| Blue |
@@ -95,58 +108,75 @@
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
+Population- and Phenotype-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
+ - AllOfUs: African, Indigenous American, East Asian, European, Oceanian, South
+ Asian (from local ancestry inference)
+ - GenomeAsia: Northeast Asian, Southeast Asian, South Asian, Oceanian, American,
+ African, Western European Reference
- 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)
+ - NPM Singapore: Chinese, Malay, Indian
+ - WBBC: North Han, Central Han, South Han, Lingnan Han
+
+Three sources also expose phenotype-stratified counts:
+
+
+ - SPARK WES and SFARI WGS: ASD proband AC/AF versus non-ASD family
+ member AC/AF.
+ - GREGoR: Affected, Unaffected, and Unknown disease-status AC/AF.
+
+
+The disease-related Disease-related Databases Combined
+track exposes additional phenotype splits for SCHEMA (Schizophrenia case vs control).
+
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
+Variant frequency VCF files from 28 sequencing-based 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).
+GRCh38 release 115 gene annotation (GFF3). The same pipeline, run on different subsets of
+source VCFs, produces the
+Disease-related Databases Combined and
+Genotyping Array Databases Combined tracks.
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)
so that future updates only require editing these files.
@@ -173,39 +203,39 @@
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
+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.
References
For primary citations of each source dataset, see the References section on the
-Variant Frequencies supertrack page. The merged-track
+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.