EpiMiner: A three-stage co-information based method for detecting and visualizing epistatic interactions

Digital Signal Processing: A Review Journal

Volumn 24, Issue , 2014, Pages 1-13

  Shang, Junliang ^a,b   Zhang, Junying ^b   Sun, Yan ^a   Zhang, Yuanke ^a  

^a QUFU NORMAL UNIVERSITY   (China)

^b XIDIAN UNIVERSITY   (China)

Author keywords

Co information; Epistatic interactions; Genomic signal processing; Single nucleotide polymorphisms

Indexed keywords

COMPUTATION THEORY; MATLAB; NUCLEOTIDES; SIGNAL PROCESSING;

AGE-RELATED MACULAR DEGENERATION; COMPUTATIONAL CHALLENGES; EPISTATIC INTERACTIONS; GENOMIC-SIGNAL-PROCESSING; INTERACTIVE EFFECT; SIMULATED DATASETS; SINGLE NUCLEOTIDE POLYMORPHISMS; SUPPORT VECTOR MACHINE CLASSIFIERS;

CLASSIFICATION (OF INFORMATION);

EID: 84883626554     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2013.08.007     Document Type: Article

References (53)

1. [1] Graphviz homepage http://graphviz.org/.
2. [2] Adams, M.K.M., Simpson, J.A., Richardson, A.J., Guymer, R.H., Williamson, E., Cantsilieris, S., English, D.R., Aung, K.Z., Makeyeva, G.A., Giles, G.G., Can genetic associations change with age? CFH and age-related macular degeneration. Hum. Mol. Genet., 2012, 5229–5236.
3. [3] Anastassiou, D., Genomic signal processing. IEEE Signal Process. Mag. 18 (2001), 8–20.
4. [4] Andrew, A.S., Nelson, H.H., Kelsey, K.T., Moore, J.H., Meng, A.C., Casella, D.P., Tosteson, T.D., Schned, A.R., Karagas, M.R., Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility. Carcinogenesis 27 (2006), 1030–1037.
5. [5] Bell, A.J., The co-information lattice. Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation, 2003, 921–926.
6. [6] Chanda, P., Sucheston, L., Liu, S., Zhang, A., Ramanathan, M., Information-theoretic gene–gene and gene–environment interaction analysis of quantitative traits. BMC Genomics, 10, 2009, 509.
7. [7] Chanda, P., Sucheston, L., Zhang, A., Brazeau, D., Freudenheim, J.L., Ambrosone, C., Ramanathan, M., AMBIENCE: A novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes. Genetics 180 (2008), 1191–1210.
8. [8] Chanda, P., Sucheston, L., Zhang, A., Ramanathan, M., The interaction index, a novel information-theoretic metric for prioritizing interacting genetic variations and environmental factors. Eur. J. Hum. Genet. 17 (2009), 1274–1286.
9. [9] Chanda, P., Zhang, A., Brazeau, D., Sucheston, L., Freudenheim, J.L., Ambrosone, C., Ramanathan, M., Information-theoretic metrics for visualizing gene–environment interactions. Am. J. Hum. Genet. 81 (2007), 939–963.
10. [10] Chen, L., Yu, G., Langefeld, C.D., Miller, D.J., Guy, R.T., Raghuram, J., Yuan, X., Herrington, D.M., Wang, Y., Comparative analysis of methods for detecting interacting loci. BMC Genomics, 12, 2011, 344.
11. [11] Chen, L., Yu, G., Miller, D.J., Song, L., Langefeld, C., Herrington, D., Liu, Y., Wang, Y., A ground truth based comparative study on detecting epistatic SNPs. Proceedings of 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2009, 26–31.
12. [12] Cordell, H.J., Epistasis: What it means, what it doesnʼt mean, and statistical methods to detect it in humans. Hum. Mol. Genet. 11 (2002), 2463–2468.
13. [13] Dougherty, E.R., Shmulevich, I., Chen, J., Wang, Z.J., Genomic Signal Processing and Statistics. 2005, Hindawi Publishing Corporation.
14. [14] Ebermann, I., Wiesen, M.H., Zrenner, E., Lopez, I., Pigeon, R., Kohl, S., Lowenheim, H., Koenekoop, R.K., Bolz, H.J., GPR98 mutations cause Usher syndrome type 2 in males. J. Med. Genet. 46 (2009), 277–280.
15. [15] Fontanarosa, J., Dai, Y., A block-based evolutionary optimization strategy to investigate gene–gene interactions in genetic association studies. Proceedings of 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2010, 330–335.
16. [16] Greene, C.S., Penrod, N.M., Kiralis, J., Moore, J.H., Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene–gene interactions. BioData Mining 2 (2009), 1–9.
17. [17] Hota, M.K., Srivastava, V.K., Identification of protein coding regions using antinotch filters. Digital Signal Process. 22 (2012), 869–877.
18. [18] Hu, T., Sinnott-Armstrong, N.A., Kiralis, J.W., Andrew, A.S., Karagas, M.R., Moore, J.H., Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinform., 12, 2011, 364.
19. [19] Ik-Soo, H., Park, T., A chi-square test for detecting multiple joint genetic variants in genome-wide association studies. Proceedings of 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2011, 708–713.
20. [20] Istepanian, R.S., Sungoor, A., Nebel, J.C., Comparative analysis of genomic signal processing for microarray data clustering. IEEE Trans. Nanobiosci. 10 (2011), 225–238.
21. [21] Jacobson, S.G., Cideciyan, A.V., Aleman, T.S., Sumaroka, A., Roman, A.J., Gardner, L.M., Prosser, H.M., Mishra, M., Bech-Hansen, N.T., Herrera, W., Schwartz, S.B., Liu, X.Z., Kimberling, W.J., Steel, K.P., Williams, D.S., Usher syndromes due to MYO7A, PCDH15, USH2A or GPR98 mutations share retinal disease mechanism. Hum. Mol. Genet. 17 (2008), 2405–2415.
22. [22] Jakulin, A., Bratko, I., Analyzing attribute interactions. Lecture Notes in Artificial Intelligence 2838 (2003), 229–240.
23. [23] Jakulin, A., Bratko, I., Quantifying and visualizing attribute interactions: An approach based on entropy. arXiv:cs.AI/0308002, 2004.
24. [24] Klein, R.J., Zeiss, C., Chew, E.Y., Tsai, J.Y., Sackler, R.S., Haynes, C., Henning, A.K., SanGiovanni, J.P., Mane, S.M., Mayne, S.T., Bracken, M.B., Ferris, F.L., Ott, J., Barnstable, C., Hoh, J., Complement factor H polymorphism in age-related macular degeneration. Science 308 (2005), 385–389.
25. [25] Liang, X.J., Yuan, L., Hu, J., Yu, H.H., Li, T., Lin, S.F., Tang, S.B., Phosphomannopentaose sulfate (PI-88) suppresses angiogenesis by downregulating heparanase and vascular endothelial growth factor in an oxygen-induced retinal neovascularization animal model. Mol. Vis. 18 (2012), 1649–1657.
26. [26] Lim, L.S., Mitchell, P., Seddon, J.M., Holz, F.G., Wong, T.Y., Age-related macular degeneration. Lancet 379 (2012), 1728–1738.
27. [27] Lorenzo-Ginori, J.V., Rodriguez-Fuentes, A., Abalo, R.G., Rodriguez, R.S., Digital signal processing in the analysis of genomic sequences. Curr. Bioinform. 4 (2009), 28–40.
28. [28] Marhon, S.A., Kremer, S.C., Gene prediction based on DNA spectral analysis: A literature review. J. Comput. Biol. 18 (2011), 639–676.
29. [29] McGill, W.J., Multivariate information transmission. Psychometrika 19 (1954), 97–116.
30. [30] McKinney, B.A., Crowe, J.E., Guo, J., Tian, D., Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis. PLoS Genet., 5, 2009, e1000432.
31. [31] Miller, D.J., Zhang, Y., Yu, G., Liu, Y., Chen, L., Langefeld, C.D., Herrington, D., Wang, Y., An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions. Bioinformatics 25 (2009), 2478–2485.
32. [32] Moore, J.H., Gilbert, J.C., Tsai, C.T., Chiang, F.T., Holden, T., Barney, N., White, B.C., A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J. Theoret. Biol. 241 (2006), 252–261.
33. [33] Noor, A., Serpedin, E., Nounou, M., Nounou, H., Mohamed, N., Chouchane, L., Information theoretic methods for modeling of gene regulatory networks. Proceedings of 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2012, 418–423.
34. [34] Ruiz-Marin, M., Matilla-Garcia, M., Cordoba, J.A., Susillo-Gonzalez, J.L., Romo-Astorga, A., Gonzalez-Perez, A., Ruiz, A., Gayan, J., An entropy test for single-locus genetic association analysis. BMC Genetics, 11, 2010, 19.
35. [35] Sarkis, M., Goebel, B., Dawy, Z., Hagenauer, J., Hanus, P., Mueller, J.C., Gene mapping of complex diseases. IEEE Signal Process. Mag. 24 (2007), 83–90.
36. [36] Shang, J., Zhang, J., Lei, X., Zhang, Y., Chen, B., Incorporating heuristic information into ant colony optimization for epistasis detection. Genes Genom. 34 (2012), 321–327.
37. [37] Shang, J., Zhang, J., Lei, X., Zhao, W., Dong, Y., EpiSIM: Simulation of multiple epistasis, linkage disequilibrium patterns and haplotype blocks for genome-wide interaction analysis. Genes Genom. 35 (2013), 305–316.
38. [38] Shang, J., Zhang, J., Sun, Y., Liu, D., Ye, D., Yin, Y., Performance analysis of novel methods for detecting epistasis. BMC Bioinform., 12, 2011, 475.
39. [39] Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T., Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13 (2003), 2498–2504.
40. [40] Shen, Y., Liu, Z., Ott, J., Systematic removal of outliers to reduce heterogeneity in case-control association studies. Hum. Hered. 70 (2010), 227–231.
41. [41] Su, S.C., Kuo, C.C.J., Chen, T., Single nucleotide polymorphism data analysis. IEEE Signal Process. Mag. 24 (2007), 75–82.
42. [42] Sucheston, L., Chanda, P., Zhang, A., Tritchler, D., Ramanathan, M., Comparison of information-theoretic to statistical methods for gene–gene interactions in the presence of genetic heterogeneity. BMC Genomics, 11, 2010, 487.
43. [43] Han, T. Sun, Multiple mutual informations and multiple interactions in frequency data. Inform. Control 46 (1980), 26–45.
44. [44] Tang, W., Wu, X., Jiang, R., Li, Y., Epistatic module detection for case-control studies: A Bayesian model with a Gibbs sampling strategy. PLoS Genet., 5, 2009, e1000464.
45. [45] Vaidyanathan, P., Genomics and proteomics: A signal processorʼs tour. IEEE Circuits Syst. Mag. 4 (2004), 6–29.
46. [46] Vaidyanathan, P., Yoon, B.J., The role of signal-processing concepts in genomics and proteomics. J. Franklin Inst. 341 (2004), 111–135.
47. [47] Wan, X., Yang, C., Yang, Q., Xue, H., Fan, X., Tang, N.L., Yu, W., BOOST: A fast approach to detecting gene–gene interactions in genome-wide case-control studies. Am. J. Hum. Genet. 87 (2010), 325–340.
48. [48] Wan, X., Yang, C., Yang, Q., Xue, H., Tang, N.L., Yu, W., Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics 26 (2010), 30–37.
49. [49] Wang, M., Zhang, M., Chen, X., Zhang, H., Detecting genes and gene–gene interactions for age-related macular degeneration with a forest-based approach. Stat. Biopharm. Res. 1 (2009), 424–430.
50. [50] Wang, Y., Liu, G., Feng, M., Wong, L., An empirical comparison of several recent epistatic interaction detection methods. Bioinformatics 27 (2011), 2936–2943.
51. [51] Wang, Y., Liu, X., Robbins, K., Rekaya, R., AntEpiSeeker: Detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm. BMC Res. Notes, 3, 2010, 117.
52. [52] Xie, M., Li, J., Jiang, T., Detecting genome-wide epistases based on the clustering of relatively frequent items. Bioinformatics 28 (2012), 5–12.
53. [53] Zhang, Y., Liu, J.S., Bayesian inference of epistatic interactions in case-control studies. Nat. Genet. 39 (2007), 1167–1173.