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Evaluation of Chromatin Accessibility in Prefrontal Cortex of Individuals with Schizophrenia

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Evaluation of Chromatin Accessibility in Prefrontal Cortex of Individuals with Schizophrenia ARTICLE DOI: 10.1038/s41467-018-05379-y OPEN Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia Julien Bryois 1, Melanie E. Garrett2, Lingyun Song3, Alexias Safi3, Paola Giusti-Rodriguez 4, Graham D. Johnson 3, Annie W. Shieh13, Alfonso Buil5, John F. Fullard6, Panos Roussos 6,7,8, Pamela Sklar6, Schahram Akbarian 6, Vahram Haroutunian 6,9, Craig A. Stockmeier 10, Gregory A. Wray3,11, Kevin P. White12, Chunyu Liu13, Timothy E. Reddy 3,14, Allison Ashley-Koch2,15, Patrick F. Sullivan 1,4,16 & Gregory E. Crawford 3,17 1234567890():,; Schizophrenia genome-wide association studies have identified >150 regions of the genome associated with disease risk, yet there is little evidence that coding mutations contribute to this disorder. To explore the mechanism of non-coding regulatory elements in schizophrenia, we performed ATAC-seq on adult prefrontal cortex brain samples from 135 individuals with schizophrenia and 137 controls, and identified 118,152 ATAC-seq peaks. These accessible chromatin regions in the brain are highly enriched for schizophrenia SNP heritability. Accessible chromatin regions that overlap evolutionarily conserved regions exhibit an even higher heritability enrichment, indicating that sequence conservation can further refine functional risk variants. We identify few differences in chromatin accessibility between cases and controls, in contrast to thousands of age-related differential accessible chromatin regions. Altogether, we characterize chromatin accessibility in the human prefrontal cortex, the effect of schizophrenia and age on chromatin accessibility, and provide evidence that our dataset will allow for fine mapping of risk variants. 1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17177 Stockholm, Sweden. 2 Duke Molecular Physiology Institute, Durham, NC 27701, USA. 3 Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA. 4 Department of Genetics, University of North Carolina, Chapel Hill, NC 27599-7264, USA. 5 Research Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Roskilde 4000, Denmark. 6 Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 7 Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 8 Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY 10468, USA. 9 MIRECC, JJ Peters VA Medical Center, Bronx, NY 10468, USA. 10 Department of Psychiatry and Human Behavior, Center for Psychiatric Neuroscience, University of Mississippi Medical Center, Jackson, MS 39216, USA. 11 Department of Biology, Duke University, Durham, NC 27708, USA. 12 Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. 13 Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA. 14 Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA. 15 Department of Medicine, Duke University, Durham, NC 27708, USA. 16 Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7264, USA. 17 Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC 27708, USA. These authors contributed equally: Julien Bryois, Melanie E. Garrett, Lingyun Song. Correspondence and requests for materials should be addressed to P.F.S. (email: [email protected]) or to G.E.C. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:3121 | DOI: 10.1038/s41467-018-05379-y | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-05379-y chizophrenia genomics is progressing rapidly, and our Results mechanistic understanding of this common and often Overview. In the Methods section, we provide the rationale for S 23 devastating neuropsychiatric disorder is markedly better our choices of ATAC-seq , brain region, and study design. Key than 5 years ago1. Evidence for a non-specific genetic component features of our approach include the use of the same samples for schizophrenia has been known for decades (e.g., sibling subjected to messenger RNA-sequencing (mRNA-seq) and gen- recurrence risk of 8.6 and phenotype heritability estimates of at otyping analysis by the CommonMind Consortium11, as well as least 60%)2,3. The bulk of the genetic basis of schizophrenia is due careful experimentation, including randomization, blinding, to common variation4. A 2014 paper identified 108 genetic comprehensive quality control, empirical covariate selection, and regions and a subsequent report has added over 40 new regions, verification of subject identity. but the implicated regions are broad and usually do not implicate We performed ATAC-seq on 314 brain samples (142 schizo- specific genes4–6. It was hypothesized that schizophrenia risk phrenia, 143 control, 23 mood disorders, and 6 other). After would include many exonic variants of strong effect, but sub- quality control, the analysis dataset consisted of ATAC-seq on sequent large whole-exome sequencing studies provide minimal postmortem Brodmann area 9 (dorsolateral prefrontal cortex support for this hypothesis4. Identifying actionable genes has (DLPFC)) tissue from 135 cases with schizophrenia and 137 proven complex, with a few exceptions such as rare exon variants controls (Fig. 1a). Sixteen individuals with mood disorders were in SETD1A7 and copy number variation in single genes like included for open chromatin peak calling and cQTL analyses, but NRXN1 and C48,9. otherwise excluded. Table 1 summarizes the demographic and These studies provide strong evidence that genetic risk for clinical features of the subjects. Cases and controls were schizophrenia results from the concerted effects of many genes. comparable for sex, ethnicity, age at death, and postmortem Schizophrenia may be a disorder of subtly altered amounts of brain pH. Cases had greater postmortem intervals (PMIs) and protein isoforms rather than changes in individual amino acids. lower RNA integrity number (RIN) scores relative to controls. Non-coding regulatory variation is a major contributor to risk for These differences appeared to have a lesser impact on DNA-based schizophrenia4,5, and genomic regions associated with schizo- ATAC-seq assays as cases were comparable to controls for unique phrenia are enriched for gene expression quantitative trait loci aligned reads and normalized open chromatin peak calls. (eQTLs) identified in human brains10,11. Combining functional However, these differences motivated comprehensive and careful genomic data with genome-wide association (GWA) results may selection of covariates (see Methods). be crucial to deciphering connections to specific genes and disease mechanisms in schizophrenia11–13. ENCODE14 and the Road- map Epigenomics Mapping Consortium15 provided considerable ATAC-seq evaluation. ATAC-seq data were aligned and peaks human functional genomic data and insights into genomic were called using MACS2 (false discovery ratio (FDR) <0.01, function. However, these studies provided limited insight into Fig. 1b and Supplementary Fig. 1a–d). We performed extensive psychiatric disorders as most samples were non-neuronal and quality control (see Methods) to ensure that our final peak set none were from affected individuals. This study is part of the accurately represented the peaks detected in individual samples PsychENCODE consortium which intends to provide functional (Supplementary Fig. 1c–h), that the samples were enriched in our genomic data from the brains of individuals with and without final peak set (Supplementary Fig. 2a, b), and that our peak set severe neuropsychiatric disorders16. was of comparable quality to ATAC-seq from other tissues and Epigenetic changes in the brain are widely hypothesized to from sorted brain nuclei (Supplementary Fig. 3). We identified partly mediate risk for schizophrenia17. Multiple epigenetic 118,152 open chromatin peaks totaling 35.5 Mb. As a crude changes have been assessed in schizophrenia (reviewed in ref. 18), comparison, the human exome has around 131 K exons totaling and methylation differences in peripheral blood19 and brain20 47 Mb. We compared these regions of open chromatin to those have been associated with schizophrenia. Of the many epigenetic from smaller experiments (Supplementary Table 1). Allowing for changes, chromatin accessibility is particularly important and is a small sample sizes, overlap of our open chromatin results with conserved eukaryotic feature characteristic of active regulatory that identified in these other studies was greatest for the most elements, including promoters, enhancers, silencers, insulators, similar studies (DLPFC in adults), somewhat lower in adult transcription factor binding sites, and active histone modifica- cortical samples of sorted neurons, and lower in fetal cortex. tions21. Chromatin accessibility has not been systematically Overlap with the diverse ENCODE samples was low, but higher evaluated in human brain for schizophrenia, except for a small for CNS-relevant samples. These results show congruence of our study22. Preferentially accessible regions of chromatin can readily larger ATAC-seq data with prior experiments, and underscore the be identified by high-throughput sequencing following the need to study brain. transposition of sequencing adaptors into the DNA backbone via About a quarter of open chromatin regions map near a the Tn5 transposase
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