Abstract
Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction–based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches’ strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively.
Graphical abstract
Similar content being viewed by others
References
National Cancer I (2007) What is cancer? In: Cancer.gov. www.cancer.gov/about-cancer/understanding/what-is-cancer
Guan X (2015) Cancer metastases: challenges and opportunities. Acta Pharm Sin B 5:402–418. https://doi.org/10.1016/j.apsb.2015.07.005
Barnes JL, Zubair M, John K, Poirier MC, Martin FL (2018) Carcinogens and DNA damage. Biochem Soc Trans 46:1213–1224. https://doi.org/10.1042/BST20180519
WHO (2019) Cancer. www.who.int/health-topics/cancer#tab=tab_1
Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I BF (2018) Global cancer observatory: cancer today. In: Int. Agency Res. Cancer. https://gco.iarc.fr/today
Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90:7–24. https://doi.org/10.1016/j.ajhg.2011.11.029
Niel C, Sinoquet C, Dina C, Rocheleau G (2015) A survey about methods dedicated to epistasis detection. Front Genet 6:6. https://doi.org/10.3389/fgene.2015.00285
National Cancer I (2017) Genetics. In: Cancer.gov. https://www.cancer.gov/about-cancer/causes-prevention/genetics
Moore JH, Williams SM (2002) New strategies for identifying gene-gene interactions in hypertension. Ann Med 34:88–95. https://doi.org/10.1080/07853890252953473
Talseth-Palmer BA, Scott RJ (2011) Genetic variation and its role in malignancy. Int J Biomed Sci 7:158–171
Altshuler D, Gibbs R, Peltonen L et al (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467:52–58. https://doi.org/10.1038/nature09298
García-González I, López-Díaz RI, Canché-Pech JR et al (2018) Epistasis analysis of metabolic genes polymorphisms associated with ischemic heart disease in Yucatan. Clín Investig Arterioscler (English Ed) 30:102–111. https://doi.org/10.1016/j.artere.2017.11.004
Genetics Home Reference (2018) What are single nucleotide polymorphisms (SNPs)? https://ghr.nlm.nih.gov/primer/genomicresearch/snp
Wienbrandt L, Kassens J, Hübenthal M, Ellinghaus D (2017) fast genome-wide third-order SNP interaction tests with information gain on a low-cost heterogeneous parallel FPGA-GPU computing architecture. Procedia Comput Sci 108:596–605. https://doi.org/10.1016/j.procs.2017.05.210
Roy T, Bhattacharjee P (2020) Performance analysis of melanoma classifier using electrical modeling technique. Med Biol Eng Comput 58:2443–2454. https://doi.org/10.1007/s11517-020-02241-6
Roy T, Bhattacharjee P (2020) A LabVIEW-based real-time modeling approach for detection of abnormalities in cancer cells. Gene Reports 20:100788. https://doi.org/10.1016/j.genrep.2020.100788
Roy T (2019) Analysis of cancer gene attributes using electrical sensor. Gene 685:62–69. https://doi.org/10.1016/j.gene.2018.10.073
WHO Genetics in Prevention and Treatment of Cancer. https://www.who.int/genomics/about/Cancer.pdf
Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69:138–147. https://doi.org/10.1086/321276
Moore J, Gilbert J, Tsai C-T et al (2006) A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol 241:252–261. https://doi.org/10.1016/j.jtbi.2005.11.036
Manuguerra M, Matullo G, Veglia F, Autrup H, Dunning AM, Garte S, Gormally E, Malaveille C, Guarrera S, Polidoro S, Saletta F, Peluso M, Airoldi L, Overvad K, Raaschou-Nielsen O, Clavel-Chapelon F, Linseisen J, Boeing H, Trichopoulos D, Kalandidi A, Palli D, Krogh V, Tumino R, Panico S, Bueno-de-Mesquita HB, Peeters PH, Lund E, Pera G, Martinez C, Amiano P, Barricarte A, Tormo MJ, Quiros JR, Berglund G, Janzon L, Jarvholm B, Day NE, Allen NE, Saracci R, Kaaks R, Ferrari P, Riboli E, Vineis P (2007) Multi-factor dimensionality reduction applied to a large prospective investigation on gene-gene and gene-environment interactions. Carcinogenesis 28:414–422. https://doi.org/10.1093/carcin/bgl159
Cao G, Lu H, Feng J, Shu J, Zheng D, Hou Y (2008) Lung cancer risk associated with thr495pro polymorphism of GHR in chines population. Jpn J Clin Oncol 38:308–316. https://doi.org/10.1093/jjco/hyn007
Milne RI, Fagerholm R, Nevanlinna H, BenÍtez J (2008) The importance of replication in gene-gene interaction studies: multifactor dimensionality reduction applied to a two-stage breast cancer case-control study. Carcinogenesis 29:1215–1218. https://doi.org/10.1093/carcin/bgn120
Pattin KA, White BC, Barney N, Gui J, Nelson HH, Kelsey KT, Andrew AS, Karagas MR, Moore JH (2009) A computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction. Genet Epidemiol 33:87–94. https://doi.org/10.1002/gepi.20360
Huo X, Lu C, Huang X, Hu Z, Jin G, Ma H, Wang X, Qin J, Wang X, Shen H, Tang J (2009) Polymorphisms in BRCA1, BRCA1-interacting genes and susceptibility of breast cancer in Chinese women. J Cancer Res Clin Oncol 135:1569–1575. https://doi.org/10.1007/s00432-009-0604-6
Gui J, Andrew AS, Andrews P, Nelson HM, Kelsey KT, Karagas MR, Moore JH (2010) A simple and computationally efficient sampling approach to covariate adjustment for multifactor dimensionality reduction analysis of epistasis. Hum Hered 70:219–225. https://doi.org/10.1159/000319175
Wu Y, Zhang L, Liu L et al (2011) A multifactor dimensionality reduction-logistic regression model of gene polymorphisms and an environmental interaction analysis in cancer research. Asian Pac J Cancer Prev 12:2887–2892
Landa I, Boullosa C, Inglada-Pérez L, Sastre-Perona A, Pastor S, Velázquez A, Mancikova V, Ruiz-Llorente S, Schiavi F, Marcos R, Malats N, Opocher G, Diaz-Uriarte R, Santisteban P, Valencia A, Robledo M (2013) An epistatic Interaction between the PAX8 and STK17B genes in papillary thyroid cancer susceptibility. PLoS One 8:e74765. https://doi.org/10.1371/journal.pone.0074765
Mostowska A, Sajdak S, Pawlik P, Lianeri M, Jagodzinski PP (2013) DNMT1, DNMT3A and DNMT3B gene variants in relation to ovarian cancer risk in the Polish population. Mol Biol Rep 40:4893–4899. https://doi.org/10.1007/s11033-013-2589-0
Luzón-Toro B, Bleda M, Navarro E, García-Alonso L, Ruiz-Ferrer M, Medina I, Martín-Sánchez M, Gonzalez CY, Fernández RM, Torroglosa A, Antiñolo G, Dopazo J, Borrego S (2015) Identification of epistatic interactions through genome-wide association studies in sporadic medullary and juvenile papillary thyroid carcinomas. BMC Med Genet 8:1–9. https://doi.org/10.1186/s12920-015-0160-7
Marcus MW, Raji OY, Duffy SW et al (2016) Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model. Int J Oncol 49:361–370. https://doi.org/10.3892/ijo.2016.3499
Gui J, Andrew AS, Andrews P, Nelson HM, Kelsey KT, Karagas MR, Moore JH (2011) A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility. Ann Hum Genet 75:20–28. https://doi.org/10.1111/j.1469-1809.2010.00624.x
Gui J, Moore JH, Kelsey KT, Marsit CJ, Karagas MR, Andrew AS (2011) A novel survival multifactor dimensionality reduction method for detecting gene-gene interactions with application to bladder cancer prognosis. Hum Genet 129:101–110. https://doi.org/10.1007/s00439-010-0905-5
Luyapan J, Ji X, Zhu D, et al (2019) An efficient survival multifactor dimensionality reduction method for detecting gene-gene interactions of lung cancer onset age. Proc - 2018 IEEE Int Conf Bioinforma Biomed BIBM 2018 2779–2781. https://doi.org/10.1109/BIBM.2018.8621534
Fu OY, Chang HW, Da Lin Y et al (2016) Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER). Oncol Rep 36:1739–1747. https://doi.org/10.3892/or.2016.4956
Li CF, Luo FT, Zeng YX, Jia WH (2014) Weighted risk score-based multifactor dimensionality reduction to detect gene-gene interactions in nasopharyngeal carcinoma. Int J Mol Sci 15:10724–10737. https://doi.org/10.3390/ijms150610724
Cao X, Yu G, Ren W, Guo M, Wang J (2020) DualWMDR: detecting epistatic interaction with dual screening and multifactor dimensionality reduction. Hum Mutat 41:719–734. https://doi.org/10.1002/humu.23951
Park M, Lee J, Park T, Lee S (2020) Gene-gene interaction analysis for the survival phenotype based on the Kaplan-Meier median estimate. Biomed Res Int 2020:1–10. https://doi.org/10.1155/2020/5282345
Musani S, Shriner D, Liu N et al (2007) Detection of gene × gene interactions in genome-wide association studies of human population data. Hum Hered 63:67–84. https://doi.org/10.1159/000099179
Fritsch A, Ickstadt K (2007) Comparing logic regression based methods for identifying SNP interactions. Lect Notes Comput Sci 4414 LNBI:90–103. https://doi.org/10.1007/978-3-540-71233-6_8
Schwender H, Ickstadt K (2008) Identification of SNP interactions using logic regression. Biostatistics 9:187–198. https://doi.org/10.1093/biostatistics/kxm024
Wei Z, Li M, Rebbeck T, Li H (2008) U-statistics-based tests for multiple genes in genetic association studies. Ann Hum Genet 72:821–833. https://doi.org/10.1111/j.1469-1809.2008.00473.x
Wang T, Ho G, Ye K, Strickler H, Elston RC (2009) A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped. Genet Epidemiol 33:6–15. https://doi.org/10.1002/gepi.20351
Lin C, Chu CM, Su SL (2016) Epistasis test in meta-analysis: a multi-parameter Markov chain Monte Carlo model for consistency of evidence. PLoS One 11:1–17. https://doi.org/10.1371/journal.pone.0152891
Che K, Liu X, Guo M, et al (2017) Epistasis detection using a permutation-based gradient boosting machine. Proc - 2016 IEEE Int Conf Bioinforma Biomed BIBM 2016 1247–1252. https://doi.org/10.1109/BIBM.2016.7822697
Stanislas V, Dalmasso C, Ambroise C (2017) Eigen-epistasis for detecting gene-gene interactions. BMC Bioinform 18:1–14. https://doi.org/10.1186/s12859-017-1488-0
Matlak D, Szczurek E (2017) Epistasis in genomic and survival data of cancer patients. PLoS Comput Biol 13:1–16. https://doi.org/10.1371/journal.pcbi.1005626
Dorani (2018) A genetic algorithm for community. Appl Evol Comput:159–170. https://doi.org/10.1007/978-3-319-77538-8
Muellner MK, Duernberger G, Ganglberger F, Kerzendorfer C, Uras IZ, Schoenegger A, Bagienski K, Colinge J, Nijman SMB (2014) TOPS: a versatile software tool for statistical analysis and visualization of combinatorial gene-gene and gene-drug interaction screens. BMC Bioinform 15. https://doi.org/10.1186/1471-2105-15-98
Li J, Li H, Lv X et al (2018) Polymorphism in lncRNA AC016683.6 and its interaction with smoking exposure on the susceptibility of lung cancer. Cancer Cell Int 18:2. https://doi.org/10.1186/s12935-018-0591-2
Ghorbian S, M. N, S. T et al (2018) Association of genetic variations in XRCC1 and ERCC1 genes with sporadic breast cancer. Gene, Cell Tissue 5:e80166. https://doi.org/10.5812/gct.80166
Xue W, Mengyun W, Li Z et al (2018) Genetic variants within MTORC1 genes predict gastric cancer prognosis in Chinese populations. J Cancer 9:1448–1454. https://doi.org/10.7150/jca.23566
Gunathilake MN, Lee J, Cho YA et al (2018) Interaction between physical activity, PITX1 rs647161 genetic polymorphism and colorectal cancer risk in a Korean population: a case-control study. Oncotarget 9:7590–7603. https://doi.org/10.18632/oncotarget.24136
Hind J, Lisboa P, Hussain A, Al-Jumeily D (2019) A novel approach to detecting epistasis using random sampling regularisation. IEEE/ACM Trans Comput Biol Bioinforma PP:1. https://doi.org/10.1109/TCBB.2019.2948330
Catalano C, da Silva Filho MI, Frank C et al (2020) Epistatic effect of TLR3 and cGAS-STING-IKKε-TBK1-IFN signaling variants on colorectal cancer risk. Cancer Med 9:1473–1484. https://doi.org/10.1002/cam4.2804
Li H, Duan N, Zhang Q, Shao Y (2019) IL1A & IL1B genetic polymorphisms are risk factors for thyroid cancer in a Chinese Han population. Int Immunopharmacol 76:105869. https://doi.org/10.1016/j.intimp.2019.105869
Li W, Jia M, Wang J, Lu J, Deng J, Tang J, Liu C (2019) Association of MMP9-1562C/T and MMP13-77A/G polymorphisms with non-small cell lung cancer in southern Chinese population. Biomolecules 9. https://doi.org/10.3390/biom9030107
Sastre Tomas J, Cardenas J, Heine Suñer D, Capriotti E (2018) Detecting cancer-associated epistatic gene variants in lung adenocarcinoma
Li Y, Xiao X, Bossé Y et al (2019) Genetic interaction analysis among oncogenesis-related genes revealed novel genes and networks in lung cancer development. Oncotarget 10:1760–1774. https://doi.org/10.18632/oncotarget.26678
Tang D, Liu H, Zhao Y, Qian D, Luo S, Patz EF Jr, Su L, Shen S, ChristianI D, Gao W, Wei Q (2020) Genetic variants of BIRC3 and NRG1 in the NLRP3 inflammasome pathway are associated with non-small cell lung cancer survival. Am J Cancer Res 10:2582–2595
Wu Y, Yang S, Liu H, Luo S, Stinchcombe TE, Glass C, Su L, Shen S, Christiani DC, Wang Q, Wei Q (2020) Novel genetic variants of KIR3DL2 and PVR involved in immunoregulatory interactions are associated with non-small cell lung cancer survival. Am J Cancer Res 10:1770–1784
Kudhair B, Alabid N, Zayed K et al (2020) The correlation of combined OGG1, CYP1A1 and GSTP1 gene variants and risk of lung cancer of male Iraqi waterpipe tobacco smokers. Mol Biol Rep 47. https://doi.org/10.1007/s11033-020-05589-y
Haoyu Z, Zhao N, Ahearn T, et al (2018) A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics
Kapoor PM, Lindström S, Behrens S, Wang X, Michailidou K, Bolla MK, Wang Q, Dennis J, Dunning AM, Pharoah PDP, Schmidt MK, Kraft P, García-Closas M, Easton DF, Milne RL, Chang-Claude J, on behalf of Breast Cancer Association Consortium (2020) Assessment of interactions between 205 breast cancer susceptibility loci and 13 established risk factors in relation to breast cancer risk, in the Breast Cancer Association Consortium. Int J Epidemiol 49:216–232. https://doi.org/10.1093/ije/dyz193
Rahmani F, Hasanzadeh M, Hassanian S et al (2020) Association of a genetic variant in the angiopoietin-like protein 4 gene with cervical cancer. Pathol Res Pract 216:153011. https://doi.org/10.1016/j.prp.2020.153011
Fan Y, Gu X, Pan H, Dai Z, Zou C, Gao Z, Zhang H (2020) Association of genetic polymorphisms in TNFRSF11 with the Progression of Genetic Susceptibility to Gastric Cancer. J Oncol 2020:1–9. https://doi.org/10.1155/2020/4103264
Andrew AS, Hu T, Gu J et al (2012) HSD3B and gene-gene interactions in a pathway-based analysis of genetic susceptibility to bladder cancer. PLoS One 7. https://doi.org/10.1371/journal.pone.0051301
Hu T, Andrew AS, Karagas MR, Moore JH (2015) Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks. BioData Min 8:1–11. https://doi.org/10.1186/s13040-015-0062-4
Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH (2011) Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinform 12:364. https://doi.org/10.1186/1471-2105-12-364
Hu T, Pan Q, Andrew AS, Langer JM, Cole MD, Tomlinson CR, Karagas MR, Moore JH (2014) Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility. BioData Min 7:1–9. https://doi.org/10.1186/1756-0381-7-5
Larson NB, Jenkins GD, Larson MC et al (2014) Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer. Eur J Hum Genet 22:126–131. https://doi.org/10.1038/ejhg.2013.69
Wakefield J, De Vocht F, Hung RJ (2010) Bayesian mixture modeling of gene-environment and gene-gene interactions. Genet Epidemiol 34:16–25. https://doi.org/10.1002/gepi.20429
Anunciação O, Vinga S, Oliveira AL (2013) Using Information Interaction to discover epistatic effects in complex diseases. PLoS One 8:e76300. https://doi.org/10.1371/journal.pone.0076300
Fan R, Zhong M, Wang S, Zhang Y, Andrew A, Karagas M, Chen H, Amos CI, Xiong M, Moore JH (2011) Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases. Genet Epidemiol 35:706–721. https://doi.org/10.1002/gepi.20621
Talluri R, Shete S (2015) Evaluating methods for modeling epistasis networks with application to head and neck cancer. Cancer Informat 14:17–23. https://doi.org/10.4137/CIN.S17289
Tian XW, Lim JS (2015) Interactive naive Bayesian network: a new approach of constructing gene-gene interaction network for cancer classification. Biomed Mater Eng 26:S1929–S1936. https://doi.org/10.3233/BME-151495
Zeng Z, Jiang X, Neapolitan R (2016) Discovering causal interactions using Bayesian network scoring and information gain. BMC Bioinform 17:1–14. https://doi.org/10.1186/s12859-016-1084-8
Assareh A, Volkert LG, Li J (2012) Interaction trees: optimizing ensembles of decision trees for gene-gene interaction detections. In: 2012 11th International Conference on Machine Learning and Applications. pp 616–621
Cook NR, Zee RYL, Ridker PM (2004) Tree and spline based association analysis of gene-gene interaction models for ischemic stroke. Stat Med 23:1439–1453. https://doi.org/10.1002/sim.1749
Lin HY, Wang W, Liu YH, Soong SJ, York TP, Myers L, Hu JJ (2008) Comparison of multivariate adaptive regression splines and logistic regression in detecting SNP-SNP interactions and their application in prostate cancer. J Hum Genet 53:802–811. https://doi.org/10.1007/s10038-008-0313-z
Wolf BJ, Hill EG, Slate EH, Neumann CA, Kistner-Griffin E (2012) LBoost: a boosting algorithm with application for epistasis discovery. PLoS One 7:1–8. https://doi.org/10.1371/journal.pone.0047281
Wu X, Tang H, Guan A, Sun F, Wang H, Shu J (2016) Finding gastric cancer related genes and clinical biomarkers for detection based on gene-gene interaction network. Math Biosci 276:1–7. https://doi.org/10.1016/j.mbs.2015.12.001
Li J, Malley JD, Andrew AS, Karagas MR, Moore JH (2016) Detecting gene-gene interactions using a permutation-based random forest method. BioData Min 9:1–17. https://doi.org/10.1186/s13040-016-0093-5
Dorani F, Hu T, Woods MO, Zhai G (2018) Ensemble learning for detecting gene-gene interactions in colorectal cancer. PeerJ 6:e5854. https://doi.org/10.7717/peerj.5854
Zhu R, Zhao H, Ma S (2014) Identifying gene-environment and gene-gene interactions using a progressive penalization approach. Genet Epidemiol 38:353–368. https://doi.org/10.1002/gepi.21807
Wu M, Huang J, Ma S (2018) Identifying gene-gene interactions using penalized tensor regression. Stat Med 37:598–610. https://doi.org/10.1002/sim.7523
Auton A, Abecasis G, Durbin RM et al (2015) A global reference for human genetic variation. Nature 526:68–74
Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J (2017) 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101:5–22. https://doi.org/10.1016/j.ajhg.2017.06.005
Dasgupta A, Sun Y, König I et al (2011) Brief review of regression-based and machine learning methods in genetic epidemiology: the genetic analysis workshop 17 experience. Genet Epidemiol 35(Suppl 1):S5–S11. https://doi.org/10.1002/gepi.20642
Xu C, Jackson S (2019) Machine learning and complex biological data. Genome Biol 20:76. https://doi.org/10.1186/s13059-019-1689-0
Okser S, Pahikkala T, Airola A, Salakoski T, Ripatti S, Aittokallio T (2014) Regularized machine learning in the genetic prediction of complex traits. PLoS Genet 10:e1004754
Abraham G, Inouye M (2015) Genomic risk prediction of complex human disease and its clinical application. Curr Opin Genet Dev 33:10–16. https://doi.org/10.1016/j.gde.2015.06.005
Chen XW, Kim S, Wu C, Xu D (2009) 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009: Preface. 2009 IEEE Int Conf Bioinforma Biomed BIBM 2009. https://doi.org/10.1109/BIBM.2009.4
Bhattacharjee S, Wang Z, Ciampa J, Kraft P, Chanock S, Yu K, Chatterjee N (2010) Using principal components of genetic variation for robust and powerful detection of gene-gene interactions in case-control and case-only studies. Am J Hum Genet 86:331–342. https://doi.org/10.1016/j.ajhg.2010.01.026
Seal DB, Saha S, Chatterjee M, et al (2016) Gene - gene interaction: a clustering, correlation & entropy based approach. 2016 IEEE 7th Annu Ubiquitous Comput Electron Mob Commun Conf UEMCON 2016. https://doi.org/10.1109/UEMCON.2016.7777833
Bornelöv S, Marillet S, Komorowski J (2014) Ciruvis: a web-based tool for rule networks and interaction detection using rule-based classifiers. BMC Bioinform 15:1–12. https://doi.org/10.1186/1471-2105-15-139
Uppu S, Krishna A, Gopalan RP (2016) A deep learning approach to detect SNP interactions. J Softw 11:965–975. https://doi.org/10.17706/jsw.11.10.965-975
Cao X, Yu G, Liu J, Jia L, Wang J (2018) ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information. Int J Mol Sci 19:19. https://doi.org/10.3390/ijms19082267
Kim D, Li R, Dudek SM, Frase AT, Pendergrass SA, Ritchie MD (2014) Knowledge-driven genomic interactions: an application in ovarian cancer. BioData Min 7:1–11. https://doi.org/10.1186/1756-0381-7-20
Zhang L, Liu H, Huang Y, Wang X, Chen Y, Meng J (2017) Cancer progression prediction using gene interaction regularized elastic net. IEEE/ACM Trans Comput Biol Bioinforma 14:145–154. https://doi.org/10.1109/TCBB.2015.2511758
Yung L, Yang C, Wan X, Yu W (2011) GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies. Bioinformatics 27:1309–1310. https://doi.org/10.1093/bioinformatics/btr114
Yang CH, Yang HS, Chuang LY (2019) PBMDR: a particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions. J Theor Biol 461:68–75. https://doi.org/10.1016/j.jtbi.2018.10.012
Yang CH, Chuang LY, Chen YJ, Tseng HF, Chang HW (2011) Computational analysis of simulated SNP interactions between 26 growth factor-related genes in a breast cancer association study. Omi A J Integr Biol 15:399–407. https://doi.org/10.1089/omi.2010.0028
Nunkesser R, Bernholt T, Schwender H, Ickstadt K, Wegener I (2007) Detecting high-order interactions of single nucleotide polymorphisms using genetic programming. Bioinformatics 23:3280–3288. https://doi.org/10.1093/bioinformatics/btm522
Ou-Yang F, Da Lin Y, Chuang LY et al (2015) The combinational polymorphisms of ORAI1 gene are associated with preventive models of breast cancer in the Taiwanese. Biomed Res Int 2015. https://doi.org/10.1155/2015/281263
Wang X, Peng Q, Fan Y (2016) Detecting susceptibility to breast cancer with SNP-SNP interaction using BPSOHS and emotional neural networks. Biomed Res Int 2016:1–7. https://doi.org/10.1155/2016/5164347
Liu J, Yu G, Jiang Y, Wang J (2017) HiSeeker: detecting high-order SNP interactions based on pairwise SNP combinations. Genes (Basel) 8:2–19. https://doi.org/10.3390/genes8060153
Li X, Zhang S, Wong KC (2018) Nature-inspired multiobjective epistasis elucidation from genome-wide association studies. IEEE/ACM Trans Comput Biol Bioinforma 1–12. https://doi.org/10.1109/TCBB.2018.2849759
Baryshnikova A, Costanzo M, Myers CL, Andrews B, Boone C (2013) Genetic interaction networks: toward an understanding of heritability. Annu Rev Genomics Hum Genet 14:111–133. https://doi.org/10.1146/annurev-genom-082509-141730
Mair B, Moffat J, Boone C, Andrews BJ (2019) Genetic interaction networks in cancer cells. Curr Opin Genet Dev 54:64–72. https://doi.org/10.1016/j.gde.2019.03.002
Montojo J, Zuberi K, Rodriguez H et al (2014) GeneMANIA: fast gene network construction and function prediction for Cytoscape. F1000Research 3:153. https://doi.org/10.12688/f1000research.4572.1
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
ESM 1
(DOCX 24 kb)
Rights and permissions
About this article
Cite this article
Manavalan, R., Priya, S. Genetic interactions effects for cancer disease identification using computational models: a review. Med Biol Eng Comput 59, 733–758 (2021). https://doi.org/10.1007/s11517-021-02343-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-021-02343-9