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Compression for population genetic data through finite-state entropy.

Winfield Chen¹, Lloyd T Elliott¹

¹Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, V5A 1S6, Canada.

Journal of Bioinformatics and Computational Biology

|September 30, 2021

Summary

This summary is machine-generated.

We developed a new compression method for population genetic data, significantly improving file size and computation speed for Genome-Wide Association Studies (GWAS). This method enhances efficiency in genetic data analysis.

Keywords:

Statistical genetics big data genome-wide association study genotype compression multi-phenotype analysis

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Population genetic data files are large, posing challenges for storage and analysis.
Current compression methods like Zstd and Zlib offer limited efficiency for genetic datasets.
Genome-Wide Association Studies (GWAS) require efficient processing of large-scale genetic information.

Purpose of the Study:

To enhance the efficiency of population genetic file formats and GWAS computation.
To introduce a novel compression approach leveraging data distribution and conditional exchangeability.
To demonstrate significant improvements in speed and size reduction for genetic data.

Main Methods:

Utilizing the distribution of samples in population-level genetic data.
Identifying conditional exchangeability for data compression.
Applying finite state entropy algorithms as an arithmetic code for compression.
Developing open-source prototype software for multi-phenotype GWAS.

Main Results:

Achieved between 2- and 3-fold speed and size improvements over Zstd and Zlib.
Demonstrated significant space savings in population genetic data compression.
Showcased computation and decompression speeds comparable to state-of-the-art methods.
Validated the effectiveness of finite state entropy for genetic data.

Conclusions:

Finite state entropy algorithms are well-suited for compressing population genetic data.
The proposed method offers substantial efficiency gains for GWAS and genetic data handling.
Open-source software is available for practical application of these advancements.