Nature GeneticsGenomicsPeer-reviewed journal article
Disease variants alter transcription factor levels and methylation of their binding sites
Marc Jan BonderiD, René Luijk, Daria Zhernakova, Matthijs Moed, Patrick DeeleniD, Martijn VermaatiD, Maarten van ItersoniD, Freerk van Dijk, Michiel van Galen, Jan Bot, Roderick C. Slieker, P. Mila Jhamai, Michael Verbiest, H. Eka D. SuchimaniD, Marijn VerkerkiD, Ruud van der BreggeniD, Jeroen van RooijiD, Nico Lakenberg, Wibowo ArindrartoiD, Szymon M. Kielbasa, Iris Jonkers, Peter van t Hof, Irene Nooren, Marian BeekmaniD, Joris DeeleniD, , C
Abstract
Most disease associated genetic risk factors are non-coding, making it challenging to design experiments to understand their functional consequences1,2. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer downstream effects of disease variants but the large majority remains unexplained.3,4. The analysis of DNA methylation, a key component of the epigenome5, offers highly complementary data on the regulatory potential of genomic regions6,7. However, a large-scale, combined analysis of methylome and transcriptome data to infer downstream effects of disease variants is lacking. Here, we show that disease variants have wide-spread effects on DNA methylation in trans that likely reflect the downstream effects on binding sites of cis-regulated transcription factors. Using data on 3,841 Dutch samples, we detected 272,037 independent cis-meQTLs (FDR < 0.05) and identified 1,907 trait-associated SNPs that affect methylation levels of 10,141 different CpG sites in trans (FDR < 0.05), an eight-fold increase in the number of downstream effects that was known from trans-eQTL studies3,8,9. Trans-meQTL CpG sites are enriched for active regulatory regions, being correlated with gene expression and overlap with Hi-C determined interchromosomal contacts10,11. We detected many trans-meQTL SNPs that affect expression levels of nearby transcription factors (including NFKB1, CTCF and NKX2-3), while the corresponding trans-meQTL CpG sites frequently coincide with its respective binding site. Trans-meQTL mapping therefore provides a strategy for identifying and better understanding downstream functional effects of many disease-associated variants.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: eQTL / molecular QTL mapping
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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