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Ensemble learning for detecting gene-gene interactions in colorectal cancer
Colorectal cancer (CRC) has a high incident rate in both men and women and is affecting millions of people every year. Genome-wide association studies (GWAS) on CRC have successfully revealed common single-nucleotide polymorphisms (SNPs) associated with CRC risk. However, they can only explain a ver...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6211269/ https://www.ncbi.nlm.nih.gov/pubmed/30397551 http://dx.doi.org/10.7717/peerj.5854 |
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author | Dorani, Faramarz Hu, Ting Woods, Michael O. Zhai, Guangju |
author_facet | Dorani, Faramarz Hu, Ting Woods, Michael O. Zhai, Guangju |
author_sort | Dorani, Faramarz |
collection | PubMed |
description | Colorectal cancer (CRC) has a high incident rate in both men and women and is affecting millions of people every year. Genome-wide association studies (GWAS) on CRC have successfully revealed common single-nucleotide polymorphisms (SNPs) associated with CRC risk. However, they can only explain a very limited fraction of the disease heritability. One reason may be the common uni-variable analyses in GWAS where genetic variants are examined one at a time. Given the complexity of cancers, the non-additive interaction effects among multiple genetic variants have a potential of explaining the missing heritability. In this study, we employed two powerful ensemble learning algorithms, random forests and gradient boosting machine (GBM), to search for SNPs that contribute to the disease risk through non-additive gene-gene interactions. We were able to find 44 possible susceptibility SNPs that were ranked most significant by both algorithms. Out of those 44 SNPs, 29 are in coding regions. The 29 genes include ARRDC5, DCC, ALK, and ITGA1, which have been found previously associated with CRC, and E2F3 and NID2, which are potentially related to CRC since they have known associations with other types of cancer. We performed pairwise and three-way interaction analysis on the 44 SNPs using information theoretical techniques and found 17 pairwise (p < 0.02) and 16 three-way (p ≤ 0.001) interactions among them. Moreover, functional enrichment analysis suggested 16 functional terms or biological pathways that may help us better understand the etiology of the disease. |
format | Online Article Text |
id | pubmed-6211269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-62112692018-11-05 Ensemble learning for detecting gene-gene interactions in colorectal cancer Dorani, Faramarz Hu, Ting Woods, Michael O. Zhai, Guangju PeerJ Bioinformatics Colorectal cancer (CRC) has a high incident rate in both men and women and is affecting millions of people every year. Genome-wide association studies (GWAS) on CRC have successfully revealed common single-nucleotide polymorphisms (SNPs) associated with CRC risk. However, they can only explain a very limited fraction of the disease heritability. One reason may be the common uni-variable analyses in GWAS where genetic variants are examined one at a time. Given the complexity of cancers, the non-additive interaction effects among multiple genetic variants have a potential of explaining the missing heritability. In this study, we employed two powerful ensemble learning algorithms, random forests and gradient boosting machine (GBM), to search for SNPs that contribute to the disease risk through non-additive gene-gene interactions. We were able to find 44 possible susceptibility SNPs that were ranked most significant by both algorithms. Out of those 44 SNPs, 29 are in coding regions. The 29 genes include ARRDC5, DCC, ALK, and ITGA1, which have been found previously associated with CRC, and E2F3 and NID2, which are potentially related to CRC since they have known associations with other types of cancer. We performed pairwise and three-way interaction analysis on the 44 SNPs using information theoretical techniques and found 17 pairwise (p < 0.02) and 16 three-way (p ≤ 0.001) interactions among them. Moreover, functional enrichment analysis suggested 16 functional terms or biological pathways that may help us better understand the etiology of the disease. PeerJ Inc. 2018-10-29 /pmc/articles/PMC6211269/ /pubmed/30397551 http://dx.doi.org/10.7717/peerj.5854 Text en © 2018 Dorani et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Dorani, Faramarz Hu, Ting Woods, Michael O. Zhai, Guangju Ensemble learning for detecting gene-gene interactions in colorectal cancer |
title | Ensemble learning for detecting gene-gene interactions in colorectal cancer |
title_full | Ensemble learning for detecting gene-gene interactions in colorectal cancer |
title_fullStr | Ensemble learning for detecting gene-gene interactions in colorectal cancer |
title_full_unstemmed | Ensemble learning for detecting gene-gene interactions in colorectal cancer |
title_short | Ensemble learning for detecting gene-gene interactions in colorectal cancer |
title_sort | ensemble learning for detecting gene-gene interactions in colorectal cancer |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6211269/ https://www.ncbi.nlm.nih.gov/pubmed/30397551 http://dx.doi.org/10.7717/peerj.5854 |
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