Integrative approaches to identification and interpretation of genes underlying psychiatric disorders

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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.