This document describes "regular" (variants-only) VCF files. For information on the gVCF format produced by HaplotypeCaller in
-ERC GVCF mode, please see this companion document.
VCF stands for Variant Call Format. It is a standardized text file format for representing SNP, indel, and structural variation calls. The VCF specification used to be maintained by the 1000 Genomes Project, but its management and expansion has been taken over by the Global Alliance for Genomics and Health Data Working group file format team.
VCF is the primary (and only well-supported) format used by the GATK for variant calls. We prefer it above all others because while it can be a bit verbose, the VCF format is very explicit about the exact type and sequence of variation as well as the genotypes of multiple samples for this variation.
That being said, this highly detailed information can be challenging to understand. The information provided by the GATK tools that infer variation from NGS data, such as the UnifiedGenotyper and the HaplotypeCaller, is especially complex. This document describes some specific features and annotations used in the VCF files output by the GATK tools.
The following text is a valid VCF file describing the first few SNPs found by the UG in a deep whole genome data set from our favorite test sample, NA12878:
##fileformat=VCFv4.0 ##FILTER=<ID=LowQual,Description="QUAL < 50.0"> ##FORMAT=<ID=AD,Number=.,Type=Integer,Description="Allelic depths for the ref and alt alleles in the order listed"> ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth (only filtered reads used for calling)"> ##FORMAT=<ID=GQ,Number=1,Type=Float,Description="Genotype Quality"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=PL,Number=3,Type=Float,Description="Normalized, Phred-scaled likelihoods for AA,AB,BB genotypes where A=ref and B=alt; not applicable if site is not biallelic"> ##INFO=<ID=AC,Number=.,Type=Integer,Description="Allele count in genotypes, for each ALT allele, in the same order as listed"> ##INFO=<ID=AF,Number=.,Type=Float,Description="Allele Frequency, for each ALT allele, in the same order as listed"> ##INFO=<ID=AN,Number=1,Type=Integer,Description="Total number of alleles in called genotypes"> ##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP Membership"> ##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth"> ##INFO=<ID=DS,Number=0,Type=Flag,Description="Were any of the samples downsampled?"> ##INFO=<ID=Dels,Number=1,Type=Float,Description="Fraction of Reads Containing Spanning Deletions"> ##INFO=<ID=HRun,Number=1,Type=Integer,Description="Largest Contiguous Homopolymer Run of Variant Allele In Either Direction"> ##INFO=<ID=HaplotypeScore,Number=1,Type=Float,Description="Consistency of the site with two (and only two) segregating haplotypes"> ##INFO=<ID=MQ,Number=1,Type=Float,Description="RMS Mapping Quality"> ##INFO=<ID=MQ0,Number=1,Type=Integer,Description="Total Mapping Quality Zero Reads"> ##INFO=<ID=QD,Number=1,Type=Float,Description="Variant Confidence/Quality by Depth"> ##INFO=<ID=SB,Number=1,Type=Float,Description="Strand Bias"> ##INFO=<ID=VQSLOD,Number=1,Type=Float,Description="log10-scaled probability of variant being true under the trained gaussian mixture model"> ##UnifiedGenotyperV2="analysis_type=UnifiedGenotyperV2 input_file=[TEXT CLIPPED FOR CLARITY]" #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878 chr1 873762 . T G 5231.78 PASS AC=1;AF=0.50;AN=2;DP=315;Dels=0.00;HRun=2;HaplotypeScore=15.11;MQ=91.05;MQ0=15;QD=16.61;SB=-1533.02;VQSLOD=-1.5473 GT:AD:DP:GQ:PL 0/1:173,141:282:99:255,0,255 chr1 877664 rs3828047 A G 3931.66 PASS AC=2;AF=1.00;AN=2;DB;DP=105;Dels=0.00;HRun=1;HaplotypeScore=1.59;MQ=92.52;MQ0=4;QD=37.44;SB=-1152.13;VQSLOD= 0.1185 GT:AD:DP:GQ:PL 1/1:0,105:94:99:255,255,0 chr1 899282 rs28548431 C T 71.77 PASS AC=1;AF=0.50;AN=2;DB;DP=4;Dels=0.00;HRun=0;HaplotypeScore=0.00;MQ=99.00;MQ0=0;QD=17.94;SB=-46.55;VQSLOD=-1.9148 GT:AD:DP:GQ:PL 0/1:1,3:4:25.92:103,0,26 chr1 974165 rs9442391 T C 29.84 LowQual AC=1;AF=0.50;AN=2;DB;DP=18;Dels=0.00;HRun=1;HaplotypeScore=0.16;MQ=95.26;MQ0=0;QD=1.66;SB=-0.98 GT:AD:DP:GQ:PL 0/1:14,4:14:60.91:61,0,255
It seems a bit complex, but the structure of the file is actually quite simple:
[HEADER LINES] #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878 chr1 873762 . T G 5231.78 PASS [ANNOTATIONS] GT:AD:DP:GQ:PL 0/1:173,141:282:99:255,0,255 chr1 877664 rs3828047 A G 3931.66 PASS [ANNOTATIONS] GT:AD:DP:GQ:PL 1/1:0,105:94:99:255,255,0 chr1 899282 rs28548431 C T 71.77 PASS [ANNOTATIONS] GT:AD:DP:GQ:PL 0/1:1,3:4:25.92:103,0,26 chr1 974165 rs9442391 T C 29.84 LowQual [ANNOTATIONS] GT:AD:DP:GQ:PL 0/1:14,4:14:60.91:61,0,255
After the header lines and the field names, each line represents a single variant, with various properties of that variant represented in the columns. Note that here everything is a SNP, but some could be indels or CNVs.
The first 6 columns of the VCF, which represent the observed variation, are easy to understand because they have a single, well-defined meaning.
CHROM and POS : The CHROM and POS gives the contig on which the variant occurs. For indels this is actually the base preceding the event, due to how indels are represented in a VCF.
ID: The dbSNP
rs identifier of the SNP, based on the contig and position of the call and whether a record exists at this site in dbSNP.
REF and ALT: The reference base and alternative base that vary in the samples, or in the population in general. Note that REF and ALT are always given on the forward strand. For indels the REF and ALT bases always include at least one base each (the base before the event).
QUAL: The Phred scaled probability that a REF/ALT polymorphism exists at this site given sequencing data. Because the Phred scale is -10 * log(1-p), a value of 10 indicates a 1 in 10 chance of error, while a 100 indicates a 1 in 10^10 chance. These values can grow very large when a large amount of NGS data is used for variant calling.
FILTER: In a perfect world, the QUAL field would be based on a complete model for all error modes present in the data used to call. Unfortunately, we are still far from this ideal, and we have to use orthogonal approaches to determine which called sites, independent of QUAL, are machine errors and which are real SNPs. Whatever approach is used to filter the SNPs, the VCFs produced by the GATK carry both the PASSing filter records (the ones that are good have PASS in their FILTER field) as well as those that fail (the filter field is anything but PASS or a dot). If the FILTER field is a ".", then no filtering has been applied to the records, meaning that all of the records will be used for analysis but without explicitly saying that any PASS. You should avoid such a situation by always filtering raw variant calls before analysis.
For more details about these fields, please see this page.
In the excerpt shown above, here is how we interpret the line corresponding to each variant:
The genotype fields of the VCF look more complicated but they're actually not that hard to interpret once you understand that they're just sets of tags and values. Let's take a look at three of the records shown earlier, simplified to just show the key genotype annotations:
chr1 873762 . T G [CLIPPED] GT:AD:DP:GQ:PL 0/1:173,141:282:99:255,0,255 chr1 877664 rs3828047 A G [CLIPPED] GT:AD:DP:GQ:PL 1/1:0,105:94:99:255,255,0 chr1 899282 rs28548431 C T [CLIPPED] GT:AD:DP:GQ:PL 0/1:1,3:4:25.92:103,0,26
Looking at that last column, here is what the tags mean:
GT : The genotype of this sample. For a diploid organism, the GT field indicates the two alleles carried by the sample, encoded by a 0 for the REF allele, 1 for the first ALT allele, 2 for the second ALT allele, etc. When there's a single ALT allele (by far the more common case), GT will be either:
GQ: The Genotype Quality, or Phred-scaled confidence that the true genotype is the one provided in GT. In the diploid case, if GT is 0/1, then GQ is really L(0/1) / (L(0/0) + L(0/1) + L(1/1)), where L is the likelihood that the sample is 0/0, 0/1/, or 1/1 under the model built for the NGS dataset. The GQ is simply the second most likely PL - the most likely PL. Because the most likely PL is always 0, GQ = second highest PL - 0. If the second most likely PL is greater than 99, we still assign a GQ of 99, so the highest value of GQ is 99.
AD and DP: These are complementary fields that represent two important ways of thinking about the depth of the data for this sample at this site. See the Technical Documentation for details on AD (DepthPerAlleleBySample) and DP (Coverage).
PL: This field provides the likelihoods of the given genotypes (here, 0/0, 0/1, and 1/1). These are normalized, Phred-scaled likelihoods for each of the 0/0, 0/1, and 1/1, without priors. To be concrete, for the heterozygous case, this is L(data given that the true genotype is 0/1). The most likely genotype (given in the GT field) is scaled so that it's P = 1.0 (0 when Phred-scaled), and the other likelihoods reflect their Phred-scaled likelihoods relative to this most likely genotype.
With that out of the way, let's interpret the genotypes for NA12878 at chr1:899282.
chr1 899282 rs28548431 C T [CLIPPED] GT:AD:DP:GQ:PL 0/1:1,3:4:25.92:103,0,26
At this site, the called genotype is
GT = 0/1, which is C/T. The confidence indicated by
GQ = 25.92 isn't so good, largely because there were only a total of 4 reads at this site (
DP =4), 1 of which was REF (=had the reference base) and 3 of which were ALT (=had the alternate base) (indicated by
AD=1,3). The lack of certainty is evident in the PL field, where
PL(0/1) = 0 (the normalized value that corresponds to a likelihood of 1.0). There's a chance that the subject is "hom-var" (=homozygous with the variant allele) since
PL(1/1) = 26, which corresponds to 10^(-2.6), or 0.0025, but either way, it's clear that the subject is definitely not "hom-ref" (=homozygous with the reference allele) since
PL(0/0) = 103, which corresponds to 10^(-10.3), a very small number.
Finally, variants in a VCF can be annotated with a variety of additional tags, either by the built-in tools or with others that you add yourself. The way they're formatted is similar to what we saw in the Genotype fields, except instead of being in two separate fields (tags and values, respectively) the annotation tags and values are grouped together, so tag-value pairs are written one after another.
chr1 873762 [CLIPPED] AC=1;AF=0.50;AN=2;DP=315;Dels=0.00;HRun=2;HaplotypeScore=15.11;MQ=91.05;MQ0=15;QD=16.61;SB=-1533.02;VQSLOD=-1.5473 chr1 877664 [CLIPPED] AC=2;AF=1.00;AN=2;DB;DP=105;Dels=0.00;HRun=1;HaplotypeScore=1.59;MQ=92.52;MQ0=4;QD=37.44;SB=-1152.13;VQSLOD= 0.1185 chr1 899282 [CLIPPED] AC=1;AF=0.50;AN=2;DB;DP=4;Dels=0.00;HRun=0;HaplotypeScore=0.00;MQ=99.00;MQ0=0;QD=17.94;SB=-46.55;VQSLOD=-1.9148
Here are some commonly used built-in annotations and what they mean:
|Annotation tag in VCF||Meaning|
|AC,AF,AN||See the Technical Documentation for Chromosome Counts.|
|DB||If present, then the variant is in dbSNP.|
|DP||See the Technical Documentation for Coverage.|
|DS||Were any of the samples downsampled because of too much coverage?|
|Dels||See the Technical Documentation for SpanningDeletions.|
|MQ and MQ0||See the Technical Documentation for RMS Mapping Quality and Mapping Quality Zero.|
|BaseQualityRankSumTest||See the Technical Documentation for Base Quality Rank Sum Test.|
|MappingQualityRankSumTest||See the Technical Documentation for Mapping Quality Rank Sum Test.|
|ReadPosRankSumTest||See the Technical Documentation for Read Position Rank Sum Test.|
|HRun||See the Technical Documentation for Homopolymer Run.|
|HaplotypeScore||See the Technical Documentation for Haplotype Score.|
|QD||See the Technical Documentation for Qual By Depth.|
|VQSLOD||Only present when using Variant quality score recalibration. Log odds ratio of being a true variant versus being false under the trained gaussian mixture model.|
|FS||See the Technical Documentation for Fisher Strand|
|SB||How much evidence is there for Strand Bias (the variation being seen on only the forward or only the reverse strand) in the reads? Higher SB values denote more bias (and therefore are more likely to indicate false positive calls).|
I'm using VariantAnnotator to add annotations to variants from a bunch of sources. One issue that I have is that for some variants, there are multiple annotations in a supplied resource. In the docs, I read
"Note that if there are multiple records in the resource file that overlap the given position, one is chosen randomly."
Can this behaviour be altered? I need to output all annotations for a record, either on a single line, or on multiple.
In the case i'm working on, one line has the annotation "CLNSIG=5" (i.e. a known pathogenic variant) and the other (likely older record) is "CLNSIG=1" i.e. a variant of unknown significance. I need to output both so I can filter downstream (using SelectVariants) to select those where "CLNSIG=5".
I have a vcf that I want to annotate the info DP field matching on the rs IDs. According to the documentation this should be able to be done via the VariantAnnotator and using the -A for the field I want.
java -jar GenomeAnalysisTK.jar \ -T VariantAnnotator \ -R ref.fasta \ --variant input.vcf \ -L input.vcf \ -o output.vcf \ --dbsnp dbSnp.vcf \ -U LENIENT_VCF_PROCESSING \ -A DepthOfCoverage
It is running and annotating the INFO field with the DB annotation with no value. Why is the DP annotation not being annotated? Thanks!
The documentation on the HaplotypeScore annotation reads:
HaplotypeCaller does not output this annotation because it already evaluates haplotype segregation internally. This annotation is only informative (and available) for variants called by Unified Genotyper.
The annotation used to be part of the best practices:
I will include it in the VQSR model for UG calls from low coverage data. Is this an unwise decision? I guess this is for myself to evaluate. I thought I would ask, in case I have missed something obvious.
I have been trying to find documentation for understanding the annotations and numbers in the VCF file. Something like below, can someone guide me how to understand/interpret the numbers? How is the quality of the variant calling for this particular indel?
AC=2; AF=0.100; AN=20; BaseQRankSum=6.161; ClippingRankSum=-2.034; DP=313; FS=5.381; InbreedingCoeff=-0.1180; MLEAC=2; MLEAF=0.100; MQ=58.49; MQ0=0; MQRankSum=-0.456; QD=1.46; ReadPosRankSum=-4.442; VQSLOD=0.348; topculprit=ReadPosRankSum
Could you tell me how to encourage GATK to annotate my genotype columns (i.e. add annotations to the FORMAT and PANC_R columns in the following file):
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT PANC_R chrX 259221 . GA G 136.74 . AC=2;AF=1.00;AN=2;DP=15;FS=0.000;MLEAC=2;MLEAF=1.00;MQ=8.82;MQ0=1;QD=3.04 GT:AD:GQ:PL 1/1:0,2:6:164,6,0
The file was generated with HaplotypeCaller. I used a command line similar to this one to no effect:
java -jar $GATKROOT/GenomeAnalysisTK.jarT VariantAnnotator -R hg19_random.fa -I chr7_recalibrated.bam -V chr7.vcf --dbsnpdbSNP135_chr.vcf -A Coverage -A QualByDepth -A FisherStrand -A MappingQualityRankSumTest -A ReadPosRankSumTest -o chr7_annotated-again.vcf
Does anyone have any suggestions? Thanks in advance!
I had a few questions about the haplotype score.
In the technical documentation it states that "Higher scores are indicative of regions with bad alignments, often leading to artifactual SNP and indel calls. Note that the Haplotype Score is only calculated for sites with read coverage."
How is the haplotype group for each variant site determined? e.g. Does it take the closest two variants to the query site and then treat the query variant + closest two variants as the haplotype group?
Also, in the case of multiallelic SNPs (>2 SNPs), haplotype score is inappropriate since it only looks at whether a site can be explained by the segregation of two and only two haplotypes, correct? So multiallelic snps will be assigned poor haplotype scores OR will these sites not be annotated at all? If we have a case where there is a truly biallelic SNP and a couple of samples have some reads that are erroneously calling a third allele, this variant site will be assigned a poor haplotype score overall, correct?
Just in the process of updating our pipeline from v2.3-4-gb8f1308 Lite to v2.4-7-g5e89f01 Academic and have run into a small issue. The command line:
-T UnifiedGenotyper -glm SNP -R /lustre/scratch111/resources/ref/Homo_sapiens/1000Genomes_hs37d5/hs37d5.fa -I /lustre/scratch111/projects/helic/vcf-newpipe/lists/chr1-pooled.list --alleles /lustre/scratch111/projects/helic/vcf-newpipe/pooled/1/1:1-1000000.snps.vcf.gz -L 1:1-1000000 -U LENIENT_VCF_PROCESSING -baq CALCULATE_AS_NECESSARY -gt_mode GENOTYPE_GIVEN_ALLELES -out_mode EMIT_ALL_SITES --standard_min_confidence_threshold_for_calling 4.0 --standard_min_confidence_threshold_for_emitting 4.0 -l INFO -A QualByDepth -A HaplotypeScore -A MappingQualityRankSumTest -A ReadPosRankSumTest -A FisherStrand -A InbreedingCoeff -A DepthOfCoverage -o /lustre/scratch111/projects/helic/vcf-newpipe/pooled/1/1:1-1000000.asnps.vcf.gz.part.vcf.gz
This worked in 2.3.4. But now gives:
Invalid command line: Argument annotation has a bad value: Class DepthOfCoverage is not found; please check that you have specified the class name correctly
I've looked at the release notes but it's not giving me a clue as to what has changed. Has the DepthOfCoverage annotation now been dropped? I've checked and I can reproduce this on the latest nightly (nightly-2013-03-11-g184e5ac)
Hi the GATK team;
I use the UnifiedGenotyper the following way:
java -jar GenomeAnalysisTK-2.1-13-g1706365/GenomeAnalysisTK.jar \ -R /human_g1k_v37.fasta \ -T UnifiedGenotyper \ -glm BOTH \ -S SILENT \ -L ../align/capture.bed \ -I myl.bam \ --dbsnp broadinstitute.org/bundle/1.5/b37/dbsnp_135.b37.vcf.gz \ -o output.vcf
When I look at the generated VCF , the variation 18:55997929 (CTTCT/C) is said to be rs4149608
18 55997929 rs4149608 CTTCT C (...)
but in the dbsnp_135.b37.vcf.gz, you can see that the right rs## should be rs144384654
$ gunzip -c broadinstitute.org/bundle/1.5/b37/dbsnp_135.b37.vcf.gz |grep -E -w '(rs4149608|rs144384654)' 18 55997929 rs4149608 CT C,CTTCT (...) 18 55997929 rs144384654 CTTCT C (...)
does UnifiedGenotyper uses the first rs## it finds at a given position ? Or should I use another method/tool to get the 'right' rs## ?
Please look at lines 1 and 2 taken from a vcf file, which have same Chromosome and Position and one of the Alt allele is same in both lines, different allele count and have different rsID.
1 1229111 rs70949568 A ACGCCCCTGCCCTGGAGGCCCCGCCCCTGCCCTGGAGGCCC,C 2629.32 TruthSensitivityTranche99.50to99.90;TruthSensitivityTranche99.30to99.50 AC=80,31;AF=0.1273;AN=284;BaseQRankSum=1.124;DB;DP=426;Dels=0.00;FS=4.620;HRun=1;HaplotypeScore=0.2101;InbreedingCoeff=-0.0029;MQ0=0;MQ=58.46;MQRankSum=1.211;QD=5.26;ReadPosRankSum=-5.748;SB=-36.94;SF=0f,1f;SNPEFF_EFFECT=DOWNSTREAM;SNPEFF_FUNCTIONAL_CLASS=NONE;SNPEFF_GENE_BIOTYPE=protein_coding;SNPEFF_GENE_NAME=ACAP3;SNPEFF_IMPACT=MODIFIER;SNPEFF_TRANSCRIPT_ID=ENST00000379037;VQSLOD=-2.3894;culprit=MQ GT:DP:GQ:AD:PL
1 1229111 . A C 89.94 TruthSensitivityTranche99.00to99.30 AC=7;AF=0.0614;AN=114;BaseQRankSum=0.801;DP=175;Dels=0.00;FS=1.668;HRun=1;HaplotypeScore=0.2276;InbreedingCoeff=-0.0538;MQ0=0;MQ=57.90;MQRankSum=0.501;QD=4.28;ReadPosRankSum=-4.531;SB=-15.19;SF=0f;SNPEFF_EFFECT=DOWNSTREAM;SNPEFF_FUNCTIONAL_CLASS=NONE;SNPEFF_GENE_BIOTYPE=protein_coding;SNPEFF_GENE_NAME=ACAP3;SNPEFF_IMPACT=MODIFIER;SNPEFF_TRANSCRIPT_ID=ENST00000379037;VQSLOD=-1.4433;culprit=MQ GT:DP:GQ:AD:PL
Could you tell me when we can use new version of SnpEff with GATK?
I have some bugs :
caused by exception org.broadinstitute.sting.gatk.walkers.annotator.interfaces.ExperimentalAnnotation.
I don't know if I forget some other options linked these annotations. These options are important for me. So I deleted them but if somebody want to use them ...
I'm curious about the experience of the community at large with VQSR, and specifically with which sets of annotations people have found to work well. The GATK team's recommendations are valuable, but my impression is that they have fairly homogenous data types - I'd like to know if anyone has found it useful to deviate from their recommendations.
For instance, I no longer include InbreedingCoefficient with my exome runs. This was spurred by a case where previously validated variants were getting discarded by VQSR. It turned out that these particular variants were homozygous alternate in the diseased samples and homozygous reference in the controls, yielding an InbreedingCoefficient very close to 1. We decided that the all-homozygous case was far more likely to be genuinely interesting than a sequencing/variant calling artifact, so we removed the annotation from VQSR. In order to catch the all-heterozygous case (which is more likely to be an error), we add a VariantFiltration pass for 'InbreedingCoefficient < -0.8' following ApplyRecalibration.
In my case, I think InbreedingCoefficient isn't as useful because my UG/VQSR cohorts tend to be smaller and less diverse than what the GATK team typically runs (and to be honest, I'm still not sure we're doing the best thing). Has anyone else found it useful to modify these annotations? It would be helpful if we could build a more complete picture of these metrics in a diverse set of experiments.
Broad recommends using snpEff to add annotations to VCF files created by GATK. This gives annotations about the effect of a given variant: is it in a coding region? Does it cause a frameshift? What transcripts are impacted? etc. However, snpEff does not provide other annotations you might want, such as 1000 genomes minor allele frequency, SIFT scores, phyloP conservation scores, and so on. I've previously used annovar to get those sorts of things, and that worked well enough, though I did not find it to be especially user-friendly.
So my question is, what other ways have users found of getting this sort of annotation information? I'm interested specifically in human exomes, but I am sure other users reading this Ask the Community post will be interested in answers for other organisms as well. I'm looking for recommendations on what's quick, simple, easy to use, and has been used successfully with VCFs produced by GATK. I'm open to answers in the form of other software tools or sources of raw data that I can easily manipulate on my own.
Thanks in advance.
I am doing human exome sequencing with hg19 as a reference, and I want UnifiedGenotyper to give me whatever annotations are available and I will worry later about which ones are useful and which are not.
I am confused about the behavior of the --annotation option in UnifiedGenotyper. The default value is listed as , implying that unless we explicitly list what annotations we want, we get no annotations at all? Is that correct? Then in order to get a list of available annotations, we are directed to the VariantAnnotator --list option but it appears that it is not possible to just run:
java -Xmx2g -jar GenomeAnalysisTK.jar \ -R ref.fasta \ -T VariantAnnotator \ --list
In order to get a list of annotations. Instead, one not only needs to include a --variants flag, but the vcf file you point to actually has to be well-formatted, etc., otherwise you get errors like this
##### ERROR MESSAGE: Argument with name '--variant' (-V) is missing.
##### ERROR MESSAGE: Invalid command line: No tribble type was provided on the command line and the type of the file could not be determined dynamically. Please add an explicit type tag :NAME listing the correct type from among the supported types:
So, that having failed, is anyone able to just provide me with a list of possible arguments to the UnifiedGenotyper --annotation option?