Annotating Variants: Introduction
As we've already seen, determining the presence or absence of a variant from NGS data is not trivial. It is software dependent and has inherent trade-offs between sensitivity and specificity. Inevitably, the number of putative variants in a real data set is very large; for example the first samples from the 1000 Genomes project typically found 0.1% variants (3 million variants), approximately 10% of which had never been previously observed (300,000 novel variants per individual.) False positive discovery rates are also typically very high at this stage.
Auxiliary data is often used to reduce putative variants without compromising sensitivity. Examples of auxiliary data include other samples within a cohort, existing SNP databases, gene or other feature annotations, and sample-specific information such as pedigree:
- By comparing genotypes across a set of samples and defining one as "reference" (or "wild type") enables other samples to be properly genotyped (i.e. 0/0 for hom. WT, 0/1 for het, 1/1 for hom. alt)
- Existing SNP databases such as dbSNP or the
vcffiles from the 1000 genomes project may be used to reject "common" variants under the assertion that "common" means "non-disease causing".
- Gene annotations allow for codon analysis to determine whether mutations are synonymous, non-synonymous, nonsense, mis-sense, or create early stop codons.
- Pedigree information is particularly effective in mendelian autosomal recessive diseases; filtering for heterozygous mutations in parents and unaffected siblings which are homozygous in the proband usually yields a very small set of candidate variants.
Variant annotation tools perform the function of combining the raw putative variant calls with auxiliary data to add meaning ("annotation") to the variants. In many cases, the variant detection tool itself will add certain elements of annotation, such as a definition of the variant, a genotype call, a measure of likelihood, a haplotype score, and other measures of the raw data useful to reduce false positives. In other cases, the annotator will only require a
vcf file combined with other auxiliary data.
Because these tools draw in information from may disparate sources, they can be very difficult to install, configure, use, and maintain. For example, the
vcf files from the 1000 Genomes project are arranged in a deep ftp tree by date of data generation. Large genome centers spend significant resources managing these tools. Our objective
Annovar - one of the simpler variant annotators available
Annovar is a variant annotator. Given a vcf file from an unknown sample and a host of existing data about genes, other known SNPs, gene variants, etc., Annovar will place the discovered variants in context.
Annovar comes pre-packaged with human auxiliary data which is updated by the authors on a regular basis. It is a well-constructed package in that there is one core program
annotate_variation.pl which can perform a variety of different types of annotation AND download the reference databases required.
The authors have also included a wrapper script
summarize_anovar.pl which runs a fairly comprehensive set of annotations automatically.
This next exercise will give you some idea of how Annovar works; we've taken the liberty of writing the bash script
annovar_pipe.sh around the existing
summarize_annovar.pl wrapper (a wrapper within a wrapper - a common trick) to even further simplify the process for this course.
First, look at the code for our
annovar_pipe.sh command. Here is an easy one-liner to
cat the contents of a script (note ` is a back-tick, not apostrophe):
This script simply does a format conversion and then calls
summarize_annovar.pl. Now let's run it on all the vcf files - you could simply edit the
commands file and type in the 6 lines, or you can use this fancier command line that calls Perl to custom-create the 6 command lines needed and put them straight into
While Annovar is running, have a look at the code to
(Note the ` characters are "backtick", not apostrophe)
Other variant annotators:
- http://www.broadinstitute.org/gatk/gatkdocs/ VariantAnnotator annotations
- Now count how many GT are het in both of the second two (parents) but hom in the first (child): (would you have expected this result?)
- Now find which GT are hom in both of the second two (parents) but het in the first (child):
Compare samtools to GATK on exome 20.
These are oddities:
Variants consist of single base base changes, insertions and deletions, and larger scale structural changes. "Larger scale" is usually defined relative to the capabilities of the technology; for example, a "small indel" usually means "detectable within a single sequence read". In 2009, sequence reads were about 50 bp but in 2011 they were 100 bp.