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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies
Ling Sing Yung1, Can Yang, Xiang Wan
1Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. timyung@ust.hk
Bioinformatics (Oxford, England)
|March 5, 2011
Summary
Graphic processing units (GPUs) accelerate gene-gene interaction analysis in genome-wide association studies (GWAS). The GBOOST method, utilizing GPUs, achieves a 40-fold speedup, significantly reducing analysis time for large genetic datasets.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Advanced genotyping enables collection of millions of genetic variations.
- Efficient gene-gene interaction analysis is crucial for genome-wide association studies (GWAS).
- Existing methods like Boolean operation-based screening and testing (BOOST) are computationally intensive.
Purpose of the Study:
- To leverage graphic processing units (GPUs) for accelerating gene-gene interaction analysis.
- To implement and evaluate a GPU-accelerated version of the BOOST algorithm.
Main Methods:
- Developed GBOOST, a GPU-based implementation of the BOOST algorithm.
- Utilized Nvidia GeForce GTX 285 display card for analysis.
- Applied GBOOST to Wellcome Trust Case Control Consortium Type 2 Diabetes (WTCCC T2D) genome data.
Main Results:
- GBOOST achieved a 40-fold speedup compared to the original BOOST method.
- GBOOST completed the WTCCC T2D genome data analysis in 1.34 hours.
- Demonstrated the efficiency of GPU computing for large-scale genetic analyses.
Conclusions:
- GPU acceleration significantly enhances the speed of gene-gene interaction analysis in GWAS.
- GBOOST provides a powerful and efficient tool for researchers analyzing large genetic datasets.
- The GBOOST framework offers a practical solution for timely genetic association studies.
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