VarSeq CNV Caller Tutorial

Welcome to the VarSeq CNV Caller Tutorial!

This tutorial covers the basics of the VarSeq CNV calling algorithm with an emphasis on visualization and interpretation of results.

Requirements

To complete this tutorial you will need to download and unzip the following file, which includes a starter project.

Important

This workflow requires an active VarSeq license with the CNV Caller on Target Regions feature included. You can go to Discover VarSeq or email info@goldenhelix.com to request an evaluation license with the CNV functionality included.

Download

VarSeq_CNV_Tutorial.zip

Files included in the above ZIP file: VarSeq CNV Caller Tutorial – Starter project containing the variants and coverage dat a for 48 samples over 31 tumor suppressor and oncogenes mutated frequently in myeloid malignancies.

Note

Please note there may be slight variations in numbers of filtered variants from what is shown in the tutorial based on the versions of software and annotations available to the user.

CNV Calling Algorithm Overview

VarSeq ® software supports calling CNVs from coverage data computed from imported BAM files. This tutorial focuses on calling and interpretation of CNVs using VarSeq.

In this tutorial, we will begin by opening an existing project containing computed coverage data for a number of cancer gene panel samples. Using this coverage data, we will call CNVs, plot the CNV data, and interpret the results.

The project files are contained in the ZIP folder that accompanies this tutorial. This project contains variant and coverage data for 48 samples over 31 tumor suppressor and oncogenes mutated frequently in myeloid malignancies. After the ZIP folder has been downloaded, extract the contents to a convenient location.

The VarSeq CNV calling algorithm relies on coverage information computed from BAM files. The algorithm uses changes in coverage relative to a collection of reference samples as evidence of CNV events. Using these reference samples, the algorithm computes two evidence metrics: Z-score and Ratio. The Z-score measures the number of standard deviations from the reference sample mean, while the Ratio is the normalized mean for the sample of interest divided by the average normalized mean for the reference samples. The utility of these metrics can be seen by looking at the duplication event shown below.

In the figure above, the spike in both Z-score and Ratio over four exons of this gene provide supporting evidence for the called Duplication event.

A third metric used by the CNV caller is Variant Allele Frequency (VAF). While VAF is not a primary metric used for identification of CNVs, it can provide supporting evidence for, or against certain types of events. For example, values of 0 or 1 are supporting evidence for heterozygous deletion events, while values of 1/3 and 2/3 provides supporting evidence for duplications. The advantage provided by VAF can be seen in the figure below.

In the above figure, two exons were called as deletions prior to utilizing VAF. However, the presence of two variants with VAF of 0.5 within the region provides the algorithm with evidence against a deletion, allowing us to successfully classify the exons as diploid.

Using these three metrics, the algorithm assigns a CNV state to each target region and then merges these regions to obtain contiguous CNV events.

Once a set of CNV events have been called, quality control flagging is performed to identify unreliable samples and potentially problematic CNV calls. These QC flags are applied to both CNV events as well as samples.

The following are examples of CNV event flags:

• Low reference sample read depth in the surrounding region;
• High variation in the region between reference samples; and
• If Ratio or Z-score fall within the noise of the surrounding region.

The following are examples of Sample flags:

• Their metrics have extremely high variation;
• Samples have very low mean depth; and
• Samples differ significantly from the selected reference samples.

By flagging these events and samples, we provide a second layer of heuristics, which can be used to reduce false positives and identify questionable CNV calls.

Scoring CNVs with the ACMG and ClinGen Guidelines

The ACMG group in conjunction with the Clinical Genome Resource (ClinGen) working group has published guidelines specific to the interpretation of copy number variants called on NGS data. These CNV scoring criteria have been incorporated into the ACMG Guidelines workflow within VSClinical so now single nucleotide variants and CNVs can be classified according to the ACMG Guidelines in one interface.

For more information about VSClinical and scoring single nucleotide variants with the ACMG Guidelines see our VarSeq VSClinical ACMG Tutorial.

It is highly encouraged to read the new ACMG/ClinGen CNV guidelines publication as these guidelines are quite complex: https://clinicalgenome.org/

The publication describes 5 sections composed of around 80 distinct criteria describing the different processes for scoring both gains and losses. Each of the criteria has an associated numeric strength, which can be either positive, indicating evidence for pathogenicity, or negative indicating evidence against pathogenicity. Once all of the criteria have been applied, the numeric strengths are combined to obtain a final score for the CNV. Scores above 1 indicate a pathogenic classification, while scores below -1 indicate a benign classification.

The 5 sections that make up the CNV guidelines can be summarized into three overarching categories. Sections 1-3 assess the impact of the CNV on the gene. Section 4 determines whether or not a gene tolerates the regional deletion or duplication, ultimately determining whether a gene is haploinsufficient or triplosensitive. The third category involves a review of patient-specific information.

VSClinical provides a means of simplifying not only the process of scoring and classifying variants and CNVs, but also provides both a simple yet sophisticated means of presenting all evidence and criteria visually.

As you work through the classification process, you will be presented with questions you are left to answer to connect the evidence to the best criteria, but VarSeq also computes recommended answers while providing all the supporting evidence for each recommendation.

This is a simple interpretation into the immense power behind the VSClinical ACMG workflows for SNVs and CNVs, but we have several other resources/publications that can shed more light on the content, but for now let’s focus on looking at an example workflow.

Importing Variant and Alignment Data

Important

The starter project provided in this tutorial already contains the variants and coverage data for 48 samples. In this portion of the tutorial we will show you how the import of the VCF variant data was completed and how the coverage data was computed on the BAM files so you can also follow along using your own data instead of using the provided project.

If you are already familiar with this process or will be working with the project provided for this tutorial please skip to the Running the CNV Caller section of the tutorial.

As mentioned earlier, The VS-CNV algorithm uses changes in coverage relative to a collection of reference samples as evidence of CNV events. To create a set reference samples to be used as a basis for CNV calling, users can compute coverage on BAM files using the Reference Sample Manager.

1.Open VarSeq and click Tools > Manage Reference Samples. This menu computes coverage on BAM files and subsequently adds CNV Reference samples to the reference sample library.

Click on the Add References button and select Add Files on the first screen of the Add CNV References to add sample BAMs.

Ensure that Target Region is selected. Next click on Select Track to browse to the interval track (BED file) that defines the regions that coverage will be calculated over. Note users can import their own BED files using the Convert Wizard. Once an interval track has been selected, ckick Create to create a set reference samples to be used as a basis for CNV calling.

Now that you have added samples to the reference sample set. You can create a VarSeq Project and import samples to call CNVs on.

2. Open VarSeq and click Create New Project. Select either the Empty Project option. Select your genome assembly and a name for the project and click OK.

Click on the Import Variants button and select Add Files on the first screen of the Import Variants Wizard.

Navigate to the directory where your VCF files are saved and select them for import. See Figure 2-5 and then click Next >.

Note:

If you do not use the Manage Reference Samples option to import your reference samples as mentioned above, you will need to import enough samples to build your Reference Panel. 30 samples is the minimum number of recommended reference samples. Therefore, you will want to import at least 31 samples, 30 used for reference and an additional sample for analysis.

Once the 31 samples are processed through the CNV tool, VarSeq will save the coverage profile for these samples in the Coverage Reference Samples folder found in the VarSeq User Data location on your computer (Tools > Open Folder > Reference Samples Folder).

For any subsequent run of the algorithm you can import any number of samples for analysis and VarSeq will pull a reference set of samples from those available in the Reference Sample Folder.

If importing into an Empty Project you will need to select your Sample Relationships on the next dialog, for this tutorial the Cancer Samples option was selected. Click Next >.

On the next dialog we will be associating the BAM files with the imported VCF files so that Targeted Region Coverage can be computed.

Click Associate BAM File at the top of the dialog and navigate to the directory where your BAM files are stored. If your BAM files have a similar naming convention to what is listed in the VCF file then they should be automatically associated, if not then manually select each BAM file. Click OK once done.

The BAM file paths should now be filled out for each sample on the import dialog.

Click Next > and Finish to complete the VCF variant data import.

Now to compute the coverage calculations required to detect CNVs.

3. Go to Add > Secondary Tables > Add Coverage Regions and follow directions on the new window to select the interval track that defines the regions to calculate target coverage.

In the next dialog you will need to select either a BED file or Interval Annotation file that defines the targeted regions in your samples.

Click Select Track and navigate to the location of your target region file.

Note:

BED files are required to be indexed, if your file does not already have an index (TBI file) it can be computed through the Data Source Library by right-clicking on the file and selecting Computations on Source.

Once this computation finishes you are ready to begin CNV calling.

Running the CNV Caller

When you open the example project accompanying this tutorial, you will be greeted by the VarSeq Variants, Samples and Coverage Regions table. Close the Samples table. The Coverage Regions table includes information about the read depth of each coverage region for the sample of interest.

To call CNVs over these coverage regions:

• Click the Add button in the upper left-hand corner of the window
• Select Secondary Tables
• Select Add CNVs

This will launch the CNV calling configuration window, which allows you to set the various parameters associated with the algorithm.

The options presented here include the following:

• Sensitivity/Precision: Determines the sensitivity and precision of the CNV calling algorithm. A value of “Very High Precision” will result in fewer false positives but will increase the number of false negatives, while a value of “Very High Sensitivity” will produce more false positives but will result in fewer false negatives.
• Minimum Number of Reference Samples: The minimum number of reference samples to be selected by the algorithm.
• Maximum Number of Reference Samples: The maximum number of reference samples to be selected by the algorithm.
• Exclude reference samples with percent difference greater than: This option will filter reference samples with a percent difference above the specified value after a minimum of 10 samples have been selected.
• Add samples to reference set: This option adds the current project’s sample to the set of reference samples.
• Normalize sex chromosomes using only controls with matching sex: If this option is selected, non-autosomal chromosomes will only be normalized using reference samples whose sex matches that of the sample of interest.
• Controls average target mean depth below: Flags targets with average reference sample depth below the specified value.
• Controls variation coefficient above: Flags targets for which the variation coefficient is above the specified value. A high variation coefficient indicates that there is extreme variation in reference sample coverage for the target region.
• Targets Excluded From Normalization and CNV Calling: Here a region track can be selected that provides coordinates for regions that will be excluded from the normalization and CNV calling process.

For this tutorial, we will update the default sensitivity, by moving the slider at the top of this window to the “High Sensitivity” setting. Generally, higher sensitivity is desired when calling CNVs on gene panel data as the total number of calls is generally low in small gene panels, and we want to avoid missing any true positive CNV calls. After updating this option, run the CNV calling algorithm by clicking OK.

When the algorithm runs, it will select a set of reference samples for each sample in the project. The reference set is chosen from the collection of samples in the reference folder that share the same target regions as the sample of interest. The algorithm selects those samples that are most similar to the sample of interest in terms of normalized coverage.

Because we chose to Add samples to reference set, the 48 samples in our coverage table will first be placed in our reference set and then used by the algorithm.

Performing Sample QC

Once that CNV caller finishes computing results, a new table will be created labeled CNVs. Drag that table and place it beside the Coverage Regions table if needed. This table contains the information related to each CNV called by the algorithm, but, before examining these results, we should perform sample-level quality control. This can be done by exploring the Samples table, which is now populated with several useful metrics related to the CNV algorithm.

To open the samples table in VarSeq, select Samples from the drop down directly above the left side of the current table.

You will notice that the CNV caller has populated with sample table with a number of fields under the heading “Copy Number Variants”.

The most useful field for sample QC is the “Sample Flags” field. This field will list one or more of the following flags if the sample fails any of our quality tests:

• High IQR: High interquartile range for Z-score and ratio. This flag indicates that there is high variance between targets for one or more of the evidence metrics.
• Low Sample Mean Depth: Sample mean depth below 30.
• Mismatch to reference samples: Match score indicates low similarity to control samples.
• Mismatch to non-autosomal reference samples: Match score indicates low similarity to non-autosomal control samples.
• Few Gender Matches: Not enough reference samples with matching gender to call X and Y CNVs.

If any of the first first three flags are listed for a given sample, then all CNV calls associated with the sample will most likely be unreliable, while if last two flags are present, then CNV calls in non-autosomal will be unreliable.

The “Mismatch to reference samples” flag can often be resolved by rerunning the algorithm once more samples have been added to the reference set.

In the current project, two samples have been flagged. Samples 34 and 41 have been flagged with “Low Sample Mean Depth”.

In addition to QC flags, the sample table also provides summary information about the number of CNVs called, the inferred gender of the sample, the reference samples chosen, and the percent difference between each sample and it’s the references set.

Plotting CNV Data

Now that we have performed sample-level QC, we can plot our CNV calls, along with the relevant evidence.

To add the CNV table, click on the Plus Icon + next to open a new tab and select Table from the dropdown menu. Then from the “Select Table Type” drop down in the new tab, select CNV.

While this table provides many useful pieces of information associated with the CNV calls, plotting the CNV data associated can be helpful when performing analysis. Before plotting, though, the CNV State column can be queried to exclude missing values. This is done by right clicking on the CNV State column header and select Query Column Values.

This opens up a filter tag in the title header. Here, click on the question mark to present options for the query. See Figure 5-4.

Checking all of the options here will keep them in the column, and then remove any missing values. Select ‘Het Deletion’ and ‘Duplicate’ from the list and these selected items will now appear in the query filter in the header, and clicking anywhere on the screen not on the value query selection window will set the currently selected configuration and close the window.

Once the missing CNV State values are eliminated, the CNV State field can be plotted. To plot the CNV State for the current sample:

• Right click the CNV State column
• Select Plot for Current Sample

This will open a GenomeBrowse view containing the CNV State of the current sample plotted along side the gene track.

In addition to the CNV state, it is also useful to plot the evidence used to call the CNVs. To do this:

• Open the coverage table by selecting Coverage Regions from the table selection menu.

This table contains the CNV data associated with each target region. This includes the regional CNV state, QC flags, Ratio, Z-score, and the number of variants for which VAF was considered.

The two primary pieces of evidence used to call CNVs are the Z-score and Ratio.

To plot these fields:

• Right click on the Z-score column, then select Plot for Current Sample.
• Then, right click on the Ratio column, then select Plot for Current Sample.

GenomeBrowse provides a number of controls that can be used to customize the appearance of the plots. We will use these controls to add a mid step connector to our Ratio and Z-score plots.

To access the control menu for a plot:

• Right click the Z-score plot
• Select Controls… from the pop-up menu

This will display a control panel to the left of the GenomeBrowse view. From this panel:

• Click the dropdown labeled Connector and select Mid step.

This will add a mid step connector to the currently selected plot. Repeat these steps for the Ratio plot.

Annotating and Interpreting CNVs

The CNVs can now be annotated against CNV and gene annotation tracks. To call CNV annotations over these coverage regions:

• Click the Add button in the upper left-hand corner of the window
• Select Secondary Tables
• Select CNV Annotation…

This brings up the Select Data Source window which allows the annotation track selection. Multiple annotations can be selected at the same time by selecting multiple annotation tracks and checking the associated boxes next to the annotation name.

In this tutorial, three annotations are used:

• 1kg Phase3 CNVs and Large Variants 5b V2, GHI found under Public Annotations > CNV and Large Variants
• RefSeq Genes 105.20201022 v2, NCBI found under Public Annotations > Genes and Regulation
• OMIM Genes 2021-01-08, GHI found under Secure Annotations

Once all of the desired annotation tracks are selected, click Select to apply the annotations. Any annotation tracks not saved on the local hard drive will be downloaded, and a progress bar window presented that shows the download status.

This creates a series of new columns under the CNV table tab. Depending upon the annotation source, the the top level categories can include overlapping CNVs, Transcripts, Regions, and Genes. Each of these top level column headers has a variety of specific annotation information; all of which can be sorted, plotted, queried, and added to the filter chain for additional filtering.

Filtering on CNV Metrics and Annotations

Depending on the size of your panel, you may only have a handful of CNV events called and can at this point inspect and interpret them all. If you are working with larger panels and exomes, it will be useful to filter out CNVs that are low-quality calls and those that may not be safely ignored due to their annotations. We will create a filter chain to specify a range of sample values to analyze. To construct a filter chain from the CNV table, right click on the desired filter column and select Add to Filter Chain.

For this example, select Cancer Sample 34, select the CNV table, and right click on the Flags column and select Add to Filter Chain.

This will show the Filter card and show the newly created Flags filter. Select the options, High Controls Variation and Within Regional IQR. We can then right-click on the filter card and Invert this selection by choosing Inverted. This change is reflected in the ! NOT note now in the filter card title.

Now return to the CNVs tab and add a filter to the p-value column by right clicking on the column header and select Add to Filter Chain. This will take you to the Filter CNVs tab. In the newly added p-value filter, enter a value of 0.01 and hit Enter. This will bring up the four filter options:

• Less than 0.01
• Equal to 0.01
• Greater than 0.01
• Missing

Select the Less than 0.01 option.

For one more filter, return to the CNVs tab, scroll over to Overlapping Regions OMIM Genes 2021-01-08, GHI and right click on the Disorders column header to add a Disorder filter to the filter chain. In the Filter CNVs tab, on the Disorders filter card, type in Breast Cancer and choose Contains Breast cancer on the filter card.

Interpreting and Validating CNV Calls

Next, we will examine a few of the CNVs called by the algorithm. We will begin by looking at a heterozygous deletion in BRCA2.

To examine this CNV:

• Select Cancer Sample 12 from the sample selection dropdown at the top of the VarSeq window.
• Click on the first row of the CNV table (Heterozygous Deletion in BRCA2)
• Next, zoom out using the minus button to see the surrounding region in GenomeBrowse.

The GenomeBrowse plot shows that there is strong evidence for this event. Since it is called as a heterozygous deletion, we expect that the normalized coverage will be about half that of the reference samples, leading to a Ratio of around 0.5, which is exactly what we see for the target within this CNV event.

Additionally we would expect a low Z-score indicating three or more standard deviations form the reference sample mean. The Z-score shown in the plot is less than -3.5 indicating extreme deviation from the reference samples, supporting the heterozygous deletion call.

Next, we will examine a duplication event in BRCA2.

To analyze this CNV:

• Select Cancer Sample 13 from the sample selection dropdown
• Then click on the first row of the CNV table (Duplicate in BRCA2)
• Once again zoom out using the minus button to see the surrounding region.

For a copy number 3 duplication we expect a Ratio of around 1.5 and a Z-score greater than 3. The duplication shown above falls in line with these expectations, with all targets having Z-scores around 4 and Ratios around 1.5. Thus, it appears that we have strong evidence for this event.

Finally, we will look at a heterozygous deletion in BRIP1.

To examine this CNV,

• Deselect Within Regional IQR from the Flags filter card.
• Select Cancer Sample 34 from the sample selection drop down menu.
• Then, select the third row of the table and zoom out using the minus button.

While this event has a low Z-score, the surrounding region is very noisy in terms of both Z-score and Ratio. Additionally, the average Ratio value of around 0.588 is not as low as we would expect for a heterozygous deletion. Our suspicions are verified by the Within Regional IQR flag shown in the CNV table, which indicates that the signal used as evidence for the event falls too close to the noise of the surrounding region. These factors lead us to view this deletion as questionable.

ACMG CNV Algorithm

Now that we have become familiarized with working with CNVs in VarSeq, we are now ready to set up our workflow to filter down to clinically relevant CNVs that we want to analyze with the ACMG CNV Guidelines.

To start:

• Select Cancer Sample 12 from the sample selection dropdown.
• Reset the NOT Flags(current) filter card at the top of the filter chain to only Enabled (right- click on the filter card, deselect Inverted and choose Enabled) and set the value to Missing.

Next, we will add the Sample ACMG CNV Classifier algorithm. This is performed by selecting Add in the title bar and selecting Computed Data…. From the Table Type dropdown menu, select CNV. Then choose the ACMG Sample CNV Classifier and click OK.

Click OK on the next window to download the sources necessary to run the algorithm and leave all default settings.

On the next dialog, leave the default value of 0.75 for the similarity coefficient. Setting this value is particularly useful in trio analysis when looking at the inheritance of a CNV.

Scroll to the right in the CNV Table to see the output of the ACMG Sample CNV Classifier algorithm. We can see that this BRCA2 CNV in our sample is auto-classified by the algorithm to be likely pathogenic. Additionally, the criteria that contribute to the classification are listed in the table.

In the next section, we will analyze this CNV within VSClinical’s automated ACMG guidelines workflow.

ACMG CNV Guidelines

Opening the ACMG Guidelines is as easy as opening a new tab by clicking on the + icon and selecting VSClinical. Then, select ACMG Guidelines from the drop-down menu.

This will open up a dialog that will ask you to specify which assessment catalogs to use, or create new catalogs using either the Create icons to the right or you can choose to create all missing catalogs at once using the Create Missing Catalogs button at the bottom of the interface. Assessment catalogs will be used to save variant, CNV, and gene level interpretations.

If you choose to create the catalogs with Create icons on the right, the create catalog will first ask you to determine the database type. The options are SQLite, PostgreSQL, MySQL, or if you have the added feature connected, VSWarehouse.

If not using VSWarehouse, a common choice is using the SQLite type and saving the catalog locally. In either case, select a name or location for the new assessment catalog and select, OK.

The Create Missing Catalogs button at the bottom of the screen will automatically create SQLite catalogs, name them, and save them to your assessment catalogs folder.

Once the assessment catalogs are selected or created click, OK.

The next dialog will prompt you to download required and optionally download recommended annotations which will be used in the evaluation of the CNV. In this dialog, you can also lock the versions of the annotations that are being used so even when a new version of the annotation is available, VSClinical will used the locked version for evaluations. Once the required annotations are downloaded click Close.

At this point, there is one last step before creating an evaluation which is to create record sets to track not only CNVs but also our variants for reporting. Click on the Create Default CNV icon for both the Primary Findings and Secondary Finding sections, then click Apply.

The evaluation is started by selecting the blue Start New Evaluation button.

The ACMG Guidelines tab will open up to the Evaluation tab. In the Evaluation tab, variants and CNVs can be added from the project or manually. Across the top are the Genes, Variants, CNVs, Phenotypes, and Report tabs which can be used to navigate to these sections at any time.

To add CNVs to the evaluation click on the Add CNVs icon shown in Figure 8-6, or scroll down until the CNVs to Evaluate in Sample Cancer Sample 12 window appears and then click on Add CNVs From Project.

Notice that the BRCA2 exon 7 deletion from Cancer Sample 12 is listed as a result of the filtered CNVs from the project. To add this CNV to the evaluation click Prepare to Add, then click Add 1 CNVs once the CNV loads into the dialog.

Next, click on the CNV listed in the CNVs to Evaluate in Sample Cancer Sample 12 window and this will redirect the evaluation to analyze this CNV in the CNVs tab. Alternatively, you can click on the CNVs tab at the top to open the CNVs tab.

At the top of the CNV evaluation tab, there is a summary and description of the BRCA2 ex7 deletion. Scroll down to the CNV Interpretation for Sample Cancer Sample 12 section. All of the CNV level interpretation will be collected here.

Next, let’s fill in some of this information. First, change the Reporting As dropdown from Don’t Report to Primary Findings. Then, set the CNV Origin dropdown to Maternal. Now we will add the CNV Summary information from the scoring section on the right to the Interpretation box on the left. To do this, click on More.. underneath the CNV Summary to expand the paragraph. Then click on the small blue plus sign and choose Add Text to My Interpretation. Your screen should look like Figure 8-10 when completed.

To save this interpretation click Review & Save Now… then click Save and Close in the next dialog.

Next, let’s collect interpretation on the gene level for our CNV and finish up the scoring process. Interpretation for this CNV in the context of the BRCA2 gene begins in the Genomic Region section. Graphically, we can see the genomic region and the information displayed from a wide range of CNV annotation sources. The first graph contains information from the ClinGen Gene/Region Dosage Sensitivity track. The red bar in the graph graph indicates that the BRCA2 gene is a known dosage sensitive gene. We also notice that a CNV in this region is not common according DECIPHER, GnomAD, DGV, and 1KG Phase3. You can click on any of the graphs to get more information from these annotations.

At the bottom of the graphs is the scoring for whether the CNV overlaps common regions, and if the CNV overlaps 1 or more genes. For this BRCA2 CNV, a 3A is scored as the CNV partially overlaps the BRCA2 gene so only 1 gene is affected by the CNV. The reasoning for the auto-recommended score is displayed underneath the scores.

To continue the CNV interpretation and scoring on a gene level scroll down to the Overlapping Genes section. Now, we see the scoring for all 5 sections. Section 1 is scored 1A as this CNV contains protein-coding or other known functionally important elements, and 3A as it overlaps one gene. Section 2 is scored 2E, but let’s click on the Edit button next to the section 2 scoring and we will take a look at the reason behind the auto-scored +0.9 score.

The Section 2 scoring wizard is set up in a decision-tree structure to ultimately asses the impact the CNV has on the gene. The 2E score indicates that this CNV deletion is a frameshift and is predicted to result in non-sense mediated decay. Displayed at the bottom of the scoring wizard are the previously answered questions that lead to the final 2E classifications. You can click through the other decision points to explore the scoring wizard.

Click on the small x at the top of the wizard and return the CNV scoring.

Notice that the scoring for Section 4 has been “Skipped for HI gene”. Because BRCA2 is a gene with well established evidence for haploinsufficiency according to ClinGen Gene Dosage, this section can be skipped.

The last section to score is section 5. This section does not have any auto-recommended scoring as this section is sample specific and needs to be scored manually. Click on the Start button for Section 5.

This opens up the Section 5 scoring wizard. The first question is “Is the inheritance status of the CNV known?” In this case the CNV inheritance in known, so click on the top option Inheritance Known.

Continue through the scoring wizard answering the questions as follows:

• What is the inheritance status? Inherited
• Are there any apparent non-segregations? 5D

Remember to allows check the decision tracking below the scoring wizard for more information.

The CNV scoring for section 5 adds a score of +0.05 to the existing +0.90 score. Exit the Section 5 wizard to see the scoring summary and classification for the BRCA2 CNV.

Now we can add some interpretation for the BRCA2 gene in the Notes on Relevance to Patient and Notes on the Gene sections. Click on the blue + icon under Auto Interpretation of Gene Impact and select Append to Notes on Relevance to Patient. Then under Gene Summary, click on the blue + icon and select Add to Notes on Gene. Your screen should look like Figure 8-18.

Now we are ready to generate a clinical report including the BRCA2 ex7 deletion.

Using CNV Interpretations in Reports

Start by clicking on the Report tab. Once in the Report tab, click on the grayed out Word Template Export on the right.

To create a word template, click on the blue + New Report Template.

There are three Word-based templates to choose from, but we will use the Gene Panel Template and under New Template Name: type CNV Tutorial. Then click Create.

Next click Render to create the report.

As you scroll through the report, notice that patient level information fills into the top of the report. Scrolling to the Primary Findings section, all of the interpretation that was collected about the BRCA2 ex7 deletion has been automatically filled in.

Conclusion

This tutorial was designed to provide an demonstration of VarSeq’s CNV calling capabilities in the context of cancer gene panels and the application of the ACMG/ClinGen ACMG Guidelines.

If you are interested in getting a demo license to try out this and other features please request a demo from: Discover VarSeq

Additional features and capabilities are being added all the time, so if you do not see a feature you need for your workflows please do not hesitate to let us know!