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Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex

Nature Neuroscience volume 19, pages 40–47 (2016)Cite this article

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Abstract

DNA methylation (DNAm) is important in brain development and is potentially important in schizophrenia. We characterized DNAm in prefrontal cortex from 335 non-psychiatric controls across the lifespan and 191 patients with schizophrenia and identified widespread changes in the transition from prenatal to postnatal life. These DNAm changes manifest in the transcriptome, correlate strongly with a shifting cellular landscape and overlap regions of genetic risk for schizophrenia. A quarter of published genome-wide association studies (GWAS)-suggestive loci (4,208 of 15,930, P < 10−100) manifest as significant methylation quantitative trait loci (meQTLs), including 59.6% of GWAS-positive schizophrenia loci. We identified 2,104 CpGs that differ between schizophrenia patients and controls that were enriched for genes related to development and neurodifferentiation. The schizophrenia-associated CpGs strongly correlate with changes related to the prenatal-postnatal transition and show slight enrichment for GWAS risk loci while not corresponding to CpGs differentiating adolescence from later adult life. These data implicate an epigenetic component to the developmental origins of this disorder.

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Figure 1: Differentially methylated loci comparing pre- and postnatal control subjects show large differences in DNA methylation.
Figure 2: A changing neuronal phenotype across brain development.
Figure 3: Examples of meQTLs for six GWAS-associated variants with nearby DNA methylation levels.
Figure 4: Examples of meQTLs for 12 GWAS-positive loci for schizophrenia.

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Gene Expression Omnibus

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Gene Expression Omnibus

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Acknowledgements

We are grateful for the vision and generosity of the Lieber and Maltz Families who made this work possible. We thank the families who donated to this research and we thank A. Feinberg for helpful input on data analyses. This work was supported by the Lieber Institute for Brain Development. A.E.J. was partially supported by 1R21MH102791.

Author information

Author notes

  1. Daniel R Weinberger and Joel E Kleinman: These authors contributed equally to this work.

Authors and Affiliations

  1. Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA

    Andrew E Jaffe, Yuan Gao, Amy Deep-Soboslay, Ran Tao, Thomas M Hyde, Daniel R Weinberger & Joel E Kleinman

  2. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

    Andrew E Jaffe

  3. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

    Andrew E Jaffe

  4. Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA

    Thomas M Hyde & Daniel R Weinberger

  5. Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, Maryland, USA

    Thomas M Hyde & Daniel R Weinberger

  6. Department of Neuroscience and the Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA

    Daniel R Weinberger

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Contributions

A.E.J. designed the study, performed the data analysis and oversaw the writing of the manuscript. Y.G. oversaw the data generation. A.D.-S. collected phenotype data on all subjects. R.T. performed DNA extractions and contributed to the data generation. T.M.H. collected brain samples and performed tissue dissections to obtain biological materials. D.R.W. designed the study, contributed to the data analysis and interpretation of the results, and oversaw the writing of the manuscript. J.E.K. collected brain samples and provided clinical interpretation of the results. All authors contributed to the writing of the manuscript.

Corresponding author

Correspondence to Andrew E Jaffe.

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Integrated supplementary information

Supplementary Figure 1 Principal component analysis (PCA) demonstrates genome-wide changes in DNAm comparing pre- and post-natal samples.

The first principal component (PC, explaining 55.6% of the variance in the data) cleanly separately pre- and post-natal samples, with samples from children (ages 0-13) demonstrating directional consistency across aging.

Supplementary Figure 2 Example differentially methylated regions (DMRs) for pre- versus post-natal differences in DNA methylation, as Figure 1B.

Proportion methylation is shown on the y-axis of each respective top-most panel. Gene annotation panels in (B) and (C) are based on Ensembl annotation – dark blue represents exons and light blue represents introns.

Supplementary Figure 3 Directionally balanced associations between DNAm and nearby gene expression levels at individual CpGs.

Pearson correlations between DNAm levels at individual CpGs/probes and nearby genes, which contains approximately equal numbers of positive and negative correlations.

Supplementary Figure 4 Directionally balanced associations between DNAm and nearby gene expression levels at DMRs.

Pearson correlations between DNAm levels at DMRs to nearby genes, stratified by the location of the DMR relative to that gene. These correlations are relatively balanced for positive and negative correlations overall, and by many classes of annotation.

Supplementary Figure 5 Proportion of variance explained in gene expression levels by cell composition estimated from DNAm data.

Supplementary Figure 6 Composition change across fetal brain development.

Shown are the composition estimates versus post-conception data for the 4 additional cell types, with Pearson correlation printed in the top right corner, as Figure 2F

Supplementary Figure 7 Neuronal composition differences by brain region and age in the BrainSpan dataset.

Each point is a sample, colored by age, and stratified by brain regions. The vertical dashed line separates the cortical brain regions from non-cortical regions.

Supplementary Figure 8 Effect of adult meQTLs in fetal samples.

Supplementary Figure 9 Cellular composition profiles by diagnosis.

(A) Distributions of NeuN- estimates per sample by processing plate and diagnosis; all p-values for diagnosis within a plate were > 0.01. (B) NeuN- proportion explains the first principal component of autosomal DNAm levels in adult samples.

Supplementary Figure 10 Negative control principal components associate with processing plate and slide.

Supplementary Figure 11 Samples on one processing plate (“Plate2”) show magnified differential methylation effects for schizophrenia.

While the T-statistics within each plate are correlated, those calculated only within samples on Plate2 were almost an order of magnitude larger than similar statistics calculated only within samples on Plate3. Red: identity line.

Supplementary Figure 12 Sensitivity analyses of differentially methylated CpGs for schizophrenia.

We compared the effects sizes of schizophrenia differences from the original statistical model (adjusting for age, sex, race, and 4 negative control PCs) to also including (A) composition estimates from all 5 cell types and (B) smoking status determined by toxicology and (C) antipsychotics by self-report in the final model.

Supplementary Figure 13 Effect of adult control meQTLs in adult SZ samples.

Supplementary Figure 14 Concordance between schizophrenia and developmental effects across 2,104 CpGs associated with schizophrenia.

X-axis: change in the DNA methylation (DNAm) levels comparing prenatal versus postnatal samples, Y-axis: change in DNAm level comparing patients with schizophrenia to adult controls. Each point is one CpG probe.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14 and Supplementary Analysis (PDF 6627 kb)

Supplementary Methods Checklist (PDF 384 kb)

Supplementary Table 1

Demographic data for the samples analyzed, stratified by age group and diagnosis. P-values depict the differences between the demographic data by the cases and adult controls. (XLSX 10 kb)

Supplementary Table 2

Differentially methylated regions (DMRs) and corresponding annotation comparing prenatal and postnatal samples. (CSV 1893 kb)

Supplementary Table 3

Gene ontology (GO) enrichment statistics for those DMRs that increase/“up” or decrease/“down” across the transition from pre to postnatal life (XLSX 933 kb)

Supplementary Table 4

Differentially methylated blocks comparing prenatal and postnatal samples (CSV 110 kb)

Supplementary Table 5

Gene ontology (GO) analysis on the genes contained with the differentially methylated blocks. (CSV 526 kb)

Supplementary Table 6

Overlap between DNAm changes and chromatin state data from the Epigenome Roadmap project for adult DLPFC Bolded cells indicated >2 fold enrichment or depletion compared to the relevant background CpGs/regions (XLSX 13 kb)

Supplementary Table 7

Overlap between PGC2 risk regions and DMRs associated with the transition from prenatal to postnatal life, n=31 regions. Rank: PGC Rank, P.value: p-value for the region, Position: range of LD block for region, numDMRs: the number of DMRs within the region. (CSV 4 kb)

Supplementary Table 8

NHGRI GWAS catalog annotated by whether each SNP has an meQTL in the DLPFC dataset. (XLSX 6171 kb)

Supplementary Table 9

meQTLs within the PGC2 SNPs and their proxies (XLSX 1465 kb)

Supplementary Table 10

List of differentially methylated CpGs comparing patients with schizophrenia to adult controls. (CSV 543 kb)

Supplementary Table 11

Gene ontology (GO) analysis for genes near differentially methylated CpGs for diagnosis. (CSV 883 kb)

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Jaffe, A., Gao, Y., Deep-Soboslay, A. et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci 19, 40–47 (2016). https://doi.org/10.1038/nn.4181

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