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


Most disease-associated genetic variants are noncoding, 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 the downstream effects of disease-associated variants, but most of these variants remain unexplained3,4. The analysis of DNA methylation, a key component of the epigenome5,6, offers highly complementary data on the regulatory potential of genomic regions7,8. 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.

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Figure 1: Overview of a genomic region around TMEM176B and characteristics of CpG sites associated with meQTLs and eQTMs.
Figure 2: Characterization of identified cis- and trans-meQTL and eQTM effects.
Figure 3: Trans-meQTLs are enriched for Hi-C interchromosomal contacts and for GWAS variants related to immune traits.
Figure 4: An imbalance in the effect directions of trans-meQTLs implies involvement of transcription factors.
Figure 5: Trans-meQTL CpGs related to rs8060686 show overlap with CTCF-binding sites.


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This work was performed within the framework of the Biobank-Based Integrative Omics Studies (BIOS) consortium funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007). Samples were contributed by LifeLines, the Leiden Longevity Study, the Netherlands Twin Registry (NTR), the Rotterdam Study, the Genetic Research in Isolated Populations program, the CODAM study and the PAN study. We thank the participants of all aforementioned biobanks and acknowledge the contributions of the investigators to this study (Supplementary Note). This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. L.F. is supported by a grant from the Dutch Research Council (ZonMW-VIDI 917.14.374) and is supported by FP7/2007–2013, grant agreement 259867 and by an ERC Starting Grant, grant agreement 637640 (ImmRisk).

Author information





B.T.H., P.A.C.'t.H., J.B.J.v.M., A.I., R.J. and L.F. formed the management team of the BIOS consortium. D.I.B., R.P., J.v.D., J.J.H., M.M.J.v.G., C.D.A.S., C.J.H.v.d.K., C.G.S., C.W., L.F., A.Z., E.F.T., P.E.S., M.B., J.D., D.v.H., J.H.V., L.H.v.d.B., C.M.v.D., A.H., A.I. and A.G.U. managed and organized the biobanks. J.B.J.v.M., P.M.J., M. Verbiest, H.E.D.S., M. Verkerk, R.v.d.B., J.v.R. and N.L. generated RNA–seq and Illumina 450K data. H.M., M.v.I., M.v.G., J.B., D.V.Z., R.J., P.v.'t.H., P.D., I.N., P.A.C.'t.H., B.T.H. and M.M. were responsible for data management and the computational infrastructure. M.J.B., R.L., M. Vermaat, D.V.Z., R.C.S., I.J., M.v.I., P.D., F.v.D., M.v.G., W.A., S.M.K., M.A.S., E.W.v.Z., Y.L., M.L., T.J.H., R.J., P.A.C.'t.H., L.F. and B.T.H. performed the data analysis. M.J.B., R.L., L.F. and B.T.H. drafted the manuscript. D.V.Z., M.M., P.D. and M. Vermaat contributed equally. A.I., R.J. and J.B.J.v.M. contributed equally.

Corresponding authors

Correspondence to Lude Franke or Bastiaan T Heijmans.

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Competing interests

The authors declare no competing financial interests.

Additional information

A full list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Density of distances between CpG sites and the most strongly associated meQTL SNP.

Density plot of the distances between the 139,566 CpGs harboring a cis-meQTL and the most strongly associated SNP. Most SNP–CpG pairs are in close proximity (median distance = 10 kb), as indicated by the narrow peak around zero.

Supplementary Figure 2 Relationship between methylation variation and meQTL-associated CpGs.

The proportion of CpGs harboring an identified trans-meQTL increases with increasing variability in DNA methylation. The proportion of CpGs with evidence of a trans-meQTL is calculated per decile of variability in methylation (x axis).

Supplementary Figure 3 Characterization of cis-meQTLs.

(a) The number of cis-meQTLs found is strongly dependent on the variability in DNA methylation at a CpG site. Variances for 405,709 CpGs interrogated in the analyses were calculated using the 3,841 samples for which 450K data were available. Next, the CpGs were divided into deciles and the number of effects was counted for each decile. The different stacked colors correspond to primary, secondary, etc., effects. (b) The proportion of variance explained remains limited, even for highly variable CpG sites. The x axis shows the variances calculated for the 405,709 CpGs interrogated. The y axis shows the proportion of that variance explained by our identified cis-meQTLs. The limited proportion of variance explained, even for highly variable probes, suggests that increased statistical power contributes to but does not fully explain the increased number of cis-meQTLs identified. (c) DNA methylation variability differs across genomic contexts. Each line represents the proportion of the 405,709 CpGs used present in each genomic region. This clearly shows that some CpGs on the array are over-represented in certain genomic contexts. For example, weakly variable CpGs (0–10%) are over-represented in CpG islands. This may confound any enrichment analyses if variability in DNA methylation is influencing the likelihood of a given CpG harboring a meQTL. (d) DNA methylation variability seems to be the driving factor for identifying cis-meQTLs, even within genomic contexts. Each line again represents a distinct genomic context. (e) Reported enrichments of cis-meQTL effects for certain genomic contexts are strongly attenuated after accounting for the differential variability in DNA methylation between those genomic regions. Gray bars show uncorrected odds ratios. Blue bars show odds ratios corrected for methylation variability and the distance to the nearest SNP.

Supplementary Figure 4 Characterization of cis-eQTMs in relation to the direction of the eQTM effect.

Over-representation of positive (blue bars) and negative (red bars) e-CpGs in CpG islands and predicted chromatin states. The x axis shows this over-representation in terms of odds ratios and error bars (95% confidence intervals). e-CpGs with negative associations are over-represented in active regions (for example, active TSSs and enhancers), whereas e-CpGs with positive associations are often found in repressed regions (for example, quiescent regions). CGI, CpG island; TssA, active TSS; TssAFlnk, flanking active TSS; TxFlnk, transcribed at gene 5′ or 3′ end; Tx, strong transcription; TxWk, weak transcription; EnhG, genic enhancer; Enh, enhancer; ZNF/Rpts, ZNF genes and repeats; Het, heterochromatin; TssBiv, bivalent/poised TSS; BivFlnk, flanking bivalent TSS/enhancer; EnhBiv: bivalent enhancer.

Supplementary Figure 5 Trans-meQTLs identified for a risk factor for inflammatory bowel disease, rs11190140, and the overlap with NKX2-3.

(a) Depiction of the NKX2-3 gene and rs11190140, associated with inflammatory bowel disease. The plot shows increased expression of NKX2-3 for the T risk allele. (b) In addition to influencing NKX2-3 expression, rs11190140 also influences DNA methylation at 228 CpGs in trans, decreasing methylation levels at 81.1% of the affected CpG sites (red). In addition, many of the CpG sites overlap with NKX2-1 and NKX2-5 motifs (there is no NKX2-3 motif or ChIP–seq data available). (c) Gene network of the genes associated with 15 of the 228 CpGs (6.6%) with a trans-meQTL: blue, cis-eQTL-affected gene; red, genes associated both in methylation and expression.

Supplementary Figure 6 Trans-meQTLs identified for a risk factor for height, rs6763931, and the overlap with ZBTB38.

(a) Depiction of the ZBTB38 gene and rs6763931, associated with height. The plot shows increased expression of ZBTB38 for the T risk allele. (b) In addition to influencing ZBTB38 expression, rs6763931 also influences DNA methylation at 267 CpGs in trans, decreasing methylation levels at 99.2% of the affected CpG sites (red). In addition, depletion of overlap with H3K27me3 is observed (7.4-fold depletion, P = 3.8 × 10–28), shown in the outer chart. (c) Gene network of the genes associated with 60 of the 779 CpGs (7.7%) with a trans-meQTL: blue, cis-eQTL effected gene; red, genes associated both in methylation and expression.

Supplementary Figure 7 Trans-meQTLs identified for a risk factor related to lung carcinoma, rs7216064, and overlap with BPTF.

(a) Depiction of the BPTF gene and rs7216064, associated with lung carcinoma. (b) rs7216064 influences DNA methylation at 64 CpGs in trans, decreasing methylation levels at 82.8% of the affected CpG sites (red). In addition, many of the CpG sites (81.3%) overlap with CTCF-binding sites (16.8-fold enrichment, P = 5.1 × 10–25), shown in the outer chart.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Note. (PDF 1911 kb)

Supplementary Table 1

Descriptions and number of samples per cohort. (XLSX 8 kb)

Supplementary Table 2

Number of independent cis-meQTLs per QTL mapping round. (XLSX 8 kb)

Supplementary Table 3

GWAS SNPs tested for trans-meQTLs. (XLSX 90 kb)

Supplementary Table 4

Replication of lymphocyte trans-meQTLs in blood and vice versa. (XLSX 1434 kb)

Supplementary Table 5

Results of trans-meQTLs in non-corrected data. (XLSX 651 kb)

Supplementary Table 6

Results of trans-meQTLs in data corrected for blood cell composition. (XLSX 722 kb)

Supplementary Table 7

Results of trans-meQTL mapping on SNPs related to blood cell composition. (XLSX 162 kb)

Supplementary Table 8

Trans-meQTL effects replicated in expression. (XLSX 350 kb)

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Bonder, M., Luijk, R., Zhernakova, D. et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet 49, 131–138 (2017).

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