Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci

Abstract

We characterized DNA methylation quantitative trait loci (mQTLs) in a large collection (n = 166) of human fetal brain samples spanning 56–166 d post-conception, identifying >16,000 fetal brain mQTLs. Fetal brain mQTLs were primarily cis-acting, enriched in regulatory chromatin domains and transcription factor binding sites, and showed substantial overlap with genetic variants that were also associated with gene expression in the brain. Using tissue from three distinct regions of the adult brain (prefrontal cortex, striatum and cerebellum), we found that most fetal brain mQTLs were developmentally stable, although a subset was characterized by fetal-specific effects. Fetal brain mQTLs were enriched amongst risk loci identified in a recent large-scale genome-wide association study (GWAS) of schizophrenia, a severe psychiatric disorder with a hypothesized neurodevelopmental component. Finally, we found that mQTLs can be used to refine GWAS loci through the identification of discrete sites of variable fetal brain methylation associated with schizophrenia risk variants.

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Figure 1: mQTLs in the developing human brain are predominantly cis-acting with effect size related to distance, although notable trans-effects are also present.
Figure 2: Despite a highly significant overall correlation of individual mQTL effects between fetal brain and adult brain regions, a subset of loci are characterized by fetal-specific mQTLs.
Figure 3: mQTL mapping can localize putative causal loci associated with disease.
Figure 4: Fetal mQTLs in schizophrenia-associated regions have larger effects on DNA methylation during neurodevelopment than the in adult brain.

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References

  1. 1

    Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    CAS  Article  Google Scholar 

  2. 2

    Jones, P.A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13, 484–492 (2012).

    CAS  Article  Google Scholar 

  3. 3

    Ziller, M.J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013).

    CAS  Article  Google Scholar 

  4. 4

    Wagner, J.R. et al. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol. 15, R37 (2014).

    Article  Google Scholar 

  5. 5

    Maunakea, A.K. et al. Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466, 253–257 (2010).

    CAS  Article  Google Scholar 

  6. 6

    Spiers, H. et al. Methylomic trajectories across human fetal brain development. Genome Res. 25, 338–352 (2015).

    CAS  Article  Google Scholar 

  7. 7

    Gibbs, J.R. et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 6, e1000952 (2010).

    Article  Google Scholar 

  8. 8

    Gamazon, E.R. et al. Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants. Mol. Psychiatry 18, 340–346 (2013).

    CAS  Article  Google Scholar 

  9. 9

    Gutierrez-Arcelus, M. et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Elife 2, e00523 (2013).

    Article  Google Scholar 

  10. 10

    Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  Article  Google Scholar 

  11. 11

    Nicolae, D.L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    Article  Google Scholar 

  12. 12

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  13. 13

    Fatemi, S.H. & Folsom, T.D. The neurodevelopmental hypothesis of schizophrenia, revisited. Schizophr. Bull. 35, 528–548 (2009).

    Article  Google Scholar 

  14. 14

    Weinberger, D.R. From neuropathology to neurodevelopment. Lancet 346, 552–557 (1995).

    CAS  Article  Google Scholar 

  15. 15

    Teh, A.L. et al. The effect of genotype and in utero environment on inter-individual variation in neonate DNA methylomes. Genome Res. 24, 1064–1074 (2014).

    CAS  Article  Google Scholar 

  16. 16

    Olsson, A.H. et al. Genome-wide associations between genetic and epigenetic variation influence mRNA expression and insulin secretion in human pancreatic islets. PLoS Genet. 10, e1004735 (2014).

    Article  Google Scholar 

  17. 17

    Drong, A.W. et al. The presence of methylation quantitative trait loci indicates a direct genetic influence on the level of DNA methylation in adipose tissue. PLoS ONE 8, e55923 (2013).

    CAS  Article  Google Scholar 

  18. 18

    Gutierrez-Arcelus, M. et al. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLoS Genet. 11, e1004958 (2015).

    Article  Google Scholar 

  19. 19

    Lemire, M. et al. Long-range epigenetic regulation is conferred by genetic variation located at thousands of independent loci. Nat. Commun. 6, 6326 (2015).

    CAS  Article  Google Scholar 

  20. 20

    Dekker, J., Marti-Renom, M.A. & Mirny, L.A. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 14, 390–403 (2013).

    CAS  Article  Google Scholar 

  21. 21

    Spilianakis, C.G., Lalioti, M.D., Town, T., Lee, G.R. & Flavell, R.A. Interchromosomal associations between alternatively expressed loci. Nature 435, 637–645 (2005).

    CAS  Article  Google Scholar 

  22. 22

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  23. 23

    Slieker, R.C. et al. Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array. Epigenetics Chromatin 6, 26 (2013).

    CAS  Article  Google Scholar 

  24. 24

    Roadmap Epigenomics Consortium. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  25. 25

    Elliott, G. et al. Intermediate DNA methylation is a conserved signature of genome regulation. Nat. Commun. 6, 6363 (2015).

    CAS  Article  Google Scholar 

  26. 26

    McRae, A.F. et al. Contribution of genetic variation to transgenerational inheritance of DNA methylation. Genome Biol. 15, R73 (2014).

    Article  Google Scholar 

  27. 27

    Wang, H. et al. Widespread plasticity in CTCF occupancy linked to DNA methylation. Genome Res. 22, 1680–1688 (2012).

    CAS  Article  Google Scholar 

  28. 28

    Ong, C.T. & Corces, V.G. CTCF: an architectural protein bridging genome topology and function. Nat. Rev. Genet. 15, 234–246 (2014).

    CAS  Article  Google Scholar 

  29. 29

    Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).

    CAS  Article  Google Scholar 

  30. 30

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  Article  Google Scholar 

  31. 31

    Lambert, J.C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat. Genet. 45, 1452–1458 (2013).

    CAS  Article  Google Scholar 

  32. 32

    Locke, A.E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  Article  Google Scholar 

  33. 33

    Morris, A.P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    CAS  Article  Google Scholar 

  34. 34

    Giambartolomei, C. et al. Bayesian test for co-localization between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  Google Scholar 

  35. 35

    Jaffe, A.E. et al. Mapping DNA methylation across development, genotype, and schizophrenia in the human frontal cortex. Nat. Neurosci. advance online publication, doi:10.1038/nn.4181 (2015).

  36. 36

    Meaburn, E.L., Schalkwyk, L.C. & Mill, J. Allele-specific methylation in the human genome: implications for genetic studies of complex disease. Epigenetics 5, 578–582 (2010).

    CAS  Article  Google Scholar 

  37. 37

    van Eijk, K.R. et al. Identification of schizophrenia-associated loci by combining DNA methylation and gene expression data from whole blood. Eur. J. Hum. Genet. 23, 1106–1110 (2015).

    CAS  Article  Google Scholar 

  38. 38

    Hill, M.J. & Bray, N.J. Evidence that schizophrenia risk variation in the ZNF804A gene exerts its effects during fetal brain development. Am. J. Psychiatry 169, 1301–1308 (2012).

    Article  Google Scholar 

  39. 39

    Tao, R. et al. Expression of ZNF804A in human brain and alterations in schizophrenia, bipolar disorder, and major depressive disorder: a novel transcript fetally regulated by the psychosis risk variant rs1344706. JAMA Psychiatry 71, 1112–1120 (2014).

    Article  Google Scholar 

  40. 40

    Pidsley, R. et al. Methylomic profiling of human brain tissue supports a neurodevelopmental origin for schizophrenia. Genome Biol. 15, 483 (2014).

    Article  Google Scholar 

  41. 41

    Davis, S., Du, P., Bilke, S., Triche, J. & Bootwalla, M. methylumi: Handle Illumina methylation data. R package version 2.14.0. <https://www.bioconductor.org/packages/release/bioc/html/methylumi.html> (2015).

  42. 42

    Aryee, M.J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363–1369 (2014).

    CAS  Article  Google Scholar 

  43. 43

    Pidsley, R. et al. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14, 293 (2013).

    CAS  Article  Google Scholar 

  44. 44

    Chen, Y.A. et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8, 203–209 (2013).

    CAS  Article  Google Scholar 

  45. 45

    Price, M.E. et al. Additional annotation enhances potential for biologically relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin 6, 4 (2013).

    CAS  Article  Google Scholar 

  46. 46

    Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    CAS  Article  Google Scholar 

  47. 47

    Ernst, J. & Kellis, M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol. 33, 364–376 (2015).

    CAS  Article  Google Scholar 

  48. 48

    Chang, C.C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  Google Scholar 

  49. 49

    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    CAS  Article  Google Scholar 

  50. 50

    Delaneau, O., Marchini, J. & Zagury, J.F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2012).

    CAS  Article  Google Scholar 

  51. 51

    Delaneau, O., Zagury, J.F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    CAS  Article  Google Scholar 

  52. 52

    Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank M. Weale for providing eQTL data from the BRAINEAC database. This work was supported by grants from the UK Medical Research Council (MRC; MR/K013807/1 to J.M. and MR/L010674/1 to N.J.B.) and the US National Institutes of Health (AG036039) to J.M. R.P. and H.S. were funded by MRC PhD studentships. The human embryonic and fetal material was provided by the Joint MRC/Wellcome Trust (grant #099175/Z/12/Z) Human Developmental Biology Resource.

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J.M. and N.J.B. conceived and supervised the study and obtained funding. E.H. undertook primary data analysis and bioinformatics. L.C.S. provided analytical support. H.S., J.V., R.P., T.M.M. and J.B. performed laboratory work. C.T. and G.T. provided samples for analysis. M.C.O'D. provided support for GWAS enrichment analyses. E.H., N.J.B. and J.M. drafted the manuscript. All of the authors read and approved the final submission.

Corresponding author

Correspondence to Jonathan Mill.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 The distribution of effect sizes across all Bonferroni significant fetal brain mQTLs.

Shown is the DNA methylation difference (% DNA methylation) associated with each minor allele for all fetal brain mQTLs, cis-acting mQTLs, and trans-acting mQTLs. The average effect size for trans-mQTLs is significantly lower than that observed for cis-mQTLs (two-sided Wilcoxon rank sum test, P = 6.74x10−7). Source data

Supplementary Figure 2 Frequency distribution of mQTL SNPs and associated DNA methylation sites.

A) mQTL SNPs are associated with DNA methylation at relatively few sites, but B) genetically-mediated DNA methylation sites are often associated with several mQTL SNPs. Source data

Supplementary Figure 3 The statistical significance of association between genotype and DNA methylation is related to the distance between the Illumina 450K array probe and mQTL SNP.

Shown is the relationship between mQTL significance (–log10 P-value) and distance between the DNA methylation site and genetic variant in fetal brain samples. Source data

Supplementary Figure 4 There is a highly-significant correlation of individual mQTL effects between fetal brain and each of the individual adult brain regions.

Shown are scatterplots showing the relationship in effect size (% DNA methylation change per allele) for all fetal brain mQTLs between fetal brain and a) PFC (r = 0.911, P < 2.2x10−16), b) STR (r = 0.899, P < 2.2x10−16) and c) CER (r = 0.835, P < 2.2x10−16). Source data

Supplementary Figure 5 The correlation of mQTL effect sizes (% DNA methylation change per allele) between fetal brain and adult brain is stronger for replicating variants (left) than non-replicating variants (right).

Of note, fetal brain mQTLs that do not replicate in adult brain are characterized by significantly lower effect sizes across all brain regions (Supplementary Fig. 6). Source data

Supplementary Figure 6 Fetal brain mQTLs that do not replicate in adult brain are characterized by significantly smaller effect sizes across all brain regions, including the initial fetal brain discovery sample (P = 3.18×10−141).

Supplementary Figure 7 SNPs associated with DNA methylation are more significantly associated with gene expression than non-mQTL variants.

Density plot comparing the distribution of the minimum brain eQTL P value across all fetal brain mQTLs (red) and non-mQTLs (blue) in the mQTL dataset (Wilcoxon rank sum test P < 2.2x10−16). Source data

Supplementary Figure 8 The overlap between independent fetal brain mQTL signals with the sentinelized subsignals from a brain eQTL dataset.

Histogram of overlap between simulated mQTL SNPs set (n = 1,000,000 simulations, matched for allele frequency) with the sentinelized eQTL set published by Ramasamy et al.. The vertical red line represents the true overlap, which is greater than any of the simulations (fold change = 4.23, P < 1.00x10−6 after 1 million simulations).1. Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci 17, 1418-28 (2014).

Supplementary Figure 9 Fetal brain mQTLs ar1e significantly enriched for schizophrenia genetic risk variants.

Different significance thresholds (represented by different colored bars) were used to classify SNP associations from a recent large GWAS analysis of schizophrenia1. There is an enrichment of schizophrenia-associated variants amongst fetal brain mQTLs at all tested GWAS significance thresholds. Shown is the enrichment fold-change with 95% confidence intervals based on 1 million simulated SNP-sets. See also Table 1.1. Schizophrenia Working Group of the Psychiatric Genomics Consortium,. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421-427 (2014). Source data

Supplementary Figure 10 Boxplot of mQTL effects observed for rs2535627, an index SNP from the recent schizophrenia GWAS.

Shown are boxplots demonstrating the relationship between genotype and DNA methylation for rs2535627 and cg11645453. There is a significant association in the fetal brain (P = 3.05x10−13) but none of the adult brain regions tested. Source data

Supplementary Figure 11 Boxplot of mQTL effect observed for rs4648845, an index SNP from a recent schizophrenia GWAS.

This boxplot demonstrates the relationship between genotype and DNA methylation for rs4648845 and cg02275930 in the fetal brain (P = 4.54x10−15). This mQTL was not tested in the adult brain regions because of the low minor allele frequency of rs4648845. Source data

Supplementary Figure 12 mQTLs identified using imputed genetic data reflected the non-imputed dataset in terms of genomic distribution and observed effect sizes.

A) The genomic distribution of all imputed mQTLs in fetal brain, where the position on the x-axis indicates the location of Illumina 450K HumanMethylation array probes and the position on the y-axis indicates the location of all imputed SNPs. The color of the point corresponds to the difference in DNA methylation per allele, with the largest effects plotted in dark red. A clear positive diagonal can be observed demonstrating that the majority of mQTLs in fetal brain are associated with genotype in cis, reflecting the distribution shown in Fig. 1a. B) The distribution of effect sizes across all imputed fetal brain mQTLs. The distribution is similar to that for the non-imputed dataset (Supplementary Fig. 1). Source data

Supplementary Figure 13 Effect sizes at fetal brain mQTLs identified in this study are highly correlated with those identified in an independent study of cortical mQTLs across development.

Effect sizes at mQTLs identified in this study (Hannon et al., X-axis) are highly correlated with those reported in a companion study (Jaffe et al., Y-axis). Each point represents a cis-mQTL identified in the current fetal brain dataset using the thresholds used by Jaffe et al [Nature Neuroscience, in press] (<20kb distance between SNP and DNA methylation site, FDR < 0.1). Source data

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 (PDF 2544 kb)

Supplementary Methods Checklist (PDF 384 kb)

Supplementary Table 1: Summary of demographic data for each of the four brain mQTL datasets generated in this study.

Abbreviations: PFC = prefrontal cortex, STR = striatum, CER = cerebellum. (XLSX 98 kb)

Supplementary Table 2: Summary of Bonferroni-significant fetal brain mQTLs. (XLSX 39 kb)

Supplementary Table 3: Annotated list of all Bonferroni significant fetal brain mQTLs.

For each significant (P < 3.69×10-13) mQTL, associated DNA methylation sites are annotated with genomic location, Illumina gene annotation, and ENCODE transcription factor binding site or DNaseI hypersensitivity site (DHS) overlap. mQTL SNPs are annotated with genomic location. Regression coefficients and P-values are provided for each mQTL across for fetal brain, and three adult brain region datasets. A list of all significant fetal brain mQTLs generated using imputed genotypes is available for download from: http://epigenetics.essex.ac.uk/mQTL/. Abbreviations: PFC = prefrontal cortex, STR = striatum, CER = cerebellum, nt = not tested in that dataset, ns = not significant (P > 0.0001) in that dataset. (XLSX 3569 kb)

Supplementary Table 4: Many fetal brain trans-mQTLs are observed in non-neural tissues.

For each DNA methylation probe identified as being influenced by a trans-mQTL in fetal brain, the strongest corresponding trans-mQTL effect in two published genome-wide mQTL analyses in pancreatic islets [Olsson, A.H. et al. PLoS Genet 10, e1004735 (2014)] and lymphocytes [Lemire, M. et al. Nat Commun 6, 6326 (2015)] are presented. (XLSX 53 kb)

Supplementary Table 5: Overlap of fetal brain mQTLs in non-neural tissues.

The set of DNA methylation probes associated with mQTL SNPs in fetal brain were compared to the set of DNA methylation probes associated with mQTL SNPs in two published genome-wide mQTL analyses undertaken in pancreatic islets [Olsson, A.H. et al. PLoS Genet 10, e1004735 (2014)] and lymphocytes [Lemire, M. et al. Nat Commun 6, 6326 (2015)]. (XLSX 29 kb)

Supplementary Table 6: Enrichment of human fetal brain mQTLs in ChIP-seq peaks for regulatory histone modifications in fetal brain.

We tested for enrichment of genetically-mediated DNA methylation sites in fetal brain histone modification ChIP-seq peaks identified by the Roadmap Epigenomics Project (http://www.roadmapepigenomics.org/). (XLSX 30 kb)

Supplementary Table 7: Enrichment of human fetal brain mQTLs in DNase hypersensitivity sites (DHSs).

We tested for enrichment of genetically-mediated DNA methylation sites in DHSs identified by the ENCODE project. (XLSX 31 kb)

Supplementary Table 8: Enrichment of human fetal brain mQTLs in transcription factor binding sites (TFBSs).

We tested for enrichment of genetically-mediated DNA methylation sites in TFBSs identified by the ENCODE project. (XLSX 41 kb)

Supplementary Table 9: Comparison of mQTLs across fetal and adult brain regions.

Summary statistics for Bonferroni significant fetal brain mQTLs are presented at a range of P-value replication thresholds. Abbreviations: PFC = prefrontal cortex, STR = striatum, CER = cerebellum. (XLSX 35 kb)

Supplementary Table 10: Fetal brain mQTLs with significant heterogeneity between fetal and adult datasets.

Abbreviations: PFC = prefrontal cortex, STR = striatum, CER = cerebellum. (XLSX 2357 kb)

Supplementary Table 11: Fetal brain mQTLs characterized by opposite direction of effects in at least one adult brain region.

Abbreviations: PFC = prefrontal cortex, STR = striatum, CER = cerebellum. (XLSX 51 kb)

Supplementary Table 12: Summary of overlap between fetal brain mQTLs and brain expression QTLs (eQTLs). (XLSX 46 kb)

Supplementary Table 13: Genetic variants associated with DNA methylation and gene expression in the human brain.

All pairs of mQTLs and eQTLs for the intersecting set of genetic variants where the same SNP was found to influence DNA methylation and gene expression in cis. (XLSX 280 kb)

Supplementary Table 14: Enrichment of brain eQTLs in human fetal brain mQTLs. (XLSX 23 kb)

Supplementary Table 15: GWAS variants identified as human fetal brain mQTLs.

All genome-wide significant variants (P < 5 ×10−8) identified for Alzheimer's disease and BMI prior to LD clumping that may mediate DNA methylation are included in this table along with their Bonferroni significant mQTLs. There were no genome-wide significant type 2 diabetes variants that were Bonferroni significant mQTLs. A comparable table identifying the overlap with mQTLs generated using imputed genotypes is available for download from: http://epigenetics.essex.ac.uk/mQTL/. Abbreviations: PFC = prefrontal cortex, STR = striatum, CER = cerebellum. (XLSX 45 kb)

Supplementary Table 16: Robustly-associated schizophrenia GWAS variants characterized by human fetal brain mQTLs.

The set of 'likely causal variants' for schizophrenia was defined as all index variants representing the 125 independently associated autosomal loci in the latest PGC schizophrenia GWAS3. Given that the causal variant at these loci is yet to be established, this list was extended to include additional variants in strong LD (r2 < 0.8 based on 1000 Genomes European populations) with these 125 autosomal index variants. For this list of 'likely causal variants', all Bonferroni significant fetal brain mQTLs were identified. A comparable table identifying the overlap with mQTLs generated using imputed genotypes is available for download from: http://epigenetics.iop.kcl.ac.uk/mQTL/. Abbreviations: PFC = prefrontal cortex, STR = striatum, CER = cerebellum. (XLSX 50 kb)

Supplementary Table 17

Genomic loci with evidence for association with both schizophrenia and DNA methylation. Co-localization analysis was performed using the publically available summary statistics from the PGC schizophrenia GWAS and imputed mQTLs generated in this study. This table contains the 306 pairs where the association for schizophrenia and a DNA methylation site overlap the same genomic region. This includes instances where either the same causal variant is associated with both phenotypes or there are separate signals for each disorder. The posterior probabilities for each hypothesis are reported (see Online Methods for details). (XLSX 98 kb)

Supplementary Analysis Scripts: Rscripts

R code for analyses and figures. (ZIP 27 kb)

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Hannon, E., Spiers, H., Viana, J. et al. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci 19, 48–54 (2016). https://doi.org/10.1038/nn.4182

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