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. 2024 Sep 3;30(12):3578–3589. doi: 10.1038/s41591-024-03239-5

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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Fig. 1 — a, Consortium network. Network is made up of biomedical institutions. All sites share data on a decentralized blockchain platform maintained by all nodes. New joining institutions are verified via cryptographic tokens. Once joined, they can upload new data and access existing data. b, High-level indexing. Indexing of data into three levels: EHR, Genetic and Audit. EHR and Genetic levels are further divided into Domain and Person views and Variant, Person, Gene, MAF counter and Analysis views, respectively. Each view is made up of multiple streams with streams organized by property. c, Indexing of Domain view is by OMOP clinical table. Within each clinical table, we index streams using the OMOP vocabulary hierarchy. d, Indexing of Person views is by person ID with all data for a patient under one stream. This is done for both clinical and genetic data. e, Indexing of Variant view is by chromosome and genomic coordinates. f, MAF counter is organized by MAF range. MAF calculation occurs at every insertion. g, Analysis view records metadata to harmonize sequencing data, assess relatedness among samples and conduct population stratification.