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Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer

BACKGROUND: Single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with a breast cancer risk. Identifying possible SNP-SNP interactions in genome-wide case–control studies is an important task when investigating genetic factors that influence common complex tra...

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Autores principales: Chuang, Li-Yeh, Chang, Hsueh-Wei, Lin, Ming-Cheng, Yang, Cheng-Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626712/
https://www.ncbi.nlm.nih.gov/pubmed/23410245
http://dx.doi.org/10.1186/2043-9113-3-4
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author Chuang, Li-Yeh
Chang, Hsueh-Wei
Lin, Ming-Cheng
Yang, Cheng-Hong
author_facet Chuang, Li-Yeh
Chang, Hsueh-Wei
Lin, Ming-Cheng
Yang, Cheng-Hong
author_sort Chuang, Li-Yeh
collection PubMed
description BACKGROUND: Single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with a breast cancer risk. Identifying possible SNP-SNP interactions in genome-wide case–control studies is an important task when investigating genetic factors that influence common complex traits; the effects of SNP-SNP interaction need to be characterized. Furthermore, observations of the complex interplay (interactions) between SNPs for high-dimensional combinations are still computationally and methodologically challenging. An improved branch and bound algorithm with feature selection (IBBFS) is introduced to identify SNP combinations with a maximal difference of allele frequencies between the case and control groups in breast cancer, i.e., the high/low risk combinations of SNPs. RESULTS: A total of 220 real case and 334 real control breast cancer data are used to test IBBFS and identify significant SNP combinations. We used the odds ratio (OR) as a quantitative measure to estimate the associated cancer risk of multiple SNP combinations to identify the complex biological relationships underlying the progression of breast cancer, i.e., the most likely SNP combinations. Experimental results show the estimated odds ratio of the best SNP combination with genotypes is significantly smaller than 1 (between 0.165 and 0.657) for specific SNP combinations of the tested SNPs in the low risk groups. In the high risk groups, predicted SNP combinations with genotypes are significantly greater than 1 (between 2.384 and 6.167) for specific SNP combinations of the tested SNPs. CONCLUSIONS: This study proposes an effective high-speed method to analyze SNP-SNP interactions in breast cancer association studies. A number of important SNPs are found to be significant for the high/low risk group. They can thus be considered a potential predictor for breast cancer association.
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spelling pubmed-36267122013-04-24 Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer Chuang, Li-Yeh Chang, Hsueh-Wei Lin, Ming-Cheng Yang, Cheng-Hong J Clin Bioinforma Methodology BACKGROUND: Single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with a breast cancer risk. Identifying possible SNP-SNP interactions in genome-wide case–control studies is an important task when investigating genetic factors that influence common complex traits; the effects of SNP-SNP interaction need to be characterized. Furthermore, observations of the complex interplay (interactions) between SNPs for high-dimensional combinations are still computationally and methodologically challenging. An improved branch and bound algorithm with feature selection (IBBFS) is introduced to identify SNP combinations with a maximal difference of allele frequencies between the case and control groups in breast cancer, i.e., the high/low risk combinations of SNPs. RESULTS: A total of 220 real case and 334 real control breast cancer data are used to test IBBFS and identify significant SNP combinations. We used the odds ratio (OR) as a quantitative measure to estimate the associated cancer risk of multiple SNP combinations to identify the complex biological relationships underlying the progression of breast cancer, i.e., the most likely SNP combinations. Experimental results show the estimated odds ratio of the best SNP combination with genotypes is significantly smaller than 1 (between 0.165 and 0.657) for specific SNP combinations of the tested SNPs in the low risk groups. In the high risk groups, predicted SNP combinations with genotypes are significantly greater than 1 (between 2.384 and 6.167) for specific SNP combinations of the tested SNPs. CONCLUSIONS: This study proposes an effective high-speed method to analyze SNP-SNP interactions in breast cancer association studies. A number of important SNPs are found to be significant for the high/low risk group. They can thus be considered a potential predictor for breast cancer association. BioMed Central 2013-02-14 /pmc/articles/PMC3626712/ /pubmed/23410245 http://dx.doi.org/10.1186/2043-9113-3-4 Text en Copyright © 2013 Chuang 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
Chuang, Li-Yeh
Chang, Hsueh-Wei
Lin, Ming-Cheng
Yang, Cheng-Hong
Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer
title Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer
title_full Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer
title_fullStr Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer
title_full_unstemmed Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer
title_short Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer
title_sort improved branch and bound algorithm for detecting snp-snp interactions in breast cancer
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626712/
https://www.ncbi.nlm.nih.gov/pubmed/23410245
http://dx.doi.org/10.1186/2043-9113-3-4
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