Description

This track shows small variants (SNVs and short indels) that were observed in affected or case individuals of disease-study cohorts, annotated with their predicted protein consequence and colored by severity. It is one half of a matched pair: the companion Population reference track shows the same kind of variants seen in population reference cohorts and in unaffected relatives or controls. Displaying the two together lets you compare, for example, how often a loss-of-function variant in a gene of interest is seen in affected individuals versus the general/unaffected background. For the full list of contributing projects, see the SNV Frequencies collection page.

The affected counts are drawn from the affected or case arm of six disease-study cohorts: SFARI SPARK WES, SFARI SPARK iWGS v1.1 Pilot and SFARI SPARK 45k WGS (autism spectrum disorder probands), SCHEMA (schizophrenia cases), GREGoR (affected rare-disease participants), and GA4K (a pediatric rare-disease cohort). From SFARI SPARK 45k WGS, the 25 million insertions flagged as non-human sequence (FILTER NonHumanIns, mostly oral bacteria DNA from the saliva samples, see the subtrack's description) were left out. For the three SPARK cohorts, SCHEMA, and GREGoR, the source data carries an explicit affected/unaffected (or case/control) label, and only the affected arm feeds this track. GA4K reports a single cohort-wide frequency with no per-individual label; because it is a rare-disease cohort, it is counted as affected here, with the caveat that it enrolls parent-child trios, so a minority of its carriers are unaffected parents. Genotyping-array cohorts are not included in either track.

Display Conventions

Color by Consequence

Variants are colored by their most severe predicted consequence:

Color	Consequence class	Examples
	Protein-truncating / loss-of-function	stop_gained, frameshift, splice_donor, splice_acceptor, stop_lost, start_lost
	Missense / in-frame	missense, inframe_insertion, inframe_deletion, protein_altering
	Synonymous	synonymous, stop_retained
	Non-coding / intergenic	intron, non_coding, intergenic, UTR

The score (used for shading) is the pooled affected/case allele frequency times 1000.

Pooled allele frequency

Affected AF is the pooled rate across contributing affected arms: affectedAF = sum(AC) / sum(AN), where affectedAC sums the allele counts and affectedAN sums the allele numbers across each cohort/arm that provides both AC and AF (the per-arm AN is derived as round(AC / AF)). Cohorts that publish only AF (with no AC or AN of their own) are still pooled by assigning them an assumed allele number, set as a default_an in the build configuration; their per-arm AC is then derived as round(AF × default_an). Cohorts that publish only AC and have no default_an set (currently GREGoR's per-arm AC_AFFECTED/UNAFFECTED/UNKNOWN) are listed in affectedCohorts but do not contribute to the pool numerator or denominator; their carriers are visible in the per-database AC column instead. The pooled rate is preferred over a max-across-cohorts statistic so a small cohort with a high local AF cannot dominate the displayed frequency.

The pooled rate also inherits a shared-sample bias: the SFARI SPARK iWGS v1.1 Pilot cohort (~12k probands) is a subset of the larger SFARI SPARK WES cohort (~155k probands), so probands sequenced in both contribute their AC and AN twice to the affected pool. Where this happens, pooled AN is inflated and pooled AF is skewed toward the frequency in the shared subset. Treat the pooled rate as a cross-cohort summary rather than an unbiased population estimate; the per-cohort AC/AF/AN fields on each variant give the single-cohort numbers.

Top affected sources by AF

Alongside the pooled rate, the mouseover lists the top 3 contributing affected arms ranked by their own per-source AF, formatted as Source (AF). This surfaces case cohorts where the variant is specifically enriched, even when the pooled rate across all arms is small. For disease cohorts that ship a phenotype split (SPARK, SFARI WGS, SCHEMA, GREGoR), the displayed AF is the affected-arm AF and the label includes the arm (for example SPARK ASD, SCHEMA case); for cohorts with no split (GA4K) the label is just the cohort name and the AF is the whole-cohort AF. Arms that ship only AC and no AF (currently GREGoR per-arm) are not included in this ranking because no AF is available; they still appear in affectedCohorts.

Finding case-enriched loss-of-function variants

To look for protein-truncating variants that are common in affected individuals but rare in the background, set the Consequence filter to Stop Gained, Frameshift, Splice Donor and Splice Acceptor (these appear red), then add an upper limit on the Background AF filter. Each variant here carries both its affected frequency and its background frequency, so this isolates variants seen in cases with little or no presence in the population/unaffected set. Comparing visually against the Population reference track shows the same contrast across a whole gene.

Filters

• Variant Type and Consequence: restrict to SNV/insertion/deletion/MNV and to predicted consequence classes (the Consequence filter uses OR logic over the comma-separated tokens on each variant).
• Affected/case AF, AC, AN: the pooled allele frequency (sum AC / sum AN), summed allele count, and summed allele number across the contributing affected arms. See "Pooled allele frequency" above.
• Background AF, AC, AN: the same triple computed across the population + unaffected background, for filtering case-enriched variants.
• Affected/case cohort: restrict to variants seen in specific disease cohorts (for example, only the two autism cohorts).
• Reference/Alternate Length and Length Change: filter by allele length.

Methods

Variant-frequency VCFs from the contributing cohorts were stripped of unneeded INFO fields, normalized with bcftools norm (splitting multi-allelic sites), and merged with bcftools merge. The merged callset was annotated with predicted protein consequences using bcftools csq against the Ensembl GRCh38 release 115 gene models.

A custom Python script (vcfToBigBed.py) then read the per-cohort allele counts and frequencies and, for each variant, pooled the allele counts and allele numbers across the affected arms (case/proband subgroups, plus GA4K whole-cohort) to produce this track, and across the population cohorts and unaffected/control subgroups to produce the companion Population reference track. A variant seen in both groups appears in both tracks. The build is documented in the makeDoc, and the scripts are on GitHub.

Data Access

Because the merged callset combines cohorts whose redistribution licenses differ, this track is not available for download and is not in the Table Browser. It can be reconstructed from the individual source VCFs using the conversion scripts and the build documentation. The per-project subtracks on the SNV Frequencies collection page document how to obtain each source dataset.

Credits

This track is only possible thanks to the data from the participants and families of the SFARI SPARK, SCHEMA, GREGoR and GA4K studies. Click the individual project subtracks on the SNV Frequencies collection page for the specific credits and citations of each cohort. Thanks to Alex Ioannidis, UCSC, for the inspiration for this track and to Andreas Lahner, MGZ, for feedback.

References

For the primary citation of each source cohort, see the References section on the SNV Frequencies collection page. The merged-track build 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