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Joukhadar R, Li Y, Thistlethwaite R, Forrest KL, Tibbits JF, Trethowan R, Hayden MJ. Optimising desired gain indices to maximise selection response. FRONTIERS IN PLANT SCIENCE 2024; 15:1337388. [PMID: 38978519 PMCID: PMC11228337 DOI: 10.3389/fpls.2024.1337388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 05/23/2024] [Indexed: 07/10/2024]
Abstract
Introduction In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit. Methods Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits. Results We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction. Discussion Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.
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Affiliation(s)
- Reem Joukhadar
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
| | - Yongjun Li
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
| | - Rebecca Thistlethwaite
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
| | - Kerrie L. Forrest
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
| | - Josquin F. Tibbits
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
| | - Richard Trethowan
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Cobbitty, NSW, Australia
| | - Matthew J. Hayden
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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Wang X, Xiang M, Li H, Li X, Mu K, Huang S, Zhang Y, Cheng X, Yang S, Yuan X, Singh RP, Bhavani S, Zeng Q, Wu J, Kang Z, Liu S, Han D. High-density mapping of durable and broad-spectrum stripe rust resistance gene Yr30 in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:152. [PMID: 38850423 DOI: 10.1007/s00122-024-04654-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 05/21/2024] [Indexed: 06/10/2024]
Abstract
KEY MESSAGE The durable stripe rust resistance gene Yr30 was fine-mapped to a 610-kb region in which five candidate genes were identified by expression analysis and sequence polymorphisms. The emergence of genetically diverse and more aggressive races of Puccinia striiformis f. sp. tritici (Pst) in the past twenty years has resulted in global stripe rust outbreaks and the rapid breakdown of resistance genes. Yr30 is an adult plant resistance (APR) gene with broad-spectrum effectiveness and its durability. Here, we fine-mapped the YR30 locus to a 0.52-cM interval using 1629 individuals derived from residual heterozygous F5:6 plants in a Yaco"S"/Mingxian169 recombinant inbred line population. This interval corresponded to a 610-kb region in the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq version 2.1 on chromosome arm 3BS harboring 30 high-confidence genes. Five genes were identified as candidate genes based on functional annotation, expression analysis by RNA-seq and sequence polymorphisms between cultivars with and without Yr30 based on resequencing. Haplotype analysis of the target region identified six haplotypes (YR30_h1-YR30_h6) in a panel of 1215 wheat accessions based on the 660K feature genotyping array. Lines with YR30_h6 displayed more resistance to stripe rust than the other five haplotypes. Near-isogenic lines (NILs) with Yr30 showed a 32.94% higher grain yield than susceptible counterparts when grown in a stripe rust nursery, whereas there was no difference in grain yield under rust-free conditions. These results lay a foundation for map-based cloning Yr30.
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Affiliation(s)
- Xiaoting Wang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Mingjie Xiang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Huaizhou Li
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Xiaoxiao Li
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Keqing Mu
- State Key Laboratory of Crop Stress Biology for Arid Areas, Plant Protection, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Shuo Huang
- State Key Laboratory of Crop Stress Biology for Arid Areas, Plant Protection, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Yibo Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Xiangrui Cheng
- State Key Laboratory of Crop Stress Biology for Arid Areas, Plant Protection, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Shuqing Yang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Xunying Yuan
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT), 56237, El Batan, Texcoco, Estado de Mexico, Mexico
- Hubei Hongshan Laboratory, National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, No. 1 Shizishan Street, Hongshan District, Wuhan City, 430070, Hubei Province, China
| | - Sridhar Bhavani
- International Maize and Wheat Improvement Center (CIMMYT), 56237, El Batan, Texcoco, Estado de Mexico, Mexico
| | - Qingdong Zeng
- State Key Laboratory of Crop Stress Biology for Arid Areas, Plant Protection, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Jianhui Wu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Zhensheng Kang
- State Key Laboratory of Crop Stress Biology for Arid Areas, Plant Protection, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Shengjie Liu
- State Key Laboratory of Crop Stress Biology for Arid Areas, Plant Protection, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China.
| | - Dejun Han
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China.
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Pradhan AK, Budhlakoti N, Chandra Mishra D, Prasad P, Bhardwaj SC, Sareen S, Sivasamy M, Jayaprakash P, Geetha M, Nisha R, Shajitha P, Peter J, Kaur A, Kaur S, Vikas VK, Singh K, Kumar S. Identification of Novel QTLs/Defense Genes in Spring Wheat Germplasm Panel for Seedling and Adult Plant Resistance to Stem Rust and Their Validation Through KASP Marker Assays. PLANT DISEASE 2023:PDIS09222242RE. [PMID: 37311158 DOI: 10.1094/pdis-09-22-2242-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Stem rust is one of the major diseases threatening wheat production globally. To identify novel resistance quantitative trait loci (QTLs), we performed 35K Axiom Array SNP genotyping assays on an association mapping panel of 400 germplasm accessions, including Indian landraces, in conjunction with phenotyping for stem rust at seedling and adult plant stages. Association analyses using three genome wide association study (GWAS) models (CMLM, MLMM, and FarmCPU) revealed 20 reliable QTLs for seedling and adult plant resistance. Among these 20 QTLs, five QTLs were found consistent with three models, i.e., four QTLs on chromosome 2AL, 2BL, 2DL, and 3BL for seedling resistance and one QTL on chromosome 7DS for adult plant resistance. Further, we identified a total of 21 potential candidate genes underlying QTLs using gene ontology analysis, including a leucine rich repeat receptor (LRR) and P-loop nucleoside triphosphate hydrolase, which have a role in pathogen recognition and disease resistance. Furthermore, four QTLs (Qsr.nbpgr-3B_11, QSr.nbpgr-6AS_11, QSr.nbpgr-2AL_117-6, and QSr.nbpgr-7BS_APR) were validated through KASP located on chromosomes 3B, 6A, 2A, and 7B. Out of these QTLs, QSr.nbpgr-7BS_APR was identified as a novel QTL for stem rust resistance which has been found effective in both seedling as well as the adult plant stages. Identified novel genomic regions and validated QTLs have the potential to be deployed in wheat improvement programs to develop disease resistant varieties for stem rust and can diversify the genetic basis of resistance.
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Affiliation(s)
| | - Neeraj Budhlakoti
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India
| | | | - Pramod Prasad
- ICAR-Indian Institute of Wheat and Barley Research, Flowerdale, Shimla, Himachal Pradesh 171002, India
| | - S C Bhardwaj
- ICAR-Indian Institute of Wheat and Barley Research, Flowerdale, Shimla, Himachal Pradesh 171002, India
| | - Sindhu Sareen
- ICAR-Indian Institute of Wheat and Barley Research, Karnal 132001, India
| | - M Sivasamy
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - P Jayaprakash
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - M Geetha
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - R Nisha
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - P Shajitha
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - John Peter
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - Amandeep Kaur
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141004, India
| | - Satinder Kaur
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141004, India
| | - V K Vikas
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - Kuldeep Singh
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Telangana 502324, India
| | - Sundeep Kumar
- ICAR-National Bureau of Plant Genetic Resources, New Delhi 110012, India
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Norman M, Bariana H, Bansal U, Periyannan S. The Keys to Controlling Wheat Rusts: Identification and Deployment of Genetic Resistance. PHYTOPATHOLOGY 2023; 113:667-677. [PMID: 36897760 DOI: 10.1094/phyto-02-23-0041-ia] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Rust diseases are among the major constraints for wheat production worldwide due to the emergence and spread of highly destructive races of Puccinia. The most common approach to minimize yield losses due to rust is to use cultivars that are genetically resistant. Modern wheat cultivars, landraces, and wild relatives can contain undiscovered resistance genes, which typically encode kinase or nucleotide-binding site leucine rich repeat (NLR) domain containing receptor proteins. Recent research has shown that these genes can provide either resistance in all growth stages (all-stage resistance; ASR) or specially in later growth stages (adult-plant resistance; APR). ASR genes are pathogen and race-specific, meaning can function against selected races of the Puccinia fungus due to the necessity to recognize specific avirulence molecules in the pathogen. APR genes are either pathogen-specific or multipathogen resistant but often race-nonspecific. Prediction of resistance genes through rust infection screening alone remains complex when more than one resistance gene is present. However, breakthroughs during the past half century such as the single-nucleotide polymorphism-based genotyping techniques and resistance gene isolation strategies like mutagenesis, resistance gene enrichment, and sequencing (MutRenSeq), mutagenesis and chromosome sequencing (MutChromSeq), and association genetics combined with RenSeq (AgRenSeq) enables rapid transfer of resistance from source to modern cultivars. There is a strong need for combining multiple genes for better efficacy and longer-lasting resistance. Hence, techniques like gene cassette creation speeds up the gene combination process, but their widespread adoption and commercial use is limited due to their transgenic nature.
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Affiliation(s)
- Michael Norman
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney Plant Breeding Institute, 107 Cobbitty Road, Cobbitty, NSW 2570, Australia
- Commonwealth Scientific and Industrial Research Organization Agriculture and Food, Canberra, ACT 2601, Australia
| | - Harbans Bariana
- School of Science, Western Sydney University, Bourke Road, Richmond, NSW 2753, Australia
| | - Urmil Bansal
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney Plant Breeding Institute, 107 Cobbitty Road, Cobbitty, NSW 2570, Australia
| | - Sambasivam Periyannan
- School of Agriculture and Environmental Science & Centre for Crop Health, University of Southern Queensland, Toowoomba, Qld 4350, Australia
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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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Kaur S, Gill HS, Breiland M, Kolmer JA, Gupta R, Sehgal SK, Gill U. Identification of leaf rust resistance loci in a geographically diverse panel of wheat using genome-wide association analysis. FRONTIERS IN PLANT SCIENCE 2023; 14:1090163. [PMID: 36818858 PMCID: PMC9929074 DOI: 10.3389/fpls.2023.1090163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Leaf rust, caused by Puccinia triticina (Pt) is among the most devastating diseases posing a significant threat to global wheat production. The continuously evolving virulent Pt races in North America calls for exploring new sources of leaf rust resistance. A diversity panel of 365 bread wheat accessions selected from a worldwide population of landraces and cultivars was evaluated at the seedling stage against four Pt races (TDBJQ, TBBGS, MNPSD and, TNBJS). A wide distribution of seedling responses against the four Pt races was observed. Majority of the genotypes displayed a susceptible response with only 28 (9.8%), 59 (13.5%), 45 (12.5%), and 29 (8.1%) wheat accessions exhibiting a highly resistant response to TDBJQ, TBBGS, MNPSD and, TNBJS, respectively. Further, we conducted a high-resolution multi-locus genome-wide association study (GWAS) using a set of 302,524 high-quality single nucleotide polymorphisms (SNPs). The GWAS analysis identified 27 marker-trait associations (MTAs) for leaf rust resistance on different wheat chromosomes of which 20 MTAs were found in the vicinity of known Lr genes, MTAs, or quantitative traits loci (QTLs) identified in previous studies. The remaining seven significant MTAs identified represent genomic regions that harbor potentially novel genes for leaf rust resistance. Furthermore, the candidate gene analysis for the significant MTAs identified various genes of interest that may be involved in disease resistance. The identified resistant lines and SNPs linked to the QTLs in this study will serve as valuable resources in wheat rust resistance breeding programs.
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Affiliation(s)
- Shivreet Kaur
- Department of Plant Pathology, North Dakota State University, Fargo, ND, United States
| | - Harsimardeep S. Gill
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Matthew Breiland
- Department of Plant Pathology, North Dakota State University, Fargo, ND, United States
| | - James A. Kolmer
- Cereal Disease Laboratory, United States Department of Agriculture-Agricultural Research Service (USDA-ARS), St. Paul, MN, United States
| | - Rajeev Gupta
- Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Fargo, ND, United States
| | - Sunish K. Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Upinder Gill
- Department of Plant Pathology, North Dakota State University, Fargo, ND, United States
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Li Y, Shi F, Lin Z, Robinson H, Moody D, Rattey A, Godoy J, Mullan D, Keeble-Gagnere G, Hayden MJ, Tibbits JFG, Daetwyler HD. Benefit of Introgression Depends on Level of Genetic Trait Variation in Cereal Breeding Programmes. FRONTIERS IN PLANT SCIENCE 2022; 13:786452. [PMID: 35783964 PMCID: PMC9240786 DOI: 10.3389/fpls.2022.786452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
We investigated the benefit from introgression of external lines into a cereal breeding programme and strategies that accelerated introgression of the favourable alleles while minimising linkage drag using stochastic computer simulation. We simulated genomic selection for disease resistance and grain yield in two environments with a high level of genotype-by-environment interaction (G × E) for the latter trait, using genomic data of a historical barley breeding programme as the base generation. Two populations (existing and external) were created from this base population with different allele frequencies for few (N = 10) major and many (N ~ 990) minor simulated disease quantitative trait loci (QTL). The major disease QTL only existed in the external population and lines from the external population were introgressed into the existing population which had minor disease QTL with low, medium and high allele frequencies. The study revealed that the benefit of introgression depended on the level of genetic variation for the target trait in the existing cereal breeding programme. Introgression of external resources into the existing population was beneficial only when the existing population lacked variation in disease resistance or when minor disease QTL were already at medium or high frequency. When minor disease QTL were at low frequencies, no extra genetic gain was achieved from introgression. More benefit in the disease trait was obtained from the introgression if the major disease QTL had larger effect sizes, more selection emphasis was applied on disease resistance, or more external lines were introgressed. While our strategies to increase introgression of major disease QTL were generally successful, most were not able to completely avoid negative impacts on selection for grain yield with the only exception being when major introgression QTL effects were very large. Breeding programmes are advised to carefully consider the level of genetic variation in a trait available in their breeding programme before deciding to introgress germplasms.
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Affiliation(s)
- Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Fan Shi
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Zibei Lin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | | | | | | | | | | | - Matthew J. Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | | | - Hans D. Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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Vikas VK, Pradhan AK, Budhlakoti N, Mishra DC, Chandra T, Bhardwaj SC, Kumar S, Sivasamy M, Jayaprakash P, Nisha R, Shajitha P, Peter J, Geetha M, Mir RR, Singh K, Kumar S. Multi-locus genome-wide association studies (ML-GWAS) reveal novel genomic regions associated with seedling and adult plant stage leaf rust resistance in bread wheat (Triticum aestivum L.). Heredity (Edinb) 2022; 128:434-449. [PMID: 35418669 DOI: 10.1038/s41437-022-00525-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 01/02/2023] Open
Abstract
Leaf rust is one of the important diseases limiting global wheat production and productivity. To identify quantitative trait nucleotides (QTNs) or genomic regions associated with seedling and adult plant leaf rust resistance, multilocus genome-wide association studies (ML-GWAS) were performed on a panel of 400 diverse wheat genotypes using 35 K single-nucleotide polymorphism (SNP) genotyping assays and trait data of leaf rust resistance. Association analyses using six multi-locus GWAS models revealed a set of 201 significantly associated QTNs for seedling and 65 QTNs for adult plant resistance (APR), explaining 1.98-31.72% of the phenotypic variation for leaf rust. Among these QTNs, 51 reliable QTNs for seedling and 15 QTNs for APR were consistently detected in at least two GWAS models and were considered reliable QTNs. Three genomic regions were pleiotropic, each controlling two to three pathotype-specific seedling resistances to leaf rust. We also identified candidate genes, such as leucine-rich repeat receptor-like (LRR) protein kinases, P-loop containing nucleoside triphosphate hydrolase and serine-threonine/tyrosine-protein kinases (STPK), which have a role in pathogen recognition and disease resistance linked to the significantly associated genomic regions. The QTNs identified in this study can prove useful in wheat molecular breeding programs aimed at enhancing resistance to leaf rust and developing next-generation leaf rust-resistant varieties.
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Affiliation(s)
- V K Vikas
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, 643 231, India
| | | | - Neeraj Budhlakoti
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
| | | | - Tilak Chandra
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India
| | - S C Bhardwaj
- ICAR-Indian Institute of Wheat and Barley Research, Flowerdale, Shimla, Himachal Pradesh, 171002, India
| | - Subodh Kumar
- ICAR-Indian Institute of Wheat and Barley Research, Flowerdale, Shimla, Himachal Pradesh, 171002, India
| | - M Sivasamy
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, 643 231, India
| | - P Jayaprakash
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, 643 231, India
| | - R Nisha
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, 643 231, India
| | - P Shajitha
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, 643 231, India
| | - John Peter
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, 643 231, India
| | - M Geetha
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, 643 231, India
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Srinagar, India
| | - Kuldeep Singh
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India.,Genetic Resource Division, ICRISAT, Patancheru, Hyderabad, India
| | - Sundeep Kumar
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India.
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Kumar K, Jan I, Saripalli G, Sharma PK, Mir RR, Balyan HS, Gupta PK. An Update on Resistance Genes and Their Use in the Development of Leaf Rust Resistant Cultivars in Wheat. Front Genet 2022; 13:816057. [PMID: 35432483 PMCID: PMC9008719 DOI: 10.3389/fgene.2022.816057] [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: 11/16/2021] [Accepted: 02/28/2022] [Indexed: 11/19/2022] Open
Abstract
Wheat is one of the most important cereal crops in the world. The production and productivity of wheat is adversely affected by several diseases including leaf rust, which can cause yield losses, sometimes approaching >50%. In the present mini-review, we provide updated information on (i) all Lr genes including those derived from alien sources and 14 other novel resistance genes; (ii) a list of QTLs identified using interval mapping and MTAs identified using GWAS (particular those reported recently i.e., after 2018) and their association with known Lr genes; (iii) introgression/pyramiding of individual Lr genes in commercial/prominent cultivars from 18 different countries including India. Challenges and future perspectives of breeding for leaf rust resistance are also provided at the end of this mini-review. We believe that the information in this review will prove useful for wheat geneticists/breeders, not only in the development of leaf rust-resistant wheat cultivars, but also in the study of molecular mechanism of leaf rust resistance in wheat.
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Affiliation(s)
- Kuldeep Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - Irfat Jan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- Division of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Kashmir University of Agricultural Sciences and Technology, Wadura, India
| | - Gautam Saripalli
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, United States
| | - P. K. Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Kashmir University of Agricultural Sciences and Technology, Wadura, India
| | - H. S. Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - P. K. Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- *Correspondence: P. K. Gupta, ,
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Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:1. [PMID: 37309486 PMCID: PMC10248672 DOI: 10.1007/s11032-021-01272-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.
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Affiliation(s)
- Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| | - Anand Kumar
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 202002 India
| | - Sumandeep Bazzer
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
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11
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Francki MG, Stainer GS, Walker E, Rebetzke GJ, Stefanova KT, French RJ. Phenotypic Evaluation and Genetic Analysis of Seedling Emergence in a Global Collection of Wheat Genotypes ( Triticum aestivum L.) Under Limited Water Availability. FRONTIERS IN PLANT SCIENCE 2021; 12:796176. [PMID: 35003185 PMCID: PMC8739788 DOI: 10.3389/fpls.2021.796176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 11/15/2021] [Indexed: 06/14/2023]
Abstract
The challenge in establishing an early-sown wheat crop in southern Australia is the need for consistently high seedling emergence when sowing deep in subsoil moisture (>10 cm) or into dry top-soil (4 cm). However, the latter is strongly reliant on a minimum soil water availability to ensure successful seedling emergence. This study aimed to: (1) evaluate 233 Australian and selected international wheat genotypes for consistently high seedling emergence under limited soil water availability when sown in 4 cm of top-soil in field and glasshouse (GH) studies; (2) ascertain genetic loci associated with phenotypic variation using a genome-wide association study (GWAS); and (3) compare across loci for traits controlling coleoptile characteristics, germination, dormancy, and pre-harvest sprouting. Despite significant (P < 0.001) environment and genotype-by-environment interactions within and between field and GH experiments, eight genotypes that included five cultivars, two landraces, and one inbred line had consistently high seedling emergence (mean value > 85%) across nine environments. Moreover, 21 environment-specific quantitative trait loci (QTL) were detected in GWAS analysis on chromosomes 1B, 1D, 2B, 3A, 3B, 4A, 4B, 5B, 5D, and 7D, indicating complex genetic inheritance controlling seedling emergence. We aligned QTL for known traits and individual genes onto the reference genome of wheat and identified 16 QTL for seedling emergence in linkage disequilibrium with coleoptile length, width, and cross-sectional area, pre-harvest sprouting and dormancy, germination, seed longevity, and anthocyanin development. Therefore, it appears that seedling emergence is controlled by multifaceted networks of interrelated genes and traits regulated by different environmental cues.
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Affiliation(s)
- Michael G. Francki
- Department of Primary Industries and Regional Development, South Perth, WA, Australia
- State Agricultural Biotechnology Centre, Murdoch University, Murdoch, WA, Australia
| | - Grantley S. Stainer
- Department of Primary Industries and Regional Development, Merredin, WA, Australia
| | - Esther Walker
- Department of Primary Industries and Regional Development, South Perth, WA, Australia
- State Agricultural Biotechnology Centre, Murdoch University, Murdoch, WA, Australia
| | - Gregory J. Rebetzke
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, Canberra, ACT, Australia
| | - Katia T. Stefanova
- Department of Primary Industries and Regional Development, South Perth, WA, Australia
| | - Robert J. French
- Department of Primary Industries and Regional Development, Merredin, WA, Australia
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12
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Joukhadar R, Thistlethwaite R, Trethowan RM, Hayden MJ, Stangoulis J, Cu S, Daetwyler HD. Genomic selection can accelerate the biofortification of spring wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3339-3350. [PMID: 34254178 DOI: 10.1007/s00122-021-03900-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/28/2021] [Indexed: 06/13/2023]
Abstract
KEY MESSAGE Genomic selection enabled accurate prediction for the concentration of 13 nutritional element traits in wheat. Wheat biofortification is one of the most sustainable strategies to alleviate mineral deficiency in human diets. Here, we investigated the potential of genomic selection using BayesR and Bayesian ridge regression (BRR) models to predict grain yield (YLD) and the concentration of 13 nutritional elements in grains (B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P and Zn) using a population of 1470 spring wheat lines. The lines were grown in replicated field trials with two times of sowing (TOS) at 3 locations (Narrabri-NSW, all lines; Merredin-WA and Horsham-VIC, 200 core lines). Narrow-sense heritability across environments (locations/TOS) ranged from 0.09 to 0.45. Co, K, Na and Ca showed low to negative genetic correlations with other traits including YLD, while the remaining traits were negatively correlated with YLD. When all environments were included in the reference population, medium to high prediction accuracy was observed for the different traits across environments. BayesR had higher average prediction accuracy for mineral concentrations (r = 0.55) compared to BRR (r = 0.48) across all traits and environments but both methods had comparable accuracies for YLD. We also investigated the utility of one or two locations (reference locations) to predict the remaining location(s), as well as the ability of one TOS to predict the other. Under these scenarios, BayesR and BRR showed comparable performance but with lower prediction accuracy compared to the scenario of predicting reference environments for new lines. Our study demonstrates the potential of genomic selection for enriching wheat grain with nutritional elements in biofortification breeding.
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Affiliation(s)
- Reem Joukhadar
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia.
| | - Rebecca Thistlethwaite
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
| | - Richard M Trethowan
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Cobbitty, NSW, Australia
| | - Matthew J Hayden
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - James Stangoulis
- College of Science and Engineering, Flinders University, Sturt Road, Bedford Park, South Australia, 5042, Australia
| | - Suong Cu
- College of Science and Engineering, Flinders University, Sturt Road, Bedford Park, South Australia, 5042, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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13
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Huang C, Shen C, Wen T, Gao B, Zhu D, Li D, Lin Z. Genome-wide association mapping for agronomic traits in an 8-way Upland cotton MAGIC population by SLAF-seq. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2459-2468. [PMID: 33912997 DOI: 10.1007/s00122-021-03835-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 04/12/2021] [Indexed: 06/12/2023]
Abstract
One sub-MAGIC population was genotyped using SLAF-seq, and QTLs and candidate genes for agronomic traits were identified in Upland cotton. The agronomic traits of Upland cotton have serious impacts on cotton production, as well as economic benefits. To discover the genetic basis of important agronomic traits in Upland cotton, a subset MAGIC (multi-parent advanced generation inter-cross) population containing 372 lines (SMLs) was selected from an 8-way MAGIC population with 960 lines. The 372 lines and 8 parents were phenotyped in six environments and deeply genotyped by SLAF-seq with 60,495 polymorphic SNPs. The genetic diversity indexes of all SNPs were 0.324 and 0.362 for the parents and MAGIC lines, respectively. The LD decay distance of the SMLs was 600 kb (r2 = 0.1). Genome-wide association mapping was performed using 60,495 SNPs and the phenotypic data of the SMLs, and 177 SNPs were identified to be significantly associated with 9 stable agronomic traits in multiple environments. The identified SNPs were divided into 117 QTLs (quantitative trait loci) by LD decay distance, explaining 5.44% to 31.64% of the phenotypic variation. Among the 117 QTLs, 3 QTLs were stable in multiple environments, and 11 QTL regions were proven to have pleiotropism associated with multiple traits. Within QTL regions, 154 genes were preferentially expressed in correlated tissues, and 8 genes with known functions were identified as priori candidate genes. Two genes, GhACT1 and GhGASL3, reported to have clear functions, were, respectively, located in qFE-A05-4 and qFE-D04-3, two stable QTLs for FE. This study revealed the genetic basis of important agronomic traits of Upland cotton, and the results will facilitate molecular breeding in cotton.
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Affiliation(s)
- Cong Huang
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Chao Shen
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Tianwang Wen
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Bin Gao
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - De Zhu
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Dingguo Li
- Institute of Crop Genetic and Breeding, Yangtze University, Jingzhou, 434025, Hubei, China.
| | - Zhongxu Lin
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China.
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14
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Emebiri L, Hildebrand S, Tan MK, Juliana P, Singh PK, Fuentes-Davila G, Singh RP. Pre-emptive Breeding Against Karnal Bunt Infection in Common Wheat: Combining Genomic and Agronomic Information to Identify Suitable Parents. FRONTIERS IN PLANT SCIENCE 2021; 12:675859. [PMID: 34394138 PMCID: PMC8358121 DOI: 10.3389/fpls.2021.675859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/18/2021] [Indexed: 06/13/2023]
Abstract
Wheat (Triticum aestivum L.) is the most widely grown cereal crop in the world and is staple food to half the world's population. The current world population is expected to reach 9.8 billion people by 2050, but food production is not expected to keep pace with demand in developing countries. Significant opportunities exist for traditional grain exporters to produce and export greater amounts of wheat to fill the gap. Karnal bunt, however, is a major threat, due to its use as a non-tariff trade barrier by several wheat-importing countries. The cultivation of resistant varieties remains the most cost-effective approach to manage the disease, but in countries that are free of the disease, genetic improvement is difficult due to quarantine restrictions. Here we report a study on pre-emptive breeding designed to identify linked molecular markers, evaluate the prospects of genomic selection as a tool, and prioritise wheat genotypes suitable for use as parents. In a genome-wide association (GWAS) study, we identified six DArTseq markers significantly linked to Karnal bunt resistance, which explained between 7.6 and 29.5% of the observed phenotypic variation. The accuracy of genomic prediction was estimated to vary between 0.53 and 0.56, depending on whether it is based solely on the identified Quantitative trait loci (QTL) markers or the use of genome-wide markers. As genotypes used as parents would be required to possess good yield and phenology, further research was conducted to assess the agronomic value of Karnal bunt resistant germplasm from the International Maize and Wheat Improvement Center (CIMMYT). We identified an ideal genotype, ZVS13_385, which possessed similar agronomic attributes to the highly successful Australian wheat variety, Mace. It is phenotypically resistant to Karnal bunt infection (<1% infection) and carried all the favourable alleles detected for resistance in this study. The identification of a genotype combining Karnal bunt resistance with adaptive agronomic traits overcomes the concerns of breeders regarding yield penalty in the absence of the disease.
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Affiliation(s)
- Livinus Emebiri
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia
- Graham Centre for Agricultural Innovation (NSW Department of Primary Industries and Charles Sturt University), Wagga Wagga, NSW, Australia
| | - Shane Hildebrand
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia
| | - Mui-Keng Tan
- NSW Department of Primary Industries, Menangle, NSW, Australia
| | - Philomin Juliana
- International Maize and Wheat Improvement Center, Mexico City, Mexico
| | - Pawan K. Singh
- International Maize and Wheat Improvement Center, Mexico City, Mexico
| | | | - Ravi P. Singh
- International Maize and Wheat Improvement Center, Mexico City, Mexico
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15
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Joukhadar R, Thistlethwaite R, Trethowan R, Keeble-Gagnère G, Hayden MJ, Ullah S, Daetwyler HD. Meta-analysis of genome-wide association studies reveal common loci controlling agronomic and quality traits in a wide range of normal and heat stressed environments. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2113-2127. [PMID: 33768282 DOI: 10.1007/s00122-021-03809-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 03/01/2021] [Indexed: 06/12/2023]
Abstract
Several stable QTL were detected using metaGWAS analysis for different agronomic and quality traits under 26 normal and heat stressed environments. Heat stress, exacerbated by global warming, has a negative influence on wheat production worldwide and climate resilient cultivars can help mitigate these impacts. Selection decisions should therefore depend on multi-environment experiments representing a range of temperatures at critical stages of development. Here, we applied a meta-genome wide association analysis (metaGWAS) approach to detect stable QTL with significant effects across multiple environments. The metaGWAS was applied to 11 traits scored in 26 trials that were sown at optimal or late times of sowing (TOS1 and TOS2, respectively) at five locations. A total of 2571 unique wheat genotypes (13,959 genotypes across all environments) were included and the analysis conducted on TOS1, TOS2 and both times of sowing combined (TOS1&2). The germplasm was genotyped using a 90 k Infinium chip and imputed to exome sequence level, resulting in 341,195 single nucleotide polymorphisms (SNPs). The average accuracy across all imputed SNPs was high (92.4%). The three metaGWAS analyses revealed 107 QTL for the 11 traits, of which 16 were detected in all three analyses and 23 were detected in TOS1&2 only. The remaining QTL were detected in either TOS1 or TOS2 with or without TOS1&2, reflecting the complex interactions between the environments and the detected QTL. Eight QTL were associated with grain yield and seven with multiple traits. The identified QTL provide an important resource for gene enrichment and fine mapping to further understand the mechanisms of gene × environment interaction under both heat stressed and unstressed conditions.
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Affiliation(s)
- Reem Joukhadar
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia.
| | - Rebecca Thistlethwaite
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
| | - Richard Trethowan
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Cobbitty, NSW, Australia
| | | | - Matthew J Hayden
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Smi Ullah
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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16
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Genome-wide association studies: assessing trait characteristics in model and crop plants. Cell Mol Life Sci 2021; 78:5743-5754. [PMID: 34196733 PMCID: PMC8316211 DOI: 10.1007/s00018-021-03868-w] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 05/28/2021] [Accepted: 05/29/2021] [Indexed: 01/19/2023]
Abstract
GWAS involves testing genetic variants across the genomes of many individuals of a population to identify genotype–phenotype association. It was initially developed and has proven highly successful in human disease genetics. In plants genome-wide association studies (GWAS) initially focused on single feature polymorphism and recombination and linkage disequilibrium but has now been embraced by a plethora of different disciplines with several thousand studies being published in model and crop species within the last decade or so. Here we will provide a comprehensive review of these studies providing cases studies on biotic resistance, abiotic tolerance, yield associated traits, and metabolic composition. We also detail current strategies of candidate gene validation as well as the functional study of haplotypes. Furthermore, we provide a critical evaluation of the GWAS strategy and its alternatives as well as future perspectives that are emerging with the emergence of pan-genomic datasets.
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17
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Francki MG, Walker E, McMullan CJ, Morris WG. Evaluation of Septoria Nodorum Blotch (SNB) Resistance in Glumes of Wheat ( Triticum aestivum L.) and the Genetic Relationship With Foliar Disease Response. Front Genet 2021; 12:681768. [PMID: 34267781 PMCID: PMC8276050 DOI: 10.3389/fgene.2021.681768] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Abstract
Septoria nodorum blotch (SNB) is a necrotrophic disease of wheat prominent in some parts of the world, including Western Australia (WA) causing significant losses in grain yield. The genetic mechanisms for resistance are complex involving multiple quantitative trait loci. In order to decipher comparable or independent regulation, this study identified the genetic control for glume compared to foliar resistance across four environments in WA against 37 different isolates. High proportion of the phenotypic variation across environments was contributed by genotype (84.0% for glume response and 82.7% for foliar response) with genotype-by-environment interactions accounting for a proportion of the variation for both glume and foliar response (14.7 and 16.2%, respectively). Despite high phenotypic correlation across environments, most of the eight and 14 QTL detected for glume and foliar resistance using genome wide association analysis (GWAS), respectively, were identified as environment-specific. QTL for glume and foliar resistance neither co-located nor were in LD in any particular environment indicating autonomous genetic mechanisms control SNB response in adult plants, regulated by independent biological mechanisms and influenced by significant genotype-by- environment interactions. Known Snn and Tsn loci and QTL were compared with 22 environment-specific QTL. None of the eight QTL for glume or the 14 for foliar response were co-located or in linkage disequilibrium with Snn and only one foliar QTL was in LD with Tsn loci on the physical map. Therefore, glume and foliar response to SNB in wheat is regulated by multiple environment-specific loci which function independently, with limited influence of known NE-Snn interactions for disease progression in Western Australian environments. Breeding for stable resistance would consequently rely on recurrent phenotypic selection to capture and retain favorable alleles for both glume and foliar resistance relevant to a particular environment.
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Affiliation(s)
- Michael G Francki
- Department of Primary Industries and Regional Development, South Perth, WA, Australia.,State Agricultural Biotechnology Centre, Murdoch University, Murdoch, WA, Australia
| | - Esther Walker
- Department of Primary Industries and Regional Development, South Perth, WA, Australia.,State Agricultural Biotechnology Centre, Murdoch University, Murdoch, WA, Australia
| | | | - W George Morris
- Department of Primary Industries and Regional Development, South Perth, WA, Australia
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18
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Zhang Y, Wang Z, Quan W, Zhang X, Feng J, Ren J, Jiang X, Zhang Z. Mapping of a QTL with major effect on reducing leaf rust severity at the adult plant growth stage on chromosome 2BL in wheat landrace Hongmazha. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1363-1376. [PMID: 33550471 DOI: 10.1007/s00122-021-03776-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 01/15/2021] [Indexed: 06/12/2023]
Abstract
A major QTL (QLr.cau-2BL) for APR to leaf rust was detected on 2BL; an SSR marker was developed to closely link with QLr.cau-2BL and validated for effectiveness of MAS. The wheat landrace Hongmazha (HMZ) possesses adult plant resistance (APR) to leaf rust. To detect and validate quantitative trait locus (QTL) for the APR, four wheat populations were assessed for leaf rust severity in a total of eight field and greenhouse experiments. The mapping population Aquileja × HMZ (120 recombinant inbred lines, RILs) was genotyped using 90 K SNP markers. A major QTL (QLr.cau-2BL) was detected between the markers IWB3854 and IWB21922 on chromosome 2BL. IWB3854 and IWB21922 were positioned at approximately 531.14 Mb and 616.48 Mb, respectively, on 2BL of IWGSC RefSeq v1.0 physical map. Based on the sequences between 531.14 and 616.48 Mb on 2BL of IWGSC RefSeq v1.0, 415 simple sequence repeat (SSR) markers were developed. These markers and 28 previously published SSR makers were screened; the resulted polymorphic markers were used to genotype the relatively larger population RL6058 × HMZ (371 RILs). QLr.cau-2BL was mapped within a 1.5 cM interval on 2BL map of RL6058 × HMZ, and a marker (Ta2BL_ssr7) was identified to closely link with QLr.cau-2BL. Effectiveness of selection for QLr.cau-2BL based on Ta2BL_ssr7 was validated using two populations (RL6058 × HMZ F2:3 and Jimai22 × HMZ BC4F2:3). In addition, polymorphism at Ta2BL_ssr7 was detected among a panel of 282 commercial wheat cultivars. We believe, therefore, that Ta2BL_ssr7 should be useful for introducing QLr.cau-2BL into commercial wheat cultivars and for accumulating QLr.cau-2BL with other APR QTL.
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Affiliation(s)
- Yibin Zhang
- Department of Plant Pathology, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Zhen Wang
- Department of Plant Pathology, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Wei Quan
- Beijing Engineering and Technique Research Center for Hybrid Wheat, Beijing Academy of Agricultural and Forestry Sciences, Beijing, 100097, People's Republic of China.
| | - Xiang Zhang
- National Fisheries Technology Extension Center, Beijing, 100125, People's Republic of China
| | - Jing Feng
- Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China
| | - Junda Ren
- Key Laboratory for Northern Urban Agriculture of Ministry of Agriculture and Rural Affairs, Beijing University of Agriculture, Beijing, 102206, People's Republic of China
| | - Xu Jiang
- Department of Plant Pathology, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Zhongjun Zhang
- Department of Plant Pathology, China Agricultural University, Beijing, 100193, People's Republic of China.
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