=Paper=
{{Paper
|id=Vol-1149/bd2014_kowalczyk
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1149/bd2014_kowalczyk.pdf
|volume=Vol-1149
}}
==None==
https://ceur-ws.org/Vol-1149/bd2014_kowalczyk.pdf
ABSTRACTS : scientific
GPU enables search for 2-way and 3-way
interactions in GWAS
Adam Kowalczyka,b, Qiao Wanga,b , Fan Shia,b, Andrew Kowalczyka, David Rawlinsona, Benjamin Goudeya,b,
Richard Campbella, Herman Ferraa
NICTA, Victorian Research Laboratories, The University of Melbourne, Parkville, VIC 3010, Australia
a
Computing and Information Systems, The University of Melbourne, Parkville, VIC 3010, Australia
b
SUMMARY
Genome-wide association studies (GWAS) probe millions of DNA loci in an attempt to associate DNA mutations
with a given disease. Complex aetiologies of many common diseases involve combinations of different genes
which require individual evaluation of trillions (non-additive) combinations of loci for association in an average
size study. We have developed solutions using a single GPU to evaluate association of each and every one
Dr Adam Kowalczyk bivariate feature within minutes (available via free webserver). Although an exhaustive tri-variate analysis
requires currently a medium size GPU cluster, many focused tri-variate analysis tasks can be accomplished
Principal Scientist routinely on a single GPU within hours of computation.
NICTA
INTRODUCTION
In recent years, GWAS has been considered a fundamental instrument in unveiling the genetic aetiology of
non-Mendelian complex diseases. More than 600 GWAS have been conducted for 150 different diseases and
adam.kowalczyk@nicta.com.au
traits. Current genotyping technologies such as Affymetrix 6.0 allow GWAS assays of several million single-
nucleotide polymorphisms (SNPs). In order to reveal the underlying biological mechanisms of disease, most
analytical methods analyse each SNP individually for association with disease1. However, interactions between
loci are believed to contribute to complex diseases with non-negligible joint effects2, even while each SNP may
Dr Adam Kowalczyk is currently a principal researcher in show little effect independently3. As recently as 2010 it was considered technically impractical to exhaustively
Victorian Research Laboratories of National ICT Australia search for second-order (bivariate) interactions without access to state of the art computing facilities due
(NICTA). He leads projects in molecular medicine and to the large search space (of ~1011 for all pairs and ~1016 for all triples in typical GWAS with 500,000
biology, leveraging many years of research and commercial
SNPs). In response, researchers proposed several pre-filtering and stochastic partial search methods using
experience in pure and applied mathematics, mathematical
univariate analysis for cutting the number of probes to be considered for multi-locus investigation. However,
physics, artificial intelligence, telecommunications and,
the worry remains that such methods may miss critical genuine interactions which may show only very
recently, bioinformatics.
weak marginal effects3,1,5.
DESCRIPTION
Spectacular progress in commodity computing technology in the last few years has led to development of a
number of algorithms capable of exhaustive bivariate analysis not only on moderate computer clusters but also
on standard desktop computers equipped with General Purpose Graphics Processing Units (GPUs). In order
to fully exploit the potential of these devices for medical and biotechnological research there is a need for
efficient software tools that reduce implementation and performance difficulties, so researchers can focus on
comparison and evaluation of results rather than software tools development and low level algorithm tweaking;
see a recent elaboration of this point in6. It has been demonstrated7 that the number of novel putative epistatic
loci can be detected using such techniques.
In this talk we present an efficient library of GPU kernels that can be used for fast implementation of any
bivariate GWAS statistics that can be derived from contingency table counts. In order to illustrate the point we
have implemented nine different algorithms from the literature. All algorithms can execute exhaustive search on
typical case/control GWAS (500K SNPs, 5K samples) within 10 minutes. This performance makes comparative
analysis of different statistical methods easy. These results are also significantly improved compared to original
implementations of the nine algorithms considered. Speedup factors of over 300 are observed compared to
some original GPU implementations in literature and even larger factors of over 10,000 are seen with respect
to the CPU implementations, e.g. a popular Fast Epistasis algorithm in PLINK 1.07 software package.
Consider that timing scales quadratically with the density of genotyping markers used. Future high-density
SNP arrays will include up to 5 million SNPs, and forthcoming GWAS based on NGS data will have even higher
marker density and may include other technology such as methylation markers. Together, these expectations
mean that the computational burden of exhaustive bivariate analysis will continue to be challenging: between
one hundred and one thousand times more complex than existing GWAS. Thus our GPU approach, which
32 #bd14 | big data conference
efficiently applies a battery of statistical tests to exhaustive search of all SNP pairs in minutes for current GWAS data, will still require hours or days for near-future
data. This is achievable on a single GPU-equipped desktop or laptop computer, but time scales down accordingly if GPU clusters are used. Additionally, the users can
define and add their own statistics to our platform and make use of our high performance library that generates contingency tables and ranks scores. To facilitate
this we have insured compatibility with popular input data formats, a common interface for defining statistical tests. The runtime of ~10 minutes for a typical dataset
and computer is fast enough that researchers can experiment with methods interactively, reviewing the effects of varied algorithms almost immediately.
The practical consequences of such an improvement in productivity are not a matter of degree. By enabling researchers to conduct GWAS experiments in less
time than a coffee break it becomes possible to focus effort on statistical methods and results rather than avoiding performance bottlenecks. New ideas can be
implemented and evaluated in very fast cycles, without the need to book time on shared high end computing resources.
Most recently, we have extended GWIS to exhaustive search for 3-way interactions, a previously impossible computational task. Using our methods, an exhaustive
3-way analysis of Celiac disease GWAS from UK containing ~310K SNPs and 2200 samples using a cluster of 200 GPUs requires 7 days of computing time. To our
knowledge this is the first time such an analysis has been shown to be practical. The runtime reduces significantly for more targeted analysis, for example a specific
DNA region or a preselected set of SNPs. Exhaustive filtering through all SNP-triplets in ~2500 SNPs, including the extended MHC region, requires <3 minutes on
a standard PC with a single GTX470 NVIDIA GPU.
CONCLUSION
In conclusion, analysis of two-way and three-way interactions in modern GWAS using multiple methods is practical today. Once such analyses are practiced in
labs around the world faster progress in unveiling the genetic aetiology of complex diseases may result. To date, it has been difficult to compare methods across a
range of datasets due to implementation difficulties and prohibitive runtime. Many existing benchmark studies were forced to use only small size, typically synthetic,
datasets. What we claim is a paradigm shift, towards routine usage real life data for methods development, benchmarking and then “production” deployment of
novel GWAS data analysis paradigms.
REFERENCES
1. Cordell, 2009, Nat.Rev.Genet, 2009, 10(6), 392–404.
2. Marchini et al., 2005, Nat Genet, 37(4), 413–417.
3. Zhang and Liu, 2007, Nature Genetics, 39(9), 1167–1173.
4. Culverhouse et al., 2002, Am. J. Hum. Genet., 70, 461-471
5. Zhang et al., 2010, J. Comp. Biol. 17(3), 401–415.
6. Wilson et al. (2014, PLoS Biol, 12(1).
7. Godey et al. (2013), BMC Genomics 14.
3 - 4 april 2014 | melbourne 33