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Sharma D, Budhlakoti N, Kumari A, Saini DK, Sharma A, Yadav A, Mir RR, Singh AK, Vikas VK, Singh GP, Kumar S. Exploring the genetic architecture of powdery mildew resistance in wheat through QTL meta-analysis. FRONTIERS IN PLANT SCIENCE 2024; 15:1386494. [PMID: 39022610 PMCID: PMC11251950 DOI: 10.3389/fpls.2024.1386494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 06/11/2024] [Indexed: 07/20/2024]
Abstract
Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici, poses a significant threat to wheat production, necessitating the development of genetically resistant varieties for long-term control. Therefore, exploring genetic architecture of PM in wheat to uncover important genomic regions is an important area of wheat research. In recent years, the utilization of meta-QTL (MQTL) analysis has gained prominence as an essential tool for unraveling the complex genetic architecture underlying complex quantitative traits. The aim of this research was to conduct a QTL meta-analysis to pinpoint the specific genomic regions in wheat responsible for governing PM resistance. This study integrated 222 QTLs from 33 linkage-based studies using a consensus map with 54,672 markers. The analysis revealed 39 MQTLs, refined to 9 high-confidence MQTLs (hcMQTLs) with confidence intervals of 0.49 to 12.94 cM. The MQTLs had an average physical interval of 41.00 Mb, ranging from 0.000048 Mb to 380.71 Mb per MQTL. Importantly, 18 MQTLs co-localized with known resistance genes like Pm2, Pm3, Pm8, Pm21, Pm38, and Pm41. The study identified 256 gene models within hcMQTLs, providing potential targets for marker-assisted breeding and genomic prediction programs to enhance PM resistance. These MQTLs would serve as a foundation for fine mapping, gene isolation, and functional genomics studies, facilitating a deeper understanding of molecular mechanisms. The identification of candidate genes opens up exciting possibilities for the development of PM-resistant wheat varieties after validation.
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Affiliation(s)
- Divya Sharma
- Divison of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Neeraj Budhlakoti
- Centre for Agriculture Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anita Kumari
- Department of Botany, University of Delhi, Delhi, India
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, Ludhiana, India
| | - Anshu Sharma
- Divison of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Aakash Yadav
- Divison of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Reyazul Rouf Mir
- Department of Genetics and Plant Breeding , Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir (SKUAST-K), Srinagar, Kashmir, India
| | - Amit Kumar Singh
- Divison of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - V. K. Vikas
- Divison of Crop Improvement, ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, Tamilnadu, India
| | - Gyanendra Pratap Singh
- Divison of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Sundeep Kumar
- Divison of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
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Schierenbeck M, Alqudah AM, Thabet SG, Avogadro EG, Dietz JI, Simón MR, Börner A. Natural allelic variation confers diversity in the regulation of flag leaf traits in wheat. Sci Rep 2024; 14:13316. [PMID: 38858489 PMCID: PMC11164900 DOI: 10.1038/s41598-024-64161-x] [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: 11/08/2023] [Accepted: 06/05/2024] [Indexed: 06/12/2024] Open
Abstract
Flag leaf (FL) dimension has been reported as a key ecophysiological aspect for boosting grain yield in wheat. A worldwide winter wheat panel consisting of 261 accessions was tested to examine the phenotypical variation and identify quantitative trait nucleotides (QTNs) with candidate genes influencing FL morphology. To this end, four FL traits were evaluated during the early milk stage under two growing seasons at the Leibniz Institute of Plant Genetics and Crop Plant Research. The results showed that all leaf traits (Flag leaf length, width, area, and length/width ratio) were significantly influenced by the environments, genotypes, and environments × genotypes interactions. Then, a genome-wide association analysis was performed using 17,093 SNPs that showed 10 novel QTNs that potentially play a role in modulating FL morphology in at least two environments. Further analysis revealed 8 high-confidence candidate genes likely involved in these traits and showing high expression values from flag leaf expansion until its senescence and also during grain development. An important QTN (wsnp_RFL_Contig2177_1500201) was associated with FL width and located inside TraesCS3B02G047300 at chromosome 3B. This gene encodes a major facilitator, sugar transporter-like, and showed the highest expression values among the candidate genes reported, suggesting their positive role in controlling flag leaf and potentially being involved in photosynthetic assimilation. Our study suggests that the detection of novel marker-trait associations and the subsequent elucidation of the genetic mechanism influencing FL morphology would be of interest for improving plant architecture, light capture, and photosynthetic efficiency during grain development.
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Affiliation(s)
- Matías Schierenbeck
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, 06466, Seeland, Germany.
- Faculty of Agricultural Sciences and Forestry, National University of La Plata, La Plata, Argentina.
- CONICET CCT La Plata, La Plata, Argentina.
| | - Ahmad Mohammad Alqudah
- Biological Science Program, Department of Biological and Environmental Sciences, College of Art and Science, Qatar University, Doha, Qatar.
| | - Samar Gamal Thabet
- Department of Botany, Faculty of Science, Fayoum University, Fayoum, Egypt
| | - Evangelina Gabriela Avogadro
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, 06466, Seeland, Germany
| | - Juan Ignacio Dietz
- CONICET CCT La Plata, La Plata, Argentina
- EEA INTA Bordenave, Ruta 76 km 36, Bordenave, Argentina
| | - María Rosa Simón
- Faculty of Agricultural Sciences and Forestry, National University of La Plata, La Plata, Argentina
- CONICET CCT La Plata, La Plata, Argentina
| | - Andreas Börner
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, 06466, Seeland, Germany
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Vasistha NK, Sharma V, Singh S, Kaur R, Kumar A, Ravat VK, Kumar R, Gupta PK. Meta-QTL analysis and identification of candidate genes for multiple-traits associated with spot blotch resistance in bread wheat. Sci Rep 2024; 14:13083. [PMID: 38844568 PMCID: PMC11156910 DOI: 10.1038/s41598-024-63924-w] [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: 01/04/2024] [Accepted: 06/03/2024] [Indexed: 06/09/2024] Open
Abstract
In bread wheat, a literature search gave 228 QTLs for six traits, including resistance against spot blotch and the following five other related traits: (i) stay green; (ii) flag leaf senescence; (iii) green leaf area duration; (iv) green leaf area of the main stem; and (v) black point resistance. These QTLs were used for metaQTL (MQTL) analysis. For this purpose, a consensus map with 72,788 markers was prepared; 69 of the above 228 QTLs, which were suitable for MQTL analysis, were projected on the consensus map. This exercise resulted in the identification of 16 meta-QTLs (MQTLs) located on 11 chromosomes, with the PVE ranging from 5.4% (MQTL7) to 21.8% (MQTL5), and the confidence intervals ranging from 1.5 to 20.7 cM (except five MQTLs with a range of 36.1-57.8 cM). The number of QTLs associated with individual MQTLs ranged from a maximum of 17 in MQTL3 to 8 each in MQTL5 and MQTL8 and 5 each in MQTL7 and MQTL14. The 16 MQTLs, included 12 multi-trait MQTLs; one of the MQTL also overlapped a genomic region carrying the major spot blotch resistance gene Sb1. Of the total 16 MQTLs, 12 MQTLs were also validated through marker-trait associations that were available from earlier genome-wide association studies. The genomic regions associated with MQTLs were also used for the identification of candidate genes (CGs) and led to the identification of 516 CGs encoding 508 proteins; 411 of these proteins are known to be associated with resistance against several biotic stresses. In silico expression analysis of CGs using transcriptome data allowed the identification of 71 differentially expressed CGs, which were examined for further possible studies. The findings of the present study should facilitate fine-mapping and cloning of genes, enabling Marker Assisted Selection.
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Affiliation(s)
- Neeraj Kumar Vasistha
- Department of Genetics and Plant Breeding, Rajiv Gandhi University, Rono Hills, Itanagar, India
- Department of Genetics-Plant Breeding and Biotechnology, Dr K. S. Gill, Akal College of Agriculture, Eternal University, Baru Sahib, Sirmour, India
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, India
| | - Vaishali Sharma
- Department of Genetics-Plant Breeding and Biotechnology, Dr K. S. Gill, Akal College of Agriculture, Eternal University, Baru Sahib, Sirmour, India
| | - Sahadev Singh
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, India
- Meerut Institute of Technology, NH-58 Baral Partapur Bypass Road, Meerut, India
| | - Ramandeep Kaur
- Department of Genetics-Plant Breeding and Biotechnology, Dr K. S. Gill, Akal College of Agriculture, Eternal University, Baru Sahib, Sirmour, India
| | - Anuj Kumar
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, India
| | - Vikas Kumar Ravat
- Department of Plant Pathology, Rajiv Gandhi University, Rono Hills, Itanagar, India
| | - Rahul Kumar
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, India
| | - Pushpendra K Gupta
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, India.
- Murdoch's Centre for Crop and Food Innovation, Murdoch University, Murdoch, WA, Australia.
- Borlaug Institute for South Asia (BISA), National Agricultural Science Complex (NASC), Dev Prakash Shastri (DPS) Marg, New Delhi, India.
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Aloryi KD, Okpala NE, Guo H, Karikari B, Amo A, Bello SF, Saini DK, Akaba S, Tian X. Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice. BMC Genomics 2024; 25:338. [PMID: 38575927 PMCID: PMC10993490 DOI: 10.1186/s12864-024-10219-z] [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: 11/13/2023] [Accepted: 03/13/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND Due to rising costs, water shortages, and labour shortages, farmers across the globe now prefer a direct seeding approach. However, submergence stress remains a major bottleneck limiting the success of this approach in rice cultivation. The merger of accumulated rice genetic resources provides an opportunity to detect key genomic loci and candidate genes that influence the flooding tolerance of rice. RESULTS In the present study, a whole-genome meta-analysis was conducted on 120 quantitative trait loci (QTL) obtained from 16 independent QTL studies reported from 2004 to 2023. These QTL were confined to 18 meta-QTL (MQTL), and ten MQTL were successfully validated by independent genome-wide association studies from diverse natural populations. The mean confidence interval (CI) of the identified MQTL was 3.44 times narrower than the mean CI of the initial QTL. Moreover, four core MQTL loci with genetic distance less than 2 cM were obtained. By combining differentially expressed genes (DEG) from two transcriptome datasets with 858 candidate genes identified in the core MQTL regions, we found 38 common differentially expressed candidate genes (DECGs). In silico expression analysis of these DECGs led to the identification of 21 genes with high expression in embryo and coleoptile under submerged conditions. These DECGs encode proteins with known functions involved in submergence tolerance including WRKY, F-box, zinc fingers, glycosyltransferase, protein kinase, cytochrome P450, PP2C, hypoxia-responsive family, and DUF domain. By haplotype analysis, the 21 DECGs demonstrated distinct genetic differentiation and substantial genetic distance mainly between indica and japonica subspecies. Further, the MQTL7.1 was successfully validated using flanked marker S2329 on a set of genotypes with phenotypic variation. CONCLUSION This study provides a new perspective on understanding the genetic basis of submergence tolerance in rice. The identified MQTL and novel candidate genes lay the foundation for marker-assisted breeding/engineering of flooding-tolerant cultivars conducive to direct seeding.
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Grants
- 2023AFA022 Hubei Provincial Natural Science Foundation of China
- 2023AFA022 Hubei Provincial Natural Science Foundation of China
- 2023AFA022 Hubei Provincial Natural Science Foundation of China
- 2023AFA022 Hubei Provincial Natural Science Foundation of China
- 2023AFA022 Hubei Provincial Natural Science Foundation of China
- 2023AFA022 Hubei Provincial Natural Science Foundation of China
- 2023AFA022 Hubei Provincial Natural Science Foundation of China
- 2023AFA022 Hubei Provincial Natural Science Foundation of China
- 2023AFA022 Hubei Provincial Natural Science Foundation of China
- 2020BBB060 Key R&D Project in Hubei Province, China
- 2020BBB060 Key R&D Project in Hubei Province, China
- 2020BBB060 Key R&D Project in Hubei Province, China
- 2020BBB060 Key R&D Project in Hubei Province, China
- 2020BBB060 Key R&D Project in Hubei Province, China
- 2020BBB060 Key R&D Project in Hubei Province, China
- 2020BBB060 Key R&D Project in Hubei Province, China
- 2020BBB060 Key R&D Project in Hubei Province, China
- 2020BBB060 Key R&D Project in Hubei Province, China
- 2018YFD0301306 the National Key Research and Development Program of China
- 2018YFD0301306 the National Key Research and Development Program of China
- 2018YFD0301306 the National Key Research and Development Program of China
- 2018YFD0301306 the National Key Research and Development Program of China
- 2018YFD0301306 the National Key Research and Development Program of China
- 2018YFD0301306 the National Key Research and Development Program of China
- 2018YFD0301306 the National Key Research and Development Program of China
- 2018YFD0301306 the National Key Research and Development Program of China
- 2018YFD0301306 the National Key Research and Development Program of China
- Key R&D Project in Hubei Province, China
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Affiliation(s)
- Kelvin Dodzi Aloryi
- Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China
| | - Nnaemeka Emmanuel Okpala
- Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China
| | - Hong Guo
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Benjamin Karikari
- Département de phytologie, Université Laval, Québec, QC, Canada
- Department of Agricultural Biotechnology, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Tamale, Ghana
| | - Aduragbemi Amo
- Department of Horticultural Sciences, Texas A&M University, College Station, TX, USA
- Texas A&M AgriLife Research and Extension Center, Weslaco, TX, USA
| | - Semiu Folaniyi Bello
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
- Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, USA
| | - Selorm Akaba
- School of Agriculture, University of Cape Coast, Cape Coast, Ghana
| | - Xiaohai Tian
- Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China.
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Azad R, Krępski T, Olechowski M, Biernacik B, Święcicka M, Matuszkiewicz M, Dmochowska-Boguta M, Rakoczy-Trojanowska M. Genotype-Specific Expression of Selected Candidate Genes Conferring Resistance to Leaf Rust of Rye ( Secale cereale L.). Genes (Basel) 2024; 15:275. [PMID: 38540334 PMCID: PMC10970619 DOI: 10.3390/genes15030275] [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: 01/26/2024] [Revised: 02/13/2024] [Accepted: 02/19/2024] [Indexed: 06/15/2024] Open
Abstract
Leaf rust (LR) caused by Puccinia recondita f. sp. secalis (Prs) is a highly destructive disease in rye. However, the genetic mechanisms underlying the rye immune response to this disease remain relatively uncharacterised. In this study, we analysed the expression of four genes in 12 rye inbred lines inoculated with Prs at 20 and 36 h post-treatment (hpt): DXS (1-deoxy-D-xylulose 5-phosphate synthase), Glu (β-1,3-glucanase), GT (UDP-glycosyltransferase) and PR-1 (pathogenesis-related protein 1). The RT-qPCR analysis revealed the upregulated expression of the four genes in response to Prs in all inbred lines and at both time-points. The gene expression data were supported by microscopic and macroscopic examinations, which revealed that eight lines were susceptible to LR and four lines were highly resistant to LR. A relationship between the infection profiles and the expression of the analysed genes was observed: in the resistant lines, the expression level fold changes were usually higher at 20 hpt than at 36 hpt, while the opposite trend was observed in the susceptible lines. The study results indicate that DXS, Glu, GT and PR-1 may encode proteins crucial for the rye defence response to the LR pathogen.
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Affiliation(s)
- Rumana Azad
- Department of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warszawa, Poland; (R.A.); (T.K.); (M.O.); (B.B.); (M.Ś.); (M.M.)
| | - Tomasz Krępski
- Department of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warszawa, Poland; (R.A.); (T.K.); (M.O.); (B.B.); (M.Ś.); (M.M.)
| | - Mateusz Olechowski
- Department of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warszawa, Poland; (R.A.); (T.K.); (M.O.); (B.B.); (M.Ś.); (M.M.)
| | - Bartosz Biernacik
- Department of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warszawa, Poland; (R.A.); (T.K.); (M.O.); (B.B.); (M.Ś.); (M.M.)
| | - Magdalena Święcicka
- Department of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warszawa, Poland; (R.A.); (T.K.); (M.O.); (B.B.); (M.Ś.); (M.M.)
| | - Mateusz Matuszkiewicz
- Department of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warszawa, Poland; (R.A.); (T.K.); (M.O.); (B.B.); (M.Ś.); (M.M.)
| | - Marta Dmochowska-Boguta
- Plant Breeding and Acclimatization Institute—National Research Institute, Radzikow, 05-870 Blonie, Poland;
| | - Monika Rakoczy-Trojanowska
- Department of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warszawa, Poland; (R.A.); (T.K.); (M.O.); (B.B.); (M.Ś.); (M.M.)
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Sharma D, Kumari A, Sharma P, Singh A, Sharma A, Mir ZA, Kumar U, Jan S, Parthiban M, Mir RR, Bhati P, Pradhan AK, Yadav A, Mishra DC, Budhlakoti N, Yadav MC, Gaikwad KB, Singh AK, Singh GP, Kumar S. Meta-QTL analysis in wheat: progress, challenges and opportunities. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:247. [PMID: 37975911 DOI: 10.1007/s00122-023-04490-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 10/16/2023] [Indexed: 11/19/2023]
Abstract
Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture.
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Affiliation(s)
- Divya Sharma
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India
| | - Anita Kumari
- Department of Botany, University of Delhi, Delhi, India
| | - Priya Sharma
- Department of Botany, University of Delhi, Delhi, India
| | - Anupma Singh
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India
| | - Anshu Sharma
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India
| | - Zahoor Ahmad Mir
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India
| | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | - Sofora Jan
- Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Srinagar, Kashmir, India
| | - M Parthiban
- Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Srinagar, Kashmir, India
| | - Reyazul Rouf Mir
- Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Srinagar, Kashmir, India
| | - Pradeep Bhati
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | - Anjan Kumar Pradhan
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India
| | - Aakash Yadav
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India
| | | | - Neeraj Budhlakoti
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Mahesh C Yadav
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India
| | - Kiran B Gaikwad
- Division of Genetics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Amit Kumar Singh
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India
| | | | - Sundeep Kumar
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India.
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Kumar S, Saini DK, Jan F, Jan S, Tahir M, Djalovic I, Latkovic D, Khan MA, Kumar S, Vikas VK, Kumar U, Kumar S, Dhaka NS, Dhankher OP, Rustgi S, Mir RR. Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat. BMC Genomics 2023; 24:259. [PMID: 37173660 PMCID: PMC10182688 DOI: 10.1186/s12864-023-09336-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. RESULTS Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker-trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. CONCLUSION The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.
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Affiliation(s)
- Sandeep Kumar
- Division of Genetics and Plant Breeding, Faculty of Agriculture, SKUAST-Kashmir, Wadura, 193201, India
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004, Punjab, India
| | - Farkhandah Jan
- Division of Genetics and Plant Breeding, Faculty of Agriculture, SKUAST-Kashmir, Wadura, 193201, India
| | - Sofora Jan
- Division of Genetics and Plant Breeding, Faculty of Agriculture, SKUAST-Kashmir, Wadura, 193201, India
| | - Mohd Tahir
- Division of Genetics and Plant Breeding, Faculty of Agriculture, SKUAST-Kashmir, Wadura, 193201, India
| | - Ivica Djalovic
- Institute of Field and Vegetable Crops, National Institute of the Republic of Serbia, Maxim Gorki 30, Novi Sad, Serbia
| | - Dragana Latkovic
- Department of Field and Vegetable Crops, Faculty of Agriculture, University of Novi Sad, Trg Dositeja Obradovića 8, 21000, Novi Sad, Serbia
| | - Mohd Anwar Khan
- Division of Genetics and Plant Breeding, Faculty of Agriculture, SKUAST-Kashmir, Wadura, 193201, India
| | - Sundeep Kumar
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - V K Vikas
- ICAR-IARI, Regional Station, Wellington, 643 231, The Nilgiris, India
| | - Upendra Kumar
- Department of Molecular Biology & Biotechnology., CCS Haryana Agriculture University, Hisar, India
| | - Sundip Kumar
- Department of Molecular Biology and Genetic Engineering, Molecular Cytogenetics Laboratory, College of Basic Science and Humanities, G. B. Pant University of Agriculture and Technology, Pantnagar-263145, U.S. Nagar, Uttarakhand, India
| | - Narendra Singh Dhaka
- Department of Genetics and Plant Breeding, College of Agriculture, G. B. Pant, University of Agriculture & Technology, Pantnagar-263145, U. S. Nagar, Uttarakhand, India
| | - Om Parkash Dhankher
- School of Agriculture, University of Massachusetts Amherst, Stockbridge Amherst, MA, 01003, USA
| | - Sachin Rustgi
- Department of Plant and Environmental Sciences, Clemson University, 2200 Pocket Road, Florence, SC, 29506, USA
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture, SKUAST-Kashmir, Wadura, 193201, India.
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8
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Li N, Miao Y, Ma J, Zhang P, Chen T, Liu Y, Che Z, Shahinnia F, Yang D. Consensus genomic regions for grain quality traits in wheat revealed by Meta-QTL analysis and in silico transcriptome integration. THE PLANT GENOME 2023:e20336. [PMID: 37144681 DOI: 10.1002/tpg2.20336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 05/06/2023]
Abstract
Grain quality traits are the key factors that determine the economic value of wheat and are largely influenced by genetics and the environment. In this study, using a meta-analysis of quantitative trait loci (QTLs) and a comprehensive in silico transcriptome assessment, we identified key genomic regions and putative candidate genes for the grain quality traits protein content, gluten content, and test weight. A total of 508 original QTLs were collected from 41 articles on QTL mapping for the three quality traits in wheat published from 2003 to 2021. When these original QTLs were projected onto a high-density consensus map consisting of 14,548 markers, 313 QTLs resulted in the identification of 64 MQTLs distributed across 17 of the 21 chromosomes. Most of the meta-QTLs (MQTLs) were distributed on sub-genomes A and B. Compared with the original QTLs, the confidence interval (CI) of the MQTLs was smaller, with an average CI of 4.47 cM, while the projected QTLs CI was 11.13 cM (2.49-fold lower). The corresponding physical length of the MQTL ranged from 0.45 to 239.01 Mb. Thirty-one of these 64 MQTLs were validated in at least one genome-wide association study. In addition, five of the 64 MQTLs were selected and designated as core MQTLs. The 211 quality-related genes from rice were used to identify wheat homologs in MQTLs. In combination with transcriptional and omics analyses, 135 putative candidate genes were identified from 64 MQTL regions. The findings should contribute to a better understanding of the molecular genetic mechanisms underlying grain quality and the improvement of these traits in wheat breeding.
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Affiliation(s)
- Na Li
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Yongping Miao
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Jingfu Ma
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Peipei Zhang
- State Key Laboratory of Aridland Crop Science, Gansu, China
| | - Tao Chen
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Yuan Liu
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Zhuo Che
- Plant Seed Master Station of Gansu Province, Gansu, China
| | - Fahimeh Shahinnia
- Institute for Crop Science and Plant Breeding, Bavarian State Research Centre for Agriculture, Freising, Germany
| | - Delong Yang
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
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9
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Guo K, Chen T, Zhang P, Liu Y, Che Z, Shahinnia F, Yang D. Meta-QTL analysis and in-silico transcriptome assessment for controlling chlorophyll traits in common wheat. THE PLANT GENOME 2023; 16:e20294. [PMID: 36636827 DOI: 10.1002/tpg2.20294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/02/2022] [Indexed: 05/10/2023]
Abstract
Chlorophyll is an important plant molecule for absorbing light and transferring electrons to produce energy for photosynthesis, which has a significant impact on crop yield. To identify quantitative trait loci (QTL) controlling chlorophyll traits in wheat (Triticum aestivum L.), a comprehensive meta-analysis of 411 original QTLs for six chlorophyll traits was performed, including the evolution of soil plant analysis development (SPAD), chlorophyll content index (CCI), chlorophyll a content (Chla), chlorophyll b content (Chlb), chlorophyll content (Chl), and ratio of chlorophyll a to b (Chla/b), derived from 41 independent experiments conducted over the past two decades. Fifty-six consensus meta-QTLs (MQTLs) were detected, unevenly distributed on chromosomes 1A, 1B, 2A, 2B, 2D, 3B, 3D, 4B, 4D, 5A, 5D, 6A, 6D, 7B, and 7D. The confidence interval (CI) of the identified MQTLs was 0.18 to 15.07 cM, with an average of 5.74 cM, and 3.17-times narrower than that of the original QTLs. A total of 30 MQTLs were aligned with marker-trait associations (MTAs) reported in genome-wide association studies (GWAS) for chlorophyll traits in wheat. Based on MQTL-flanking marker information and homology analyses combined with RNA-seq data, 136 putative candidate genes were identified in MQTL regions, involved in porphyrin metabolism, photosynthesis, terpene biosynthesis, glyoxylate and dicarboxylate metabolism, and secondary metabolites. The results of this study contribute to the understanding of the genetic basis for controlling chlorophyll traits and can be used in breeding wheat with high photosynthetic efficiency.
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Affiliation(s)
- Kaiqi Guo
- State Key Lab of Aridland Crop Science, Lanzhou, 730070, China
- College of Life Science and Technology, Gansu Agricultural Univ., Lanzhou, 730070, China
| | - Tao Chen
- State Key Lab of Aridland Crop Science, Lanzhou, 730070, China
- College of Life Science and Technology, Gansu Agricultural Univ., Lanzhou, 730070, China
| | - Peipei Zhang
- State Key Lab of Aridland Crop Science, Lanzhou, 730070, China
| | - Yuan Liu
- State Key Lab of Aridland Crop Science, Lanzhou, 730070, China
- College of Life Science and Technology, Gansu Agricultural Univ., Lanzhou, 730070, China
| | - Zhuo Che
- Plant Seed Master Station of Gansu Province, Lanzhou, 730000, China
| | - Fahimeh Shahinnia
- Bavarian State Research Centre for Agriculture, Institute for Crop Science and Plant Breeding, Freising, 85354, Germany
| | - Delong Yang
- State Key Lab of Aridland Crop Science, Lanzhou, 730070, China
- College of Life Science and Technology, Gansu Agricultural Univ., Lanzhou, 730070, China
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10
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Delfan S, Bihamta MR, Dadrezaei ST, Abbasi A, Alipoor H. Exploring genomic regions involved in bread wheat resistance to leaf rust at seedling/adult stages by using GWAS analysis. BMC Genomics 2023; 24:83. [PMID: 36810004 PMCID: PMC9945389 DOI: 10.1186/s12864-022-09096-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/22/2022] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Global wheat productivity is seriously challenged by a range of rust pathogens, especially leaf rust derived from Puccinia triticina. Since the most efficient approach to control leaf rust is genetic resistance, many efforts have been made to uncover resistance genes; however, it demands an ongoing exploration for effective resistance sources because of the advent of novel virulent races. Thus, the current study was focused on detecting leaf rust resistance-related genomic loci against the P. triticina prevalent races by GWAS in a set of Iranian cultivars and landraces. RESULTS Evaluation of 320 Iranian bread wheat cultivars and landraces against four prevalent rust pathotypes of P. triticina (LR-99-2, LR-98-12, LR-98-22, and LR-97-12) indicated the diversity in wheat accessions responses to P. triticina. From GWAS results, 80 leaf rust resistance QTLs were located in the surrounding known QTLs/genes on almost chromosomes, except for 1D, 3D, 4D, and 7D. Of these, six MTAs (rs20781/rs20782 associated with resistance to LR-97-12; rs49543/rs52026 for LR-98-22; rs44885/rs44886 for LR-98-22/LR-98-1/LR-99-2) were found on genomic regions where no resistance genes previously reported, suggesting new loci conferring resistance to leaf rust. The GBLUP genomic prediction model appeared better than RR-BLUP and BRR, reflecting that GBLUP is a potent model for genomic selection in wheat accessions. CONCLUSIONS Overall, the newly identified MTAs as well as the highly resistant accessions in the recent work provide an opportunity towards improving leaf rust resistance.
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Affiliation(s)
- Saba Delfan
- grid.46072.370000 0004 0612 7950Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | - Mohammad Reza Bihamta
- Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran.
| | - Seyed Taha Dadrezaei
- grid.473705.20000 0001 0681 7351Department of Cereal Research, Seed and Plant Improvement Institute, Agricultural Research and Education Organization (AREEO), Karaj, Iran
| | - Alireza Abbasi
- grid.46072.370000 0004 0612 7950Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | - Hadi Alipoor
- grid.412763.50000 0004 0442 8645Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
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11
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Kong B, Ma J, Zhang P, Chen T, Liu Y, Che Z, Shahinnia F, Yang D. Deciphering key genomic regions controlling flag leaf size in wheat via integration of meta-QTL and in silico transcriptome assessment. BMC Genomics 2023; 24:33. [PMID: 36658498 PMCID: PMC9854125 DOI: 10.1186/s12864-023-09119-5] [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: 09/28/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Grain yield is a complex and polygenic trait influenced by the photosynthetic source-sink relationship in wheat. The top three leaves, especially the flag leaf, are considered the major sources of photo-assimilates accumulated in the grain. Determination of significant genomic regions and candidate genes affecting flag leaf size can be used in breeding for grain yield improvement. RESULTS With the final purpose of understanding key genomic regions for flag leaf size, a meta-analysis of 521 initial quantitative trait loci (QTLs) from 31 independent QTL mapping studies over the past decades was performed, where 333 loci eventually were refined into 64 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs was 5.28 times less than that of the initial QTLs. Thirty-three MQTLs overlapped the marker trait associations (MTAs) previously reported in genome-wide association studies (GWAS) for flag leaf traits in wheat. A total of 2262 candidate genes for flag leaf size, which were involved in the peroxisome, basal transcription factor, and tyrosine metabolism pathways were identified in MQTL regions by the in silico transcriptome assessment. Of these, the expression analysis of the available genes revealed that 134 genes with > 2 transcripts per million (TPM) were highly and specifically expressed in the leaf. These candidate genes could be critical to affect flag leaf size in wheat. CONCLUSIONS The findings will make further insight into the genetic determinants of flag leaf size and provide some reliable MQTLs and putative candidate genes for the genetic improvement of flag leaf size in wheat.
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Affiliation(s)
- Binxue Kong
- State Key Laboratory of Aridland Crop Science, Lanzhou, 730070, China
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China
| | - Jingfu Ma
- State Key Laboratory of Aridland Crop Science, Lanzhou, 730070, China
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China
| | - Peipei Zhang
- State Key Laboratory of Aridland Crop Science, Lanzhou, 730070, China
| | - Tao Chen
- State Key Laboratory of Aridland Crop Science, Lanzhou, 730070, China
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China
| | - Yuan Liu
- State Key Laboratory of Aridland Crop Science, Lanzhou, 730070, China
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China
| | - Zhuo Che
- Plant Seed Master Station of Gansu Province, Lanzhou, 730000, China
| | - Fahimeh Shahinnia
- Bavarian State Research Centre for Agriculture, Institute for Crop Science and Plant Breeding, 85354, Freising, Germany
| | - Delong Yang
- State Key Laboratory of Aridland Crop Science, Lanzhou, 730070, China.
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
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12
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Lhamo D, Sun Q, Zhang Q, Li X, Fiedler JD, Xia G, Faris JD, Gu YQ, Gill U, Cai X, Acevedo M, Xu SS. Genome-wide association analyses of leaf rust resistance in cultivated emmer wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:20. [PMID: 36683081 DOI: 10.1007/s00122-023-04281-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
Fifteen and eleven loci, with most loci being novel, were identified to associate with seedling and adult resistances, respectively, to the durum-specific races of leaf rust pathogen in cultivated emmer. Leaf rust, caused by Puccinia triticina (Pt), constantly threatens durum (Triticum turgidum ssp. durum) and bread wheat (Triticum aestivum) production worldwide. A Pt race BBBQD detected in California in 2009 poses a potential threat to durum production in North America because resistance source to this race is rare in durum germplasm. To find new resistance sources, we assessed a panel of 180 cultivated emmer wheat (Triticum turgidum ssp. dicoccum) accessions for seedling resistance to BBBQD and for adult resistance to a mixture of durum-specific races BBBQJ, CCMSS, and MCDSS in the field, and genotyped the panel using genotype-by-sequencing (GBS) and the 9 K SNP (Single Nucleotide Polymorphism) Infinium array. The results showed 24 and nine accessions consistently exhibited seedling and adult resistance, respectively, with two accessions providing resistance at both stages. We performed genome-wide association studies using 46,383 GBS and 4,331 9 K SNP markers and identified 15 quantitative trait loci (QTL) for seedling resistance located mostly on chromosomes 2B and 6B, and 11 QTL for adult resistance on 2B, 3B and 6A. Of these QTL, one might be associated with leaf rust resistance (Lr) gene Lr53, and two with the QTL previously reported in durum or hexaploid wheat. The remaining QTL are potentially associated with new Lr genes. Further linkage analysis and gene cloning are necessary to identify the causal genes underlying these QTL. The emmer accessions with high levels of resistance will be useful for developing mapping populations and adapted durum germplasm and varieties with resistance to the durum-specific races.
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Affiliation(s)
- Dhondup Lhamo
- USDA-ARS, Crop Improvement and Genetics Research Unit, Western Regional Research Center, Albany, CA, 94710, USA
| | - Qun Sun
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA
| | - Qijun Zhang
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA
| | - Xuehui Li
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA
| | - Jason D Fiedler
- USDA-ARS, Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, ND, 58102, USA
| | - Guangmin Xia
- Key Laboratory of Plant Development and Environmental Adaptation Biology, School of Life Science, Shandong University, Qingdao, 266237, China
| | - Justin D Faris
- USDA-ARS, Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, ND, 58102, USA
| | - Yong-Qiang Gu
- USDA-ARS, Crop Improvement and Genetics Research Unit, Western Regional Research Center, Albany, CA, 94710, USA
| | - Upinder Gill
- Department of Plant Pathology, North Dakota State University, Fargo, ND, 58108, USA
| | - Xiwen Cai
- USDA-ARS, Wheat, Sorghum and Forage Research Unit, and Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, 68583, USA
| | - Maricelis Acevedo
- Department of Global Development, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, 14853, USA.
| | - Steven S Xu
- USDA-ARS, Crop Improvement and Genetics Research Unit, Western Regional Research Center, Albany, CA, 94710, USA.
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13
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Ma J, Liu Y, Zhang P, Chen T, Tian T, Wang P, Che Z, Shahinnia F, Yang D. Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.). BMC PLANT BIOLOGY 2022; 22:607. [PMID: 36550393 PMCID: PMC9784057 DOI: 10.1186/s12870-022-03989-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Kernel size-related traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR) and kernel thickness (KT), are critical determinants for wheat kernel weight and yield and highly governed by a type of quantitative genetic basis. Genome-wide identification of major and stable quantitative trait loci (QTLs) and functional genes are urgently required for genetic improvement in wheat kernel yield. A hexaploid wheat population consisting of 120 recombinant inbred lines was developed to identify QTLs for kernel size-related traits under different water environments. The meta-analysis and transcriptome evaluation were further integrated to identify major genomic regions and putative candidate genes. RESULTS The analysis of variance (ANOVA) revealed more significant genotypic effects for kernel size-related traits, indicating the moderate to high heritability of 0.61-0.89. Thirty-two QTLs for kernel size-related traits were identified, explaining 3.06%-14.2% of the phenotypic variation. Eleven stable QTLs were detected in more than three water environments. The 1103 original QTLs from the 34 previous studies and the present study were employed for the MQTL analysis and refined into 58 MQTLs. The average confidence interval of the MQTLs was 3.26-fold less than that of the original QTLs. The 1864 putative candidate genes were mined within the regions of 12 core MQTLs, where 70 candidate genes were highly expressed in spikes and kernels by comprehensive analysis of wheat transcriptome data. They were involved in various metabolic pathways, such as carbon fixation in photosynthetic organisms, carbon metabolism, mRNA surveillance pathway, RNA transport and biosynthesis of secondary metabolites. CONCLUSIONS Major genomic regions and putative candidate genes for kernel size-related traits in wheat have been revealed by an integrative strategy with QTL linkage mapping, meta-analysis and transcriptomic assessment. The findings provide a novel insight into understanding the genetic determinants of kernel size-related traits and will be useful for the marker-assisted selection of high yield in wheat breeding.
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Grants
- GHSJ 2020-Z4 Research Program Sponsored by State Key Laboratory of Aridland Crop Science, China
- GHSJ 2020-Z4 Research Program Sponsored by State Key Laboratory of Aridland Crop Science, China
- GHSJ 2020-Z4 Research Program Sponsored by State Key Laboratory of Aridland Crop Science, China
- GHSJ 2020-Z4 Research Program Sponsored by State Key Laboratory of Aridland Crop Science, China
- GHSJ 2020-Z4 Research Program Sponsored by State Key Laboratory of Aridland Crop Science, China
- GHSJ 2020-Z4 Research Program Sponsored by State Key Laboratory of Aridland Crop Science, China
- GHSJ 2020-Z4 Research Program Sponsored by State Key Laboratory of Aridland Crop Science, China
- GHSJ 2020-Z4 Research Program Sponsored by State Key Laboratory of Aridland Crop Science, China
- 21YF5NA089 Key Research and Development Program of Gansu Province, China
- 21YF5NA089 Key Research and Development Program of Gansu Province, China
- 21YF5NA089 Key Research and Development Program of Gansu Province, China
- 21YF5NA089 Key Research and Development Program of Gansu Province, China
- 21YF5NA089 Key Research and Development Program of Gansu Province, China
- 21YF5NA089 Key Research and Development Program of Gansu Province, China
- 21YF5NA089 Key Research and Development Program of Gansu Province, China
- 21YF5NA089 Key Research and Development Program of Gansu Province, China
- 2022CYZC-44 Industrial Support Plan of Colleges and Universities in Gansu Province
- 2022CYZC-44 Industrial Support Plan of Colleges and Universities in Gansu Province
- 2022CYZC-44 Industrial Support Plan of Colleges and Universities in Gansu Province
- 2022CYZC-44 Industrial Support Plan of Colleges and Universities in Gansu Province
- 2022CYZC-44 Industrial Support Plan of Colleges and Universities in Gansu Province
- 2022CYZC-44 Industrial Support Plan of Colleges and Universities in Gansu Province
- 2022CYZC-44 Industrial Support Plan of Colleges and Universities in Gansu Province
- 2022CYZC-44 Industrial Support Plan of Colleges and Universities in Gansu Province
- 31760385 National Natural Science Foundation of China
- 31760385 National Natural Science Foundation of China
- 31760385 National Natural Science Foundation of China
- 31760385 National Natural Science Foundation of China
- 31760385 National Natural Science Foundation of China
- 31760385 National Natural Science Foundation of China
- 31760385 National Natural Science Foundation of China
- 31760385 National Natural Science Foundation of China
- 22ZD6NA010 Key Sci & Tech Special Project of Gansu Province
- 22ZD6NA010 Key Sci & Tech Special Project of Gansu Province
- 22ZD6NA010 Key Sci & Tech Special Project of Gansu Province
- 22ZD6NA010 Key Sci & Tech Special Project of Gansu Province
- 22ZD6NA010 Key Sci & Tech Special Project of Gansu Province
- 22ZD6NA010 Key Sci & Tech Special Project of Gansu Province
- 22ZD6NA010 Key Sci & Tech Special Project of Gansu Province
- 22ZD6NA010 Key Sci & Tech Special Project of Gansu Province
- Key Sci & Tech Special Project of Gansu Province
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Affiliation(s)
- Jingfu Ma
- State Key Lab of Aridland Crop Science, Lanzhou, Gansu, China
- College of Agronomy, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Yuan Liu
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Peipei Zhang
- State Key Lab of Aridland Crop Science, Lanzhou, Gansu, China
| | - Tao Chen
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Tian Tian
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Peng Wang
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Zhuo Che
- Plant Seed Master Station of Gansu Province, Lanzhou, Gansu, China
| | - Fahimeh Shahinnia
- Institute for Crop Science and Plant Breeding, Bavarian State Research Centre for Agriculture, Freising, Germany
| | - Delong Yang
- State Key Lab of Aridland Crop Science, Lanzhou, Gansu, China.
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China.
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14
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Aloryi KD, Okpala NE, Amo A, Bello SF, Akaba S, Tian X. A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice. FRONTIERS IN PLANT SCIENCE 2022; 13:1035851. [PMID: 36466247 PMCID: PMC9709451 DOI: 10.3389/fpls.2022.1035851] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 10/19/2022] [Indexed: 06/17/2023]
Abstract
Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.
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Affiliation(s)
- Kelvin Dodzi Aloryi
- Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China
| | - Nnaemeka Emmanuel Okpala
- Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China
| | - Aduragbemi Amo
- Institute of Plant Breeding, Genetics and Genomics University of Georgia, Athens, GA, United States
| | - Semiu Folaniyi Bello
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
| | - Selorm Akaba
- School of Agriculture, University of Cape Coast, Cape Coast, Ghana
| | - Xiaohai Tian
- Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China
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15
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Saini P, Sheikh I, Saini DK, Mir RR, Dhaliwal HS, Tyagi V. Consensus genomic regions associated with grain protein content in hexaploid and tetraploid wheat. Front Genet 2022; 13:1021180. [PMID: 36246648 PMCID: PMC9554612 DOI: 10.3389/fgene.2022.1021180] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 09/13/2022] [Indexed: 11/13/2022] Open
Abstract
A meta-analysis of QTLs associated with grain protein content (GPC) was conducted in hexaploid and tetraploid wheat to identify robust and stable meta-QTLs (MQTLs). For this purpose, as many as 459 GPC-related QTLs retrieved from 48 linkage-based QTL mapping studies were projected onto the newly developed wheat consensus map. The analysis resulted in the prediction of 57 MQTLs and 7 QTL hotspots located on all wheat chromosomes (except chromosomes 1D and 4D) and the average confidence interval reduced 2.71-fold in the MQTLs and QTL hotspots compared to the initial QTLs. The physical regions occupied by the MQTLs ranged from 140 bp to 224.02 Mb with an average of 15.2 Mb, whereas the physical regions occupied by QTL hotspots ranged from 1.81 Mb to 36.03 Mb with a mean of 8.82 Mb. Nineteen MQTLs and two QTL hotspots were also found to be co-localized with 45 significant SNPs identified in 16 previously published genome-wide association studies in wheat. Candidate gene (CG) investigation within some selected MQTLs led to the identification of 705 gene models which also included 96 high-confidence CGs showing significant expressions in different grain-related tissues and having probable roles in GPC regulation. These significantly expressed CGs mainly involved the genes/gene families encoding for the following proteins: aminotransferases, early nodulin 93, glutamine synthetases, invertase/pectin methylesterase inhibitors, protein BIG GRAIN 1-like, cytochrome P450, glycosyl transferases, hexokinases, small GTPases, UDP-glucuronosyl/UDP-glucosyltransferases, and EamA, SANT/Myb, GNAT, thioredoxin, phytocyanin, and homeobox domains containing proteins. Further, eight genes including GPC-B1, Glu-B1-1b, Glu-1By9, TaBiP1, GSr, TaNAC019-A, TaNAC019-D, and bZIP-TF SPA already known to be associated with GPC were also detected within some of the MQTL regions confirming the efficacy of MQTLs predicted during the current study.
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Affiliation(s)
- Pooja Saini
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, India
| | - Imran Sheikh
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, India
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punajb Agricultural University, Ludhiana, India
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture SKUAST-Kashmir, Srinagar, India
| | - Harcharan Singh Dhaliwal
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, India
| | - Vikrant Tyagi
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, India
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Kolmer JA, Rouse MN. Adult plant leaf rust resistance QTL derived from wheat line CI13227 maps to chromosomes 2AL, 4BS, and 7AL. THE PLANT GENOME 2022; 15:e20215. [PMID: 35542982 DOI: 10.1002/tpg2.20215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
The winter wheat (Triticum aestivum L.) line CI13227 has been characterized as having adult plant resistance to leaf rust caused by Puccinia triticina Eriks (Pt). Line CI13227 was crossed with the susceptible spring wheat 'Thatcher' (Tc) and a Tc*2/CI13227 F6 line with adult plant leaf rust resistance designated as 411A was derived. Line 411A was crossed with Tc to develop an F6 recombinant inbred line (RIL) population. The parents and 120 F6 lines were assessed for leaf rust severity at the flag leaf stage in five field plot tests from 2011 through 2015 and were genotyped for single-nucleotide polymorphism (SNP) markers with the Illumina iSelect 90K wheat bead array. A total of 2,384 SNP markers segregated among the RILs. Completely linked SNPs were removed, and 474 markers that covered 2,605 centimorgans (cM) were used for linkage map construction. Quantitative trait loci (QTL) on chromosome 2AL with logarithm of odds (LOD) values 2.34-7.88, on chromosome 4BS with LOD values 1.35- 4.66, and on chromosome 7AL with LOD values 2.92-7.81 were associated with significant reduction in leaf rust severity in the field plot tests. Recombinant inbred lines that had combinations of two or three of the QTL had significantly lower leaf rust severity than RILs that lacked any resistance QTL. Kompetitive allele specific polymerase chain reaction (KASP) markers were developed for the SNPs that were closely linked with the three QTL to facilitate marker-based selection of the leaf rust resistance in breeding programs.
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Affiliation(s)
- James A Kolmer
- USDA-ARS, Cereal Disease Laboratory, 1551 Lindig St., St. Paul, MN, 55108, USA
| | - Matthew N Rouse
- USDA-ARS, Cereal Disease Laboratory, 1551 Lindig St., St. Paul, MN, 55108, USA
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