Download University at Buffalo, State University of New York

IBM Software
Information Management
Case Study
University at Buffalo, State
University of New York
IBM Netezza powers the study of exponentially large gene
interaction associated with multiple sclerosis
Overview
The need
Need for supercomputer-level processing
against millions of single nucleotide
polymorphisms (SNPs) to handle
combinatorial explosion involved in
breakthrough data mining methods for gene
interaction in disease discovery
The solution
IBM Netezza data warehouse appliance
provides faster performance than
high-performance computer (HPC)
platforms for efficient execution of the
AMBIENCE algorithm; AMBIENCE uses the
“K-Way Interaction Index” to identify series
of SNPs that correlate with the presence of a
phenotype, such as multiple sclerosis
The benefit
Processes exponentially complex
gene-environment interactions more than
6,000 times as fast as large symmetric
multiprocessing (SMP) systems
The State University of New York (SUNY) at Buffalo is home to one of
the leading multiple sclerosis (MS) research centers in the world. MS is
a devastating, chronic neurological disease that affects nearly one
million people worldwide. The disease causes physical and cognitive
disabilities in individuals and is characterized by inflammation and
neurodegeneration of the brain and spinal cord. From the beginning,
the genetics of MS were known to be complex and it was apparent that
no single gene was the likely cause for the disease.
Since 2007, the SUNY team has been looking at data obtained from
scanned genomes of MS patients to identify genes whose variations
could contribute to the risk of developing MS. New technologies now
enable hundreds of thousands of genetic variations, called single
nucleotide polymorphisms (SNPs), to be obtained from single samples.
According to research lead, Dr. Murali Ramanathan, a critical fact in
the study of MS is that “gene products work by interacting with both
other gene products and environmental factors.”
Because of this, researchers have postulated that multiple SNPs
— combined with environmental variables — would better explain the
risk of developing MS. Finding these combinations is analogous to the
proverbial search for a needle in a haystack says Dr. Ramanathan.
Identifying a candidate’s environmental factors that could be used to
prevent the disease from progressing in patients was of great
importance. Examples of this include sun exposure and vitamin D
levels, Epstein-Barr virus infections and smoking.
The researchers needed to create algorithms that would efficiently
identify interactions and attempt ‘parsimony’ — an extreme efficiency.
The team was looking for the fewest number of SNPs, environmental
and phenotypic variable combinations that would help explain the
presence of MS.
IBM Software
Information Management
“Developing knowledge of
pertinent environmental
factors opens the way for
new therapeutic and
prevention strategies and
can enable individualized
treatment and management of MS patients.”
Case Study
1
A
T
A
T
C
G
C
C
G
A
T T
A
G
C
C
G
A C
T G
SNP
2
—Dr. Murali Ramanathan, Research Lead,
The State University of New York (SUNY)
at Buffalo
A
T
A
T
C
T
A
C
G
A
T T
A
G
C
C
G
A C
T G
Figure 1: A single nucleotide polymorphism (SNP) is a DNA sequence variation oc-
curring when a single nucleotide—A, C, G or T—in the shared genome differs in an
individual. If many individuals with a disease (or ‘phenotype’) have the same polymorphisms that healthy individuals do not, this may be a clue to finding out genetic clues
to what causes a disease (Wikipedia).
The researchers at the State University of New York developed an
approach they called AMBIENCE that is distinctive in its use of new
information theoretic methods along with its versatility and scalability.
The theoretic underpinnings of AMBIENCE enable the detection of
both linear and non-linear dependencies in the data. The AMBIENCE
algorithm is capable of conducting an efficient search of the large
combinatorial space because of the unique nature of the information
theoretic metrics, which allows for greedy search identification of the
most promising combinations.
The need
The computational challenge in gene-environmental interaction
analyses is due to a phenomenon called ‘combinatorial explosion.’
Considering that there are thousands of SNPs, the number of
combinations of SNPs that have to be assessed for uncovering potential
interaction becomes astronomical.
2
IBM Software
Information Management
Solution Components
Software
•
IBM® Netezza® 1000-24
“The IBM Netezza 1000
data warehouse appliance
not only allowed us to
greatly speed up our run
time, it also gave us the
added ability to accomplish
more within that time.”
Case Study
The sheer number of SNPs combined with environmental variables
and phenotype values mean that the amount of computations necessary
for data mining could number in the quintillions (18 zeros).
To identify how many interactions could exist, based on the number of
SNPs included, the SUNY researchers graphed a mathematical
function. If they were to mine through one million SNPs to look for
the number of combinations containing four SNPs, they’d be looking
for a possible sextillion interactions, or 10 to the 21st power.
The researchers attempted to run the algorithms against commodity
hardware. In this method they found that a simple run of the algorithm
took almost a week. Extrapolating this performance, they realized it
would take many weeks to run the algorithm against the volume of data
they wanted. Meanwhile, they knew that the algorithm results would
lead to additional questions, algorithm adjustments, data changes and
more, which would be untenable. They needed processing power that
would speed this up by 100 times in order to be able to make
meaningful discoveries and publish them.
—Dr. Murali Ramanathan
Figure 2: A heuristic example of the SUNY data table
The solution
The researchers knew they needed the kind of processing only available
in a high-performance computer (HPC), previously known as a
supercomputer. In addition, the research team needed capabilities
found in relational databases — not often included in HPC platforms.
HPCs typically ran their processing only in parallel, breaking up
calculations to run simultaneously across many processors. They also
often used field-programmable gate array (FPGA) architectures for
additional speed.
3
IBM Software
Information Management
“With the IBM Netezza
research analytics
infrastructure in place
running an algorithm
became a matter of minutes
instead of days”
—Dr. Murali Ramanathan
Case Study
Unfortunately HPCs can cost tens of millions of dollars and do not
include storage for large volumes of data. They are designed for data to
be read ‘into memory’ making their use for the ‘big data’ computations
needed by the SUNY team implausible.
That is when the researchers discovered IBM® Netezza®. Testing the
IBM Netezza data warehouse appliance confirmed it delivered all the
performance they wanted and much more. IBM Netezza’s architecture
is designed to accommodate ‘big data’ storage and processing,
combined with the ‘in memory’ performance of an HPC.
IBM Netezza, a pioneer of data-intensive HPC that are easy to administer
at a fraction of the cost of today’s HPC platforms, easily emerged as the
comprehensive solution the researchers at SUNY were seeking.
The benefit
Once deploying IBM Netezza data warehousing appliance as their
research analytics infrastructure, the SUNY researchers were able to:
•
•
•
•
•
Use the new algorithms and commence analyses that were previously
nearly impossible to conduct.
Reduce the time required to conduct analysis from 27.2 hours without
Netezza to 11.7 minutes with it. “We went from days to minutes,” said
Dr. Ramanathan.
Carry out their research with little to no database administration.
(Unlike other HPC platforms or databases available, Netezza was
designed to require an absolute minimum of maintenance.)
Publish multiple articles in scientific journals, with more in-process.
Proceed with studies based on ‘vector phenotypes’ — a more complex
variable that will further push the IBM Netezza platform.
Figure 3: One of the articles published by SUNY researchers
4
IBM Software
Information Management
“The IBM Netezza
appliance was very easy to
install and required little to
no database administration.
It performed as they said it
would…low maintenance
and with the high speed
analytics our computationally intense work
demanded.”
—Dr. Murali Ramanathan
Case Study
A call to action
One of the most debilitating hurdles to productive scientific research is
the latency between being ready to start — having a solid question, the
right data, and a robust calculation — and getting an answer. With the
aggressive price/performance ratio of IBM® Netezza®, researchers can
take advantage of modern, secure compute and storage power. And one
of the chief benefits is that the researchers can ask and answer many
more questions – in the same time period – than ever before.
For more information
To learn more about IBM Netezza data warehouse appliances, please
contact your IBM or Netezza sales representative or IBM Business
Partner, or visit the following website: www.netezza.com.
For more information on SUNY Buffalo, visit: www.buffalo.edu/, or
SUNY’s research, consult the following sample publications:
AMBIENCE: a novel approach and efficient algorithm for identifying
informative genetic and environmental associations with complex phenotypes.
Chanda P, Sucheston L, Zhang A, Brazeau D, Freudenheim JL,
Ambrosone C, Ramanathan M.
Genetics. 2008 Oct; 180(2): 1191-210.
Epub 2008 Sep 9. PMID: 18780753 [PubMed - indexed for
MEDLINE]
Information-theoretic gene-gene and gene-environment interaction analysis of
quantitative traits.
Chanda P, Sucheston L, Liu S, Zhang A, Ramanathan M.
BMC Genomics. 2009 Nov 4; 10:509.
PMID: 19889230.
Comparison of information-theoretic to statistical methods for gene-gene
interactions in the presence of genetic heterogeneity.
Sucheston L, Chanda P, Zhang A, Tritchler D, Ramanathan M.
BMC Genomics. 2010 Sep 3; 11:487. PMID: 20815886.
The interaction index, a novel information-theoretic metric for prioritizing
interacting genetic variations and environmental factors.
Chanda P, Sucheston L, Zhang A, Ramanathan M.
Eur J Hum Genet. 2009 Oct;17(10): 1274-86.
Epub 2009 Mar 18. PMID: 19293841.
5
© Copyright IBM Corporation 2011
IBM Corporation
Software Group
Route 100
Somers, NY 10589
U.S.A.
Produced in the United States of America
April 2011
All Rights Reserved
IBM, the IBM logo, and ibm.com, are trademarks or registered trademarks of
International Business Machines Corporation in the United States, other countries, or
both. If these and other IBM trademarked terms are marked on their first occurrence
in this information with a trademark symbol (® or ™), these symbols indicate U.S.
registered or common law trademarks owned by IBM at the time this information was
published. Such trademarks may also be registered or common law trademarks in
other countries. A current list of IBM trademarks is available on the Web at
“Copyright and trademark information” at
ibm.com/legal/copytrade.shtml
Netezza is a registered trademark of Netezza Corporation, an IBM Company.
Other company, product and service names may be trademarks or service marks of others.
References in this publication to IBM products or services do not imply that IBM
intends to make them available in all countries in which IBM operates.
Please Recycle
an IBM Company
IMC14645-USEN-00