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Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions
BACKGROUND: Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2761303/ https://www.ncbi.nlm.nih.gov/pubmed/19772641 http://dx.doi.org/10.1186/1756-0381-2-5 |
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author | Greene, Casey S Penrod, Nadia M Kiralis, Jeff Moore, Jason H |
author_facet | Greene, Casey S Penrod, Nadia M Kiralis, Jeff Moore, Jason H |
author_sort | Greene, Casey S |
collection | PubMed |
description | BACKGROUND: Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs) which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low success rate for noisy datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF), which improves the estimation of weights in noisy data but does not fundamentally change the underlying ReliefF algorithm. To improve the sensitivity of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF). RESULTS: SURF's ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this success rate increase does not require an increase in algorithmic complexity and allows for increased success rate, even with the removal of a nuisance parameter from the algorithm. CONCLUSION: Researchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be used instead of ReliefF to filter a dataset before an exhaustive MDR analysis. This change increases the ability of a study to detect gene-gene interactions. The SURF algorithm is implemented in the open source Multifactor Dimensionality Reduction (MDR) software package available from . |
format | Text |
id | pubmed-2761303 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-27613032009-10-14 Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions Greene, Casey S Penrod, Nadia M Kiralis, Jeff Moore, Jason H BioData Min Methodology BACKGROUND: Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs) which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low success rate for noisy datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF), which improves the estimation of weights in noisy data but does not fundamentally change the underlying ReliefF algorithm. To improve the sensitivity of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF). RESULTS: SURF's ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this success rate increase does not require an increase in algorithmic complexity and allows for increased success rate, even with the removal of a nuisance parameter from the algorithm. CONCLUSION: Researchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be used instead of ReliefF to filter a dataset before an exhaustive MDR analysis. This change increases the ability of a study to detect gene-gene interactions. The SURF algorithm is implemented in the open source Multifactor Dimensionality Reduction (MDR) software package available from . BioMed Central 2009-09-22 /pmc/articles/PMC2761303/ /pubmed/19772641 http://dx.doi.org/10.1186/1756-0381-2-5 Text en Copyright © 2009 Greene et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Greene, Casey S Penrod, Nadia M Kiralis, Jeff Moore, Jason H Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions |
title | Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions |
title_full | Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions |
title_fullStr | Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions |
title_full_unstemmed | Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions |
title_short | Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions |
title_sort | spatially uniform relieff (surf) for computationally-efficient filtering of gene-gene interactions |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2761303/ https://www.ncbi.nlm.nih.gov/pubmed/19772641 http://dx.doi.org/10.1186/1756-0381-2-5 |
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