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Genomic data storage is inefficient. The new Genotype Representation Graph (GRG) compactly stores phased whole-genome polymorphisms, enabling faster analysis of large genetic datasets.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Structures

Background:

  • Large-scale genetic polymorphism data storage is computationally burdensome.
  • Current tabular formats (e.g., VCF) are inefficient for massive datasets like the UK Biobank (350TB).

Purpose of the Study:

  • Introduce a compact and efficient data structure for genomic data.
  • Enable faster computational analysis of large-scale genetic variation.

Main Methods:

  • Developed the Genotype Representation Graph (GRG), a hierarchical graph structure.
  • GRG exploits variant-sharing across samples for lossless compression.
  • Created command-line tools and libraries (C++, Python) for GRG construction and processing.

Main Results:

  • GRG compresses biobank-scale human data to fit in server RAM (5-26GB/chromosome).
  • 200,000 UK Biobank genomes compressed to 160GB in GRG format (13x smaller than VCF).
  • GRG enables significantly faster computation of variant summaries (e.g., allele frequency, association effects) via graph traversal.

Conclusions:

  • GRG offers a scalable solution for analyzing massive genomic datasets.
  • GRG facilitates efficient repeated calculations and interactive data analysis.
  • GRG-based algorithms are expected to lower the cost and improve scalability of genomic computations.