1259dcfba3a263d92d2602665fd866dc44b47996 lrnassar Sun Jun 21 11:17:10 2026 -0700 Clarify varFreqs description page wording per code review feedback. refs #37733 Reword the default_an sentence in the Pooled allele frequency sections of varFreqsAffected.html and varFreqsBackground.html to explain that cohorts publishing only AF are pooled via an assigned default_an, with per-arm AC derived as round(AF * default_an). Change "tokens" to "terms" in the Consequence filter section of varFreqs.html. diff --git src/hg/makeDb/trackDb/human/varFreqs.html src/hg/makeDb/trackDb/human/varFreqs.html index 44840eb1a7c..fb6fc359c6c 100644 --- src/hg/makeDb/trackDb/human/varFreqs.html +++ src/hg/makeDb/trackDb/human/varFreqs.html @@ -1,766 +1,766 @@ <h2>Description</h2> <p> This track collection gathers variant allele frequencies from population-scale sequencing and genotyping projects worldwide, from a total of ~1.7 million genomes/exomes/arrays. Unlike gnomAD, the data was not reprocessed in a harmonized way; the variant VCFs were collected from the projects as-is. The goal is a single place to compare how common a variant is across different populations, ancestries, and cohorts, for projects that gnomAD is unlikely to reprocess soon. Three combined tracks aggregate the source data along different lines, and there is also one subtrack per project with the original VCF data and all the annotations that the project provides. The different projects use different pipelines and sequencing technologies. Click any of the projects above or below for a summary of their sample selection, sequencing assay and software pipeline. Many projects do not allow us to distribute the data, but we document how to request it and provide all converters, see Data Download below. </p> <p> The browser has other tracks with variant frequencies. We have of course the data from <a href="hgTrackUi?g=gnomadVariants">gnomAD</a> in separate tracks. Two projects that provide haplotype-phased genotypes can also be found in their own tracks: <a href="hgTrackUi?g=tgpArchive">1000 Genomes</a> is a separate track, and the phased genotypes HGDP, SGDP, HGDP+1000 Genomes and Mexico Biobank are in the <a href="hgTrackUi?g=phasedVars">Phased Variants</a> track. Their VCF versions below show only the allele frequency per variant, not the phased genotypes. </p> <p>Please contact us (<a href="mailto:genome@soe.ucsc.edu">genome@soe.ucsc.edu</a><!-- above address is genome at soe.ucsc.edu -->) if you know of a project that we should add. So far, we have requested data from Regeneron's Million Exomes and the Mexico City studies (both requests rejected); Taiwan Biobank and the full UK Biobank WGS data requests are pending.</p> <h2>Combined Tracks</h2> <p> Three combined tracks merge variants from the individual subtracks into single bigBed files with predicted protein consequences and cross-database filtering. All three use the same filter conventions (variant type, consequence, source database, allele frequency, allele count, and per-database AF/AC). </p> <ul> <li><a href="hgTrackUi?g=varFreqsBackground"><b>Population reference</b></a> — the default summary view: variants seen in the population reference cohorts (gnomAD HGDP+1kG, TOPMed, ALFA, HRC and the national WGS projects) and in the unaffected/control arms of the disease cohorts. Excludes the genotyping-array cohorts.</li> <li><a href="hgTrackUi?g=varFreqsAffected"><b>Disease cohorts</b></a> — variants seen in the affected or case arm of five disease-study cohorts (SFARI SPARK WES and WGS autism probands, SCHEMA schizophrenia cases, GREGoR affected, GA4K rare-disease). Each variant also carries its background frequency, so case-enriched variants can be isolated by filtering Background AF.</li> <li><a href="hgTrackUi?g=varFreqsArray"><b>Genotyping Array Databases Combined</b></a> — 14.7 million variants from three array cohorts (TPMI Taiwan, Mexico Biobank, UK Biobank imputed). Kept separate because chip data has different per-variant confidence than sequencing.</li> </ul> <p> On the Disease and Population reference tracks, <b>Affected AF</b> and <b>Background AF</b> are pooled across contributing cohort arms (sum of allele counts divided by sum of allele numbers), not the maximum across arms, so the displayed frequency matches the carrier-count scale and a small cohort with a high local frequency does not dominate the value. See the "Pooled allele frequency" section on each combined track's description page for which cohorts contribute to the pool numerator and denominator. </p> <h3>Consequence filter — the "Other" bucket</h3> <p> All three combined tracks share the same Consequence filter (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 +Intergenic, Other). The filter uses OR logic across the comma-separated consequence terms on each variant: a variant tagged <code>stop_gained,frameshift</code> is selected by either the "Stop Gained" or the "Frameshift" filter. The "Other" bucket catches the less common <a href="http://www.sequenceontology.org/" target="_blank">Sequence Ontology</a> consequence that don't fit the named buckets above. Examples include <code>splice_region</code> (variant near a splice site but outside the canonical donor/acceptor), <code>start_lost</code> / <code>stop_lost</code> (variant disrupts the start codon or replaces the stop codon with a coding amino acid), <code>stop_retained</code> (variant changes the stop codon but keeps it a stop), <code>inframe_insertion</code> / <code>inframe_deletion</code> (in-frame indel that adds or removes whole codons), and <code>coding_sequence</code> (CDS variant where the precise impact is undetermined). If you include "Other" in the filter selection, no records will be hidden by the consequence filter. </p> <h3>Available Datasets</h3> <style> /* varFreqs dataset table: the three combined tracks and the per-project datasets are logically two tables. Give the column headers a strong background so they stand out, and a light group-heading bar to separate the two sections. */ #varFreqsTbl th { background-color: #00457c; color: #ffffff; } #varFreqsTbl tr.varFreqsGroup td { background-color: #d9e4f8; font-weight: bold; font-size: 1.05em; } </style> <table class="stdTbl" id="varFreqsTbl"> <tr class="varFreqsGroup"><td colspan="7">Combined tracks</td></tr> <tr> <th>Database</th> <th>Region</th> <th>N</th> <th>Data Type</th> <th>Cohort</th> <th>Sub-populations</th> <th>Downloadable from UCSC</th> </tr> <tr> <td><a href="hgTrackUi?g=varFreqsAffected">Disease cohorts</a></td> <td>Sequencing-based disease cohorts</td> <td>~130k</td> <td>WGS/WES/long-read</td> <td>Affected/case arms of SFARI SPARK WES/WGS, SCHEMA, GREGoR, GA4K</td> <td>Affected/case AF and AC; background AF for contrast</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=varFreqsBackground">Population reference</a></td> <td>Sequencing-based, population + unaffected</td> <td>~1.5mil</td> <td>WGS/WES/long-read</td> <td>Population cohorts + unaffected/control arms</td> <td>Background AF and AC; per-cohort and ancestry breakdowns</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=varFreqsArray">Genotyping Array Databases Combined</a></td> <td>TPMI, MexBB, UKBB</td> <td>~530k</td> <td>Array / imputed</td> <td>14.7M variants</td> <td>—</td> <td>No</td> </tr> <tr class="varFreqsGroup"><td colspan="7">Individual project datasets</td></tr> <tr> <th>Database</th> <th>Region</th> <th>N</th> <th>Data Type</th> <th>Cohort</th> <th>Sub-populations</th> <th>Downloadable from UCSC</th> </tr> <tr> <td><a href="hgTrackUi?g=allofus">AllOfUs v7</a></td> <td>USA</td> <td>245k</td> <td>WGS</td> <td>General population, diverse</td> <td>African, Indigenous American, East Asian, European, Oceanian, South Asian (<b>local ancestry</b>; see Notes below)</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=topmed">TOPMED Freeze 10</a></td> <td>USA</td> <td>151k</td> <td>WGS</td> <td>Heart, lung, blood, sleep disorder cohorts</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=sfariSparkExomes">SFARI SPARK WES</a></td> <td>USA</td> <td>140k</td> <td>WES</td> <td>Autism families (parents + affected children)</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=sfariSparkWgs">SFARI SPARK WGS</a></td> <td>USA</td> <td>12.5k</td> <td>WGS</td> <td>Autism families (parents + affected children)</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=alfaVcf">NCBI ALFA R4</a></td> <td>USA</td> <td>408k</td> <td>WGS/WES/array mix</td> <td>Aggregated dbGaP studies, mixed phenotypes</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=finngen">FinnGen R12</a></td> <td>Finland</td> <td>500k</td> <td>Imputed (8.5k WGS ref panel)</td> <td>National biobank, ~10% of population</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=ukbb">UK Biobank (Neale Lab v3)</a></td> <td>UK</td> <td>361k</td> <td>Imputed array (HRC+UK10K+1KGp3 ref panel)</td> <td>White British subset of UK Biobank, Neale Lab Round 2 GWAS</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=swefreq">SweGen</a></td> <td>Sweden</td> <td>1k</td> <td>WGS</td> <td>Cross-section of Swedish population</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=gonl">GoNL</a></td> <td>Netherlands</td> <td>498</td> <td>WGS (~13x)</td> <td>250 unrelated Dutch trios (parents only)</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=schema">SCHEMA</a></td> <td>Multi-national</td> <td>121k</td> <td>WES</td> <td>Schizophrenia: 24k cases, 97k controls (Singh 2022 primary); VCF aggregates up to ~73k/~182k</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=tommo60kjpn">Japan ToMMO 61k</a></td> <td>Japan</td> <td>61k</td> <td>WGS</td> <td>General population</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=wbbc">WBBC China</a></td> <td>China</td> <td>4.5k</td> <td>WGS</td> <td>Westlake BioBank for Chinese pilot (now part of China Precision BioBank), autosomes only</td> <td>North Han, Central Han, South Han, Lingnan Han (by recruitment region)</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=chinamap">ChinaMAP phase 1</a></td> <td>China</td> <td>10.5k</td> <td>WGS</td> <td>China Metabolic Analytics Project, ~40x depth, 27 provinces and 8 ethnic groups, autosomes only</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=tpmi">Taiwan TPMI</a></td> <td>Taiwan</td> <td>165k</td> <td>Axiom SNP array (TPM1)</td> <td>Taiwan Precision Medicine Initiative, Han Chinese</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=mgrb">Australia MGRB</a></td> <td>Australia</td> <td>4k</td> <td>WGS</td> <td>Healthy elderly (age ≥70)</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=gasp">GenomeAsia Pilot</a></td> <td>Asia (219 groups)</td> <td>1.7k</td> <td>WGS</td> <td>Diverse populations across Asia</td> <td>Northeast Asian, Southeast Asian, South Asian, Oceanian, American, African, Western European Reference</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=abraom">ABraOM Brazil</a></td> <td>Brazil</td> <td>1.2k</td> <td>WGS</td> <td>Elderly admixed individuals (São Paulo)</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=indigenomes">IndiGenomes</a></td> <td>India</td> <td>1k</td> <td>WGS</td> <td>Healthy individuals</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=genomeindia">GenomeIndia 9.7k</a></td> <td>India</td> <td>9.8k</td> <td>WGS (≥23x)</td> <td>83 anthropologically defined endogamous populations across India</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=kova">KOVA Korea</a></td> <td>Korea</td> <td>5.3k</td> <td>1.9k WGS + 3.4k WES</td> <td>Normal tissue from cancer patients, healthy parents, volunteers</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=npm">NPM Singapore</a></td> <td>Singapore</td> <td>9.8k</td> <td>WGS</td> <td>Chinese, Indian, Malay ancestry</td> <td>—</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=saudi">Saudi Genome</a></td> <td>Saudi Arabia</td> <td>302</td> <td>WGS (30x)</td> <td>Saudi population</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=hrc">HRC</a></td> <td>Multi-national</td> <td>~30k</td> <td>Low-coverage WGS (7x)</td> <td>Imputation reference panel (excl. 1000 Genomes)</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=mxbFreq">MXB Mexico Biobank</a></td> <td>Mexico</td> <td>6k</td> <td>Genotyping array</td> <td>Diverse Mexican ancestries, 898 recruitment sites</td> <td>By state, by ancestry</td> <td>No</td> </tr> <tr> <td><a href="hgTrackUi?g=sgdpFreq">SGDP</a></td> <td>Global</td> <td>279</td> <td>WGS</td> <td>142 diverse populations worldwide</td> <td>By population</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=gregor">GREGoR R4</a></td> <td>USA</td> <td>3.6k</td> <td>WGS</td> <td>Rare disease families (10.7k participants, 4.4k families)</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=hgdp1kFreq">gnomAD HGDP+1kG</a></td> <td>Global</td> <td>4k</td> <td>WGS</td> <td>80 populations (HGDP + 1000 Genomes reprocessed)</td> <td>4k-cohort total AF only; per-population AF columns are <b>full gnomAD v3.1.2</b> release values (~76k genomes), see Notes below</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=ga4kSnv">GA4K</a></td> <td>USA</td> <td>552</td> <td>PacBio HiFi long-read WGS</td> <td>Genomic Answers for Kids: pediatric rare-disease probands and families (Children's Mercy)</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=colorsDbSnv">CoLoRSdb v1.2.0</a></td> <td>Multi-national</td> <td>1,027</td> <td>PacBio HiFi long-read WGS</td> <td>Consortium of Long Read Sequencing: aggregated population-consented samples across multiple research cohorts</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=svatalogSnv">SVatalog 101</a></td> <td>Canada (SickKids)</td> <td>101</td> <td>10X Genomics linked short-read WGS</td> <td>GWAS SVatalog cohort: 101 samples with matched long-read SVs (see <a href="hgTrackUi?g=chirmade101Sv">chirmade101Sv</a>)</td> <td>—</td> <td>Yes</td> </tr> <tr> <td><a href="hgTrackUi?g=tishkoff180">Indigenous Africans 180</a></td> <td>Africa (Ethiopia, Tanzania, Cameroon, Botswana)</td> <td>180</td> <td>WGS (>30x)</td> <td>12 indigenous populations across all four African language phyla (Khoesan, Niger-Congo, Nilo-Saharan, Afroasiatic)</td> <td>—</td> <td>No</td> </tr> </table> <h2>Display Conventions</h2> <p>Most tracks only show the variant and allele frequencies on mouseover or clicks. When zoomed in, tracks display alleles with base-specific coloring. Homozygote data are shown as one letter; heterozygotes are shown with both letters. All VCF files are normalized, with one allele per annotation (no multi-allele lines). </p> <h2>Methods</h2> <p> Each subtrack includes the upstream project's VCF largely as-released, sometimes converted from other file formats; per-subtrack pipelines (coordinate liftover, format conversion, header normalization) are documented on each subtrack's own description page and recorded in the <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/varFreqs.txt" target="_blank">build documentation</a>. The conversion scripts live alongside the makedoc in the <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/varFreqs" target="_blank">scripts directory</a>. </p> <p> The combined Disease cohorts and Population reference tracks are built by a separate pipeline: each per-subtrack VCF is normalized (<code>bcftools norm</code>), all sites are merged into a single callset, consequence annotations are recomputed against Ensembl with <code>bcftools csq</code>, and the merged callset is split by phenotype. Within each combined track, the <b>Affected AF</b> and <b>Background AF</b> columns are <i>pooled</i> across contributing cohort arms (sum of allele counts divided by sum of allele numbers, with the per-arm AN derived from each cohort's AC and AF), so the displayed frequency matches the carrier-count. The Genotyping Array Databases Combined track is built the same way from the array cohorts only. </p> <h2>Data Access</h2> <p>Many of these databases have restrictions on redistribution and download. The table above indicates if we are allowed to distribute it in VCF format. Click the database link in the table above and see the "Data Access" section of the respective track for a description of where to download the data. When the data is freely available from our website, the Data Access section will also indicate the VCF file location on our download server. Because it contains some licensed data, the combined track is not available for download, but can be recreated using the conversion scripts in our <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/varFreqs" target="_blank">GitHub repository</a> and the accompanying <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/varFreqs.txt" target="_blank">documentation file</a>. </p> <h2>Credits</h2> <p>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 any of the tracks in the list above to see the specific credits for each project. Thanks to Alex Ioannidis, UCSC, for the inspiration for this track and to Andreas Lahner, MGZ, for feedback.</p> <h2>References</h2> <p> All of Us Research Program Genomics Investigators. <a href="https://doi.org/10.1038/s41586-023-06957-x" target="_blank"> Genomic data in the All of Us Research Program</a>. <em>Nature</em>. 2024 Mar;627(8003):340-346. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/38374255" target="_blank">38374255</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10937371/" target="_blank">PMC10937371</a> </p> <p> Ameur A, Dahlberg J, Olason P, Vezzi F, Karlsson R, Martin M, Viklund J, Kahari AK, Lundin P, Che H <em>et al</em>. <a href="https://doi.org/10.1038/ejhg.2017.130" target="_blank"> SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population</a>. <em>Eur J Hum Genet</em>. 2017 Nov;25(11):1253-1260. 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