{"entity": "researcher", "timestamp": "2026-09-23T15:44:39.342Z", "family": "Karczewski", "given": "Konrad J", "initials": "KJ", "orcid": "0000-0003-2878-4671", "affiliations": ["Stanley Center for Psychiatric Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.", "Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/e7a94f87fd1940f08ac575109197a665.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/e7a94f87fd1940f08ac575109197a665"}}, "publications": [{"entity": "publication", "iuid": "4bd0a58c3c3e4866a4d32967d240db09", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/4bd0a58c3c3e4866a4d32967d240db09.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/4bd0a58c3c3e4866a4d32967d240db09"}}, "title": "Analysis-ready VCF at Biobank scale using Zarr.", "authors": [{"family": "Czech", "given": "Eric", "initials": "E", "orcid": "0000-0002-4254-4255", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/516d032de5014798a465798429c1e19a.json"}}, {"family": "Millar", "given": "Timothy R", "initials": "TR", "orcid": "0000-0002-5142-8811", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9e7e0935207345baa6d548405afaa749.json"}}, {"family": "Tyler", "given": "Will", "initials": "W"}, {"family": "White", "given": "Tom", "initials": "T"}, {"family": "Elsworth", "given": "Benjamin", "initials": "B", "orcid": "0000-0001-7328-4233", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d86620ba498a491ebeb85dbadcbb6412.json"}}, {"family": "Guez", "given": "J\u00e9r\u00e9my", "initials": "J", "orcid": "0009-0007-6406-5187", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b761bc4e249a4f71b544370cc7f0ddbf.json"}}, {"family": "Hancox", "given": "Jonny", "initials": "J", "orcid": "0009-0003-5799-5299", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/50b097e242d447b59adf98549819f720.json"}}, {"family": "Jeffery", "given": "Ben", "initials": "B", "orcid": "0000-0002-1982-6801", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a2715e3f36b14f4f8be8d4c6d6782f20.json"}}, {"family": "Karczewski", "given": "Konrad J", "initials": "KJ", "orcid": "0000-0003-2878-4671", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e7a94f87fd1940f08ac575109197a665.json"}}, {"family": "Miles", "given": "Alistair", "initials": "A", "orcid": "0000-0001-9018-4680", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e17e3a81512140fa9fec02294ba2727b.json"}}, {"family": "Tallman", "given": "Sam", "initials": "S", "orcid": "0000-0001-7183-6276", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5fba12d6c38e40f2b5e8704bcc3123d1.json"}}, {"family": "Unneberg", "given": "Per", "initials": "P", "orcid": "0000-0001-5735-3315", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/47dae455224d4af4a35865907088f0b1.json"}}, {"family": "Wojdyla", "given": "Rafal", "initials": "R", "orcid": "0009-0005-0735-7090", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f37a6ca6cb7c4d35bddc612c0becb440.json"}}, {"family": "Zabad", "given": "Shadi", "initials": "S", "orcid": "0000-0002-8003-9284", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5ff8167ae75471fa0d087754cb0a1ba.json"}}, {"family": "Hammerbacher", "given": "Jeff", "initials": "J", "orcid": "0000-0001-6596-8563", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d940f250429e4fddb97df0e1ce65fd5d.json"}}, {"family": "Kelleher", "given": "Jerome", "initials": "J", "orcid": "0000-0002-7894-5253", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/27cc071d41f24cdf8585b3ab25b4f346.json"}}], "type": "journal article", "published": "2025-02-06", "journal": {"title": "bioRxiv", "issn": "2692-8205", "issn-l": null}, "abstract": "Variant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasises efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. Biobank scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed.\n\nZarr is a format for storing multi-dimensional data that is widely used across the sciences, and is ideally suited to massively parallel processing. We present the VCF Zarr specification, an encoding of the VCF data model using Zarr, along with fundamental software infrastructure for efficient and reliable conversion at scale. We show how this format is far more efficient than standard VCF based approaches, and competitive with specialised methods for storing genotype data in terms of compression ratios and single-threaded calculation performance. We present case studies on subsets of three large human datasets (Genomics England: n=78,195; Our Future Health: n=651,050; All of Us: n=245,394) along with whole genome datasets for Norway Spruce (n=1,063) and SARS-CoV-2 (n=4,484,157). We demonstrate the potential for VCF Zarr to enable a new generation of high-performance and cost-effective applications via illustrative examples using cloud computing and GPUs.\n\nLarge row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely-used, open-source technologies has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores, while maintaining compatibility with existing file-oriented workflows.", "doi": "10.1101/2024.06.11.598241", "pmid": "38915693", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11195102"}, {"db": "pii", "key": "2024.06.11.598241"}], "notes": [], "created": "2026-09-23T14:37:10.132Z", "modified": "2026-09-23T14:37:10.475Z"}, {"entity": "publication", "iuid": "b8e75853cb4b464c88e5709e948b1646", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b8e75853cb4b464c88e5709e948b1646.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b8e75853cb4b464c88e5709e948b1646"}}, "title": "Analysis-ready VCF at Biobank scale using Zarr.", "authors": [{"family": "Czech", "given": "Eric", "initials": "E", "orcid": "0000-0002-4254-4255", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/516d032de5014798a465798429c1e19a.json"}}, {"family": "Tyler", "given": "Will", "initials": "W"}, {"family": "White", "given": "Tom", "initials": "T"}, {"family": "Jeffery", "given": "Ben", "initials": "B", "orcid": "0000-0002-1982-6801", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a2715e3f36b14f4f8be8d4c6d6782f20.json"}}, {"family": "Millar", "given": "Timothy R", "initials": "TR", "orcid": "0000-0002-5142-8811", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9e7e0935207345baa6d548405afaa749.json"}}, {"family": "Elsworth", "given": "Benjamin", "initials": "B", "orcid": "0000-0001-7328-4233", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d86620ba498a491ebeb85dbadcbb6412.json"}}, {"family": "Guez", "given": "J\u00e9r\u00e9my", "initials": "J", "orcid": "0009-0007-6406-5187", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b761bc4e249a4f71b544370cc7f0ddbf.json"}}, {"family": "Hancox", "given": "Jonny", "initials": "J", "orcid": "0009-0003-5799-5299", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/50b097e242d447b59adf98549819f720.json"}}, {"family": "Karczewski", "given": "Konrad J", "initials": "KJ", "orcid": "0000-0003-2878-4671", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e7a94f87fd1940f08ac575109197a665.json"}}, {"family": "Miles", "given": "Alistair", "initials": "A", "orcid": "0000-0001-9018-4680", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e17e3a81512140fa9fec02294ba2727b.json"}}, {"family": "Tallman", "given": "Sam", "initials": "S", "orcid": "0000-0001-7183-6276", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5fba12d6c38e40f2b5e8704bcc3123d1.json"}}, {"family": "Unneberg", "given": "Per", "initials": "P", "orcid": "0000-0001-5735-3315", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/47dae455224d4af4a35865907088f0b1.json"}}, {"family": "Wojdyla", "given": "Rafal", "initials": "R", "orcid": "0009-0005-0735-7090", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f37a6ca6cb7c4d35bddc612c0becb440.json"}}, {"family": "Zabad", "given": "Shadi", "initials": "S", "orcid": "0000-0002-8003-9284", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5ff8167ae75471fa0d087754cb0a1ba.json"}}, {"family": "Hammerbacher", "given": "Jeff", "initials": "J", "orcid": "0000-0001-6596-8563", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d940f250429e4fddb97df0e1ce65fd5d.json"}}, {"family": "Kelleher", "given": "Jerome", "initials": "J", "orcid": "0000-0002-7894-5253", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/27cc071d41f24cdf8585b3ab25b4f346.json"}}], "type": "journal article", "published": "2025-01-06", "journal": {"title": "Gigascience", "issn": "2047-217X", "volume": "14", "issn-l": "2047-217X"}, "abstract": "Variant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasizes efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. The Biobank-scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed.\n\nZarr is a format for storing multidimensional data that is widely used across the sciences, and is ideally suited to massively parallel processing. We present the VCF Zarr specification, an encoding of the VCF data model using Zarr, along with fundamental software infrastructure for efficient and reliable conversion at scale. We show how this format is far more efficient than standard VCF-based approaches, and competitive with specialized methods for storing genotype data in terms of compression ratios and single-threaded calculation performance. We present case studies on subsets of 3 large human datasets (Genomics England: $n$=78,195; Our Future Health: $n$=651,050; All of Us: $n$=245,394) along with whole genome datasets for Norway Spruce ($n$=1,063) and SARS-CoV-2 ($n$=4,484,157). We demonstrate the potential for VCF Zarr to enable a new generation of high-performance and cost-effective applications via illustrative examples using cloud computing and GPUs.\n\nLarge row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely used, open-source technologies, has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores, while maintaining compatibility with existing file-oriented workflows.", "doi": "10.1093/gigascience/giaf049", "pmid": "40451243", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12127038"}, {"db": "pii", "key": "8154315"}], "notes": [], "created": "2026-09-23T11:35:21.666Z", "modified": "2026-09-23T11:35:22.228Z"}, {"entity": "publication", "iuid": "1b379affbc0d40389086f03a5b7bb3d3", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1b379affbc0d40389086f03a5b7bb3d3.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1b379affbc0d40389086f03a5b7bb3d3"}}, "title": "Exome-wide association study to identify rare variants influencing COVID-19 outcomes: 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{"family": "Miyano", "given": "Satoru", "initials": "S", "orcid": "0000-0002-1753-6616", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b26047b7517441ad91bb2934cf66a337.json"}}, {"family": "Mangul", "given": "Serghei", "initials": "S", "orcid": "0000-0003-4770-3443", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8e87a9f05e2d42779ce7a7357b8d6b62.json"}}, {"family": "Abedalthagafi", "given": "Malak S", "initials": "MS"}, {"family": "Zeberg", "given": "Hugo", "initials": "H", "orcid": "0000-0001-7118-1249", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1c306297a4a0490c84a1d748b9f302a2.json"}}, {"family": "Grzymski", "given": "Joseph J", "initials": "JJ", "orcid": "0000-0003-2646-8958", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ff7de90ac4c14fbe899846adec65fb96.json"}}, {"family": "Washington", "given": "Nicole L", "initials": "NL"}, {"family": "Ossowski", "given": "Stephan", "initials": "S", "orcid": "0000-0002-7416-9568", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2d59334f547645f0afc211c8f63b8936.json"}}, {"family": "Ludwig", "given": "Kerstin U", "initials": "KU", "orcid": "0000-0002-8541-2519", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8e34872452274bc583bf0ee7af758e07.json"}}, {"family": "Schulte", "given": "Eva C", "initials": "EC", "orcid": "0000-0003-3105-5672", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/95d6999d62034b6fbc2c46d997962030.json"}}, {"family": "Riess", "given": "Olaf", "initials": "O"}, {"family": "Moniuszko", "given": "Marcin", "initials": "M"}, {"family": "Kwasniewski", "given": "Miroslaw", "initials": "M"}, {"family": "Mbarek", "given": "Hamdi", "initials": "H"}, {"family": "Ismail", "given": "Said I", "initials": "SI"}, {"family": "Verma", "given": "Anurag", "initials": "A", "orcid": "0000-0002-5063-9107", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9988534363024616bc739738f1c9a854.json"}}, {"family": "Goldstein", "given": "David B", "initials": "DB", "orcid": "0000-0001-7627-0259", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f27e71f7fef9461ebf98935546a4cde7.json"}}, {"family": "Kiryluk", "given": "Krzysztof", "initials": "K", "orcid": "0000-0002-5047-6715", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2f5e2ff65a7a425a91eb7b03ce370d9a.json"}}, {"family": "Renieri", "given": "Alessandra", "initials": "A", "orcid": "0000-0002-0846-9220", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2113adef51ff4b7b9cbbc56f8d78e95f.json"}}, {"family": "Ferreira", "given": "Manuel A R", "initials": "MAR"}, {"family": "Richards", "given": "J Brent", "initials": "JB", "orcid": "0000-0002-3746-9086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ed87dd6607bd4dcdbc150d5432ebfb05.json"}}], "type": "journal article", "published": "2022-11-00", "journal": {"title": "PLoS Genet", "issn": "1553-7404", "volume": "18", "issue": "11", "pages": "e1010367", "issn-l": "1553-7390"}, "abstract": "Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.", "doi": "10.1371/journal.pgen.1010367", "pmid": "36327219", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9632827"}, {"db": "pii", "key": "PGENETICS-D-22-00434"}], "notes": [], "created": "2026-09-23T08:37:18.986Z", "modified": "2026-09-23T08:37:22.559Z"}]}