ef70dfff0e8710e8aa4bc369a939f838c75947fb lrnassar Fri Jun 5 14:59:06 2026 -0700 varFreqs: Phase-7 audit cleanup on the supertrack and combined-track description pages. Supertrack varFreqs.html: - Restore the consequence-filter "Other" bucket explanation that was lost when varFreqsAll.html was replaced by the Affected+Background pair (now documented once on the supertrack page, since all three combined tracks share the filter). - Add 6 primary citations that were already in standalone subtrack pages but not carried up: Bycroft (UK Biobank), Cao (ChinaMAP), Cong (WBBC), Genome of the Netherlands Consortium (GoNL), Malomane (Saudi), Yang (TPMI). - Reorder Ameur, Singh into correct alphabetical position. - Lowercase

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 "Phased Variants" track. Their VCF versions below show only the isolate frequency per variant.

Please contact us (genome@soe.ucsc.edu) if you know of a project that we should add. So far, +

Please contact us (genome@soe.ucsc.edu) if you know of a project that we should add. So far, Regeneron's Million Exomes and Mexico City Studies (request rejected) and Taiwan Biobank (pending).

Combined Tracks

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).

Affected/Case Individuals — 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.
Population + Unaffected — 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.
Genotyping Array Databases Combined — 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.

Consequence filter — the "Other" bucket

+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 +on each variant: a variant tagged stop_gained,frameshift is selected by either +the "Stop Gained" or the "Frameshift" filter. The "Other" +bucket catches the less common +Sequence Ontology consequence +terms emitted by bcftools csq that don't fit the named buckets above. Examples +include splice_region (variant near a splice site but outside the canonical +donor/acceptor), start_lost / stop_lost (variant disrupts the +start codon or replaces the stop codon with a coding amino acid), +stop_retained (variant changes the stop codon but keeps it a stop), +inframe_insertion / inframe_deletion (in-frame indel that adds or +removes whole codons), and coding_sequence (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. +

Available Datasets

@@ -455,90 +475,127 @@

Credits

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.

References

All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program. Nature. 2024 Mar;627(8003):340-346. PMID: 38374255; PMC: PMC10937371

+Ameur A, Dahlberg J, Olason P, Vezzi F, Karlsson R, Martin M, Viklund J, Kahari AK, Lundin P, Che H +et al. + +SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish +population. +Eur J Hum Genet. 2017 Nov;25(11):1253-1260. +PMID: 28832569; PMC: PMC5765326 +

Bhattacharyya C, Subramanian K, Uppili B, Biswas NK, Ramdas S, Tallapaka KB, Arvind P, Rupanagudi KV, Maitra A, Nagabandi T et al. Mapping genetic diversity with the GenomeIndia project. Nat Genet. 2025 Apr;57(4):767-773. PMID: 40200122

-Ameur A, Dahlberg J, Olason P, Vezzi F, Karlsson R, Martin M, Viklund J, Kahari AK, Lundin P, Che H -et al. - -SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish -population. -Eur J Hum Genet. 2017 Nov;25(11):1253-1260. -PMID: 28832569; PMC: PMC5765326 +Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, +O'Connell J et al. + +The UK Biobank resource with deep phenotyping and genomic data. +Nature. 2018 Oct;562(7726):203-209. +PMID: 30305743; PMC: PMC6786975 +

+ +

+Cao Y, Li L, Xu M, Feng Z, Sun X, Lu J, Xu Y, Du P, Wang T, Hu R et al. + +The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals. +Cell Res. 2020 Sep;30(9):717-731. +PMID: 32355288; PMC: PMC7609296

Chirmade S, Wang Z, Mastromatteo S, Sanders E, Thiruvahindrapuram B, Nalpathamkalam T, Pellecchia G, Lin F, Keenan K, Patel RV et al. GWAS SVatalog: a visualization tool to aid fine-mapping of GWAS loci with structural variations. Heredity (Edinb). 2025 Sep;135(3):199-210. PMID: 41203876; PMC: PMC13031531

Cohen ASA, Farrow EG, Abdelmoity AT, Alaimo JT, Amudhavalli SM, Anderson JT, Bansal L, Bartik L, Baybayan P, Belden B et al. Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes. Genet Med. 2022 Jun;24(6):1336-1348. PMID: 35305867

+Cong PK, Bai WY, Li JC, Yang MY, Khederzadeh S, Gai SR, Li N, Liu YH, Yu SH, Zhao WW et al. + +Genomic analyses of 10,376 individuals in the Westlake BioBank for Chinese (WBBC) pilot project. +Nat Commun. 2022 May 26;13(1):2939. +PMID: 35618720; PMC: PMC9135724 +

Fan S, Spence JP, Feng Y, Hansen MEB, Terhorst J, Beltrame MH, Ranciaro A, Hirbo J, Beggs W, Thomas N et al. Whole-genome sequencing reveals a complex African population demographic history and signatures of local adaptation. Cell. 2023 Mar 2;186(5):923-939.e14. PMID: 36868214; PMC: PMC10568978

Feliciano P, Daniels AM, Snyder LG, Beaumont A, Camba A, Esler A, Gulsrud AG, Mason A, Nicholson A, Paolicelli AM et al; The SPARK Consortium. SPARK: A US Cohort of 50,000 Families to Accelerate Autism Research. Neuron. 2018 Feb 7;97(3):488-493. PMID: 29420931; PMC: PMC7444276

+Genome of the Netherlands Consortium. + +Whole-genome sequence variation, population structure and demographic history of the Dutch +population. +Nat Genet. 2014 Aug;46(8):818-25. +PMID: 24974849 +

GenomeAsia100K Consortium. The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature. 2019 Dec;576(7785):106-111. PMID: 31802016; PMC: PMC7054211

Jain A, Bhoyar RC, Pandhare K, Mishra A, Sharma D, Imran M, Senthivel V, Divakar MK, Rophina M, Jolly B et al. IndiGenomes: a comprehensive resource of genetic variants from over 1000 Indian genomes. Nucleic Acids Res. 2021 Jan 8;49(D1):D1225-D1232. @@ -596,71 +653,80 @@ Sci Rep. 2017 Jun 27;7(1):4287. PMID: 28655895; PMC: PMC5487339

Mallick S, Li H, Lipson M, Mathieson I, Gymrek M, Racimo F, Zhao M, Chennagiri N, Nordenfelt S, Tandon A et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature. 2016 Oct 13;538(7624):201-206. PMID: 27654912; PMC: PMC5161557

+Malomane DK, Williams MP, Huber CD, Mangul S, Abedalthagafi M, Chiang CWK. + +Patterns of population structure and genetic variation within the Saudi Arabian population. +bioRxiv. 2025 Jan 13;. +PMID: 39868174; PMC: PMC11761371 +

McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, Kang HM, Fuchsberger C, Danecek P, Sharp K et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016 Oct;48(10):1279-83. PMID: 27548312; PMC: PMC5388176

Naslavsky MS, Scliar MO, Yamamoto GL, Wang JYT, Zverinova S, Karp T, Nunes K, Ceroni JRM, de Carvalho DL, da Silva Simões CE et al. Whole-genome sequencing of 1,171 elderly admixed individuals from São Paulo, Brazil. Nat Commun. 2022 Mar 4;13(1):1004. PMID: 35246524; PMC: PMC8897431

+Singh T, Poterba T, Curtis D, Akil H, Al Eissa M, Barchas JD, Bass N, Bigdeli TB, Breen G, +Bromet EJ et al. + +Rare coding variants in ten genes confer substantial risk for schizophrenia. +Nature. 2022 Apr;604(7906):509-516. +PMID: 35396579; PMC: PMC9805802 +

Sohail M, Palma-Martínez MJ, Chong AY, Quinto-Cortés CD, Barberena-Jonas C, Medina-Muñoz SG, Ragsdale A, Delgado-Sánchez G, Cruz-Hervert LP, Ferreyra-Reyes L et al. Mexican Biobank advances population and medical genomics of diverse ancestries. Nature. 2023 Oct;622(7984):775-783. PMID: 37821706; PMC: PMC10600006

-Singh T, Poterba T, Curtis D, Akil H, Al Eissa M, Barchas JD, Bass N, Bigdeli TB, Breen G, -Bromet EJ et al. - -Rare coding variants in ten genes confer substantial risk for schizophrenia. -Nature. 2022 Apr;604(7906):509-516. -PMID: 35396579; PMC: PMC9805802 -

Tadaka S, Kawashima J, Hishinuma E, Saito S, Okamura Y, Otsuki A, Kojima K, Komaki S, Aoki Y, Kanno T et al. jMorp: Japanese Multi-Omics Reference Panel update report 2023. Nucleic Acids Res. 2024 Jan 5;52(D1):D622-D632. PMID: 37930845; PMC: PMC10767895

Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, Taliun SAG, Corvelo A, Gogarten SM, Kang HM et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. @@ -673,15 +739,24 @@ Wong E, Bertin N, Hebrard M, Tirado-Magallanes R, Bellis C, Lim WK, Chua CY, Tong PML, Chua R, Mak K et al. The Singapore National Precision Medicine Strategy. Nat Genet. 2023 Feb;55(2):178-186. PMID: 36658435

Wu D, Dou J, Chai X, Bellis C, Wilm A, Shih CC, Soon WWJ, Bertin N, Lin CB, Khor CC et al. Large-scale whole-genome sequencing of three diverse Asian populations in Singapore. Cell. 2019 Oct 17;179(3):736-749.e15. PMID: 31626772

+ +

+Yang HC, Kwok PY, Li LH, Liu YM, Jong YJ, Lee KY, Wang DW, Tsai MF, Yang JH, Chen CH et al. + +The Taiwan Precision Medicine Initiative provides a cohort for large-scale studies. +Nature. 2025 Dec;648(8092):117-127. +PMID: 41092961; PMC: PMC12675286 +

Database	Region	N	Data Type	Cohort	Sub-populations	Downloadable from UCSC
Affected/Case Individuals	Sequencing-based disease cohorts