ArticlebioRxiv : the preprint server for biology2026
General, orders-of-magnitude faster whole-genome analysis with genotype representation graphs.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Whole-genome sequencing (WGS) of biobank-scale cohorts have generated datasets that traditional tabular genotype formats cannot efficiently store or analyze. Genotype Representation Graphs (GRGs) offer a compelling alternative: a biologically-motivated, hierarchical, graph-based representation that compactly and losslessly encodes the genotypes, and that supports computation directly on the graph rather than on a materialized genotype matrix. Here we introduce two advances that together make GRG a practical foundation for biobank-scale population and statistical genetics. First, we present GRG v2, a substantially improved format and construction algorithm that reduces construction time by 10-20×, halves the disk and RAM footprint of the resulting files, and improves load time by more than 20×. Applied to the recently phased UK Biobank WGS dataset (490,541 individuals, 706,556,181 variants), GRG v2 produces files 25 times smaller than
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