CINOEDV: A co-information based method for detecting and visualizing n-order epistatic interactions

BMC Bioinformatics

Volumn 17, Issue 1, 2016, Pages

  Shang, Junliang ^a   Sun, Yingxia ^a   Liu, Jin Xing ^a,b   Xia, Junfeng ^c   Zhang, Junying ^d   Zheng, Chun Hou ^c  

^a QUFU NORMAL UNIVERSITY   (China)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

^c ANHUI UNIVERSITY   (China)

^d XIDIAN UNIVERSITY   (China)

Author keywords

Co information; Epistatic interactions; Hypergraph; Particle swarm optimization; Single nucleotide polymorphisms

Indexed keywords

NUCLEOTIDES; PARTICLE SWARM OPTIMIZATION (PSO);

AGE-RELATED MACULAR DEGENERATION; COMPUTATIONAL CHALLENGES; EPISTATIC INTERACTIONS; GENETIC ARCHITECTURE; HYPERGRAPH; NONLINEAR INTERACTIONS; SINGLE NUCLEOTIDE POLYMORPHISMS; STATE-OF-THE-ART METHODS;

ASSOCIATION REACTIONS;

ALGORITHM; BIOLOGY; EPISTASIS; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HUMAN; MACULAR DEGENERATION; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM;

ALGORITHMS; COMPUTATIONAL BIOLOGY; EPISTASIS, GENETIC; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MACULAR DEGENERATION; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84969579622     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-016-1076-8     Document Type: Article

References (56)

1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747-53.
2. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci. 2009;106(23):9362-7.
3. Jia P, Zhao Z. Network-assisted analysis to prioritize GWAS results: principles, methods and perspectives. Hum Genet. 2014;133(2):125-38.
4. Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet. 2008;40(6):695-701.
5. Frazer KA, Murray SS, Schork NJ, Topol EJ. Human genetic variation and its contribution to complex traits. Nat Rev Genet. 2009;10(4):241-51.
6. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010;11(6):446-50.
7. Maher B. The case of the missing heritability. Nature. 2008;456(7218):18-21.
8. Cordell HJ. Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet. 2002;11(20):2463-8.
9. Phillips PC. Epistasis-the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet. 2008;9(11):855-67.
10. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet. 2009;10(6):392-404.
11. Wei W-H, Hemani G, Haley CS. Detecting epistasis in human complex traits. Nat Rev Genet. 2014;15(11):722-33.
12. Herold C, Steffens M, Brockschmidt FF, Baur MP, Becker T. INTERSNP: genome-wide interaction analysis guided by a priori information. Bioinformatics. 2009;25(24):3275-81.
13. 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.
14. Moore JH, Asselbergs FW, Williams SM. Bioinformatics challenges for genome-wide association studies. Bioinformatics. 2010;26(4):445-55.
15. Zhang X, Huang S, Zou F, Wang W. TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics. 2010;26(12):i217-27.
16. Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NLS, Yu W. BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet. 2010;87(3):325.
17. Wan X, Yang C, Yang Q, Xue H, Tang NL, Yu W. Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics. 2010;26(1):30-7.
18. 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. 2010;3(1):117.
19. Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nat Genet. 2007;39(9):1167-73.
20. 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. 2009;5(5):e1000464.
21. 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. 2009;17(10):1274-86.
22. Chanda P, Sucheston L, Zhang A, Brazeau D, Freudenheim JL, Ambrosone C, Ramanathan M. AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes. Genetics. 2008;180(2):1191-210.
23. Chanda P, Zhang A, Brazeau D, Sucheston L, Freudenheim JL, Ambrosone C, Ramanathan M. Information-theoretic metrics for visualizing gene-environment interactions. Am J Hum Gen. 2007;81(5):939-63.
24. Chanda P, Sucheston L, Liu S, Zhang A, Ramanathan M. Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits. BMC Genomics. 2009;10(1):509.
25. 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. 2010;11(1):487.
26. Shang J, Zhang J, Sun Y, Zhang Y. EpiMiner: A three-stage co-information based method for detecting and visualizing epistatic interactions. Digital Signal Processing. 2014;24:1-13.
27. Wu Y, Zhu X, Chen J, Zhang X. EINVis: a visualization tool for analyzing and exploring genetic interactions in large-scale association studies. Genet Epidemiol. 2013;37(7):675-85.
28. Moore JH, Gilbert JC, Tsai C-T, Chiang F-T, Holden T, Barney N, White BC. 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.
29. McKinney BA, Crowe JE, Guo J, Tian D. Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis. PLoS Genet. 2009;5(3):e1000432.
30. Davis N, Crowe J, Pajewski N, McKinney B. Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine. Genes Immun. 2010;11(8):630-6.
31. Pandey A, Davis N, White B, Pajewski N, Savitz J, Drevets W, McKinney B. Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder. Transl Psychiatry. 2012;2(8):e154.
32. Davis NA, Lareau CA, White BC, Pandey A, Wiley G, Montgomery CG, Gaffney PM, McKinney B. Encore: Genetic association interaction network centrality pipeline and application to sle exome data. Genet Epidemiol. 2013;37(6):614-21.
33. Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH. Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinformatics. 2011;12:364.
34. Hu T, Pan Q, Andrew AS, Langer JM, Cole MD, Tomlinson CR, Karagas MR, Moore JH. Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility. BioData Mining. 2014;7(1):5.
35. Andrew AS, Hu T, Gu J, Gui J, Ye Y, Marsit CJ, Kelsey KT, Schned AR, Tanyos SA, Pendleton EM. HSD3B and gene-gene interactions in a pathway-based analysis of genetic susceptibility to bladder cancer. PLoS One. 2012;7(12):e51301.
36. Lavender NA, Rogers EN, Yeyeodu S, Rudd J, Hu T, Zhang J, Brock GN, Kimbro KS, Moore JH, Hein DW. Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer. BMC Med Genet. 2012;5(1):11.
37. Hu T, Andrew AS, Karagas MR, Moore JH: Statistical epistasis networks reduce the computational complexity of searching three-locus genetic models. In: Pac Symp Biocomput: 2013. World Scientific: 397-408.
38. Hu T, Chen Y, Kiralis JW, Moore JH. ViSEN: methodology and software for visualization of statistical epistasis networks. Genet Epidemiol. 2013;37(3):283-5.
39. Bell AJ: The co-information lattice. The 4th International Symposium on Independent Component Analysis and Blind Signal Separation 2003:921-926.
40. Sun Han T. Multiple mutual informations and multiple interactions in frequency data. Inf Control. 1980;46(1):26-45.
41. James K, Russell E: Particle swarm optimization. In: Proceedings of 1995 IEEE International Conference on Neural Networks: 1995. 1942-1948.
42. Hwang M-L, Lin Y-D, Chuang L-Y, Yang C-H. Determination of the SNP-SNP Interaction between Breast Cancer Related Genes to Analyze the Disease Susceptibility. Int J Mach Learn Comput. 2014;4(5):468-73.
43. Tizhoosh HR. Opposition-Based Learning: A New Scheme for Machine Intelligence. In: Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce 2006. Vienna: IEEE Press; 2005. p. 695-701.
44. 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 Genomics 2013;35(3):305-16.
45. Parida L, Haiminen N. SimBA: simulation algorithm to fit extant-population distributions. BMC bioinformatics. 2015;16(1):1.
46. Haiminen N, Lebreton C, Parida L: Best-fit in linear time for non-generative population simulation. In: Algorithms in Bioinformatics. Wroclaw, Poland: Springer; 2014: 247-262.
47. Shang J, Zhang J, Sun Y, Liu D, Ye D, Yin Y. Performance analysis of novel methods for detecting epistasis. BMC Bioinformatics. 2011;12(1):475.
48. Shang J, Zhang J, Lei X, Zhang Y, Chen B. Incorporating heuristic information into ant colony optimization for epistasis detection. Genes Genomics. 2012;34(3):321-7.
49. Aflakparast M, Salimi H, Gerami A, Dubé M, Visweswaran S, Masoudi-Nejad A. Cuckoo search epistasis: a new method for exploring significant genetic interactions. Heredity. 2014;112(6):666-74.
50. Himmelstein DS, Greene CS, Moore JH. Evolving hard problems: generating human genetics datasets with a complex etiology. BioData Mining. 2011;4(1):1-13.
51. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308(5720):385-9.
52. Wan X, Yang C, Yang Q, Xue H, Tang NL, Yu W. Detecting two-locus associations allowing for interactions in genome-wide association studies. Bioinformatics. 2010;26(20):2517-25.
53. Xie M, Li J, Jiang T. Detecting genome-wide epistases based on the clustering of relatively frequent items. Bioinformatics. 2012;28(1):5-12.
54. Liao Z, Zeng Q, Liao B, Li X. A Novel Two-Stage Approach for Epistasis Detection in Genome-Wide Case-Control Studies. Biochemical Genetics 2014;52(9-10):403-14.
55. Li W, Reich J. A complete enumeration and classification of two-locus disease models. Hum Hered. 2000;50(6):334-49.
56. Frankel WN, Schork NJ. Who's afraid of epistasis? Nat Genet. 1996;14(4):371-3.