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High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies. | LitMetric

High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies.

Health Inf Sci Syst

IBM Research - Australia, 204 Lygon Street, Carlton, VIC, 3053, Australia ; Department of Computing and Information Systems, University of Melbourne, Parkville, VIC, 3010, Australia.

Published: April 2015

AI Article Synopsis

  • Genome-wide association studies (GWAS) aim to find single nucleotide polymorphisms (SNPs) linked to diseases, but traditional analysis methods often overlook significant interactions unless multivariate techniques are used.
  • * The authors developed a computational framework utilizing supercomputers to tackle the complexity of multivariate SNP analysis, achieving results significantly faster than previous CPU and GPU methods.
  • * Their analysis on a massive dataset of 1.1 million SNPs could be completed in under 3 months on a leading supercomputer, suggesting a promising future for high-scale interaction studies in GWAS.

Article Abstract

Genome-wide association studies (GWAS) are a common approach for systematic discovery of single nucleotide polymorphisms (SNPs) which are associated with a given disease. Univariate analysis approaches commonly employed may miss important SNP associations that only appear through multivariate analysis in complex diseases. However, multivariate SNP analysis is currently limited by its inherent computational complexity. In this work, we present a computational framework that harnesses supercomputers. Based on our results, we estimate a three-way interaction analysis on 1.1 million SNP GWAS data requiring over 5.8 years on the full "Avoca" IBM Blue Gene/Q installation at the Victorian Life Sciences Computation Initiative. This is hundreds of times faster than estimates for other CPU based methods and four times faster than runtimes estimated for GPU methods, indicating how the improvement in the level of hardware applied to interaction analysis may alter the types of analysis that can be performed. Furthermore, the same analysis would take under 3 months on the currently largest IBM Blue Gene/Q supercomputer "Sequoia" at the Lawrence Livermore National Laboratory assuming linear scaling is maintained as our results suggest. Given that the implementation used in this study can be further optimised, this runtime means it is becoming feasible to carry out exhaustive analysis of higher order interaction studies on large modern GWAS.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383059PMC
http://dx.doi.org/10.1186/2047-2501-3-S1-S3DOI Listing

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