Recommendations for the design and analysis of epigenome-wide association studies

Nature Methods

Volumn 10, Issue 10, 2013, Pages 949-955

  Michels, Karin B ^a,b   Binder, Alexandra M ^b   Dedeurwaerder, Sarah ^c   Epstein, Charles B ^d   Greally, John M ^e   Gut, Ivo ^f   Houseman, E Andres ^g   Izzi, Benedetta ^h   Kelsey, Karl T ⁱ   Meissner, Alexander ^d,j   Milosavljevic, Aleksandar ^k   Siegmund, Kimberly D ^l   Bock, Christoph ^m,n,o   Irizarry, Rafael A ^p  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

^d BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^e ALBERT EINSTEIN COLLEGE OF MEDICINE   (United States)

^f Centro Nacional de Analisis Genomico   (Spain)

^g OREGON STATE UNIVERSITY   (United States)

^h UNIVERSITY OF FREIBURG   (Germany)

ⁱ BROWN UNIVERSITY   (United States)

^j Harvard University   (United States)

^k BAYLOR COLLEGE OF MEDICINE   (United States)

^l UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^m ERICH SCHMID INSTITUTE OF MATERIALS SCIENCE   (Austria)

ⁿ MEDICAL UNIVERSITY OF VIENNA   (Austria)

^o MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^p JOHNS HOPKINS UNIVERSITY   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

CELL HETEROGENEITY; CELL POPULATION; CELL SPECIFICITY; CELL SUBPOPULATION; DNA METHYLATION; ENVIRONMENTAL EXPOSURE; EPIGENOME WIDE ASSOCIATION STUDY; FALSE POSITIVE RESULT; GENE EXPRESSION; GENETIC ASSOCIATION; HIGH THROUGHPUT SEQUENCING; HUMAN; POPULATION STRUCTURE; PRIORITY JOURNAL; REVIEW; TISSUE SPECIFICITY;

ANIMALS; CPG ISLANDS; DATA INTERPRETATION, STATISTICAL; DNA METHYLATION; EPIGENESIS, GENETIC; GENOME-WIDE ASSOCIATION STUDY; HUMANS; RESEARCH DESIGN; SEQUENCE ANALYSIS, DNA;

EID: 84884828422     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.2632     Document Type: Review

References (52)

1. McCarthy, M.I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9, 356-369 (2008). (Pubitemid 351556063)
2. Bernstein, B.E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045-1048 (2010).
3. Satterlee, J.S., Schubeler, D. & Ng, H.H. Tackling the epigenome: challenges and opportunities for collaboration. Nat. Biotechnol. 28, 1039-1044 (2010).
4. Adams, D. et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat. Biotechnol. 30, 224-226 (2012).
5. Bock, C. et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat. Biotechnol. 28, 1106-1114 (2010).
6. Harris, R.A. et al. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat. Biotechnol. 28, 1097-1105 (2010).
7. Bock, C. Analysing and interpreting DNA methylation data. Nat. Rev. Genet. 13, 705-719 (2012).
8. Hansen, K.D., Wu, Z., Irizarry, R.A. & Leek, J.T. Sequencing technology does not eliminate biological variability. Nat. Biotechnol. 29, 572-573 (2011).
9. Jaffe, A.E., Feinberg, A.P., Irizarry, R.A. & Leek, J.T. Significance analysis and statistical dissection of variably methylated regions. Biostatistics 13, 166-178 (2012).
10. Bibikova, M. et al. High-throughput DNA methylation profiling using universal bead arrays. Genome Res. 16, 383-393 (2006). (Pubitemid 43327509)
11. Michels, K.B. Epigenetic Epidemiology (Springer, 2012).
12. Mill, J. & Heijmans, B.T. From promises to practical strategies in epigenetic epidemiology. Nat. Rev. Genet. 14, 585-594 (2013). 13.
13. Rakyan, V.K., Down, T.A., Balding, D.J. & Beck, S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529-541 (2011).
14. Silviera, M.L., Smith, B.P., Powell, J. & Sapienza, C. Epigenetic differences in normal colon mucosa of cancer patients suggest altered dietary metabolic pathways. Cancer Prev. Res. (Phila.) 5, 374-384 (2012).
15. Houseman, E.A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012).
16. Reinius, L.E. et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS ONE 7, e41361 (2012).
17. Koestler, D.C. et al. Peripheral blood immune cell methylation profiles are associated with nonhematopoietic cancers. Cancer Epidemiol. Biomarkers Prev. 21, 1293-1302 (2012).
18. Abbas, A.R., Wolslegel, K., Seshasayee, D., Modrusan, Z. & Clark, H.F. Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS ONE 4, e6098 (2009).
19. Liu, Y. et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol. 31, 142-147 (2013).
20. Saferali, A. et al. Cell culture-induced aberrant methylation of the imprinted IG DMR in human lymphoblastoid cell lines. Epigenetics 5, 50-60 (2010).
21. Sugawara, H. et al. Comprehensive DNA methylation analysis of human peripheral blood leukocytes and lymphoblastoid cell lines. Epigenetics 6, 508-515 (2011).
22. Caliskan, M., Cusanovich, D.A., Ober, C. & Gilad, Y. The effects of EBV transformation on gene expression levels and methylation profiles. Hum. Mol. Genet. 20, 1643-1652 (2011).
23. Michels, K.B. The promises and challenges of epigenetic epidemiology. Exp. Gerontol. 45, 297-301 (2010).
24. Teschendorff, A.E., Zhuang, J. & Widschwendter, M. Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics 27, 1496-1505 (2011).
25. Leek, J.T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733-739 (2010).
26. Dedeurwaerder, S. et al. Evaluation of the Infinium Methylation 450K technology. Epigenomics 3, 771-784 (2011).
27. Smith, Z.D. et al. A unique regulatory phase of DNA methylation in the early mammalian embryo. Nature 484, 339-344 (2012).
28. Boyle, P. et al. Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling. Genome Biol. 13, R92 (2012).
29. Bock, C. et al. DNA methylation dynamics during in vivo differentiation of blood and skin stem cells. Mol. Cell 47, 633-647 (2012).
30. Bis-SNP: combined DNA methylation and SNP calling for Bisulfite-seq data, Genome Biol.13
31. Du, P., Kibbe, W.A. & Lin, S.M. lumi: a pipeline for processing Illumina microarray. Bioinformatics 24, 1547-1548 (2008). (Pubitemid 351911712)
32. Goecks, J., Nekrutenko, A. & Taylor, J. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010).
33. Halachev, K., Bast, H., Albrecht, F., Lengauer, T. & Bock, C. EpiExplorer: live exploration and global analysis of large epigenomic datasets. Genome Biol. 13, R96 (2012).
34. Smyth, G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 3 (2004).
35. Akey, J.M., Biswas, S., Leek, J.T. & Storey, J.D. On the design and analysis of gene expression studies in human populations. Nat. Genet. 39, 807-808; author reply 808-809 (2007). (Pubitemid 47014480)
36. Johnson, W.E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118-127 (2007). (Pubitemid 44906106)
37. Jaffe, A.E. et al. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int. J. Epidemiol. 41, 200-209 (2012).
38. Hansen, K.D., Langmead, B. & Irizarry, R.A. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13, R83 (2012).
39. Langevin, S.M. et al. The influence of aging, environmental exposures and local sequence features on the variation of DNA methylation in blood. Epigenetics 6, 908-919 (2011).
40. Feinberg, A.P. & Irizarry, R.A. Evolution in health and medicine Sackler colloquium: stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proc. Natl. Acad. Sci. USA 107 (suppl. 1), 1757-1764 (2010).
41. Teschendorff, A.E. & Widschwendter, M. Differential variability improves the identification of cancer risk markers in DNA methylation studies profiling precursor cancer lesions. Bioinformatics 28, 1487-1494 (2012).
42. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440-9445 (2003). (Pubitemid 37033947)
43. Xu, J. et al. Pioneer factor interactions and unmethylated CpG dinucleotides mark silent tissue-specific enhancers in embryonic stem cells. Proc. Natl. Acad. Sci. USA 104, 12377-12382 (2007). (Pubitemid 47206145)
44. Nativio, R. et al. Disruption of genomic neighbourhood at the imprinted IGF2-H19 locus in Beckwith-Wiedemann syndrome and Silver-Russell syndrome. Hum. Mol. Genet. 20, 1363-1374 (2011).
45. Gibney, E.R. & Nolan, C.M. Epigenetics and gene expression. Heredity 105, 4-13 (2010).
46. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29 (2000). (Pubitemid 30257031)
47. Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30 (2000). (Pubitemid 30047706)
48. Huang da, W. et al. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35, W169-W175 (2007).
49. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545-15550 (2005). (Pubitemid 41528093)
50. McLean, C.Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495-501 (2010).
51. Bock, C., Halachev, K., Buch, J. & Lengauer, T. EpiGRAPH: user-friendly software for statistical analysis and prediction of (epi)genomic data. Genome Biol. 10, R14 (2009).
52. Bailey, T.L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202-W208 (2009).