机器学习方法在基因交互作用探测中的研究进展

doi:10.16288/j.yczz.17-254

遗传 ›› 2018, Vol. 40 ›› Issue (3): 218-226.doi: 10.16288/j.yczz.17-254

机器学习方法在基因交互作用探测中的研究进展

彭哲也,唐紫珺,谢民主()

湖南师范大学物理与信息科学学院,长沙 410081

收稿日期:2017-09-20 修回日期:2017-12-28 出版日期:2018-03-20 发布日期:2018-01-30
基金资助:
国家自然科学基金(编号：61772197,61370172)资助

Research progress in machine learning methods for gene-gene interaction detection

Peng Zheye,Tang Zijun,Xie Minzhu()

College of Physics and Information Science, Hunan Normal University, Changsha 410081, China

Received:2017-09-20 Revised:2017-12-28 Online:2018-03-20 Published:2018-01-30
Supported by:
[Supported by the National Natural Science Foundation of China (Nos. 61772197,61370172)]

摘要/Abstract

摘要：

复杂疾病是基因与基因、基因与环境交互作用的结果,高维基因交互作用的探测给计算带来了极大的挑战。在过去20年间,机器学习方法被用于探测基因-基因交互作用,并取得了一定的效果。本文综述了机器学习方法在基因交互作用探测中的研究进展,系统地介绍了神经网络(neural networks, NN)、随机森林(random forest, RF)、支持向量机(support vector machines, SVM)和多因子降维法(multifactor dimensionality reduction, MDR)等机器学习方法在全基因组关联研究(genome wide association study, GWAS)中探测基因交互作用的原理和局限性,并对未来的研究进行了展望。

关键词: 机器学习, 基因交互, 全基因组关联分析, 单核苷酸多态性, 上位性

Abstract:

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

Key words: machine learning, gene-gene interactions, genome wide association studies, single nucleotide polymorphism, epistasis

彭哲也,唐紫珺,谢民主. 机器学习方法在基因交互作用探测中的研究进展[J]. 遗传, 2018, 40(3): 218-226.

Peng Zheye,Tang Zijun,Xie Minzhu. Research progress in machine learning methods for gene-gene interaction detection[J]. Hereditas(Beijing), 2018, 40(3): 218-226.

参考文献

[1]	Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ , McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TFC, McCarroll SA, Visscher PM. Finding the missing heritability of complex diseases. Nature, 2009,461(7265):747-753.
[2]	Pecanka J, Jonker MA, Bochdanovits Z, Van AW . A powerful and efficient two-stage method for detecting gene-to- gene interactions in GWAS. Biostatistics, 2017,18(3):477-494.
[3]	Li FG, Wang ZP, Hu G, Li H . Current status of SNPs interaction in genome-wide association syudy. Hereditas (Beijing), 2011,33(9):901-910.
[3]	李放歌, 王志鹏, 户国, 李辉 . 全基因组关联研究中的交互作用研究现状. 遗传, 2011,33(9):901-910.
[4]	Li J, Malley JD, Andrew AS, Karagas MR, Moore JH . Detecting gene-gene interactions using a permutation-based random forest method. Biod Min, 2016,9(1):14-31.
[5]	Young JH, Marcotte EM . Predictability of genetic interactions from functional gene modules. G3, 2017,7(2):617-624.
[6]	Wang XG, Lv C, Xu Q, Liu YF . Interactions among polymorphisms of NER genes prompt the risk of transplantation rejection. Hereditas(Beijing), 2017,39(1):22-31.
[6]	王本刚, 吕执, 徐倩, 刘永峰 . 多NER基因多态的交互作用与移植排斥的发病风险相关. 遗传, 2017,39(1):22-31.
[7]	Zhao JY, Zhu Y, Xiong MM . Genome-wide gene-gene interaction analysis for next-generation sequencing. Eur J Hum Genet, 2016,24(3):421-428.
[8]	Anusha AR, Vinodchandra SS. Probabilistic neural network inferences on oligonucleotide classification based on oligo: target interaction. In: Nguyen N, Tojo S, Nguyen L, eds. Intelligent Information and Database Systems. Cham: Springer, 2017: 733-740.
[9]	Li RW, Dudek SM, Kim D, Hall MA, Bradford Y, Peissig PL, Brilliant MH, Linneman JG , McCarty CA, Bao L, Ritchie MD. Identification of genetic interaction networks via an evolutionary algorithm evolved bayesian network. BioData Min, 2016,9:18.
[10]	Tong DL, Boocock DJ, Dhondalay GK, Lemetre C, Ball GR . Artificial neural network inference (ANNI): a study on gene-gene interaction for biomarkers in childhood sarcomas. PLoS One, 2014,9(7):e102483.
[11]	De Poswar FO, Farias LC, De Fraga CA, Bambirra W Jr, Brito-Júnior M, Sousa-Neto MD, Santos SHS, De Paula AMB , D'Angelo MFSV, Guimar?es AL. Interaction network analysis, and neural networks to characterize gene expression of radicular cyst and periapical granuloma. Journal of Endodontics. J Endod, 2015,41(6):877-883.
[12]	Motsinger-Reif AA, Dudek SM, Hahn LW, Ritchie MD . Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology. Genet Epidemiol, 2008,32(4):325-340.
[13]	Tomita Y, Tomida S, Hasegawa Y, Suzuki Y, Shirakawa T, Kobayashi T, Honda H . Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma. BMC Bioinf, 2004,5:120.
[14]	Leung FHF, Lam HK, Ling SH, Tam PKS . Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Netw, 2003,14(1):79-88.
[15]	Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH . Optimization of neural network architecture using genetic programming improves detection and modeling of gene- gene interactions in studies of human diseases. BMC Bioinformatics, 2003,4(1):28-42.
[16]	Manshad AK, Manshad MK, Ashoori S . The application of an artificial neural network (ANN) and a genetic programming neural network (GPNN) for the modeling of experimental data of slim tube permeability reduction by asphaltene precipitation in Iranian crude oil reservoirs. Petroleum Science and Technology, 2012,30(23):2450-2459.
[17]	Motsinger AA, Lee SL, Mellick G, Ritchie MD . GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC Bioinf, 2006,7:39.
[18]	De Campos LML, De Oliveira RCL, Roisenberg M . Optimization of neural networks through grammatical evolution and a genetic algorithm. Expert Systems with Applications, 2016,56:368-384.
[19]	Breiman L . Random forests. Machine Learning, 2001,45(1):5-32.
[20]	Yoo W, Ference BA, Cote ML, Schwartz A . A comparison of logistic regression, logic regression, classification tree, and random forests to identify effective gene-gene and gene-environmental interactions. Int J Appl Sci Technol, 2012,2(7):268.
[21]	Austin PC, Tu JV, Ho JE, Levy D, Lee DS . Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. J Clin Epidemiol, 2013,66(4):398-407.
[22]	Nguyen TT, Huang JZ, Wu Q, Nguyen T, Li M . Genome-wide association data classification and SNPs selection using two-stage quality-based random forests. BMC Genomics, 2015,16(Suppl.2):S5.
[23]	Chen J, Zhou Y, Gao YQ, Cao WJ, Sun H, Liu YF, Wang C . A genetic features and gene interaction study for identifying the genes that cause hereditary spherocytosis. Hematology, 2017,22(4):240-247.
[24]	Bureau A, Dupuis J, Falls K, Lunetta KL, Hayward B, Keith TP, Van Eerdewegh P . Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol, 2005,28(2):171-182.
[25]	Chen X, Ishwaran H . Pathway hunting by random survival forests. Bioinformatics, 2013,29(1):99-105.
[26]	Winham SJ, Colby CL, Freimuth RR, Wang X, De Andrade M, Huebner M, Biernacka JM . SNP interaction detection with Random Forests in high-dimensional genetic data. BMC Bioinf, 2012,13:164.
[27]	Yoshida M, Koike A . SNPInterForest: a new method for detecting epistatic interactions. BMC Bioinf, 2011,12(1):469-479.
[28]	Pan QX, Hu T, Malley JD, Andrew AS, Karagas MR, Moore JH. Supervising random forest using attribute interaction networks. In: Vanneschi L, Bush WS, Giacobini M, eds. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2013. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 104-116.
[29]	Cortes C, Vapnik V . Support-vector networks. Machine Learning, 1995,20(3):273-297.
[30]	Sehhati M R, Dehnavi A M, Rabbani H, Javanmard SH . Using protein interaction database and support vector machines to improve gene signatures for prediction of breast cancer recurrence. J Med Signals Sens, 2013,3(2):87-93.
[31]	Qi ZQ, Tian YJ, Shi Y . Robust twin support vector machine for pattern classification. Patt Recognit, 2013,46(1):305-316.
[32]	Listgarten J, Damaraju S, Poulin B, Cook L, Dufour J, Driga A, Mackey J, Wishart D, Greiner R, Zanke B . Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms. Clin Cancer Res, 2004,10(8):2725-2737.
[33]	Chen SH, Sun JL, Dimitrov L, Turner AR, Adams TS, Meyers DA, Chang BL, Zheng SL, Gr?nberg H, Xu JF, Hsu FC . A support vector machine approach for detecting gene-gene interaction. Genet Epidemiol, 2008,32(2):152-167.
[34]	Shen YY, Liu Z, Ott J . Support vector machines with L1 penalty for detecting gene-gene interactions. Int J Data Min Bioinform, 2012,6(5):463-470.
[35]	Ban HJ, Heo JY, Oh KS, Park KJ . Identification of type 2 diabetes-associated combination of SNPs using support vector machine. BMC Genet, 2010,11:26.
[36]	Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH . Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet, 2001,69(1):138-147.
[37]	Lee S, Son D, Yu WB, Park T . Gene-gene interaction analysis for the accelerated failure time model using a unified model-based multifactor dimensionality reduction method. Genom Inform, 2016,14(4):166-172.
[38]	Park MY, Hastie T . Penalized logistic regression for detecting gene interactions. Biostatistics, 2007,9(1):30-50.
[39]	Gui J, Moore JH, Williams SM, Andrews P, Hillege HL , Van Der Harst P, Navis G, Van Gilst WH, Asselbergs FW, Gilbert-Diamond D. A simple and computationally efficient approach to multifactor dimensionality reduction analysis of gene-gene interactions for quantitative traits. PLoS One, 2013,8(6):e66545.
[40]	Tsai CT, Lai LP, Lin JL, Chiang FT, Hwang JJ, Ritchie MD, Moore JH, Hsu KL, Tseng CD, Liau CS, Tseng YZ . Renin-angiotensin system gene polymorphisms and atrial fibrillation. Circulation, 2004,13(6):1640-1646.
[41]	Su MW, Tung KY, Liang PH, Tsai CH, Kuo NW, Lee YL . Gene-gene and gene-environmental interactions of childhood asthma: a multifactor dimension reduction approach. PLoS One, 2012,7(2):e30694.
[42]	He H, Oetting WS, Brott MJ, Basu S . Power of multifactor dimensionality reduction and penalized logistic regression for detecting gene-gene interaction in a case-control study. BMC Med Genet, 2009,10(1):127-144.
[43]	Yu W, Lee S, Park T . A unified model based multifactor dimensionality reduction framework for detecting gene- gene interactions. Bioinformatics, 2016,32(17):i605-i610.
[44]	Leem S, Park T . An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions. BMC Genom, 2017,18(S2):115-127.
[45]	Gui J, Andrew AS, Andrews P, Nelson HM, Kelsey KT, Karagas MR, Moore JH . A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility. Ann Hum Genet, 2011,75(1):20-28.
[46]	Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, Elston RC, Li MD . A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. AJHG, 2007,80(6):1125-1137.
[47]	Chen GB, Liu NJ, Klimentidis YC, Zhu XF, Zhi DG, Wang XJ, Lou XY . A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence. Hum Genet, 2014,133(2):139-150.
[48]	Kwon MS, Kim K, Lee S, Chung W, Yi SG, Namkung J, Park T . GWAS-GMDR: a program package for genome- wide scan of gene-gene interactions with covariate adjusttment based on multifactor dimensionality reduction. IEEE International Conference on Bioinformatics and Biomedicine Workshops. Washington, DC: IEEE, 2011: 703-707.
[49]	Wang XS, Cheng YH, Zhang L. Machine learning method in bioinformatics. Beijing: Science Press, 2014.
[49]	王雪松, 程玉虎, 张林 . 生物信息学中的机器学习分析方法. 北京: 科学出版社, 2014.
[50]	Li SY, Cui YH . Gene-centric gene-gene interaction: a model-based kernel machine method. Annals of Applied Statistics, 2012,6(3):1134-1161.
[51]	Mellick GD, Silburn PA, Prince JA, Brookes AJ . A novel screen for nuclear mitochondrial gene associations with Parkinson's disease. J Neural Transm, 2004,111(2):191-199.
[52]	Li Q, Kim Y, Suktitipat B, Hetmanski JB, Marazita ML, Duggal P, Beaty TH, Bailey-Wilson JE . Gene-gene interaction among WNT genes for oral cleft in trios. Genet Epidemiol, 2015,39(5):385-394.

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