Psychiatric disorders contribute substantially to the disease burden in the United States and worldwide. There
is strong evidence for a genetic contribution to many psychiatric illnesses. In recent years, with the
advancement of high throughput genomic technologies and the availability of large samples, remarkable
success has been made in risk gene discovery for major psychiatric disorders [e.g., schizophrenia (SCZ),
bipolar disorder (BD) and major depressive disorder (MDD)] through genome-wide association studies
(GWAS). However, due to the high complexity of the human genome, few causal genes or variants have been
identified within GWAS risk loci, thus, to date, limiting the potential of translating these genetic findings into
biological mechanisms. There is now a great need to pinpoint causal genes/variants at the known GWAS risk
loci and to understand their causal mechanisms, as well as to discover novel genes from novel risk loci. There
is also growing evidence that risk variants from GWAS tend to be located in regulatory DNA regions in
disease-relevant tissues or cell types, suggesting that risk variants may act through regulation of gene
expression. Studies leveraging diverse functional genomic resources may benefit psychiatric risk gene
discovery and result in better prediction of their biological relevance. This proposal aims to employ highly
integrative approaches to identify causal genes and regulatory noncoding variants underlying SCZ, BD, and
MDD. Our specific aims are: 1) Integrate GWAS with brain methylome for risk gene discovery, by leveraging a
dense high-resolution reference panel of DNAm from whole genome bisulfite sequencing of DNA from three
different brain regions (frontal cortex, hippocampus, and caudate) and an enlarged array-based reference
panel; 2) Apply a deep learning approach to predicting disease-relevant regulatory variants, by employing
features from disease-relevant gene regulatory networks and functional genomic annotations within brain
tissues and neural cell types; and 3) Map prioritized genes and variants to specific brain cell types and brain
function. We have assembled an outstanding multidisciplinary team with expertise in psychiatric genetics,
bioinformatics, machine learning, and neuroimaging. Our goal is to apply multidisciplinary and cutting-edge
analytical strategies to help address the challenges arising in the post-GWAS era. The identification and
characterization of risk genes and noncoding regulatory variants would help improve our understanding of the
biological mechanisms that underlie psychiatric illnesses, moving us closer to designing effective prevention
and treatment for these disorders.