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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Benjamin Goudey, Mani Abedini, John L Hopper, Michael Inouye, Enes Makalic, Daniel F Schmidt, John Wagner, Zeyu Zhou, Justin Zobel, Matthias Reumann

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. More... »

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Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/2047-2501-3-s1-s3

DOI

http://dx.doi.org/10.1186/2047-2501-3-s1-s3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020029142

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25870758


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