442e433a90b25deb87f10e6cf1b7b608bb0a6d67 max Sat Sep 26 21:56:06 2026 -0700 sfariSparkWgs45kAsd: flag 25M insertions of non-human (oral bacteria) sequence as FILTER NonHumanIns and hide them by default; add SFARI SPARK 45k WGS to the combined tracks without those insertions and relabel the 12k pilot as SFARI SPARK iWGS v1.1 Pilot, refs #38424 diff --git src/hg/makeDb/trackDb/human/varFreqsBackground.html src/hg/makeDb/trackDb/human/varFreqsBackground.html index 56fdfc599b7..6ab74cc94ac 100644 --- src/hg/makeDb/trackDb/human/varFreqsBackground.html +++ src/hg/makeDb/trackDb/human/varFreqsBackground.html @@ -1,180 +1,183 @@ <h2>Description</h2> <p> This track shows small variants (SNVs and short indels) seen in <b>population reference cohorts and in unaffected or control individuals</b> of disease-study cohorts, annotated with their predicted protein consequence and colored by severity. It is the background half of a matched pair: the companion <a href="hgTrackUi?g=varFreqsAffected">Disease cohorts</a> track shows the same kind of variants seen in affected or case individuals. Displaying the two together lets you see how common a variant is in the general/unaffected population compared with affected individuals. For the full list of contributing projects, see the <a href="hgTrackUi?g=varFreqs">SNV Frequencies</a> collection page. </p> <p> The background combines two kinds of data: the population/biobank reference cohorts (such as gnomAD HGDP+1kG, TOPMed, ALFA, HRC and the many national WGS projects), and the unaffected/control or unknown-phenotype arms of the disease-study cohorts (non-ASD family -members in SFARI SPARK WES/WGS, SCHEMA controls, and GREGoR unaffected/unknown -participants). Genotyping-array cohorts are not included. A variant that also appears in +members in SFARI SPARK WES, iWGS v1.1 Pilot and 45k WGS, SCHEMA controls, and GREGoR +unaffected/unknown participants). Genotyping-array cohorts are not included. 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 +<a href="hgTrackUi?g=sfariSparkWgs45kAsd">subtrack's description</a>) were left out. A variant that also appears in affected individuals is shown in both this track and the <a href="hgTrackUi?g=varFreqsAffected">Disease cohorts</a> track. </p> <h2>Display Conventions</h2> <h3>Color by Consequence</h3> <p>Variants are colored by their most severe predicted consequence:</p> <table class="stdTbl"> <tr><th>Color</th><th>Consequence class</th><th>Examples</th></tr> <tr><th style="background-color:#FF0000;width:2em"> </th> <td>Protein-truncating / loss-of-function</td> <td>stop_gained, frameshift, splice_donor, splice_acceptor, stop_lost, start_lost</td></tr> <tr><th style="background-color:#1F77B4;width:2em"> </th> <td>Missense / in-frame</td> <td>missense, inframe_insertion, inframe_deletion, protein_altering</td></tr> <tr><th style="background-color:#008000;width:2em"> </th> <td>Synonymous</td> <td>synonymous, stop_retained</td></tr> <tr><th style="background-color:#808080;width:2em"> </th> <td>Non-coding / intergenic</td> <td>intron, non_coding, intergenic, UTR</td></tr> </table> <p> The score (used for shading) is the pooled background allele frequency times 1000. </p> <h3>Pooled allele frequency</h3> <p> <b>Background AF</b> is the pooled rate across contributing population cohorts and unaffected/control arms: <code>backgroundAF = sum(AC) / sum(AN)</code>, where <b>backgroundAC</b> sums the allele counts and <b>backgroundAN</b> sums the allele numbers across each cohort/arm that provides both AC and AF (the per-arm AN is derived as <code>round(AC / AF)</code>). Two cohorts that publish only AF (ABraOM, ALFA) are still pooled by assigning them an assumed allele number, set as a <code>default_an</code> in the build configuration; their per-arm AC is then derived as <code>round(AF × default_an)</code>. Cohorts that publish only AC with no <code>default_an</code> set (currently MGRB and the GREGoR unaffected and unknown arms), and cohorts that contribute only through per-population AC/AF (currently AllOfUs), are listed in <b>backgroundSources</b> but do not contribute to the pool numerator or denominator; their data remain visible in the per-database and per-population AC/AF columns. The pooled rate is preferred over a max-across-cohorts statistic so a small cohort with a high local AF (for example AllOfUs Oceanian) cannot dominate the displayed frequency. </p> <p> The pooled rate also inherits a <b>shared-sample bias</b>: several source cohorts overlap in the individuals they include. For example, 1000 Genomes samples appear in both gnomAD HGDP+1kG and HRC; HGDP and SGDP overlap; AllOfUs and TOPMed share participants; ALFA aggregates dbGaP studies used elsewhere in the pool. Where a variant sits in a shared sample, both its AC and AN are counted more than once, so 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. </p> <h3>Top population sources by AF</h3> <p> Alongside the pooled rate, the mouseover lists the top 3 contributing background sources ranked by their own per-source AF, formatted as <code>Source (AF)</code>. This surfaces population cohorts where a variant is specifically enriched, even when the pooled rate is small; the East-Asian founder allele <a href="/cgi-bin/hgTracks?db=hg38&position=chr10:111751803-111751805&varFreqs=full&varFreqsBackground=pack" target="_blank">rs4986893</a>, for example, ranks ToMMo Japan and KOVA Korea at the top while the pooled rate across all contributing sources sits much lower. For disease cohorts that ship a phenotype split (SPARK, SFARI WGS, SCHEMA, GREGoR), the displayed AF is the unaffected-arm AF and the label includes the arm (for example <code>SPARK non-ASD</code>, <code>SCHEMA ctrl</code>); for population cohorts, the label is the cohort name and the AF is the unified cohort AF. Per-population sub-ancestries of a cohort (such as gnomAD HGDP+1kG continental groups) are deliberately excluded from this ranking so sub-population frequencies do not crowd out actual project-level signals. </p> <p> Two source cohorts are also excluded from the Top-3 ranking: <b>SGDP</b> and <b>SVatalog</b>. Their VCFs encode allele counts per genotyped site rather than per population (each variant in a single individual produces <code>AC=1, AN=2, AF=0.5</code>), so the per-source AF is not a population frequency and would always sit near the top of the ranking with a meaningless value. Both cohorts still appear in <b>backgroundSources</b> and still contribute their (small) AC and AN to the pooled <b>backgroundAF</b>; they are only suppressed from the Top-3 list. </p> <h2>Filters</h2> <ul> <li><b>Variant Type</b> and <b>Consequence</b>: 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).</li> <li><b>Background AF</b>, <b>AC</b>, <b>AN</b>: the pooled allele frequency (sum AC / sum AN), summed allele count, and summed allele number across the contributing population cohorts and unaffected/control arms. See "Pooled allele frequency" above.</li> <li><b>Affected/case AF</b>, <b>AC</b>, <b>AN</b>: the same triple computed across affected individuals, for context.</li> <li><b>Background source</b>: restrict to variants seen in specific cohorts.</li> <li><b>Per-database AF/AC</b> and ancestry-specific allele frequencies (AllOfUs, GenomeAsia, gnomAD HGDP+1kG, NPM Singapore, WBBC) let you filter to a single cohort or ancestry group.</li> <li><b>Reference/Alternate Length</b> and <b>Length Change</b>: filter by allele length.</li> </ul> <h2>Methods</h2> <p> Variant-frequency VCFs from the contributing cohorts were stripped of unneeded INFO fields, normalized with <code>bcftools norm</code> (splitting multi-allelic sites), and merged with <code>bcftools merge</code>. The merged callset was annotated with predicted protein consequences using <a href="https://samtools.github.io/bcftools/howtos/csq-calling.html" target="_blank">bcftools csq</a> against the <a href="https://www.ensembl.org/info/data/ftp/index.html" target="_blank">Ensembl</a> GRCh38 release 115 gene models. </p> <p> A custom Python script (<code>vcfToBigBed.py</code>) then read the per-cohort allele counts and frequencies and, for each variant, pooled the allele counts and allele numbers across the population cohorts and unaffected/control subgroups to produce this track, and across the affected arms to produce the companion <a href="hgTrackUi?g=varFreqsAffected">Disease cohorts</a> track. A variant seen in both groups appears in both tracks. The build is documented in the <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/varFreqs.txt" target="_blank">makeDoc</a>, and the scripts are on <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/varFreqs" target="_blank">GitHub</a>. </p> <h2>Data Access</h2> <p> Because the merged callset combines cohorts whose redistribution licenses differ, this track is <b>not available for download</b> and is not in the Table Browser. It can be reconstructed from the individual source VCFs using the <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/varFreqs" target="_blank">conversion scripts</a> and the <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/varFreqs.txt" target="_blank">build documentation</a>. The per-project subtracks on the <a href="hgTrackUi?g=varFreqs">SNV Frequencies</a> collection page document how to obtain each source dataset. </p> <h2>Credits</h2> <p> This track is only possible thanks to the data from millions of volunteers around the world who contributed to the population reference projects and to the unaffected/control arms of the disease cohorts. Click the individual project subtracks on the <a href="hgTrackUi?g=varFreqs">SNV Frequencies</a> 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. </p> <h2>References</h2> <p> For the primary citation of each source cohort, see the References section on the <a href="hgTrackUi?g=varFreqs">SNV Frequencies</a> collection page. The merged-track build uses the following tools: </p> <p> Danecek P, McCarthy SA. <a href="https://doi.org/10.1093/bioinformatics/btx100" target="_blank"> BCFtools/csq: haplotype-aware variant consequences</a>. <em>Bioinformatics</em>. 2017 Jul 1;33(13):2037-2039. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/28205675" target="_blank">28205675</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870570/" target="_blank">PMC5870570</a> </p>