SNPHarvester: A filtering-based approach for detecting epistatic interactions in genome-wide association studies

Bioinformatics

Volumn 25, Issue 4, 2009, Pages 504-511

  Yang, Can ^a   He, Zengyou ^a   Wan, Xiang ^a   Yang, Qiang ^a   Xue, Hong ^a   Yu, Weichuan ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOINFORMATICS; COMPUTER SIMULATION; EPISTASIS; GENETIC ALGORITHM; GENETIC ASSOCIATION; GENOME ANALYSIS; PRIORITY JOURNAL; PROGNOSIS; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL ANALYSIS; STOCHASTIC MODEL;

ALGORITHMS; EPISTASIS, GENETIC; GENOME; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; POLYMORPHISM, SINGLE NUCLEOTIDE; SOFTWARE;

EID: 60149109479     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btn652     Document Type: Article

References (26)

1. Breiman,L. et al. (2001) Random forests. Mach. Learn., 45 5-32.
2. Cho,Y. et al. (2004) Multifactor-dimensionality reduction shows a two-locus interaction associated with type 2 diabetes mellitus. Diabetologia, 47, 549-554.
3. Cordell,H.J. (2002) Epistasis: What it means, what it doesn't mean, and statistical methods to detect it in humans. Hum. Mol. Genet., 11, 2463-2468.
4. Culverhouse,R. et al. (2002) A perspective on epistasis: Limits of models displaying no main effect. Am. J. Hum. Genet., 70, 461-471.
5. Greene,C.S. et al. (2008) Ant colony optimization for genome-wide genetic analysis. In Dorigo,M. et al. (eds), Procedings of the 6th International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS 2008), vol. 5217 of Lecture Notes in Computer Science. Springer, Brussels, Belgium, pp. 37-47.
6. Gregersen,P.K. et al. (1987) The shared epitope hypothesis. an approach to understanding the molecular genetics of susceptibility to rheumatoid arthritis. Arthritis Rheum., 30, 1205-1213.
7. Griffiths,A.J. et al. (2008) Introduction to Genetic Analysis W.H.Freeman and Co Ltd., New York, USA.
8. Hirschhorn,J.N. and Daly,M.J. (2005) Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet., 6, 95-108.
9. Liang,Y. and Kelemen,A. (2008) Statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic study for complex diseases. Stat. Surv., 2, 43-60.
10. Marchini,J. et al. (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat. Genet., 37 413-417.
11. McCarthy,M. et al. (2008) Genome-wide association studies for complex traits: Consensus, uncertainty and challenges. Nat. Rev., 9, 356-369.
12. Moore,J.H. (2007) Genome-wide analysis of epistasis using multifactor dimensionality reduction: Feature selection and construction in the domain of human genetics. In Zhu,X. and Davidson,I. (eds), Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data IGI Global, Hershey, USA, pp. 17-30.
13. Moore,J.H. and White,B.C. (2006) Exploiting expert knowledge in genetic programming for genome-wide genetic analysis. In Runarsson,T.P. et al. (eds), Procedings of the 9th International Conference on Parallel Problem Solving from Nature, vol. 4193 of Lecture Notes in Computer Science. Springer, Reykjavik, Iceland, pp. 969-977.
14. Moore,J.H. and White,B.C. (2007) Genome-wide genetic analysis using genetic programming: The critical need for expert knowledge. In Riolo,R. et al. (eds), Genetic Programming Theory and Practice IV. Springer, New York, USA, pp. 11-28.
15. Mori,S. et al. (2008) Association of genetic variations of genes encoding thrombospondin, type 1, domain-containing 4 and 7a with low bone mineral density in Japanese women with osteoporosis. J. Hum. Genet., 53, 694-697.
16. Motsinger-Reif,A.A. et al. (2008) Power of grammatical evolution neural networks to detect gene-gene interactions in the presence of error. BMC Res. Notes, 1, 65.
17. Musani,S. et al. (2007) Detection of gene-gene interactions in genome-wide association studies of human population data. Hum. Hered. 63, 67-84.
18. Park,M. and Hastie,T. (2008) Penalized logistic regression for detecting gene interactions. Biostatistics, 9, 30-50.
19. Province,M.A. and Borecki,I.B. (2008) Gathering the gold dust: Methods for assessing the aggregate impact of small effect genes in genomic scans. In Proceedings of Pacific Symposium on Biocomputing, Maui, Hawaii, USA.
20. Ritchie,M. 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.
21. Thomson,W. et al. (2007) Rheumatoid arthritis association at 6q23. Nat. Genet., 39, 1431-1433.
22. Velez,D. et al. (2007) A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet. Epidemiol., 31, 306-315.
23. Wang,K. et al. (2007) Pathway-based approaches for analysis of genomewide association studies. Am. J. Hum. Genet., 81, 1278-1283.
24. Wang,W. et al. (2005) Genome-wide association studies: Theoretical and practical concerns. Nat. Rev. Genet., 6, 109-118.
25. WTCCC (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661-678.
26. Zhang,Y. and Liu,J.S. (2007) Bayesian inference of epistatic interactions in case-control studies. Nat. Genet., 39, 1167-1173.