Illumina DRAGEN Resources for PopGen:https://developer.illumina.com/dragen/dragen-popgen
Accurate and efficient calling of small and large variants from popgen datasets using the DRAGEN Bio-IT Platform
DRAGEN reanalysis of the 1000 Genomes Dataset now available on the Registry of Open Data
UKBB Command Line for DRAGEN
dragen -r <hg38-ref-dir> \
--bam-input <input BAM file> \
--output-directory <out-dir> \
--output-file-prefix <prefix> \
--enable-variant-caller=true \
--vc-emit-ref-confidence=GVCF \
--vc-enable-vcf-output=true \
--enable-duplicate-marking=true \
--enable-map-align=true \
--enable-map-align-output=true \
--output-format=CRAM \
--vc-hard-filter 'DRAGENHardQUAL:all:QUAL<5.0;LowDepth:all:DP<=1' \
--vc-frd-max-effective-depth=40 \
--qc-cross-cont-vcf <path-to>/SNP_NCBI_GRCh38.vcf \
--qc-coverage-region-1 <path-to>/wgs_coverage_regions.hg38_minus_N.interval_list.bed \
--qc-coverage-reports-1 cov_report \
--qc-coverage-region-2 <path-to>/acmg59_allofus_19dec2019.GRC38.wGenes.NEW.bed \
--qc-coverage-reports-2 cov_report \
--qc-coverage-ignore-overlaps=true \
--qc-coverage-count-soft-clipped-bases=true \
--read-trimmers polyg \
--soft-read-trimmers none \
--intermediate-results-dir=/ephemeral/staging/tmp/ \
--repeat-genotype-enable=true \
--enable-cyp2d6=true \
--enable-sv=true \
--enable-cnv=true \
--cnv-enable-self-normalization=true \
--vc-enable-joint-detection=true
PopGen data processing and analysis workflows using the DRAGEN Platform (left) and GATK best practices (right) workflows
Global genomic biobanks and studies
Carress H, Lawson D J, Elhaik E. Population genetic considerations for using biobanks as international resources in the pandemic era and beyond[J]. BMC genomics, 2021, 22: 1-19.
Tanjo T, Kawai Y, Tokunaga K, et al. Practical guide for managing large-scale human genome data in research[J]. Journal of Human Genetics, 2021, 66(1): 39-52.
Pre-loaded software in the UKB-RAP: https://community.ukbiobank.ac.uk/hc/en-gb/articles/19459498669981-Pre-loaded-software-in-the-UKB-RAP
UK Biobank Allele Frequency Browser：https://afb.ukbiobank.ac.uk/
UK Biobank Whole-Genome Sequencing Consortium. Whole-genome sequencing of 490,640 UK Biobank participants[J]. Nature, 2025, 645(8081): 692.
Halldorsson B V, Eggertsson H P, Moore K H S, et al. The sequences of 150,119 genomes in the UK Biobank[J]. Nature, 2022, 607(7920): 732-740.
WGS-based genome study of patient with rare disease and their families and cancer patients in England.
Cancer_rare-disease_Covid-19
Estonian Biobank
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Milani L, Alver M, Laur S, et al. The Estonian Biobank’s journey from biobanking to personalized medicine[J]. Nature communications, 2025, 16(1): 3270.
All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program[J]. Nature.
Mahmoud M, Huang Y, Garimella K, et al. Utility of long-read sequencing for All of Us[J]. bioRxiv, 2023: 2023.01. 23.525236.
Lennon N J, Kottyan L C, Kachulis C, et al. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations[J]. Nature Medicine, 2024: 1-8.
Ying D, Cheung C L, Lam W K J, et al. Population-scale genomic medicine with the Hong Kong Genome Project[J]. Nature Medicine, 2026: 1-11.
SG10K:Wu D, Dou J, Chai X, et al. Large-scale whole-genome sequencing of three diverse Asian populations in Singapore[J]. Cell, 2019, 179(3): 736-749. e15.
SG10K_Health:Wong E, Bertin N, Hebrard M, et al. The Singapore National Precision Medicine Strategy[J]. Nature Genetics, 2023, 55(2): 178-186.
SG10K_med:Chan S H, Bylstra Y, Teo J X, et al. Analysis of clinically relevant variants from ancestrally diverse Asian genomes[J]. Nature Communications, 2022, 13(1): 6694.
Precision Medicine Research Highlights:https://www.npm.sg/research/research-highlights/
Call for Proposals – Driver Projects for the PRECISE-SG100K Dataset:https://www.npm.sg/research/call-for-proposals/
“Call
for Proposals” meeting:https://file.for.sg/sg100k-cfp.pdf
ChinaMAP:Cao Y, Li L, Xu M, et al. The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals[J]. Cell research, 2020, 30(9): 717-731.
Westlake BioBank:Cong P K, Bai W Y, Li J C, et al. Genomic analyses of 10,376 individuals in the Westlake BioBank for Chinese (WBBC) pilot project[J]. Nature Communications, 2022, 13(1): 2939.
Tian Z, Chen F, Wang J, et al. CAS Array: design and assessment of a genotyping array for Chinese biobanking[J]. Precision Clinical Medicine, 2023, 6(1): pbad002.
Zhang P, Luo H, Li Y, et al. NyuWa Genome resource: a deep whole-genome sequencing-based variation profile and reference panel for the Chinese population[J]. Cell reports, 2021, 37(7).
Jain A, Bhoyar R C, Pandhare K, et al. IndiGenomes: a comprehensive resource of genetic variants from over 1000 Indian genomes[J]. Nucleic Acids Research, 2021, 49(D1): D1225-D1232.
Mbarek H, Devadoss Gandhi G, Selvaraj S, et al. Qatar genome: Insights on genomics from the Middle East[J]. Human mutation, 2022, 43(4): 499-510.
Qatar Genome Program is about to enter a new era thanks to Illumina #DRAGEN.
Hsu J S, Wu D C, Shih S H, et al. Complete genomic profiles of 1,496 Taiwanese reveal curated medical insights[J]. Journal of Advanced Research, 2023.
Determining the human genetic variation by means of whole-genome sequencing in population scale.
Byrska-Bishop M, Evani U S, Zhao X, et al. High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios[J]. Cell, 2022, 185(18): 3426-3440. e19.
1KG Project reference panel:http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/
WGS-based genome study of people in South and East Asia.
The GenomeAsia 100K Project enables genetic discoveries across Asia[J]. Nature, 2019, 576(7785): 106-111.
Ziyatdinov A, Torres J, Alegre-Díaz J, et al. Genotyping, sequencing and analysis of 140,000 adults from Mexico City[J]. Nature, 2023, 622(7984): 784-793.
Nationwide biobank and genome cohort study in Finland.
Kurki M I, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population[J]. Nature, 2023, 613(7944): 508-518.
Simons genome diversity project Mallick S, Li H, Lipson M, et al. The Simons genome diversity project: 300 genomes from 142 diverse populations[J]. Nature, 2016, 538(7624): 201-206.
Choi J, Kim S, Kim J, et al. A whole-genome reference panel of 14,393 individuals for East Asian populations accelerates discovery of rare functional variants[J]. Science Advances, 2023, 9(32): eadg6319.
Zhou W, Kanai M, Wu K H H, et al. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease[J]. Cell Genomics, 2022, 2(10).
Lessons from national biobank projects
Lee H, Kim W, Kwon N, et al. Lessons from national biobank projects utilizing whole-genome sequencing for population-scale genomics[J]. Genomics & Informatics, 2025, 23(1): 8.
Zou X Z, Hu F, Lou H, et al. Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes[J]. Nature, 2026: 1-11.
Wang H, Dombroski B A, Cheng P L, et al. Structural variation detection and association analysis of whole‐genome‐sequence data from 16,543 Alzheimer's disease sequencing project subjects[J]. Alzheimer's & Dementia, 2025, 21(6): e70277.
Lee W P, Tucci A A, Conery M, et al. Copy number variation identification on 3,800 Alzheimer’s disease whole genome sequencing data from the Alzheimer’s disease sequencing project[J]. Frontiers in genetics, 2021, 12: 752390.
VCF to aGDS (vcf2agds): https://github.com/drarwood/vcf2agds_overview
Li X, Wood A R, Yuan Y, et al. Streamlining large-scale genomic data management: Insights from the UK Biobank whole-genome sequencing data[J]. Cell Genomics, 2025, 5(12).
Hail:two implementations of SVCR: SVCR-VCF, which encodes SVCR in VCF format, and VDS, which uses Hail’s native format. https://github.com/hail-is/hail/
Scaling of dataset size of sequenced whole genomes represented in Hail LZ4-compressed VDS, gzip-compressed SVCR-VCF and gzip-compressed PVCF. In (a) total size in Gibibytes is plotted on the y axis, and in (b), plotting the size per sample (total size divided by number of samples) provides a clear view of the super-linear scaling of PVCF.
Poterba T, Vittal C, King D, et al. The scalable variant call representation: enabling genetic analysis beyond one million genomes[J]. Bioinformatics, 2025, 41(1): btae746.
Al Ali A, Kandavel P K, Al Mabrazi H, et al. CRAM compression: practical across-technologies considerations for large-scale sequencing projects[J]. bioRxiv, 2022: 2022.12. 21.521516.
Jurgens S J, Wang X, Choi S H, et al. Rare coding variant analysis for human diseases across biobanks and ancestries[J]. Nature Genetics, 2024, 56(9): 1811-1820.
Ribeiro D M, Hofmeister R J, Rubinacci S, et al. Noncoding rare variant associations with blood traits in 166,740 UK Biobank genomes[J]. Nature Genetics, 2025, 57(9): 2146-2155.
