Gene-gene interaction analysis for the survival phenotype based on the cox model

Bioinformatics

Volumn 28, Issue 18, 2012, Pages

  Lee, Seungyeoun ^a   Kwon, Min Seok ^b   Oh, Jung Mi ^c   Park, Taesung ^b,d  

^a Sejong University   (South Korea)

^b Interdisciplinary Program in Bioinformatics   (South Korea)

^c Tumor Microenvironment Global Core Research Center   (South Korea)

^d Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

VISCUM ALBUM;

ACUTE GRANULOCYTIC LEUKEMIA; ALGORITHM; ARTICLE; BIOLOGICAL MODEL; FEMALE; GENE FREQUENCY; GENETIC ASSOCIATION; GENETICS; GENOTYPE; HUMAN; MALE; METHODOLOGY; MORTALITY; MULTIFACTOR DIMENSIONALITY REDUCTION; PHENOTYPE; PROPORTIONAL HAZARDS MODEL; SINGLE NUCLEOTIDE POLYMORPHISM;

ALGORITHMS; FEMALE; GENE FREQUENCY; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; LEUKEMIA, MYELOID, ACUTE; MALE; MODELS, GENETIC; MULTIFACTOR DIMENSIONALITY REDUCTION; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; PROPORTIONAL HAZARDS MODELS;

EID: 84866441336     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts415     Document Type: Article

References (16)

1. Andrew,A.S. et al. (2009) Bladder cancer SNPpanel predicts susceptibility and survival. Hum. Genet., 125, 527-539.
2. Calle,M.L. et al. (2008) Improving strategies for detecting genetic patterns of disease susceptibility in association studies. Stat Med., 27, 6532-6546.
3. Chung,Y. et al. (2007) Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions. Bioinformatics, 23, 71-76.
4. Cox,D.R. (1972) Regression models and life tables (with discussion). J.R. Statist. Soc. B, 34, 187-220.
5. Gui,J. et al. (2011) A novel survival multifactor dimensionality reduction method for detecting gene-gene interactions with application to bladder cancer prognosis. Hum. Genet., 129, 101-110.
6. Hahn,L.W. et al. (2003) Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics, 19, 376-382.
7. Hahn,L.W. and Moore,J.H. (2004) Ideal discrimination of discrete clinical end-points using multilocus genotypes. In Silico Biol., 4, 183-194.
8. Lee,S.Y. et al. (2007) Log-linear model-based multifactor dimensionality reduction method to detect gene-gene interactions. Bioinformatics, 23, 2589-2595.
9. Lou,X.Y. et al. (2007)Ageneralized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am. J. Hum. Genet., 80, 1125-1137.
10. Manolio,T.A. (2010) Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med., 363, 166-176.
11. Moore,J.H. (2004) Computational analysis of gene-gene interaction in common human diseases using multifactor dimensionality reduction. Expert Rev. Mol. Diagn., 4, 795-803.
12. Namkung,J. et al. (2009) Identification of gene-gene interactions in the presence of missing data using the multifactor dimensionality reduction method. Genet. Epidemiol., 33, 646-656.
13. Moore,J.J. andWillams,S.M. (2009) Epistasis and its implications for personal genetics. Am. J. Hum. Genet., 86, 309-320.
14. Ritchie,M.D. 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.
15. Therneau,T.M. et al. (1990) Martingale-based residuals for survival models. Biometrika 77, 147-160.
16. Velez,D.R. et al. (2007) A balanced accuracy metric for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet. Epidemiol., 31, 306-315.