SYMPHONY, an information-theoretic method for gene-gene and gene-environment interaction analysis of disease syndromes

Heredity

Volumn 110, Issue 6, 2013, Pages 548-559

  Knights, J ^a   Yang, J ^b   Chanda, P ^b   Zhang, A ^b   Ramanathan, M ^a  

^a UNIVERSITY AT BUFFALO   (United States)

^b the State University of New York   (United States)

Author keywords

complex diseases; gene environment interactions; gene gene interactions; K way interaction information; syndromes

Indexed keywords

DISEASE INCIDENCE; DISEASE VECTOR; GENE EXPRESSION; GENOTYPE-ENVIRONMENT INTERACTION; HERITABILITY; OSTEOLOGY; PHENOTYPE; PROTEIN; PUBLIC HEALTH; THEORETICAL STUDY;

ALGORITHM; ANIMAL; ARTICLE; BIOLOGICAL MODEL; COMPUTER SIMULATION; DISEASES; EPISTASIS; GENETIC ASSOCIATION; GENETICS; GENOTYPE; GENOTYPE ENVIRONMENT INTERACTION; HUMAN; MOUSE; PHENOTYPE; QUANTITATIVE TRAIT LOCUS; SINGLE NUCLEOTIDE POLYMORPHISM; THEORETICAL MODEL;

ALGORITHMS; ANIMALS; COMPUTER SIMULATION; DISEASE; EPISTASIS, GENETIC; GENE-ENVIRONMENT INTERACTION; GENETIC ASSOCIATION STUDIES; GENOTYPE; HUMANS; MICE; MODELS, GENETIC; MODELS, THEORETICAL; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; QUANTITATIVE TRAIT LOCI;

EID: 84878538736     PISSN: 0018067X     EISSN: 13652540     Source Type: Journal    
DOI: 10.1038/hdy.2012.123     Document Type: Article

References (35)

1. Bush WS, Dudek SM, Ritchie MD (2006). Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene-gene interactions. Bioinformatics 22: 2173-2174. (Pubitemid 44390915)
2. Chanda P, Sucheston L, Liu S, Zhang A, Ramanathan M (2009). Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits. BMC Genomics 10: 509.
3. Chanda P, Sucheston L, Zhang A, Brazeau D, Freudenheim JL, Ambrosone Cet al. (2008). AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes. Genetics 180: 1191-1210.
4. Chanda P, Zhang A, Brazeau D, Sucheston L, Freudenheim JL, Ambrosone Cet al. (2007). Information-theoretic metrics for visualizing gene-environment interactions. Am J Hum Genet 81: 939-963. (Pubitemid 47580248)
5. Cox DR, Small NJH (1978). Testing multivariate normality. Biometrika 65: 263-272. (Pubitemid 8379487)
6. Culverhouse R, Klein T, Shannon W (2004). Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol 27: 141-152. (Pubitemid 39121794)
7. Dirckx JH (2001). In: Dirckx JH (ed.) Stedman's Concise Medical Dictionary for the Health Professionals. Lippincottt, Williams and Wilkins: Philadelphia, PA.
8. Fano RM (1961). Transmission of Information: A Statistical Theory of Communications. MIT Press: Cambridge, MA.
9. Ford ES, Giles WH, Dietz WH (2002). Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA 287: 356-359. (Pubitemid 34251969)
10. Friedman JH, Rafsky LC (1979). Multivariate generalizations of the Wald-Wolfowitz and Smirnov two sample tests. Ann Stat 7: 697-717.
11. Gokhale DV, Ahmed NV, Res BC, Piscataway NJ (1989). Entropy expressions and their estimators for multivariate distributions. IEEE Trans Inform Theory 35: 688-692. (Pubitemid 20606244)
12. Hahn LW, Ritchie MD, Moore JH (2003). Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19: 376-382. (Pubitemid 36284937)
13. Han B, Park M, Chen XW (2010). A Markov blanket-based method for detecting causal SNPs in GWAS. BMC Bioinformatics 11:Suppl 3 S5.
14. Han TS (1980). Multiple mutual informations and multiple interactions in frequency data. Inform Control 46: 26-45.
15. Henze (2002). Invariant tests for multivariate normality: a critical review. Stat Pap 43: 467-506. (Pubitemid 36170835)
16. Ishimori N, Li R, Kelmenson PM, Korstanje R, Walsh KA, Churchill GAet al. (2004). Quantitative trait loci analysis for plasma HDL-cholesterol concentrations and atherosclerosis susceptibility between inbred mouse strains C57BL/6J and 129S1/SvImJ. Arterioscler Thromb Vasc Biol 24: 161-166. (Pubitemid 38082428)
17. Jakulin A (2005). Machine learning based on attribute interactions. PhD thesis. University of Ljubljana: Ljubljana, Slovenia.
18. Jakulin A, Bratko I (2004). In: Greiner R, Schuurmans D (eds). Proceedings of the Twenty- First International Conference on Machine Learning (ICML-2004). Banff, Canada, pp 409-416.
19. Kim Y, Wojciechowski R, Sung H, Mathias RA, Wang L, Klein APet al. (2009). Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects. BMC Proc 3:Suppl 7 S64.
20. Knights J, Ramanathan M (2012). An information theory analysis of gene-environmental interactions in count/rate data. Hum Hered 73: 123-138.
21. Martin ER, Monks SA, Warren LL, Kaplan NL (2000). A test for linkage and association in general pedigrees: the pedigree disequilibrium test. Am J Hum Genet 67: 146-154. (Pubitemid 30481552)
22. Martin ER, Ritchie MD, Hahn L, Kang S, Moore JH (2006). A novel method to identify gene-gene effects in nuclear families: the MDR-PDT. Genet Epidemiol 30: 111-123. (Pubitemid 43201584)
23. McGill WJ (1954). Multivariate information transmission. Psychometrika 19: 97-116.
24. Meng YA, Yu Y, Cupples LA, Farrer LA, Lunetta KL (2009). Performance of random forest when SNPs are in linkage disequilibrium. BMC Bioinformatics 10: 78.
25. Miller MB, Lind GR, Li N, Jang S-Y (2007). Genetic Analysis Workshop 15: simulation of a complex genetic model for rheumatoid arthritis in nuclear families including a dense SNP map with linkage disequilibrium between marker loci and trait loci. BMC Genet 1:Suppl 1 S4.
26. Moore JH, Gilbert JC, Tsai CT, Chiang FT, Holden T, Barney Net al. (2006). A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol 241: 252-261. (Pubitemid 43993963)
27. Nelson MR, Kardia SL, Ferrell RE, Sing CF (2001). A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 11: 458-470. (Pubitemid 32243856)
28. 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. (Pubitemid 36158211)
29. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FFet al. (2001). Multifactordimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69: 138-147. (Pubitemid 32614026)
30. Shannon CE (1997). The mathematical theory of communication. 1963. MD Comput 14: 306-317.
31. Smith SP, Jain AK (1988). A test to determine the multivariate normality of a dataset. IEEE Trans Pattern Anal Mach Intell 10: 757-761.
32. Srivastava S, Gupta MR (2008). IEEE International Symposium on Information Theory (ISIT). Toronto.
33. Sucheston L, Chanda P, Zhang A, Tritchler D, Ramanathan M (2010). Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity. BMC Genomics 11: 487.
34. Watanabe S (1960). Information theoretical analysis of multivariate correlation. IBM J Res Develop 4: 66-82.
35. Wu Q (2011). IEEE International Conference on Bioinformatics and Biomedicine.