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Enabling efficient analysis of biobank-scale data with genotype representation graphs.

Drew DeHaas1, Ziqing Pan1, Xinzhu Wei2

  • 1Department of Computational Biology, Cornell University, Ithaca, NY, USA.

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|December 5, 2024
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Summary
This summary is machine-generated.

A new data structure, the genotype representation graph (GRG), efficiently encodes large genomic datasets. This approach significantly compresses whole-genome data and speeds up computational analysis, making large-scale genomics more accessible.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Data Structures and Algorithms

Background:

  • Analyzing large-scale genomic datasets presents significant computational challenges due to the size and complexity of genetic data.
  • Existing tabular data structures and file formats are becoming costly and unsustainable for encoding vast amounts of genetic information.
  • Efficient representation and processing of whole-genome polymorphisms across numerous samples are critical for advancing genomic research.

Purpose of the Study:

  • To introduce a novel data structure, the genotype representation graph (GRG), for compact and efficient representation of large genomic datasets.
  • To demonstrate the scalability and performance benefits of GRG for analyzing whole-genome polymorphisms.
  • To reduce the cost and increase the feasibility of large-scale genomic data analysis.

Main Methods:

  • Developed the genotype representation graph (GRG), a fully connected hierarchical data structure.
  • Implemented lossless encoding of phased whole-genome polymorphisms within the GRG structure.
  • Utilized graph-traversal algorithms for efficient data processing and computation on the GRG.

Main Results:

  • GRG achieved significant data compression, reducing 200,000 UK Biobank phased human genomes to 5-26 GB per chromosome.
  • The data structure enables efficient reuse of computed values in random access memory through graph traversal.
  • GRG-based computations, such as allele frequencies and association effects, demonstrated superior speed compared to existing methods.

Conclusions:

  • The genotype representation graph (GRG) offers a scalable and cost-effective solution for managing and analyzing large genomic datasets.
  • GRG facilitates faster and more efficient computational analysis, paving the way for broader accessibility to large-scale genomics.
  • This novel data structure has the potential to revolutionize how large-scale genomic data is stored, processed, and analyzed.