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Xu L, Hao J, Lv M, Liu P, Ge Q, Zhang S, Yang J, Niu H, Wang Y, Xue Y, Lu X, Tang J, Zheng J, Gou M. A genome-wide association study identifies genes associated with cuticular wax metabolism in maize. PLANT PHYSIOLOGY 2024; 194:2616-2630. [PMID: 38206190 DOI: 10.1093/plphys/kiae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/20/2023] [Accepted: 12/11/2023] [Indexed: 01/12/2024]
Abstract
The plant cuticle is essential in plant defense against biotic and abiotic stresses. To systematically elucidate the genetic architecture of maize (Zea mays L.) cuticular wax metabolism, 2 cuticular wax-related traits, the chlorophyll extraction rate (CER) and water loss rate (WLR) of 389 maize inbred lines, were investigated and a genome-wide association study (GWAS) was performed using 1.25 million single nucleotide polymorphisms (SNPs). In total, 57 nonredundant quantitative trait loci (QTL) explaining 5.57% to 15.07% of the phenotypic variation for each QTL were identified. These QTLs contained 183 genes, among which 21 strong candidates were identified based on functional annotations and previous publications. Remarkably, 3 candidate genes that express differentially during cuticle development encode β-ketoacyl-CoA synthase (KCS). While ZmKCS19 was known to be involved in cuticle wax metabolism, ZmKCS12 and ZmKCS3 functions were not reported. The association between ZmKCS12 and WLR was confirmed by resequencing 106 inbred lines, and the variation of WLR was significant between different haplotypes of ZmKCS12. In this study, the loss-of-function mutant of ZmKCS12 exhibited wrinkled leaf morphology, altered wax crystal morphology, and decreased C32 wax monomer levels, causing an increased WLR and sensitivity to drought. These results confirm that ZmKCS12 plays a vital role in maize C32 wax monomer synthesis and is critical for drought tolerance. In sum, through GWAS of 2 cuticular wax-associated traits, this study reveals comprehensively the genetic architecture in maize cuticular wax metabolism and provides a valuable reference for the genetic improvement of stress tolerance in maize.
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Affiliation(s)
- Liping Xu
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
- The Shennong Laboratory, Zhengzhou 450002, China
| | - Jiaxin Hao
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Mengfan Lv
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Peipei Liu
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Qidong Ge
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Sainan Zhang
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Jianping Yang
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Hongbin Niu
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Yiru Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yadong Xue
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Xiaoduo Lu
- Institute of Advanced Agricultural Technology, Qilu Normal University, Jinan 250200, China
| | - Jihua Tang
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
- The Shennong Laboratory, Zhengzhou 450002, China
| | - Jun Zheng
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Mingyue Gou
- State Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
- The Shennong Laboratory, Zhengzhou 450002, China
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Qiu Y, Adhikari P, Balint-Kurti P, Jamann T. Identification of loci conferring resistance to 4 foliar diseases of maize. G3 (BETHESDA, MD.) 2024; 14:jkad275. [PMID: 38051956 PMCID: PMC10849323 DOI: 10.1093/g3journal/jkad275] [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: 04/04/2023] [Revised: 11/06/2023] [Accepted: 11/10/2023] [Indexed: 12/07/2023]
Abstract
Foliar diseases of maize are among the most important diseases of maize worldwide. This study focused on 4 major foliar diseases of maize: Goss's wilt, gray leaf spot, northern corn leaf blight, and southern corn leaf blight. QTL mapping for resistance to Goss's wilt was conducted in 4 disease resistance introgression line populations with Oh7B as the common recurrent parent and Ki3, NC262, NC304, and NC344 as recurrent donor parents. Mapping results for Goss's wilt resistance were combined with previous studies for gray leaf spot, northern corn leaf blight, and southern corn leaf blight resistance in the same 4 populations. We conducted (1) individual linkage mapping analysis to identify QTL specific to each disease and population; (2) Mahalanobis distance analysis to identify putative multiple disease resistance regions for each population; and 3) joint linkage mapping to identify QTL across the 4 populations for each disease. We identified 3 lines that were resistant to all 4 diseases. We mapped 13 Goss's wilt QTLs in the individual populations and an additional 6 using joint linkage mapping. All Goss's wilt QTL had small effects, confirming that resistance to Goss's wilt is highly quantitative. We report several potentially important chromosomal bins associated with multiple disease resistance including 1.02, 1.03, 3.04, 4.06, 4.08, and 9.03. Together, these findings indicate that disease QTL distribution is not random and that there are locations in the genome that confer resistance to multiple diseases. Furthermore, resistance to bacterial and fungal diseases is not entirely distinct, and we identified lines resistant to both fungi and bacteria, as well as loci that confer resistance to both bacterial and fungal diseases.
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Affiliation(s)
- Yuting Qiu
- Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Pragya Adhikari
- Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Peter Balint-Kurti
- Department of Entomology and Plant Pathology, North Carolina State University, Box 7616, Raleigh, NC 27695, USA
- Plant Science Research Unit, USDA-ARS, Raleigh, NC 27695, USA
| | - Tiffany Jamann
- Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
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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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Zhang Y, Zhang M, Ye J, Xu Q, Feng Y, Xu S, Hu D, Wei X, Hu P, Yang Y. Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice ( Oryza sativa L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:81. [PMID: 37965378 PMCID: PMC10641074 DOI: 10.1007/s11032-023-01423-y] [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: 07/11/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023]
Abstract
Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01423-y.
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Affiliation(s)
- Yuanyuan Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Mengchen Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Junhua Ye
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Qun Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yue Feng
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Siliang Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Dongxiu Hu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Xinghua Wei
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Peisong Hu
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yaolong Yang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
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Hao Y, Hu Y, Jaqueth J, Lin J, He C, Lin G, Zhao M, Ren J, Tamang TM, Park S, Robertson AE, White FF, Fu J, Li B, Liu S. Genetic and transcriptomic dissection of host defense to Goss's bacterial wilt and leaf blight of maize. G3 (BETHESDA, MD.) 2023; 13:jkad197. [PMID: 37652038 PMCID: PMC10627284 DOI: 10.1093/g3journal/jkad197] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 01/28/2023] [Accepted: 08/22/2023] [Indexed: 09/02/2023]
Abstract
Goss's wilt, caused by the Gram-positive actinobacterium Clavibacter nebraskensis, is an important bacterial disease of maize. The molecular and genetic mechanisms of resistance to the bacterium, or, in general, Gram-positive bacteria causing plant diseases, remain poorly understood. Here, we examined the genetic basis of Goss's wilt through differential gene expression, standard genome-wide association mapping (GWAS), extreme phenotype (XP) GWAS using highly resistant (R) and highly susceptible (S) lines, and quantitative trait locus (QTL) mapping using 3 bi-parental populations, identifying 11 disease association loci. Three loci were validated using near-isogenic lines or recombinant inbred lines. Our analysis indicates that Goss's wilt resistance is highly complex and major resistance genes are not commonly present. RNA sequencing of samples separately pooled from R and S lines with or without bacterial inoculation was performed, enabling identification of common and differential gene responses in R and S lines. Based on expression, in both R and S lines, the photosynthesis pathway was silenced upon infection, while stress-responsive pathways and phytohormone pathways, namely, abscisic acid, auxin, ethylene, jasmonate, and gibberellin, were markedly activated. In addition, 65 genes showed differential responses (up- or down-regulated) to infection in R and S lines. Combining genetic mapping and transcriptional data, individual candidate genes conferring Goss's wilt resistance were identified. Collectively, aspects of the genetic architecture of Goss's wilt resistance were revealed, providing foundational data for mechanistic studies.
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Affiliation(s)
- Yangfan Hao
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Ying Hu
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | | | - Jinguang Lin
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Cheng He
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Guifang Lin
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Mingxia Zhao
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Jie Ren
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Tej Man Tamang
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Sunghun Park
- Department of Horticulture and Natural Resources, Kansas State University, Manhattan, KS 66506, USA
| | - Alison E Robertson
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA 50010, USA
| | - Frank F White
- Department of Plant Pathology, University of Florida, Gainesville, FL 32611, USA
| | - Junjie Fu
- Chinese Academy of Agricultural Sciences, Institute of Crop Science, Beijing 100081, China
| | - Bailin Li
- Corteva Agriscience, Johnston, IA 50131, USA
| | - Sanzhen Liu
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
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Shumilak A, El-Shetehy M, Soliman A, Tambong JT, Daayf F. Goss's Wilt Resistance in Corn Is Mediated via Salicylic Acid and Programmed Cell Death but Not Jasmonic Acid Pathways. PLANTS (BASEL, SWITZERLAND) 2023; 12:1475. [PMID: 37050101 PMCID: PMC10097360 DOI: 10.3390/plants12071475] [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: 01/28/2023] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
A highly aggressive strain (CMN14-5-1) of Clavibacter nebraskensis bacteria, which causes Goss's wilt in corn, induced severe symptoms in a susceptible corn line (CO447), resulting in water-soaked lesions followed by necrosis within a few days. A tolerant line (CO450) inoculated with the same strain exhibited only mild symptoms such as chlorosis, freckling, and necrosis that did not progress after the first six days following infection. Both lesion length and disease severity were measured using the area under the disease progression curve (AUDPC), and significant differences were found between treatments. We analyzed the expression of key genes related to plant defense in both corn lines challenged with the CMN14-5-1 strain. Allene oxide synthase (ZmAOS), a gene responsible for the production of jasmonic acid (JA), was induced in the CO447 line in response to CMN14-5-1. Following inoculation with CMN14-5-1, the CO450 line demonstrated a higher expression of salicylic acid (SA)-related genes, ZmPAL and ZmPR-1, compared to the CO447 line. In the CO450 line, four genes related to programmed cell death (PCD) were upregulated: respiratory burst oxidase homolog protein D (ZmrbohD), polyphenol oxidase (ZmPPO1), ras-related protein 7 (ZmRab7), and peptidyl-prolyl cis-trans isomerase (ZmPPI). The differential gene expression in response to CMN14-5-1 between the two corn lines provided an indication that SA and PCD are involved in the regulation of corn defense responses against Goss's wilt disease, whereas JA may be contributing to disease susceptibility.
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Affiliation(s)
- Alexander Shumilak
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Mohamed El-Shetehy
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- Department of Botany, Faculty of Science, Tanta University, Tanta 31527, Egypt
| | - Atta Soliman
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- Department of Genetics, Faculty of Agriculture, University of Tanta, Tanta 31527, Egypt
| | - James T Tambong
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
| | - Fouad Daayf
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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Li J, Cheng D, Guo S, Chen C, Wang Y, Zhong Y, Qi X, Liu Z, Wang D, Wang Y, Liu W, Liu C, Chen S. Genome-wide association and genomic prediction for resistance to southern corn rust in DH and testcross populations. FRONTIERS IN PLANT SCIENCE 2023; 14:1109116. [PMID: 36778694 PMCID: PMC9908600 DOI: 10.3389/fpls.2023.1109116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Southern corn rust (SCR), caused by Puccinia polysora Underw, is a destructive disease that can severely reduce grain yield in maize (Zea mays L.). Owing to P. polysora being multi-racial, it is very important to explore more resistance genes and develop more efficient selection approaches in maize breeding programs. Here, four Doubled Haploid (DH) populations with 384 accessions originated from selected parents and their 903 testcross hybrids were used to perform genome-wide association (GWAS). Three GWAS processes included the additive model in the DH panel, additive and dominant models in the hybrid panel. As a result, five loci were detected on chromosomes 1, 7, 8, 8, and 10, with P-values ranging from 4.83×10-7 to 2.46×10-41. In all association analyses, a highly significant locus on chromosome 10 was detected, which was tight chained with the known SCR resistance gene RPPC and RPPK. Genomic prediction (GP), has been proven to be effective in plant breeding. In our study, several models were performed to explore predictive ability in hybrid populations for SCR resistance, including extended GBLUP with different genetic matrices, maker based prediction models, and mixed models with QTL as fixed factors. For GBLUP models, the prediction accuracies ranged from 0.56-0.60. Compared with traditional prediction only with additive effect, prediction ability was significantly improved by adding additive-by-additive effect (P-value< 0.05). For maker based models, the accuracy of BayesA and BayesB was 0.65, 8% higher than other models (i.e., RRBLUP, BRR, BL, BayesC). Finally, by adding QTL into the mixed linear prediction model, the accuracy can be further improved to 0.67, especially for the G_A model, the prediction performance can be increased by 11.67%. The prediction accuracy of the BayesB model can be further improved significantly by adding QTL information (P-value< 0.05). This study will provide important valuable information for understanding the genetic architecture and the application of GP for SCR in maize breeding.
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Affiliation(s)
- Jinlong Li
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Dehe Cheng
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Shuwei Guo
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Chen Chen
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
- Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Yuwen Wang
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Yu Zhong
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Xiaolong Qi
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Zongkai Liu
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Dong Wang
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Yuandong Wang
- Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wenxin Liu
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Chenxu Liu
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
| | - Shaojiang Chen
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization Ministry of Education (MOE), China Agricultural University, Beijing, China
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Wu S, Wang J, Zhao Y, Wen W, Zhang Y, Lu X, Wang C, Liu K, Chen B, Guo X, Zhao C. Characterization and genetic dissection of maize ear leaf midrib acquired by 3D digital technology. FRONTIERS IN PLANT SCIENCE 2022; 13:1063056. [PMID: 36531364 PMCID: PMC9754214 DOI: 10.3389/fpls.2022.1063056] [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: 10/06/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
The spatial morphological structure of plant leaves is an important index to evaluate crop ideotype. In this study, we characterized the three-dimensional (3D) data of the ear leaf midrib of maize at the grain-filling stage using the 3D digitization technology and obtained the phenotypic values of 15 traits covering four different dimensions of the ear leaf midrib, of which 13 phenotypic traits were firstly proposed for featuring plant leaf spatial structure. Cluster analysis results showed that the 13 traits could be divided into four groups, Group I, -II, -III and -IV. Group I contains HorizontalLength, OutwardGrowthMeasure, LeafAngle and DeviationTip; Group II contains DeviationAngle, MaxCurvature and CurvaturePos; Group III contains LeafLength and ProjectionArea; Group IV contains TipTop, VerticalHeight, UpwardGrowthMeasure, and CurvatureRatio. To investigate the genetic basis of the ear leaf midrib curve, 13 traits with high repeatability were subjected to genome-wide association study (GWAS) analysis. A total of 828 significantly related SNPs were identified and 1365 candidate genes were annotated. Among these, 29 candidate genes with the highest significant and multi-method validation were regarded as the key findings. In addition, pathway enrichment analysis was performed on the candidate genes of traits to explore the potential genetic mechanism of leaf midrib curve phenotype formation. These results not only contribute to further understanding of maize leaf spatial structure traits but also provide new genetic loci for maize leaf spatial structure to improve the plant type of maize varieties.
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Affiliation(s)
- Sheng Wu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Jinglu Wang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Yanxin Zhao
- Beijing Key Laboratory of Maize DNA (DeoxyriboNucleic Acid) Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Weiliang Wen
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Ying Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xianju Lu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chuanyu Wang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Kai Liu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Bo Chen
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xinyu Guo
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chunjiang Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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9
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Wang J, Wang C, Lu X, Zhang Y, Zhao Y, Wen W, Song W, Guo X. Dissecting the Genetic Structure of Maize Leaf Sheaths at Seedling Stage by Image-Based High-Throughput Phenotypic Acquisition and Characterization. FRONTIERS IN PLANT SCIENCE 2022; 13:826875. [PMID: 35837446 PMCID: PMC9274118 DOI: 10.3389/fpls.2022.826875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 02/17/2022] [Indexed: 06/15/2023]
Abstract
The rapid development of high-throughput phenotypic detection techniques makes it possible to obtain a large number of crop phenotypic information quickly, efficiently, and accurately. Among them, image-based phenotypic acquisition method has been widely used in crop phenotypic identification and characteristic research due to its characteristics of automation, non-invasive, non-destructive and high throughput. In this study, we proposed a method to define and analyze the traits related to leaf sheaths including morphology-related, color-related and biomass-related traits at V6 stage. Next, we analyzed the phenotypic variation of leaf sheaths of 418 maize inbred lines based on 87 leaf sheath-related phenotypic traits. In order to further analyze the mechanism of leaf sheath phenotype formation, 25 key traits (2 biomass-related, 19 morphology-related and 4 color-related traits) with heritability greater than 0.3 were analyzed by genome-wide association studies (GWAS). And 1816 candidate genes of 17 whole plant leaf sheath traits and 1,297 candidate genes of 8 sixth leaf sheath traits were obtained, respectively. Among them, 46 genes with clear functional descriptions were annotated by single nucleotide polymorphism (SNPs) that both Top1 and multi-method validated. Functional enrichment analysis results showed that candidate genes of leaf sheath traits were enriched into multiple pathways related to cellular component assembly and organization, cell proliferation and epidermal cell differentiation, and response to hunger, nutrition and extracellular stimulation. The results presented here are helpful to further understand phenotypic traits of maize leaf sheath and provide a reference for revealing the genetic mechanism of maize leaf sheath phenotype formation.
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Affiliation(s)
- Jinglu Wang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chuanyu Wang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ying Zhang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Yanxin Zhao
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Weiliang Wen
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wei Song
- Key Laboratory of Crop Genetics and Breeding of Hebei Province, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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10
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Mullens A, Jamann TM. Colonization and Movement of Green Fluorescent Protein-Labeled Clavibacter nebraskensis in Maize. PLANT DISEASE 2021; 105:1422-1431. [PMID: 33190611 DOI: 10.1094/pdis-08-20-1823-re] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Clavibacter nebraskensis causes Goss's bacterial wilt and leaf blight, a major disease of maize. Infected crop residue is the primary inoculum source and infection can occur via wounds or natural openings, such as stomata or hydathodes. The use of resistant hybrids is the primary control method for Goss's wilt. In this study, colonization and movement patterns of C. nebraskensis during infection were examined using green fluorescent protein (GFP)-labeled bacterial strains. We successfully introduced a plasmid to C. nebraskensis via electroporation, which resulted in GFP accumulation. Fluorescence microscopy revealed that in the absence of wounding, bacteria colonize leaf tissue via entry through the hydathodes when guttation droplets are present. Stomatal penetration was not observed under natural conditions. Bacteria initially colonize the xylem and subsequently the mesophyll, which creates the freckles that are characteristic of the disease. Bacteria infiltrated into the mesophyll did not cause disease symptoms, could not enter the vasculature, and did not spread from the initial inoculation point. Bacteria were observed exuding through stomata onto the leaf surface, resulting in the characteristic sheen of diseased leaves. Resistant maize lines exhibited decreased bacterial spread in the vasculature and the mesophyll. These tools to examine C. nebraskensis movement offer opportunities and new insights into the pathogenesis process and can form the basis for improved Goss's wilt management through host resistance.
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Affiliation(s)
- Alexander Mullens
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801
| | - Tiffany M Jamann
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801
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Shikha K, Shahi JP, Vinayan MT, Zaidi PH, Singh AK, Sinha B. Genome-wide association mapping in maize: status and prospects. 3 Biotech 2021; 11:244. [PMID: 33968587 PMCID: PMC8085158 DOI: 10.1007/s13205-021-02799-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 04/19/2021] [Indexed: 12/11/2022] Open
Abstract
Genome-wide association study (GWAS) provides a robust and potent tool to retrieve complex phenotypic traits back to their underlying genetics. Maize is an excellent crop for performing GWAS due to diverse genetic variability, rapid decay of linkage disequilibrium, availability of distinct sub-populations and abundant SNP information. The application of GWAS in maize has resulted in successful identification of thousands of genomic regions associated with many abiotic and biotic stresses. Many agronomic and quality traits of maize are severely affected by such stresses and, significantly affecting its growth and productivity. To improve productivity of maize crop in countries like India which contribute only 2% to the world's total production in 2019-2020, it is essential to understand genetic complexity of underlying traits. Various DNA markers and trait associations have been revealed using conventional linkage mapping methods. However, it has achieved limited success in improving polygenic complex traits due to lower resolution of trait mapping. The present review explores the prospects of GWAS in improving yield, quality and stress tolerance in maize besides, strengths and challenges of using GWAS for molecular breeding and genomic selection. The information gathered will facilitate elucidation of genetic mechanisms of complex traits and improve efficiency of marker-assisted selection in maize breeding. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s13205-021-02799-4.
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Affiliation(s)
- Kumari Shikha
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
| | - J. P. Shahi
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
| | - M. T. Vinayan
- International Maize and Wheat Improvement Centre (CIMMYT)-Asia, ICRISAT Campus, Patancheru, Hyderabad, Telangana India
| | - P. H. Zaidi
- International Maize and Wheat Improvement Centre (CIMMYT)-Asia, ICRISAT Campus, Patancheru, Hyderabad, Telangana India
| | - A. K. Singh
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
| | - B. Sinha
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
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Stanley A, Menkir A, Paterne A, Ifie B, Tongoona P, Unachukwu N, Meseka S, Mengesha W, Gedil M. Genetic Diversity and Population Structure of Maize Inbred Lines with Varying Levels of Resistance to Striga Hermonthica Using Agronomic Trait-Based and SNP Markers. PLANTS (BASEL, SWITZERLAND) 2020; 9:E1223. [PMID: 32957613 PMCID: PMC7570130 DOI: 10.3390/plants9091223] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/07/2020] [Accepted: 09/14/2020] [Indexed: 11/17/2022]
Abstract
Striga hermonthica is a serious biotic stress limiting maize production in sub-Saharan Africa. The limited information on the patterns of genetic diversity among maize inbred lines derived from source germplasm with mixed genetic backgrounds limits the development of inbred lines, hybrids, and synthetics with durable resistance to S. hermonthica. This study was conducted to assess the level of genetic diversity in a panel of 150 diverse maize inbred lines using agronomic and molecular data and also to infer the population structure among the inbred lines. Ten Striga-resistance-related traits were used for the phenotypic characterization, and 16,735 high-quality single-nucleotide polymorphisms (SNPs), identified by genotyping-by-sequencing (GBS), were used for molecular diversity. The phenotypic and molecular hierarchical cluster analyses grouped the inbred lines into five clusters, respectively. However, the grouping patterns between the phenotypic and molecular hierarchical cluster analyses were inconsistent due to non-overlapping information between the phenotypic and molecular data. The correlation between the phenotypic and molecular diversity matrices was very low (0.001), which is in agreement with the inconsistencies observed between the clusters formed by the phenotypic and molecular diversity analyses. The joint phenotypic and genotypic diversity matrices grouped the inbred lines into three groups based on their reaction patterns to S. hermonthica, and this was able to exploit a broad estimate of the actual diversity among the inbred lines. The joint analysis shows an invaluable insight for measuring genetic diversity in the evaluated materials. The result indicates that wide genetic variability exists among the inbred lines and that the joint diversity analysis can be utilized to reliably assign the inbred lines into heterotic groups and also to enhance the level of resistance to Striga in new maize varieties.
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Affiliation(s)
- Adekemi Stanley
- West Africa Centre for Crop Improvement University of Ghana, Legon PMB 30, Ghana; (A.S.); (B.I.); (P.T.)
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria; (A.P.); (N.U.); (S.M.); (W.M.); (M.G.)
| | - Abebe Menkir
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria; (A.P.); (N.U.); (S.M.); (W.M.); (M.G.)
| | - Agre Paterne
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria; (A.P.); (N.U.); (S.M.); (W.M.); (M.G.)
| | - Beatrice Ifie
- West Africa Centre for Crop Improvement University of Ghana, Legon PMB 30, Ghana; (A.S.); (B.I.); (P.T.)
| | - Pangirayi Tongoona
- West Africa Centre for Crop Improvement University of Ghana, Legon PMB 30, Ghana; (A.S.); (B.I.); (P.T.)
| | - Nnanna Unachukwu
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria; (A.P.); (N.U.); (S.M.); (W.M.); (M.G.)
| | - Silvestro Meseka
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria; (A.P.); (N.U.); (S.M.); (W.M.); (M.G.)
| | - Wende Mengesha
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria; (A.P.); (N.U.); (S.M.); (W.M.); (M.G.)
| | - Melaku Gedil
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria; (A.P.); (N.U.); (S.M.); (W.M.); (M.G.)
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Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize. G3-GENES GENOMES GENETICS 2020; 10:2819-2828. [PMID: 32571803 PMCID: PMC7407448 DOI: 10.1534/g3.120.401104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Crops are hosts to numerous plant pathogenic microorganisms. Maize has several major disease issues; thus, breeding multiple disease resistant (MDR) varieties is critical. While the genetic basis of resistance to multiple fungal pathogens has been studied in maize, less is known about the relationship between fungal and bacterial resistance. In this study, we evaluated a disease resistance introgression line (DRIL) population for the foliar disease Goss’s bacterial wilt and blight (GW) and conducted quantitative trait locus (QTL) mapping. We identified a total of ten QTL across multiple environments. We then combined our GW data with data on four additional foliar diseases (northern corn leaf blight, southern corn leaf blight, gray leaf spot, and bacterial leaf streak) and conducted multivariate analysis to identify regions conferring resistance to multiple diseases. We identified 20 chromosomal bins with putative multiple disease effects. We examined the five chromosomal regions (bins 1.05, 3.04, 4.06, 8.03, and 9.02) with the strongest statistical support. By examining how each haplotype effected each disease, we identified several regions associated with increased resistance to multiple diseases and three regions associated with opposite effects for bacterial and fungal diseases. In summary, we identified several promising candidate regions for multiple disease resistance in maize and specific DRILs to expedite interrogation.
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