Advertisement

Determination of DNA Methylation Levels Using Illumina HumanMethylation450 BeadChips

  • Melanie A. CarlessEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1288)

Abstract

DNA methylation is a modifiable epigenetic phenomenon that has a strong influence over transcriptional regulation and as such has been consistently implicated in development and disease. Several platforms are targeted toward the identification of DNA methylation changes that might be pertinent to the disease process and include regional analysis (e.g., pyrosequencing) as well as genome-wide analysis (e.g., next-generation sequencing and microarray). The Illumina HumanMethylation450 BeadChip is one of the most comprehensive microarray platforms available, and due to the high costs associated with next-generation sequencing, it is becoming a widely used tool for the analysis of genome-wide DNA methylation levels. Providing quantitative DNA methylation levels at 482,421 CpG sites within CpG islands, shores, and shelves, as well as intergenic regions, the HumanMethylation450 BeadChip can allow accurate assessment of differential methylation across large studies. This chapter outlines the laboratory methodologies associated with performing the Illumina Infinium Methylation Assay, including bisulfite conversion, whole-genome amplification, BeadChip hybridization, XStain procedures, and imaging systems. Furthermore, this chapter provides an outline of data analysis tools, including the GenomeStudio pipeline, quality control measures, and additional statistical considerations. This comprehensive overview can aid not only in performing the Illumina Infinium Methylation Assay but also in the interpretation of data derived from this platform.

Key words

DNA methylation Bisulfite conversion Infinium assay CpG site GenomeStudio 

References

  1. 1.
    Gardiner-Garden M, Frommer M (1987) CpG islands in vertebrate genomes. J Mol Biol 196(2):261–282CrossRefPubMedGoogle Scholar
  2. 2.
    Bibikova M et al (2011) High density DNA methylation array with single CpG site resolution. Genomics 98(4):288–295CrossRefPubMedGoogle Scholar
  3. 3.
    Takai D, Jones PA (2002) Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc Natl Acad Sci U S A 99(6):3740–3745CrossRefPubMedCentralPubMedGoogle Scholar
  4. 4.
    Illingworth RS et al (2010) Orphan CpG islands identify numerous conserved promoters in the mammalian genome. PLoS Genet 6(9):e1001134CrossRefPubMedCentralPubMedGoogle Scholar
  5. 5.
    Lorincz MC et al (2004) Intragenic DNA methylation alters chromatin structure and elongation efficiency in mammalian cells. Nat Struct Mol Biol 11(11):1068–1075CrossRefPubMedGoogle Scholar
  6. 6.
    Cokus SJ et al (2008) Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452(7184):215–219CrossRefPubMedCentralPubMedGoogle Scholar
  7. 7.
    Rauch TA et al (2009) A human B cell methylome at 100-base pair resolution. Proc Natl Acad Sci U S A 106(3):671–678CrossRefPubMedCentralPubMedGoogle Scholar
  8. 8.
    Sandoval J et al (2011) Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6(6):692–702CrossRefPubMedGoogle Scholar
  9. 9.
    Teschendorff AE et al (2013) A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29(2):189–196CrossRefPubMedCentralPubMedGoogle Scholar
  10. 10.
    Roessler J et al (2012) Quantitative cross-validation and content analysis of the 450 k DNA methylation array from Illumina Inc. BMC Res Notes 5:210CrossRefPubMedCentralPubMedGoogle Scholar
  11. 11.
    Dedeurwaerder S et al (2011) Evaluation of the Infinium Methylation 450 K technology. Epigenomics 3(6):771–784CrossRefPubMedGoogle Scholar
  12. 12.
    Touleimat N, Tost J (2012) Complete pipeline for Infinium((R)) Human Methylation 450 K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics 4(3):325–341CrossRefPubMedGoogle Scholar
  13. 13.
    Maksimovic J, Gordon L, Oshlack A (2012) SWAN: subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol 13(6):R44CrossRefPubMedCentralPubMedGoogle Scholar
  14. 14.
    Zhang X, Mu W, Zhang W (2012) On the analysis of the Illumina 450 k array data: probes ambiguously mapped to the human genome. Front Genet 3:73PubMedCentralPubMedGoogle Scholar
  15. 15.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4):357–359CrossRefPubMedCentralPubMedGoogle Scholar
  16. 16.
    Du P, Kibbe WA, Lin SM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24(13):1547–1548CrossRefPubMedGoogle Scholar
  17. 17.
    Davis S, et al. (2010) Methylumi: handle illumina methylation data (version 2.0.1). www.bioconductor.org/packages/release/bioc/html/methylumi.html
  18. 18.
    minfi: Analyze Illumina’s 450 k methylation arrays. http://www.bioconductor.org/packages/release/bioc/html/minfi.html
  19. 19.
    Almasy L, Blangero J (1998) Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62(5):1198–1211CrossRefPubMedCentralPubMedGoogle Scholar
  20. 20.
    Barfield RT et al (2012) CpGassoc: an R function for analysis of DNA methylation microarray data. Bioinformatics 28(9):1280–1281CrossRefPubMedCentralPubMedGoogle Scholar
  21. 21.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B 57:289–300Google Scholar
  22. 22.
    Du P et al (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11:587CrossRefPubMedCentralPubMedGoogle Scholar
  23. 23.
    Chen YA et al (2013) Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8(2):203–209CrossRefPubMedCentralPubMedGoogle Scholar
  24. 24.
    Noushmehr H et al (2010) Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 17(5):510–522CrossRefPubMedCentralPubMedGoogle Scholar
  25. 25.
    Laffaire J et al (2011) Methylation profiling identifies 2 groups of gliomas according to their tumorigenesis. Neuro Oncol 13(1):84–98CrossRefPubMedCentralPubMedGoogle Scholar
  26. 26.
    Lange CP et al (2012) Genome-scale discovery of DNA-methylation biomarkers for blood-based detection of colorectal cancer. PLoS ONE 7(11):e50266CrossRefPubMedCentralPubMedGoogle Scholar
  27. 27.
    Liu Y et al (2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol 31(2):142–147CrossRefPubMedCentralPubMedGoogle Scholar
  28. 28.
    Wang D et al (2012) IMA: an R package for high-throughput analysis of Illumina’s 450 K Infinium methylation data. Bioinformatics 28(5):729–730CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Texas Biomedical Research InstituteSan AntonioUSA

Personalised recommendations