Targeted Caller
Repetitive regions in the human genome pose a challenge for general variant calling approaches which typically cannot make use of potentially misplaced MAPQ0 reads. Furthermore, high sequence homology of some genes with a pseudogene paralog can lead to a wide variety of common structural variants (SVs) in the population, requiring specialized targeted calling approaches. DRAGEN supports targeted calling for a number of genes/targets as described in subsequent target-specific sections.
The targeted caller can be enabled using the command line option --enable-targeted=true or a subset of targets can be enabled by providing a space-separated list of target names. The supported target names for WGS are: cyp2b6, cyp2d6, cyp21a2, gba, hba, lpa, rh, and smn. The supported target names for WES are: hba and smn. For a list of all supported targeted caller options along with their default values, see Targeted Caller Options. The targeted caller produces a <output-file-prefix>.targeted.json file containing a summary of the variant caller results for each target. Additional detail of individual variant calls are reported in VCF format in the <output-file-prefix>.targeted.vcf.gz output file.
Input Data
The targeted caller requires WES data or WGS data aligned to a human reference genome. WGS data should be at least 30x coverage as the caller may be less reliable at lower coverage. Human reference genome builds based on hg19, hs37d5 (including GRCh37), or hg38 are supported.
Output Files
Targeted JSON File
The targeted caller generates a <output-file-prefix>.targeted.json file in the output directory. The output file is a JSON formatted file containing the fields below.
sampleId
The sample name.
string
always
softwareVersion
The version of DRAGEN.
string
always
genomeBuild
The reference genome build.
string
always
phenotypeDatabaseSources
Resources used for calling metabolism status (phenotype).
json array of strings
CYP2B6 or CYP2D6 is enabled
cyp2b6
The CYP2B6 caller fields.
dictionary
CYP2B6 caller is enabled
cyp2d6
The CYP2D6 caller fields.
dictionary
CYP2D6 caller is enabled
cyp21a2
The CYP21A2 caller fields.
dictionary
CYP21A2 caller is enabled
gba
The GBA caller fields.
dictionary
GBA caller is enabled
hba
The HBA caller fields.
dictionary
HBA caller is enabled
lpa
The LPA caller fields.
dictionary
LPA caller is enabled
rh
The RH caller fields.
dictionary
RH caller is enabled
smn
The SMN caller fields.
dictionary
SMN caller is enabled
locusAnnotations
The Star Allele caller fields, see Star Allele Caller
dictionary
Star Allele caller is enabled
Targeted VCF File
The targeted caller generates a <output-file-prefix>.targeted.vcf.gz file in the output directory. The output file is a VCFv4.2 formatted file. The targets that have VCF output are: cyp21a2, gba, hba, lpa, rh, and smn.
Small variants, structural variants, and copy number variants are reported in the same VCF file.
The <output-file-prefix>.targeted.vcf.gz file includes the following source header line:
For lpa, rh and smn targets, the EVENT and EVENTTYPE INFO fields are used to identify the called variants.
The EVENT and EVENTTYPE INFO fields are formally introduced in VCFv4.4 to enable the representation of complex rearrangements. This is achieved using the EVENT field to group all the related VCF records together, and the EVENTTYPE to classify the event. The corresponding header lines are the following.
However, the use of EVENT is not limited to complex rearrangements and can be used to associate nonsymbolic alleles, for example in cases of variant position ambiguity in high homology regions.
Since the EVENTTYPE values are implementation-defined, custom EVENTTYPE header lines are included to describe each EVENTTYPE.
For cyp2d6, cyp21a2, gba, and hba targets, the ALLELE_ID INFO field is used to identify the called variant alleles.
The missing value . is used when no identifier is available (e.g. a wild type allele) or applicable (e.g. allele index 0 for a structural variant record).
Additionally, the 'TargetedCaller' INFO field is used to indicate which targeted caller the current VCF record is generated from
Nonrecombinant-like Variants In High Homology Regions
In the case of target variants in a high homology region, each variant is reported ambiguously at all corresponding homologous positions (i.e. in both the pseudogene and in the target gene). Additional analysis for these variants can be performed if absolute certainty that these variants are located in the target gene (e.g. in gba or cyp21a2) is required.
For lpa and smn the ploidy of the called genotype (FORMAT/GT field) corresponds to the combined copy number from all the homologous positions. For cyp21a2, gba and hba, this "joint" genotype from all the homologous positions is instead reported in a separate FORMAT/JGT field which is then collapsed into a diploid genotype and reported in the FORMAT/GT field. The following fields are reported for "joint" calls:
Note that the FORMAT/GQ and FORMAT/JGQ fields contain the unconditional genotype quality, unlike the VCF spec where FORMAT/GQ is defined as the genotype quality conditioned on the site being variant.
In the depicted example there are two genes A and B that include a high homology region. The usual process to call variants in this regions is to make a joint pileup of the reads aligning in both genes A and B and call the variants using a model with a ploidy proportional to the total copy number of the regions. This generates divergent possible genotypes that are equally likely since the variant cannot be confidently placed in either gene A or gene B. For lpa and smn the variant would be reported as follows:
Given the unconventional ploidy of the FORMAT/GT field used in this representation, a TargetedRepeatConflict filter is applied to these records. The header line for the filter is the following.
For cyp21a2, gba and hba, a conventional diploid FORMAT/GT is reported and so no TargetedRepeatConflict filter is applied. Due to the ambiguity in placing target variants in high homology regions, the corresponding QUAL and FORMAT/GQ fields can be much lower than conventional small variant calls (i.e. Phred 3 for a single variant allele copy across two homologous diploid positions). Therefore, instead of filtering on QUAL and FORMAT/GQ for these records, the records are filtered based on the FORMAT/JVQL and FORMAT/JGQ fields:
Since the wild type alleles at homologous positions may be different from each other or different from the reference alleles, an additional filter is applied when only wild type alleles are detected across the homologous positions. This avoids making ambiguous variant calls when no target variant of interest is detected.
Rh Gene Conversion Events
In the case of an identified gene conversion even in rh, a small variant is reported at each differentiating site in the acceptor region.
In the depicted example there are two genes A and B and gene A is the acceptor of a gene conversion from gene B (green box in the figure). Gene conversion are identified by observing variations in copy number at differentiating sites (blue and pink bars in the figure) in consecutive regions. Copy number variations between regions define the breakends of the gene conversion. An equivalent VCF representation for gene conversion would be using CNV and SV entries with breakends corresponding to the donor/acceptor regions, however, only the small variant representation is currently supported.
In the case of a detected gene conversion event, there may be differentiating sites with a genotype that is inconsistent with that gene conversion event. In these cases the RecombinantConflict filter is applied. The RecombinantConflict is defined by the following header line.
In the example, the resulting representation is as follows.
Nonallelic Homologous Recombination
For cyp21a2 and gba, nonallelic homologous recombination can result in gene deletion or duplication in the case of reciprocal recombination or gene conversion in the case of nonreciprocal recombination. Both gene deletion and gene conversion can introduce loss-of-function variants and in both cases the targeted caller will report these variants in the target gene. In the case of gene deletion, the differentiating sites at the nontarget (i.e. pseudogene) positions will contain the overlapping deletion allele * while the differentiating sites in the target will contain any variant alleles. Although an equivalent VCF representation would be to simply report the deletion with a single structural variant VCF record, reporting small variant VCF records in the target gene allows for identification of the specific mutations that may occur in a gene transcript and matches well with annotation using HGVS nomenclature. Similarly, for gene conversions, variants are reported at differentiating sites in the target gene, rather than as pairs of structural variant breakends.
Calls at differentiating sites within the recombinant variant calling region will contain the same "joint" fields as are reported for nonrecombinant-like variants in high homology regions (see Nonrecombinant-like Variants In High Homology Regions). However, the collapsed diploid FORMAT/GT will be based on any detected recombination events. Because detected recombinant variants are placed in the target gene, these records are filtered differently than the ambiguously placed, nonrecombinant-like variants in high homology regions. The INFO/Recombinant flag is added to calls derived from recombinant variant calling to distinguish them from nonrecombinant-like variant calls in high homology regions. The FORMAT/VQL field is used to apply the RecombinantLowVQL filter for low quality recombinant variants and the RecombinantREF filter is applied when the collapsed diploid FORMAT/GT contains only reference alleles.
Overlapping Structural Variant Representation
The use of GT=0 for symbolic structural variant alleles is formally disambiguated in VCFv4.4, specifying that "GT=0 indicates the absence of any of the ALT symbolic structural variants defined in the record". With this convention we can report compound overlapping heterozygous structural variants.
In the hba genotype depicted above, two overlapping SVs can be represented as follows:
The relevant header lines for the VCF records above are:
Variable Number Tandem Repeat Representation
In the depicted example there is a Variable Number Tandem Repeat (VNTR) region composed of three repeat units in the reference. The CN INFO field is used to report the allele copy number, the CN FORMAT field to is used report the region total copy number given by the sum of the allele copy numbers, and the REPCN FORMAT field is used to report the repeat unit copy number equal to the allele copy number multiplied by the number of repeat units in the reference.
This VNTR can be represented as follows:
The REPCN and CN header lines are:
Additional Filters
For lpa, rh and smn, the TargetedLowQual filter is applied if the QUAL of a target variant is less than 3.00.
Similarly, for cyp21a2 and gba the TargetedLowVQL filter is applied if the VQL of a target variant in low-homology region is less than 3.00.
The TargetedLowGQ filter is applied if the targeted variant has GQ smaller than 3.
Merging Targeted Calls In The hard-filtered Files
hard-filtered FilesWhen the small variant caller is enabled, the targeted small variant VCF calls can be merged into the <output-file-prefix>.hard-filtered.vcf.gz and <output-file-prefix>.hard-filtered.gvcf.gz files, briefly hard-filtered files. The --targeted-merge-vc command line option can be used to control which targets will have their small variant VCF records merged into the hard-filtered files. For example, --targeted-merge-vc rh will enable merging of the calls from the rh caller into the hard-filtered files and --targeted-merge-vc rh hba will enable merging of the calls from the rh and hba targets into the hard-filtered files. The true value will merge all calls from all supported targets into the hard-filtered files, while the false value will merge no calls into the hard-filtered files.
The targeted calls merged into the hard-filtered files are marked with a TARGETED INFO flag.
When enabled, targeted small variants are merged into the hard-filtered files regardless of any regions that may be provided using the --vc-target-bed option.
Merging Strategy
The merging strategy for targeted small variant calls is to prioritize the targeted calls over small variant calls from the germline small variant caller. When a germline small variant call overlaps a targeted caller call, then the small variant call is filtered with a TargetedConflict filter if any of the following holds:
The targeted caller call is
PASS.The small variant call and targeted caller call have incompatible genotypes and the targeted caller call is not filtered with the
TargetedLowGQfilter.
The strategy is summarized in the following examples.
The
TARGETEDcall isPASS.
The
TARGETEDcall and the small variant call are not overlapping
The
TARGETEDcall is filtered withVARIANT_IN_HOMOLOGY_REGIONand has a discordant variant representation with the overlapping small variant call.
The
TARGETEDcall is filtered withTargetedLowQualand has a discordant genotype with the overlapping small variant call.
The
TARGETEDcall is filtered withTargetedLowGQand has a discordant genotype with the overlapping small variant call.
Exome calling using in-run PON
Targeted calling from WES data is supported for hba and smn. It uses an in-run panel of normals (PON) for coverage normalization of the various target regions by automatically identifying copy-neutral samples from a single sequencing run. All samples in the panel are expected to be from the same sequencing run and library prep batch as the case samples being analyzed. Samples must be prepared using the Illumina CS/PGx Custom Enrichment Research Panel. If targeted calling is enabled on WES data without a PON then targeted calling is skipped and no targeted calling output files will be generated. The first step in targeted calling from WES data is to generate exome counts files for each of the samples in the PON. A minimum of 30 samples is required in the PON and the PON must be sufficiently diverse such that for a given target region, a large subset of samples is copy-neutral. For example, a PON where all samples are positive for alpha thalassemia (HBA1/2 deletion) would not be sufficiently diverse for accurately calling variants in HBA1/2. Similarly, a PON consisting of a large pedigree of related samples would not be sufficiently diverse. No more than ~6% of the samples in the PON should be related to any case sample being analyzed; a PON of 50 samples containing a quad would be acceptable since it would contain 3 samples related to a proband (Mother/Father/Sibling) or ~6% of the samples in the PON. If the samples in the sequencing run are sufficiently diverse, then it is recommended that the PON consist of as many samples from the sequencing run as possible, but can be limited to 96 samples without significantly impacting the accuracy of coverage normalization.
The table below summarizes the available options and high-level steps for running the Targeted Caller using an in-run PON. CNV and Targeted Caller require separate PON files, but the intermediate counts files can be generated in the same DRAGEN command line invocation. For additional details click on the link for each option.
Create run using the Run Planning tool in BSSH
Start planned run in Control Software on instrument
Run DRAGEN Germline Enrichment from BCLs App
Run DRAGEN Germline Enrichment App
Run DRAGEN Enrichment App
BCL to FASTQ conversion
Generate CNV target counts and Targeted Caller exome counts for each PON sample
Generate CNV combined counts PON file
Generate Targeted Caller PON file
Perform case sample analyses
Exome counts generation
Exome counts file generation can be enabled using the command line option --targeted-generate-exome-counts=true. A <output-file-prefix>.targeted.exome.counts.json.gz file will be generated in the output directory. Note that the --enable-targeted option is not required, but can be used to specify a subset of targets.
Exome PON generation
An exome PON file can be generated, using the command line option --targeted-pon-counts-list to pass a text file containing a list of exome counts files, one for each sample in the panel. A <output-file-prefix>.targeted.pon.json.gz file will be generated in the output directory. Note that this is a stand-alone independent dragen run that cannot be combined with other dragen components. A read input (bam/cram/fastq) file is not used.
Exome case sample analysis
Exome targeted calling on a case sample is performed by passing in a PON file and a systematic noise file using the command line options --targeted-pon and --targeted-systematic-noise, respectively. Note that the PON file should be for the same batch as the case sample. A systematic noise file and corresponding pre-built pangenome reference can be downloaded from the DRAGEN Software Support Site page. A json file, <output-file-prefix>.targeted.json and a vcf file, <output-file-prefix>.targeted.vcf.gz will be generated in the output directory with the calls for the enabled targets. For WES mode, an additional field ponQualityFilter, is added to the JSON output for each enabled target. It denotes the quality of the PON and the confidence of the resulting calls. If the case sample does not correlate well with the PON, the ponQualityFilter gets set to LowPonCorrelation, signaling that the calls are considered to have low confidence. Note that the --enable-targeted option is not required, but can be used to specify a subset of targets.
Command-Line Examples
The Targeted Caller can be enabled in parallel with other components as part of a human WGS germline analysis workflow (see DRAGEN Recipe - Germline WGS).
The Targeted Caller can be enabled in parallel with other components as part of a human WES germline analysis workflow (see DRAGEN Recipe - Germline WES).
FASTQ Input Example
The following command-line example runs the targeted caller from FASTQ input:
Prealigned BAM Input Example
The following command-line example runs cyp21a2 only using BAM input without realignment:
Exome counts generation from prealigned BAM Input Example
Exome PON generation from exome counts files from a single sequencing run
Exome case sample analysis from prealigned BAM Input Example
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