Disease variants alter transcription factor levels and methylation of their binding sites

Abstract

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.

Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease

“…We undertook a replication analysis for the 128 CpG-trait associations for which independent data was available, repeating analyses using meQTL data from the BIOS QTL browser (17) and complex trait data from the UK Biobank (Supplementary Material, Table S5) (18). There was evidence of replication for 101 of the 128 associations based on multiple testing corrections ( P  < 3.91 × 10 −04 ) and the direction of effect between DNA methylation and complex traits (Supplementary Material, Table S6).…”

“…The BIOS QTL browser contains results from meQTL analyses in whole blood using a sample of 3841 Dutch individuals (http://www.genenetwork.nl/biosqtlbrowser/; date last accessed September 9, 2017) (17). The full list of primary cis -meQTLs was downloaded to evaluate effects identified in the discovery MR analysis conducted in ARIES.…”

“…For CpG-trait associations where DNA methylation and complex trait were driven by the same causal variant, as inferred by the JLIM method, we repeated our initial analysis using meta-analysed meQTL data from the BIOS QTL browser (17) and trait data from the UK Biobank project (18). …”

“…Effects on complex traits were obtained using findings from large-scale genome-wide association studies (GWAS). Replication of results with evidence of a putative causal relationship for a selection of traits was undertaken using DNA methylation data from the BIOS QTL browser (17) and complex trait data from up to 334 398 individuals enrolled in the UK Biobank study (18). Functional annotation and enrichment analyses, including data for histone mark peaks and DNAse I hypersensitivity sites across 113 different tissue types, were undertaken for selected variants and CpG sites (19,20).…”

Hoxa5 Promotes Adipose Differentiation via Increasing DNA Methylation Level and Inhibiting PKA/HSL Signal Pathway in Mice

“…DNA methylation is reported to block the binding of some transcription factor in vitro [30, 31]. Intriguingly, some transcription factors such as REST and CTCF have been shown to bind methylated regions and trigger their demethylation [32].…”