Abstract
Hundreds of epigenome-wide association studies (EWAS) have been performed, successfully identifying replicated epigenomic signals in processes such as aging and smoking. Despite this progress, it remains a major challenge in EWAS to detect both cell type-specific and cell type confounding effects impacting study results. One way to identify these effects is through eFORGE (experimentally derived Functional element Overlap analysis of ReGions from EWAS), a published tool that uses 815 datasets from large-scale mapping studies to detect enriched tissues, cell types, and genomic regions. Here, I show that eFORGE analysis can be extended to EWAS differentially variable positions (DVPs), identifying target cell types and tissues. In addition, I also show that eFORGE tissue-specific enrichment can be detected for sites below EWAS significance threshold. I develop on these and other analysis examples, extending our knowledge of eFORGE cell type- and tissue-specific enrichment results for different EWAS.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Visscher PM, Wray NR, Zhang Q et al (2017) 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101:5–22. https://doi.org/10.1016/j.ajhg.2017.06.005
Maurano MT, Humbert R, Rynes E et al (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science 337:1190–1195. https://doi.org/10.1126/science.1222794
ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74. https://doi.org/10.1038/nature11247
Roadmap Epigenomics Consortium, Kundaje A, Meuleman W et al (2015) Integrative analysis of 111 reference human epigenomes. Nature 518:317–330. https://doi.org/10.1038/nature14248
Stunnenberg HG, Abrignani S, Adams D et al (2016) The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery. Cell 167:1145–1149. https://doi.org/10.1016/j.cell.2016.11.007
Claussnitzer M, Dankel SN, Kim K-H et al (2015) FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med 373:895–907. https://doi.org/10.1056/NEJMoa1502214
Dunham I, Kulesha E, Iotchkova V et al (2014) FORGE: a tool to discover cell specific enrichments of GWAS associated SNPs in regulatory regions. bioRxiv:013045. https://doi.org/10.1101/013045
Breeze CE, Paul DS, van Dongen J et al (2016) eFORGE: a tool for identifying cell type-specific signal in epigenomic data. Cell Rep 17:2137–2150. https://doi.org/10.1016/j.celrep.2016.10.059
Eckhardt F, Lewin J, Cortese R et al (2006) DNA methylation profiling of human chromosomes 6, 20 and 22. Nat Genet 38:1378–1385. https://doi.org/10.1038/ng1909
Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188. https://doi.org/10.1214/aos/1013699998
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. https://doi.org/10.1038/nbt.2487
Lowe R, Rakyan VK (2014) Correcting for cell-type composition bias in epigenome-wide association studies. Genome Med 6:23. https://doi.org/10.1186/gm540
Tsai P-C, Glastonbury CA, Eliot MN et al (2018) Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health. Clin Epigenetics 10:126. https://doi.org/10.1186/s13148-018-0558-0
Teschendorff AE, Menon U, Gentry-Maharaj A et al (2009) An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS One 4:e8274. https://doi.org/10.1371/journal.pone.0008274
Phipson B, Maksimovic J, Oshlack A (2016) missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics 32:286–288. https://doi.org/10.1093/bioinformatics/btv560
Ecker S, Chen L, Pancaldi V et al (2017) Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types. Genome Biol 18(1):18. https://doi.org/10.1186/s13059-017-1156-8
Wang X, Tucker NR, Rizki G et al (2016) Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures. eLife 5:e10557. https://doi.org/10.7554/eLife.10557
Teixeira VH, Pipinikas CP, Pennycuick A et al (2019) Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions. Nat Med 25(3):517–525. https://doi.org/10.1038/s41591-018-0323-0
van Dongen J, Nivard MG, Willemsen G et al (2016) Genetic and environmental influences interact with age and sex in shaping the human methylome. Nat Commun 7:11115. https://doi.org/10.1038/ncomms11115
Altorok N, Coit P, Hughes T et al (2014) Genome-wide DNA methylation patterns in naive CD4+ T cells from patients with primary Sjögren’s syndrome. Arthritis Rheumatol 66:731–739. https://doi.org/10.1002/art.38264
Breeze CE, Reynolds AP, van Dongen J et al (2019) eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data. Bioinformatics 35(22):4767–4769. https://doi.org/10.1093/bioinformatics/btz456
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Breeze, C.E. (2022). Cell Type-Specific Signal Analysis in Epigenome-Wide Association Studies. In: Guan, W. (eds) Epigenome-Wide Association Studies. Methods in Molecular Biology, vol 2432. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1994-0_5
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1994-0_5
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1993-3
Online ISBN: 978-1-0716-1994-0
eBook Packages: Springer Protocols