# Tagged with #joint-analysis 1 documentation article | 1 announcement | 0 forum discussions

Created 2014-04-03 20:20:08 | Updated 2014-10-22 19:22:34 | Tags: haplotypecaller genotypegvcfs combinegvcfs gvcf joint-analysis

### Overview

GVCF stands for Genomic VCF. A GVCF is a kind of VCF, so the basic format specification is the same as for a regular VCF (see the spec documentation here), but a Genomic VCF contains extra information.

This document explains what that extra information is and how you can use it to empower your variants analyses.

### Important caveat

What we're covering here is strictly limited to GVCFs produced by HaplotypeCaller in GATK versions 3.0 and above. The term GVCF is sometimes used simply to describe VCFs that contain a record for every position in the genome (or interval of interest) regardless of whether a variant was detected at that site or not (such as VCFs produced by UnifiedGenotyper with --output_mode EMIT_ALL_SITES). GVCFs produced by HaplotypeCaller 3.x contain additional information that is formatted in a very specific way. Read on to find out more.

### General comparison of VCF vs. gVCF

The key difference between a regular VCF and a gVCF is that the gVCF has records for all sites, whether there is a variant call there or not. The goal is to have every site represented in the file in order to do joint analysis of a cohort in subsequent steps. The records in a gVCF include an accurate estimation of how confident we are in the determination that the sites are homozygous-reference or not. This estimation is generated by the HaplotypeCaller's built-in reference model.

Note that some other tools (including the GATK's own UnifiedGenotyper) may output an all-sites VCF that looks superficially like the BP_RESOLUTION gVCFs produced by HaplotypeCaller, but they do not provide an accurate estimate of reference confidence, and therefore cannot be used in joint genotyping analyses.

### The two types of gVCFs

As you can see in the figure above, there are two options you can use with -ERC: GVCF and BP_RESOLUTION. With BP_RESOLUTION, you get a gVCF with an individual record at every site: either a variant record, or a non-variant record. With GVCF, you get a gVCF with individual variant records for variant sites, but the non-variant sites are grouped together into non-variant block records that represent intervals of sites for which the genotype quality (GQ) is within a certain range or band. The GQ ranges are defined in the ##GVCFBlock line of the gVCF header. The purpose of the blocks (also called banding) is to keep file size down, and there is no downside for the downstream analysis, so we do recommend using the -GVCF option.

### Example gVCF file

This is a banded gVCF produced by HaplotypeCaller with the -GVCF option.

As you can see in the first line, the basic file format is a valid version 4.1 VCF:

##fileformat=VCFv4.1
##ALT=<ID=NON_REF,Description="Represents any possible alternative allele at this location">
##FILTER=<ID=LowQual,Description="Low quality">
##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="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
##FORMAT=<ID=SB,Number=4,Type=Integer,Description="Per-sample component statistics which comprise the Fisher's Exact Test to detect strand bias.">
##GVCFBlock=minGQ=0(inclusive),maxGQ=5(exclusive)
##GVCFBlock=minGQ=20(inclusive),maxGQ=60(exclusive)
##GVCFBlock=minGQ=5(inclusive),maxGQ=20(exclusive)
##GVCFBlock=minGQ=60(inclusive),maxGQ=2147483647(exclusive)
##INFO=<ID=BaseQRankSum,Number=1,Type=Float,Description="Z-score from Wilcoxon rank sum test of Alt Vs. Ref base qualities">
##INFO=<ID=ClippingRankSum,Number=1,Type=Float,Description="Z-score From Wilcoxon rank sum test of Alt vs. Ref number of hard clipped bases">
##INFO=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth; some reads may have been filtered">
##INFO=<ID=DS,Number=0,Type=Flag,Description="Were any of the samples downsampled?">
##INFO=<ID=END,Number=1,Type=Integer,Description="Stop position of the interval">
##INFO=<ID=HaplotypeScore,Number=1,Type=Float,Description="Consistency of the site with at most two segregating haplotypes">
##INFO=<ID=InbreedingCoeff,Number=1,Type=Float,Description="Inbreeding coefficient as estimated from the genotype likelihoods per-sample when compared against the Hardy-Weinberg expectation">
##INFO=<ID=MLEAC,Number=A,Type=Integer,Description="Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed">
##INFO=<ID=MLEAF,Number=A,Type=Float,Description="Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed">
##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=MQRankSum,Number=1,Type=Float,Description="Z-score From Wilcoxon rank sum test of Alt vs. Ref read mapping qualities">
##INFO=<ID=ReadPosRankSum,Number=1,Type=Float,Description="Z-score from Wilcoxon rank sum test of Alt vs. Ref read position bias">
##contig=<ID=20,length=63025520,assembly=b37>
##reference=file:///humgen/1kg/reference/human_g1k_v37.fasta


Toward the middle you see the ##GVCFBlock lines (after the ##FORMAT lines) (repeated here for clarity):

##GVCFBlock=minGQ=0(inclusive),maxGQ=5(exclusive)
##GVCFBlock=minGQ=20(inclusive),maxGQ=60(exclusive)
##GVCFBlock=minGQ=5(inclusive),maxGQ=20(exclusive)


which indicate the GQ ranges used for banding (corresponding to the boundaries [5, 20, 60]).

You can also see the definition of the MIN_DP annotation in the ##FORMAT lines.

#### Records

The first thing you'll notice, hopefully, is the <NON_REF> symbolic allele listed in every record's ALT field. This provides us with a way to represent the possibility of having a non-reference allele at this site, and to indicate our confidence either way.

The second thing to look for is the END tag in the INFO field of non-variant block records. This tells you at what position the block ends. For example, the first line is a non-variant block that starts at position 20:10000000 and ends at 20:10000116.

#CHROM  POS ID  REF ALT QUAL    FILTER  INFO    FORMAT  NA12878
20  10000000    .   T   <NON_REF>   .   .   END=10000116    GT:DP:GQ:MIN_DP:PL  0/0:44:99:38:0,89,1385
20  10000117    .   C   T,<NON_REF> 612.77  .   BaseQRankSum=0.000;ClippingRankSum=-0.411;DP=38;MLEAC=1,0;MLEAF=0.500,0.00;MQ=221.39;MQ0=0;MQRankSum=-2.172;ReadPosRankSum=-0.235   GT:AD:DP:GQ:PL:SB   0/1:17,21,0:38:99:641,0,456,691,519,1210:6,11,11,10
20  10000118    .   T   <NON_REF>   .   .   END=10000210    GT:DP:GQ:MIN_DP:PL  0/0:42:99:38:0,80,1314
20  10000211    .   C   T,<NON_REF> 638.77  .   BaseQRankSum=0.894;ClippingRankSum=-1.927;DP=42;MLEAC=1,0;MLEAF=0.500,0.00;MQ=221.89;MQ0=0;MQRankSum=-1.750;ReadPosRankSum=1.549    GT:AD:DP:GQ:PL:SB   0/1:20,22,0:42:99:667,0,566,728,632,1360:9,11,12,10
20  10000212    .   A   <NON_REF>   .   .   END=10000438    GT:DP:GQ:MIN_DP:PL  0/0:52:99:42:0,99,1403
20  10000439    .   T   G,<NON_REF> 1737.77 .   DP=57;MLEAC=2,0;MLEAF=1.00,0.00;MQ=221.41;MQ0=0 GT:AD:DP:GQ:PL:SB   1/1:0,56,0:56:99:1771,168,0,1771,168,1771:0,0,0,0
20  10000440    .   T   <NON_REF>   .   .   END=10000597    GT:DP:GQ:MIN_DP:PL  0/0:56:99:49:0,120,1800
20  10000598    .   T   A,<NON_REF> 1754.77 .   DP=54;MLEAC=2,0;MLEAF=1.00,0.00;MQ=185.55;MQ0=0 GT:AD:DP:GQ:PL:SB   1/1:0,53,0:53:99:1788,158,0,1788,158,1788:0,0,0,0
20  10000599    .   T   <NON_REF>   .   .   END=10000693    GT:DP:GQ:MIN_DP:PL  0/0:51:99:47:0,120,1800
20  10000694    .   G   A,<NON_REF> 961.77  .   BaseQRankSum=0.736;ClippingRankSum=-0.009;DP=54;MLEAC=1,0;MLEAF=0.500,0.00;MQ=106.92;MQ0=0;MQRankSum=0.482;ReadPosRankSum=1.537 GT:AD:DP:GQ:PL:SB   0/1:21,32,0:53:99:990,0,579,1053,675,1728:9,12,10,22
20  10000695    .   G   <NON_REF>   .   .   END=10000757    GT:DP:GQ:MIN_DP:PL  0/0:48:99:45:0,120,1800
20  10000758    .   T   A,<NON_REF> 1663.77 .   DP=51;MLEAC=2,0;MLEAF=1.00,0.00;MQ=59.32;MQ0=0  GT:AD:DP:GQ:PL:SB   1/1:0,50,0:50:99:1697,149,0,1697,149,1697:0,0,0,0
20  10000759    .   A   <NON_REF>   .   .   END=10001018    GT:DP:GQ:MIN_DP:PL  0/0:40:99:28:0,65,1080
20  10001019    .   T   G,<NON_REF> 93.77   .   BaseQRankSum=0.058;ClippingRankSum=-0.347;DP=26;MLEAC=1,0;MLEAF=0.500,0.00;MQ=29.65;MQ0=0;MQRankSum=-0.925;ReadPosRankSum=0.000 GT:AD:DP:GQ:PL:SB   0/1:19,7,0:26:99:122,0,494,179,515,694:12,7,4,3
20  10001020    .   C   <NON_REF>   .   .   END=10001020    GT:DP:GQ:MIN_DP:PL  0/0:26:72:26:0,72,1080
20  10001021    .   T   <NON_REF>   .   .   END=10001021    GT:DP:GQ:MIN_DP:PL  0/0:25:37:25:0,37,909
20  10001022    .   C   <NON_REF>   .   .   END=10001297    GT:DP:GQ:MIN_DP:PL  0/0:30:87:25:0,72,831
20  10001298    .   T   A,<NON_REF> 1404.77 .   DP=41;MLEAC=2,0;MLEAF=1.00,0.00;MQ=171.56;MQ0=0 GT:AD:DP:GQ:PL:SB   1/1:0,41,0:41:99:1438,123,0,1438,123,1438:0,0,0,0
20  10001299    .   C   <NON_REF>   .   .   END=10001386    GT:DP:GQ:MIN_DP:PL  0/0:43:99:39:0,95,1226
20  10001387    .   C   <NON_REF>   .   .   END=10001418    GT:DP:GQ:MIN_DP:PL  0/0:41:42:39:0,21,315
20  10001419    .   T   <NON_REF>   .   .   END=10001425    GT:DP:GQ:MIN_DP:PL  0/0:45:12:42:0,9,135
20  10001426    .   A   <NON_REF>   .   .   END=10001427    GT:DP:GQ:MIN_DP:PL  0/0:49:0:48:0,0,1282
20  10001428    .   T   <NON_REF>   .   .   END=10001428    GT:DP:GQ:MIN_DP:PL  0/0:49:21:49:0,21,315
20  10001429    .   G   <NON_REF>   .   .   END=10001429    GT:DP:GQ:MIN_DP:PL  0/0:47:18:47:0,18,270
20  10001430    .   G   <NON_REF>   .   .   END=10001431    GT:DP:GQ:MIN_DP:PL  0/0:45:0:44:0,0,1121
20  10001432    .   A   <NON_REF>   .   .   END=10001432    GT:DP:GQ:MIN_DP:PL  0/0:43:18:43:0,18,270
20  10001433    .   T   <NON_REF>   .   .   END=10001433    GT:DP:GQ:MIN_DP:PL  0/0:44:0:44:0,0,1201
20  10001434    .   G   <NON_REF>   .   .   END=10001434    GT:DP:GQ:MIN_DP:PL  0/0:44:18:44:0,18,270
20  10001435    .   A   <NON_REF>   .   .   END=10001435    GT:DP:GQ:MIN_DP:PL  0/0:44:0:44:0,0,1130
20  10001436    .   A   AAGGCT,<NON_REF>    1845.73 .   DP=43;MLEAC=2,0;MLEAF=1.00,0.00;MQ=220.07;MQ0=0 GT:AD:DP:GQ:PL:SB   1/1:0,42,0:42:99:1886,125,0,1888,126,1890:0,0,0,0
20  10001437    .   A   <NON_REF>   .   .   END=10001437    GT:DP:GQ:MIN_DP:PL  0/0:44:0:44:0,0,0


Note that toward the end of this snippet, you see multiple consecutive non-variant block records. These were not merged into a single record because the sites they contain belong to different ranges of GQ (which are defined in the header).

Created 2014-03-17 23:32:16 | Updated | Tags: release rnaseq version-highlights multisample gatk-3-0 reference-model joint-analysis

Better late than never, here is the now-traditional "Highlights" document for GATK version 3.0, which was released two weeks ago. It will be a very short one since we've already gone over the new features in detail in separate articles --but it's worth having a recap of everything in one place. So here goes.

### Work smarter, not harder

We are delighted to present our new Best Practices workflow for variant calling in which multisample calling is replaced by a winning combination of single-sample calling in gVCF mode and joint genotyping analysis. This allows us to both bypass performance issues and solve the so-called "N+1 problem" in one fell swoop. For full details of why and how this works, please see this document. In the near future, we will update our Best Practices page to make it clear that the new workflow is now the recommended way to go for calling variants on cohorts of samples. We've already received some pretty glowing feedback from early adopters, so be sure to try it out for yourself!

### Jumping on the RNAseq bandwagon

All the cool kids were doing it, so we had to join the party. It took a few months of experimentation, a couple of new tools and some tweaks to the HaplotypeCaller, but you can now call variants on RNAseq with GATK! This document details our Best Practices recommendations for doing so, along with a non-trivial number of caveats that you should keep in mind as you go.

### Goodbye to ReduceReads

Nice try, but no. This tool is obsolete now that we have the gVCF/reference model pipeline (see above). Note that this means that GATK 3.0 will not support BAM files that were processed using ReduceReads!

### Changes for developers

We've switched the build system from Ant to Maven, which should make it much easier to use GATK as a library against which you can develop your own tools. And on a related note, we're also making significant changes to the internal structure of the GATK codebase. Hopefully this will not have too much impact on external projects, but there will be a doc very shortly describing how the new build system works and how the codebase is structured.

### Hardware optimizations held for 3.1

For reasons that will be made clear in the near future, we decided to hold the previously announced hardware optimizations until version 3.1, which will be released very soon. Stay tuned!

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