# Characterizing and targeting subphenotypes of schizophrenia and bipolar disorder via individually imputed tissue and cell-type specific transcriptomes

> **NIH NIH K08** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $191,944

## Abstract

PROJECT SUMMARY
 Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable, severe and complex brain disorders
characterized by substantial clinical and biological heterogeneity. Despite this, case-control studies often ignore
such heterogeneity through their focus on the average patient, which may be the core reason for a lack of robust
biomarkers indicative of an individual’s treatment response and outcome. Although they are classified as
independent diagnostic entities, SCZ and BD are highly genetically correlated, exhibit high relative risks among
relatives of both BD & SCZ patients, and have partially overlapping symptomatology and treatment. In this project
we will use tissue and cell-type specific imputed transcriptomes for individuals with SCZ or BD in our VA
discovery cohort comprising the Million Veteran Program (MVP) and Cooperative Studies Program 572 (CSP
#572, “The Genetics of Functional Disability in Schizophrenia and Bipolar Illness”), as an intermediate molecular
phenotype, to identify, characterize and target subphenotypes of these disorders. Findings from the VA discovery
cohort will be validated in the PsycheMERGE and BioMe cohorts.
 First, we will impute tissue and cell-type specific transcriptomes for all individuals with schizophrenia (SCZ)
or bipolar disorder (BD) in the VA discovery cohort. To achieve this, we will train tissue (brain and peripheral
tissues) and cell-type (glutamatergic & GABAergic neurons, astrocytes, oligodendrocytes, and microglia from
DLPFC) specific EpiXcan transcriptomic imputation models at the gene and isoform level. Secondly, we will use
the imputed transcriptomes as an intermediate molecular phenotype to identify genetically-regulated gene
expression (GReX) based subpopulations and within them the key molecular drivers using deep neural networks
(DNNs). Lastly, we will identify key non-genetic biomarkers and effective treatments for each validated
subphenotype. Non-genetic biomarkers will be based on pre-mined features available from the electronic health
records (EHR) and features extracted from the EHR via natural language processing (NLP). The subphenotypes
will be validated in the civilian cohorts PsycheMERGE and BioMe.
 This project will take place at the Icahn School of Medicine, one of the leading centers of data science,
genomics and precision medicine. The mentoring committee comprises experts in the fields of computational
and functional genomics, integrative analysis, machine learning (including DNNs and NLP), and EHR mining.
Dr. Voloudakis will develop the skills necessary to launch an independent academic career in genetically based
EHR-informed precision psychiatry.

## Key facts

- **NIH application ID:** 10055546
- **Project number:** 1K08MH122911-01A1
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Georgios Voloudakis
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $191,944
- **Award type:** 1
- **Project period:** 2020-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10055546

## Citation

> US National Institutes of Health, RePORTER application 10055546, Characterizing and targeting subphenotypes of schizophrenia and bipolar disorder via individually imputed tissue and cell-type specific transcriptomes (1K08MH122911-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10055546. Licensed CC0.

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