Variant Annotation

Variant annotation is the process of adding functional and biological information to genetic variants identified in genome-wide association studies (GWAS). After identifying variants associated with a trait, annotation helps researchers understand the potential biological consequences of these variants by providing information such as:

• Gene location: Which genes are affected by the variant (exonic, intronic, intergenic, etc.)
• Functional consequences: Whether the variant affects protein-coding sequences (missense, nonsense, synonymous), regulatory regions, or non-coding RNAs
• Population frequency: How common the variant is in different populations (e.g., from dbSNP, gnomAD, 1000 Genomes)
• Pathogenicity: Clinical significance and disease associations (e.g., from ClinVar)
• Conservation scores: Evolutionary conservation and predicted functional impact (e.g., CADD, SIFT, PolyPhen)

This information is crucial for prioritizing variants for follow-up studies, understanding biological mechanisms, and translating GWAS findings into actionable insights for personalized medicine and drug discovery.

On this page

ANNOVAR

ANNOVAR is a simple and efficient command line tool for variant annotation.

In this tutorial, we will use ANNOVAR to annotate the variants in our summary statistics (hg19).

Install

Download ANNOVAR from here (registration required; freely available to personal, academic and non-profit use only.)

You will receive an email with the download link after registration. Download it and decompress:

tar -xvzf annovar.latest.tar.gz

For refGene annotation for hg19, we do not need to download additional files.

Format input file

The default input file for ANNOVAR is a 1-based coordinate file.

We will only use the first 100000 variants as an example.

awk 'NR>1 && NR<=100000 {print $1,$2,$2,$4,$5}' ../06_Association_tests/1kgeas.B1.glm.firth > annovar_input.txt

head annovar_input.txt 
1 13273 13273 G C
1 14599 14599 T A
1 14604 14604 A G
1 14930 14930 A G
1 69897 69897 T C
1 86331 86331 A G
1 91581 91581 G A
1 122872 122872 T G
1 135163 135163 C T
1 233473 233473 C G

Annotation

Annotate the variants with gene information.

input=annovar_input.txt
humandb=/home/he/tools/annovar/annovar/humandb
table_annovar.pl ${input} ${humandb} -buildver hg19 -out myannotation -remove -protocol refGene -operation g -nastring . -polish

Chr Start   End Ref Alt Func.refGene    Gene.refGene    GeneDetail.refGene  ExonicFunc.refGene  AAChange.refGene
1   13273   13273   G   C   ncRNA_exonic    DDX11L1;LOC102725121    .   .   .
1   14599   14599   T   A   ncRNA_exonic    WASH7P  .   .   .
1   14604   14604   A   G   ncRNA_exonic    WASH7P  .   .   .
1   14930   14930   A   G   ncRNA_intronic  WASH7P  .   .   .
1   69897   69897   T   C   exonic  OR4F5   .   synonymous SNV  OR4F5:NM_001005484:exon1:c.T807C:p.S269S
1   86331   86331   A   G   intergenic  OR4F5;LOC729737 dist=16323;dist=48442   .   .
1   91581   91581   G   A   intergenic  OR4F5;LOC729737 dist=21573;dist=43192   .   .
1   122872  122872  T   G   intergenic  OR4F5;LOC729737 dist=52864;dist=11901   .   .
1   135163  135163  C   T   ncRNA_exonic    LOC729737   .   .   .

Additional databases

ANNOVAR supports a wide range of commonly used databases including dbsnp , dbnsfp, clinvar, gnomad, 1000g, cadd and so forth. For details, please check ANNOVAR's official documents

You can check the Table Name listed in the link above and download the database you need using the following command.

annotate_variation.pl -buildver hg19 -downdb -webfrom annovar avsnp150 humandb/

# input file is in vcf format
table_annovar.pl \
  ${in_vcf} \
  ${humandb} \
  -buildver hg19 \
  -protocol refGene,avsnp150,clinvar_20200316,gnomad211_exome \
  -operation g,f,f,f \
  -remove \
  -out ${out_prefix} \
  -vcfinput

VEP

Ensembl VEP (Variant Effect Predictor) adds transcript-level consequences, regulatory overlap, and optional frequency or pathogenicity annotations. Ensembl recommends Docker or Singularity on modern systems so you do not install Perl, BioPerl, HTSlib, and the Ensembl API yourself.

In this folder: a small tracked VCF, vep_sample.vcf (same variant style as annovar_input.txt), 2_vep.sh (runs VEP in Docker and writes vep_output.vcf), and vep_output.example.vcf (checked-in header + first variant so you can inspect output without running Docker).

Open the release directory (change 115 to your Ensembl release if needed):

https://ftp.ensembl.org/pub/release-115/variation/indexed_vep_cache/

Quick setup (Docker image + cache)

1. Pull the image (Docker section of the Ensembl guide):

docker pull ensemblorg/ensembl-vep

1. Install GRCh37 cache and FASTA (hg19, same assembly as the ANNOVAR example above):

mkdir -p "$HOME/vep_data/tmp"
docker run --rm -it \
  --user "$(id -u):$(id -g)" \
  -v "$HOME/vep_data:/data" \
  ensemblorg/ensembl-vep \
  INSTALL.pl -a cf -s homo_sapiens -y GRCh37 -c /data

• -a cf: cache + FASTA. -c /data: store everything on the mounted volume (not the image default $HOME/.vep).
• --user "$(id -u):$(id -g)": the image user vep often cannot write bind-mounted host directories; without matching UID/GID, INSTALL.pl may report “downloading” then fail on /data/tmp/…. 2_vep.sh uses the same --user so it can read the cache and write outputs here.

Optional: on a shared machine you may use -u 0 instead of --user "$(id -u):$(id -g)" for install; leave the cache world-readable (default umask is usually enough) if you later run vep as the image’s vep user without --user.

If install failed halfway: clear $HOME/vep_data and repeat step 2.

Run the tutorial script

From 07_Annotation, after setup:

./2_vep.sh

The script requires Docker, a non-empty $VEP_DATA (default $HOME/vep_data), then runs vep on vep_sample.vcf and writes vep_output.vcf. If you run with sudo, the default cache path is ~<invoking-user>/vep_data (via SUDO_USER) so it still matches a cache you installed as yourself—prefer ./2_vep.sh without sudo if your user can use Docker.

VEP_DATA=/path/to/vep_cache ./2_vep.sh
INPUT_VCF="$PWD/my.vcf" OUT_VCF="$PWD/my_vep.vcf" ./2_vep.sh

INPUT_VCF and OUT_VCF must live under this directory (mounted as /work; subpaths such as subdir/file.vcf are fine).

##fileformat=VCFv4.2
##reference=GRCh37
##VEP="v115.2" API="v115" time="2026-04-06 06:52:15" cache="/data/homo_sapiens/115_GRCh37" ensembl=115.266b84d ensembl-compara=115.ae48a7a ensembl-funcgen=115.57f7061 ensembl-io=115.25061d3 ensembl-variation=115.b7c2637 1000genomes="phase3" COSMIC="98" ClinVar="202306" HGMD-PUBLIC="20204" assembly="GRCh37.p13" dbSNP="156" gencode="GENCODE 19" genebuild="2011-04" gnomADe="v4.1" gnomADg="v4.1" polyphen="2.2.2" regbuild="1.0" sift="sift5.2.2"
##INFO=<ID=CSQ,Number=.,Type=String,Description="Consequence annotations from Ensembl VEP. Format: Allele|Consequence|IMPACT|SYMBOL|Gene|Feature_type|Feature|BIOTYPE|EXON|INTRON|HGVSc|HGVSp|cDNA_position|CDS_position|Protein_position|Amino_acids|Codons|Existing_variation|DISTANCE|STRAND|FLAGS|SYMBOL_SOURCE|HGNC_ID">
##VEP-command-line='vep --assembly GRCh37 --cache --database 0 --dir_cache /data --dir_plugins /plugins --force_overwrite --format vcf --input_file vep_sample.vcf --offline --output_file vep_output.vcf --vcf'
#CHROM  POS ID  REF ALT QUAL    FILTER  INFO
1   15774   1:15774:G:A G   A   .   PASS    CSQ=A|intron_variant&non_coding_transcript_variant|MODIFIER|WASH7P|ENSG00000227232|Transcript|ENST00000423562|unprocessed_pseudogene||8/9||||||||||-1||HGNC|38034,A|intron_variant&non_coding_transcript_variant|MODIFIER|WASH7P|ENSG00000227232|Transcript|ENST00000438504|unprocessed_pseudogene||10/11||||||||||-1||HGNC|38034,A|downstream_gene_variant|MODIFIER|DDX11L1|ENSG00000223972|Transcript|ENST00000450305|transcribed_unprocessed_pseudogene|||||||||||2104|1||HGNC|37102,A|downstream_gene_variant|MODIFIER|DDX11L1|ENSG00000223972|Transcript|ENST00000456328|processed_transcript|||||||||||1365|1||HGNC|37102,A|intron_variant&non_coding_transcript_variant|MODIFIER|WASH7P|ENSG00000227232|Transcript|ENST00000488147|unprocessed_pseudogene||9/10||||||||||-1||HGNC|38034,A|downstream_gene_variant|MODIFIER|DDX11L1|ENSG00000223972|Transcript|ENST00000515242|transcribed_unprocessed_pseudogene|||||||||||1362|1||HGNC|37102,A|downstream_gene_variant|MODIFIER|DDX11L1|ENSG00000223972|Transcript|ENST00000518655|transcribed_unprocessed_pseudogene|||||||||||1365|1||HGNC|37102,A|intron_variant&non_coding_transcript_variant|MODIFIER|WASH7P|ENSG00000227232|Transcript|ENST00000538476|unprocessed_pseudogene||11/12||||||||||-1||HGNC|38034,A|intron_variant&non_coding_transcript_variant|MODIFIER|WASH7P|ENSG00000227232|Transcript|ENST00000541675|unprocessed_pseudogene||7/8||||||||||-1||HGNC|38034

Larger VCFs from your data

From the same PLINK2-style file as in the ANNOVAR section (../06_Association_tests/1kgeas.B1.glm.firth):

awk 'BEGIN{OFS="\t"; print "##fileformat=VCFv4.2"; print "##reference=GRCh37"; print "#CHROM","POS","ID","REF","ALT","QUAL","FILTER","INFO"} \
  NR>1 && NR<=1001 {print $1,$2,$3,$4,$5,".","PASS","."}' \
  ../06_Association_tests/1kgeas.B1.glm.firth > vep_input.vcf
INPUT_VCF="$PWD/vep_input.vcf" OUT_VCF="$PWD/vep_large_output.vcf" ./2_vep.sh

From annovar_input.txt (space-separated Chr Start End Ref Alt), first ~1000 variants:

awk 'BEGIN{OFS="\t"; print "##fileformat=VCFv4.2"; print "##reference=GRCh37"; print "#CHROM","POS","ID","REF","ALT","QUAL","FILTER","INFO"} \
  NF>=5 && !/^#/ {id=$1":"$2":"$4":"$5; print $1,$2,id,$4,$5,".","PASS","."}' \
  annovar_input.txt | head -n 1003 > vep_from_annovar.vcf

(head limits to header + 1000 lines of variants; omit head for the full file.)

Annotated VCFs carry consequences in INFO (e.g. CSQ). More options: Running VEP, examples.

Without Docker: clone ensembl-vep and run perl INSTALL.pl per the download and install guide (again, cache downloads are large and slow the first time).

References

ANNOVAR

• ANNOVAR: Wang, K., Li, M., & Hakonarson, H. (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38(16), e164. https://doi.org/10.1093/nar/gkq603

• ANNOVAR website: https://annovar.openbioinformatics.org/en/latest/

VEP (Variant Effect Predictor)

• VEP: McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R., Thormann, A., ... & Cunningham, F. (2016). The Ensembl Variant Effect Predictor. Genome Biology, 17(1), 1-14. https://doi.org/10.1186/s13059-016-0974-4

• VEP documentation: https://www.ensembl.org/info/docs/tools/vep/index.html

• VEP download and install (Docker, cache, Singularity): https://useast.ensembl.org/info/docs/tools/vep/script/vep_download.html

General annotation resources

• dbSNP: Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., & Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic Acids Research, 29(1), 308-311. https://doi.org/10.1093/nar/29.1.308

• ClinVar: Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., ... & Maglott, D. R. (2018). ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Research, 46(D1), D1062-D1067. https://doi.org/10.1093/nar/gkx1153

• gnomAD: Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., ... & MacArthur, D. G. (2020). The mutational constraint spectrum quantified from variation in 141,456 humans. Nature, 581(7809), 434-443. https://doi.org/10.1038/s41586-020-2308-7