Journal article
High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies
Benjamin Goudey, Mani Abedini, John L Hopper, Michael Inouye, Enes Makalic, Daniel F Schmidt, John Wagner, Zeyu Zhou, Justin Zobel, Matthias Reumann
HEALTH INFORMATION SCIENCE AND SYSTEMS | SPRINGER | Published : 2015
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 Scie..
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Awarded by NHMRC
Awarded by Victorian Life Sciences Computation Initiative (VLSCI)
Funding Acknowledgements
This research was partially funded by NHMRC grant 1033452 and was supported by a Victorian Life Sciences Computation Initiative (VLSCI) grant number 0126 on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government, Australia. This article has been published as part of Health Information Science and Systems Volume 3 Supplement 1, 2015: Proceedings of the Health Informatics Society of Australia Big Data Conference (HISA 2013). The full contents of the supplement are available online at http://www.hissjournal.com/supplements/3/S1/