RT Journal Article SR Electronic T1 Disease variants alter transcription factor levels and methylation of their binding sites JF bioRxiv FD Cold Spring Harbor Laboratory SP 033084 DO 10.1101/033084 A1 Marc Jan Bonder A1 René Luijk A1 Daria V. Zhernakova A1 Matthijs Moed A1 Patrick Deelen A1 Martijn Vermaat A1 Maarten van Iterson A1 Freerk van Dijk A1 Michiel van Galen A1 Jan Bot A1 Roderick C. Slieker A1 P. Mila Jhamai A1 Michael Verbiest A1 H. Eka D. Suchiman A1 Marijn Verkerk A1 Ruud van der Breggen A1 Jeroen van Rooij A1 Nico Lakenberg A1 Wibowo Arindrarto A1 Szymon M. Kielbasa A1 Iris Jonkers A1 Peter van ’t Hof A1 Irene Nooren A1 Marian Beekman A1 Joris Deelen A1 Diana van Heemst A1 Alexandra Zhernakova A1 Ettje F. Tigchelaar A1 Morris A. Swertz A1 Albert Hofman A1 André G. Uitterlinden A1 René Pool A1 Jenny van Dongen A1 Jouke J. Hottenga A1 Coen D.A. Stehouwer A1 Carla J.H. van der Kallen A1 Casper G. Schalkwijk A1 Leonard H. van den Berg A1 Erik. W van Zwet A1 Hailiang Mei A1 Mathieu Lemire A1 Thomas J. Hudson A1 the BIOS Consortium A1 P. Eline Slagboom A1 Cisca Wijmenga A1 Jan H. Veldink A1 Marleen M.J. van Greevenbroek A1 Cornelia M. van Duijn A1 Dorret I. Boomsma A1 Aaron Isaacs A1 Rick Jansen A1 Joyce B.J. van Meurs A1 Peter A.C. ’t Hoen A1 Lude Franke A1 Bastiaan T. Heijmans YR 2015 UL http://biorxiv.org/content/early/2015/11/30/033084.abstract AB 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 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.