Using integrated omics to identify dysfunctional genetic mechanisms influencing schizophrenia and sleep disturbances

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The proposed work aims to decipher genetic factors dysregulated in the brains of individuals with schizophrenia with pleiotropic effects influencing sleep issues. The goals are to inform genomic driven medical care for improved treatment of sleep problems, which are among the most common co-occurring conditions in these patients. Sleep disruptions are associated with more severe schizophrenia-related symptoms. Healthy sleep is important for neurodevelopment, indicating that managing sleep disturbances may have significant impacts on reducing severity of schizophrenia symptoms and improving long-term outcomes. Characterizing pleiotropic genetic effects using multiomics data holds promise for informing precision medicine approaches to treatment of sleep problems in these individuals. Investigators at the University of Kansas Medical Center have whole brain specimens from human donors diagnosed with schizophrenia and confirmed controls. This brain bank reflects a diverse collection of specimens from males and females with different reported race. This project will generate multi-omics data from sleep-wake regulating brain regions in these specimens and identify genetic variation impacting function of pleiotropic genes and proteins evidenced to increase risk for both schizophrenia and insomnia-related symptoms. Variants with evidence for pharmacogenetic and regulatory effects on genes encoding drug targets will be evaluated. Genetic risk scores calculated from sequence data that are useful to predicting risk and aiding in early detection and intervention will be functionally validated. In addition, this project will comprehensively characterize gene and protein coexpression connecting two important brain regions known to regulate human sleep behaviors. Combining evidence from the genome, transcriptome and proteome will allow for discerning the biochemical pathways and genetic mechanisms dysregulated in sleep-wake regulating brain regions from these patients and help identify proteins that can be targeted by small molecule compounds to treat sleep problems more effectively. This work should also provide knowledge of how convergent mechanisms influence risk for multiple disorders in the same individual. The approaches developed and data generated in this project will provide a rich resource that will be shared with the larger scientific community allowing for investigations of the sleep regulation network in a diverse representation of individuals with mental health conditions.