Identify germline short variants (SNPs and Indels) in one or more individuals to produce a joint callset in VCF format.
|Prod* germline short variant per-sample calling||uBAM to GVCF||optimized for GCP||yes||pending|
|Prod* germline short variant joint genotyping||GVCFs to cohort VCF||optimized for GCP||yes||pending|
|$5 Genome Analysis Pipeline||uBAM to GVCF or cohort VCF||optimized for GCP (see blog)||yes||hg38|
|Generic germline short variant per-sample calling||analysis-ready BAM to GVCF||universal||yes||hg38|
|Generic germline short variant joint genotyping||GVCFs to cohort VCF||universal||yes||hg38 & b37|
|Intel germline short variant per-sample calling||uBAM to GVCF||Intel optimized for local architectures||yes||NA|
* Prod refers to the Broad Institute's Data Sciences Platform production pipelines, which are used to process sequence data produced by the Broad's Genomic Sequencing Platform facility.
This workflow is designed to operate on a set of samples constituting a study cohort. Specifically, a set of per-sample BAM files that have been pre-processed as described in the GATK Best Practices for data pre-processing.
We begin by calling variants per sample in order to produce a file in GVCF format. Next, we consolidate GVCFs from multiple samples into a GenomicsDB datastore. We then perform joint genotyping, and finally, apply VQSR filtering to produce the final multisample callset with the desired balance of precision and sensitivity.
Additional steps such as Genotype Refinement and Variant Annotation may be included depending on experimental design; those are not documented here.
In the past, variant callers specialized in either SNPs or Indels, or (like the GATK's own UnifiedGenotyper) could call both but had to do so them using separate models of variation. The HaplotypeCaller is capable of calling SNPs and indels simultaneously via local de-novo assembly of haplotypes in an active region. In other words, whenever the program encounters a region showing signs of variation, it discards the existing mapping information and completely reassembles the reads in that region. This allows the HaplotypeCaller to be more accurate when calling regions that are traditionally difficult to call, for example when they contain different types of variants close to each other. It also makes the HaplotypeCaller much better at calling indels than position-based callers like UnifiedGenotyper.
In the GVCF mode used for scalable variant calling in DNA sequence data, HaplotypeCaller runs per-sample to generate an intermediate file called a GVCF , which can then be used for joint genotyping of multiple samples in a very efficient way. This enables rapid incremental processing of samples as they roll off the sequencer, as well as scaling to very large cohort sizes
In practice, this step can be appended to the pre-processing section to form a single pipeline applied per-sample, going from the original unmapped BAM containing raw sequence all the way to the GVCF for each sample. This is the implementation used in production at the Broad Institute.
This step consists of consolidating the contents of GVCF files across multiple samples in order to improve scalability and speed the next step, joint genotyping. Note that this is NOT equivalent to the joint genotyping step; variants in the resulting merged GVCF cannot be considered to have been called jointly.
Prior to GATK4 this was done through hierarchical merges with a tool called CombineGVCFs. This tool is included in GATK4 for legacy purposes, but performance is far superior when using GenomicsDBImport, which produces a datastore instead of a GVCF file.
At this step, we gather all the per-sample GVCFs (or combined GVCFs if we are working with large numbers of samples) and pass them all together to the joint genotyping tool, GenotypeGVCFs. This produces a set of joint-called SNP and indel calls ready for filtering. This cohort-wide analysis empowers sensitive detection of variants even at difficult sites, and produces a squared-off matrix of genotypes that provides information about all sites of interest in all samples considered, which is important for many downstream analyses.
This step runs quite quickly and can be rerun at any point when samples are added to the cohort, thereby solving the so-called N+1 problem.
The GATK's variant calling tools are designed to be very lenient in order to achieve a high degree of sensitivity. This is good because it minimizes the chance of missing real variants, but it does mean that we need to filter the raw callset they produce in order to reduce the amount of false positives, which can be quite large.
The established way to filter the raw variant callset is to use variant quality score recalibration (VQSR), which uses machine learning to identify annotation profiles of variants that are likely to be real, and assigns a VQSLOD score to each variant that is much more reliable than the QUAL score calculated by the caller. In the first step of this two-step process, the program builds a model based on training variants, then applies that model to the data to assign a well-calibrated probability to each variant call. We can then use this variant quality score in the second step to filter the raw call set, thus producing a subset of calls with our desired level of quality, fine-tuned to balance specificity and sensitivity.
The downside of how variant recalibration works is that the algorithm requires high-quality sets of known variants to use as training and truth resources, which for many organisms are not yet available. It also requires quite a lot of data in order to learn the profiles of good vs. bad variants, so it can be difficult or even impossible to use on small datasets that involve only one or a few samples, on targeted sequencing data, on RNAseq, and on non-model organisms. If for any of these reasons you find that you cannot perform variant recalibration on your data (after having tried the workarounds that we recommend, where applicable), you will need to use hard-filtering instead. This consists of setting flat thresholds for specific annotations and applying them to all variants equally. See the methods articles and FAQs for more details on how to do this.
We are currently experimenting with neural network-based approaches with the goal of eventually replacing VQSR with a more powerful and flexible filtering process.
The central tenet that governs the variant discovery part of the workflow is that the accuracy and sensitivity of the germline variant discovery algorithm are significantly increased when it is provided with data from many samples at the same time. Specifically, the variant calling program needs to be able to construct a squared-off matrix of genotypes representing all potentially variant genomic positions, across all samples in the cohort. Note that this is distinct from the primitive approach of combining variant calls generated separately per-sample, which lack information about the confidence of homozygous-reference or other uncalled genotypes.
In earlier versions of the variant discovery phase, multiple per-sample BAM files were presented directly to the variant calling program for joint analysis. However, that scaled very poorly with the number of samples, posing unacceptable limits on the size of the study cohorts that could be analyzed in that way. In addition, it was not possible to add samples incrementally to a study; all variant calling work had to be redone when new samples were introduced.
Starting with GATK version 3.x, a new approach was introduced, which decoupled the two internal processes that previously composed variant calling: (1) the initial per-sample collection of variant context statistics and calculation of all possible genotype likelihoods given each sample by itself, which require access to the original BAM file reads and is computationally expensive, and (2) the calculation of genotype posterior probabilities per-sample given the genotype likelihoods across all samples in the cohort, which is computationally cheap. These were made into the separate steps described below, enabling incremental growth of cohorts as well as scaling to large cohort sizes.