Transcription at enhancers is a widespread phenomenon which produces so-called enhancer RNA (eRNA) and occurs in an activity-dependent manner. However, the role of eRNA and its utility in exploring disease-associated changes in enhancer function, and the downstream coding transcripts that they regulate, is not well established. We used transcriptomic and epigenomic data to interrogate the relationship of eRNA transcription to disease status and how genetic variants alter enhancer transcriptional activity in the human brain. We combined RNA-seq data from 537 postmortem brain samples from the CommonMind Consortium with cap analysis of gene expression and enhancer identification, using the assay for transposase-accessible chromatin followed by sequencing (ATACseq). We find 118 differentially transcribed eRNAs in schizophrenia and identify schizophrenia-associated gene/eRNA co-expression modules. Perturbations of a key module are associated with the polygenic risk scores. Furthermore, we identify genetic variants affecting expression of 927 enhancers, which we refer to as enhancer expression quantitative loci or eeQTLs. Enhancer expression patterns are consistent across studies, including differentially expressed eRNAs and eeQTLs. Combining eeQTLs with a genome-wide association study of schizophrenia identifies a genetic variant that alters enhancer function and expression of its target gene, GOLPH3L. Our novel approach to analyzing enhancer transcription is adaptable to other large-scale, non-poly-A depleted, RNA-seq studies.
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This work was supported by the National Institutes of Health—R01AG050986 (Roussos), R01MH109677 (Roussos), R37MH057881 (Devlin, Roeder) and R01MH109900 (Roeder)—Brain Behavior Research Foundation (20540 Roussos), Alzheimer’s Association (NIRG-340998 Roussos) and the Veterans Affairs (Merit grant BX002395 Roussos). This study was additionally funded by The Lundbeck Foundation, Denmark (Grant number R102-A9118). Further, this work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. This paper is dedicated to the memory of PS. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. RNA-seq and genotyping data were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd. and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, R01-MH-075916, P50M096891, P50MH084053S1, R37MH057881 and R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories and the NIMH Human Brain Collection Core. CMC Leadership: PS, Joseph Buxbaum (Icahn School of Medicine at Mount Sinai), BD, David Lewis (University of Pittsburgh), Raquel Gur, Chang-Gyu Hahn (University of Pennsylvania), Keisuke Hirai, Hiroyoshi Toyoshiba (Takeda Pharmaceuticals Company Limited), ED, Laurent Essioux (F. Hoffman-La Roche Ltd.), Lara Mangravite, MP (Sage Bionetworks), Thomas Lehner, Barbara Lipska (NIMH).
The CommonMind Consortium
Menachem Fromer, Douglas M Ruderfer, Hardik R Shah, Lambertus L Klei, Kristen K Dang, Thanneer M Perumal, Benjamin A Logsdon, Milind C Mahajan, Lara M Mangravite, Laurent Essioux, Hiroyoshi Toyoshiba, Raquel E Gur, Chang-Gyu Hahn, David A Lewis, Vahram Haroutunian, Barbara K Lipska, Joseph D Buxbaum, Keisuke Hirai
Conflict of interest
The authors declare that they have no conflict of interest.
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