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BiForce Toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies
Genome-wide association studies (GWAS) have discovered many loci associated with common disease and quantitative traits. However, most GWAS have not studied the gene–gene interactions (epistasis) that could be important in complex trait genetics. A major challenge in analysing epistasis in GWAS is t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3394281/ https://www.ncbi.nlm.nih.gov/pubmed/22689639 http://dx.doi.org/10.1093/nar/gks550 |
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author | Gyenesei, Attila Moody, Jonathan Laiho, Asta Semple, Colin A.M. Haley, Chris S. Wei, Wen-Hua |
author_facet | Gyenesei, Attila Moody, Jonathan Laiho, Asta Semple, Colin A.M. Haley, Chris S. Wei, Wen-Hua |
author_sort | Gyenesei, Attila |
collection | PubMed |
description | Genome-wide association studies (GWAS) have discovered many loci associated with common disease and quantitative traits. However, most GWAS have not studied the gene–gene interactions (epistasis) that could be important in complex trait genetics. A major challenge in analysing epistasis in GWAS is the enormous computational demands of analysing billions of SNP combinations. Several methods have been developed recently to address this, some using computers equipped with particular graphical processing units, most restricted to binary disease traits and all poorly suited to general usage on the most widely used operating systems. We have developed the BiForce Toolbox to address the demand for high-throughput analysis of pairwise epistasis in GWAS of quantitative and disease traits across all commonly used computer systems. BiForce Toolbox is a stand-alone Java program that integrates bitwise computing with multithreaded parallelization and thus allows rapid full pairwise genome scans via a graphical user interface or the command line. Furthermore, BiForce Toolbox incorporates additional tests of interactions involving SNPs with significant marginal effects, potentially increasing the power of detection of epistasis. BiForce Toolbox is easy to use and has been applied in multiple studies of epistasis in large GWAS data sets, identifying interesting interaction signals and pathways. |
format | Online Article Text |
id | pubmed-3394281 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-33942812012-07-30 BiForce Toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies Gyenesei, Attila Moody, Jonathan Laiho, Asta Semple, Colin A.M. Haley, Chris S. Wei, Wen-Hua Nucleic Acids Res Stand Alone Programs Genome-wide association studies (GWAS) have discovered many loci associated with common disease and quantitative traits. However, most GWAS have not studied the gene–gene interactions (epistasis) that could be important in complex trait genetics. A major challenge in analysing epistasis in GWAS is the enormous computational demands of analysing billions of SNP combinations. Several methods have been developed recently to address this, some using computers equipped with particular graphical processing units, most restricted to binary disease traits and all poorly suited to general usage on the most widely used operating systems. We have developed the BiForce Toolbox to address the demand for high-throughput analysis of pairwise epistasis in GWAS of quantitative and disease traits across all commonly used computer systems. BiForce Toolbox is a stand-alone Java program that integrates bitwise computing with multithreaded parallelization and thus allows rapid full pairwise genome scans via a graphical user interface or the command line. Furthermore, BiForce Toolbox incorporates additional tests of interactions involving SNPs with significant marginal effects, potentially increasing the power of detection of epistasis. BiForce Toolbox is easy to use and has been applied in multiple studies of epistasis in large GWAS data sets, identifying interesting interaction signals and pathways. Oxford University Press 2012-07 2012-06-09 /pmc/articles/PMC3394281/ /pubmed/22689639 http://dx.doi.org/10.1093/nar/gks550 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Stand Alone Programs Gyenesei, Attila Moody, Jonathan Laiho, Asta Semple, Colin A.M. Haley, Chris S. Wei, Wen-Hua BiForce Toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies |
title | BiForce Toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies |
title_full | BiForce Toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies |
title_fullStr | BiForce Toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies |
title_full_unstemmed | BiForce Toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies |
title_short | BiForce Toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies |
title_sort | biforce toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies |
topic | Stand Alone Programs |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3394281/ https://www.ncbi.nlm.nih.gov/pubmed/22689639 http://dx.doi.org/10.1093/nar/gks550 |
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