An efficiency analysis of high-order combinations of gene–gene interactions using multifactor-dimensionality reduction

BMC Genomics

Li-Yeh Chuang¹  Cheng-San Yang³  Yu-Da Lin²  Cheng-Hong Yang² 

[1] Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan;Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan;Department of Plastic Surgery, Chia-Yi Christian Hospital, Chiayi, Taiwan

关键词: Multifactor dimensionality reduction;    Gene–gene interactions;    SNPs;   

Others  :  1219238
DOI  :  10.1186/s12864-015-1717-8

 received in 2015-02-21, accepted in 2015-06-24,  发布年份 2015

【 摘 要 】

Background

Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.

Results

Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis.

Conclusion

FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar.

【 授权许可】

   
2015 Yang et al.

【 图 表 】

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

【 参考文献 】

• [1]Li X, Liao B, Chen H. A new technique for generating pathogenic barcodes in breast cancer susceptibility analysis. J Theor Biol. 2015; 366:84-90.
• [2]Yang CH, Lin YD, Chuang LY, Chang HW. Evaluation of Breast Cancer Susceptibility Using Improved Genetic Algorithms to Generate Genotype SNP Barcodes. IEEE ACM T Comput Bi. 2013; 10(2):361-71.
• [3]Yang CH, Chuang LY, Cheng YH, Lin YD, Wang CL, Wen CH et al.. Single nucleotide polymorphism barcoding to evaluate oral cancer risk using odds ratio-based genetic algorithms. Kaohsiung J Med Sci. 2012; 28(7):362-8.
• [4]Steen KV. Travelling the world of gene-gene interactions. Brief. Bioinform. 2012; 13(1):1-19.
• [5]Chuang LY, Lin YD, Chang HW, Yang CH. An improved PSO algorithm for generating protective SNP barcodes in breast cancer. PLoS One. 2012; 7(5): Article ID e37018
• [6]Yang P, Ho JW, Yang YH, Zhou BB. Gene-gene interaction filtering with ensemble of filters. BMC Bioinformatics. 2011; 12 Suppl 1:S10. BioMed Central Full Text
• [7]Shang JL, Zhang JY, Sun Y, Liu D, Ye DJ, Yin YL. Performance analysis of novel methods for detecting epistasis. BMC Bioinformatics. 2011; 12:475. BioMed Central Full Text
• [8]Moore JH, Asselbergs FW, Williams SM. Bioinformatics challenges for genome-wide association studies. Bioinformatics. 2010; 26(4):445-55.
• [9]Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF et al.. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001; 69(1):138-47.
• [10]Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics. 2003; 19(3):376-82.
• [11]Moore JH, Gilbert JC, Tsai CT, Chiang FT, Holden T, Barney N et al.. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol. 2006; 241(2):252-61.
• [12]Chung YJ, Lee SY, Elston RC, Park T. Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions. Bioinformatics. 2007; 23(1):71-6.
• [13]Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, Elston RC et al.. A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet. 2007; 80(6):1125-37.
• [14]Lee SY, Chung Y, Elston RC, Kim Y, Park T. Log-linear model-based multifactor dimensionality reduction method to detect genegene interactions. Bioinformatics. 2007; 23(19):2589-95.
• [15]Li CF, Luo FT, Zeng YX, Jia WH. Weighted risk score-based multifactor dimensionality reduction to detect gene-gene interactions in nasopharyngeal carcinoma. Int J Mol Sci. 2014; 15(6):10724-37.
• [16]Calle ML, Urrea V, Vellalta G, Malats N, Steen KV. Improving strategies for detecting genetic patterns of disease susceptibility in association studies. Stat Med. 2008; 27(30):6532-46.
• [17]Yang CH, Lin YD, Chuang LY, Chen JB, Chang HW. MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction. PLoS One. 2013; 8(11): Article ID e79387
• [18]Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM et al.. A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Gen. Epidemiol. 2007; 31(4):306-15.
• [19]Pattin KA, White BC, Barney N, Gui J, Nelson HH, Kelsey KT et al.. A Computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction. Gen. Epidemiol. 2009; 33(1):87-94.
• [20]Namkung J, Kim K, Yi S, Chung W, Kwon MS, Park T. New evaluation measures for multifactor dimensionality reduction classifiers in gene-gene interaction analysis. Bioinformatics. 2009; 25(3):338-45.
• [21]Agirbasli M, Guney AI, Ozturhan HS, Agirbasli D, Ulucan K, Sevinc D et al.. Multifactor dimensionality reduction analysis of MTHFR, PAI-1, ACE, PON1, and eNOS gene polymorphisms in patients with early onset coronary artery disease. Eur. J. Cardiovasc. Prev. Rehabil. 2011; 18(6):803-9.
• [22]Tsai CT, Hwang JJ, Ritchie MD, Moore JH, Chiang FT, Lai LP et al.. Renin-angiotensin system gene polymorphisms and coronary artery disease in a large angiographic cohort: Detection of high order gene-gene interaction. Atherosclerosis. 2007; 195(1):172-80.
• [23]Moore JH, Williams SM. New strategies for identifying gene-gene interactions in hypertension. Ann. Med. 2002; 34(2):88-95.
• [24]Williams SM, Ritchie MD, Phillips JA, Dawson E, Prince M, Dzhura E et al.. Multilocus analysis of hypertension: A hierarchical approach. Hum Hered. 2004; 57(1):28-38.
• [25]Sanada H, Yatabe J, Midorikawa S, Hashimoto S, Watanabe T, Moore JH et al.. Single-nucleotide polymorphisms for diagnosis of salt-sensitive hypertension. Clin Chem. 2006; 52(3):352-60.
• [26]Gui J, Andrew AS, Andrews P, Nelson HM, Kelsey KT, Karagas MR et al.. A robust multifactor dimensionality reduction method for detecting gene–gene interactions with application to the genetic analysis of bladder cancersusceptibility. Ann. Hum. Genet. 2011; 75(1):20-8.
• [27]Coutinho AM, Sousa I, Martins M, Correia C, Morgadinho T, Bento C et al.. Evidence for epistasis between SLC6A4 and ITGB3 in autism etiology and in the determination of platelet serotonin levels. Hum Genet. 2007; 121(2):243-56.
• [28]Bush WS, Dudek SM, Ritchie MD. Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene-gene interactions. Bioinformatics. 2006; 22(17):2173-4.
• [29]Sinnott-Armstrong NA, Greene CS, Cancare F, Moore JH. Accelerating epistasis analysis in human genetics with consumer graphics hardware. BMC Res Notes. 2009; 2(1):149. BioMed Central Full Text
• [30]Greene CS, Sinnott-Armstrong NA, Himmelstein DS, Park PJ, Moore JH, Harris BT. Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS. Bioinformatics. 2010; 26(5):694-5.
• [31]Urbanowicz RJ, Kiralis J, Sinnott-Armstrong NA, Heberling T, Fisher JM, Moore JH. GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. Biodata Min. 2012; 5:16. BioMed Central Full Text
• [32]Chen JB, Yang YH, Lee WC, Liou CW, Lin TK, Chung YH et al.. Sequence-based polymorphisms in the mitochondrial d-loop and potential SNP predictors for chronic dialysis. PLoS One. 2012; 7(7):e41125.
• [33]Greene CS, White BC, Moore JH: Ant colony optimization for genome-wide genetic analysis. In: Ant Colony Optimization and Swarm Intelligence. Springer; 2008:37–47
• [34]Faigle U, Fujishige S. A general model for matroids and the greedy algorithm. Math Program. 2009; 119(2):353-69.
• [35]Ye K. Experiments: Planning, analysis, and parameter design optimization. Interfaces. 2003; 33(5):96-8.