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STABIX: summary-statistic-based GWAS indexing and compression.

Kristen Schneider1,2, Simon Walker1, Chris Gignoux3,4

  • 1Department of Computer Science, University of Colorado, Boulder, Boulder, 80309 CO, United States.

Bioinformatics (Oxford, England)
|May 2, 2025
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Summary
This summary is machine-generated.

STABIX offers improved compression and faster querying for large Genome-Wide Association Studies (GWAS) data. This new tool enables efficient summary-statistic-based queries, outperforming standard methods for biobanks.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-Wide Association Studies (GWAS) generate large files, hindering efficient data management, storage, and sharing, particularly for large biobanks like UK Biobank publishing thousands of traits.
  • Existing compression (bgzip) and query (Tabix) tools facilitate genomic position-based queries but lack functionality for efficient summary-statistic-based retrieval, such as finding variants within a specific p-value range, which necessitates full file decompression and scanning.

Purpose of the Study:

  • To introduce STABIX, a novel tool designed to address the limitations of current GWAS data management methods.
  • To enable efficient summary-statistic-based queries on GWAS data, complementing existing genomic position-based queries.
  • To improve both compression ratios and decompression speeds compared to standard bgzip and Tabix tools.

Main Methods:

  • Development of STABIX, a new software tool incorporating summary-statistic-based query capabilities.
  • Comparative analysis of STABIX against the standard bgzip and Tabix tools using ten GWAS files from PanUKBB.
  • Evaluation of compression efficiency (file size) and query performance (decompression speed per gene).

Main Results:

  • STABIX achieved superior compression, producing files and indices on average 1.2 times smaller than bgzip and tbi.
  • STABIX demonstrated significantly faster per-gene decompression, averaging 7x speed improvement over Tabix.
  • STABIX provided faster per-gene decompression for over 99% of nearly 20,000 genes analyzed.

Conclusions:

  • STABIX offers a substantial improvement in managing and querying large GWAS datasets.
  • The tool enhances data accessibility and analytical efficiency by enabling rapid retrieval based on summary statistics.
  • STABIX represents a valuable advancement for researchers working with large-scale genomic association data, particularly within biobanking contexts.