A Method to Detect Differentially Methylated Loci With Next-Generation Sequencing

Genetic Epidemiology

Volumn 37, Issue 4, 2013, Pages 377-382

  Xu, Hongyan ^a   Podolsky, Robert H ^a   Ryu, Duchwan ^a   Wang, Xiaoling ^a   Su, Shaoyong ^a   Shi, Huidong ^a   George, Varghese ^a  

^a MEDICAL COLLEGE OF GEORGIA   (United States)

Author keywords

Differential methylation test; DNA methylation; Next generation sequencing

Indexed keywords

ALLELE; ARTICLE; CHRONIC LYMPHATIC LEUKEMIA; CPG ISLAND; DNA METHYLATION; GENE LOCUS; GENOME; MICROARRAY ANALYSIS; NEXT GENERATION SEQUENCING; SIMULATION; STATISTICAL ANALYSIS;

ALGORITHMS; ALLELES; CASE-CONTROL STUDIES; CLUSTER ANALYSIS; COMPUTER SIMULATION; CPG ISLANDS; DNA METHYLATION; HUMANS; LEUKEMIA, LYMPHOCYTIC, CHRONIC, B-CELL; MODELS, STATISTICAL; RESEARCH DESIGN; SEQUENCE ANALYSIS, DNA; SOFTWARE;

EID: 84876406263     PISSN: 07410395     EISSN: 10982272     Source Type: Journal    
DOI: 10.1002/gepi.21726     Document Type: Article

References (21)

1. Bell JT, Tsai PC, Yang TP, Pidsley R, Nisbet J, Glass D, Mangino M, Zhai G, Zhang F, Valdes A and others. 2012. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet 8:e1002629.
2. Chen Z, Liu Q, Nadarajah S. 2012. A new statistical approach to detecting differentially methylated loci for case control illumina array methylation data. Bioinformatics 28:1109-1113.
3. Damle RN, Wasil T, Fais F, Ghiotto F, Valetto A, Allen SL, Buchbinder A, Budman D, Dittmar K, Kolitz J and others. 1999. Ig V gene mutation status and cd38 expression as novel prognostic indicators in chronic lymphocytic leukemia. Blood 94:1840-1847.
4. Del Poeta G, Maurillo L, Venditti A, Buccisano F, Epiceno AM, Capelli G, Tamburini A, Suppo G, Battaglia A, Del Principe MI and others. 2001. Clinical significance of CD38 expression in chronic lymphocytic leukemia. Blood 98:2633-2639.
5. Del Poeta G, Maurillo L, Venditti A, Buccisano F, Epiceno AM, Capelli G, Tamburini A, Suppo G, Battaglia A, Del Principe MI and others. 2012. Distinct DNA methylomes of newborns and centenarians. Proc Natl Acad Sci USA 109:10522-10527.
6. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH. 2010. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11:446-450.
7. Hamblin TJ, Davis Z, Gardiner A, Oscier DG, Stevenson FK. 1999. Unmutated ig v(h) genes are associated with a more aggressive form of chronic lymphocytic leukemia. Blood 94:1848-1854.
8. Hansen KD, Langmead B, Irizarry RA. 2012. Bsmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol 13:R83.
9. Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD, Feinberg AP, Irizarry RA. 2012. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol 41:200-209.
10. Kibriya MG, Raza M, Jasmine F, Roy S, Paul-Brutus R, Rahaman R, Dodsworth C, Rakibuz-Zaman M, Kamal M, Ahsan H. 2011. A genome-wide DNA methylation study in colorectal carcinoma. BMC Med Genomics 4:50.
11. Kuan PF, Wang S, Zhou X, Chu H. 2010. A statistical framework for illumina DNA methylation arrays. Bioinformatics 26:2849-2855.
12. Kulis M, Esteller M. 2010. DNA methylation and cancer. Adv Genet 70:27-56.
13. Liu J, Morgan M, Hutchison K, Calhoun VD. 2010. A study of the influence of sex on genome wide methylation. PLoS One 5:e10028.
14. Meissner A, Gnirke A, Bell GW, Ramsahoye B, Lander ES, Jaenisch R. 2005. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res 33:5868-5877.
15. Pei L, Choi JH, Liu J, Lee EJ, McCarthy B, Wilson JM, Speir E, Awan F, Tae H, Arthur G and others. 2012. Genome-wide DNA methylation analysis reveals novel epigenetic changes in chronic lymphocytic leukemia. Epigenetics 7:567-578.
16. Rao JN, Scott AJ. 1992. A simple method for the analysis of clustered binary data. Biometrics 48:577-585.
17. Satterlee JS, Schbeler D, Ng H-H. 2010. Tackling the epigenome: challenges and opportunities for collaboration. Nat Biotechnol 28:1039-1044.
18. Spisák S, Kalmár A, Galamb O, Wichmann B, Sipos F, Péterfia B, Csabai I, Kovalszky I, Semsey S, Tulassay Z and others. 2012. Genome-wide screening of genes regulated by DNA methylation in colon cancer development. PLoS One 7:e46215.
19. Sun H, Wang S, 2012. Penalized logistic regression for high-dimensional DNA methylation data with case-control studies. Bioinformatics 28:1368-1375.
20. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP and others. 2010. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res 20:440-446.
21. Wang S. 2011. Method to detect differentially methylated loci with case-control designs using illumina arrays. Genet Epidemiol 35:686-694.