Genome-wide association (GWA) studies have identified >100 regions of the genome that contribute to risk for schizophrenia. As observed for other complex disorders, the identified regions are overwhelmingly non- coding, strongly suggesting that genetic variation in gene regulatory elements is a major mechanistic contributor. Further investigation of those regulatory mechanisms is precluded by a fundamental gap in the ability to identify disorder-specific regulatory elements in the brain, and limited understand of how genetic variation within those elements influences their function. To address that knowledge gap, this project will comprehensively identify, characterize, and validate non-coding functional regulatory elements in brain tissues relevant to schizophrenia. The central hypothesis of the proposal is that non-coding variation contributes to schizophrenia by directly altering the function of regulatory elements in the brain. The motivation for the proposed study is that identifying regulatory mechanisms of schizophrenia has the potential to translate into improved diagnosis and treatment of this common, chronically debilitating disorder. Powered by a team with strong interdisciplinary expertise in psychiatric disorders, functional genomics, comparative primate genomics, and statistical genetics, this hypothesis will be tested by completing three specific aims: 1) Comprehensively identify active gene regulatory elements in three brain regions from 100 schizophrenia cases and 100 controls using ATAC-seq;2) Identify chromatin QTLs (cQTLs) that impact chromatin accessibility and gene expression, and perform targeted association tests using the most up to date PGC GWA mega analysis results;3) Prioritize and quantify regulatory variant function using high-throughput reporter-gene expression assays, and validate by genome editing. The approach is innovative because it utilizes a highly complementary and diverse set of experimental approaches to drive targeted genetic and functional investigation into the regulatory mechanisms of schizophrenia. Ultimately, the data produced and the experimental and statistical approaches developed will enable related studies of other disorders and diseases. In doing so, the proposed research provides a much-needed path forward to understand how non- coding variation contributes to complex human phenotypes.

Public Health Relevance

The goal of this grant is to identify, characterize, and validate functional non-coding mutations that contribute to schizophrenia. Understanding how specific DNA variants directly contribute to schizophrenia will be critical for developing improved diagnostics and treatment therapies.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Genetics of Health and Disease Study Section (GHD)
Program Officer
Senthil, Geetha
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Duke University
Schools of Medicine
United States
Zip Code
Khan, Atlas; Liu, Qian; Wang, Kai (2018) iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes. BMC Bioinformatics 19:501
Toker, Lilah; Mancarci, Burak Ogan; Tripathy, Shreejoy et al. (2018) Transcriptomic Evidence for Alterations in Astrocytes and Parvalbumin Interneurons in Subjects With Bipolar Disorder and Schizophrenia. Biol Psychiatry 84:787-796
Wang, Daifeng; Liu, Shuang; Warrell, Jonathan et al. (2018) Comprehensive functional genomic resource and integrative model for the human brain. Science 362:
Bryois, Julien; Garrett, Melanie E; Song, Lingyun et al. (2018) Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia. Nat Commun 9:3121
Gusev, Alexander; Mancuso, Nicholas; Won, Hyejung et al. (2018) Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet 50:538-548
Gandal, Michael J; Zhang, Pan; Hadjimichael, Evi et al. (2018) Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362:
Girdhar, Kiran; Hoffman, Gabriel E; Jiang, Yan et al. (2018) Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome. Nat Neurosci 21:1126-1136
Doostparast Torshizi, Abolfazl; Duan, Jubao; Wang, Kai (2018) Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders. BMC Res Notes 11:489
Gandal, Michael J; Haney, Jillian R; Parikshak, Neelroop N et al. (2018) Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359:693-697
Finucane, Hilary K; Reshef, Yakir A; Anttila, Verneri et al. (2018) Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat Genet 50:621-629

Showing the most recent 10 out of 12 publications