title: High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies.
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.
authors: IBM Research - Australia, 204 Lygon Street, Carlton, VIC, 3053, Australia ; Department of Computing and Information Systems, University of Melbourne, Parkville, VIC, 3010, Australia.
authors: Benjamin
authors: B
authors: Goudey
authors: IBM Research - Australia, 204 Lygon Street, Carlton, VIC, 3053, Australia.
authors: Mani
authors: M
authors: Abedini
authors: Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, 3010, Australia.
authors: John L
authors: JL
authors: Hopper
authors: Department of Pathology and Department Microbiology and Immunology, University of Melbourne, Parkville, VIC, 3010, Australia.
authors: Michael
authors: M
authors: Inouye
authors: Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, 3010, Australia.
authors: Enes
authors: E
authors: Makalic
authors: Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, 3010, Australia.
authors: Daniel F
authors: DF
authors: Schmidt
authors: IBM Research Collaboratory for Life Sciences-Melbourne, 187 Grattan Street, Carlton, VIC 3010, Australia.
authors: John
authors: J
authors: Wagner
authors: IBM Research - Australia, 204 Lygon Street, Carlton, VIC, 3053, Australia ; Department of Mathematics and Statistics, University of Melbourne, Parkville, VIC, 3010, Australia.
authors: Zeyu
authors: Z
authors: Zhou
authors: Department of Computing and Information Systems, University of Melbourne, Parkville, VIC, 3010, Australia.
authors: Justin
authors: J
authors: Zobel
authors: IBM Research - Australia, 204 Lygon Street, Carlton, VIC, 3053, Australia ; Department of Computing and Information Systems, University of Melbourne, Parkville, VIC, 3010, Australia.
authors: Matthias
authors: M
authors: Reumann