| Title: | Enabling efficient analysis of biobank-scale data with genotype representation graphs |
| Journal: | Nature Computational Science |
| Published: | 5 Dec 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39639156/ |
| DOI: | https://doi.org/10.1038/s43588-024-00739-9 |
Publication 14046
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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.</p>
3 Authors
- Drew DeHaas
- Ziqing Pan
- Xinzhu Wei
1 Application
| Application ID | Title |
|---|---|
| 97908 | Scalable and accurate methods for understanding human complex traits |
Enabling scientific discoveries that improve human health