ArticleNature computational science2025
Enabling efficient analysis of biobank-scale data with genotype representation graphs.
Article in Nature computational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Genetic prediction with ARG-powered linear algebra.Genetics · 2026Article
- Highly efficient genotype compression leveraging genealogical relatedness.bioRxiv : the preprint server for biology · 2026Article
- General, orders-of-magnitude faster whole-genome analysis with genotype representation graphs.bioRxiv : the preprint server for biology · 2026Article
- Leveraging ancestral recombination graphs for scalable mixed-model analysis of complex traits.Cell genomics · 2026Article
- On ARGs, pedigrees, and genetic relatedness matrices.Genetics · 2026Article
- Fast phenotype simulation for genotype representation graphs.Bioinformatics advances · 2026Article
- Power and Limitations of Inferring Genetic Ancestry.Annals of human genetics · 2025Review
- On ARGs, pedigrees, and genetic relatedness matrices.bioRxiv : the preprint server for biology · 2025Article
- Analysis-ready VCF at Biobank scale using Zarr.bioRxiv : the preprint server for biology · 2025Article
- Analysis-ready VCF at Biobank scale using Zarr.GigaScience · 2025Article
- A vision of how low-coverage sequence data should contribute to genetic evaluation in the future.Journal of animal science · 2025Article
- IGD: a simple, efficient genotype data format.Bioinformatics advances · 2025Article
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3 authors.
Funding
Abstract
Computational analysis of a large number of genomes requires a data structure that can represent the dataset compactly while also enabling efficient operations on variants and samples. However, encoding genetic data in existing tabular data structures and file formats has become costly and unsustainable. Here we introduce the genotype representation graph (GRG), a fully connected hierarchical data structure that losslessly encodes phased whole-genome polymorphisms. Exploiting variant-sharing across samples enables GRG to compress 200,000 UK Biobank phased human genomes to 5-26 gigabytes per chromosome, also enabling graph-traversal algorithms to reuse computed values in random access memory. Constructing and processing GRG files scales to a million whole genomes. Using allele frequencies and association effects as examples, we show that computation on GRG via graph traversal runs the fastest among all tested alternatives. GRG-based algorithms have the potential to increase the scalability and reduce the cost of analyzing large genomic datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.