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DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits

Nature Genetics volume 55, pages 112–122 (2023)Cite this article

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Abstract

Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.

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Fig. 1: Scheme of data generation and analysis overview.
Fig. 2: mQTL discovery and e/mQTL functional mechanism characterization.
Fig. 3: Characterization of HOXD-locus e/mQTL pleiotropy in the ovary.
Fig. 4: Colocalization of mQTLs and eQTLs with GWAS traits.
Fig. 5: Examples of trait-linked e/mQTLs.
Fig. 6: Characteristics of trait-linked mQTLs.

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Data availability

Summary statistics of mQTLs are available at the GTEx Portal (https://gtexportal.org/home/datasets). DNAm normalized data is available at GEO (GSE213478). All GTEx protected data are available via dbGaP (phs000424.v9); access to the DNAm raw data is provided through the AnVIL platform (https://anvil.terra.bio/#workspaces/anvil-datastorage/AnVIL_GTEx_V9_hg38). Independent linkage disequilibrium blocks coordinates to define GWAS hit loci, colocalization summary statistics and priors, single-tissue functional annotation enrichment statistics, and data to generate figures, are available at Figshare (https://figshare.com/projects/DNA_methylation_QTL_mapping_across_diverse_human_tissues_provides_molecular_links_between_genetic_variation_and_complex_traits/149524).

Code availability

Code for QTL and eQTM mapping, functional enrichment, and colocalization, as well as code to to generate manuscript figures, is available at the github repository (https://github.com/meritxellop/eGTEx_mQTLs_eQTLs_GWAS) and archived at zenodo (https://doi.org/10.5281/zenodo.7106660)99.

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Acknowledgements

This work was supported by grants U01 HG007601 (to B.L.P.), R35ES028379 (to B.L.P.), 2R01 GM108711 (to L.S.C) and U24 CA210993-SUB (to L.S.C) and was completed in part with computational resources provided by the Center for Research Informatics at the University of Chicago. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the donors and their families for their generous gifts of biospecimens to the GTEx research project; the Genomics Platform at the Broad Institute for data generation; F. Aguet, J. Nedzel and K. Ardlie for sample delivery logistics and data release management; D. Delgado and L. Tong for assistance with assessing mQTL replication; and M. Goraj, J. Witkos, J. Degner and J. Resztak for comments on an earlier version of the manuscript.

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Authors and Affiliations

  1. Department of Public Health Sciences, University of Chicago, Chicago, IL, USA

    Meritxell Oliva, Yihao Lu, Meytal Chernoff, Farzana Jasmine, Habibul Ahsan, Muhammad G. Kibriya, Lin S. Chen & Brandon L. Pierce

  2. Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Kathryn Demanelis

  3. Department of Human Genetics, University of Chicago, Chicago, IL, USA

    Habibul Ahsan & Brandon L. Pierce

  4. Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA

    Habibul Ahsan & Brandon L. Pierce

  5. Department of Medicine, University of Chicago, Chicago, IL, USA

    Habibul Ahsan

  6. Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA

    Habibul Ahsan & Muhammad G. Kibriya

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  1. Meritxell Oliva
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Contributions

B.L.P. and M.O. conceived the study; M.O. conceived and led all analysis supervised by B.L.P. and L.S.C.; M.O. performed all bioinformatic analysis, granted K.D., M.C. and Y.L. contributions; M.O. led the writing and editing of the manuscript and supplement; B.L.P., L.S.C. and H.A. contributed to the editing of the manuscript and supplement; M.O., B.L.P. and L.S.C. coordinated analyses of all contributing authors; F.J. generated the DNAm data; M.G.K. supervised the generation of the DNAm data; K.D. processed and QC-ed the DNAm data; Y.L. and M.C. contributed to the mQTL functional characterization analysis. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Meritxell Oliva, Lin S. Chen or Brandon L. Pierce.

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Extended data

Extended Data Fig. 1 Characterization of methylomes across tissues, eQTM discovery and tissue specificity patterns.

(a) Sample similarity based on DNAm profiles. Dimensionality reduction was performed with a t-Distributed Stochastic Neighbor Embedding approach (t-SNE). (b) Hierarchical tissue clustering based on complete methylomes (left panel) and transcriptomes (right panel) of nine tissues (x axis). The molecular phenotypes displayed (y axis) correspond to the top 20,000 most divergent CpG sites and genes across tissues. DNAm and gene expression values are column-wise scaled. (c) Number of eQTMs per tissue, defined at LFSR < 0.05 or FDR < 0.05, shown with per-tissue eQTM-mapping sample sizes in parentheses. FDR: False Discovery Rate. LFSR: Local False Sign Rate. (d) Tissue sharing profile of eQTMs. (e) Contribution (x axis, square-root transformed log(OR)) of selected factors to eQTM likelihood (presence) for different gene regulatory elements (y axis). Dist.: Distance. OR: Odds Ratio. Factor units: CpG–gene distance [Kb], eQTM Sign [‘1’ for negative correlation between methylation and expression, ‘0’ otherwise], CpG DNAm [M-value], gene expression [log2(TPM + 1)]. OR estimates were derived from across-tissue meta-analysis (nine tissues) of predictor coefficients of eQTM likelihood, fitted with a logistic regression model (Methods).

Extended Data Fig. 2 DNAm-derived PEER factors association with technical, clinical and epidemiological covariates.

Proportion of variance - mean adjusted R2 across top three PEERs (R2adj) - of the PEER factors explained in part by known donor and sample clinical and biological covariates. Each cell shows the proportion of variance explained by the covariate with respect to the three top PEERs in a specific tissue. Only covariates with R2adj ≥ 0.02 in any tissue are shown. Tissues and covariates are ordered based on hierarchical clustering with complete agglomeration with Euclidean distance. Gray cells indicate unavailable data. The cells at the bottom of the panel shows that the PEERs are capturing batch effects, as expected.

Extended Data Fig. 3 DNAm-derived PEER factors association with tissue cellular abundances.

Proportion of variance - mean adjusted R2 across top three PEERs (R2adj) - of the PEER factors explained in part by tissue cellular abundances. Each bar shows the proportion of variance explained by the cell abundance with respect to the three top PEERs in a specific tissue. (b) Fraction of cell abundance (y axis) estimated by DNAm cell-type deconvolution with EpiSCORE, stratified by cell type (x axis) in corresponding tissue. Breast cell abundances are stratified by sex to illustrate sex-differential cell abundances. Cell abundances were estimated for all available samples per tissue: NBreast,Males = 14, NBreast,Females = 38, NBlood,Pooled = 54, NColon,Pooled = 224. B: B cells, NK: Natural Killer cells, CD4T: CD4+ T-cells, CD8T: CD8+ T-cells, Mono: Monocytes, Neutro: Neutrophils, Eosino: Eosinophils, Basal: Basal Epithelial cells, EC: Endothelial cells, Fat: Adipocytes, Luminal: Luminal Epithelial cells, Lym: Lymphocytes, Macro: Macrophages, Epi: Epithelial cells, Mye: Myeloid cells, Stromal: Stromal cells.

Extended Data Fig. 4 Tissue specificity of QTLs.

(a) Tissue sharing profile of mQTLs and eQTLs. (b-c) Cross-tissue sharing of mCpGs and eGenes. Cross-tissue mean percent of mCpGs and eGenes per tissue-sharing category is shown in parentheses. Of note, testis is an outlier for eQTL tissue specificity, as 23.5% of eGenes were not detected in any other tissue. Avg.: average (mean). (c) Cross-tissue sharing of mQTL tissue-leveraged effect magnitudes (y axis) per gene regulatory region (x axis, 36 data points per box plot). P-values of paired two-sided Wilcoxon signed rank tests are shown for corresponding pairwise comparisons; p-value of Kruskal-Wallis rank sum test is shown for the three-way comparison. Enh.: Enhancer. (d, e) Validation of tissue-specific mQTLs in muscle, blood and brain mQTL external cohorts, see (b) for tissue color legend. In (d), for each cohort, the average of absolute mQTL effect sizes and corresponding standard error is displayed for each set of tissue-specific mQTLs identified in each GTEx tissue (x axis). In (e), Spearman correlation between external and GTEx mQTL effect sizes, and associated standard error, is shown. Fisher’s z transformation was applied to Spearman correlation coefficients; standard errors were calculated based on the transformed coefficients. In (d,e), the number of QTL associations (N) tested for each pairwise comparison is as follows: NFUSION,GTEx = 195|4142|336,4630,287,4643|1428|360|369, NGoDMC,GTEx = 22|1353|47|1478|70|1019|292|83|102 and NROSMAP,GTEx = 57|2428|156|2587|163|2673|791|214|109, where GTEx corresponds to Breast|Colon|Kidney|Lung|Muscle|Ovary|Prostate|Testis|Blood tissues, respectively.

Extended Data Fig. 5 Representativity of GTEx mCpGs in external cohorts.

Overlap of mCpGs identified in GTEx (FDR < 0.05) with mCpGs identified in external mQTL cohorts at different nominal p-value thresholds (P < 1e-03 and P < 1e-05). Results are represented for all mCpGs - detected in EPIC and/or 450 K Illumina array - and for 450 K Illumina array CpG sites exclusively. P-value thresholds correspond to external cohort nominal mQTL associations, derived from QTL mapping by multiple regression two-sided t-tests.

Extended Data Fig. 6 Enrichment of QTLs in chromatin states.

(a) QTL enrichment (x-axis) in tissue-matching open chromatin regions derived from ENCODE DNase-seq profiles per tissue (y-axis). Whole blood is excluded due to lack of a tissue-matching DNase-seq profile. Enrichment differences between tissues may be due in part to per-tissue DNase-seq data quality. (b) QTL enrichment (x-axis) in active chromatin states. OR: Odds Ratio. Enrichment values correspond to maximum-likelihood estimated log(ORs) for single-tissue in (a) and from across-tissue (nine tissues) meta-analysis in (b). In all panels, whiskers represent the 95% confidence interval of the enrichment value.

Extended Data Fig. 7 Characterization of mQTL pleiotropy.

(a) Scheme of possible scenarios of eQTL-mQTL colocalization regarding QTL variants’ pleiotropic effect on multiple mCpGs and eGenes. (b) Quantification of mQTL-eQTL pleiotropy per tier per tissue, in percent of mCpGs belonging to each tier. Tier details illustrated in (a). Avg.: average (mean). (c) Distribution of the number of eGenes per mCpG (left panel) and mCpGs per eGene (right panel) involved in mQTL-eQTL colocalization events, stratified by tissue. Avg.: average (mean).

Extended Data Fig. 8 Evaluation of mQTL-GWAS colocalization approach.

(a) Density plot of mQTL-GWAS colocalization scores based on coloc run with default (y axis) and fastenloc-derived priors (x axis) on UKB standing height GWAS; Spearman’s rho is shown. Each dot corresponds to a colocalization test for a particular GWAS hit, independent mQTL and tissue combination. (b) Density plot of mQTL-GWAS colocalization scores based on coloc (x axis) and fastenloc (y axis) approaches on all GWASs; Spearman’s rho is shown. Each dot corresponds to a colocalization test for a particular GWAS, GWAS hit, independent mQTL and tissue combination. Dots within the top-right quadrant correspond to significant (RCP > 0.3 and PP4 > 0.3) colocalizations. PP4: coloc-derived posterior probability where the two traits share a single causal variant. RCP: fastenloc-derived probability of a genomic region harboring a colocalized signal.

Extended Data Fig. 9 Signatures of QTL-GWAS colocalizations and trait-linked QTLs.

(a) Percent of QTL-colocalized GWAS hits (y axis) per GWAS trait (x axis) stratified by GWAS trait category and colocalization group (see Fig. 4). Only GWAS traits with > = 10 colocalized GWAS hits are displayed. (b) Mean DNAm - in M-values - of mCpGs (left panel) and gene expression - in log2(TPM + 1) - of eGenes (right panel) tested for colocalization, stratified by tissue and colocalization group (see Fig. 4). Mean DNAm and gene expression across tissues is indicated by a dashed line. Whiskers represent the 95% confidence interval of the mean, calculated based on 5,000 replications of bootstrapped samples (random sampling with replacement). The number of mCpGs/eGenes (N) tested per bootstrap is as follows: NMeanMethylation = (12472|51|124), (147806|306|1111), (17574|24|221), (157356|359|1234), (13623|45|162), (127008|147|1041), (65147|103|60), (13576|38|101), (20127|126|254) and NMeanGeneExpression = (10050|27|168), (10800|110|103), (1147|4|22), (13053|144|149), (12594|33|218), (5120|55|47), (6744|43|75), (17025|18|164), (11545|95|291) for QTL-GWAS tested, e/mQTL-shared and e/mQTL-specific eGenes/mCpGs in Breast|Colon|Kidney|Lung|Muscle|Ovary|Prostate|Testis|Blood tissues, respectively.

Extended Data Fig. 10 Breast cancer linked e/mQTLs in the TERT-CLPTM1L locus.

Colocalized molecular phenotypes for this locus, identified by breast cancer GWAS-QTL multivariate colocalization approach (Methods), are provided in the top summary table. The mQTLs colocalizing with these breast cancer GWAS signals (that is, mCpGs cg03935379 and cg07380026) are shown in Fig. 5a, b. Additional details are provided in Supplementary Table 8. Plots illustrate association p-values in the locus for breast cancer estrogen positive (ER+) GWAS (top panel), breast cancer estrogen negative (ER−) GWAS (middle panel) and TERT eQTL signal in induced pluripotent stem cells (iPSCs) (bottom panel). Genotype-phenotype association p-values correspond to rs10069690, lead signal for breast cancer ER- GWAS. Linkage disequilibrium between loci is quantified by squared Pearson coefficient of correlation (r2) in population from European origin. Breast GWAS p-values were obtained from Milne et al. 2017, and iPSC TERT eQTL p-values from Bonder et al 2021. Mb: mega base. P-values correspond to nominal GWAS or QTL associations, derived from multiple regression two-sided t-tests.

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Oliva, M., Demanelis, K., Lu, Y. et al. DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits. Nat Genet 55, 112–122 (2023). https://doi.org/10.1038/s41588-022-01248-z

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