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

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Abstract

DNA methylation (DNAm) is important in brain development, and potentially 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 GWAS-suggestive loci (4,208/15,930, 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, 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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Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome.

Marina Bibikova, Sebastian Moran, Juan Sandoval … (2011)

DNA methylation is the most studied epigenetic mark and CpG methylation is central to many biological processes and human diseases. Since cancer has highlighted the contribution to disease of aberrant DNA methylation patterns, such as the presence of promoter CpG island hypermethylation-associated silencing of tumor suppressor genes and global DNA hypomethylation defects, their importance will surely become apparent in other pathologies. However, advances in obtaining comprehensive DNA methylomes are hampered by the high cost and time-consuming aspects of the single nucleotide methods currently available for whole genome DNA methylation analyses. Following the success of the standard CpG methylation microarrays for 1,505 CpG sites and 27,000 CpG sites, we have validated in vivo the newly developed 450,000 (450K) cytosine microarray (Illumina). The 450K microarray includes CpG and CNG sites, CpG islands/shores/shelves/open sea, non-coding RNA (microRNAs and long non-coding RNAs) and sites surrounding the transcription start sites (-200 bp to -1,500 bp, 5'-UTRs and exons 1) for coding genes, but also for the corresponding gene bodies and 3'-UTRs, in addition to intergenic regions derived from GWAS studies. Herein, we demonstrate that the 450K DNA methylation array can consistently and significantly detect CpG methylation changes in the HCT-116 colorectal cancer cell line in comparison with normal colon mucosa or HCT-116 cells with defective DNA methyltransferases (DKO). The provided validation highlights the potential use of the 450K DNA methylation microarray as a useful tool for ongoing and newly designed epigenome projects.

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Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

Andrew E Jaffe, Peter Murakami, Hwajin Lee … (2012)

During the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature. New statistical challenges arise when the measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal, and measurement errors are characterized by spatial correlation along the genome. Perhaps the most challenging complication is that continuous genomic data from large studies are measured throughout long periods, making them susceptible to 'batch effects'. An example that combines all three characteristics is genome-wide DNA methylation measurements. Here, we present a data analysis pipeline that effectively models measurement error, removes batch effects, detects regions of interest and attaches statistical uncertainty to identified regions. We illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with a continuous trait in a well-characterized population of newborns. Additionally, we show that addressing unexplained heterogeneity like batch effects reduces the number of false-positive regions. Our framework offers a comprehensive yet flexible approach for identifying genomic regions of biological interest in large epidemiological studies using quantitative high-throughput methods.

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Temporal dynamics and genetic control of transcription in the human prefrontal cortex.

Carlo Colantuoni, Barbara K. Lipska, Tianzhang Ye … (2011)

Previous investigations have combined transcriptional and genetic analyses in human cell lines, but few have applied these techniques to human neural tissue. To gain a global molecular perspective on the role of the human genome in cortical development, function and ageing, we explore the temporal dynamics and genetic control of transcription in human prefrontal cortex in an extensive series of post-mortem brains from fetal development through ageing. We discover a wave of gene expression changes occurring during fetal development which are reversed in early postnatal life. One half-century later in life, this pattern of reversals is mirrored in ageing and in neurodegeneration. Although we identify thousands of robust associations of individual genetic polymorphisms with gene expression, we also demonstrate that there is no association between the total extent of genetic differences between subjects and the global similarity of their transcriptional profiles. Hence, the human genome produces a consistent molecular architecture in the prefrontal cortex, despite millions of genetic differences across individuals and races. To enable further discovery, this entire data set is freely available (from Gene Expression Omnibus: accession GSE30272; and dbGaP: accession phs000417.v1.p1) and can also be interrogated via a biologist-friendly stand-alone application (http://www.libd.org/braincloud).

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Author and article information

Journal

Journal ID (nlm-journal-id): 9809671

Journal ID (pubmed-jr-id): 21092

Journal ID (nlm-ta): Nat Neurosci

Journal ID (iso-abbrev): Nat. Neurosci.

Title: Nature neuroscience

ISSN (Print): 1097-6256

ISSN (Electronic): 1546-1726

Publication date Nihms-submitted: 3 November 2015

Publication date (Electronic): 30 November 2015

Publication date (Print): January 2016

Publication date PMC-release: 30 May 2016

Volume: 19

Issue: 1

Pages: 40-47

Affiliations

[1 ]Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 N Wolfe St, Ste 300, Baltimore MD, USA

[2 ]Department of Mental Health, 655 N Wolfe St, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

[3 ]Department of Biostatistics, 655 N Wolfe St, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

[4 ]Departments of Neurology and Psychiatry, Johns Hopkins School of Medicine, 615 N Wolfe St, Baltimore, MD 21205

[5 ]Department of Neuroscience and the Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland 21205

Author notes

[* ] andrew.jaffe@ 123456libd.org , phone: 1-443-287-6864

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Manuscript ID: NIHMS734923

DOI: 10.1038/nn.4181

PMC ID: 4783176

PubMed ID: 26619358

SO-VID: 7571f46b-700e-4054-881c-76719da6faaa

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

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Most cited references 30

Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome.

Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

Temporal dynamics and genetic control of transcription in the human prefrontal cortex.

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