196b96d04435ca93e8a7366127a41afe6b791b98
lrnassar
  Fri Jun 5 14:22:48 2026 -0700
varFreqs.html: clarify SCHEMA cohort size in the supertrack Cohort cell -
keep the 24k/97k Singh 2022 primary-meta-analysis headline, note that the
SCHEMA browser VCF we use aggregates further cohorts up to ~73k/~182k.
Per QA feedback. refs #36642

diff --git src/hg/makeDb/trackDb/human/varFreqs.html src/hg/makeDb/trackDb/human/varFreqs.html
index 39f9d48bdad..d8786a9e002 100644
--- src/hg/makeDb/trackDb/human/varFreqs.html
+++ src/hg/makeDb/trackDb/human/varFreqs.html
@@ -1,687 +1,687 @@
 <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.
 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 cannot be recomputed by
 gnomAD 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.
 </p>
 
 <p>
 Data from projects that provide haplotype-phased genotypes can also be found
 elsewhere: 1000 Genomes is also a separate track, and the phased genotypes HGDP, SGDP,
 HGDP+1000 Genomes and Mexico Biobank can also be found in the &quot;Phased Variants&quot; track.
 Their VCF versions below show only the isolate frequency per variant.
 </p>
 
 <p>Please contact us (<A HREF="mailto:&#103;en&#111;&#109;&#101;&#64;&#115;&#111;&#101;.&#117;&#99;s&#99;.&#101;&#100;u">&#103;en&#111;&#109;&#101;&#64;&#115;&#111;&#101;.&#117;&#99;s&#99;.&#101;&#100;u</A><!-- above address is genome at soe.ucsc.edu -->) if you know of a project that we should add. So far,
 Regeneron&apos;s Million Exomes and Mexico City Studies (request rejected) and Taiwan Biobank (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=varFreqsAffected"><b>Affected/Case Individuals</b></a> &mdash;
       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 frequency in the background, so
       case-enriched variants can be isolated.</li>
   <li><a href="hgTrackUi?g=varFreqsBackground"><b>Population + Unaffected</b></a> &mdash;
       the matched background: 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. Showing this together with the
       Affected track lets you compare case versus background frequency across a gene. Both
       tracks exclude the genotyping-array cohorts.</li>
   <li><a href="hgTrackUi?g=varFreqsArray"><b>Genotyping Array Databases Combined</b></a>
       &mdash; 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>
 
 <h3>Available Datasets</h3>
 
 <table class="stdTbl">
 <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">Affected/Case Individuals</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 + Unaffected</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>&mdash;</td>
   <td>No</td>
 </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>Yes</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>&mdash;</td>
   <td>Yes</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>&mdash;</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>&mdash;</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>&mdash;</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>&mdash;</td>
   <td>Yes</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>&mdash;</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>&mdash;</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>&mdash;</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</td>
+  <td>Schizophrenia: 24k cases, 97k controls (Singh 2022 primary); VCF aggregates up to ~73k/~182k</td>
   <td>&mdash;</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>&mdash;</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>&mdash;</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>&mdash;</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 &ge;70)</td>
   <td>&mdash;</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 (S&atilde;o Paulo)</td>
   <td>&mdash;</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>&mdash;</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 (&ge;23x)</td>
   <td>83 anthropologically defined endogamous populations across India</td>
   <td>&mdash;</td>
   <td>Yes</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>&mdash;</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>&mdash;</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>&mdash;</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>&mdash;</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>&mdash;</td>
   <td>No</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>&mdash;</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>&mdash;</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>&mdash;</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 (&gt;30x)</td>
   <td>12 indigenous populations across all four African language phyla (Khoesan, Niger-Congo, Nilo-Saharan, Afroasiatic)</td>
   <td>&mdash;</td>
   <td>No</td>
 </tr>
 </table>
 
 <h2>Notes on Specific Sub-tracks</h2>
 
 <h3>AllOfUs &mdash; local-ancestry-stratified frequencies</h3>
 <p>
 The AllOfUs subtrack provides <b>local-ancestry-stratified</b> allele frequencies, not the
 global ancestry categories used in the All of Us Research Program 2024 Nature paper
 (see References). Each variant's per-ancestry AF/AC counts only the haplotypes whose
 inferred local ancestry at that exact genomic position belongs to the named group
 (strict-both-haps mode). The six ancestry classes
 (African, Indigenous American, East Asian, European, Oceanian, South Asian) match HGDP-derived
 local-ancestry reference panels and so include Oceanian, which is not one of the
 paper's six global Rye categories (those are AFR, AMR, EAS, EUR, Middle Eastern, SAS).
 For an admixed individual, the local-ancestry AF at a position can therefore differ
 substantially from the AF among self-reported members of the same ancestry group.
 The Ioannidis lab (Phoenix, UCSC) developed the pipeline that produced this VCF
 and applied it to the AllOfUs v7 release; only variants with cohort allele count &ge; 20
 were retained.
 </p>
 
 <h3>gnomAD HGDP+1kG &mdash; cohort vs full-release frequencies</h3>
 <p>
 This subtrack derives from the gnomAD v3.1.2 release, which embeds the
 4,094-genome jointly-called HGDP+1kG cohort (Koenig et al. 2024) inside the larger
 gnomAD aggregation. To save space, we kept only INFO fields useful for clinical and
 population-genetic interpretation. Two allele-frequency
 sets are exposed:
 </p>
 <ul>
   <li>The <b>cohort-level</b> AC/AF/AN fields (no prefix) are computed across the
       ~3,400 unrelated HGDP+1kG individuals (allele number &asymp; 6,800).</li>
   <li>The <b>per-population</b> filter fields (gnomAD v3.1.2 African AF, gnomAD v3.1.2
       Latino AF, etc.) are values from the <b>full gnomAD v3.1.2 release</b>
       (~76,000 genomes), not just the 4,094-genome HGDP+1kG cohort.
       The corresponding allele numbers are typically tens of thousands per population.</li>
 </ul>
 <p>
 The filter labels on the track configuration page, and the field descriptions in the
 combined-track bigBed, reflect this distinction. Per-population
 HGDP+1kG-cohort frequencies are not exposed because the cohort is too small for
 stable per-population estimates in many populations.
 </p>
 
 <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; 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 (<em>e.g.</em> <code>finngen_to_vcf.py</code>, <code>kovaToVcf.py</code>,
 <code>schema_addAcAnAf.py</code>, <code>svatalogFreqToVcf.py</code>) 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 Affected and Background 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 into the two bigBed files via
 <code>vcfToBigBed.py</code> + <code>bedToBigBed</code>. The mapping from upstream INFO fields to
 bigBed columns is driven by two configuration files in the scripts directory:
 <code>databases.tsv</code> (one row per source dataset, flagging which cohorts study a disease)
 and <code>populations.tsv</code> (per-population AC/AF columns within each source, including the
 affected and unaffected arm of each disease cohort). Editing those two files and rerunning
 <code>mergeAndAnnotate.sh</code> followed by <code>vcfToBigBed.py --split-affected</code> rebuilds
 the two tracks. The Genotyping Array Databases Combined track is built the same way from the
 array cohorts only.
 </p>
 
 <h2>Data Access</h2>
 <p>All the data is publicly available. The table above indicates if we are allowed to distribute it in VCF format. Most of the databases do not allow us to redistribute the data files directly from our website, but it can always be downloaded from the original websites in some form. Click the database link in the table above and see the &quot;Data Access&quot; 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>
 Bhattacharyya C, Subramanian K, Uppili B, Biswas NK, Ramdas S, Tallapaka KB, Arvind P, Rupanagudi
 KV, Maitra A, Nagabandi T <em>et al</em>.
 <a href="https://doi.org/10.1038/s41588-025-02153-x" target="_blank">
 Mapping genetic diversity with the GenomeIndia project</a>.
 <em>Nat Genet</em>. 2025 Apr;57(4):767-773.
 PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/40200122" target="_blank">40200122</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.
 PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/28832569" target="_blank">28832569</a>; PMC: <a
 href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5765326/" target="_blank">PMC5765326</a>
 </p>
 
 <p>
 Chirmade S, Wang Z, Mastromatteo S, Sanders E, Thiruvahindrapuram B, Nalpathamkalam T, Pellecchia G,
 Lin F, Keenan K, Patel RV <em>et al</em>.
 <a href="https://doi.org/10.1038/s41437-025-00809-2" target="_blank">
 GWAS SVatalog: a visualization tool to aid fine-mapping of GWAS loci with structural variations</a>.
 <em>Heredity (Edinb)</em>. 2025 Sep;135(3):199-210.
 PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/41203876" target="_blank">41203876</a>; PMC: <a
 href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13031531/" target="_blank">PMC13031531</a>
 </p>
 
 <p>
 Cohen ASA, Farrow EG, Abdelmoity AT, Alaimo JT, Amudhavalli SM, Anderson JT, Bansal L, Bartik L,
 Baybayan P, Belden B <em>et al</em>.
 <a href="https://doi.org/10.1016/j.gim.2022.02.007" target="_blank">
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