-assisted selection of foxtail millet with high grain weight. The results provide a fundamental resource for foxtail millet genetic research and breeding and demonstrate the power of integrating RTM- and MLM-GWAS approaches as a complementary strategy for investigating complex traits in foxtail millet.
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Affiliation(s)
- Keli Dai
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Xin Wang
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Hanxiao Liu
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Pengfei Qiao
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Jiaxue Wang
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Weiping Shi
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China.
| | - Jie Guo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China.
| | - Xianmin Diao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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9
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Corut AK, Wallace JG. kGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS. G3 (BETHESDA, MD.) 2023; 14:jkad246. [PMID: 37976215 PMCID: PMC10755180 DOI: 10.1093/g3journal/jkad246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/15/2023] [Indexed: 11/19/2023]
Abstract
Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow).
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Affiliation(s)
- Adnan Kivanc Corut
- Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
| | - Jason G Wallace
- Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
- Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Athens, GA 30602, USA
- Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602, USA
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10
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Su J, Lu Z, Zeng J, Zhang X, Yang X, Wang S, Zhang F, Jiang J, Chen F. Multi-locus genome-wide association study and genomic prediction for flowering time in chrysanthemum. PLANTA 2023; 259:13. [PMID: 38063918 DOI: 10.1007/s00425-023-04297-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023]
Abstract
MAIN CONCLUSION Multi-locus GWAS detected several known and candidate genes responsible for flowering time in chrysanthemum. The associations could greatly increase the predictive ability of genome selection that accelerates the possible application of GS in chrysanthemum breeding. Timely flowering is critical for successful reproduction and determines the economic value for ornamental plants. To investigate the genetic architecture of flowering time in chrysanthemum, a multi-locus genome-wide association study (GWAS) was performed using a collection of 200 accessions and 330,710 single-nucleotide polymorphisms (SNPs) via 3VmrMLM method. Five flowering time traits including budding (FBD), visible colouring (VC), early opening (EO), full-bloom (OF) and senescing (SF) stages, plus five derived conditional traits were recorded in two environments. Extensive phenotypic variations were observed for these flowering time traits with coefficients of variation ranging from 6.42 to 38.27%, and their broad-sense heritability ranged from 71.47 to 96.78%. GWAS revealed 88 stable quantitative trait nucleotides (QTNs) and 93 QTN-by-environment interactions (QEIs) associated with flowering time traits, accounting for 0.50-8.01% and 0.30-10.42% of the phenotypic variation, respectively. Amongst the genes around these stable QTNs and QEIs, 21 and 10 were homologous to known flowering genes in Arabidopsis; 20 and 11 candidate genes were mined by combining the functional annotation and transcriptomics data, respectively, such as MYB55, FRIGIDA-like, WRKY75 and ANT. Furthermore, genomic selection (GS) was assessed using three models and seven unique marker datasets. We found the prediction accuracy (PA) using significant SNPs identified by GWAS under SVM model exhibited the best performance with PA ranging from 0.90 to 0.95. Our findings provide new insights into the dynamic genetic architecture of flowering time and the identified significant SNPs and candidate genes will accelerate the future molecular improvement of chrysanthemum.
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Affiliation(s)
- Jiangshuo Su
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Zhaowen Lu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Junwei Zeng
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Xuefeng Zhang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Xiuwei Yang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Siyue Wang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Fei Zhang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
- Zhongshan Biological Breeding Laboratory, No. 50 Zhongling Street, Nanjing, 210014, China
| | - Jiafu Jiang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
- Zhongshan Biological Breeding Laboratory, No. 50 Zhongling Street, Nanjing, 210014, China
| | - Fadi Chen
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China.
- Zhongshan Biological Breeding Laboratory, No. 50 Zhongling Street, Nanjing, 210014, China.
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11
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Jia J, Zhao G, Li D, Wang K, Kong C, Deng P, Yan X, Zhang X, Lu Z, Xu S, Jiao Y, Chong K, Liu X, Cui D, Li G, Zhang Y, Du C, Wu L, Li T, Yan D, Zhan K, Chen F, Wang Z, Zhang L, Kong X, Ru Z, Wang D, Gao L. Genome resources for the elite bread wheat cultivar Aikang 58 and mining of elite homeologous haplotypes for accelerating wheat improvement. MOLECULAR PLANT 2023; 16:1893-1910. [PMID: 37897037 DOI: 10.1016/j.molp.2023.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 07/12/2023] [Accepted: 10/23/2023] [Indexed: 10/29/2023]
Abstract
Despite recent progress in crop genomics studies, the genomic changes brought about by modern breeding selection are still poorly understood, thus hampering genomics-assisted breeding, especially in polyploid crops with compound genomes such as common wheat (Triticum aestivum). In this work, we constructed genome resources for the modern elite common wheat variety Aikang 58 (AK58). Comparative genomics between AK58 and the landrace cultivar Chinese Spring (CS) shed light on genomic changes that occurred through recent varietal improvement. We also explored subgenome diploidization and divergence in common wheat and developed a homoeologous locus-based genome-wide association study (HGWAS) approach, which was more effective than single homoeolog-based GWAS in unraveling agronomic trait-associated loci. A total of 123 major HGWAS loci were detected using a genetic population derived from AK58 and CS. Elite homoeologous haplotypes (HHs), formed by combinations of subgenomic homoeologs of the associated loci, were found in both parents and progeny, and many could substantially improve wheat yield and related traits. We built a website where users can download genome assembly sequence and annotation data for AK58, perform blast analysis, and run JBrowse. Our work enriches genome resources for wheat, provides new insights into genomic changes during modern wheat improvement, and suggests that efficient mining of elite HHs can make a substantial contribution to genomics-assisted breeding in common wheat and other polyploid crops.
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Affiliation(s)
- Jizeng Jia
- College of Agronomy, Collaborative Innovation Center of Henan Grain Crops, State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, Henan, China; State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Guangyao Zhao
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Danping Li
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Kai Wang
- Xi'An Shansheng Biosciences Co., Ltd., Xi'an 710000, China
| | - Chuizheng Kong
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Pingchuan Deng
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, Zhejiang 310058, China; State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 612100, China
| | - Xueqing Yan
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xueyong Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zefu Lu
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shujuan Xu
- University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Yuannian Jiao
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kang Chong
- University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Xu Liu
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Dangqun Cui
- College of Agronomy, Collaborative Innovation Center of Henan Grain Crops, State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Guangwei Li
- College of Agronomy, Collaborative Innovation Center of Henan Grain Crops, State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Yijing Zhang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Department of Biochemistry, Institute of Plant Biology, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Chunguang Du
- College of Agronomy, Collaborative Innovation Center of Henan Grain Crops, State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Liang Wu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, Zhejiang 310058, China; Hainan Yazhou Bay Seed Laboratory, Hainan Institute of Zhejiang University, Sanya, Hainan 562000, China
| | - Tianbao Li
- College of Agronomy, Collaborative Innovation Center of Henan Grain Crops, State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, Henan, China; State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Dong Yan
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Kehui Zhan
- College of Agronomy, Collaborative Innovation Center of Henan Grain Crops, State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Feng Chen
- College of Agronomy, Collaborative Innovation Center of Henan Grain Crops, State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Zhiyong Wang
- College of Agronomy, Collaborative Innovation Center of Henan Grain Crops, State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Lichao Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiuying Kong
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Zhengang Ru
- School of Life Science and Technology, Henan Institute of Science and Technology, Xinxiang, Henan 453003, China.
| | - Daowen Wang
- College of Agronomy, Collaborative Innovation Center of Henan Grain Crops, State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, Henan, China.
| | - Lifeng Gao
- State Key Laboratory of Crop Gene Resources and Breeding, the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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12
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Yu T, Zhang J, Cao J, Li S, Cai Q, Li X, Li S, Li Y, He C, Ma X. Identification of Multiple Genetic Loci Related to Low-Temperature Tolerance during Germination in Maize ( Zea maize L.) through a Genome-Wide Association Study. Curr Issues Mol Biol 2023; 45:9634-9655. [PMID: 38132448 PMCID: PMC10742315 DOI: 10.3390/cimb45120602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 11/13/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Low-temperature stress during the germination stage is an important abiotic stress that affects the growth and development of northern spring maize and seriously restricts maize yield and quality. Although some quantitative trait locis (QTLs) related to low-temperature tolerance in maize have been detected, only a few can be commonly detected, and the QTL intervals are large, indicating that low-temperature tolerance is a complex trait that requires more in-depth research. In this study, 296 excellent inbred lines from domestic and foreign origins (America and Europe) were used as the study materials, and a low-coverage resequencing method was employed for genome sequencing. Five phenotypic traits related to low-temperature tolerance were used to assess the genetic diversity of maize through a genome-wide association study (GWAS). A total of 14 SNPs significantly associated with low-temperature tolerance were detected (-log10(P) > 4), and an SNP consistently linked to low-temperature tolerance in the field and indoors during germination was utilized as a marker. This SNP, 14,070, was located on chromosome 5 at position 2,205,723, which explained 4.84-9.68% of the phenotypic variation. The aim of this study was to enrich the genetic theory of low-temperature tolerance in maize and provide support for the innovation of low-temperature tolerance resources and the breeding of new varieties.
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Affiliation(s)
- Tao Yu
- Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (J.C.); (Q.C.); (X.L.); (X.M.)
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
- Key Laboratory of Germplasm Resources Creation and Utilization of Maize, Harbin 150086, China
| | - Jianguo Zhang
- Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (J.C.); (Q.C.); (X.L.); (X.M.)
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
- Key Laboratory of Germplasm Resources Creation and Utilization of Maize, Harbin 150086, China
| | - Jingsheng Cao
- Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (J.C.); (Q.C.); (X.L.); (X.M.)
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
- Key Laboratory of Germplasm Resources Creation and Utilization of Maize, Harbin 150086, China
| | - Shujun Li
- Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (J.C.); (Q.C.); (X.L.); (X.M.)
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
- Key Laboratory of Germplasm Resources Creation and Utilization of Maize, Harbin 150086, China
| | - Quan Cai
- Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (J.C.); (Q.C.); (X.L.); (X.M.)
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
- Key Laboratory of Germplasm Resources Creation and Utilization of Maize, Harbin 150086, China
| | - Xin Li
- Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (J.C.); (Q.C.); (X.L.); (X.M.)
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
| | - Sinan Li
- Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (J.C.); (Q.C.); (X.L.); (X.M.)
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
| | - Yunlong Li
- Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (J.C.); (Q.C.); (X.L.); (X.M.)
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
| | - Changan He
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
- Keshan Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihaer 161000, China
| | - Xuena Ma
- Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (J.C.); (Q.C.); (X.L.); (X.M.)
- Key Laboratory of Biology and Genetics Improvement of Maize in Northern Northeast Region, Ministry of Agriculture and Rural Affairs, Harbin 150086, China
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13
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Luo X, Yang Y, Lin X, Xiao J. Deciphering spike architecture formation towards yield improvement in wheat. J Genet Genomics 2023; 50:835-845. [PMID: 36907353 DOI: 10.1016/j.jgg.2023.02.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 03/12/2023]
Abstract
Wheat is the most widely grown crop globally, providing 20% of the daily consumed calories and protein content around the world. With the growing global population and frequent occurrence of extreme weather caused by climate change, ensuring adequate wheat production is essential for food security. The architecture of the inflorescence plays a crucial role in determining the grain number and size, which is a key trait for improving yield. Recent advances in wheat genomics and gene cloning techniques have improved our understanding of wheat spike development and its applications in breeding practices. Here, we summarize the genetic regulation network governing wheat spike formation, the strategies used for identifying and studying the key factors affecting spike architecture, and the progress made in breeding applications. Additionally, we highlight future directions that will aid in the regulatory mechanistic study of wheat spike determination and targeted breeding for grain yield improvement.
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Affiliation(s)
- Xumei Luo
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiman Yang
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Xuelei Lin
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Jun Xiao
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
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14
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Chawla R, Poonia A, Samantara K, Mohapatra SR, Naik SB, Ashwath MN, Djalovic IG, Prasad PVV. Green revolution to genome revolution: driving better resilient crops against environmental instability. Front Genet 2023; 14:1204585. [PMID: 37719711 PMCID: PMC10500607 DOI: 10.3389/fgene.2023.1204585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 08/11/2023] [Indexed: 09/19/2023] Open
Abstract
Crop improvement programmes began with traditional breeding practices since the inception of agriculture. Farmers and plant breeders continue to use these strategies for crop improvement due to their broad application in modifying crop genetic compositions. Nonetheless, conventional breeding has significant downsides in regard to effort and time. Crop productivity seems to be hitting a plateau as a consequence of environmental issues and the scarcity of agricultural land. Therefore, continuous pursuit of advancement in crop improvement is essential. Recent technical innovations have resulted in a revolutionary shift in the pattern of breeding methods, leaning further towards molecular approaches. Among the promising approaches, marker-assisted selection, QTL mapping, omics-assisted breeding, genome-wide association studies and genome editing have lately gained prominence. Several governments have progressively relaxed their restrictions relating to genome editing. The present review highlights the evolutionary and revolutionary approaches that have been utilized for crop improvement in a bid to produce climate-resilient crops observing the consequence of climate change. Additionally, it will contribute to the comprehension of plant breeding succession so far. Investing in advanced sequencing technologies and bioinformatics will deepen our understanding of genetic variations and their functional implications, contributing to breakthroughs in crop improvement and biodiversity conservation.
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Affiliation(s)
- Rukoo Chawla
- Department of Genetics and Plant Breeding, Maharana Pratap University of Agriculture and Technology, Udaipur, Rajasthan, India
| | - Atman Poonia
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh Haryana Agricultural University, Bawal, Haryana, India
| | - Kajal Samantara
- Institute of Technology, University of Tartu, Tartu, Estonia
| | - Sourav Ranjan Mohapatra
- Department of Forest Biology and Tree Improvement, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India
| | - S. Balaji Naik
- Institute of Integrative Biology and Systems, University of Laval, Quebec City, QC, Canada
| | - M. N. Ashwath
- Department of Forest Biology and Tree Improvement, Kerala Agricultural University, Thrissur, Kerala, India
| | - Ivica G. Djalovic
- Institute of Field and Vegetable Crops, National Institute of the Republic of Serbia, Novi Sad, Serbia
| | - P. V. Vara Prasad
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
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15
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Peng L, Li Y, Tan W, Wu S, Hao Q, Tong N, Wang Z, Liu Z, Shu Q. Combined genome-wide association studies and expression quantitative trait locus analysis uncovers a genetic regulatory network of floral organ number in a tree peony ( Paeonia suffruticosa Andrews) breeding population. HORTICULTURE RESEARCH 2023; 10:uhad110. [PMID: 37577399 PMCID: PMC10419549 DOI: 10.1093/hr/uhad110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/16/2023] [Indexed: 08/15/2023]
Abstract
Great progress has been made in our understanding of floral organ identity determination and its regulatory network in many species; however, the quantitative genetic basis of floral organ number variation is far less well understood for species-specific traits from the perspective of population variation. Here, using a tree peony (Paeonia suffruticosa Andrews, Paeoniaceae) cultivar population as a model, the phenotypic polymorphism and genetic variation based on genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) analysis were analyzed. Based on 24 phenotypic traits of 271 representative cultivars, the transcript profiles of 119 cultivars were obtained, which indicated abundant genetic variation in tree peony. In total, 86 GWAS-related cis-eQTLs and 3188 trans-eQTL gene pairs were found to be associated with the numbers of petals, stamens, and carpels. In addition, 19 floral organ number-related hub genes with 121 cis-eQTLs were obtained by weighted gene co-expression network analysis, among which five hub genes belonging to the ABCE genes of the MADS-box family and their spatial-temporal co-expression and regulatory network were constructed. These results not only help our understanding of the genetic basis of floral organ number variation during domestication, but also pave the way to studying the quantitative genetics and evolution of flower organ number and their regulatory network within populations.
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Affiliation(s)
- Liping Peng
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
| | - Yang Li
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
| | - Wanqing Tan
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shangwei Wu
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qing Hao
- College of Landscape Architecture and Forestry, Qingdao Agricultural University, Qingdao 266109, China
| | - Ningning Tong
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
| | - Zhanying Wang
- Peony Research Institute, Luoyang Academy of Agricultural and Forestry Sciences, Luoyang 471000, China
| | - Zheng’an Liu
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qingyan Shu
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
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16
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Li B, Wei A, Tong X, Han Y, Liu N, Chen Z, Yang H, Wu H, Lv M, Wang NN, Du S. A Genome-Wide Association Study to Identify Novel Candidate Genes Related to Low-Nitrogen Tolerance in Cucumber (Cucumis sativus L.). Genes (Basel) 2023; 14:genes14030662. [PMID: 36980933 PMCID: PMC10048605 DOI: 10.3390/genes14030662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/09/2023] Open
Abstract
Cucumber is one of the most important vegetables, and nitrogen is essential for the growth and fruit production of cucumbers. It is crucial to develop cultivars with nitrogen limitation tolerance or high nitrogen efficiency for green and efficient development in cucumber industry. To reveal the genetic basis of cucumber response to nitrogen starvation, a genome-wide association study (GWAS) was conducted on a collection of a genetically diverse population of cucumber (Cucumis sativus L.) comprising 88 inbred and DH accessions including the North China type, the Eurasian type, the Japanese and South China type mixed subtype, and the South China type subtype. Phenotypic evaluation of six traits under control (14 mM) and treatment (3.5 mM) N conditions depicted the presence of broad natural variation in the studied population. The GWAS results showed that there were significant differences in the population for nitrogen limitation treatment. Nine significant loci were identified corresponding to six LD blocks, three of which overlapped. Sixteen genes were selected by GO annotation associated with nitrogen. Five low-nitrogen stress tolerance genes were finally identified by gene haplotype analysis: CsaV3_3G003630 (CsNRPD1), CsaV3_3G002970 (CsNRT1.1), CsaV3_4G030260 (CsSnRK2.5), CsaV3_4G026940, and CsaV3_3G011820 (CsNPF5.2). Taken together, the experimental data and identification of candidate genes presented in this study offer valuable insights and serve as a useful reference for the genetic enhancement of nitrogen limitation tolerance in cucumbers.
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Affiliation(s)
- Bowen Li
- College of Life Science, Nankai University, Tianjin 300071, China
| | - Aimin Wei
- Cucumber Research Institute, Tianjin Academy of Agricultural Sciences, Tianjin 300192, China
- State Key Laboratory of Vegetable Biobreeding, Tianjin 300192, China
| | - Xueqiang Tong
- College of Life Science, Nankai University, Tianjin 300071, China
| | - Yike Han
- Cucumber Research Institute, Tianjin Academy of Agricultural Sciences, Tianjin 300192, China
- State Key Laboratory of Vegetable Biobreeding, Tianjin 300192, China
| | - Nan Liu
- Cucumber Research Institute, Tianjin Academy of Agricultural Sciences, Tianjin 300192, China
| | - Zhengwu Chen
- Cucumber Research Institute, Tianjin Academy of Agricultural Sciences, Tianjin 300192, China
- State Key Laboratory of Vegetable Biobreeding, Tianjin 300192, China
| | - Hongyu Yang
- College of Life Science, Nankai University, Tianjin 300071, China
| | - Huaxiang Wu
- College of Life Science, Nankai University, Tianjin 300071, China
| | - Mingjie Lv
- Institute of Germplasm Resources and Biotechnology, Tianjin Academy of Agricultural Sciences, Tianjin 300061, China
| | - Ning Ning Wang
- College of Life Science, Nankai University, Tianjin 300071, China
- College of Agricultural Science, Nankai University, Tianjin 300071, China
| | - Shengli Du
- College of Life Science, Nankai University, Tianjin 300071, China
- Cucumber Research Institute, Tianjin Academy of Agricultural Sciences, Tianjin 300192, China
- State Key Laboratory of Vegetable Biobreeding, Tianjin 300192, China
- Correspondence:
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Kumar M, Kumar S, Sandhu KS, Kumar N, Saripalli G, Prakash R, Nambardar A, Sharma H, Gautam T, Balyan HS, Gupta PK. GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:14. [PMID: 37313293 PMCID: PMC10248620 DOI: 10.1007/s11032-023-01357-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/26/2023] [Indexed: 06/15/2023]
Abstract
In wheat, a genome-wide association study (GWAS) and genomic prediction (GP) analysis were conducted for pre-harvest sprouting (PHS) tolerance and two of its related traits. For this purpose, an association panel of 190 accessions was phenotyped for PHS (using sprouting score), falling number, and grain color over two years and genotyped with 9904 DArTseq based SNP markers. GWAS for main-effect quantitative trait nucleotides (M-QTNs) using three different models (CMLM, SUPER, and FarmCPU) and epistatic QTNs (E-QTNs) using PLINK were performed. A total of 171 M-QTNs (CMLM, 47; SUPER, 70; FarmCPU, 54) for all three traits, and 15 E-QTNs involved in 20 first-order epistatic interactions were identified. Some of the above QTNs overlapped the previously reported QTLs, MTAs, and cloned genes, allowing delineating 26 PHS-responsive genomic regions that spread over 16 wheat chromosomes. As many as 20 definitive and stable QTNs were considered important for use in marker-assisted recurrent selection (MARS). The gene, TaPHS1, for PHS tolerance (PHST) associated with one of the QTNs was also validated using the KASP assay. Some of the M-QTNs were shown to have a key role in the abscisic acid pathway involved in PHST. Genomic prediction accuracies (based on the cross-validation approach) using three different models ranged from 0.41 to 0.55, which are comparable to the results of previous studies. In summary, the results of the present study improved our understanding of the genetic architecture of PHST and its related traits in wheat and provided novel genomic resources for wheat breeding based on MARS and GP. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01357-5.
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Affiliation(s)
- Manoj Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Sachin Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | | | - Neeraj Kumar
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC USA
| | - Gautam Saripalli
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD USA
| | - Ram Prakash
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Akash Nambardar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Hemant Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Tinku Gautam
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
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Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T. Microorganisms 2023; 11:microorganisms11020331. [PMID: 36838296 PMCID: PMC9961961 DOI: 10.3390/microorganisms11020331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 02/03/2023] Open
Abstract
Plant growth-promoting bacteria (PGPB) can enhance plant health by facilitating nutrient uptake, nitrogen fixation, protection from pathogens, stress tolerance and/or boosting plant productivity. The genetic determinants that drive the plant-bacteria association remain understudied. To identify genetic loci highly correlated with traits responsive to PGPB, we performed a genome-wide association study (GWAS) using an Arabidopsis thaliana population treated with Azoarcus olearius DQS-4T. Phenotypically, the 305 Arabidopsis accessions tested responded differently to bacterial treatment by improving, inhibiting, or not affecting root system or shoot traits. GWA mapping analysis identified several predicted loci associated with primary root length or root fresh weight. Two statistical analyses were performed to narrow down potential gene candidates followed by haplotype block analysis, resulting in the identification of 11 loci associated with the responsiveness of Arabidopsis root fresh weight to bacterial inoculation. Our results showed considerable variation in the ability of plants to respond to inoculation by A. olearius DQS-4T while revealing considerable complexity regarding statistically associated loci with the growth traits measured. This investigation is a promising starting point for sustainable breeding strategies for future cropping practices that may employ beneficial microbes and/or modifications of the root microbiome.
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Singh S, Gaurav SS, Vasistha NK, Kumar U, Joshi AK, Mishra VK, Chand R, Gupta PK. Genetics of spot blotch resistance in bread wheat ( Triticum aestivum L.) using five models for GWAS. FRONTIERS IN PLANT SCIENCE 2023; 13:1036064. [PMID: 36743576 PMCID: PMC9891466 DOI: 10.3389/fpls.2022.1036064] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 12/28/2022] [Indexed: 06/18/2023]
Abstract
Genetic architecture of resistance to spot blotch in wheat was examined using a Genome-Wide Association Study (GWAS) involving an association panel comprising 303 diverse genotypes. The association panel was evaluated at two different locations in India including Banaras Hindu University (BHU), Varanasi (Uttar Pradesh), and Borlaug Institute for South Asia (BISA), Pusa, Samastipur (Bihar) for two consecutive years (2017-2018 and 2018-2019), thus making four environments (E1, BHU 2017-18; E2, BHU 2018-19; E3, PUSA, 2017-18; E4, PUSA, 2018-19). The panel was genotyped for 12,196 SNPs based on DArT-seq (outsourced to DArT Ltd by CIMMYT); these SNPs included 5,400 SNPs, which could not be assigned to individual chromosomes and were therefore, described as unassigned by the vendor. Phenotypic data was recorded on the following three disease-related traits: (i) Area Under Disease Progress Curve (AUDPC), (ii) Incubation Period (IP), and (iii) Lesion Number (LN). GWAS was conducted using each of five different models, which included two single-locus models (CMLM and SUPER) and three multi-locus models (MLMM, FarmCPU, and BLINK). This exercise gave 306 MTAs, but only 89 MTAs (33 for AUDPC, 30 for IP and 26 for LN) including a solitary MTA detected using all the five models and 88 identified using four of the five models (barring SUPER) were considered to be important. These were used for further analysis, which included identification of candidate genes (CGs) and their annotation. A majority of these MTAs were novel. Only 70 of the 89 MTAs were assigned to individual chromosomes; the remaining 19 MTAs belonged to unassigned SNPs, for which chromosomes were not known. Seven MTAs were selected on the basis of minimum P value, number of models, number of environments and location on chromosomes with respect to QTLs reported earlier. These 7 MTAs, which included five main effect MTAs and two for epistatic interactions, were considered to be important for marker-assisted selection (MAS). The present study thus improved our understanding of the genetics of resistance against spot blotch in wheat and provided seven MTAs, which may be used for MAS after due validation.
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Affiliation(s)
- Sahadev Singh
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - Shailendra Singh Gaurav
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - Neeraj Kumar Vasistha
- Department of Genetics-Plant Breeding and Biotechnology, Dr Khem Singh Gill, Akal College of Agriculture, Eternal University, Sirmaur, India
| | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | - Arun Kumar Joshi
- The International Maize and Wheat Improvement Center (CIMMYT), Borlaug Institute for South Asia (BISA), G-2, B-Block, NASC Complex, DPS Marg, New Delhi, India
| | - Vinod Kumar Mishra
- Department of Genetics and Plant Breeding, Indian Institute of Agricultural Science, Banaras Hindu University, Varanasi, India
| | - Ramesh Chand
- Department of Mycology and Plant Pathology, Indian Institute of Agricultural Science Banaras Hindu University, Varanasi, India
| | - Pushpendra Kumar Gupta
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- Borlaug Institute for South Asia (BISA), Ludhiana, India
- Murdoch’s Centre for Crop & Food Innovation, Murdoch University, Murdoch, WA, Australia
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20
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Hu B, Wang W, Chen J, Liu Y, Chu C. Genetic improvement toward nitrogen-use efficiency in rice: Lessons and perspectives. MOLECULAR PLANT 2023; 16:64-74. [PMID: 36380584 DOI: 10.1016/j.molp.2022.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 06/16/2023]
Abstract
The indispensable role of nitrogen fertilizer in ensuring world food security together with the severe threats it poses to the ecosystem makes the usage of nitrogen fertilizer a major challenge for sustainable agriculture. Genetic improvement of crops with high nitrogen-use efficiency (NUE) is one of the most feasible solutions for tackling this challenge. In the last two decades, extensive efforts toward dissecting the variation of NUE-related traits and the underlying genetic basis in different germplasms have been made, and a series of achievements have been obtained in crops, especially in rice. Here, we summarize the approaches used for genetic dissection of NUE and the functions of the causal genes in modulating NUE as well as their applications in NUE improvement in rice. Strategies for exploring the variants controlling NUE and breeding future crops with "less-input-more-output" for sustainable agriculture are also proposed.
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Affiliation(s)
- Bin Hu
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; Key Laboratory for Enhancing Resource Use Efficiency of Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, 510642, Guangzhou, China.
| | - Wei Wang
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; Key Laboratory for Enhancing Resource Use Efficiency of Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, 510642, Guangzhou, China
| | - Jiajun Chen
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, South China Agricultural University, Guangzhou 510642, China
| | - Yongqiang Liu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China
| | - Chengcai Chu
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; Key Laboratory for Enhancing Resource Use Efficiency of Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, 510642, Guangzhou, China.
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Gao Z, Liang Y, Wang Y, Xiao Y, Chen J, Yang X, Shi T. Genome-wide association study of traits in sacred lotus uncovers MITE-associated variants underlying stamen petaloid and petal number variations. FRONTIERS IN PLANT SCIENCE 2022; 13:973347. [PMID: 36212363 PMCID: PMC9539442 DOI: 10.3389/fpls.2022.973347] [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: 06/20/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Understanding the genetic variants responsible for floral trait diversity is important for the molecular breeding of ornamental flowers. Widely used in water gardening for thousands of years, the sacred lotus exhibits a wide range of diversity in floral organs. Nevertheless, the genetic variations underlying various morphological characteristics in lotus remain largely unclear. Here, we performed a genome-wide association study of sacred lotus for 12 well-recorded ornamental traits. Given a moderate linkage disequilibrium level of 32.9 kb, we successfully identified 149 candidate genes responsible for seven flower traits and plant size variations, including many pleiotropic genes affecting multiple floral-organ-related traits, such as NnKUP2. Notably, we found a 2.75-kb presence-and-absence genomic fragment significantly associated with stamen petaloid and petal number variations, which was further confirmed by re-examining another independent population dataset with petal number records. Intriguingly, this fragment carries MITE transposons bound by siRNAs and is related to the expression differentiation of a nearby candidate gene between few-petalled and double-petalled lotuses. Overall, these genetic variations and candidate genes responsible for diverse lotus traits revealed by our GWAS highlight the role of transposon variations, particularly MITEs, in shaping floral trait diversity.
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Affiliation(s)
- Zhiyan Gao
- Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China
- Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yuting Liang
- Wuhan Institute of Landscape Architecture, Wuhan, China
| | - Yuhan Wang
- Wuhan Institute of Design and Sciences, Wuhan, China
| | - Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jinming Chen
- Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China
- Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, China
| | - Xingyu Yang
- Wuhan Institute of Landscape Architecture, Wuhan, China
| | - Tao Shi
- Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China
- Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, China
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22
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Fayaz H, Tyagi S, Wani AA, Pandey R, Akhtar S, Bhat MA, Chitikineni A, Varshney RK, Thudi M, Kumar U, Mir RR. Genome-wide association analysis to delineate high-quality SNPs for seed micronutrient density in chickpea (Cicer arietinum L.). Sci Rep 2022; 12:11357. [PMID: 36064952 PMCID: PMC9445022 DOI: 10.1038/s41598-022-14487-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 06/07/2022] [Indexed: 11/16/2022] Open
Abstract
Chickpea is the most important nutrient-rich grain legume crop in the world. A diverse core set of 147 chickpea genotypes was genotyped with a Axiom(®)50K CicerSNP array and trait phenotyped in two different environments for four seed micronutrients (Zn, Cu, Fe and Mn). The trait data and high-throughput 50K SNP genotypic data were used for the genome-wide association study (GWAS). The study led to the discovery of genes/QTLs for seed Zn, Cu, Fe and Mn, concentrations in chickpea. The analysis of seed micronutrient data revealed significant differences for all four micronutrient concentrations (P ≤ 0.05). The mean concentrations of seed Zn, Cu, Fe and Mn pooled over the 2 years were 45.9 ppm, 63.8 ppm 146.1 ppm, and 27.0 ppm, respectively. The analysis of results led to the identification of 35 SNPs significantly associated with seed Zn, Cu, Fe and Mn concentrations. Among these 35 marker-trait associations (MTAs), 5 were stable (consistently identified in different environments), 6 were major (explaining more than 15% of the phenotypic variation for an individual trait) and 3 were both major and stable MTAs. A set of 6 MTAs, MTAs (3 for Mn, 2 for Fe, and 1 for Cu) reported by us during the present study have been also reported in the same/almost same genomic regions in earlier studies and therefore declared as validated MTAs. The stable, major and validated MTAs identified during the present study will prove useful in future chickpea molecular breeding programs aimed at enhancing the seed nutrient density of chickpea.
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Affiliation(s)
- Humara Fayaz
- Division of Genetics and Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences & Technology (SKUAST)-Kashmir, Wadura Campus, Sopore, India.,Cytogenetics and Reproductive Biology Laboratory, Department of Botany, University of Kashmir, Srinagar, India
| | - Sandhya Tyagi
- Division of Plant Physiology, Indian Agricultural Research Institute (IARI), New Delhi, India
| | - Aijaz A Wani
- Cytogenetics and Reproductive Biology Laboratory, Department of Botany, University of Kashmir, Srinagar, India
| | - Renu Pandey
- Division of Plant Physiology, Indian Agricultural Research Institute (IARI), New Delhi, India
| | - Sabina Akhtar
- College of Education, American University in the Emirates, Dubai, UAE
| | - Mohd Ashraf Bhat
- Division of Genetics and Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences & Technology (SKUAST)-Kashmir, Wadura Campus, Sopore, India
| | - Annapurna Chitikineni
- Center of Excellence in Genomics & Systems Biology (CEGSB), Iinternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, Telangana, India
| | - Rajeev Kumar Varshney
- Center of Excellence in Genomics & Systems Biology (CEGSB), Iinternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, Telangana, India.,State Agricultural Biotechnology Centre, Crop & Food Innovation Centre, Food Futures Institute, Murdoch University, Murdoch, WA, Australia
| | - Mahendar Thudi
- Center of Excellence in Genomics & Systems Biology (CEGSB), Iinternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, Telangana, India. .,Department of Agricultural Biotechnology and Biotechnology, Rajendra Prasad Central Agricultural University, Pusa, Samasthipur, India. .,University of Southern Queensland (USQ), Toowoomba, Australia.
| | - Upendra Kumar
- Department of Molecular Biology, Biotechnology and Bioinformatics, College of Biotechnology, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences & Technology (SKUAST)-Kashmir, Wadura Campus, Sopore, India.
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Earley AM, Temme AA, Cotter CR, Burke JM. Genomic regions associate with major axes of variation driven by gas exchange and leaf construction traits in cultivated sunflower (Helianthus annuus L.). THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:1425-1438. [PMID: 35815412 PMCID: PMC9545426 DOI: 10.1111/tpj.15900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/01/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Stomata and leaf veins play an essential role in transpiration and the movement of water throughout leaves. These traits are thus thought to play a key role in the adaptation of plants to drought and a better understanding of the genetic basis of their variation and coordination could inform efforts to improve drought tolerance. Here, we explore patterns of variation and covariation in leaf anatomical traits and analyze their genetic architecture via genome-wide association (GWA) analyses in cultivated sunflower (Helianthus annuus L.). Traits related to stomatal density and morphology as well as lower-order veins were manually measured from digital images while the density of minor veins was estimated using a novel deep learning approach. Leaf, stomatal, and vein traits exhibited numerous significant correlations that generally followed expectations based on functional relationships. Correlated suites of traits could further be separated along three major principal component (PC) axes that were heavily influenced by variation in traits related to gas exchange, leaf hydraulics, and leaf construction. While there was limited evidence of colocalization when individual traits were subjected to GWA analyses, major multivariate PC axes that were most strongly influenced by several traits related to gas exchange or leaf construction did exhibit significant genomic associations. These results provide insight into the genetic basis of leaf trait covariation and showcase potential targets for future efforts aimed at modifying leaf anatomical traits in sunflower.
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Affiliation(s)
- Ashley M. Earley
- Department of Plant BiologyUniversity of GeorgiaAthensGeorgiaUSA
| | - Andries A. Temme
- Department of Plant BiologyUniversity of GeorgiaAthensGeorgiaUSA
- Division of Intensive Plant Food SystemsHumboldt‐Universität zu Berlin10117BerlinGermany
| | | | - John M. Burke
- Department of Plant BiologyUniversity of GeorgiaAthensGeorgiaUSA
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Rai KK. Integrating speed breeding with artificial intelligence for developing climate-smart crops. Mol Biol Rep 2022; 49:11385-11402. [PMID: 35941420 PMCID: PMC9360691 DOI: 10.1007/s11033-022-07769-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 07/05/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass production, which is imperative for the maintenance of ecosystem services worldwide. Since conventional breeding technologies for crop improvement are limited, time-consuming, and involve laborious selection processes to foster new and improved crop varieties. An urgent need is to accelerate the plant breeding cycle using artificial intelligence (AI) to depict plant responses to environmental perturbations in real-time. MATERIALS AND METHODS The review is a collection of authorized information from various sources such as journals, books, book chapters, technical bulletins, conference papers, and verified online contents. CONCLUSIONS Speed breeding has emerged as an essential strategy for accelerating the breeding cycles of crop plants by growing them under artificial light and temperature conditions. Furthermore, speed breeding can also integrate marker-assisted selection and cutting-edged gene-editing tools for early selection and manipulation of essential crops with superior agronomic traits. Scientists have recently applied next-generation AI to delve deeper into the complex biological and molecular mechanisms that govern plant functions under environmental cues. In addition, AIs can integrate, assimilate, and analyze complex OMICS data sets, an essential prerequisite for successful speed breeding protocol implementation to breed crop plants with superior yield and adaptability.
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Affiliation(s)
- Krishna Kumar Rai
- Centre of Advanced Study in Botany, Department of Botany, Institute of Science, Banaras Hindu University (BHU), 221005, Varanasi, Uttar Pradesh, India.
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25
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Dmitriev AA, Pushkova EN, Melnikova NV. Plant Genome Sequencing: Modern Technologies and Novel Opportunities for Breeding. Mol Biol 2022. [DOI: 10.1134/s0026893322040045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Kulwal PL, Mir RR, Varshney RK. Cytogenetics to functional genomics: six decades journey of Professor P.K. Gupta. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:1021-1030. [PMID: 35199459 PMCID: PMC9129079 DOI: 10.1111/pbi.13795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/02/2022] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
We had the fortune of starting our scientific/research careers in the Molecular Biology and Crop Biotechnology Laboratory of Professor P.K. Gupta at Ch. Charan Singh University, Meerut, UP, India. Here, we describe the most important scientific contributions of our beloved mentor in the area of cytotaxonomy, cytogenetics, mutation breeding, quantitative genetics, molecular biology, crop biotechnology and plant genomics, on his 85th birthday. Important contributions made in the development and use of genomics resources including the development and use of different kinds of molecular markers, genetic and physical mapping, quantitative trait locus (QTL) interval mapping, genome-wide association mapping and molecular breeding including marker-assisted selection have been briefly summarized. Efforts have been also made to give readers a glimpse of important contributions in the study of cytology/apomixis of grasses, cytotaxonomic studies in asteraceae/fabaceae, nuclear/repetitive DNA content in Lolium, interspecific/intergeneric relationships involving the genus Hordeum and re-examining taxonomy of the tribe Triticeae.
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Affiliation(s)
- Pawan L. Kulwal
- State Level Biotechnology CentreMahatma Phule Krishi Vidyapeeth (Agricultural University)RahuriIndia
| | - Reyazul Rouf Mir
- Division of Genetics & Plant BreedingFaculty of AgricultureSher‐e‐Kashmir University of Agricultural Science and Technology, KashmirSoporeIndia
| | - Rajeev K. Varshney
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid TropicsPatancheruIndia
- State Agricultural Biotechnology CentreCentre for Crop & Food InnovationFood Futures InstituteMurdoch UniversityMurdochWAAustralia
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Wang Y, Bao J, Wei X, Wu S, Fang C, Li Z, Qi Y, Gao Y, Dong Z, Wan X. Genetic Structure and Molecular Mechanisms Underlying the Formation of Tassel, Anther, and Pollen in the Male Inflorescence of Maize (Zea mays L.). Cells 2022; 11:cells11111753. [PMID: 35681448 PMCID: PMC9179574 DOI: 10.3390/cells11111753] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 02/08/2023] Open
Abstract
Maize tassel is the male reproductive organ which is located at the plant’s apex; both its morphological structure and fertility have a profound impact on maize grain yield. More than 40 functional genes regulating the complex tassel traits have been cloned up to now. However, the detailed molecular mechanisms underlying the whole process, from male inflorescence meristem initiation to tassel morphogenesis, are seldom discussed. Here, we summarize the male inflorescence developmental genes and construct a molecular regulatory network to further reveal the molecular mechanisms underlying tassel-trait formation in maize. Meanwhile, as one of the most frequently studied quantitative traits, hundreds of quantitative trait loci (QTLs) and thousands of quantitative trait nucleotides (QTNs) related to tassel morphology have been identified so far. To reveal the genetic structure of tassel traits, we constructed a consensus physical map for tassel traits by summarizing the genetic studies conducted over the past 20 years, and identified 97 hotspot intervals (HSIs) that can be repeatedly mapped in different labs, which will be helpful for marker-assisted selection (MAS) in improving maize yield as well as for providing theoretical guidance in the subsequent identification of the functional genes modulating tassel morphology. In addition, maize is one of the most successful crops in utilizing heterosis; mining of the genic male sterility (GMS) genes is crucial in developing biotechnology-based male-sterility (BMS) systems for seed production and hybrid breeding. In maize, more than 30 GMS genes have been isolated and characterized, and at least 15 GMS genes have been promptly validated by CRISPR/Cas9 mutagenesis within the past two years. We thus summarize the maize GMS genes and further update the molecular regulatory networks underlying male fertility in maize. Taken together, the identified HSIs, genes and molecular mechanisms underlying tassel morphological structure and male fertility are useful for guiding the subsequent cloning of functional genes and for molecular design breeding in maize. Finally, the strategies concerning efficient and rapid isolation of genes controlling tassel morphological structure and male fertility and their application in maize molecular breeding are also discussed.
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Affiliation(s)
- Yanbo Wang
- Zhongzhi International Institute of Agricultural Biosciences, Shunde Graduate School, Research Center of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100024, China; (Y.W.); (J.B.); (X.W.); (S.W.); (C.F.); (Y.Q.); (Y.G.)
| | - Jianxi Bao
- Zhongzhi International Institute of Agricultural Biosciences, Shunde Graduate School, Research Center of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100024, China; (Y.W.); (J.B.); (X.W.); (S.W.); (C.F.); (Y.Q.); (Y.G.)
| | - Xun Wei
- Zhongzhi International Institute of Agricultural Biosciences, Shunde Graduate School, Research Center of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100024, China; (Y.W.); (J.B.); (X.W.); (S.W.); (C.F.); (Y.Q.); (Y.G.)
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co., Ltd., Beijing 100192, China;
| | - Suowei Wu
- Zhongzhi International Institute of Agricultural Biosciences, Shunde Graduate School, Research Center of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100024, China; (Y.W.); (J.B.); (X.W.); (S.W.); (C.F.); (Y.Q.); (Y.G.)
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co., Ltd., Beijing 100192, China;
| | - Chaowei Fang
- Zhongzhi International Institute of Agricultural Biosciences, Shunde Graduate School, Research Center of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100024, China; (Y.W.); (J.B.); (X.W.); (S.W.); (C.F.); (Y.Q.); (Y.G.)
| | - Ziwen Li
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co., Ltd., Beijing 100192, China;
| | - Yuchen Qi
- Zhongzhi International Institute of Agricultural Biosciences, Shunde Graduate School, Research Center of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100024, China; (Y.W.); (J.B.); (X.W.); (S.W.); (C.F.); (Y.Q.); (Y.G.)
| | - Yuexin Gao
- Zhongzhi International Institute of Agricultural Biosciences, Shunde Graduate School, Research Center of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100024, China; (Y.W.); (J.B.); (X.W.); (S.W.); (C.F.); (Y.Q.); (Y.G.)
| | - Zhenying Dong
- Zhongzhi International Institute of Agricultural Biosciences, Shunde Graduate School, Research Center of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100024, China; (Y.W.); (J.B.); (X.W.); (S.W.); (C.F.); (Y.Q.); (Y.G.)
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co., Ltd., Beijing 100192, China;
- Correspondence: (Z.D.); (X.W.); Tel.: +86-152-1092-0373 (Z.D.); +86-186-0056-1850 (X.W.)
| | - Xiangyuan Wan
- Zhongzhi International Institute of Agricultural Biosciences, Shunde Graduate School, Research Center of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100024, China; (Y.W.); (J.B.); (X.W.); (S.W.); (C.F.); (Y.Q.); (Y.G.)
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co., Ltd., Beijing 100192, China;
- Correspondence: (Z.D.); (X.W.); Tel.: +86-152-1092-0373 (Z.D.); +86-186-0056-1850 (X.W.)
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Theeuwen TPJM, Logie LL, Harbinson J, Aarts MGM. Genetics as a key to improving crop photosynthesis. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3122-3137. [PMID: 35235648 PMCID: PMC9126732 DOI: 10.1093/jxb/erac076] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/23/2022] [Indexed: 05/02/2023]
Abstract
Since the basic biochemical mechanisms of photosynthesis are remarkably conserved among plant species, genetic modification approaches have so far been the main route to improve the photosynthetic performance of crops. Yet, phenotypic variation observed in wild species and between varieties of crop species implies there is standing natural genetic variation for photosynthesis, offering a largely unexplored resource to use for breeding crops with improved photosynthesis and higher yields. The reason this has not yet been explored is that the variation probably involves thousands of genes, each contributing only a little to photosynthesis, making them hard to identify without proper phenotyping and genetic tools. This is changing, though, and increasingly studies report on quantitative trait loci for photosynthetic phenotypes. So far, hardly any of these quantitative trait loci have been used in marker assisted breeding or genomic selection approaches to improve crop photosynthesis and yield, and hardly ever have the underlying causal genes been identified. We propose to take the genetics of photosynthesis to a higher level, and identify the genes and alleles nature has used for millions of years to tune photosynthesis to be in line with local environmental conditions. We will need to determine the physiological function of the genes and alleles, and design novel strategies to use this knowledge to improve crop photosynthesis through conventional plant breeding, based on readily available crop plant germplasm. In this work, we present and discuss the genetic methods needed to reveal natural genetic variation, and elaborate on how to apply this to improve crop photosynthesis.
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Affiliation(s)
- Tom P J M Theeuwen
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
- Correspondence:
| | - Louise L Logie
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
| | - Jeremy Harbinson
- Biophysics, Wageningen University & Research, Wageningen, The Netherlands
| | - Mark G M Aarts
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
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Jambuthenne DT, Riaz A, Athiyannan N, Alahmad S, Ng WL, Ziems L, Afanasenko O, Periyannan SK, Aitken E, Platz G, Godwin I, Voss-Fels KP, Dinglasan E, Hickey LT. Mining the Vavilov wheat diversity panel for new sources of adult plant resistance to stripe rust. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1355-1373. [PMID: 35113190 PMCID: PMC9033734 DOI: 10.1007/s00122-022-04037-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 01/12/2022] [Indexed: 06/14/2023]
Abstract
Multi-year evaluation of the Vavilov wheat diversity panel identified new sources of adult plant resistance to stripe rust. Genome-wide association studies revealed the key genomic regions influencing resistance, including seven novel loci. Wheat stripe rust (YR) caused by Puccinia striiformis f. sp. tritici (Pst) poses a significant threat to global food security. Resistance genes commonly found in many wheat varieties have been rendered ineffective due to the rapid evolution of the pathogen. To identify novel sources of adult plant resistance (APR), 292 accessions from the N.I. Vavilov Institute of Plant Genetic Resources, Saint Petersburg, Russia, were screened for known APR genes (i.e. Yr18, Yr29, Yr46, Yr33, Yr39 and Yr59) using linked polymerase chain reaction (PCR) molecular markers. Accessions were evaluated against Pst (pathotype 134 E16 A + Yr17 + Yr27) at seedling and adult plant stages across multiple years (2014, 2015 and 2016) in Australia. Phenotypic analyses identified 132 lines that potentially carry novel sources of APR to YR. Genome-wide association studies (GWAS) identified 68 significant marker-trait associations (P < 0.001) for YR resistance, representing 47 independent quantitative trait loci (QTL) regions. Fourteen genomic regions overlapped with previously reported Yr genes, including Yr29, Yr56, Yr5, Yr43, Yr57, Yr30, Yr46, Yr47, Yr35, Yr36, Yrxy1, Yr59, Yr52 and YrYL. In total, seven QTL (positioned on chromosomes 1D, 2A, 3A, 3D, 5D, 7B and 7D) did not collocate with previously reported genes or QTL, indicating the presence of promising novel resistance factors. Overall, the Vavilov diversity panel provides a rich source of new alleles which could be used to broaden the genetic bases of YR resistance in modern wheat varieties.
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Affiliation(s)
- Dilani T Jambuthenne
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Adnan Riaz
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Naveenkumar Athiyannan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Agriculture and Food,, Canberra, ACT, Australia
| | - Samir Alahmad
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Wei Ling Ng
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Laura Ziems
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Olga Afanasenko
- Department of Plant Resistance To Diseases, All Russian Research Institute for Plant Protection, St Petersburg, Russia, 196608
| | - Sambasivam K Periyannan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Agriculture and Food,, Canberra, ACT, Australia
| | - Elizabeth Aitken
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
| | - Greg Platz
- Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD, Australia
| | - Ian Godwin
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Eric Dinglasan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
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30
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Batstone RT. Genomes within genomes: nested symbiosis and its implications for plant evolution. THE NEW PHYTOLOGIST 2022; 234:28-34. [PMID: 34761378 DOI: 10.1111/nph.17847] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 10/20/2021] [Indexed: 06/13/2023]
Abstract
Many important plant traits are products of nested symbiosis: mobile genetic elements (MGEs) are nested within microbes, which in turn, are nested within plants. Plant trait variation is therefore not only determined by the plant's genome, but also by loci within microbes and MGEs. Yet it remains unclear how interactions and coevolution within nested symbiosis impacts the evolution of plant traits. Despite the complexities of nested symbiosis, including nonadditive interactions, understanding the evolution of plant traits is facilitated by combining quantitative genetic and functional genomic approaches that explicitly consider sources of nested genetic variation (from loci in MGEs to microbiomes). Additionally, understanding coevolution within nested symbiosis enables us to design or select for MGEs that promote plant health.
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Affiliation(s)
- Rebecca T Batstone
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana, IL, 61801, USA
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31
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Malik P, Kumar J, Sharma S, Meher PK, Balyan HS, Gupta PK, Sharma S. GWAS for main effects and epistatic interactions for grain morphology traits in wheat. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2022; 28:651-668. [PMID: 35465203 PMCID: PMC8986918 DOI: 10.1007/s12298-022-01164-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 03/05/2022] [Accepted: 03/07/2022] [Indexed: 06/05/2023]
Abstract
In the present study in wheat, GWAS was conducted for identification of marker trait associations (MTAs) for the following six grain morphology traits: (1) grain cross-sectional area (GCSA), (2) grain perimeter (GP), (3) grain length (GL), (4) grain width (GWid), (5) grain length-width ratio (GLWR) and (6) grain form-density (GFD). The data were recorded on a subset of spring wheat reference set (SWRS) comprising 225 diverse genotypes, which were genotyped using 10,904 SNPs and phenotyped for two consecutive years (2017-2018, 2018-2019). GWAS was conducted using five different models including two single-locus models (CMLM, SUPER), one multi-locus model (FarmCPU), one multi-trait model (mvLMM) and a model for Q x Q epistatic interactions. False discovery rate (FDR) [P value -log10(p) ≥ 5] and Bonferroni correction [P value -log10(p) ≥ 6] (corrected p value < 0.05) were applied to eliminate false positives due to multiple testing. This exercise gave 88 main effect and 29 epistatic MTAs after FDR and 13 main effect and 6 epistatic MTAs after Bonferroni corrections. MTAs obtained after Bonferroni corrections were further utilized for identification of 55 candidate genes (CGs). In silico expression analysis of CGs in different tissues at different parts of the seed at different developmental stages was also carried out. MTAs and CGs identified during the present study are useful addition to available resources for MAS to supplement wheat breeding programmes after due validation and also for future strategic basic research. Supplementary Information The online version contains supplementary material available at 10.1007/s12298-022-01164-w.
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Affiliation(s)
- Parveen Malik
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P 250 004 India
| | - Jitendra Kumar
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P 250 004 India
- Department of Biotechnology, National Agri-Food Biotechnology Institute (NABI), Govt. of India, Sector 81 (Knowledge City), S.A.S. Nagar, Mohali, Punjab 140306 India
| | - Shiveta Sharma
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P 250 004 India
| | - Prabina Kumar Meher
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012 India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P 250 004 India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P 250 004 India
| | - Shailendra Sharma
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P 250 004 India
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Morón-García O, Garzón-Martínez GA, Martínez-Martín MJP, Brook J, Corke FMK, Doonan JH, Camargo Rodríguez AV. Genetic architecture of variation in Arabidopsis thaliana rosettes. PLoS One 2022; 17:e0263985. [PMID: 35171969 PMCID: PMC8849614 DOI: 10.1371/journal.pone.0263985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 02/01/2022] [Indexed: 12/04/2022] Open
Abstract
Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.
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Affiliation(s)
- Odín Morón-García
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - Gina A. Garzón-Martínez
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - M. J. Pilar Martínez-Martín
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - Jason Brook
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - Fiona M. K. Corke
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - John H. Doonan
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
- * E-mail: (AVCR); (JHD)
| | - Anyela V. Camargo Rodríguez
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
- * E-mail: (AVCR); (JHD)
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Zhao J, Sauvage C, Bitton F, Causse M. Multiple haplotype-based analyses provide genetic and evolutionary insights into tomato fruit weight and composition. HORTICULTURE RESEARCH 2022; 9:uhab009. [PMID: 35039843 PMCID: PMC8771453 DOI: 10.1093/hr/uhab009] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 10/12/2021] [Accepted: 10/15/2021] [Indexed: 05/05/2023]
Abstract
Improving fruit quality traits such as metabolic composition remains a challenge for tomato breeders. To better understand the genetic architecture of these traits and decipher the demographic history of the loci controlling tomato quality traits, we applied an innovative approach using multiple haplotype-based analyses, aiming to test the potentials of haplotype based study in association and genomic prediction studies. We performed and compared haplotype vs SNP-based associations (hapQTL) with multi-locus mixed model (MLMM), focusing on tomato fruit weight and metabolite contents (i.e. sugars, organic acids and amino acids). Using a panel of 163 tomato accessions genotyped with 5995 SNPs, we detected a total of 784 haplotype blocks, with an average size of haplotype blocks ~58 kb. A total of 108 significant associations for 26 traits were detected thanks to Haplotype/SNP-based Bayes models. Haplotype-based Bayes model (97 associations) outperformed SNP-based Bayes model (50 associations) and MLMM (53 associations) in identifying marker-trait associations as well as in genomic prediction (especially for those traits with moderate to low heritability). To decipher the demographic history, we identified 24 positive selective sweeps using the integrated haplotype score (iHS). Most of the significant associations for tomato quality traits were located within selective sweeps (54.63% and 71.7% in hapQTL and MLMM models, respectively). Promising candidate genes were identified controlling tomato fruit weight and metabolite contents. We thus demonstrated the benefits of using haplotypes for evolutionary and genetic studies, providing novel insights into tomato quality improvement and breeding history.
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Affiliation(s)
- Jiantao Zhao
- INRA, UR1052, Centre de Recherche PACA, Génétique et Amélioration des Fruits et Légumes, Domaine Saint Maurice, 67 Allée des Chênes CS 60094 – 84140, Montfavet Cedex, France
- Boyce Thompson Institute for Plant Research, Cornell University, 533 Tower Road, Ithaca, NY 14853-1801, USA
| | - Christopher Sauvage
- INRA, UR1052, Centre de Recherche PACA, Génétique et Amélioration des Fruits et Légumes, Domaine Saint Maurice, 67 Allée des Chênes CS 60094 – 84140, Montfavet Cedex, France
- Syngenta SAS France, 1228 Chemin de l’Hobit, Saint Sauveur 31790, France
| | - Frédérique Bitton
- INRA, UR1052, Centre de Recherche PACA, Génétique et Amélioration des Fruits et Légumes, Domaine Saint Maurice, 67 Allée des Chênes CS 60094 – 84140, Montfavet Cedex, France
| | - Mathilde Causse
- INRA, UR1052, Centre de Recherche PACA, Génétique et Amélioration des Fruits et Légumes, Domaine Saint Maurice, 67 Allée des Chênes CS 60094 – 84140, Montfavet Cedex, France
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Kulwal PL, Singh R. Biparental Crossing and QTL Mapping for Validation of Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:273-285. [PMID: 35641770 DOI: 10.1007/978-1-0716-2237-7_16] [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
Association mapping (AM), also known as genome-wide association studies (GWAS), is increasingly being employed in crop plants for the identification of QTL/genes and marker-trait associations (MTAs) in natural populations. Large numbers of such associations have been identified for variety of traits in different crop plants. However, not many of these associations have been used practically in the crop improvement program due to lack of validation. Although there are different ways through which the results of AM/GWAS could be validated, the best approach is to develop a biparental population for the trait of interest. An overview of the steps involved in the validation of results of AM using biparental mapping population in plants is provided in this chapter.
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Affiliation(s)
- Pawan L Kulwal
- State Level Biotechnology Centre, Mahatma Phule Krishi Vidyapeeth, Rahuri, Ahmednagar, Maharashtra, India.
| | - Ravinder Singh
- School of Biotechnology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu, Jammu and Kashmir, India
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Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:1. [PMID: 37309486 PMCID: PMC10248672 DOI: 10.1007/s11032-021-01272-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.
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Affiliation(s)
- Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| | - Anand Kumar
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 202002 India
| | - Sumandeep Bazzer
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
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Yoosefzadeh-Najafabadi M, Eskandari M, Belzile F, Torkamaneh D. Genome-Wide Association Study Statistical Models: A Review. Methods Mol Biol 2022; 2481:43-62. [PMID: 35641758 DOI: 10.1007/978-1-0716-2237-7_4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Statistical models are at the core of the genome-wide association study (GWAS). In this chapter, we provide an overview of single- and multilocus statistical models, Bayesian, and machine learning approaches for association studies in plants. These models are discussed based on their basic methodology, cofactors adjustment accounted for, statistical power and computational efficiency. New statistical models and machine learning algorithms are both showing improved performance in detecting missed signals, rare mutations and prioritizing causal genetic variants; nevertheless, further optimization and validation studies are required to maximize the power of GWAS.
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Affiliation(s)
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - François Belzile
- Département de Phytologie, Université Laval, Quebec City, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, QC, Canada
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Quebec City, QC, Canada.
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, QC, Canada.
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Kanapin A, Bankin M, Rozhmina T, Samsonova A, Samsonova M. Genomic Regions Associated with Fusarium Wilt Resistance in Flax. Int J Mol Sci 2021; 22:12383. [PMID: 34830265 PMCID: PMC8623186 DOI: 10.3390/ijms222212383] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/13/2021] [Accepted: 11/15/2021] [Indexed: 01/22/2023] Open
Abstract
Modern flax cultivars are susceptible to many diseases; arguably, the most economically damaging of these is the Fusarium wilt fungal disease. Over the past decades international flax breeding initiatives resulted in the development of resistant cultivars. However, much remains to be learned about the mechanisms of resistance to Fusarium infection in flax. As a first step to uncover the genetic factors associated with resistance to Fusarium wilt disease, we performed a genome-wide association study (GWAS) using 297 accessions from the collection of the Federal Research Centre of the Bast Fiber Crops, Torzhok, Russia. These genotypes were infected with a highly pathogenic Fusarium oxysporum f.sp. lini MI39 strain; the wilt symptoms were documented in the course of three successive years. Six different single-locus models implemented in GAPIT3 R package were applied to a selected subset of 72,526 SNPs. A total of 15 QTNs (Quantitative Trait Nucleotides) were detected during at least two years of observation, while eight QTNs were found during all three years of the experiment. Of these, ten QTNs occupied a region of 640 Kb at the start of chromosome 1, while the remaining QTNs mapped to chromosomes 8, 11 and 13. All stable QTNs demonstrate a statistically significant allelic effect across 3 years of the experiment. Importantly, several QTNs spanned regions that harbored genes involved in the pathogen recognition and plant immunity response, including the KIP1-like protein (Lus10025717) and NBS-LRR protein (Lus10025852). Our results provide novel insights into the genetic architecture of flax resistance to Fusarium wilt and pinpoint potential candidate genes for further in-depth studies.
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Affiliation(s)
- Alexander Kanapin
- Centre for Computational Biology, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia; (A.K.); (A.S.)
| | - Mikhail Bankin
- Mathematical Biology & Bioinformatics Laboratory, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia;
| | - Tatyana Rozhmina
- Laboratory of Breeding Technologies, Federal Research Center for Bast Fiber Crops, 172002 Torzhok, Russia;
| | - Anastasia Samsonova
- Centre for Computational Biology, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia; (A.K.); (A.S.)
| | - Maria Samsonova
- Mathematical Biology & Bioinformatics Laboratory, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia;
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Monnot S, Desaint H, Mary-Huard T, Moreau L, Schurdi-Levraud V, Boissot N. Deciphering the Genetic Architecture of Plant Virus Resistance by GWAS, State of the Art and Potential Advances. Cells 2021; 10:3080. [PMID: 34831303 PMCID: PMC8625838 DOI: 10.3390/cells10113080] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/03/2021] [Accepted: 11/04/2021] [Indexed: 01/04/2023] Open
Abstract
Growing virus resistant varieties is a highly effective means to avoid yield loss due to infection by many types of virus. The challenge is to be able to detect resistance donors within plant species diversity and then quickly introduce alleles conferring resistance into elite genetic backgrounds. Until now, mainly monogenic forms of resistance with major effects have been introduced in crops. Polygenic resistance is harder to map and introduce in susceptible genetic backgrounds, but it is likely more durable. Genome wide association studies (GWAS) offer an opportunity to accelerate mapping of both monogenic and polygenic resistance, but have seldom been implemented and described in the plant-virus interaction context. Yet, all of the 48 plant-virus GWAS published so far have successfully mapped QTLs involved in plant virus resistance. In this review, we analyzed general and specific GWAS issues regarding plant virus resistance. We have identified and described several key steps throughout the GWAS pipeline, from diversity panel assembly to GWAS result analyses. Based on the 48 published articles, we analyzed the impact of each key step on the GWAS power and showcase several GWAS methods tailored to all types of viruses.
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Affiliation(s)
- Severine Monnot
- INRAE, Génétique et Amélioration des Fruits et Légumes (GAFL), 84143 Montfavet, France
- Bayer Crop Science, Chemin de Roque Martine, 13670 Saint-Andiol, France
| | - Henri Desaint
- INRAE, Génétique et Amélioration des Fruits et Légumes (GAFL), 84143 Montfavet, France
| | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, Université Paris-Saclay, Ferme du Moulon, 91190 Gif-sur-Yvette, France
- Mathématiques et Informatique Appliquées (MIA)-Paris, INRAE, AgroParisTech, Université Paris-Saclay, 75231 Paris, France
| | - Laurence Moreau
- INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, Université Paris-Saclay, Ferme du Moulon, 91190 Gif-sur-Yvette, France
| | | | - Nathalie Boissot
- INRAE, Génétique et Amélioration des Fruits et Légumes (GAFL), 84143 Montfavet, France
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Metabolomics for Crop Breeding: General Considerations. Genes (Basel) 2021; 12:genes12101602. [PMID: 34680996 PMCID: PMC8535592 DOI: 10.3390/genes12101602] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/05/2021] [Accepted: 10/08/2021] [Indexed: 12/16/2022] Open
Abstract
The development of new, more productive varieties of agricultural crops is becoming an increasingly difficult task. Modern approaches for the identification of beneficial alleles and their use in elite cultivars, such as quantitative trait loci (QTL) mapping and marker-assisted selection (MAS), are effective but insufficient for keeping pace with the improvement of wheat or other crops. Metabolomics is a powerful but underutilized approach that can assist crop breeding. In this review, basic methodological information is summarized, and the current strategies of applications of metabolomics related to crop breeding are explored using recent examples. We briefly describe classes of plant metabolites, cellular localization of metabolic pathways, and the strengths and weaknesses of the main metabolomics technique. Among the commercialized genetically modified crops, about 50 with altered metabolic enzyme activities have been identified in the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. These plants are reviewed as encouraging examples of the application of knowledge of biochemical pathways. Based on the recent examples of metabolomic studies, we discuss the performance of metabolic markers, the integration of metabolic and genomic data in metabolic QTLs (mQTLs) and metabolic genome-wide association studies (mGWAS). The elucidation of metabolic pathways and involved genes will help in crop breeding and the introgression of alleles of wild relatives in a more targeted manner.
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Gupta PK. GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers. Bioessays 2021; 43:e2100109. [PMID: 34486143 DOI: 10.1002/bies.202100109] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 12/22/2022]
Abstract
The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.
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Affiliation(s)
- Pushpendra K Gupta
- Department of Genetics and Plant Breeding, Ch. Charan Singh University Meerut, Meerut, Uttar Pradesh, India
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Alipour H, Abdi H, Rahimi Y, Bihamta MR. Dissection of the genetic basis of genotype-by-environment interactions for grain yield and main agronomic traits in Iranian bread wheat landraces and cultivars. Sci Rep 2021; 11:17742. [PMID: 34493739 PMCID: PMC8423731 DOI: 10.1038/s41598-021-96576-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/11/2021] [Indexed: 02/07/2023] Open
Abstract
Understanding the genetic basis of performance stability is essential to maintain productivity, especially under severe conditions. In the present study, 268 Iranian bread wheat landraces and cultivars were evaluated in four well-watered and two rain-fed conditions for different traits. According to breeding programs, cultivars were in a group with a high mean and stability in terms of GY, GN, and SW traits, while in terms of PH, they had a low mean and high stability. The stability of cultivars and landraces was related to dynamic and static stability, respectively. The highest number of marker pairs and lowest LD decay distance in both cultivars and landraces was observed on the B genome. Population structure differentiated indigenous cultivars and landraces, and the GWAS results for each were almost different despite the commonalities. Chromosomes 1B, 3B, 7B, 2A, and 4A had markers with pleiotropic effects on the stability of different traits. Due to two rain-fed environments, the Gene Ontology (GO) confirmed the accuracy of the results. The identified markers in this study can be helpful in breeding high-performance and stable genotypes and future breeding programs such as fine mapping and cloning.
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Affiliation(s)
- Hadi Alipour
- Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.
| | - Hossein Abdi
- Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
| | - Yousef Rahimi
- Department of Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Mohammad Reza Bihamta
- Department of Agronomy and Plant Breeding, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
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Buzdin AV, Patrushev MV, Sverdlov ED. Will Plant Genome Editing Play a Decisive Role in "Quantum-Leap" Improvements in Crop Yield to Feed an Increasing Global Human Population? PLANTS (BASEL, SWITZERLAND) 2021; 10:1667. [PMID: 34451712 PMCID: PMC8398637 DOI: 10.3390/plants10081667] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/04/2021] [Accepted: 08/07/2021] [Indexed: 02/08/2023]
Abstract
Growing scientific evidence demonstrates unprecedented planetary-scale human impacts on the Earth's system with a predicted threat to the existence of the terrestrial biosphere due to population increase, resource depletion, and pollution. Food systems account for 21-34% of global carbon dioxide (CO2) emissions. Over the past half-century, water and land-use changes have significantly impacted ecosystems, biogeochemical cycles, biodiversity, and climate. At the same time, food production is falling behind consumption, and global grain reserves are shrinking. Some predictions suggest that crop yields must approximately double by 2050 to adequately feed an increasing global population without a large expansion of crop area. To achieve this, "quantum-leap" improvements in crop cultivar productivity are needed within very narrow planetary boundaries of permissible environmental perturbations. Strategies for such a "quantum-leap" include mutation breeding and genetic engineering of known crop genome sequences. Synthetic biology makes it possible to synthesize DNA fragments of any desired sequence, and modern bioinformatics tools may hopefully provide an efficient way to identify targets for directed modification of selected genes responsible for known important agronomic traits. CRISPR/Cas9 is a new technology for incorporating seamless directed modifications into genomes; it is being widely investigated for its potential to enhance the efficiency of crop production. We consider the optimism associated with the new genetic technologies in terms of the complexity of most agronomic traits, especially crop yield potential (Yp) limits. We also discuss the possible directions of overcoming these limits and alternative ways of providing humanity with food without transgressing planetary boundaries. In conclusion, we support the long-debated idea that new technologies are unlikely to provide a rapidly growing population with significantly increased crop yield. Instead, we suggest that delicately balanced humane measures to limit its growth and the amount of food consumed per capita are highly desirable for the foreseeable future.
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Affiliation(s)
- Anton V Buzdin
- The Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701 Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Maxim V Patrushev
- Kurchatov Center for Genome Research, National Research Center Kurchatov Institute, 123182 Moscow, Russia
| | - Eugene D Sverdlov
- Kurchatov Center for Genome Research, National Research Center Kurchatov Institute, 123182 Moscow, Russia
- Institute of Molecular Genetics, National Research Center Kurchatov Institute, 123182 Moscow, Russia
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Genome-wide association mapping reveals key genomic regions for physiological and yield-related traits under salinity stress in wheat (Triticum aestivum L.). Genomics 2021; 113:3198-3215. [PMID: 34293475 DOI: 10.1016/j.ygeno.2021.07.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 06/27/2021] [Accepted: 07/13/2021] [Indexed: 11/21/2022]
Abstract
A genome-wide association study (GWAS) was conducted using six different multi-locus GWAS models and 35K SNP array to demarcate genomic regions underlying reproductive stage salinity tolerance. Marker-trait association analysis was performed for salt tolerance indices (STI) of 11 morpho-physiological traits, and the actual concentrations of Na+ and K+, and the Na+/K+ ratio in flag leaf. A total of 293 significantly associated quantitative trait nucleotides (QTNs) for 14 morpho-physiological traits were identified. Of these 293 QTNs, 12 major QTNs with R2 ≥ 10.0% were detected in three or more GWAS models. Novel major QTNs were identified for plant height, number of effective tillers, biomass, grain yield, thousand grain weight, Na+ and K+ content, and the Na+/K+ ratio in flag leaf. Moreover, 48 candidate genes were identified from the associated genomic regions. The QTNs identified in this study could potentially be targeted for improving salinity tolerance in wheat.
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Malik P, Kumar J, Singh S, Sharma S, Meher PK, Sharma MK, Roy JK, Sharma PK, Balyan HS, Gupta PK, Sharma S. Single-trait, multi-locus and multi-trait GWAS using four different models for yield traits in bread wheat. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:46. [PMID: 37309385 PMCID: PMC10236106 DOI: 10.1007/s11032-021-01240-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/30/2021] [Indexed: 06/14/2023]
Abstract
A genome-wide association study (GWAS) for 10 yield and yield component traits was conducted using an association panel comprising 225 diverse spring wheat genotypes. The panel was genotyped using 10,904 SNPs and evaluated for three years (2016-2019), which constituted three environments (E1, E2 and E3). Heritability for different traits ranged from 29.21 to 97.69%. Marker-trait associations (MTAs) were identified for each trait using data from each environment separately and also using BLUP values. Four different models were used, which included three single trait models (CMLM, FarmCPU, SUPER) and one multi-trait model (mvLMM). Hundreds of MTAs were obtained using each model, but after Bonferroni correction, only 6 MTAs for 3 traits were available using CMLM, and 21 MTAs for 4 traits were available using FarmCPU; none of the 525 MTAs obtained using SUPER could qualify after Bonferroni correction. Using BLUP, 20 MTAs were available, five of which also figured among MTAs identified for individual environments. Using mvLMM model, after Bonferroni correction, 38 multi-trait MTAs, for 15 different trait combinations were available. Epistatic interactions involving 28 pairs of MTAs were also available for seven of the 10 traits; no epistatic interactions were available for GNPS, PH, and BYPP. As many as 164 putative candidate genes (CGs) were identified using all the 50 MTAs (CMLM, 3; FarmCPU, 9; mvLMM, 6, epistasis, 21 and BLUP, 11 MTAs), which ranged from 20 (CMLM) to 66 (epistasis) CGs. In-silico expression analysis of CGs was also conducted in different tissues at different developmental stages. The information generated through the present study proved useful for developing a better understanding of the genetics of each of the 10 traits; the study also provided novel markers for marker-assisted selection (MAS) to be utilized for the development of wheat cultivars with improved agronomic traits. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01240-1.
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Affiliation(s)
- Parveen Malik
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India
| | - Jitendra Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India
- National Agri-Food Biotechnology Institute (NABI), Sector 81, Sahibzada Ajit Singh Nagar, 140306 Punjab India
| | - Sahadev Singh
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India
| | - Shiveta Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India
| | - Prabina Kumar Meher
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India
| | - Mukesh Kumar Sharma
- Department of Mathematics, Chaudhary Charan Singh University, Meerut 250004, India
| | - Joy Kumar Roy
- National Agri-Food Biotechnology Institute (NABI), Sector 81, Sahibzada Ajit Singh Nagar, 140306 Punjab India
| | - Pradeep Kumar Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India
| | - Shailendra Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India
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Gupta PK. Quantitative genetics: pan-genomes, SVs, and k-mers for GWAS. Trends Genet 2021; 37:868-871. [PMID: 34183185 DOI: 10.1016/j.tig.2021.05.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/20/2021] [Accepted: 05/25/2021] [Indexed: 01/30/2023]
Abstract
For identification of marker-trait associations (MTAs) for complex traits in animals and plants, thousands of genome-wide association studies (GWAS) were conducted during the past two decades. This involved regular improvement in methodology. Initially, a reference genome and SNPs were used; more recently pan-genomes and the markers structural variations (SVs)/k-mers are also being used.
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Affiliation(s)
- Pushpendra K Gupta
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, CCS University Meerut, Meerut, India.
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Egan LM, Hofmann RW, Ghamkhar K, Hoyos-Villegas V. Prospects for Trifolium Improvement Through Germplasm Characterisation and Pre-breeding in New Zealand and Beyond. FRONTIERS IN PLANT SCIENCE 2021; 12:653191. [PMID: 34220882 PMCID: PMC8242581 DOI: 10.3389/fpls.2021.653191] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/10/2021] [Indexed: 06/13/2023]
Abstract
Trifolium is the most used pastoral legume genus in temperate grassland systems, and a common feature in meadows and open space areas in cities and parks. Breeding of Trifolium spp. for pastoral production has been going on for over a century. However, the breeding targets have changed over the decades in response to different environmental and production pressures. Relatively small gains have been made in Trifolium breeding progress. Trifolium breeding programmes aim to maintain a broad genetic base to maximise variation. New Zealand is a global hub in Trifolium breeding, utilising exotic germplasm imported by the Margot Forde Germplasm Centre. This article describes the history of Trifolium breeding in New Zealand as well as the role and past successes of utilising genebanks in forage breeding. The impact of germplasm characterisation and evaluation in breeding programmes is also discussed. The history and challenges of Trifolium breeding and its effect on genetic gain can be used to inform future pre-breeding decisions in this genus, as well as being a model for other forage legumes.
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Affiliation(s)
- Lucy M. Egan
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
- Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln, New Zealand
| | - Rainer W. Hofmann
- Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln, New Zealand
| | - Kioumars Ghamkhar
- AgResearch Grasslands Research Centre, Palmerston North, New Zealand
| | - Valerio Hoyos-Villegas
- Department of Plant Science, Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, QC, Canada
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The EpiDiverse Plant Epigenome-Wide Association Studies (EWAS) Pipeline. EPIGENOMES 2021; 5:epigenomes5020012. [PMID: 34968299 PMCID: PMC8594691 DOI: 10.3390/epigenomes5020012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 11/29/2022] Open
Abstract
Bisulfite sequencing is a widely used technique for determining DNA methylation and its relationship with epigenetics, genetics, and environmental parameters. Various techniques were implemented for epigenome-wide association studies (EWAS) to reveal meaningful associations; however, there are only very few plant studies available to date. Here, we developed the EpiDiverse EWAS pipeline and tested it using two plant datasets, from P. abies (Norway spruce) and Q. lobata (valley oak). Hence, we present an EWAS implementation tested for non-model plant species and describe its use.
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48
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Torkamaneh D, Laroche J, Boyle B, Hyten DL, Belzile F. A bumper crop of SNPs in soybean through high-density genotyping-by-sequencing (HD-GBS). PLANT BIOTECHNOLOGY JOURNAL 2021; 19:860-862. [PMID: 33476468 PMCID: PMC8131051 DOI: 10.1111/pbi.13551] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 01/14/2021] [Indexed: 05/03/2023]
Affiliation(s)
- Davoud Torkamaneh
- Département de PhytologieUniversité LavalQuébec CityQCCanada
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
- Department of Plant AgricultureUniversity of GuelphGuelphONCanada
| | - Jérôme Laroche
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
| | - Brian Boyle
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
| | - David L. Hyten
- Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNEUSA
| | - François Belzile
- Département de PhytologieUniversité LavalQuébec CityQCCanada
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
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Parra-Galindo MA, Soto-Sedano JC, Mosquera-Vásquez T, Roda F. Pathway-based analysis of anthocyanin diversity in diploid potato. PLoS One 2021; 16:e0250861. [PMID: 33914830 PMCID: PMC8084248 DOI: 10.1371/journal.pone.0250861] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/14/2021] [Indexed: 12/21/2022] Open
Abstract
Anthocyanin biosynthesis is one of the most studied pathways in plants due to the important ecological role played by these compounds and the potential health benefits of anthocyanin consumption. Given the interest in identifying new genetic factors underlying anthocyanin content we studied a diverse collection of diploid potatoes by combining a genome-wide association study and pathway-based analyses. By using an expanded SNP dataset, we identified candidate genes that had not been associated with anthocyanin variation in potatoes, namely a Myb transcription factor, a Leucoanthocyanidin dioxygenase gene and a vacuolar membrane protein. Importantly, a genomic region in chromosome 10 harbored the SNPs with strongest associations with anthocyanin content in GWAS. Some of these SNPs were associated with multiple anthocyanin compounds and therefore could underline the existence of pleiotropic genes or anthocyanin biosynthetic clusters. We identified multiple anthocyanin homologs in this genomic region, including four transcription factors and five enzymes that could be governing anthocyanin variation. For instance, a SNP linked to the phenylalanine ammonia-lyase gene, encoding the first enzyme in the phenylpropanoid biosynthetic pathway, was associated with all of the five anthocyanins measured. Finally, we combined a pathway analysis and GWAS of other agronomic traits to identify pathways related to anthocyanin biosynthesis in potatoes. We found that methionine metabolism and the production of sugars and hydroxycinnamic acids are genetically correlated to anthocyanin biosynthesis. The results contribute to the understanding of anthocyanins regulation in potatoes and can be used in future breeding programs focused on nutraceutical food.
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Affiliation(s)
| | - Johana Carolina Soto-Sedano
- Departamento de Biología, Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia
| | - Teresa Mosquera-Vásquez
- Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia
| | - Federico Roda
- Max Planck Tandem Group, Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia
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Lou H, Zhang R, Liu Y, Guo D, Zhai S, Chen A, Zhang Y, Xie C, You M, Peng H, Liang R, Ni Z, Sun Q, Li B. Genome-wide association study of six quality-related traits in common wheat (Triticum aestivum L.) under two sowing conditions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:399-418. [PMID: 33155062 DOI: 10.1007/s00122-020-03704-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 10/08/2020] [Indexed: 05/20/2023]
Abstract
We identified genomic regions associated with six quality-related traits in wheat under two sowing conditions and analyzed the effects of multienvironment-significant SNPs on the stability of these traits. Grain quality affects the nutritional and commercial value of wheat (Triticum aestivum L.) and is a critical factor influencing consumer preferences for specific wheat varieties. Climate change is predicted to increase environmental stress and thereby reduce wheat quality. Here, we performed a genotyping assay involving the use of the wheat 90 K array in a genome-wide association study of six quality-related traits in 486 wheat accessions under two sowing conditions (normal and late sowing) over 4 years. We identified 64 stable quantitative trait loci (QTL), including 10 for grain protein content, 9 for wet gluten content, 4 for grain starch content, 14 for water absorption, 15 for dough stability time and 12 for grain hardness in wheat under two sowing conditions. These QTL harbored 175 single nucleotide polymorphisms (SNPs), explaining approximately 3-13% of the phenotypic variation in multiple environments. Some QTL on chromosomes 6A and 5D were associated with multiple traits simultaneously, and two (QNGPC.cau-6A, QNGH.cau-5D) harbored known genes, such as NAM-A1 for grain protein content and Pinb for grain hardness, whereas other QTL could facilitate gene discovery. Forty-three SNPs that were detected under late or both normal and late sowing conditions appear to be related to phenotypic stability. The effects of these SNP alleles were confirmed in the association population. The results of this study will be useful for further dissecting the genetic basis of quality-related traits in wheat and developing new wheat cultivars with desirable alleles to improve the stability of grain quality.
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Affiliation(s)
- Hongyao Lou
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Runqi Zhang
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Yitong Liu
- Key Laboratory of Photobiology, Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Xiangshan, Beijing, 100093, China
- University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Dandan Guo
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Shanshan Zhai
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Aiyan Chen
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Yufeng Zhang
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Chaojie Xie
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Mingshan You
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Huiru Peng
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Rongqi Liang
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
| | - Zhongfu Ni
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Qixin Sun
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Baoyun Li
- State Key Laboratory for Agrobiotechnology/Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/China Agricultural University, Beijing, 100193, China.
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