1. User Accounts
    • When you first visit the website, you have to create a new user account.
    • Create a new user account with an Email and a Password. The Email will be used to send Job Status updates.
    • Click on "New User Sign Up" to create a new user account.
    • Click on "Log In" to login with your already created user account credentials.

  2. Upload Phenotype Data File
    • Click the "Upload Phenotype File" button on the sidebar to start a new analysis.
    • Select your preferred file, and make sure it is in CSV format or Tab-delimited format. The first column should have the official CC strain name or it's Alias name. Each row is a unique animal. The order of the remaining columns containing phenotype data is not important, but one column should be named "Sex" and denoted as M or F. The file can contain multiple phenotype columns.
    • If the phenotype data does not appear on the main web page:
      • The notifications menu in the top right corner may show a specific error.
      • Open the "Field Separator Options" menu to change "Delimitor" and/or "Quote" setting and reupload the file.
    • Enter a name for the analysis session, and click "Save Analysis" to proceed.

  3. Inspect Phenotype Data
    • Click on any numeric column on the data table. Below the data table, plots will appear.
    • Histogram, QQ, Histogram (Normalized), and QQ (Normalized) plots will appear once the file is correctly uploaded.
    • You can specify the type of Data Transformation by selecting: "Sqrt", "Log", "Auto" or "Rank-Z"
    • Automatically manage outlier strains, and manually manage selected strains for a single column.

  4. Run QTL Analysis
    • Select one or more columns, then click the green "Run QTL" button to run analysis.
    • You should receive an email confirmation that your job was submitted.
    • The "Job Status" in the side panel tracks your analyses.
    • Click the refresh button in the sidebar to see updates.

  5. View QTL Analysis Results
    • If a job is labeled "F", the QTL Analysis has finished.
    • Reload the page, to get the results from the server.
    • Click a phenotype column on which the QTL Analysis was run.
    • The QTL results will appear below the data table.

  6. Generate Report of QTL Results
    • To export QTL results obtained for this session, click the "Generate PDF" button, which should appear if correct phenotype columns are selected.
    • You will be prompted to download a PDF report and / or a folder of JPEG figures at 300 dpi, which also includes a PDF and HTML report.

  7. Contact Us
    • Please email konganti[AT]tamu.edu for any unexpected errors, questions or concerns about the web app.



  • Version 1.8 - March 26 2020
    • Feature: The Genotype Effects plot for a SNP now shows CC Strain Names as possible non-overlapping labels.
    • Feature: If it is too cluttered, you can use the Check Box: Hide CC Strain Names on top of the SNP plot for Genotype Effects to hide the labels in current view or while generating the PDF report.

  • Version 1.7 - September 9 2019
    • Feature: Genome Build 37 (mm9) is being discontinued in favor of Genome Build 38 (mm10). This will allow inclusion of founder probabilities for CC strains CC078, CC079, CC080, CC081 and CC083.
    • Feature: GigaMUGA marker set is now the default genotyping array for mapping.
    • Bug Fix: Fixed a bug wherein uploading very large list of non-supported strains would block the viewport of the confirmation button (OK button).

  • Version 1.6 - February 12 2019
    • Bug Fix: Rows where phenotype values are either NA or missing / empty are automatically removed replicating the behaviour of DOQTL in the backend.
    • Bug Fix: Fixed a bug wherein zoomed Allele Effects plot would show incoherent LOD lines.

  • Version 1.5 - January 16 2019
    • Bug Fix: Fixed a bug wherein analysis completion emails were not being sent.
    • Bug Fix: Fixed a bug wherein selecting GigaMUGA or Build 38 (mm10) resulted in Error in eigen(WQK, symmetric = TRUE, only.values = TRUE): infinite or missing values in x error.
    • Bug Fix: Fixed a bug wherein Genes within the significant QTL interval plot would show Cannot get genes within QTL interval as Proximal > Distal.
    • Bug Fix: Fixed a bug wherein spaces and special characters in the column name of the uploaded phenotype file causes unexpected errors.

  • Version 1.4 - August 20 2018
    • Bug Fix: Fixed a bug wherein reports were not being generated when "QTL" analysis has not yet been done for selected Phenotype columns.
    • Feature: Yay! Added ability to handle Phenotype data with multiple individuals per strain. Please re-upload your data if you have previously uploaded the Phenotype data that contains mulitple entries per individual.

  • Version 1.3 - July 02 2018
    • Bug Fix: If a Phenotype column contains negative values, SQRT transform returns imaginary numbers. This error check now prevents the app from crashing.

  • Version 1.2 - June 26 2018
    • Bug Fix: In the generated PDF and HTML report files, "QTL" plots now include a title to differentiate between multiple Phenotypes.
    • Bug Fix: Incorrectly rendered app title in the PDF and HTML report files has been fixed.

  • Version 1.1 - June 22 2018
    • Feature: Added ability to display "Allele Effects" plot even though no significant QTL have been identified.
    • Feature: Added horizontal scroll to the display of the uploaded Phenotype table.
    • Bug Fix: Fixed a bug wherein "F" character in sex column was being converted to "FALSE" during file upload.

  • Version 1.0 - May 18 2018
    • Initial release of gQTL v1.0 in May 2018.




Phenotype Data

Multiple phenotype columns can be selected to run QTL analysis simultaneously. To edit the selected strains for a phenotype column, select a single column and the "Selected Strains" Checkbox and Plots will appear for that phenotype.
Select a Phenotype Column Click on a phenotype column of your choice on the table anywhere other than the column header.
Note: We have automatically created unique strain names as we see that your data set contains multiple individuals per strain.
Note: We have automatically removed rows where the phenotype values are either NA or missing.
Check Strains for each phenotype Select a single phenotype column to manually check selected strains. Autoremove outlier strains: Outlier values for a selected phenotype column will be removed from subsequent analysis.
Tip: Select a single phenotype column to view outlier strains and basic plots below.
auto: Shapiro-Wilk's test chooses best transform between log and sqrt. rankZ: Recommended if you have hundreds or thousands of phenotypes.
GigaMUGA is the third-generation Mouse Universal Genotyping Array. It extends MegaMUGA from 77.8K to approximately 150K markers, and includes all MUGA and most MegaMUGA markers as a subset.
* Genome Build 37 (mm9) is being discontinued in favor of Genome Build 38 (mm10). This will allow inclusion of founder probabilities for CC strains CC078, CC079, CC080, CC081 and CC083.
Prune Probablity Matrix
Checking this option will set all the CC founder genotype probability values below 0.005 to a very low value, 1e-20.
Run options

Perform QTL Analysis and Render Plots Warning: it will take a while!


Generate Report