Description

This track merges variants from all individual variant frequency databases into a single bigBed file with predicted protein consequences and cross-database filtering. It contains over 1.1 billion variants from 26 source databases worldwide. For a summary of all available databases, see the Variant Frequencies supertrack page.

Each variant is annotated with its predicted consequence on protein-coding genes (using bcftools csq with Ensembl gene models), and colored by severity. Allele counts and frequencies are shown for each source database and, where available, broken down by ancestry or population group.

Display Conventions

Color by Consequence

Variants are colored by their most severe predicted consequence:

Amino Acid Change Notation

The "AA change" field uses bcftools csq notation: 23I>23V means position 23 changed from Isoleucine (I) to Valine (V) (missense). 23I alone (no arrow) means position 23 is Isoleucine and unchanged (synonymous). A "*" indicates a stop codon (e.g. 45R>45* is a stop_gained).

Filters

This track supports extensive filtering via the track settings page. Click on the track title or use the "Configure" button to access filters:

Variant Type and Consequence

• Variant Type: Filter by SNV, Insertion, Deletion, or MNV.
• Consequence: Filter by predicted consequence (Missense, Synonymous, Stop Gained, Frameshift, Splice Donor, Splice Acceptor, Intron, 3' UTR, 5' UTR, Non-coding, Intergenic, Other). The filter uses OR logic across the comma-separated consequence tokens on each variant: a variant tagged stop_gained,frameshift is selected by either the "Stop Gained" or the "Frameshift" filter. The "Other" bucket catches the less common Sequence Ontology consequence terms emitted by bcftools csq that don't fit the named buckets above — for example splice_region (variant near a splice site but outside the canonical donor/acceptor), start_lost / stop_lost (variant disrupts the start codon or replaces the stop codon with a coding amino acid), stop_retained (variant changes the stop codon but keeps it a stop), inframe_insertion / inframe_deletion (in-frame indel adding or removing whole codons), and coding_sequence (CDS variant where the precise impact is undetermined). Including "Other" in the filter selection guarantees that no records are hidden by the consequence filter.

How to find protein-truncating variants: Set the Consequence filter to include only "Stop Gained", "Frameshift", "Splice Donor", and "Splice Acceptor". These will appear as red items in the track display.

Frequency and Count Filters

• Max Allele Frequency: Filter by the maximum allele frequency observed across all databases. Useful for finding rare variants (e.g., set max to 0.01 for variants with AF < 1% in all databases).
• Total Allele Count: Filter by the sum of allele counts across all databases. Useful for excluding singletons (e.g., set minimum to 2 to remove AC=1 variants that may be sequencing errors).
• Per-database AF and AC: Filter by allele frequency or allele count in any specific database. For example, filter to variants with TOPMed AF > 0.001.

Source Database

The Source Database filter lets you restrict to variants present in specific databases. For example, select only "GREGoR" to see variants found in the rare disease cohort. This filter uses OR logic: selecting multiple databases shows variants found in any of the selected databases.

Population-Specific Filters

Several databases provide ancestry-specific allele frequencies:

• AllOfUs: African, Indigenous American, East Asian, European, Oceanian, South Asian (from local ancestry inference)
• GenomeAsia: Northeast Asian, Southeast Asian, South Asian
• gnomAD HGDP+1kG: African, Amish, Latino, Ashkenazi Jewish, East Asian, Finnish, Middle Eastern, Non-Finnish European, Other, South Asian
• GREGoR: Affected, Unaffected, Unknown (disease status, not ancestry)

Length Filters

• Reference/Alternate Length: Filter by the length of the reference or alternate allele.
• Length Change: Filter by the size difference between alternate and reference (positive = insertion, negative = deletion, zero = SNV or MNV).

Methods

Variant frequency VCF files from 26 databases were stripped of their INFO fields (to reduce size), normalized with bcftools norm (splitting multi-allelic sites), and merged with bcftools merge. The merged VCF was then annotated with predicted protein consequences using bcftools csq with the Ensembl GRCh38 release 115 gene annotation (GFF3).

The annotated VCF was converted to bigBed format using a custom Python script (vcfToBigBed.py) that reads frequency data from each source VCF in parallel, matches variants by position/ref/alt, and writes a BED file with consequence coloring, per-database allele counts and frequencies, and population breakdowns. The database configuration (which VCFs to include, field mappings, and population definitions) is stored in two TSV files (databases.tsv and populations.tsv) to make future updates easy.

We provide documentation that indicates how all source files of the varFreqs track were converted in the makeDoc file of the track. Scripts are available from Github.

Data Access

The data can be explored interactively with the Table Browser or the Data Integrator. For programmatic access, our REST API can be used; the track name is varFreqsAll.

Because the merged callset includes data from multiple sources whose redistribution licenses differ, the combined bigBed is not available for download from our download server. The combined track can be reconstructed from the individual source VCFs using the conversion scripts on GitHub together with the build documentation. Where individual source data is downloadable from UCSC, the per-subtrack description page indicates the path on our download server.

Credits

This track is only possible thanks to the data from millions of volunteers around the world, who donated blood, signed consent forms and provided health information about themselves and sometimes their families. Click on any of the individual tracks in the Variant Frequencies supertrack to see the specific credits for each project. Thanks to Alex Ioannidis, UCSC, for the motivation for this track and to Andreas Lahner, MGZ, for feedback.

References

For primary citations of each source dataset, see the References section on the Variant Frequencies supertrack page. The merged-track build itself uses the following tools:

Danecek P, McCarthy SA. BCFtools/csq: haplotype-aware variant consequences. Bioinformatics. 2017 Jul 1;33(13):2037-2039. PMID: 28205675; PMC: PMC5870570

McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F. The Ensembl Variant Effect Predictor. Genome Biol. 2016 Jun 6;17(1):122. PMID: 27268795; PMC: PMC4893825