Sample Number

2191

Abundant

QTL Number

431

Association

Gene Number

9276

Candidate

Trait Number

28

Diversiform


🎖️ Welcome to LWRR , You can try to search for information of interest here



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🎉 Serve the future intelligent design breeding

LWRR (https://wheat.dftianyi.com) is a comprehensive web-based platform integrating extensive phenotypic and genotypic data from 2,191 wheat accessions, aiming to facilitate the application by breeders during variety cultivation. LWRR features seven integrated modules covering population structure analysis, trait exploration, GWAS results visualization, QTL information, and candidate gene analysis. Additionally, it offers cloud-based analytical tools allowing users to analyze their own data securely. The development of LWRR represents a significant step forward in bridging the gap between genetic discovery and practical breeding applications.


Population source world map

✨ Annotation: The samples come from all over the world and have a wide range of regional and genetic diversity. 272 from Africa, 929 from Asia, 640 from Europe, 298 from Americas, 52 from Oceania. This panel consisted of 684 landraces and 1,507 cultivars.

Structure of Population

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The above results are based on the Admixture(https://dalexander.github.io/admixture) software for population structure analysis of genotypes of 2191 materials, and presented the data of 2191 materials in each component uniformly. You can click on any material to obtain the corresponding calculation result, and the local selection function is provided below the X-axis, making it convenient for you to view a specific location.

Principal Component Analysis of Population

PCA 3D Plot (color by Admixture k=9 )
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PCA 3D Plot (color by Breed Group)
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Query the information and characteristics of wheat breeding group (BG)

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Diversity and selection of different breeding groups (Pi & FST)

Tips:

1. Pi was calculated using samples from BG1-BG4, landrace(LC), and non-landrace(NLC)

2. FST was calculated using the resistant samples in each breeding group compared with the landrace

3. The sliding window step was 0.1Mb and the window size was 100kb ( vcftools , https://vcftools.github.io/)


Nucleotide diversity (π) of each breeding group can reflect the polymorphism of variation sites in different physical regions. This allows researchers to quantify the genetic diversity within a population, which reflects the average percentage of base differences between individual DNA sequences within the population

FST is a measure of population differentiation and genetic distance. The greater the differentiation index, the greater the difference between subpopulations. It can be used to compare the diversity of subgroups and measure the degree of population differentiation.

The phenotype of wheat yellow rust during the seedling stage.

Please select a Pst race to view the phenotype :

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Adult-Plant Resistance of wheat yellow rust phenotypes

Best linear unbiased estimate(BLUE) of IT (Infection Type)
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Best linear unbiased estimate(BLUE) of DS (Disease Severity)
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Phenotypic identification methods and standards for wheat yellow rust

Query the phenotype and basic information of sample

Usage
Enter the sample of interest, click the green button below, and wait for a while, the page will automatically obtain the specific information of the sample you submitted, including the resistance level under different phenotypes, the distribution difference of 431 QTLS, etc.
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The phenotypic data of wheat stripe rust resistance under different environments (the larger the value, the higher the degree of susceptibility)
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What QTLs are present in this sample

Map of whole chromosome disease resistance sites

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Query the results of GWAS by different phenotypes

Results of whole-chromosome horizontal association analysis

View the result information for a specific interval

✨ Please select a region to view detail, the page will show the corresponding region of significant variation sites, and provide a table of results.

Note: Please do not enter a large range of interval, ensure that the distance from Start to End is less than 20MB

⚠️ The content of the following page is empty by default, please modify the parameters according to the need to click on the green button to get the results of the information.

Narrow the GWAS association analysis results for the specified interval
The variation information corresponding to the significant peak :

Overall Quick View (Explore QTL information of wheat rust resistance)

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Search QTL information online

Tip
Wheat is one of the most important grain crops. Currently, the global wheat production is threatened by the potential of wheat stripe rust. The best measure to control wheat stripe rust is to cultivate disease-resistant varieties with excellent resistance to wheat stripe rust.

The genotype frequency variation of this QTL


The number of samples of different alleles
Diversity between breeding times
Diversity between breeding region

Samples containing Resistance alleles in this QTL

Phenotypic box-and-line plot showing different genotypes

Search Gene (Chinese Spring 2.1)

Tip
Wheat is one of the most important grain crops. Currently, the global wheat production is threatened by the potential of wheat stripe rust. The best measure to control wheat stripe rust is to cultivate disease-resistant varieties with excellent resistance to wheat stripe rust.

Gene Infomation

Selective domestication analysis of 10MB around the gene region

The above shows the Pi and Fst of the gene you submitted your query within the surrounding 10Mb region

The variation sites of the gene region and LDblock plot

Variation sites plot




Analysis of wheat resistance candidate genes

🔗 Click here to download the example GenoType File (vcf)
🔗 Click here to download the example Trait File (xlsx)
Tip
Wheat is one of the most important grain crops. Currently, the global wheat production is threatened by the potential of wheat stripe rust. The best measure to control wheat stripe rust is to cultivate disease-resistant varieties with excellent resistance to wheat stripe rust.

Sample names and phenotype names must not contain special characters, use letters as the first character, and use NA to fill in missing values.

You need to upload the file in standard format (please download to see example data) first and then analyze it.

Output Result : (Please upload data for analysis first)



VCF file table


Note :After each parameter change, please click the button to analyze it.(Association analysis was performed based on candidate genes)


Manhattan plot of candidate gene association analysis results












📢 Select a phenotype to determine the difference between haplotypes. The HAP Number parameter specifies the expected number of haplotypes. The results are divided into different haplotypes according to the clustering algorithm








References

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  • Liu J, Chang Z, Zhang X, Yang Z, Li X, Jia J, Zhan H, Guo H, Wang J. Putative Thinopyrum intermedium-derived stripe rust resistance gene Yr50 maps on wheat chromosome arm 4BL. Theor Appl Genet, 2013, 126(1): 265-274.
  • Liu L, Wang M, Feng J, See DR, Chen X (2019a) Whole-genome mapping of stripe rust resistance quantitative trait loci and race specificity related to resistance reduction in winter wheat cultivar Eltan. Phytopathology® 109:1226–1235. https://doi.org/10.1094/PHYTO-10-18-0385-R
  • Liu L, Yuan C, Wang M, See DR, Chen X (2020a) Mapping quantitative trait loci for high-temperature adult-plant resistance to stripe rust in spring wheat PI 197734 using a doubled haploid population and genotyping by multiplexed sequencing. Front Plant Sci 11: 596962. https://doi.org/10.3389/fpls.2020.596962
  • Liu L, Yuan CY, Wang MN, See DR, Zemetra RS, Chen XM (2019b) QTL analysis of durable stripe rust resistance in the north American winter wheat cultivar Skiles. Theor Appl Genet 132:1677–1691. https://doi.org/10.1007/s00122-019-03307-2
  • Liu P, Shi C, Liu S, Lei J, Lu Q, Hu H, Ren Y, Zhang N, Sun C, Chen L, Jiang Y, Feng L, Zhang T, Zhong K, Liu J, Zhang J, Zhang Z, Sun B, Chen J, Tang Y, Chen F, Yang J. A papain-like cysteine protease-released small signal peptide confers wheat resistance to wheat yellow mosaic virus. Nat Commun, 2023, 14(1).
  • Liu Q, Deng S, Liu B, Tao Y, Ai H, Liu J, Zhang Y, Zhao Y, Xu M. A helitron-induced RabGDIα variant causes quantitative recessive resistance to maize rough dwarf disease. Nat Commun, 2020, 11(1): 495.
  • Liu S, Huang S, Zeng Q, Wang X, Yu R, Wang Q, Singh RP, Bhavani S, Kang Z, Wu J, Han D (2021) Refined mapping of stripe rust resistance gene YrP10090 within a desirable haplotype for wheat improvement on chromosome 6A. Theor Appl Genet 134:2005–2021. https://doi.org/10.1007/s00122-021-03801-6
  • Liu S, Liu D, Zhang C, Zhang W, Wang X, Mi Z, Gao X, Ren Y, Lan C, Liu X, Zhao Z, Liu J, Li H, Yuan F, Su B, Kang Z, Li C, Han D, Wang C, Cao X, Wu J. Slow stripe rusting in Chinese wheat Jimai 44 conferred by Yr29 in combination with a major QTL on chromosome arm 6AL. Theor Appl Genet, 2023, 136(8): 175.
  • Liu W., Naruoka, Y., Miller, K., Garland-Campbell, K.A. and Carter, A.H. (2018), Characterizing and Validating Stripe Rust Resistance Loci in US Pacific Northwest Winter Wheat Accessions (Triticum aestivum L.) by Genome-wide Association and Linkage Mapping. The Plant Genome, 11: 170087. https://doi.org/10.3835/plantgenome2017.10.0087
  • Liu X, Khajuria C, Li J, Trick H N, Huang L, Gill B S, Reeck G R, Antony G, White F F, Chen M. Wheat Mds-1 encodes a heat-shock protein and governs susceptibility towards the Hessian fly gall midge. Nat Commun, 2013, 4(1): 2070.
  • Liu Y, Qie Y, Li X, Wang M, Chen X (2020b) Genome-wide mapping of quantitative trait loci conferring all-stage and high-temperature adult-plant resistance to stripe rust in spring wheat landrace PI 181410. Int J Mol Sci 21:478. https://doi.org/10.3390/ijms21020478
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Thanks for the main dependent programs of this project



The R packages utilized during the development process


Background

Wheat is one of the most important cereal crops, with a global cultivation area exceeding 300 million acres. In recent years, factors such as global warming have lead to an increase in extreme weather events, which disrupt the prevalence patterns of crop diseases and pests, posing significant challenges to food security (Schulthess et al., 2022). Wheat yellow rust (YR) or stripe rust, caused by the biotrophic fungal Puccinia striiformis Westend f. sp. tritici, is one of the most critical diseases affecting wheat production worldwide, presenting a substantial threat to wheat safety and security. Breeding disease-resistant varieties is the most effective measure to control wheat yellow rust.

However, the discovery and utilization of effective resistance loci remain challenging in current wheat breeding practices. Comprehensive analysis of wheat stripe rust resistance loci at the population level is required to enhance flexibility in their application within breeding research (He et al., 2024). With the rapid advancements in genomics, the availability of multi-omics data on wheat has expanded significantly. Published online databases such as WheatOmics (Ma et al., 2021) and TGT (Chen et al., 2020) have accelerated the progress of wheat research. However, there is currently no publicly available, practical database specifically focused on wheat stripe rust resistance.

To address this gap, we have constructed a dynamic landscape of wheat yellow rust resistance loci to provide a valuable reference for research in wheat disease resistance genetics and breeding. Based on cloud computing architecture, we have developed an interactive online platform (https://wheat.dftianyi.com/), freely accessible to wheat genetic and breeding researchers worldwide. This platform offers real-time query and analysis modules for wheat rust resistance loci, aiming to expedite the intelligent design of wheat breeding in the future.

Usage method

The main functional modules of LWRR include: population genetic structure analysis, material information query, phenotype data analytics, GWAS result analysis, QTL information retrieval, disease resistance candidate gene analysis, etc., and provide online cloud tools to upload genotype and phenotype files to quickly complete candidate gene analysis. LWRR is designed with a dynamic responsive architecture, including 12 sub-menu pages (from population-level genetic analysis to individual-level phenotypic attributes, which is convenient for users to quickly browse). It has the characteristics of online access, easy use, dynamic interaction, and comprehensive . So that breeding researchers can quickly inquire about the change law of wheat rust resistance check point population level, the difference of QTL check point between different materials, and the online analysis of disease resistance candidate genes in the study, providing support for future wheat disease resistance design and breeding.

1. Population genetic structure analysis

The “Population” module provides information on material sources and population structure, as well as comparison and analysis between different breeding groups. Through Structure analysis of population genetic composition, users can select different K values to view specific results, and can zoom the horizontal axis to view the results of the specified sample. The mouse click will display the information of the corresponding sample in real time. In addition, PCA interactive 3D query of different subpopulations and different breeding groups is provided, which is convenient for obtaining population characteristics and structural grouping information. In order to facilitate the study of selective domestication patterns and genetic characteristics between different subpopulations, we calculated the results of Pi, Fst, TajimD and other results of different breeding groups and farmers at the genome level, and provided a dynamic query function. Users can enter any chromosome region to query the corresponding detailed data. We conducted statistical analysis of the four breeding groups, and displayed them according to the source region, breeding age, and population structure, so that users can easily understand the characteristics and information of the breeding groups.

2. Sample information query

The “sample” module provides information and attribute queries for individual samples, as well as the QTL status of embroidery resistance they contain. When the user enters a material number or name, the page will display the material’s attribute information and QTL distribution, such as querying the “S0073” sample. The page provides the disease resistance of the sample under different phenotypic conditions, and shows the presence of different QTLs in the material, as well as the allele types with excellent disease resistance. When the user clicks on the blue QTL hyperlinke, it will automatically jump to the corresponding QTL information interface. Through the panorama at the full chromosome level, it can quickly find which QTLs are potential key check points.

3. Phenotype data analytics

The “Phenotype” module provides statistical analysis of phenotype information of different materials. Generally speaking, the resistance of wheat to stripe rust can be divided into two types: seedling stage and adult stage. The resistance at seedling stage is specific to small species. Therefore, this module provides phenotype analysis of different physiological races of wheat stripe rust. Users can choose to view the phenotype data of different physiological races at seedling stage. Through boxplot, they can quickly understand the material resistance differences of different breeding groups. According to the frequency distribution histogram, they can clearly understand the distribution trend of phenotype data. Heat map shows the specific values corresponding to different materials in different phenotypes.

4. GWAS result analysis

The “GWAS” module provides the result query of genome-wide association analysis, including all the check points obtained by association analysis that have significant association points with phenotypes. The website provides Manhattan graphs and qq graphs obtained by each phenotype under MLM and FarmCPU models. In order to further facilitate users to obtain the results of an interval of interest, we use the online query function to provide more fine-grained functions. Users can select a phenotype, query the significant check points corresponding to a certain interval, and view the specific information of a significant SNP (physical precise location and effect value, etc.) by clicking the mouse. It is convenient for users to make full use of the significant check points for extended analysis. We label all the significance thresholds -log10 (P) greater than 3 The check points are provided in the form of tables, and when users find a GWAS candidate interval of interest, they can also perform in-depth analysis based on the check point information.

5. QTL information retrieval

The “QTL” module provides a one-stop query and analysis of QTL check points for wheat resistance to stripe rust, including specific information such as the location interval of each QTL check point, and can query the proportion of different alleles at the population level, as well as the utilization differences of different ages and breeding groups . For example, search for “Yr30” (you can enter known gene numbers, QTL numbers, reported QTL numbers, etc., the server uses a global search engine), the page will show the QTL corresponding to the Yr30 gene and the change in the utilization trend of the gene since 1950, as well as the difference in utilization among the four breeding groups. The page automatically displays the phenotypic differences of different genotypes of alleles corresponding to the check point, and statistical analysis is carried out through T-test. The above calculation results are based on population-level variant check point genotype data and statistical analysis of phenotype data in multiple environments. Users can view the panorama of the distribution of disease-resistant QTL check points at the genome-wide level, and select a green area with the mouse to jump to the corresponding QTL to view. In addition, the page also shows the list of candidate genes in the corresponding interval of QTL, which is convenient for users to view candidate genes.

6. Disease resistance candidate gene analysis

The “Tools” module integrates online analysis tools, allowing users to upload candidate genes for quick analysis by themselves. Based on the cloud computing service framework, users can upload VCF genotype files and Excel phenotype files of candidate genes (the tool provides sample data and file formats), and then calculate and analyze in real time in the LWRR cloud. One-stop gene mutation check point analysis, candidate gene association analysis, single marker check point significance statistical test, linkage imbalance analysis, gene haplotype analysis, etc. The analysis results can be quickly viewed on the page, or they can be saved locally through the download function provided by the tool. For security and confidentiality, all data is encrypted and transmitted through SSL, and each user session has a unique temporary random number. Uploaded files and results are stored in the temporary cache space, which is automatically deleted after the user exits the use.


Feedback on the issue

https://github.com/CropCoder/LWRR/issues

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