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Song J, Liu Y, Guo R, Pacheco A, Muñoz-Zavala C, Song W, Wang H, Cao S, Hu G, Zheng H, Dhliwayo T, San Vicente F, Prasanna BM, Wang C, Zhang X. Exploiting genomic tools for genetic dissection and improving the resistance to Fusarium stalk rot in tropical maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:109. [PMID: 38649662 DOI: 10.1007/s00122-024-04597-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/07/2024] [Indexed: 04/25/2024]
Abstract
KEY MESSAGE A stable genomic region conferring FSR resistance at ~250 Mb on chromosome 1 was identified by GWAS. Genomic prediction has the potential to improve FSR resistance. Fusarium stalk rot (FSR) is a global destructive disease in maize; the efficiency of phenotypic selection for improving FSR resistance was low. Novel genomic tools of genome-wide association study (GWAS) and genomic prediction (GP) provide an opportunity for genetic dissection and improving FSR resistance. In this study, GWAS and GP analyses were performed on 562 tropical maize inbred lines consisting of two populations. In total, 15 SNPs significantly associated with FSR resistance were identified across two populations and the combinedPOP consisting of all 562 inbred lines, with the P-values ranging from 1.99 × 10-7 to 8.27 × 10-13, and the phenotypic variance explained (PVE) values ranging from 0.94 to 8.30%. The genetic effects of the 15 favorable alleles ranged from -4.29 to -14.21% of the FSR severity. One stable genomic region at ~ 250 Mb on chromosome 1 was detected across all populations, and the PVE values of the SNPs detected in this region ranged from 2.16 to 5.18%. Prediction accuracies of FSR severity estimated with the genome-wide SNPs were moderate and ranged from 0.29 to 0.51. By incorporating genotype-by-environment interaction, prediction accuracies were improved between 0.36 and 0.55 in different breeding scenarios. Considering both the genome coverage and the threshold of the P-value of SNPs to select a subset of molecular markers further improved the prediction accuracies. These findings extend the knowledge of exploiting genomic tools for genetic dissection and improving FSR resistance in tropical maize.
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Affiliation(s)
- Junqiao Song
- Henan University of Science and Technology, Luoyang, 471000, Henan, China
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Anyang Academy of Agricultural Sciences, Anyang, 455000, Henan, China
| | - Yubo Liu
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 200063, China
| | - Rui Guo
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, 050035, Hebei, China
| | - Angela Pacheco
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Carlos Muñoz-Zavala
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Wei Song
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, 050035, Hebei, China
| | - Hui Wang
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 200063, China
| | - Shiliang Cao
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Maize Research, Heilongjiang Academy of Agricultural Sciences, Harbin, 150070, Heilongjiang, China
| | - Guanghui Hu
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Maize Research, Heilongjiang Academy of Agricultural Sciences, Harbin, 150070, Heilongjiang, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 200063, China
| | - Thanda Dhliwayo
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, Nairobi, 00621, Kenya
| | - Chunping Wang
- Henan University of Science and Technology, Luoyang, 471000, Henan, China.
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico.
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, 12 Zhongguancun South Street, Beijing, 100081, China.
- Nanfan Research Institute, CAAS, Sanya, 572024, Hainan, China.
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Hu D, Zhao Y, Zhu L, Li X, Zhang J, Cui X, Li W, Hao D, Yang Z, Wu F, Dong S, Su X, Huang F, Yu D. Genetic dissection of ten photosynthesis-related traits based on InDel- and SNP-GWAS in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:96. [PMID: 38589730 DOI: 10.1007/s00122-024-04607-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/21/2024] [Indexed: 04/10/2024]
Abstract
KEY MESSAGE A total of 416 InDels and 112 SNPs were significantly associated with soybean photosynthesis-related traits. GmIWS1 and GmCDC48 might be related to chlorophyll fluorescence and gas-exchange parameters, respectively. Photosynthesis is one of the main factors determining crop yield. A better understanding of the genetic architecture for photosynthesis is of great significance for soybean yield improvement. Our previous studies identified 5,410,112 single nucleotide polymorphisms (SNPs) from the resequencing data of 219 natural soybean accessions. Here, we identified 634,106 insertions and deletions (InDels) from these 219 accessions and used these InDel variations to perform principal component and linkage disequilibrium analysis of this population. The genome-wide association study (GWAS) were conducted on six chlorophyll fluorescence parameters (chlorophyll content, light energy absorbed per reaction center, quantum yield for electron transport, probability that a trapped exciton moves an electron into the electron transport chain beyond primary quinone acceptor, maximum quantum yield of photosystem II primary photochemistry in the dark-adapted state, performance index on absorption basis) and four gas-exchange parameters (intercellular carbon dioxide concentration, stomatal conductance, net photosynthesis rate, transpiration rate) and revealed 416 significant InDels and 112 significant SNPs. Based on GWAS results, GmIWS1 (encoding a transcription elongation factor) and GmCDC48 (encoding a cell division cycle protein) with the highest expression in the mapping region were determined as the candidate genes responsible for chlorophyll fluorescence and gas-exchange parameters, respectively. Further identification of favorable haplotypes with higher photosynthesis, seed weight and seed yield were carried out for GmIWS1 and GmCDC48. Overall, this study revealed the natural variations and candidate genes underlying the photosynthesis-related traits based on abundant phenotypic and genetic data, providing valuable insights into the genetic mechanisms controlling photosynthesis and yield in soybean.
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Affiliation(s)
- Dezhou Hu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
- College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yajun Zhao
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Lixun Zhu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xiao Li
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Jinyu Zhang
- Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang, 453003, China
| | - Xuan Cui
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Wenlong Li
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Derong Hao
- Jiangsu Yanjiang Institute of Agricultural Sciences, Nantong, 226012, China
| | - Zhongyi Yang
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Fei Wu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Shupeng Dong
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xiaoyue Su
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Fang Huang
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Deyue Yu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China.
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [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: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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Patel R, Menon J, Kumar S, Nóbrega MB, Patel DA, Sakure AA, Vaja MB. Modern day breeding approaches for improvement of castor. Heliyon 2024; 10:e27048. [PMID: 38463846 PMCID: PMC10920369 DOI: 10.1016/j.heliyon.2024.e27048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open
Abstract
Castor (Ricinus communis L.) is an industrially important oil producing crop belongs to Euphorbiaceae family. Castor oil has unique chemical properties make it industrially important crop. It is a member of monotypic genus even though it has ample amount of variability. Using this variability, conventionally many varieties and hybrids have been developed. But, like other crops, the modern and unconventional methods of crop improvement has not fully explored in castor. This article discusses the use of polyploidy induction, distant/wide hybridization and mutation breeding as tools for generating variety. Modern approaches accelerate the speed of crop breeding as an alternative tool. To achieve this goal, molecular markers are employed in breeding to capture the genetic variability through molecular analysis and population structuring. Allele mining is used to trace the evolution of alleles, identify new haplotypes and produce allele specific markers for use in marker aided selection using Genome wide association studies (GWAS) and quantitative trait loci (QTL) mapping. Plant genetic transformation is a rapid and effective mode of castor improvement is also discussed here. The efforts towards developing stable regeneration protocol provide a wide range of utility like embryo rescue in distant crosses, development of somaclonal variation, haploid development using anther culture and callus development for stable genetic transformation has reviewed in this article. Omics has provided intuitions to the molecular mechanisms of (a)biotic stress management in castor along with dissected out the possible genes for improving the yield. Relating genes to traits offers additional scientific inevitability leading to enhancement and sympathetic mechanisms of yield improvement and several stress tolerance.
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Affiliation(s)
- Rumit Patel
- Department of Agricultural Biotechnology, Anand Agricultural University, Anand, 388110, India
- Department of Genetics & Plant Breeding, B. A. College of Agriculture, Anand Agricultural University, Anand, 388110, India
| | - Juned Menon
- Department of Genetics & Plant Breeding, B. A. College of Agriculture, Anand Agricultural University, Anand, 388110, India
| | - Sushil Kumar
- Department of Agricultural Biotechnology, Anand Agricultural University, Anand, 388110, India
| | - Márcia B.M. Nóbrega
- Embrapa Algodão, Rua Oswaldo Cruz, nº 1.143, Centenário, CEP 58428-095, Campina Grande, PB, Brazil
| | - Dipak A. Patel
- Department of Agricultural Biotechnology, Anand Agricultural University, Anand, 388110, India
| | - Amar A. Sakure
- Department of Agricultural Biotechnology, Anand Agricultural University, Anand, 388110, India
| | - Mahesh B. Vaja
- Department of Agricultural Biotechnology, Anand Agricultural University, Anand, 388110, India
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Ndlovu N, Kachapur RM, Beyene Y, Das B, Ogugo V, Makumbi D, Spillane C, McKeown PC, Prasanna BM, Gowda M. Linkage mapping and genomic prediction of grain quality traits in tropical maize ( Zea mays L.). Front Genet 2024; 15:1353289. [PMID: 38456017 PMCID: PMC10918846 DOI: 10.3389/fgene.2024.1353289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/07/2024] [Indexed: 03/09/2024] Open
Abstract
The suboptimal productivity of maize systems in sub-Saharan Africa (SSA) is a pressing issue, with far-reaching implications for food security, nutrition, and livelihood sustainability within the affected smallholder farming communities. Dissecting the genetic basis of grain protein, starch and oil content can increase our understanding of the governing genetic systems, improve the efficacy of future breeding schemes and optimize the end-use quality of tropical maize. Here, four bi-parental maize populations were evaluated in field trials in Kenya and genotyped with mid-density single nucleotide polymorphism (SNP) markers. Genotypic (G), environmental (E) and G×E variations were found to be significant for all grain quality traits. Broad sense heritabilities exhibited substantial variation (0.18-0.68). Linkage mapping identified multiple quantitative trait loci (QTLs) for the studied grain quality traits: 13, 7, 33, 8 and 2 QTLs for oil content, protein content, starch content, grain texture and kernel weight, respectively. The co-localization of QTLs identified in our research suggests the presence of shared genetic factors or pleiotropic effects, implying that specific genomic regions influence the expression of multiple grain quality traits simultaneously. Genomic prediction accuracies were moderate to high for the studied traits. Our findings highlight the polygenic nature of grain quality traits and demonstrate the potential of genomic selection to enhance genetic gains in maize breeding. Furthermore, the identified genomic regions and single nucleotide polymorphism markers can serve as the groundwork for investigating candidate genes that regulate grain quality traits in tropical maize. This, in turn, can facilitate the implementation of marker-assisted selection (MAS) in breeding programs focused on improving grain nutrient levels.
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Affiliation(s)
- Noel Ndlovu
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Rajashekar M. Kachapur
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
- University of Agricultural Sciences, Dharwad, Karnataka, India
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Veronica Ogugo
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Charles Spillane
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | - Peter C. McKeown
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | | | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
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6
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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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Gentzel IN, Paul P, Wang GL, Ohlson EW. Effects of Maize Chlorotic Mottle Virus and Potyvirus Resistance on Maize Lethal Necrosis Disease. PHYTOPATHOLOGY 2024; 114:484-495. [PMID: 38408034 DOI: 10.1094/phyto-05-23-0171-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Maize lethal necrosis (MLN) is a viral disease caused by host co-infection by maize chlorotic mottle virus (MCMV) and a potyvirus, such as sugarcane mosaic virus (SCMV). The disease is most effectively managed by growing MLN-resistant varieties. However, the relative importance of MCMV and potyvirus resistance in managing this synergistic disease is poorly characterized. In this study, we evaluated the effects of SCMV and/or MCMV resistance on disease, virus titers, and synergism and explored expression patterns of known potyvirus resistance genes TrxH and ABP1. MLN disease was significantly lower in both the MCMV-resistant and SCMV-resistant inbred lines compared with the susceptible control Oh28. Prior to 14 days postinoculation (dpi), MCMV titers in resistant lines N211 and KS23-6 were more than 100,000-fold lower than found in the susceptible Oh28. However, despite no visible symptoms, titer differences between MCMV-resistant and -susceptible lines were negligible by 14 dpi. In contrast, systemic SCMV titers in the potyvirus-resistant line, Pa405, ranged from 130,000-fold to 2 million-fold lower than susceptible Oh28 as disease progressed. Initial TrxH expression was up to 49,000-fold lower in Oh28 compared with other genotypes, whereas expression of ABP1 was up to 4.5-fold lower. Measures of virus synergy indicate that whereas MCMV resistance is effective in early infection, strong potyvirus resistance is critical for reducing synergist effects of co-infection on MCMV titer. These results emphasize the importance of both potyvirus resistance and MCMV resistance in an effective breeding program for MLN management.
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Affiliation(s)
- Irene N Gentzel
- Department of Plant Pathology, The Ohio State University, Wooster, OH 44691
| | - Pierce Paul
- Department of Plant Pathology, The Ohio State University, Wooster, OH 44691
| | - Guo-Liang Wang
- Department of Plant Pathology, The Ohio State University, Columbus, OH 43210
| | - Erik W Ohlson
- Corn, Soybean, and Wheat Quality Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Wooster, OH 44691
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Ayesiga SB, Rubaihayo P, Oloka BM, Dramadri IO, Sserumaga JP. Genome-wide association study and pathway analysis to decipher loci associated with Fusarium ear rot resistance in tropical maize germplasm. GENETIC RESOURCES AND CROP EVOLUTION 2023; 71:2435-2448. [PMID: 39026943 PMCID: PMC11252232 DOI: 10.1007/s10722-023-01793-4] [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: 08/04/2023] [Accepted: 10/25/2023] [Indexed: 07/20/2024]
Abstract
Breeding for host resistance is the most efficient and environmentally safe method to curb the spread of fusarium ear rot (FER). However, conventional breeding for resistance to FER is hampered by the complex polygenic nature of this trait, which is highly influenced by environmental conditions. This study aimed to identify genomic regions, single nucleotide polymorphisms (SNPs), and putative candidate genes associated with FER resistance as well as candidate metabolic pathways and pathway genes involved in it. A panel of 151 tropical inbred maize lines were used to assess the genetic architecture of FER resistance over two seasons. During the study period, seven SNPs associated with FER resistance were identified on chromosomes 1, 2, 4, 5, and 9, accounting for 4-11% of the phenotypic variance. These significant markers were annotated into four genes. Seven significant metabolic pathways involved in FER resistance were identified using the Pathway Association Study Tool, the most significant being the superpathway of the glyoxylate cycle. Overall, this study confirmed that resistance to FER is indeed a complex mechanism controlled by several small to medium-effect loci. Our findings may contribute to fast-tracking the efforts to develop disease-resistant maize lines through marker-assisted selection. Supplementary Information The online version contains supplementary material available at 10.1007/s10722-023-01793-4.
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Affiliation(s)
- Stella Bigirwa Ayesiga
- Department of Agricultural Production, College of Agriculture and Environmental Sciences, Makerere University, P. O. Box 7062, Kampala, Uganda
- National Livestock Resources Research Institute, National Agricultural Research Organization, PO Box 5704, Kampala, Uganda
| | - Patrick Rubaihayo
- Department of Agricultural Production, College of Agriculture and Environmental Sciences, Makerere University, P. O. Box 7062, Kampala, Uganda
| | - Bonny Michael Oloka
- Department of Horticultural Sciences, North Carolina State University, Raleigh, NC USA
| | - Isaac Ozinga Dramadri
- Department of Agricultural Production, College of Agriculture and Environmental Sciences, Makerere University, P. O. Box 7062, Kampala, Uganda
| | - Julius Pyton Sserumaga
- National Livestock Resources Research Institute, National Agricultural Research Organization, PO Box 5704, Kampala, Uganda
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Ma P, Liu E, Zhang Z, Li T, Zhou Z, Yao W, Chen J, Wu J, Xu Y, Zhang H. Genetic variation in ZmWAX2 confers maize resistance to Fusarium verticillioides. PLANT BIOTECHNOLOGY JOURNAL 2023; 21:1812-1826. [PMID: 37293701 PMCID: PMC10440989 DOI: 10.1111/pbi.14093] [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: 01/20/2023] [Revised: 03/16/2023] [Accepted: 05/19/2023] [Indexed: 06/10/2023]
Abstract
Fusarium verticillioides (F. verticillioides) is a widely distributed phytopathogen that incites multiple destructive diseases in maize, posing a grave threat to corn yields and quality worldwide. However, there are few reports of resistance genes to F. verticillioides. Here, we reveal that a combination of two single nucleotide polymorphisms (SNPs) corresponding to ZmWAX2 gene associates with quantitative resistance variations to F. verticillioides in maize through a genome-wide association study. A lack of ZmWAX2 compromises maize resistance to F. verticillioides-caused seed rot, seedling blight and stalk rot by reducing cuticular wax deposition, while the transgenic plants overexpressing ZmWAX2 show significantly increased immunity to F. verticillioides. A natural occurrence of two 7-bp deletions within the promoter increases ZmWAX2 transcription, thus enhancing maize resistance to F. verticillioides. Upon Fusarium stalk rot, ZmWAX2 greatly promotes the yield and grain quality of maize. Our studies demonstrate that ZmWAX2 confers multiple disease resistances caused by F. verticillioides and can serve as an important gene target for the development of F. verticillioides-resistant maize varieties.
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Affiliation(s)
- Peipei Ma
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
- College of Agronomy, Synergetic Innovation Center of Henan Grain Crops and National Key Laboratory of Wheat and Maize Crop ScienceHenan Agricultural UniversityZhengzhouChina
| | - Enpeng Liu
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
| | - Zhirui Zhang
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
| | - Tao Li
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
| | - Zijian Zhou
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
| | - Wen Yao
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
| | - Jiafa Chen
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
| | - Jianyu Wu
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
- College of Agronomy, Synergetic Innovation Center of Henan Grain Crops and National Key Laboratory of Wheat and Maize Crop ScienceHenan Agricultural UniversityZhengzhouChina
| | - Yufang Xu
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
| | - Huiyong Zhang
- College of Life SciencesHenan Agricultural UniversityZhengzhouChina
- College of Agronomy, Synergetic Innovation Center of Henan Grain Crops and National Key Laboratory of Wheat and Maize Crop ScienceHenan Agricultural UniversityZhengzhouChina
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10
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Petroli CD, Subbarao GV, Burgueño JA, Yoshihashi T, Li H, Franco Duran J, Pixley KV. Genetic variation among elite inbred lines suggests potential to breed for BNI-capacity in maize. Sci Rep 2023; 13:13422. [PMID: 37591891 PMCID: PMC10435450 DOI: 10.1038/s41598-023-39720-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 07/29/2023] [Indexed: 08/19/2023] Open
Abstract
Biological nitrification inhibition (BNI) is a plant function where root systems release antibiotic compounds (BNIs) specifically aimed at suppressing nitrifiers to limit soil-nitrate formation in the root zone. Little is known about BNI-activity in maize (Zea mays L.), the most important food, feed, and energy crop. Two categories of BNIs are released from maize roots; hydrophobic and hydrophilic BNIs, that determine BNI-capacity in root systems. Zeanone is a recently discovered hydrophobic compound with BNI-activity, released from maize roots. The objectives of this study were to understand/quantify the relationship between zeanone activity and hydrophobic BNI-capacity. We assessed genetic variability among 250 CIMMYT maize lines (CMLs) characterized for hydrophobic BNI-capacity and zeanone activity, towards developing genetic markers linked to this trait in maize. CMLs with high BNI-capacity and ability to release zeanone from roots were identified. GWAS was performed using 27,085 SNPs (with unique positions on the B73v.4 reference genome, and false discovery rate = 10), and phenotypic information for BNI-capacity and zeanone production from root systems. Eighteen significant markers were identified; three associated with specific BNI-activity (SBNI), four with BNI-activity per plant (BNIPP), another ten were common between SBNI and BNIPP, and one with zeanone release. Further, 30 annotated genes were associated with the significant SNPs; most of these genes are involved in pathways of "biological process", and one (AMT5) in ammonium regulation in maize roots. Although the inbred lines in this study were not developed for BNI-traits, the identification of markers associated with BNI-capacity suggests the possibility of using these genomic tools in marker-assisted selection to improve hydrophobic BNI-capacity in maize.
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Affiliation(s)
- César D Petroli
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Km. 45, El Batán, Texcoco, C.P. 56237, Mexico.
| | - Guntur V Subbarao
- Japan International Research Center for Agricultural Science, 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - Juan A Burgueño
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Km. 45, El Batán, Texcoco, C.P. 56237, Mexico
| | - Tadashi Yoshihashi
- Japan International Research Center for Agricultural Science, 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - Huihui Li
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Km. 45, El Batán, Texcoco, C.P. 56237, Mexico
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), No 12 Zhongguancun South Street, Beijing, 10081, China
| | - Jorge Franco Duran
- Departamento de Biometría y Estadística, Facultad de Agronomía, Universidad de la República, Ruta 3, Km 363, C.P. 60000, Paysandú, Uruguay
| | - Kevin V Pixley
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Km. 45, El Batán, Texcoco, C.P. 56237, Mexico
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11
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Achola E, Wasswa P, Fonceka D, Clevenger JP, Bajaj P, Ozias-Akins P, Rami JF, Deom CM, Hoisington DA, Edema R, Odeny DA, Okello DK. Genome-wide association studies reveal novel loci for resistance to groundnut rosette disease in the African core groundnut collection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:35. [PMID: 36897398 PMCID: PMC10006280 DOI: 10.1007/s00122-023-04259-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/21/2022] [Indexed: 06/18/2023]
Abstract
We identified markers associated with GRD resistance after screening an Africa-wide core collection across three seasons in Uganda Groundnut is cultivated in several African countries where it is a major source of food, feed and income. One of the major constraints to groundnut production in Africa is groundnut rosette disease (GRD), which is caused by a complex of three agents: groundnut rosette assistor luteovirus, groundnut rosette umbravirus and its satellite RNA. Despite several years of breeding for GRD resistance, the genetics of the disease is not fully understood. The objective of the current study was to use the African core collection to establish the level of genetic variation in their response to GRD, and to map genomic regions responsible for the observed resistance. The African groundnut core genotypes were screened across two GRD hotspot locations in Uganda (Nakabango and Serere) for 3 seasons. The Area Under Disease Progress Curve combined with 7523 high quality SNPs were analyzed to establish marker-trait associations (MTAs). Genome-Wide Association Studies based on Enriched Compressed Mixed Linear Model detected 32 MTAs at Nakabango: 21 on chromosome A04, 10 on B04 and 1 on B08. Two of the significant markers were localised on the exons of a putative TIR-NBS-LRR disease resistance gene on chromosome A04. Our results suggest the likely involvement of major genes in the resistance to GRD but will need to be further validated with more comprehensive phenotypic and genotypic datasets. The markers identified in the current study will be developed into routine assays and validated for future genomics-assisted selection for GRD resistance in groundnut.
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Affiliation(s)
- Esther Achola
- Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda
| | - Peter Wasswa
- Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda
| | - Daniel Fonceka
- Regional Study Center for the Improvement of Drought Adaptation, Senegalese Institute for Agricultural Research, BP 3320, Thiès, Senegal
- UMR AGAP, CIRAD, 34398, Montpellier, France
- UMR AGAP, CIRAD, BP 3320, Thies, Senegal
| | | | - Prasad Bajaj
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Telangana, 502324, India
| | - Peggy Ozias-Akins
- Center for Applied Genetic Technologies, University of Georgia, Tifton, GA, 31793, USA
| | - Jean-François Rami
- UMR AGAP, CIRAD, 34398, Montpellier, France
- UMR AGAP, CIRAD, BP 3320, Thies, Senegal
- CIRAD, INRAE, AGAP, Univ Montpellier, Institut Agro, 34398, Montpellier, France
| | - Carl Michael Deom
- Department of Pathology, The University of Georgia, Athens, GA, 30602, USA
| | - David A Hoisington
- Feed the Future Innovation Lab for Peanut, University of Georgia, Athens, GA, 30602, USA
| | - Richard Edema
- Makerere University Regional Center for Crop Improvement Kampala, P.O. Box 7062, Kampala, Uganda
| | - Damaris Achieng Odeny
- International Crops Research Institute for the Semi-Arid Tropics, PO Box, Nairobi, 39063-00623, Kenya.
| | - David Kalule Okello
- National Semi-Arid Resources Research Institute-Serere, P.O. Box 56, Kampala, Uganda.
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12
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Wang C, Li H, Long Y, Dong Z, Wang J, Liu C, Wei X, Wan X. A Systemic Investigation of Genetic Architecture and Gene Resources Controlling Kernel Size-Related Traits in Maize. Int J Mol Sci 2023; 24:1025. [PMID: 36674545 PMCID: PMC9865405 DOI: 10.3390/ijms24021025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/31/2022] [Accepted: 01/04/2023] [Indexed: 01/07/2023] Open
Abstract
Grain yield is the most critical and complex quantitative trait in maize. Kernel length (KL), kernel width (KW), kernel thickness (KT) and hundred-kernel weight (HKW) associated with kernel size are essential components of yield-related traits in maize. With the extensive use of quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) analyses, thousands of QTLs and quantitative trait nucleotides (QTNs) have been discovered for controlling these traits. However, only some of them have been cloned and successfully utilized in breeding programs. In this study, we exhaustively collected reported genes, QTLs and QTNs associated with the four traits, performed cluster identification of QTLs and QTNs, then combined QTL and QTN clusters to detect consensus hotspot regions. In total, 31 hotspots were identified for kernel size-related traits. Their candidate genes were predicted to be related to well-known pathways regulating the kernel developmental process. The identified hotspots can be further explored for fine mapping and candidate gene validation. Finally, we provided a strategy for high yield and quality maize. This study will not only facilitate causal genes cloning, but also guide the breeding practice for maize.
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Affiliation(s)
- Cheng Wang
- Research Center of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, 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, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Huangai Li
- Research Center of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, 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, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Yan Long
- Research Center of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, 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, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Zhenying Dong
- Research Center of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, 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, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Jianhui Wang
- Research Center of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, 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, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Chang Liu
- Research Center of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, 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, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Xun Wei
- Research Center of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, 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, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Xiangyuan Wan
- Research Center of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, 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, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
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13
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Johnmark O, Indieka S, Liu G, Gowda M, Suresh LM, Zhang W, Gao X. Fighting Death for Living: Recent Advances in Molecular and Genetic Mechanisms Underlying Maize Lethal Necrosis Disease Resistance. Viruses 2022; 14:v14122765. [PMID: 36560769 PMCID: PMC9784999 DOI: 10.3390/v14122765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Maize Lethal Necrosis (MLN) disease, caused by a synergistic co-infection of maize chlorotic mottle virus (MCMV) and any member of the Potyviridae family, was first reported in EasternAfrica (EA) a decade ago. It is one of the most devastating threats to maize production in these regions since it can lead up to 100% crop loss. Conventional counter-measures have yielded some success; however, they are becoming less effective in controlling MLN. In EA, the focus has been on the screening and identification of resistant germplasm, dissecting genetic and the molecular basis of the disease resistance, as well as employing modern breeding technologies to develop novel varieties with improved resistance. CIMMYT and scientists from NARS partner organizations have made tremendous progresses in the screening and identification of the MLN-resistant germplasm. Quantitative trait loci mapping and genome-wide association studies using diverse, yet large, populations and lines were conducted. These remarkable efforts have yielded notable outcomes, such as the successful identification of elite resistant donor lines KS23-5 and KS23-6 and their use in breeding, as well as the identification of multiple MLN-tolerance promising loci clustering on Chr 3 and Chr 6. Furthermore, with marker-assisted selection and genomic selection, the above-identified germplasms and loci have been incorporated into elite maize lines in a maize breeding program, thus generating novel varieties with improved MLN resistance levels. However, the underlying molecular mechanisms for MLN resistance require further elucidation. Due to third generation sequencing technologies as well functional genomics tools such as genome-editing and DH technology, it is expected that the breeding time for MLN resistance in farmer-preferred maize varieties in EA will be efficient and shortened.
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Affiliation(s)
- Onyino Johnmark
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- China and Kenya Belt and Road Joint Laboratory on Crop Molecular Biology, Nanjing Agricultural University, Nanjing 210095, China
- Collaborative Innovation Center for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Biochemistry and Molecular Biology Department, Egerton University, Njoro P.O. Box 536-20115, Kenya
| | - Stephen Indieka
- Biochemistry and Molecular Biology Department, Egerton University, Njoro P.O. Box 536-20115, Kenya
| | - Gaoqiong Liu
- Crops Soils and Horticulture Department, Egerton University, Njoro P.O. Box 536-20115, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi P.O. Box 1041-00621, Kenya
| | - L. M. Suresh
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi P.O. Box 1041-00621, Kenya
| | - Wenli Zhang
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- China and Kenya Belt and Road Joint Laboratory on Crop Molecular Biology, Nanjing Agricultural University, Nanjing 210095, China
- Collaborative Innovation Center for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiquan Gao
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- China and Kenya Belt and Road Joint Laboratory on Crop Molecular Biology, Nanjing Agricultural University, Nanjing 210095, China
- Collaborative Innovation Center for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Correspondence:
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14
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Ndlovu N, Spillane C, McKeown PC, Cairns JE, Das B, Gowda M. Genome-wide association studies of grain yield and quality traits under optimum and low-nitrogen stress in tropical maize (Zea mays L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4351-4370. [PMID: 36131140 PMCID: PMC9734216 DOI: 10.1007/s00122-022-04224-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 09/14/2022] [Indexed: 06/15/2023]
Abstract
KEY MESSAGE Genome-wide association study (GWAS) demonstrated that multiple genomic regions influence grain quality traits under nitrogen-starved soils. Using genomic prediction, genetic gains can be improved through selection for grain quality traits. Soils in sub-Saharan Africa are nitrogen deficient due to low fertilizer use and inadequate soil fertility management practices. This has resulted in a significant yield gap for the major staple crop maize, which is undermining nutritional security and livelihood sustainability across the region. Dissecting the genetic basis of grain protein, starch and oil content under nitrogen-starved soils can increase our understanding of the governing genetic systems and improve the efficacy of future breeding schemes. An association mapping panel of 410 inbred lines and four bi-parental populations were evaluated in field trials in Kenya and South Africa under optimum and low nitrogen conditions and genotyped with 259,798 SNP markers. Genetic correlations demonstrated that these populations may be utilized to select higher performing lines under low nitrogen stress. Furthermore, genotypic, environmental and GxE variations in nitrogen-starved soils were found to be significant for oil content. Broad sense heritabilities ranged from moderate (0.18) to high (0.86). Under low nitrogen stress, GWAS identified 42 SNPs linked to grain quality traits. These significant SNPs were associated with 51 putative candidate genes. Linkage mapping identified multiple QTLs for the grain quality traits. Under low nitrogen conditions, average prediction accuracies across the studied genotypes were higher for oil content (0.78) and lower for grain yield (0.08). Our findings indicate that grain quality traits are polygenic and that using genomic selection in maize breeding can improve genetic gain. Furthermore, the identified genomic regions and SNP markers can be utilized for selection to improve maize grain quality traits.
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Affiliation(s)
- Noel Ndlovu
- Plant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland
| | - Charles Spillane
- Plant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland.
| | - Peter C McKeown
- Plant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland
| | - Jill E Cairns
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box MP163, Harare, Zimbabwe
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041-00621, Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041-00621, Nairobi, Kenya.
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15
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Biswal AK, Alakonya AE, Mottaleb KA, Hearne SJ, Sonder K, Molnar TL, Jones AM, Pixley KV, Prasanna BM. Maize Lethal Necrosis disease: review of molecular and genetic resistance mechanisms, socio-economic impacts, and mitigation strategies in sub-Saharan Africa. BMC PLANT BIOLOGY 2022; 22:542. [PMID: 36418954 PMCID: PMC9686106 DOI: 10.1186/s12870-022-03932-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Maize lethal necrosis (MLN) disease is a significant constraint for maize producers in sub-Saharan Africa (SSA). The disease decimates the maize crop, in some cases, causing total crop failure with far-reaching impacts on regional food security. RESULTS In this review, we analyze the impacts of MLN in Africa, finding that resource-poor farmers and consumers are the most vulnerable populations. We examine the molecular mechanism of MLN virus transmission, role of vectors and host plant resistance identifying a range of potential opportunities for genetic and phytosanitary interventions to control MLN. We discuss the likely exacerbating effects of climate change on the MLN menace and describe a sobering example of negative genetic association between tolerance to heat/drought and susceptibility to viral infection. We also review role of microRNAs in host plant response to MLN causing viruses as well as heat/drought stress that can be carefully engineered to develop resistant varieties using novel molecular techniques. CONCLUSIONS With the dual drivers of increased crop loss due to MLN and increased demand of maize for food, the development and deployment of simple and safe technologies, like resistant cultivars developed through accelerated breeding or emerging gene editing technologies, will have substantial positive impact on livelihoods in the region. We have summarized the available genetic resources and identified a few large-effect QTLs that can be further exploited to accelerate conversion of existing farmer-preferred varieties into resistant cultivars.
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Affiliation(s)
- Akshaya Kumar Biswal
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico-Veracruz, El Batan, Texcoco, C.P. 56237, Mexico.
| | - Amos Emitati Alakonya
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico-Veracruz, El Batan, Texcoco, C.P. 56237, Mexico
| | - Khondokar Abdul Mottaleb
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico-Veracruz, El Batan, Texcoco, C.P. 56237, Mexico
| | - Sarah J Hearne
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico-Veracruz, El Batan, Texcoco, C.P. 56237, Mexico
| | - Kai Sonder
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico-Veracruz, El Batan, Texcoco, C.P. 56237, Mexico
| | | | - Alan M Jones
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kevin Vail Pixley
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico-Veracruz, El Batan, Texcoco, C.P. 56237, Mexico
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16
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Shi J, Wang Y, Wang C, Wang L, Zeng W, Han G, Qiu C, Wang T, Tao Z, Wang K, Huang S, Yu S, Wang W, Chen H, Chen C, He C, Wang H, Zhu P, Hu Y, Zhang X, Xie C, Lu X, Li P. Linkage mapping combined with GWAS revealed the genetic structural relationship and candidate genes of maize flowering time-related traits. BMC PLANT BIOLOGY 2022; 22:328. [PMID: 35799118 PMCID: PMC9264602 DOI: 10.1186/s12870-022-03711-9] [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: 03/30/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize (Zea mays) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time. RESULTS Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time. CONCLUSIONS In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize.
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Affiliation(s)
- Jian Shi
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Yunhe Wang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Chuanhong Wang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Lei Wang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Wei Zeng
- School of Agronomy, Anhui Agricultural University, Hefei, 230036, China
| | - Guomin Han
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Chunhong Qiu
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Tengyue Wang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Zhen Tao
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Kaiji Wang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Shijie Huang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Shuaishuai Yu
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Wanyi Wang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Hongyi Chen
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Chen Chen
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Chen He
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Hui Wang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Peiling Zhu
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Yuanyuan Hu
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Xin Zhang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Chuanxiao Xie
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, National Key Facility for Crop Gene Resources and Genetic Improvement, Beijing, 100081, China
| | - Xiaoduo Lu
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China.
| | - Peijin Li
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China.
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Sadessa K, Beyene Y, Ifie BE, Suresh LM, Olsen MS, Ogugo V, Wegary D, Tongoona P, Danquah E, Offei SK, Prasanna BM, Gowda M. Identification of Genomic Regions Associated with Agronomic and Disease Resistance Traits in a Large Set of Multiple DH Populations. Genes (Basel) 2022; 13:genes13020351. [PMID: 35205395 PMCID: PMC8872035 DOI: 10.3390/genes13020351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/08/2022] [Accepted: 02/08/2022] [Indexed: 11/17/2022] Open
Abstract
Breeding maize lines with the improved level of desired agronomic traits under optimum and drought conditions as well as increased levels of resistance to several diseases such as maize lethal necrosis (MLN) is one of the most sustainable approaches for the sub-Saharan African region. In this study, 879 doubled haploid (DH) lines derived from 26 biparental populations were evaluated under artificial inoculation of MLN, as well as under well-watered (WW) and water-stressed (WS) conditions for grain yield and other agronomic traits. All DH lines were used for analyses of genotypic variability, association studies, and genomic predictions for the grain yield and other yield-related traits. Genome-wide association study (GWAS) using a mixed linear FarmCPU model identified SNPs associated with the studied traits i.e., about seven and eight SNPs for the grain yield; 16 and 12 for anthesis date; seven and eight for anthesis silking interval; 14 and 5 for both ear and plant height; and 15 and 5 for moisture under both WW and WS environments, respectively. Similarly, about 13 and 11 SNPs associated with gray leaf spot and turcicum leaf blight were identified. Eleven SNPs associated with senescence under WS management that had depicted drought-stress-tolerant QTLs were identified. Under MLN artificial inoculation, a total of 12 and 10 SNPs associated with MLN disease severity and AUDPC traits, respectively, were identified. Genomic prediction under WW, WS, and MLN disease artificial inoculation revealed moderate-to-high prediction accuracy. The findings of this study provide useful information on understanding the genetic basis for the MLN resistance, grain yield, and other agronomic traits under MLN artificial inoculation, WW, and WS conditions. Therefore, the obtained information can be used for further validation and developing functional molecular markers for marker-assisted selection and for implementing genomic prediction to develop superior elite lines.
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Affiliation(s)
- Kassahun Sadessa
- Ethiopian Institute of Agricultural Research (EIAR), Ambo Agricultural Research Center, Ambo P.O. Box 37, West Shoa, Ethiopia;
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
- International Maize and Wheat Improvement Center (CIMMYT), 12.5 KM Peg, Harare P.O. Box MP163, Zimbabwe;
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Beatrice E. Ifie
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - L. M. Suresh
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Michael S. Olsen
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Veronica Ogugo
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Dagne Wegary
- International Maize and Wheat Improvement Center (CIMMYT), 12.5 KM Peg, Harare P.O. Box MP163, Zimbabwe;
| | - Pangirayi Tongoona
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - Eric Danquah
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - Samuel Kwame Offei
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - Boddupalli M. Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
- Correspondence: ; Tel.: +254-727019454
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18
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Genomic Analysis of Resistance to Fall Armyworm (Spodoptera frugiperda) in CIMMYT Maize Lines. Genes (Basel) 2022; 13:genes13020251. [PMID: 35205295 PMCID: PMC8872412 DOI: 10.3390/genes13020251] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/19/2022] [Accepted: 01/25/2022] [Indexed: 01/08/2023] Open
Abstract
The recent invasion, rapid spread, and widescale destruction of the maize crop by the fall armyworm (FAW; Spodoptera frugiperda (J.E. Smith)) is likely to worsen the food insecurity situation in Africa. In the present study, a set of 424 maize lines were screened for their responses to FAW under artificial infestation to dissect the genetic basis of resistance. All lines were evaluated for two seasons under screen houses and genotyped with the DArTseq platform. Foliar damage was rated on a scale of 1 (highly resistant) to 9 (highly susceptible) and scored at 7, 14, and 21 days after artificial infestation. Analyses of variance revealed significant genotypic and genotype by environment interaction variances for all traits. Heritability estimates for leaf damage scores were moderately high and ranged from 0.38 to 0.58. Grain yield was negatively correlated with a high magnitude to foliar damage scores, ear rot, and ear damage traits. The genome-wide association study (GWAS) revealed 56 significant marker–trait associations and the predicted functions of the putative candidate genes varied from a defense response to several genes of unknown function. Overall, the study revealed that native genetic resistance to FAW is quantitative in nature and is controlled by many loci with minor effects.
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Bai Y, Zhao X, Yao X, Yao Y, An L, Li X, Wang Y, Gao X, Jia Y, Guan L, Li M, Wu K, Wang Z. Genome wide association study of plant height and tiller number in hulless barley. PLoS One 2021; 16:e0260723. [PMID: 34855842 PMCID: PMC8639095 DOI: 10.1371/journal.pone.0260723] [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: 07/15/2021] [Accepted: 11/15/2021] [Indexed: 11/18/2022] Open
Abstract
Hulless barley (Hordeum vulgare L. var. nudum), also called naked barley, is a unique variety of cultivated barley. The genome-wide specific length amplified fragment sequencing (SLAF-seq) method is a rapid deep sequencing technology that is used for the selection and identification of genetic loci or markers. In this study, we collected 300 hulless barley accessions and used the SLAF-seq method to identify candidate genes involved in plant height (PH) and tiller number (TN). We obtained a total of 1407 M paired-end reads, and 228,227 SLAF tags were developed. After filtering using an integrity threshold of >0.8 and a minor allele frequency of >0.05, 14,504,892 single-nucleotide polymorphisms (SNP) loci were screened out. The remaining SNPs were used for the construction of a neighbour-joining phylogenetic tree, and the three subcluster members showed no obvious differentiation among regional varieties. We used a genome wide association study approach to identify 1006 and 113 SNPs associated with TN and PH, respectively. Based on best linear unbiased predictors (BLUP), 41 and 29 SNPs associated with TN and PH, respectively. Thus, several of genes, including Hd3a and CKX5, may be useful candidates for the future genetic breeding of hulless barley. Taken together, our results provide insight into the molecular mechanisms controlling barley architecture, which is important for breeding and yield.
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Affiliation(s)
- Yixiong Bai
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, China
- Qinghai University, Qinghai Academy of Agricultural and Forestry Sciences, Qinghai Key Laboratory of Hulless Barley Genetics and Breeding, Xining, Qinghai Province, China
| | - Xiaohong Zhao
- Qinghai University, Qinghai Academy of Agricultural and Forestry Sciences, Qinghai Key Laboratory of Hulless Barley Genetics and Breeding, Xining, Qinghai Province, China
- Good Agricultural Practices Research Center of Traditional, Chongqing Institute of Medicinal Plant Cultivation, Chongqing, China
| | - Xiaohua Yao
- Qinghai University, Qinghai Academy of Agricultural and Forestry Sciences, Qinghai Key Laboratory of Hulless Barley Genetics and Breeding, Xining, Qinghai Province, China
| | - Youhua Yao
- Qinghai University, Qinghai Academy of Agricultural and Forestry Sciences, Qinghai Key Laboratory of Hulless Barley Genetics and Breeding, Xining, Qinghai Province, China
| | - Likun An
- Qinghai University, Qinghai Academy of Agricultural and Forestry Sciences, Qinghai Key Laboratory of Hulless Barley Genetics and Breeding, Xining, Qinghai Province, China
| | - Xin Li
- Qinghai University, Qinghai Academy of Agricultural and Forestry Sciences, Qinghai Key Laboratory of Hulless Barley Genetics and Breeding, Xining, Qinghai Province, China
| | - Yong Wang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, China
| | - Xin Gao
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, China
| | - Yatao Jia
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, China
| | - Lulu Guan
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, China
| | - Man Li
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, China
| | - Kunlun Wu
- Qinghai University, Qinghai Academy of Agricultural and Forestry Sciences, Qinghai Key Laboratory of Hulless Barley Genetics and Breeding, Xining, Qinghai Province, China
- * E-mail: (KW); (ZW)
| | - Zhonghua Wang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, China
- * E-mail: (KW); (ZW)
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20
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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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21
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Cao S, Song J, Yuan Y, Zhang A, Ren J, Liu Y, Qu J, Hu G, Zhang J, Wang C, Cao J, Olsen M, Prasanna BM, San Vicente F, Zhang X. Genomic Prediction of Resistance to Tar Spot Complex of Maize in Multiple Populations Using Genotyping-by-Sequencing SNPs. FRONTIERS IN PLANT SCIENCE 2021; 12:672525. [PMID: 34335648 PMCID: PMC8322742 DOI: 10.3389/fpls.2021.672525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 06/21/2021] [Indexed: 06/13/2023]
Abstract
Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.
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Affiliation(s)
- Shiliang Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Junqiao Song
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- Maize Research Institute, Anyang Academy of Agricultural Sciences, Anyang, China
| | - Yibing Yuan
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Ao Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Jiaojiao Ren
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Agronomy, Xinjiang Agricultural University, Urumqi, China
| | - Yubo Liu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Jingtao Qu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guanghui Hu
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Jianguo Zhang
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Chunping Wang
- Maize Research Institute, Anyang Academy of Agricultural Sciences, Anyang, China
| | - Jingsheng Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
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22
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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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23
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Zhu M, Tong L, Xu M, Zhong T. Genetic dissection of maize disease resistance and its applications in molecular breeding. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:32. [PMID: 37309327 PMCID: PMC10236108 DOI: 10.1007/s11032-021-01219-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 02/25/2021] [Indexed: 06/14/2023]
Abstract
Disease resistance is essential for reliable maize production. In a long-term tug-of-war between maize and its pathogenic microbes, naturally occurring resistance genes gradually accumulate and play a key role in protecting maize from various destructive diseases. Recently, significant progress has been made in deciphering the genetic basis of disease resistance in maize. Enhancing disease resistance can now be explored at the molecular level, from marker-assisted selection to genomic selection, transgenesis technique, and genome editing. In view of the continuing accumulation of cloned resistance genes and in-depth understanding of their resistance mechanisms, coupled with rapid progress of biotechnology, it is expected that the large-scale commercial application of molecular breeding of resistant maize varieties will soon become a reality.
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Affiliation(s)
- Mang Zhu
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
| | - Lixiu Tong
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
| | - Mingliang Xu
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
| | - Tao Zhong
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
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24
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Gowda M, Makumbi D, Das B, Nyaga C, Kosgei T, Crossa J, Beyene Y, Montesinos-López OA, Olsen MS, Prasanna BM. Genetic dissection of Striga hermonthica (Del.) Benth. resistance via genome-wide association and genomic prediction in tropical maize germplasm. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:941-958. [PMID: 33388884 PMCID: PMC7925482 DOI: 10.1007/s00122-020-03744-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 12/02/2020] [Indexed: 06/01/2023]
Abstract
KEY MESSAGE Genome-wide association revealed that resistance to Striga hermonthica is influenced by multiple genomic regions with moderate effects. It is possible to increase genetic gains from selection for Striga resistance using genomic prediction. Striga hermonthica (Del.) Benth., commonly known as the purple witchweed or giant witchweed, is a serious problem for maize-dependent smallholder farmers in sub-Saharan Africa. Breeding for Striga resistance in maize is complicated due to limited genetic variation, complexity of resistance and challenges with phenotyping. This study was conducted to (i) evaluate a set of diverse tropical maize lines for their responses to Striga under artificial infestation in three environments in Kenya; (ii) detect quantitative trait loci associated with Striga resistance through genome-wide association study (GWAS); and (iii) evaluate the effectiveness of genomic prediction (GP) of Striga-related traits. An association mapping panel of 380 inbred lines was evaluated in three environments under artificial Striga infestation in replicated trials and genotyped with 278,810 single-nucleotide polymorphism (SNP) markers. Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance. Heritability estimates were moderate (0.42) to high (0.92) for measured traits. GWAS revealed 57 SNPs significantly associated with Striga resistance indicator traits and grain yield (GY) under artificial Striga infestation with low to moderate effect. A set of 32 candidate genes physically near the significant SNPs with roles in plant defense against biotic stresses were identified. GP with different cross-validations revealed that prediction of performance of lines in new environments is better than prediction of performance of new lines for all traits. Predictions across environments revealed high accuracy for all the traits, while inclusion of GWAS-detected SNPs led to slight increase in the accuracy. The item-based collaborative filtering approach that incorporates related traits evaluated in different environments to predict GY and Striga-related traits outperformed GP for Striga resistance indicator traits. The results demonstrated the polygenic nature of resistance to S. hermonthica, and that implementation of GP in Striga resistance breeding could potentially aid in increasing genetic gain for this important trait.
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Affiliation(s)
- Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya.
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Christine Nyaga
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Titus Kosgei
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
- Moi University, P. O. Box 3900-30100, Eldoret, Kenya
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo, Postal 6-641, 06600, Mexico, D.F, Mexico
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | | | - Michael S Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
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Ma P, Zhang X, Luo B, Chen Z, He X, Zhang H, Li B, Liu D, Wu L, Gao S, Gao D, Zhang S, Gao S. Transcriptomic and genome-wide association study reveal long noncoding RNAs responding to nitrogen deficiency in maize. BMC PLANT BIOLOGY 2021; 21:93. [PMID: 33579187 PMCID: PMC7879672 DOI: 10.1186/s12870-021-02847-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 01/21/2021] [Indexed: 05/13/2023]
Abstract
BACKGROUND Long noncoding RNAs (lncRNAs) play important roles in essential biological processes. However, our understanding of lncRNAs as competing endogenous RNAs (ceRNAs) and their responses to nitrogen stress is still limited. RESULTS Here, we surveyed the lncRNAs and miRNAs in maize inbred line P178 leaves and roots at the seedling stage under high-nitrogen (HN) and low-nitrogen (LN) conditions using lncRNA-Seq and small RNA-Seq. A total of 894 differentially expressed lncRNAs and 38 different miRNAs were identified. Co-expression analysis found that two lncRNAs and four lncRNA-targets could competitively combine with ZmmiR159 and ZmmiR164, respectively. To dissect the genetic regulatory by which lncRNAs might enable adaptation to limited nitrogen availability, an association mapping panel containing a high-density single-nucleotide polymorphism (SNP) array (56,110 SNPs) combined with variable LN tolerant-related phenotypes obtained from hydroponics was used for a genome-wide association study (GWAS). By combining GWAS and RNA-Seq, 170 differently expressed lncRNAs within the range of significant markers were screened. Moreover, 40 consistently LN-responsive genes including those involved in glutamine biosynthesis and nitrogen acquisition in root were identified. Transient expression assays in Nicotiana benthamiana demonstrated that LNC_002923 could inhabit ZmmiR159-guided cleavage of Zm00001d015521. CONCLUSIONS These lncRNAs containing trait-associated significant SNPs could consider to be related to root development and nutrient utilization. Taken together, the results of our study can provide new insights into the potential regulatory roles of lncRNAs in response to LN stress, and give valuable information for further screening of candidates as well as the improvement of maize resistance to LN stress.
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Affiliation(s)
- Peng Ma
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Xiao Zhang
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Bowen Luo
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Zhen Chen
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Xuan He
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Haiying Zhang
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Binyang Li
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Dan Liu
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Ling Wu
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Shiqiang Gao
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Duojiang Gao
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Suzhi Zhang
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Shibin Gao
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, 611130, Sichuan, China.
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Badji A, Machida L, Kwemoi DB, Kumi F, Okii D, Mwila N, Agbahoungba S, Ibanda A, Bararyenya A, Nghituwamhata SN, Odong T, Wasswa P, Otim M, Ochwo-Ssemakula M, Talwana H, Asea G, Kyamanywa S, Rubaihayo P. Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils. PLANTS 2020; 10:plants10010029. [PMID: 33374402 PMCID: PMC7823878 DOI: 10.3390/plants10010029] [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: 07/13/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/23/2022]
Abstract
Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.
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Affiliation(s)
- Arfang Badji
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Lewis Machida
- Alliance Bioversity-CIAT, Africa-Office, Kampala P.O. Box 24384, Uganda
| | | | - Frank Kumi
- Department of Crop Science, University of Cape Coast, Cape Coast PMB, Ghana
| | - Dennis Okii
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Natasha Mwila
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | | | - Angele Ibanda
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Astere Bararyenya
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | | | - Thomas Odong
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Peter Wasswa
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Michael Otim
- National Crops Resource Research Institute, Kampala P.O. Box 7084, Uganda
| | | | - Herbert Talwana
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Godfrey Asea
- National Crops Resource Research Institute, Kampala P.O. Box 7084, Uganda
| | - Samuel Kyamanywa
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Patrick Rubaihayo
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
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Kibe M, Nair SK, Das B, Bright JM, Makumbi D, Kinyua J, Suresh LM, Beyene Y, Olsen MS, Prasanna BM, Gowda M. Genetic Dissection of Resistance to Gray Leaf Spot by Combining Genome-Wide Association, Linkage Mapping, and Genomic Prediction in Tropical Maize Germplasm. FRONTIERS IN PLANT SCIENCE 2020; 11:572027. [PMID: 33224163 PMCID: PMC7667048 DOI: 10.3389/fpls.2020.572027] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/29/2020] [Indexed: 05/05/2023]
Abstract
Gray leaf spot (GLS) is one of the major maize foliar diseases in sub-Saharan Africa. Resistance to GLS is controlled by multiple genes with additive effect and is influenced by both genotype and environment. The objectives of the study were to dissect the genetic architecture of GLS resistance through linkage mapping and genome-wide association study (GWAS) and assessing the potential of genomic prediction (GP). We used both biparental populations and an association mapping panel of 410 diverse tropical/subtropical inbred lines that were genotyped using genotype by sequencing. Phenotypic evaluation in two to four environments revealed significant genotypic variation and moderate to high heritability estimates ranging from 0.43 to 0.69. GLS was negatively and significantly correlated with grain yield, anthesis date, and plant height. Linkage mapping in five populations revealed 22 quantitative trait loci (QTLs) for GLS resistance. A QTL on chromosome 7 (qGLS7-105) is a major-effect QTL that explained 28.2% of phenotypic variance. Together, all the detected QTLs explained 10.50, 49.70, 23.67, 18.05, and 28.71% of phenotypic variance in doubled haploid (DH) populations 1, 2, 3, and F3 populations 4 and 5, respectively. Joint linkage association mapping across three DH populations detected 14 QTLs that individually explained 0.10-15.7% of phenotypic variance. GWAS revealed 10 significantly (p < 9.5 × 10-6) associated SNPs distributed on chromosomes 1, 2, 6, 7, and 8, which individually explained 6-8% of phenotypic variance. A set of nine candidate genes co-located or in physical proximity to the significant SNPs with roles in plant defense against pathogens were identified. GP revealed low to moderate prediction correlations of 0.39, 0.37, 0.56, 0.30, 0.29, and 0.38 for within IMAS association panel, DH pop1, DH pop2, DH pop3, F3 pop4, and F3 po5, respectively, and accuracy was increased substantially to 0.84 for prediction across three DH populations. When the diversity panel was used as training set to predict the accuracy of GLS resistance in biparental population, there was 20-50% reduction compared to prediction within populations. Overall, the study revealed that resistance to GLS is quantitative in nature and is controlled by many loci with a few major and many minor effects. The SNPs/QTLs identified by GWAS and linkage mapping can be potential targets in improving GLS resistance in breeding programs, while GP further consolidates the development of high GLS-resistant lines by incorporating most of the major- and minor-effect genes.
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Affiliation(s)
- Maguta Kibe
- International Maize and Wheat Improvement Center, Nairobi, Kenya
- Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
| | - Sudha K. Nair
- International Maize and Wheat Improvement Center, Hyderabad, India
| | - Biswanath Das
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Jumbo M. Bright
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Johnson Kinyua
- Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
| | - L. M. Suresh
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Michael S. Olsen
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | | | - Manje Gowda
- International Maize and Wheat Improvement Center, Nairobi, Kenya
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Combination of Linkage Mapping, GWAS, and GP to Dissect the Genetic Basis of Common Rust Resistance in Tropical Maize Germplasm. Int J Mol Sci 2020; 21:ijms21186518. [PMID: 32899999 PMCID: PMC7555316 DOI: 10.3390/ijms21186518] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/01/2020] [Accepted: 09/04/2020] [Indexed: 12/27/2022] Open
Abstract
Common rust (CR) caused by Puccina sorghi is one of the destructive fungal foliar diseases of maize and has been reported to cause moderate to high yield losses. Providing CR resistant germplasm has the potential to increase yields. To dissect the genetic architecture of CR resistance in maize, association mapping, in conjunction with linkage mapping, joint linkage association mapping (JLAM), and genomic prediction (GP) was conducted on an association-mapping panel and five F3 biparental populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Analysis of variance for the biparental populations and the association panel showed significant genotypic and genotype x environment (GXE) interaction variances except for GXE of Pop4. Heritability (h2) estimates were moderate with 0.37-0.45 for the individual F3 populations, 0.45 across five populations and 0.65 for the association panel. Genome-wide association study (GWAS) analyses revealed 14 significant marker-trait associations which individually explained 6-10% of the total phenotypic variances. Individual population-based linkage analysis revealed 26 QTLs associated with CR resistance and together explained 14-40% of the total phenotypic variances. Linkage mapping revealed seven QTLs in pop1, nine QTL in pop2, four QTL in pop3, five QTL in pop4, and one QTL in pop5, distributed on all chromosomes except chromosome 10. JLAM for the 921 F3 families from five populations detected 18 QTLs distributed in all chromosomes except on chromosome 8. These QTLs individually explained 0.3 to 3.1% and together explained 45% of the total phenotypic variance. Among the 18 QTL detected through JLAM, six QTLs, qCR1-78, qCR1-227, qCR3-172, qCR3-186, qCR4-171, and qCR7-137 were also detected in linkage mapping. GP within population revealed low to moderate correlations with a range from 0.19 to 0.51. Prediction correlation was high with r = 0.78 for combined analysis of the five F3 populations. Prediction of biparental populations by using association panel as training set reveals positive correlations ranging from 0.05 to 0.22, which encourages to develop an independent but related population as a training set which can be used to predict diverse but related populations. The findings of this study provide valuable information on understanding the genetic basis of CR resistance and the obtained information can be used for developing functional molecular markers for marker-assisted selection and for implementing GP to improve CR resistance in tropical maize.
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Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization. Genes (Basel) 2020; 11:genes11060689. [PMID: 32599710 PMCID: PMC7349181 DOI: 10.3390/genes11060689] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 05/30/2020] [Accepted: 06/01/2020] [Indexed: 01/01/2023] Open
Abstract
Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology's single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10-30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.
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30
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Boddupalli P, Suresh LM, Mwatuni F, Beyene Y, Makumbi D, Gowda M, Olsen M, Hodson D, Worku M, Mezzalama M, Molnar T, Dhugga KS, Wangai A, Gichuru L, Angwenyi S, Alemayehu Y, Grønbech Hansen J, Lassen P. Maize lethal necrosis (MLN): Efforts toward containing the spread and impact of a devastating transboundary disease in sub-Saharan Africa. Virus Res 2020; 282:197943. [PMID: 32205142 PMCID: PMC7221342 DOI: 10.1016/j.virusres.2020.197943] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 03/12/2020] [Accepted: 03/19/2020] [Indexed: 11/27/2022]
Abstract
Maize lethal necrosis (MLN), a complex viral disease, emerged as a serious threat to maize production and the livelihoods of smallholders in eastern Africa since 2011, primarily due to the introduction of maize chlorotic mottle virus (MCMV). The International Maize and Wheat Improvement Center (CIMMYT), in close partnership with national and international partners, implemented a multi-disciplinary and multi-institutional strategy to curb the spread of MLN in sub-Saharan Africa, and mitigate the impact of the disease. The strategy revolved around a) intensive germplasm screening and fast-tracked development and deployment of MLN-tolerant/resistant maize hybrids in Africa-adapted genetic backgrounds; b) optimizing the diagnostic protocols for MLN-causing viruses, especially MCMV, and capacity building of relevant public and private sector institutions on MLN diagnostics and management; c) MLN monitoring and surveillance across sub-Saharan Africa in collaboration with national plant protection organizations (NPPOs); d) partnership with the private seed sector for production and exchange of MLN pathogen-free commercial maize seed; and e) awareness creation among relevant stakeholders about MLN management, including engagement with policy makers. The review concludes by highlighting the need to keep continuous vigil against MLN-causing viruses, and preventing any further spread of the disease to the major maize-growing countries that have not yet reported MLN in sub-Saharan Africa.
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Affiliation(s)
- Prasanna Boddupalli
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi, Kenya.
| | - L M Suresh
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi, Kenya
| | - Francis Mwatuni
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi, Kenya
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi, Kenya
| | - Mike Olsen
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi, Kenya
| | - David Hodson
- CIMMYT, Km 45 México-Veracruz, El Batán, 56237, Texcoco, Mexico CDMX, Mexico
| | - Mosisa Worku
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, Nairobi, Kenya
| | - Monica Mezzalama
- CIMMYT, Km 45 México-Veracruz, El Batán, 56237, Texcoco, Mexico CDMX, Mexico
| | - Terence Molnar
- CIMMYT, Km 45 México-Veracruz, El Batán, 56237, Texcoco, Mexico CDMX, Mexico
| | - Kanwarpal S Dhugga
- CIMMYT, Km 45 México-Veracruz, El Batán, 56237, Texcoco, Mexico CDMX, Mexico
| | - Anne Wangai
- Kenya Agricultural and Livestock Research Organization (KALRO), NARL, Waiyaki Way, Nairobi, Kenya
| | - Lilian Gichuru
- Alliance for Green Revolution in Africa (AGRA), West End Towers, 4th Floor Kanjata Road, off Muthangari Drive, Off Waiyaki Way, P.O. Box 66773, Westlands, 00800, Nairobi, Kenya
| | - Samuel Angwenyi
- African Agricultural Technology Foundation (AATF), ILRI Campus, Naivasha Road, Nairobi, Kenya
| | | | - Jens Grønbech Hansen
- Dept. of Agroecology, Aarhus University, Blichers Allé 20, Postboks 50, DK-8830, Tjele, Denmark
| | - Poul Lassen
- Dept. of Agroecology, Aarhus University, Blichers Allé 20, Postboks 50, DK-8830, Tjele, Denmark
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31
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Hybrid Breeding for MLN Resistance: Heterosis, Combining Ability, and Hybrid Prediction. PLANTS 2020; 9:plants9040468. [PMID: 32276322 PMCID: PMC7238107 DOI: 10.3390/plants9040468] [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] [Received: 02/20/2020] [Revised: 03/24/2020] [Accepted: 03/25/2020] [Indexed: 11/18/2022]
Abstract
Prior knowledge on heterosis and quantitative genetic parameters on maize lethal necrosis (MLN) can help the breeders to develop numerous resistant or tolerant hybrids with optimum resources. Our objectives were to (1) estimate the quantitative genetic parameters for MLN disease severity, (2) investigate the efficiency of the prediction of hybrid performance based on parental per se and general combining ability (GCA) effects, and (3) examine the potential of hybrid prediction for MLN resistance or tolerance based on markers. Fifty elite maize inbred lines were selected based on their response to MLN under artificial inoculation. Crosses were made in a half diallel mating design to produce 307 F1 hybrids. All hybrids were evaluated in MLN quarantine facility in Naivasha, Kenya for two seasons under artificial inoculation. All 50 inbreds were genotyped with genotyping-by-sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability was moderate to high. We observed that hybrids were superior to the mean performance of the parents for disease severity (−14.57%) and area under disease progress curve (AUDPC) (14.9%). Correlations were significant and moderate between line per se and GCA; and mean of parental value with hybrid performance for both disease severity and AUDPC value. Very low and negative correlation was observed between parental lines marker based genetic distance and heterosis. Nevertheless, the correlation of GCA effects was very high with hybrid performance which can suggests as a good predictor of MLN resistance. Genomic prediction of hybrid performance for MLN is high for both traits. We therefore conclude that there is potential for prediction of hybrid performance for MLN. Overall, the estimated quantitative genetic parameters suggest that through targeted approach, it is possible to develop outstanding lines and hybrids for MLN resistance.
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Mapping-by-Sequencing via MutMap Identifies a Mutation in ZmCLE7 Underlying Fasciation in a Newly Developed EMS Mutant Population in an Elite Tropical Maize Inbred. Genes (Basel) 2020; 11:genes11030281. [PMID: 32155750 PMCID: PMC7140824 DOI: 10.3390/genes11030281] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/03/2020] [Accepted: 03/03/2020] [Indexed: 12/21/2022] Open
Abstract
Induced point mutations are important genetic resources for their ability to create hypo- and hypermorphic alleles that are useful for understanding gene functions and breeding. However, such mutant populations have only been developed for a few temperate maize varieties, mainly B73 and W22, yet no tropical maize inbred lines have been mutagenized and made available to the public to date. We developed a novel Ethyl Methanesulfonate (EMS) induced mutation resource in maize comprising 2050 independent M2 mutant families in the elite tropical maize inbred ML10. By phenotypic screening, we showed that this population is of comparable quality with other mutagenized populations in maize. To illustrate the usefulness of this population for gene discovery, we performed rapid mapping-by-sequencing to clone a fasciated-ear mutant and identify a causal promoter deletion in ZmCLE7 (CLE7). Our mapping procedure does not require crossing to an unrelated parent, thus is suitable for mapping subtle traits and ones affected by heterosis. This first EMS population in tropical maize is expected to be very useful for the maize research community. Also, the EMS mutagenesis and rapid mapping-by-sequencing pipeline described here illustrate the power of performing forward genetics in diverse maize germplasms of choice, which can lead to novel gene discovery due to divergent genetic backgrounds.
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Genetic Analysis of QTL for Resistance to Maize Lethal Necrosis in Multiple Mapping Populations. Genes (Basel) 2019; 11:genes11010032. [PMID: 31888105 PMCID: PMC7017159 DOI: 10.3390/genes11010032] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 12/17/2019] [Accepted: 12/24/2019] [Indexed: 11/17/2022] Open
Abstract
Maize lethal necrosis (MLN) occurs when maize chlorotic mottle virus (MCMV) and sugarcane mosaic virus (SCMV) co-infect maize plant. Yield loss of up to 100% can be experienced under severe infections. Identification and validation of genomic regions and their flanking markers can facilitate marker assisted breeding for resistance to MLN. To understand the status of previously identified quantitative trait loci (QTL)in diverse genetic background, F3 progenies derived from seven bi-parental populations were genotyped using 500 selected kompetitive allele specific PCR (KASP) SNPs. The F3 progenies were evaluated under artificial MLN inoculation for three seasons. Phenotypic analyses revealed significant variability (P ≤ 0.01) among genotypes for responses to MLN infections, with high heritability estimates (0.62 to 0.82) for MLN disease severity and AUDPC values. Linkage mapping and joint linkage association mapping revealed at least seven major QTL (qMLN3_130 and qMLN3_142, qMLN5_190 and qMLN5_202, qMLN6_85 and qMLN6_157 qMLN8_10 and qMLN9_142) spread across the 7-biparetal populations, for resistance to MLN infections and were consistent with those reported previously. The seven QTL appeared to be stable across genetic backgrounds and across environments. Therefore, these QTL could be useful for marker assisted breeding for resistance to MLN.
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Nyaga C, Gowda M, Beyene Y, Muriithi WT, Makumbi D, Olsen MS, Suresh LM, Bright JM, Das B, Prasanna BM. Genome-Wide Analyses and Prediction of Resistance to MLN in Large Tropical Maize Germplasm. Genes (Basel) 2019; 11:genes11010016. [PMID: 31877962 PMCID: PMC7016728 DOI: 10.3390/genes11010016] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 12/17/2019] [Accepted: 12/18/2019] [Indexed: 11/16/2022] Open
Abstract
Maize lethal necrosis (MLN), caused by co-infection of maize chlorotic mottle virus and sugarcane mosaic virus, can lead up to 100% yield loss. Identification and validation of genomic regions can facilitate marker assisted breeding for resistance to MLN. Our objectives were to identify marker-trait associations using genome wide association study and assess the potential of genomic prediction for MLN resistance in a large panel of diverse maize lines. A set of 1400 diverse maize tropical inbred lines were evaluated for their response to MLN under artificial inoculation by measuring disease severity or incidence and area under disease progress curve (AUDPC). All lines were genotyped with genotyping by sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability estimates were moderate to high. GWAS revealed 32 significantly associated SNPs for MLN resistance (at p < 1.0 × 10−6). For disease severity, these significantly associated SNPs individually explained 3–5% of the total phenotypic variance, whereas for AUDPC they explained 3–12% of the total proportion of phenotypic variance. Most of significant SNPs were consistent with the previous studies and assists to validate and fine map the big quantitative trait locus (QTL) regions into few markers’ specific regions. A set of putative candidate genes associated with the significant markers were identified and their functions revealed to be directly or indirectly involved in plant defense responses. Genomic prediction revealed reasonable prediction accuracies. The prediction accuracies significantly increased with increasing marker densities and training population size. These results support that MLN is a complex trait controlled by few major and many minor effect genes.
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Affiliation(s)
- Christine Nyaga
- Department of Agricultural Science and Technology, Kenyatta University, Nairobi 43844-00100, Kenya; (C.N.); (W.T.M.)
- International Maize and Wheat Improvement Centre (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 1041-00621, Kenya; (Y.B.); (D.M.); (M.S.O.); (L.M.S.); (J.M.B.); (B.D.); (B.M.P.)
| | - Manje Gowda
- International Maize and Wheat Improvement Centre (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 1041-00621, Kenya; (Y.B.); (D.M.); (M.S.O.); (L.M.S.); (J.M.B.); (B.D.); (B.M.P.)
- Correspondence: ; Tel.: +254-727-019-454
| | - Yoseph Beyene
- International Maize and Wheat Improvement Centre (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 1041-00621, Kenya; (Y.B.); (D.M.); (M.S.O.); (L.M.S.); (J.M.B.); (B.D.); (B.M.P.)
| | - Wilson T. Muriithi
- Department of Agricultural Science and Technology, Kenyatta University, Nairobi 43844-00100, Kenya; (C.N.); (W.T.M.)
| | - Dan Makumbi
- International Maize and Wheat Improvement Centre (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 1041-00621, Kenya; (Y.B.); (D.M.); (M.S.O.); (L.M.S.); (J.M.B.); (B.D.); (B.M.P.)
| | - Michael S. Olsen
- International Maize and Wheat Improvement Centre (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 1041-00621, Kenya; (Y.B.); (D.M.); (M.S.O.); (L.M.S.); (J.M.B.); (B.D.); (B.M.P.)
| | - L. M. Suresh
- International Maize and Wheat Improvement Centre (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 1041-00621, Kenya; (Y.B.); (D.M.); (M.S.O.); (L.M.S.); (J.M.B.); (B.D.); (B.M.P.)
| | - Jumbo M. Bright
- International Maize and Wheat Improvement Centre (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 1041-00621, Kenya; (Y.B.); (D.M.); (M.S.O.); (L.M.S.); (J.M.B.); (B.D.); (B.M.P.)
| | - Biswanath Das
- International Maize and Wheat Improvement Centre (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 1041-00621, Kenya; (Y.B.); (D.M.); (M.S.O.); (L.M.S.); (J.M.B.); (B.D.); (B.M.P.)
| | - Boddupalli M. Prasanna
- International Maize and Wheat Improvement Centre (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 1041-00621, Kenya; (Y.B.); (D.M.); (M.S.O.); (L.M.S.); (J.M.B.); (B.D.); (B.M.P.)
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Awata LAO, Ifie BE, Tongoona P, Danquah E, Jumbo MB, Gowda M, Marchelo-D’ragga PW, Sitonik C, Suresh LM. Maize lethal necrosis and the molecular basis of variability in concentrations of the causal viruses in co-infected maize plant. JOURNAL OF GENERAL AND MOLECULAR VIROLOGY 2019; 9:JGMV-09-01-0073. [PMID: 33381355 PMCID: PMC7753892 DOI: 10.5897/jgmv2019.0073] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 06/19/2019] [Indexed: 12/13/2022]
Abstract
Maize lethal necrosis (MLN) disease is new to Africa. First report was in Kenya in 2012, since then the disease has rapidly spread to most parts of eastern and central Africa region including Tanzania, Burundi, DRC Congo, Rwanda, Uganda, Ethiopia and similar symptoms were observed in South Sudan. Elsewhere, the disease was caused by infection of Maize Chlorotic Mottle Virus (MCMV) in combination with any of the potyviruses namely; maize dwarf mosaic virus (MDMV), sugarcane mosaic virus (SCMV) and tritimovirus wheat streak mosaic virus (WSMV). In Africa, the disease occurs due to combined infections of maize by MCMV and SCMV, leading to severe yield losses. Efforts to address the disease spread have been ongoing. Serological techniques including enzyme-linked immuno-sorbent assay (ELISA), polymerase chain reaction (PCR), genome-wide association (GWAS) mapping and next generation sequencing have been effectively used to detect and characterize MLN causative pathogens. Various management strategies have been adapted to control MLN including use of resistant varieties, phytosanitary measures and better cultural practices. This review looks at the current knowledge on MLN causative viruses, genetic architecture and molecular basis underlying their synergistic interactions. Lastly, some research gaps towards MLN management will be identified. The information gathered may be useful for developing strategies towards future MLN management and maize improvement in Africa.
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Affiliation(s)
- L. A. O. Awata
- Directorate of Research, Ministry of Agriculture and Food Security, Ministries Complex, Parliament Road, P. O. Box 33, Juba, South Sudan
| | - B. E. Ifie
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, PMB 30, Legon, Ghana
| | - P. Tongoona
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, PMB 30, Legon, Ghana
| | - E. Danquah
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, PMB 30, Legon, Ghana
| | - M. B. Jumbo
- International Maize and Wheat Improvement Center (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri. P. O. Box 1041-00621, Nairobi, Kenya
| | - M. Gowda
- International Maize and Wheat Improvement Center (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri. P. O. Box 1041-00621, Nairobi, Kenya
| | - P. W. Marchelo-D’ragga
- Department of Agricultural Sciences, College of Natural Resources and Environmental Studies, University of Juba, P. O. Box 82 Juba, South Sudan
| | - Chelang’at Sitonik
- International Maize and Wheat Improvement Center (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri. P. O. Box 1041-00621, Nairobi, Kenya
- Department of Plant Breeding and Biotechnology, School of Agriculture and Biotechnology, University of Eldoret, P. O. Box 1125-30100, Eldoret, Kenya
| | - L. M. Suresh
- International Maize and Wheat Improvement Center (CIMMYT), World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri. P. O. Box 1041-00621, Nairobi, Kenya
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