Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems

Autoren

Jorge Gonzalez-Dominguez
Lars Wienbrandt
Jan Christian Kassens
David Ellinghaus
Manfred Schimmler
Bertil Schmidt

DOI

10.1109/tcbb.2015.2389958

ISSN

1545-5963

Ausgabe der Veröffentlichung

5

Zeitschrift

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Paginierung

982 - 994

Datum der Veröffentlichung

2015

Status

Published

Herausgeber

Institute of Electrical and Electronics Engineers (IEEE)

Herausgeber URL

http://dx.doi.org/10.1109/tcbb.2015.2389958

Datum der Datenerfassung

2022

Titel

Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems

Ausgabe der Zeitschrift

12

Datenquelle: Crossref

Abstract

High-throughput genotyping technologies (such as SNP-arrays) allow the rapid collection of up to a few million genetic markers of an individual. Detecting epistasis (based on 2-SNP interactions) in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. Computational methods to detect epistasis therefore suffer from prohibitively long runtimes; e.g., processing a moderately-sized dataset consisting of about 500,000 SNPs and 5,000 samples requires several days using state-of-the-art tools on a standard 3 GHz CPU. In this paper, we demonstrate how this task can be accelerated using a combination of fine-grained and coarse-grained parallelism on two different computing systems. The first architecture is based on reconfigurable hardware (FPGAs) while the second architecture uses multiple GPUs connected to the same host. We show that both systems can achieve speedups of around four orders-of-magnitude compared to the sequential implementation. This significantly reduces the runtimes for detecting epistasis to only a few minutes for moderately-sized datasets and to a few hours for large-scale datasets.

Autoren

Jorge González-Domínguez
Lars Wienbrandt
Jan Christian Kässens
David Ellinghaus
Manfred Schimmler
Bertil Schmidt

DOI

10.1109/tcbb.2015.2389958

eISSN

1557-9964

Externe Identifier

PubMed Identifier: 26451813

Funding acknowledgements

Wellcome Trust: 076113
DFG:
Wellcome Trust: 085475

Open access

false

ISSN

1545-5963

Ausgabe der Veröffentlichung

5

Zeitschrift

IEEE/ACM transactions on computational biology and bioinformatics

Schlüsselwörter

Sensitivity and Specificity
Reproducibility of Results
Equipment Design
Equipment Failure Analysis
Chromosome Mapping
DNA Mutational Analysis
Epistasis, Genetic
Polymorphism, Single Nucleotide
Computer Graphics
Signal Processing, Computer-Assisted
Genome-Wide Association Study
High-Throughput Nucleotide Sequencing

Sprache

eng

Medium

Print

Paginierung

982 - 994

Datum der Veröffentlichung

2015

Status

Published

Datum der Datenerfassung

2015

Titel

Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems.

Sub types

Research Support, Non-U.S. Gov't
Journal Article

Ausgabe der Zeitschrift

12

Datenquelle: Europe PubMed Central

Abstract

High-throughput genotyping technologies (such as SNP-arrays) allow the rapid collection of up to a few million genetic markers of an individual. Detecting epistasis (based on 2-SNP interactions) in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. Computational methods to detect epistasis therefore suffer from prohibitively long runtimes; e.g., processing a moderately-sized dataset consisting of about 500,000 SNPs and 5,000 samples requires several days using state-of-the-art tools on a standard 3 GHz CPU. In this paper, we demonstrate how this task can be accelerated using a combination of fine-grained and coarse-grained parallelism on two different computing systems. The first architecture is based on reconfigurable hardware (FPGAs) while the second architecture uses multiple GPUs connected to the same host. We show that both systems can achieve speedups of around four orders-of-magnitude compared to the sequential implementation. This significantly reduces the runtimes for detecting epistasis to only a few minutes for moderately-sized datasets and to a few hours for large-scale datasets.

Autoren

Jorge González-Domínguez
Lars Wienbrandt
Jan Christian Kässens
David Ellinghaus
Manfred Schimmler
Bertil Schmidt

Autoren-URL

https://www.ncbi.nlm.nih.gov/pubmed/26451813

DOI

10.1109/TCBB.2015.2389958

eISSN

1557-9964

Funding acknowledgements

Wellcome Trust: 076113
Wellcome Trust: 085475

Ausgabe der Veröffentlichung

5

Zeitschrift

IEEE/ACM Trans Comput Biol Bioinform

Schlüsselwörter

Chromosome Mapping
Computer Graphics
DNA Mutational Analysis
Epistasis, Genetic
Equipment Design
Equipment Failure Analysis
Genome-Wide Association Study
High-Throughput Nucleotide Sequencing
Polymorphism, Single Nucleotide
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted

Sprache

eng

Country

United States

Paginierung

982 - 994

Datum der Veröffentlichung

2015

Status

Published

Datum, an dem der Datensatz öffentlich gemacht wurde

2016

Titel

Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems.

Sub types

Journal Article
Research Support, Non-U.S. Gov't

Ausgabe der Zeitschrift

12

Datenquelle: PubMed

Autoren

Jorge González-Domínguez
Lars Wienbrandt
Jan Christian Kässens
David Ellinghaus
Manfred Schimmler
Bertil Schmidt

Zeitschrift

IEEE ACM Trans. Comput. Biol. Bioinform.

Artikelnummer

5

Paginierung

982 - 994

Datum der Veröffentlichung

2015

Titel

Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems.

Ausgabe der Zeitschrift

12

Datenquelle: DBLP

Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems

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