A Novel Two-Stage Approach for Epistasis Detection in Genome-Wide Case–Control Studies

Biochemical Genetics

Volumn 52, Issue 9-10, 2014, Pages 403-414

  Liao, Zhongli ^a   Zeng, Qingguang ^a   Liao, Bo ^a   Li, Xiong ^a  

^a HUNAN UNIVERSITY   (China)

Author keywords

Criterion fusion; Epistasis detection; Genome wide case control studies; Two stage analyses

Indexed keywords

AGE RELATED MACULAR DEGENERATION; ALLELE; ARTICLE; CASE CONTROL STUDY; EPISTASIS; GENE FREQUENCY; GENETIC ASSOCIATION; GENOTYPE; HUMAN; SINGLE NUCLEOTIDE POLYMORPHISM; BIOLOGY; COMPUTER SIMULATION; GENETIC DATABASE; GENETICS; HUMAN GENOME; MACULAR DEGENERATION;

CASE-CONTROL STUDIES; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, GENETIC; EPISTASIS, GENETIC; GENOME, HUMAN; HUMANS; MACULAR DEGENERATION; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84919383777     PISSN: 00062928     EISSN: 15734927     Source Type: Journal    
DOI: 10.1007/s10528-014-9656-7     Document Type: Article

References (28)

1. Chen N-H, Reith ME, Quick MW (2004) Synaptic uptake and beyond: the sodium-and chloride-dependent neurotransmitter transporter family SLC6. Pflüg Arch 447(5):519–531
2. Cordell HJ (2009) Detecting gene–gene interactions that underlie human diseases. Nat Rev Genet 10(6):392–404
3. Evans DM, Marchini J, Morris AP, Cardon LR (2006) Two-stage two-locus models in genome-wide association. PLoS Genet 2(9):e157
4. Fontanarosa J, Dai Y (2010) A block-based evolutionary optimization strategy to investigate gene–gene interactions in genetic association studies. In: Bioinformatics and biomedicine workshops (BIBMW), 2010 IEEE international conference, pp 330–335
5. Giudici P, Castelo R (2003) Improving Markov chain Monte Carlo model search for data mining. Mach Learn 50(1–2):127–158
6. Han B, Chen X-W (2011) bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies. BMC Genomics 12(Suppl 2):S9
7. Han B, Park M, Chen X-W (2010) A Markov blanket-based method for detecting causal SNPs in GWAS. BMC Bioinform 11(Suppl 3):S5
8. Han B, Chen X-W, Talebizadeh Z, Xu H (2012) Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks. BMC Syst Biol 6(Suppl 3):S14
9. Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6(2):95–108
10. Jiang R, Tang W, Wu X, Fu W (2009) A random forest approach to the detection of epistatic interactions in case–control studies. BMC Bioinform 10(Suppl 1):S65
11. Klein RJ, Zeiss C, Chew EY, Tsai J-Y, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308(5720):385–389
12. Lin HY, Chen YA, Tsai YY, Qu X, Tseng TS, Park JY (2012) TRM: a powerful two-stage machine learning approach for identifying SNP–SNP interactions. Ann Hum Genet 76(1):53–62
13. Marchini J, Donnelly P, Cardon LR (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37(4):413–417
14. Mechanic LE, Luke BT, Goodman JE, Chanock SJ, Harris CC (2008) Polymorphism interaction analysis (PIA): a method for investigating complex gene–gene interactions. BMC Bioinform 9(1):146
15. Park MY, Hastie T (2008) Penalized logistic regression for detecting gene interactions. Biostatistics 9(1):30–50
16. Peña JM, Nilsson R, Björkegren J, Tegnér J (2007) Towards scalable and data efficient learning of Markov boundaries. Int J Approx Reason 45(2):211–232
17. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ (2007) Plink: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575
18. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69(1):138–147
19. Shang J, Zhang J, Sun Y, Zhang Y (2014) EpiMiner: a three-stage co-information based method for detecting and visualizing epistatic interactions. Digit Signal Process 24:1–13
20. Spirtes P, Glymour C, Scheines R (2000) Causation, prediction, and search, vol 81. MIT Press, Cambridge
21. Tang W, Wu X, Jiang R, Li Y (2009) Epistatic module detection for case–control studies: a Bayesian model with a Gibbs sampling strategy. PLoS Genet 5(5):e1000464
22. Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NL, Yu W (2010a) Boost: a fast approach to detecting gene–gene interactions in genome-wide case–control studies. Am J Hum Genet 87(3):325–340
23. Wan X, Yang C, Yang Q, Xue H, Tang NL, Yu W (2010b) Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics 26(1):30–37
24. Wang Y, Liu X, Robbins K, Rekaya R (2010) AntEpiSeeker: detecting epistatic interactions for case–control studies using a two-stage ant colony optimization algorithm. BMC Res Notes 3(1):117
25. Yang C, He Z, Wan X, Yang Q, Xue H, Yu W (2009) SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies. Bioinformatics 25(4):504–511
26. Yang F, Mao K (2011) Robust feature selection for microarray data based on multicriterion fusion. IEEE/ACM Trans Comput Biol Bioinform 8(4):1080–1092
27. Zhang X, Huang S, Zou F, Wang W (2010) TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics 26(12):i217–i227
28. Zhang Y, Liu JS (2007) Bayesian inference of epistatic interactions in case–control studies. Nat Genet 39(9):1167–1173