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SwiftLink: parallel MCMC linkage analysis using multicore CPU and GPU.

Alan Medlar1, Dorota Głowacka, Horia Stanescu

  • 1Division of Medicine, University College London, London WC1E 6BT, UK, Institute of Biotechnology, University of Helsinki, Helsinki 00014, Finland. alan.j.medlar@helsinki.fi

Bioinformatics (Oxford, England)
|December 15, 2012
PubMed
Summary
This summary is machine-generated.

SWIFTLINK accelerates multipoint linkage analysis using parallel processing on CPUs and GPUs. This novel application significantly speeds up genetic disease component elucidation for large pedigrees and numerous markers.

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Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Linkage analysis is crucial for identifying genetic disease components, especially with whole exome sequencing.
  • Traditional multipoint linkage analysis methods struggle with scalability concerning marker number and pedigree size.
  • Current Markov chain Monte Carlo (MCMC) methods are slow to converge and underutilize processing power.

Purpose of the Study:

  • To develop a novel application, SWIFTLINK, for efficient MCMC linkage analysis.
  • To enhance computational performance by leveraging parallel processing across multiple CPU cores and a GPU.
  • To optimize linkage analysis for large pedigrees and complex genetic traits.

Main Methods:

  • Implemented parallelized Gibbs samplers redesigned for multi-core CPUs and GPUs.
  • Developed SWIFTLINK to distribute computational load across heterogeneous hardware.
  • Focused on matching algorithmic characteristics to computer architecture for maximum performance.

Main Results:

  • SWIFTLINK achieved an 8.5× speed-up compared to the single-threaded version.
  • Demonstrated a 109× speed-up over the widely used SIMWALK program.
  • Successfully applied to a real-world dataset for parametric multipoint linkage analysis in a consanguineous pedigree with EAST syndrome.

Conclusions:

  • SWIFTLINK offers a significant performance improvement for MCMC linkage analysis.
  • The parallel architecture approach effectively addresses scalability limitations in genetic analysis.
  • SWIFTLINK provides a powerful tool for researchers studying Mendelian traits and genetic components of diseases.