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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Infinium Assay for Large-scale SNP Genotyping Applications
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PBOOST: a GPU-based tool for parallel permutation tests in genome-wide association studies.

Guangyuan Yang1, Wei Jiang1, Qiang Yang1

  • 1Laboratory of Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering and Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.

Bioinformatics (Oxford, England)
|December 24, 2014
PubMed
Summary
This summary is machine-generated.

A new GPU-accelerated tool, PBOOST, enables fast and reliable permutation testing for genetic association studies. It accurately estimates P-values, even when standard assumptions are violated, significantly outperforming CPU-based methods.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Testing for gene interactions is crucial in genetic association studies.
  • Existing methods like likelihood ratio tests (LRTs) have limitations when contingency table assumptions are violated.
  • Accurate P-value estimation for extremely small P-values requires a high number of permutations.

Purpose of the Study:

  • To develop a fast and reliable permutation testing tool for genetic association studies.
  • To address the limitations of LRTs when contingency table assumptions are not met.
  • To enable accurate P-value estimation for detecting significant genetic associations.

Main Methods:

  • Development of PBOOST, a GPU-based permutation tool.
  • Utilized simulation data to evaluate P-value accuracy compared to LRTs.
  • Benchmarked PBOOST's speed against CPU-based methods using real genome data.

Main Results:

  • PBOOST provides highly reliable P-value estimations.
  • LRT P-values show significant relative errors (>100%) when contingency table assumptions are violated.
  • PBOOST is substantially faster than CPU methods, completing 10^7 permutations in 1 minute versus 60 minutes.
  • PBOOST achieved 10^12 permutations for 280 SNP pairs in 1 week using a GPU cluster.

Conclusions:

  • PBOOST is an efficient tool for large-scale genetic association studies.
  • The tool ensures the reliability of P-values from LRTs, especially in challenging data scenarios.
  • PBOOST facilitates robust detection of significant single nucleotide polymorphism (SNP) pair associations.