676b58d841804f049f720cc9ba3fddec216dae61 max Tue Dec 2 06:22:46 2025 -0800 adding saudi arabia to variant frequencies track diff --git src/hg/makeDb/trackDb/human/varFreqs.html src/hg/makeDb/trackDb/human/varFreqs.html index 8dc145fd00a..f47b5d89e6b 100644 --- src/hg/makeDb/trackDb/human/varFreqs.html +++ src/hg/makeDb/trackDb/human/varFreqs.html @@ -1,448 +1,471 @@

Description

This container shows results from projects where the variant frequencies, aka allele frequencies, are publicly available. The tracks were collected from the projects listed below. Projects that provide haplotype-phased genotypes/variants can be found elsewhere: 1000 Genomes is a separate track, and the projects HGDP, SGDP, HGDP+1000 Genomes and Mexico Biobank can be found in the "Phased Variants" track.

If you want us to add other projects, please contact us. We asked and were unable to obtain variant frequencies from the following projects: UK Biobank (request pending), All of us (granted), SFARI SPARK (in process).

The following projects were added:

Mexico City Prospective Study (MCPS): 9,950 whole genome sequenced individuals and 141,046 exome sequenced and genotyped individuals from the Mexico City Prospective Study (MCPS), a collaboration between the Regeneron Genetics Center, University of Oxford, Universidad Nacional Autónoma de México (UNAM), National Institute of Genomic Medicine in Mexico, Abbvie Inc. and AstraZeneca UK. For details see (Ziyatdinov A, Nature 2023), the reference section.
Regeneron Million Exomes Project (ME): Whole-exomes of 983,578 individuals sequenced by the Regeneron Genetics Center (RGC). These data span dozens of collaborations including large biobanks and health systems. All data were generated by the RGC on a single, harmonized sequencing and informatics protocol. The dataset includes individuals across diverse ancestral populations, encompassing outbred and founder populations and cohorts with high rates of consanguinity. See (Sun et al, Nature 2024) for details.
NHLBI TOPMED Freeze 10: NHLBI TOPMed (Trans-Omics for Precision Medicine) program, launched by the U.S. National Heart, Lung, and Blood Institute, integrates whole-genome sequencing with molecular, clinical, and environmental data from large, well-phenotyped cohorts. Its goal is to uncover the biological mechanisms underlying heart, lung, blood, and sleep disorders to advance precision medicine and improve population health. Freeze 10 contains 868,581,653 variants from 150,899 whole genomes. VCFs were downloaded from BRAVO.
GenomeAsia Pilot (GAsP): Whole-genome sequencing data of 1,739 individuals from 219 population groups across Asia. See (GenomeAsia Consortium, Nature 2019) for details.
ALFA: The NCBI ALlele Frequency Aggregator pipeline computes allele frequencies from approved, unrestricted dbGaP studies and makes them publicly available through dbSNP. Its goal is to release frequency data from over one million dbGaP subjects to aid discoveries involving common and rare variants with biological or disease relevance. The R4 release includes 408,709 subjects and allele frequencies for 15.5 million rs sites, including nearly one million ClinVar variants. Genotype and associated individual-level data are accessible through dbGaP authorized access.
FinnGen: Imputed variants from 500,348 Biobank samples obtained using genotyping arrays in Finnland, 10% of the population. The imputation used phased variants obtained from 8,554 high-quality whole genome sequences, also from Finnland. For details, see (Kurki et al, Nature 2023). Phenotype links can be shown at FinnGen PheWeb.
JPN To61k Japan Tohoku University Tohoku Medical Megabank Organization 61k Allele frequency panel (JPN 61k): An allele frequency panel based on short-read WGS analysis of 61,000 Japanese individuals. The project includes other datatypes, such as STRs, long-read SVs and short-read CNVs. Data can be downloaded from the jMorp Website, specifically the Downloads section. For details, see (Tadaka et al, NAR 2023).
Brazil Arquivo Brasileiro Online de Mutaçõ (ABraOM): Genomic variants obtained with whole-genome sequencing from SABE, a census-based sample of elderly individuals from São Paulo, Brazil's largest city. Brazilian population is constituted by ~500 years of admixture between Africans, Europeans, and Native Americans. Additionally, the cohort presents ~3% of individuals with non-admixed Japanese ancestry (early 20th century migration). Coverage 38.6. Data can be downloaded from the AbraOM Website. For details see (Naslavsky et al, Nat Comm 2022).
IndiGenomes: Whole genome sequencing of 1,029 healthy Indian individuals under the pilot phase of the "IndiGen" program. Data can be downloaded from the IndiGen Website. For details see (Jain et al, NAR 2020). Only the allele frequency is available from this project. The website also provides SV call and Alu insertion VCFs.
Korean Variant Archive (KOVA): 1,896 whole genome sequencing and 3,409 whole exome sequencing data from healthy individuals of Korean ethnicity. Most of the samples were originated from normal tissue of cancer patients (40.16 %), healthy parents of rare disease patients (28.4 %), or healthy volunteers (31.44 %). Japanese ancestry is broken down in the INFO field. Coverage 100x for WES, 30x for WGS. For details see (Lee et al, Exp Mol Med 2022).
NPM Singapore: 9,770 whole genomes, mostly of Chinese, Indian and Malay ancestry. A minimum allele count cutoff of > 5 was applied. Data is available for download from the CHORUS browser, see "Data access" below. For details see (Wong et al, Nat Genetics 2023). CNV data is also available there.
+ Saudi Genome Program: + Variant frequencies from 302 whole genomes at 30x coverage, on Saudi Genome Program Samples. + The genotyping data and imputations from 3,352 individuals do not seem to be available publicly. + For details see (Malomane et al 2025). +

Display Conventions

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, while heterozygotes will be displayed with both letters.

For NCBI ALFA: This track has no single VCF with INFO fields, but uses multiple subtracks instead, one per ancestry.

Data Access

Most of the data in these tracks are not available for download from UCSC. Data can be browsed on our website. But the data can be downloaded for free from the original projects. Accessing the data usually requires a click-through license on the respectice websites, links are either provided above in the project description or with more details here:

MXB: Allele frequencies by geographical state and ancestry are available via the MexVar platform. Raw genotype data are available under controlled access at the EGA (Study: EGAS00001005797; Dataset: EGAD00010002361). For the VCFs, email andres.moreno@cinvestav.mx.

MCPS: VCFs with summarized allele frequencies are available from the MCPS website.

Regeneron one million exomes: VCFs with summarized allele frequencies are available from the RGC ME website.

TOPMED: VCFs with summarized allele frequencies are available from the TOPMED BRAVO website. They require a login.

GenomeAsia Pilot: VCFs are available from UCSC and also from the GenomeAsia 100K website. No license nor login.

KOVA: TSV data can be requested on the KOVA Downloads website.

Finngen: TSV data can be requested via the form at https://finngen.gitbook.io/documentation/data-download which triggers an email with the download link.

NPM: VCF access can be requested on the Chorus Browser website, which requires an account and data access request.

Methods

MXB: Genotyping was performed with the Illumina Multi-Ethnic Global Array (MEGA, ~1.8M SNPs), optimized for admixed populations and enriched for ancestry-informative and medically relevant variants. Only autosomal, biallelic SNPs passing quality control are included. Samples were selected from 898 recruitment sites, with prioritization of indigenous language speakers. Data processing included GenomeStudio → PLINK conversion, strand alignment, removal of duplicates, update of map positions using dbSNP Build 151 and low-quality variants/individuals, and relatedness filtering.

SGDP: The version used was https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/vcf_variants/, merged with bcftools and lifted to hg38 with CrossMap.

KOVA: V7 of the TSV.gz was obtained from the KOVA staff and converted to VCF. It is not available for download from our site but can be requested from the KOVA website.

Finngen: R12 annotated variants were downloaded from the Google Cloud bucket link received though an email after filling out the form linked from https://finngen.gitbook.io/documentation/data-download and converted to VCF with a custom Python script.

NPM Singapore: Whole Genome Sequencing (WGS) data processing followed GATK4 best practices. GATK4 germline variant analysis workflow written in WDL was adapted to use Nextflow and deployed at the National Supercomputing Centre, Singapore (NSCC). In short, WGS reads were aligned against GRCh38 using the BWA-MEM algorithm and used as input to GATK HaplotypeCaller to produce single sample gVCFs. The gVCF files were joint-called then loaded in Hail, an open-source python-based data analysis library suited to work with population-scale with genomic data collections. Low-quality WGS libraries and low-quality variants were removed. QC-ed variants were functionally annotated using Ensembl Variant Effect Predictor (VEP) (version 95). Functional annotations for variant impacting protein-coding were also complemented with information on the potential alteration to their cognate protein's 3D structure and drug binding ability.

Saudi Genome Program: Data was downloaded +from Figshare, +and converted to VCF. +

Credits

MXB: We thank the Center for Research and Advanced Studies (Cinvestav) of Mexico for generating and providing the frequency data, the National Institute of Medical Sciences and Nutrition (INCMNSZ) for DNA extraction, and the Ministry of Health together with the National Institute of Public Health (INSP) for the design and implementation of the National Health Survey 2000 (ENSA 2000). We also thank the ENSA-Genomics Consortium for their contributions to sample collection and data processing that made possible the construction of the MXB genomic resource.

MCPS: Data produced by Regeneron RGC and collaborators, which are the University of Oxford, Universidad Nacional Autónoma de México (UNAM) and National Institute of Genomic Medicine in Mexico. The Regeneron Genetics Center, University of Oxford, Universidad Nacional Autónoma de México (UNAM), National Institute of Genomic Medicine in Mexico, Abbvie Inc. and AstraZeneca UK Limited (collectively, the "Collaborators") bear no responsibility for the analyses or interpretations of the data presented here. Any opinions, insights, or conclusions presented herein are those of the authors and not of the Collaborators.

Regeneron Million Exomes: The Regeneron Genetics Center, and its collaborators (collectively, the "Collaborators") bear no responsibility for the analyses or interpretations of the data presented here. Any opinions, insights, or conclusions presented herein are those of the authors and not of the Collaborators. This research has been conducted using the UK Biobank Resource under application number 26041.

SGDP: This project was funded by the Simons Foundation. Thanks to David Reich and Swapan Mallick for help with importing the data.

KOVA: Thanks to Insu Jang and the KOVA director for providing variant frequencies in TSV format.

Finngen: We want to acknowledge the participants and investigators of the FinnGen study.

NPM Singapore: Thanks to the NPM Data Access Committee and Eleanor for granting our data request. By browsing the data, you agree to use the data only for academic, non-commercial research to improve human health (biology/disease). We request all data users agree to protect the confidentiality of the data subjects in any research papers or publications that they may prepare, by taking all reasonable care to limit the possibility of identification. In particular, the data users shall not to use, or attempt to use, the data to deliberately compromise or otherwise infringe the confidentiality of information on data subjects and their right to privacy. If you use any of the data obtained from the CHORUS variant browser, we request that you cite the NPM flagship paper (Wong et al, 2023). All data users of the data must take note that the data provider and relevant SG10K_Health cohort owners bear no responsibility for the further analysis or interpretation of the data.

Thanks to Alex Ioannidis, UCSC, and Andreas Lahner, MGZ, for feedback on this track.

References

Barberena-Jonas, C. et al. (2025). MexVar database: Clinical genetic variation beyond the Hispanic label in the Mexican Biobank. Nature Medicine (in press).

Sohail M, Moreno-Estrada A. The Mexican Biobank Project promotes genetic discovery, inclusive science and local capacity building. Dis Model Mech. 2024 Jan 1;17(1). PMID: 38299665; PMC: PMC10855211

Sohail M, Palma-Martínez MJ, Chong AY, Quinto-Corés 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

Ziyatdinov A, Torres J, Alegre-Díaz J, Backman J, Mbatchou J, Turner M, Gaynor SM, Joseph T, Zou Y, Liu D et al. Genotyping, sequencing and analysis of 140,000 adults from Mexico City. Nature. 2023 Oct;622(7984):784-793. PMID: 37821707; PMC: PMC10600010

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

Sun KY, Bai X, Chen S, Bao S, Zhang C, Kapoor M, Backman J, Joseph T, Maxwell E, Mitra G et al. A deep catalogue of protein-coding variation in 983,578 individuals. Nature. 2024 Jul;631(8021):583-592. PMID: 38768635; PMC: PMC11254753

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

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

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. PMID: 33095885; PMC: PMC7778947

Bergström A, McCarthy SA, Hui R, Almarri MA, Ayub Q, Danecek P, Chen Y, Felkel S, Hallast P, Kamm J et al. Insights into human genetic variation and population history from 929 diverse genomes. Science. 2020 Mar 20;367(6484). PMID: 32193295; PMC: PMC7115999

Koenig Z, Yohannes MT, Nkambule LL, Zhao X, Goodrich JK, Kim HA, Wilson MW, Tiao G, Hao SP, Sahakian N et al. A harmonized public resource of deeply sequenced diverse human genomes. Genome Res. 2024 Jun 25;34(5):796-809. PMID: 38749656; PMC: PMC11216312

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

Lee J, Lee J, Jeon S, Lee J, Jang I, Yang JO, Park S, Lee B, Choi J, Choi BO et al. A database of 5305 healthy Korean individuals reveals genetic and clinical implications for an East Asian population. Exp Mol Med. 2022 Nov;54(11):1862-1871. PMID: 36323850; PMC: PMC9628380

Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023 Jan;613(7944):508-518. PMID: 36653562; PMC: PMC9849126

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

+ + +

+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 +