Penalized logistic regression for high-dimensional DNA methylation data with case-control studies

Bioinformatics

Volumn 28, Issue 10, 2012, Pages 1368-1375

  Sun, Hokeun ^a   Wang, Shuang ^a  

^a Columbia University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CASE CONTROL STUDY; COMPUTER SIMULATION; CPG ISLAND; DNA METHYLATION; FEMALE; GENE EXPRESSION REGULATION; GENETIC PREDISPOSITION; GENETICS; HUMAN; OVARY TUMOR; STATISTICAL MODEL;

CASE-CONTROL STUDIES; COMPUTER SIMULATION; CPG ISLANDS; DNA METHYLATION; FEMALE; GENE EXPRESSION REGULATION, NEOPLASTIC; GENETIC PREDISPOSITION TO DISEASE; HUMANS; LOGISTIC MODELS; OVARIAN NEOPLASMS;

EID: 84861135864     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts145     Document Type: Article

References (23)

1. Alexander,D. and Lange,K. (2011) Stability selection for genome-wide association. Genet. Epidemiol., 35, 722-728.
2. Bibikova,M. et al. (2006) High-throughput DNA methylation profiling using universal bead arrays. Genome Res., 16, 383-393.
3. Breheny,P. and Huang,J. (2009) Penalized methods for bi-level variable selction. Stat. Interface, 2, 369-380.
4. Friedman,J. et al. (2007) Pathwise coordinate optimization. Ann. Appl. Stat., 1, 302-332.
5. Friedman,J. et al. (2010) Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw., 33, 1-22.
6. Houseman,E. et al. (2008) Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics, 9, 365.
7. Kuan,P. et al. (2010) A statistical framework for illumina DNA methylation arrays. Bioinformatics, 26, 2849-2855.
8. Li,C. and Li,H. (2008) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics, 24, 1175-1182.
9. Li,C. and Li,H. (2010) Variable selection and regression analysis for covariates with a graphical structure with an application to genomics. Ann. Appl. Stat., 4, 1498-1516.
10. London,S.J. et al. (1997) Myeloperoxidase genetic polymorphism and lung cancer risk. Cancer Res., 57, 5001-5003.
11. Marsit,C.J. et al. (2009) Epigenetic profiling reveals etiologically distinct patterns of DNA methylation in head and neck squamous cell carcinoma. Carcinogenesis, 30, 416-422.
12. Meier,L. et al. (2008) The group lasso for logistic regression. J. Roy. Stat. Soc. B, 70, 53-71.
13. Meinshausen,N. and Bühlmann,P. (2010) Stability selection. J. Roy. Stat. Soc. B, 72, 417-473.
14. Shen,J. et al. (2011) Genome-wide DNA methylation profiles in hepatocellular carcinoma. Hepatology, (in press).
15. Siegmund,K. et al. (2004) A comparision of cluster analysis methods using DNA methylation data. Bioinformatics, 20, 1896-1904.
16. Teschendorff,A. et al. (2010) Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res., 20, 332-340.
17. Tibshirani,R. (1996) Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B, 58, 267-288.
18. Tibshirani,R. et al. (2005) Sparsity and smoothness via the fused lasso. J. Roy. Stat. Soc. B, 67, 91-108.
19. Wang,S. (2011) Method to detect diffentially methylated loci with case-contol designs using illumina arrays. Genet. Epidemiol., 35, 686-694.
20. Wu,T. et al. (2009) Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics, 25, 714-721.
21. Zhang,J. and Wiemann,S. (2000) Kegggraph: a graph approach to KEGG pathway in r and bioconductor. Bioinformatics, 25, 1470-1471.
22. Zhou,H. et al. (2010) Association screening of common and rare genetic variants by penalized regression. Bioinformatics, 26, 2375-2382.
23. Zou,H. and Hastie,T. (2005) Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. B, 67, 301-320.