Epistasis analysis using relieff

Methods in Molecular Biology

Volumn 1253, Issue , 2015, Pages

  Moore, Jason H ^a  

^a DARTMOUTH MEDICAL SCHOOL   (United States)

Author keywords

Association studies; Epistasis; Genetic analysis; Gene gene interaction; Machine learning

Indexed keywords

ARTICLE; BLADDER CANCER; DATA ANALYSIS; EPISTASIS; EVAPORATIVE COOLING RELIEFF ALGORITHM; GENETIC ALGORITHM; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC IDENTIFICATION; GENETIC MODEL; HUMAN; LUNG TUBERCULOSIS; MACHINE LEARNING; PRIORITY JOURNAL; RANDOM CHEMISTRY RELIEFF ALGORITHM; RELIEFF ALGORITHM; SPATIALLY UNIFORM RELIEFF ALGORITHM; TUNED RELIEFF ALGORITHM; ALGORITHM; BLADDER TUMOR; GENETICS;

ALGORITHMS; EPISTASIS, GENETIC; HUMANS; TUBERCULOSIS, PULMONARY; URINARY BLADDER NEOPLASMS;

EID: 84955153657     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-2155-3_17     Document Type: Article

References (56)

1. Moore JH, Asselbergs FW, Williams SM (2010) Bioinformatics challenges for genome-wide association studies. Bioinformatics 26:445–455
2. McKinney BA, Reif DM, Ritchie MD et al (2006) Machine learning for detecting gene-gene interactions: a review. Appl Bioinforma 5:77–88
3. Cordell HJ (2009) Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10:392–404
4. Hahn LW, Ritchie MD, Moore JH (2003) Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19:376–382
5. Ritchie MD, Hahn LW, Roodi N et al (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69:138–147
6. Hahn LW, Moore JH (2004) Ideal discrimination of discrete clinical endpoints using multi-locus genotypes. In Silico Biol 4:183–194
7. Ritchie MD, Hahn LW, Moore JH (2003) Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 24:150–157
8. Moore JH (2004) Computational analysis of gene-gene interactions using multifactor dimensionality reduction. Expert Rev Mol Diagn 4:795–803
9. Moore JH (2010) Detecting, characterizing, and interpreting nonlinear gene-gene interactions using multifactor dimensionality reduction. Adv Genet 72:101–116
10. Velez DR, White BC, Motsinger AA et al (2007) A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 31:306–315
11. Pattin KA, White BC, Barney N et al (2009) A computationally effi cient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction. Genet Epidemiol 33:87–94
12. Moore JH (2007) Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics. In: Zhu X, Davidson I (eds) Knowledge discovery and data mining: challenges and realities. IGI Global, Hershey, PA, pp 17–30
13. Cordell HJ (2002) Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet 11:2463–2468
14. Phillips PC (2008) Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 9:855–867
15. Phillips PC (1998) The language of gene interaction. Genetics 149:1167–1171
16. Moore JH (2003) The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 56:73–82
17. Moore JH, Williams SM (2005) Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. Bioessays 27:637–646
18. Moore JH (2005) A global view of epistasis. Nat Genet 37:13–14
19. Tyler AL, Asselbergs FW, Williams SM et al (2009) Shadows of complexity: what biological networks reveal about epistasis and pleiotropy. Bioessays 31:220–227
20. Cowper-Sal lari R, Cole MD, Karagas MR et al (2011) Layers of epistasis: genome-wide regulatory networks and network approaches to genome-wide association studies, Wiley interdisciplinary reviews. Syst Biol Med 3:513–526
21. Moore JH, Williams SM (2009) Epistasis and its implications for personal genetics. Am J Hum Genet 85:309–320
22. Bush WS, Dudek SM, Ritchie MD (2009) Biofi lter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pacific symposium on biocom-puting, pp 368–379
23. Pendergrass SA, Verma SS, Holzinger ER et al (2013) Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using Biofi lter, and gene- environment interactions using the PhenX Toolkit. Pacifi c symposium on biocomputing, pp 147–158
24. Grady BJ, Torstenson ES, McLaren PJ et al (2011) Use of biological knowledge to inform the analysis of gene-gene interactions involved in modulating virologic failure with efavirenz-containing treatment regimens in ART-naïve ACTG clinical trials participants. Pacifi c symposium on biocomputing, pp 253–264
25. Kira K, Rendell LA (1992) A practical approach to feature selection. In: Proceedings of the ninth international workshop on machine learning, Morgan Kaufmann Publishers, San Francisco, CA, pp 249–256
26. Robnik-Šikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53:23–69
27. Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. Lecture Notes in Computer Science 784:171–182
28. Demšar J, Curk T, Erjavec A et al (2013) Orange: data mining toolbox in python. J Mach Learn Res 14:2349–2353
29. Hall M, Frank E, Holmes G et al (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11:10–18
30. Moore JH, White BC (2007) Tuning ReliefF for genome-wide genetic analysis. In: Marchiori E, Moore JH, Rajapakse JC (eds) Evolutionary computation, machine learning and data mining in bioinformatics. Springer, Berlin, pp 166–175
31. Hess HF (1986) Evaporative cooling of magnetically trapped and compressed spin-polarized hydrogen. Phys Rev B Condens Matter 34:3476–3479
32. McKinney BA, Reif DM, White BC et al (2007) Evaporative cooling feature selection for genotypic data involving interactions. Bioinformatics 23:2113–2120
33. McKinney BA, Reif DM, Rock MT et al (2006) Cytokine expression patterns associated with systemic adverse events following smallpox immunization. J Infect Dis 194:444–453
34. Eppstein MJ, Payne JL, White BC et al (2007) Genomic mining for complex disease traits with “random chemistry”. Genet Program Evolvable Mach 8:395–411
35. Greene CS, Penrod NM, Kiralis J et al (2009) Spatially uniform relieff (SURF) for computationally- effi cient fi ltering of gene-gene interactions. BioData Min 2:5
36. Greene CS, Himmelstein DS, Kiralis J et al (2010) The informative extremes: using both nearest and farthest individuals can improve relief algorithms in the domain of human genetics. In: Pizzuti C, Ritchie MD, Giacobini M (eds) Evolutionary computation, machine learning and data mining in bioinformatics. Springer, Berlin, pp 182–193
37. Granizo-Mackenzie D, Moore JH (2013) Multiple threshold spatially uniform ReliefF for the genetic analysis of complex human diseases. In: Vanneschi L, Bush WS, Giacobini M (eds) Evolutionary computation, machine learning and data mining in bioinformatics. Springer, Berlin, pp 1–10
38. Stokes ME, Visweswaran S (2012) Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease. BioData Min 5:20
39. Andrew AS, Nelson HH, Kelsey KT et al (2006) Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility. Carcinogenesis 27:1030–1037
40. Urbanowicz RJ, Andrew AS, Karagas MR et al (2013) Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifi er system approach. J Am Med Inform Assoc 20:603–612
41. Collins RL, Hu T, Wejse C et al (2013) Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis. BioData Min 6:4
42. Olesen R, Wejse C, Velez DR et al (2007) DC-SIGN (CD209), pentraxin 3 and vitamin D receptor gene variants associate with pulmonary tuberculosis risk in West Africans. Genes Immun 8:456–467
43. Greene CS, Himmelstein DS, Nelson HH et al (2010) Enabling personal genomics with an explicit test of epistasis. Pacifi c symposium on biocomputing, pp 327–336
44. Hu T, Chen Y, Kiralis JW et al (2013) An information-gain approach to detecting three- way epistatic interactions in genetic association studies. J Am Med Inform Assoc 20:630–636
45. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575
46. Urbanowicz RJ, Kiralis J, Sinnott-Armstrong NA et al (2012) GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Min 5:16
47. Urbanowicz RJ, Kiralis J, Fisher JM et al (2012) Predicting the diffi culty of pure, strict, epistatic models: metrics for simulated model selection. BioData Min 5:15
48. Edwards TL, Bush WS, Turner SD et al (2008) Generating linkage disequilibrium patterns in data simulations using genomeSIMLA. In: Proceedings of the 6th European conference on evolutionary computation, machine learning and data mining in bioinformatics, Springer, Berlin, pp 24–35
49. Ritchie MD, Bush WS (2010) Genome simulation approaches for synthesizing in silico datasets for human genomics. Adv Genet 72:1–24
50. Dudek SM, Motsinger AA, Velez DR et al (2006) Data simulation software for whole-genome association and other studies in human genetics. Pacific symposium on biocomputing, pp 499–510
51. Moore JH, Hahn LW, Ritchie MD et al (2004) Routine discovery of complex genetic models using genetic algorithms. Appl Soft Comput 4:79–86
52. Himmelstein DS, Greene CS, Moore JH (2011) Evolving hard problems: generating human genetics datasets with a complex etiology. BioData Min 4:21
53. Dai H, Bhandary M, Becker M et al (2012) Global tests of P-values for multifactor dimensionality reduction models in selection of optimal number of target genes. BioData Min 5:3
54. Greene CS, Hill DP, Moore JH (2010) Environmental sensing of expert knowledge in a computational evolution system for complex problem solving in human genetics. In: Riolo R, O’Reilly U-M, McConaghy T (eds) Genetic programming theory and practice, vol 7. Springer, Ann Arbor, MI, pp 19–36
55. Moore JH, Andrews PC, Barney N et al (2008) Development and evaluation of an open-ended computational evolution system for the genetic analysis of susceptibility to common human diseases. In: Marchiori E, Moore JH (eds) Evolutionary computation, machine learning and data mining in bioinformatics. Springer, Berlin, pp 129–140
56. Moore JH, Greene CS, Andrews PC et al (2009) Does complexity matter? artifi cial evolution, computational evolution and the genetic analysis of epistasis in common human diseases. In: Genetic programming theory and practice, vol 6. Springer, pp 1–19