e11e10c01c975653b7f0102601cabd52967d2c80 max Fri Aug 14 05:58:23 2026 -0700 lrSv: author-provided noyvertSv description, Vienna ONT naming, hs1 Lin update, refs #38099 - noyvertSv.html: replace the Description with the author-provided text (imputation purpose, singletons excluded, subset-of-Vienna relationship) - rename "1KG ONT Vienna" -> "1KG Vienna ONT" to match the subtrack and merged-track labels (noyvertSv.html and the hs1 lrSv page) - hs1 lrSv page: add the 1KG Lin merged subtrack (now native on T2T-CHM13, 614,522 SVs) and reorder the summary table and detail sections to match the track (priority) order - lrSv1kLin.html: link the source Lin et al. dataset on GitHub diff --git src/hg/makeDb/trackDb/human/noyvertSv.html src/hg/makeDb/trackDb/human/noyvertSv.html index e4547b64013..6548e23f0a1 100644 --- src/hg/makeDb/trackDb/human/noyvertSv.html +++ src/hg/makeDb/trackDb/human/noyvertSv.html @@ -1,147 +1,144 @@ <h2>Description</h2> <p> The structural variants (SVs) in this dataset were identified using Oxford Nanopore long-read whole-genome sequencing of 888 individuals from the 1000 -Genomes Project, representing five ancestry groups. -This dataset and the <a href="hgTrackUi?g=lrSv1kgOnt">1KG ONT Vienna</a> track -(Schloissnig et al. 2025) are based on the same underlying Oxford Nanopore -sequencing data; the 888 samples here are a subset of the 1,019 samples in that -track and only SVs that appear in a single sample (singletons) were removed from this track, -so this callset is smaller than the Schloissnig dataset. The reason is that -this callset was created primarily for imputation: The SVs here were merged with -previously identified short variants from the same individuals to generate a -multi-ancestry SV imputation reference panel. This panel was used to impute -SVs in approximately 500,000 UK Biobank participants and test their -associations with 32 disease-relevant traits. +Genomes Project, representing five ancestry groups. This callset was created +primarily for SV imputation. To generate a multi-ancestry SV imputation +reference panel, SVs observed in only one individual (singletons) were excluded, +and the remaining SVs were merged with previously identified short variants from +the same individuals. This panel was used to impute SVs in approximately 500,000 +UK Biobank participants and to test their associations with 32 disease-relevant +traits. </p> <p> The track contains all 107,445 SVs in the reference panel: 59,953 insertions, -38,459 deletions, 5,729 inversions, 2,696 breakends, and 608 duplications. -Each variant is annotated with its overall allele frequency; allele -frequencies across five superpopulations (African, Admixed American, East -Asian, European, and South Asian); Hardy-Weinberg equilibrium p-values; and -imputation accuracy metrics from internal leave-one-out validation and UK -Biobank imputation. For SVs reaching genome-wide significance, the associated -traits, p-values, and INFO scores are listed on the corresponding variant -details page. +38,459 deletions, 5,729 inversions, 2,696 breakends, and 608 duplications. Each +variant is annotated with its overall allele frequency; allele frequencies +across five superpopulations (African, Admixed American, East Asian, European, +and South Asian); Hardy-Weinberg equilibrium p-values; and imputation accuracy +metrics from internal leave-one-out validation and UK Biobank imputation. For +SVs reaching genome-wide significance, the associated traits, p-values, and INFO +scores are listed on the corresponding variant details page. </p> <p> -Although the two studies share the same raw sequencing data, they applied +The 888 samples in this dataset are a subset of the 1,019 samples included in +the <a href="hgTrackUi?g=lrSv1kgOnt">1KG Vienna ONT</a> track (Schloissnig et al. +2025). Although the two studies share the same raw sequencing data, they applied different data-processing and SV-calling pipelines to address distinct research objectives, so the individual calls are only partially concordant. The imputation reference panel, UK Biobank imputation results, and SV-wide association study (SV-WAS) results described here are specific to this track. </p> <h2>Display Conventions and Configuration</h2> <p> Items are colored by SV type, matching the other subtracks of the container: </p> <table class="stdTbl"> <tr><th style="background-color:#C80000;width:2em"> </th> <td>Deletion (DEL)</td></tr> <tr><th style="background-color:#0000C8;width:2em"> </th> <td>Insertion (INS)</td></tr> <tr><th style="background-color:#00A000;width:2em"> </th> <td>Duplication (DUP)</td></tr> <tr><th style="background-color:#E68C00;width:2em"> </th> <td>Inversion (INV)</td></tr> <tr><th style="background-color:#5A5A5A;width:2em"> </th> <td>Breakend (BND), a single junction of a larger rearrangement</td></tr> </table> <p> Insertions and breakends are drawn at a single reference base; the length of inserted sequence is reported for insertions, and the mate locus of the rearrangement junction is reported for breakends. Deletions, inversions and duplications span the affected reference interval. Because the source table does not report an allele count, the allele count and allele number shown here are approximate values derived from the reported allele frequency and the genotype missing rate (allele number = 2 × 888 × (1 − missing rate); allele count = allele frequency × allele number). </p> <p> The mouseover shows the variant name, SV type, reference and insertion lengths, allele frequency, approximate allele count, and the number of UK Biobank trait associations. Filters are available for SV type, SV length, insertion length, approximate allele count, overall and per-population allele frequency, the number of UK Biobank GWAS hits, and the leave-one-out imputation r² and minor-allele concordance. </p> <h2>Methods</h2> <p> 888 individuals from the 1000 Genomes Project (164 European, 144 Admixed American, 168 East Asian, 171 South Asian and 241 African), out of 906 sequenced, passed quality control. They were sequenced on the Oxford Nanopore PromethION P48 platform with R9.4.1 flow cells and the SQK-LSK110 ligation kit, to a median read length of about 6.2 kb and 15x median coverage. Reads were aligned to GRCh38 with minimap2 v2.24 and structural variants were jointly called across all samples with Sniffles2 v2.0.7 using tandem-repeat annotations. Variants were retained if they were 50 bp to 30 Mb long, present in at least two individuals and had a genotype missing rate below 20%, yielding 107,445 SVs. This SV panel was merged with about 45 million short variants from 1000 Genomes Phase 3 and phased with Beagle to build a multi-ancestry imputation reference panel. Leave-one-out cross-validation with Beagle v5.4 provided per-variant imputation accuracy (r²) and minor-allele concordance. The panel was then used to impute SVs into 488,130 UK Biobank participants, and an SV-wide association study (SV-WAS) with Regenie v3 tested 32 disease-relevant phenotypes and 1,463 protein levels in European-ancestry participants, using a genome-wide significance threshold of p<5×10<sup>-8</sup>. See Noyvert et al. 2025 for full details. </p> <p> The per-variant summary table (allele frequencies, quality metrics, imputation accuracy and significant UK Biobank associations for all 107,445 SVs) was provided by the authors. At UCSC it was converted to the shared long-read SV schema (signed lengths made positive, an explicit insertion-length field added, allele count and allele number approximated from allele frequency and missing rate, and colors assigned from the container's shared palette). The step-by-step commands are recorded in the UCSC makeDoc for this track container: <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/lrSv.txt" target="_blank"> doc/hg38/lrSv.txt</a>. The conversion script and autoSql schema live in <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/lrSv" target="_blank"> makeDb/scripts/lrSv</a>, and the track configuration is in <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/trackDb/human/lrSv.ra" target="_blank">trackDb/human/lrSv.ra</a>. </p> <h2>Data Access</h2> <p> The data can be explored interactively in table format with the <a href="../cgi-bin/hgTables">Table Browser</a> or the <a href="../cgi-bin/hgIntegrator">Data Integrator</a> and exported from there to spreadsheet or tab-sep tables. From scripts, the data can be accessed through our <a href="https://api.genome.ucsc.edu" target="_blank">API</a>, track=<i>noyvertSv</i>. </p> <p> The annotation is stored as a bigBed file that can be downloaded from <a href="http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/" target="_blank">our download server</a> as <tt>noyvert.bb</tt>. Individual regions or the whole annotation can be obtained with the <tt>bigBedToBed</tt> utility, available from our <a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads" target="_blank">utilities page</a>. Example: <tt>bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/noyvert.bb -chrom=chr21 -start=0 -end=100000000 stdout</tt>. </p> <h2>Credits</h2> <p> Thanks to Boris Noyvert and colleagues at Boehringer Ingelheim and the wider study team for generating this multi-ancestry long-read SV panel and for sharing the per-variant summary table, and to the 1000 Genomes Project and the UK Biobank participants whose data made the study possible. </p> <h2>References</h2> <p> Noyvert B, Erzurumluoglu AM, Drichel D, Omland S, Andlauer TFM <em>et al</em>. <a href="https://doi.org/10.7554/eLife.106115.1" target="_blank"> Imputation of structural variants using a multi-ancestry long-read sequencing panel enables identification of disease associations</a>. <em>eLife</em>. 2025. doi:10.7554/eLife.106115.1 </p> <p> A continuously updated preprint version of this study is available on medRxiv: <a href="https://doi.org/10.1101/2023.12.20.23300308" target="_blank"> doi:10.1101/2023.12.20.23300308</a>. </p>