Adjusting for cell type composition in DNA methylation data using a regression-based approach

Methods in Molecular Biology

Volumn 1589, Issue , 2017, Pages 99-106

  Jones, Meaghan J ^a   Islam, Sumaiya A ^a   Edgar, Rachel D ^a   Kobor, Michael S ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Cell type; DNA methylation; Illumina Infinium HumanMethylation450 BeadChip; R statistical software; Statistical adjustment

Indexed keywords

BISULFITE; DNA;

CELL COMPOSITION; CELLS BY BODY ANATOMY; DNA METHYLATION; GENE SEQUENCE; GENOME ANALYSIS; PYROSEQUENCING; REGRESSION ANALYSIS; BIOLOGY; CYTOLOGY; HIGH THROUGHPUT SEQUENCING; HUMAN; HUMAN GENOME; METABOLISM; MONONUCLEAR CELL; PROCEDURES; SOFTWARE; STATISTICS AND NUMERICAL DATA;

COMPUTATIONAL BIOLOGY; DNA METHYLATION; GENOME, HUMAN; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; LEUKOCYTES, MONONUCLEAR; REGRESSION ANALYSIS; SOFTWARE;

EID: 85013113048     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/7651_2015_262     Document Type: Chapter

References (18)

1. Reinius LE, Acevedo N, Joerink M et al (2012) Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One 7:e41361
2. Jaffe AE, Irizarry RA (2014) Accounting for cellular heterogeneity is critical in epigenomewide association studies. Genome Biol 15:R31
3. Lam LL, Emberly E, Fraser HB et al (2012) Factors underlying variable DNA methylation in a human community cohort. Proc Natl Acad Sci U S A 109(Suppl 2):17253-17260
4. Liu Y, Aryee MJ, Padyukov L et al (2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol 31:142-147
5. Lowe R, Rakyan VK (2014) Correcting for cell-type composition bias in epigenome-wide association studies. Genome Med 6:23
6. Guintivano J, Aryee MJ, Kaminsky ZA (2013) A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics 8:290-302
7. Jones MJ, Farré P, McEwen LM et al (2013) Distinct DNA methylation patterns of cognitive impairment and trisomy 21 in down syndrome. BMC Med Genomics 6:58
8. Smith AK, Kilaru V, Klengel T et al (2014) DNA extracted from saliva for methylation studies of psychiatric traits: evidence tissue specificity and relatedness to brain. Am J Med Genet 168:36-44
9. Houseman EA, Accomando WP, Koestler DC et al (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform 13:86
10. Montaño CM, Irizarry RA, Kaufmann WE et al (2013) Measuring cell-type specific differential methylation in human brain tissue. Genome Biol 14:R94
11. Koestler DC, Christensen B, Karagas MR et al (2013) Blood-based profiles of DNA methylation predict the underlying distribution of cell types: a validation analysis. Epigenetics 8:816-826
12. D.C.T. R (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
13. Du P, Kibbe WA, Lin SM (2008) Lumi: a pipe-line for processing Illumina microarray. Bioinformatics 24:1547-1548
14. Aryee MJ, Jaffe AE, Corrada-Bravo H et al (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30:1363-1369
15. Du P, Zhang X, Huang C-C et al (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform 11:587
16. Zou J, Lippert C, Heckerman D et al (2014) Epigenome-wide association studies without the need for cell-type composition. Nat Methods 11:309-311
17. Houseman EA, Molitor J, Marsit CJ (2014) Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30:1431-1439
18. Leek JT, Johnson WE, Parker HS et al (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28:882-883