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PeerJ. 2018 Oct 29;6:e5854. doi: 10.7717/peerj.5854. eCollection 2018.

Ensemble learning for detecting gene-gene interactions in colorectal cancer.

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Department of Computer Science, Memorial University, St. John's, Newfoundland and Labrador, Canada.
Faculty of Medicine, Memorial University, St. John's, Newfoundland and Labrador, Canada.
Contributed equally


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.


Colorectal cancer; Complex diseases; Ensemble learning; Epistasis; Gene-gene interaction; Genetic marker discovery; Gradient boosting machine; Random forests

Conflict of interest statement

The authors declare that they have no competing interests.

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