A Markov blanket-based method for detecting causal SNPs in GWAS

BMC Bioinformatics

Volumn 11, Issue SUPPL. 3, 2010, Pages

  Han, Bing ^a   Park, Meeyoung ^a   Chen, Xue wen ^a  

^a University of Kansas   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL EXPERIMENTS; COMPUTATIONAL DETECTION; EPISTATIC INTERACTIONS; GENOME-WIDE ASSOCIATION STUDIES; HEURISTIC SEARCH STRATEGY; MACHINE-LEARNING; SIMULATED DATASETS; TRAINING PROCESS;

ASSOCIATION REACTIONS; BIOINFORMATICS; GENES; HEURISTIC ALGORITHMS; LEARNING SYSTEMS;

DIAGNOSIS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CASE CONTROL STUDY; COMPUTER SIMULATION; GENETIC ASSOCIATION; GENETIC DATABASE; GENETIC EPISTASIS; GENETIC PREDISPOSITION; GENETICS; HUMAN; METHODOLOGY; PROBABILITY; RETINA MACULA DEGENERATION; SINGLE NUCLEOTIDE POLYMORPHISM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CASE-CONTROL STUDIES; COMPUTER SIMULATION; DATABASES, GENETIC; EPISTASIS, GENETIC; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MACULAR DEGENERATION; MARKOV CHAINS; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 77952257916     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-S3-S5     Document Type: Article

References (35)

1. Antonarakis SE, Beckmann JS. Mendelian disorders deserve more attention. Nat Rev Genet 2006, 7:277-282.
2. Cordell HJ. Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet 2002, 11:2463-2468.
3. McKinney BA, Reif DM, Ritchie MD, Moore JH. Machine learning for detecting gene-gene interactions: a review. Appl Bioinformatics 2006, 5:77-88.
4. Musani SK, Shriner D, Liu N, Feng R, Coffey CS, Yi N, Tiwari HK, Allison DB. Detection of gene x gene interactions in genome-wide association studies of human population data. Hum Hered 2007, 63:67-84.
5. Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger DG, Struewing JP, Morrison J, Field H, Luben R, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007, 447:1087-1093. 2714974, 17529967.
6. Fellay J, Shianna KV, Ge D, Colombo S, Ledergerber B, Weale M, Zhang K, Gumbs C, Castagna A, Cossarizza A, et al. A whole-genome association study of major determinants for host control of HIV-1. Science 2007, 317:944-947. 1991296, 17641165.
7. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, et al. Complement factor H polymorphism in age-related macular degeneration. Science 2005, 308:385-389. 1512523, 15761122.
8. The International HapMap Project. Nature 2003, 426:789-796.
9. A haplotype map of the human genome. Nature 2005, 437:1299-1320. 1880871, 16255080.
10. Marchini J, Donnelly P, Cardon LR. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nature genetics 2005, 37:413-417.
11. Kooperberg C, Ruczinski I. Identifying interacting SNPs using Monte Carlo logic regression. Genetic epidemiology 2005, 28:157-170.
12. Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics (Oxford England) 2003, 19:376-382.
13. Moore JH, Gilbert JC, Tsai CT, Chiang FT, 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. Journal of theoretical biology 2006, 241:252-261.
14. Ritchie MD, Hahn LW, Moore JH. Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genetic epidemiology 2003, 24:150-157.
15. 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. American journal of human genetics 2001, 69:138-147. 1226028, 11404819.
16. Robnik-Šikonja M, Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Machine learning 2003, 53:23-69.
17. Park MY, Hastie T. Penalized logistic regression for detecting gene interactions. Biostatistics (Oxford England) 2008, 9:30-50.
18. Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nature genetics 2007, 39:1167-1173.
19. Chen SH, Sun J, Dimitrov L, Turner AR, Adams TS, Meyers DA, Chang BL, Zheng SL, Gronberg H, Xu J, Hsu FC. A support vector machine approach for detecting gene-gene interaction. Genetic epidemiology 2008, 32:152-167.
20. Jiang R, Tang W, Wu X, Fu W. A random forest approach to the detection of epistatic interactions in case-control studies. BMC bioinformatics 2009, 10(Suppl 1):S65. 2648748, 19208169.
21. Pritchard JK, Przeworski M. Linkage disequilibrium in humans: models and data. American journal of human genetics 2001, 69:1-14. 1226024, 11410837.
22. Li J, Chen Y. Generating samples for association studies based on HapMap data. BMC bioinformatics 2008, 9:44. 2375120, 18218094.
23. Chang C, Lin C-J. LIBSVM: A library for support vector machines. 2001,
24. Hamosh A, Scott AF, Amberger J, Bocchini C, Valle D, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 2002, 30:52-55. 99152, 11752252.
25. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 2001, 29:308-311. 29783, 11125122.
26. Shriver MD, Mei R, Parra EJ, Sonpar V, Halder I, Tishkoff SA, Schurr TG, Zhadanov SI, Osipova LP, Brutsaert TD, et al. Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation. Hum Genomics 2005, 2:81-89.
27. Chen X-W, Anantha G, Lin X. Improving Bayesian Network Structure Learning with Mutual Information-Based Node Ordering in the K2 Algorithm. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2008, 20:628-640.
28. Spirtes P, Glymour CN, Scheines R. Causation, prediction, and search. 2000, MIT Press, 2.
29. Aliferis CF, Tsamardinos I, Statnikov A. HITON: a novel Markov Blanket algorithm for optimal variable selection. AMIA Annual Symposium proceedings / AMIA Symposium 2003, 21-25.
30. Sokal RR, Rohlf FJ. Biometry: the principles and practice of statistics in biological research. 1995, Freeman, 3.
31. McDonald JH. Handbook of Biological Statistics. 2009, Sparky House Publishing, 2.
32. Koller D, Sahami M. Toward Optimal Feature Selection. Proceedings of 13th conference on machine learning: 3-6 July 1996; Bari, Italy 1996, 284-292. Morgan Kaufmann, Lorenza Saitta.
33. Margaritis D, Thrun S. Bayesian Network Induction via Local Neighborhoods. Proceedings of Neural Information Processing Systems 12:29 Nov-4 Dec 1999; Denver 1999, 505-511. MIT Press, Sara A. Solla, Todd K. Leen and Klaus-Robert Müller.
34. Tsamardinos I, Aliferis C, Statnikov A, Statnikov E. Algorithms for Large Scale Markov Blanket Discovery. Proceedings of the 16th International FLAIRS Conference: 11-15 May 2003; St. Augustine 2003, 376-380. AAAI Press, Doug Dankel.
35. Tsamardinos I, Aliferis C, Statnikov A. Time and Sample Efficient Discovery of Markov Blankets And Direct Causal Relations. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining: 24-27 August 2003; Washington, D.C 2003, 673-678. ACM, Lise Getoor.