Tutorial. Identification of Variants Using GATK. Sample to Insight. November 21, 2017

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1 Identification of Variants Using GATK November 21, 2017 Sample to Insight QIAGEN Aarhus Silkeborgvej 2 Prismet 8000 Aarhus C Denmark Telephone: AdvancedGenomicsSupport@qiagen.com

2 Identification of Variants Using GATK 2 Identification of Variants Using GATK This tutorial demonstrates the usability of the GATK Integration Plugin with a small dataset, and shows how the variants identified using GATK can be annotated and visualized with the tools included in Biomedical Genomics Workbench. This tutorial covers in just a few steps all the following: Run GATK on a single sample using Broad Institute s best practices guidelines for Whole Genome and Whole Exome Illumina data. Annotate the variants called using GATK best practices with NCBI s ClinVar database to look for clinically relevant variants. Visualize and examine the results in the Biomedical Genomics Workbench. Prerequisites The GATK Integration Plugin functionality is developed for use on Biomedical-enabled CLC Genomics Server with a client plugin developed for the Biomedical Genomics Workbench. How to install this plugin is described here: manuals/gatk/current/gatk_integration_user_manual.pdf. Download and import data This tutorial makes use of a subset (chromosome 5) of the NA human whole exome. The original data set is available on the GATK website here: broadinstitute.org/gatk/discussion/1292/which-datasets-should-i-use-forreviewing-or-benchmarking-purposes. Go through the following steps to download and import the data into a Biomedical enabled Genomics Server location accessible from the Workbench. 1. Download the sample data from our website: com/testdata/gatk_tutorial.zip on your local computer. 2. Unzip the file on your local machine. 3. Start the Biomedical Genomics Workbench. 4. Log in to the Biomedical enabled Genomics Server if it is not done automatically: go to File, and choose "CLC Server Login". In the Navigation Area to the left you should see the directories associated with your Server. Open the location you would like to have your files imported to. 5. Drag and drop the WES_chr5_reads and the targeted_regions_chr5_bed files to the chosen location. You can also use the Import ( ) Standard import functionality to import both the reads and targeted regions file.

3 Identification of Variants Using GATK 3 Data management configuration In order to do this tutorial, you need to use a Reference Data Set that was generated specifically for chromosome To do this, go to: Toolbar Data Management ( ) This will open the wizard shown in figure 1. Figure 1: Open the Data Management and download the "Identification of variants using GATK" data set. 2. Choose to work on the Server. 3. Open the Reference Data Sets tab and select "Identification of variants using GATK". 4. Click on the button labeled Download. You can check the progress of the download in the Process bar below the Toolbox. 5. When the download is completed, click on the button Export, and choose in the pop-up window the CLC_ImportExport directory that was created on the server during installation of the GATK Integration Plugin. This will export the Reference Data Set from a *.clc format to a format compatible with BWA and GATK. Click on OK. Note that if you have already exported reference data prior to this tutorial, the CLC_ImportExport directory will contain a CLC_References sub-directory. You should not select the CLC_References sub-directory as destination for exporting the References, as it would create yet again a CLC_References sub-directory within the first one. Always select the directory above the CLC_References sub-directory to have your references included in the right location.

4 Identification of Variants Using GATK 4 6. Click on the button Apply. In the table, the column Applied should now have a "Yes" for all the Reference Elements included in the GATK Reference Data Set. You can now close the Data Manager. Running the Identify Variants using BWA-GATK Best Practices (WES) workflow Go to: Toolbox Ready-to-Use Workflows Whole Exome Sequencing ( ) Hereditary Disease ( ) Identify Variants using BWA-GATK Best Practices (WES-HD) 1. Double-click on the name of the workflow in the Toolbox. Note that you may be asked to update the workflow to match the GATK external application on the Biomedical enabled CLC Genomics Server. If requested, close the wizard and click on the button "Workflows" in the top right corner of the workbench. Find the GATK workflows in the list, and click on the button "Update". You can now close the Workflow manager and restart the workflow. 2. This will open the wizard shown in figure 2. Figure 2: The GATK Integration plugin runs on a Biomedical enabled CLC Genomics Server. The GATK workflow only runs on the server so this option is pre-selected. Click Next. 3. In the Select input for Reads window (figure 3), select the reads you imported in the CLC Server. Figure 3: Select the sequencing reads you want to analyze. 4. In the next window, you can select the specific Hapmap population(s) you are working with. By default, the 12 populations of Hapmap are pre-selected. Click on the Browse

5 Identification of Variants Using GATK 5 icon located to the right of the field. Select the population(s) you want to deselect, and remove them from the "Selected elements" window by clicking on the arrow that points back to the Navigation Area (figure 4). In the case of this tutorial, we only wish to keep HAPMAP_phase_3_CEU_chr5. Click on OK when only this population is selected, and then click Next. Note that it is possible to right-click on a Hapmap population when it is selected. The option "Show location" will open in the Navigation Area the location of the file that is selected. It can be helpful in case you have de-selected one population too many. Figure 4: Select only the Hapmap and 1000 Genoms Project populations of interest. 5. Repeat the steps above for the 1000 Genomes. In the case of this tutorial, we wish to only keep the 1000GENOMES_phase_1_EUR_chr5. You can also double-click the populations you would like to remove from your analysis. Click on OK, then click Next when you are done. If you have by accident deleted a population of interest, you can select them again from the CLC_References folder (CLC_References/homo_sapiens/hapmap/phase_3_chr_5 or CLC_References/homo_sapiens/1000_genomes_project/phase_1_chr_5). 6. In the GATK Parameter Selector window (figure 5), a table describes the reads that were used as input in the previous step. Below the table, you can see that filtering has been preset to Hard filtering as it is the best suited option for a single Whole Exome analysis. Leave the generic filters at their default values as they are set following GATK recommendations, and click Next. 7. The next window (figure 6) allows the user to specify a target regions file. Click on the Browse icon to the right of the field, select the targeted_regions_chr5_bed file and click OK. You can then click Next. 8. Choose to save the results in a folder you can call "GATK" on the Server and click Finish to start the analysis. Output from the Identify Variants using BWA-GATK Best Practices (WES) workflow When completed, you should see the following files in the folder you selected to save the workflow output (figure 7).

6 Identification of Variants Using GATK 6 Figure 5: Read group information and filters. Figure 6: Specify the target regions file. Figure 7: Example of output files when running the Identify Variants using BWA-GATK Best Practices (WES-HD) workflow. Open the variant track called output (Identified Variants), and click on the table icon ( ) at the bottom of the View Area. Note that this table includes all variants found by GATK, and the table must be filtered to highlight only the variants that passed the hard filtering requirements (figure 8). 1. Click on the arrow next to the "Filter" button at the top of the table to open up the advanced filter box. 2. Select FILTER in the first field, = in the second, and type PASS in the third.

7 Identification of Variants Using GATK 7 Figure 8: Filtering the variants table to highlight those that passed the GATK hard filtering requirements. 3. Add a new filtering criterion by clicking on the green + button. 4. As you did for the first filter, select Reference Allele, =, and type No. 5. Click on the button Filter. 6. Select all remaining rows in the table and click on Create Track from Selection. 7. Save the new table as "GATK Filtered Identified Variants". Annotate the variants identified using GATK best practices We now want to annotate the variants identified by GATK with ClinVar. Because we are using only chromosome 5 in this tutorial, you need download the ClinVar database that contains only the variants found on chromosome 5: Open the Data manager, and in the Reference Data Elements section, choose to download ClinVar _chr_5. 1. Go to Toolbox Tools ( ) Add Information to Variants ( ) Add Information from Variant Databases 2. You can choose to run the annotation on your local computer. 3. In the first window, choose the "GATK Filtered Identified Variants" as input. 4. In the next window, choose to annotate with the chromosome 5-specific ClinVar reference database you have just downloaded (figure 9). Leave the Auto join option unchecked and click Next. 5. Choose to Save the results in the GATK folder present on the server and click Finish. You can now visualize and inspect the annotated variants in the genome browser view generated by the Identify Variants using BWA-GATK Best Practices (WES) workflow. 1. In the folder GATK, double click on the filed called "output (Genome Browser View)" to open the Genome Browser View. 2. Right-click on the track called "output (Identified Variants)" and choose to Remove Track.

8 Identification of Variants Using GATK 8 Figure 9: Select the full ClinVar database, or the one specific to chromosome 5 you just downloaded using the Data Manager. 3. Insert the newly generated annotated variants track by selecting the file called "GATK Filtered Identified Variants (KNOWN)" and dropping it in place of the previous variant track. 4. Double-click on the annotated variants track in the Genome Browser View to open the annotated variant table in a split view. 5. Use the right hand side panel to select the column you want your table to display. Keep at least "allele", "count", "coverage", "frequency", "QUAL" and "CLNDBN Clinvar_ _chr5". 6. Sort table by clicking on the column "CLNDBN Clinvar_ _chr5". The Genome Browser View makes it very easy to inspect and interpret results: you can notice that NA12878 may be genetically disposed to asthma and colorectal cancer for example. Note also that NA12878 has the variant for Sandhoff disease although it is not very well supported: its QUAL is much lower than the other variants that passed the filters, in part because the coverage at the position is very low (figure 10).

9 Identification of Variants Using GATK 9 Figure 10: Inspect variants easily with the Genome Browser View and the annotated table of filtered variants opened in split view.