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Kumar P, Gill HS, Singh M, Kaur K, Koupal D, Talukder S, Bernardo A, Amand PS, Bai G, Sehgal SK. Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat (Triticum aestivum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:205. [PMID: 39141073 PMCID: PMC11324803 DOI: 10.1007/s00122-024-04701-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 07/27/2024] [Indexed: 08/15/2024]
Abstract
KEY MESSAGE Multi-environmental characterization of flag leaf morphology traits in the US winter wheat revealed nine stable genomic regions for different flag leaf-related traits including a major region governing flag leaf angle. Flag leaf in wheat is the primary contributor to accumulating photosynthetic assimilates. Flag leaf morphology (FLM) traits determine the overall canopy structure and capacity to intercept the light, thus influencing photosynthetic efficiency. Hence, understanding the genetic control of these traits could be useful for breeding desirable ideotypes in wheat. We used a panel of 272 accessions from the hard winter wheat (HWW) region of the USA to investigate the genetic architecture of five FLM traits including flag leaf length (FLL), width (FLW), angle (FLANG), length-width ratio, and area using multilocation field experiments. Multi-environment GWAS using 14,537 single-nucleotide polymorphisms identified 36 marker-trait associations for different traits, with nine being stable across environments. A novel and major stable region for FLANG (qFLANG.1A) was identified on chromosome 1A accounting for 9-13% variation. Analysis of spatial distribution for qFLANG.1A in a set of 2354 breeding lines from the HWW region showed a higher frequency of allele associated with narrow leaf angle. A KASP assay was developed for allelic discrimination of qFLANG.1A and was used for its independent validation in a diverse set of spring wheat accessions. Furthermore, candidate gene analysis for two regions associated with FLANG identified seven putative genes of interest for each of the two regions. The present study enhances our understanding of the genetic control of FLM in wheat, particularly FLANG, and these results will be useful for dissecting the genes underlying canopy architecture in wheat facilitating the development of climate-resilient wheat varieties.
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Affiliation(s)
- Pradeep Kumar
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Harsimardeep S Gill
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Mandeep Singh
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Karanjot Kaur
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Dante Koupal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Shyamal Talukder
- Department of Soil and Crop Sciences, Texas A&M University, Texas A&M AgriLife Research Center, Beaumont, TX, USA
| | - Amy Bernardo
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, USA
| | - Paul St Amand
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, USA
| | - Sunish K Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA.
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Zhang D, Zhao R, Xian G, Kou Y, Ma W. A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1361716. [PMID: 38571713 PMCID: PMC10987776 DOI: 10.3389/fpls.2024.1361716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/04/2024] [Indexed: 04/05/2024]
Abstract
Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing novel high-quality crop varieties. Predicting the associations between genes and traits requires the organization and analysis of multi-dimensional scientific data. The existing methods for establishing the relationships between genomic data and phenotypic data can only elucidate the associations between genes and individual traits. However, there are relatively few methods for detecting elite polyphenotype genes. In this study, a knowledge graph for traits regulating-genes was constructed by collecting data from the PubMed database and eight other databases related to the staple food crops rice, maize, and wheat as well as the model plant Arabidopsis thaliana. On the basis of the knowledge graph, a model for predicting traits regulating-genes was constructed by combining the data attributes of the gene nodes and the topological relationship attributes of the gene nodes. Additionally, a scoring method for predicting the genes regulating specific traits was developed to screen for elite polyphenotype genes. A total of 125,591 nodes and 547,224 semantic relationships were included in the knowledge graph. The accuracy of the knowledge graph-based model for predicting traits regulating-genes was 0.89, the precision rate was 0.91, the recall rate was 0.96, and the F1 value was 0.94. Moreover, 4,447 polyphenotype genes for 31 trait combinations were identified, among which the rice polyphenotype gene IPA1 and the A. thaliana polyphenotype gene CUC2 were verified via a literature search. Furthermore, the wheat gene TraesCS5A02G275900 was revealed as a potential polyphenotype gene that will need to be further characterized. Meanwhile, the result of venn diagram analysis between the polyphenotype gene datasets (consists of genes that are predicted by our model) and the transcriptome gene datasets (consists of genes that were differential expression in response to disease, drought or salt) showed approximately 70% and 54% polyphenotype genes were identified in the transcriptome datasets of Arabidopsis and rice, respectively. The application of the model driven by knowledge graph for predicting traits regulating-genes represents a novel method for detecting elite polyphenotype genes.
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Affiliation(s)
- Dandan Zhang
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ruixue Zhao
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Integration Publishing Knowledge Mining and Knowledge Service, National Press and Publication Administration, Beijing, China
| | - Guojian Xian
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yuantao Kou
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Integration Publishing Knowledge Mining and Knowledge Service, National Press and Publication Administration, Beijing, China
| | - Weilu Ma
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
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Zhang H, Li Y, Liu W, Sun Y, Tang J, Che J, Yang S, Wang X, Zhang R. Genetic Analysis of Adaptive Traits in Spring Wheat in Northeast China. Life (Basel) 2024; 14:168. [PMID: 38398677 PMCID: PMC10890535 DOI: 10.3390/life14020168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/13/2024] [Accepted: 01/17/2024] [Indexed: 02/25/2024] Open
Abstract
The dissection of the genetic architecture and the detection of the loci for adaptive traits are important for marker-assisted selection (MAS) for breeding. A spring wheat diversity panel with 251 cultivars, mainly from China, was obtained to conduct a genome-wide association study (GWAS) to detect the new loci, including the heading date (HD), maturating date (MD), plant height (PH), and lodging resistance (LR). In total, 41 loci existing in all 21 chromosomes, except for 4A and 6B, were identified, and each explained 4.3-18.9% of the phenotypic variations existing in two or more environments. Of these, 13 loci are overlapped with the known genes or quantitative trait loci (QTLs), whereas the other 28 are likely to be novel. The 1A locus (296.9-297.7 Mb) is a multi-effect locus for LR and PH, whereas the locus on chromosome 6D (464.5-471.0 Mb) affects both the HD and MD. Furthermore, four candidate genes for adaptive traits were identified, involved in cell division, signal transduction, and plant development. Additionally, two competitive, allele-specific PCR (KASP) markers, Kasp_2D_PH for PH and Kasp_6D_HD for HD, were developed and validated in another 162 spring wheat accessions. Our study uncovered the genetic basis of adaptive traits and provided the associated SNPs and varieties with more favorable alleles for wheat MAS breeding.
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Affiliation(s)
- Hongji Zhang
- Crop Resources Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (W.L.); (Y.S.); (J.T.); (S.Y.); (X.W.)
| | - Yuyao Li
- Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China;
| | - Wenlin Liu
- Crop Resources Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (W.L.); (Y.S.); (J.T.); (S.Y.); (X.W.)
| | - Yan Sun
- Crop Resources Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (W.L.); (Y.S.); (J.T.); (S.Y.); (X.W.)
| | - Jingquan Tang
- Crop Resources Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (W.L.); (Y.S.); (J.T.); (S.Y.); (X.W.)
| | - Jingyu Che
- Keshan Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161600, China;
| | - Shuping Yang
- Crop Resources Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (W.L.); (Y.S.); (J.T.); (S.Y.); (X.W.)
| | - Xiangyu Wang
- Crop Resources Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China; (W.L.); (Y.S.); (J.T.); (S.Y.); (X.W.)
| | - Rui Zhang
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China;
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Kumari J, Lakhwani D, Jakhar P, Sharma S, Tiwari S, Mittal S, Avashthi H, Shekhawat N, Singh K, Mishra KK, Singh R, Yadav MC, Singh GP, Singh AK. Association mapping reveals novel genes and genomic regions controlling grain size architecture in mini core accessions of Indian National Genebank wheat germplasm collection. FRONTIERS IN PLANT SCIENCE 2023; 14:1148658. [PMID: 37457353 PMCID: PMC10345843 DOI: 10.3389/fpls.2023.1148658] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/11/2023] [Indexed: 07/18/2023]
Abstract
Wheat (Triticum aestivum L.) is a staple food crop for the global human population, and thus wheat breeders are consistently working to enhance its yield worldwide. In this study, we utilized a sub-set of Indian wheat mini core germplasm to underpin the genetic architecture for seed shape-associated traits. The wheat mini core subset (125 accessions) was genotyped using 35K SNP array and evaluated for grain shape traits such as grain length (GL), grain width (GW), grain length, width ratio (GLWR), and thousand grain weight (TGW) across the seven different environments (E1, E2, E3, E4, E5, E5, E6, and E7). Marker-trait associations were determined using a multi-locus random-SNP-effect Mixed Linear Model (mrMLM) program. A total of 160 non-redundant quantitative trait nucleotides (QTNs) were identified for four grain shape traits using two or more GWAS models. Among these 160 QTNs, 27, 36, 38, and 35 QTNs were associated for GL, GW, GLWR, and TGW respectively while 24 QTNs were associated with more than one trait. Of these 160 QTNs, 73 were detected in two or more environments and were considered reliable QTLs for the respective traits. A total of 135 associated QTNs were annotated and located within the genes, including ABC transporter, Cytochrome450, Thioredoxin_M-type, and hypothetical proteins. Furthermore, the expression pattern of annotated QTNs demonstrated that only 122 were differentially expressed, suggesting these could potentially be related to seed development. The genomic regions/candidate genes for grain size traits identified in the present study represent valuable genomic resources that can potentially be utilized in the markers-assisted breeding programs to develop high-yielding varieties.
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Affiliation(s)
- Jyoti Kumari
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Deepika Lakhwani
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Preeti Jakhar
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Shivani Sharma
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Shailesh Tiwari
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Shikha Mittal
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
- Jaypee University of Information Technology, Solan, India
| | | | - Neelam Shekhawat
- ICAR-National Bureau of Plant Genetic Resources, Regional Station, Jodhpur, Jodhpur, India
| | - Kartar Singh
- ICAR-National Bureau of Plant Genetic Resources, Regional Station, Jodhpur, Jodhpur, India
| | | | - Rakesh Singh
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Mahesh C. Yadav
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | | | - Amit Kumar Singh
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
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