The recent increase in GWAS discovery power for psychiatric disorders has led to the recognition of an undisputed genetic basis for schizophrenia (SZ). However, the mechanistic basis of the vast majority of these loci remains uncharacterized, hindering the ability to translate genetic findings into novel drug targets and develop new treatments for SZ patients. In this proposal, we overcome these challenges and seek to identify and characterize novel SZ driver genes and causal variants by combining computational and experimental methods, integrating systems-level information to prioritize individual genes and loci, and validating their gene- regulatory and cellular effects in 10 neuronal and 3 glial cell tyes derived from iPS cells.
Aim 1 : We infer gene co-expression networks and modules using multiple brain regions and developmental stages, and use them to predict schizophrenia driver genes based on their clustering in common networks/modules, and their linking to schizophrenia-associated loci using activity correlation, chromatin conformation and eQTLs.
Aim 2 : We search for schizophrenia-enriched modules of enhancer regions, discovered by clustering patterns of H3K27ac activity across brain regions, developmental stages, and individuals, using an iterative probabilistic framework for joint prediction of causal driver genes, variants, and regulators.
Aim 3 : We experimentally validate the gene- regulatory and neuronal/glial cellular phenotypes of predicted schizophrenia driver genes and variants in neuronal and glial cell lines based on targeted sequencing of heterozygous loci overlapping 800 putative driver genes and 10,000 putative causal variants, and systematic profiling of neuronal and glial phenotypes upon knockdown and knockout of 200 candidate genes and bidirectional CRISPR-Cas9 editing of 50 candidate causal variants. If successful, this ambitious proposal has the potential to reveal dozens of new target genes and variants associated with Schizophrenia, and open up new avenues for therapeutic development that may alleviate the personal and societal burden of schizophrenia in our lifetimes.

Public Health Relevance

The recent increase in GWAS discovery power for psychiatric disorders has led to the recognition of an undisputed genetic basis for schizophrenia, but their mechanistic basis remains uncharacterized, thus hindering the ability to translate genetic findings into novel drug targets and develop new treatments for schizophrenia patients. In this proposal, we overcome these challenges and seek to identify and characterize novel schizophrenia driver genes and causal variants by combining computational and experimental methods, integrating systems-level information to prioritize individual genes and loci, and validating their gene-regulatory and cellular effects in neurons and glial cells. If successful, ths ambitious proposal has the potential to reveal dozens of new target genes and variants associated with Schizophrenia, and open up new avenues for therapeutic development that may alleviate the personal and societal burden of schizophrenia in our lifetimes.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH109978-02
Application #
9264586
Study Section
Special Emphasis Panel (ZMH1-ERB-C (02))
Program Officer
Arguello, Alexander
Project Start
2016-05-01
Project End
2021-02-28
Budget Start
2017-03-01
Budget End
2018-02-28
Support Year
2
Fiscal Year
2017
Total Cost
$427,812
Indirect Cost
$138,928
Name
Broad Institute, Inc.
Department
Type
Research Institutes
DUNS #
623544785
City
Cambridge
State
MA
Country
United States
Zip Code
02142
Marbach, Daniel; Lamparter, David; Quon, Gerald et al. (2016) Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nat Methods 13:366-70
Bekelis, Kimon; Kerley-Hamilton, Joanna S; Teegarden, Amy et al. (2016) MicroRNA and gene expression changes in unruptured human cerebral aneurysms. J Neurosurg 125:1390-1399
Ernst, Jason; Melnikov, Alexandre; Zhang, Xiaolan et al. (2016) Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions. Nat Biotechnol 34:1180-1190