skip to main content
10.1145/1830483.1830523acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
poster

Fast genome-wide epistasis analysis using ant colony optimization for multifactor dimensionality reduction analysis on graphics processing units

Published: 07 July 2010 Publication History

Abstract

Epistasis, or non-linear gene-to-gene interaction, is now thought to be at the heart of many common human diseases. A popular algorithm to detect epistasis is Multifactor Dimensionality Reduction (MDR), which exhaustively searches to determine an optimal classification. This exhaustive search is combinatorial in complexity and does not scale efficiently to large datasets. Ant Colony Opimization (ACO) is a technique to reduce this complexity by exploiting expert knowledge to spend more time looking at most likely candidates for the optimal classification. Graphics Processing Units (GPUs) are highly-parallel integrated circuits able to execute arbitrary code. The authors implemented ACO MDR on GPUs and compared it to both a Java ACO implementation and an exhaustive C++ implementation. The performance advantage of GPUs, combined with the added computational efficiency of a heuristic evolutionary algorithm such as ACO, allow larger scale problems to be tackled, something that is becoming critical with the advances in high throughput genome sequencing.

References

[1]
C. Greene, N. Penrod, J. Kiralis, and J. Moore. Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions. BioData Mining, 2(1):5, 2009.
[2]
C. S. Greene, N. A. Sinnott-Armstrong, D. S. Himmelstein, P. J. Park, J. H. Moore, and B. T. Harris. Multifactor Dimensionality Reduction for Graphics Processing Units Enables Genome-wide Testing of Epistasis in Sporadic ALS. Bioinformatics, 26(5):694--695, 2010.
[3]
C. S. Greene, B. C. White, and J. H. Moore. Ant colony optimization for genome-wide genetic analysis. In ANTS '08: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence, pages 37--47, Berlin, Heidelberg, 2008. Springer-Verlag.
[4]
A. Kloeckner, N. Pinto, Y. Lee, B. Catanzaro, P. Ivanov, and A. Fasih. PyCUDA: GPU Run-Time Code Generation for High-Performance Computing. Technical Report 2009-40, Scientific Computing Group, Brown University, Providence, RI, USA, Nov. 2009.
[5]
J. H. Moore. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Human Heredity, 56:73--82, 2003.
[6]
J. H. Moore, J. C. Gilbert, C. T. Tsai, F. T. Chiang, T. Holden, N. Barney, and B. C. White. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Journal of Theoretical Biology, 241(2):252--261, Jul 2006.
[7]
M. D. Ritchie, L. W. Hahn, N. Roodi, L. R. Bailey, W. D. Dupont, F. F. Parl, and J. H. Moore. Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. American Journal of Human Genetics, 69:138--147, 2001.
[8]
M. Schatz, C. Trapnell, A. Delcher, and A. Varshney. High-throughput sequence alignment using graphics processing units. BMC Bioinformatics, 8(1):474, 2007.
[9]
N. Sinnott-Armstrong, C. Greene, F. Cancare, and J. Moore. Accelerating epistasis analysis in human genetics with consumer graphics hardware. BMC Research Notes, 2(1):149, 2009.

Cited By

View all
  • (2024)Network medicine-based epistasis detection in complex diseases: ready for quantum computingNucleic Acids Research10.1093/nar/gkae697Online publication date: 23-Aug-2024
  • (2022)A Secure High-Order Gene Interaction Detecting Method for Infectious DiseasesComputational and Mathematical Methods in Medicine10.1155/2022/44717362022(1-19)Online publication date: 21-Apr-2022
  • (2020)A framework for modeling epistatic interactionBioinformatics10.1093/bioinformatics/btaa99037:12(1708-1716)Online publication date: 30-Nov-2020
  • Show More Cited By

Index Terms

  1. Fast genome-wide epistasis analysis using ant colony optimization for multifactor dimensionality reduction analysis on graphics processing units

            Recommendations

            Comments

            Information & Contributors

            Information

            Published In

            cover image ACM Conferences
            GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
            July 2010
            1520 pages
            ISBN:9781450300728
            DOI:10.1145/1830483

            Sponsors

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            Published: 07 July 2010

            Permissions

            Request permissions for this article.

            Check for updates

            Author Tags

            1. ACO
            2. GPU
            3. genetics

            Qualifiers

            • Poster

            Conference

            GECCO '10
            Sponsor:

            Acceptance Rates

            Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

            Contributors

            Other Metrics

            Bibliometrics & Citations

            Bibliometrics

            Article Metrics

            • Downloads (Last 12 months)0
            • Downloads (Last 6 weeks)0
            Reflects downloads up to 16 Feb 2025

            Other Metrics

            Citations

            Cited By

            View all
            • (2024)Network medicine-based epistasis detection in complex diseases: ready for quantum computingNucleic Acids Research10.1093/nar/gkae697Online publication date: 23-Aug-2024
            • (2022)A Secure High-Order Gene Interaction Detecting Method for Infectious DiseasesComputational and Mathematical Methods in Medicine10.1155/2022/44717362022(1-19)Online publication date: 21-Apr-2022
            • (2020)A framework for modeling epistatic interactionBioinformatics10.1093/bioinformatics/btaa99037:12(1708-1716)Online publication date: 30-Nov-2020
            • (2019)A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-order SNP InteractionsIEEE Access10.1109/ACCESS.2019.2951700(1-1)Online publication date: 2019
            • (2019)A Review of Ant Colony Optimization Based Methods for Detecting Epistatic InteractionsIEEE Access10.1109/ACCESS.2019.28946767(13497-13509)Online publication date: 2019
            • (2017)A Hybrid Algorithm Based on Particle Swarm Optimization and Ant Colony Optimization AlgorithmSmart Computing and Communication10.1007/978-3-319-52015-5_3(22-31)Online publication date: 13-Jan-2017
            • (2016)Boosting Multifactor Dimensionality Reduction Using Pre-evaluationETRI Journal10.4218/etrij.16.0114.004038:1(206-215)Online publication date: 1-Feb-2016
            • (2011)Graphics processing units and genetic programming: an overviewSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-011-0695-215:8(1657-1669)Online publication date: 1-Aug-2011

            View Options

            Login options

            View options

            PDF

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader

            Figures

            Tables

            Media

            Share

            Share

            Share this Publication link

            Share on social media