Supercomputing enabling exhaustive statistical analysis of genome wide association study data: Preliminary results.
Most published GWAS do not examine SNP interactions due to the high computational complexity of computing p-values for the interaction terms. Our aim is to utilize supercomputing resources to apply complex statistical techniques to the world's accumulating GWAS, epidemiology, survival and pathology data to uncover more information about genetic and environmental risk, biology and aetiology. We performed the Bayesian Posterior Probability test on a pseudo data set with 500,000 single nucleotide polymorphism and 100 samples as proof of principle. We carried out strong scaling simulations on 2 to 4,096 processing cores with factor 2 increments in partition size. On two processing cores, the run time is 317h, i.e. almost two weeks, compared to less than 10 minutes on 4,096 processing cores. The speedup factor is 2,020 that is very close to the theoretical value of 2,048. This work demonstrates the feasibility of performing exhaustive higher order analysis of GWAS studies using independence testing for contingency tables. We are now in a position to employ supercomputers with hundreds of thousands of threads for higher order analysis of GWAS data using complex statistics.
IBM Research Collaboratory for Life Sciences Melbourne, 187 Grattan Street, Carlton, VIC 3010, Australia. mreumann@ieee.org
Matthias
M
Reumann
Enes
E
Makalic
Benjamin W
BW
Goudey
Michael
M
Inouye
Adrian
A
Bickerstaffe
Minh
M
Bui
Daniel J
DJ
Park
Miroslaw K
MK
Kapuscinski
Daniel F
DF
Schmidt
Zeyu
Z
Zhou
Guoqi
G
Qian
Justin
J
Zobel
John
J
Wagner
John L
JL
Hopper
D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D009010:Monte Carlo Method; D009369:Neoplasms / Q000235:genetics; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide