-This track merges variants from six disease-focused or clinically-recruited cohorts into a -single bigBed file with predicted protein consequences and cross-database filtering. It -contains 932 million variants from SPARK WES (140k autism families), SFARI WGS (12.5k -autism families), TOPMed (NHLBI heart, lung and blood disease cohorts), SCHEMA -(schizophrenia case/control), GREGoR (rare-disease families), and GA4K (PacBio long-read -pediatric rare disease). Where the source dataset provides per-phenotype counts, those are -exposed as separate AC/AF columns and as filter widgets. -
- --For a summary of all available variant frequency databases, including the population-scale -control track and the genotyping-array track, see the -SNV 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 phenotype. -
- -Variants are colored by their most severe predicted consequence:
-| Color | Consequence class | Examples |
|---|---|---|
| Red | -Protein-truncating / Loss-of-function | -stop_gained, frameshift, splice_donor, splice_acceptor, stop_lost, start_lost | -
| Blue | -Missense / In-frame | -missense, inframe_insertion, inframe_deletion, protein_altering | -
| Green | -Synonymous | -synonymous, stop_retained | -
| Grey | -Non-coding / Intergenic | -intron, non_coding, intergenic, UTR | -
-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). -
- --This track supports filtering via the track settings page. Click the track title or use the -"Configure" button to access filters. -
- --Four of the six sources publish counts split by phenotype, which lets you compare allele -frequencies between affected and unaffected groups within the same cohort: -
-asd column.-The Source Database filter restricts the display to variants present in specific -databases. It uses OR logic: selecting multiple databases shows variants found in any of -the selected sources. -
- -
-The same merge-and-annotate pipeline used for the
-All Databases Combined track was run on the
-disease-cohort subset of source VCFs. Each VCF was stripped of its INFO fields,
-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 SPARK WES and WGS sites VCFs were rebuilt for this track so each variant carries
-phenotype-stratified counts in addition to overall AC/AN/AF. The split uses the
-asd column of the SPARK individuals_registration TSV via
-bcftools +fill-tags -S, producing AC_AUT / AN_AUT / AF_AUT and
-AC_NON_AUT / AN_NON_AUT / AF_NON_AUT. SCHEMA was processed the same way, summing
-AC_CASE/AN_CASE/AF_CASE and AC_CTRL/AN_CTRL/AF_CTRL across its 39 analysis cohorts.
-GREGoR ships AC/AN/AF triples for affected, unaffected and unknown disease status
-directly in its release.
-
-The track's -makeDoc file documents how each source VCF was converted. Scripts are -available from -Github. -
- --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 varFreqsDisease. -
--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. -
- --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 -SNV 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. -
- --For primary citations of each source dataset, see the References section on the -SNV 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 -