# Mining the Genomewide Scan: Genetic Profiles of Structural Loss in Schizophrenia

> **NIH NIH R01** · GEORGIA STATE UNIVERSITY · 2020 · $629,153

## Abstract

Project Summary.
Current studies are using genome wide scan (GWS) approaches to identify the numerous genes which might
play a role in increased risk for psychosis. Structural neuroimaging measures implicate gray matter loss in
schizophrenia; a pattern of regional loss in the medial frontal, temporal and insular gyri have been identified by
us and others in schizophrenia. However, the recognition that case/control approaches are not perhaps the most
useful, has led to an emphasis on cognitive constructs such as attention, memory, language, as in the Research
Domain Criteria (RDoC) matrix, to identify cross-diagnostic mechanisms. This has left the psychotic symptoms
per se without a clear connection to the neuroanatomical circuits and genetic mechanisms. Identifying the
relationships among patterns of gray matter reduction, symptom co-occurrence patterns, and genetic
profiles which exist across schizophrenia and bipolar disorder is the goal of this project. We propose a
multivariate method for analyzing already existing GWS data, voxelwise measures of gray matter density, and
symptom measures from an aggregated dataset of over 4000 individuals with diagnoses from the schizophrenia
and bipolar spectrum. We will apply three way parallel ICA, with reference; this technique identifies patterns of
spatial variation in the brain structure, symptom profiles, and patterns of genotypes which are linked. We begin
with over 7,000 structural imaging, symptom scores, and GWS samples from cases and controls, from
aggregated legacy data. We constrain the imaging and genetic analyses with reference vectors to incorporate a
priori information. In Aims 1 and 2 we will develop initial a priori spatial patterns, structural networks using source-
based morphometry methods, both alone and in conjunction with symptom measures; in Aim 3 we will determine
the heritability and quantitative trait loci for these networks in independent family samples; in Aim 4 we use the
quantitative trait loci as a priori constraints on the genetic data, and the heritable structural networks as
constraints on the imaging data on our three-way parallel ICA analysis. We include a split-half analysis for
replication and a follow-up high-density genotyping plan. Using these methods, we will determine the spatial
patterns and genetic profiles that covary within our sample, and which show relationships with symptom profiles
across schizophrenia and bipolar disorder; this forms the basis for linking the symptoms to the brain circuits and
genetics “units of analysis”. Using higher-order clustering on the identified patterns, we identify coherent sub-
groupings of subjects using the genetics, brain structure, and symptom measures within the larger data matrix.
The final results will be the combinations of genotypic networks which influence the patterns of structural brain
effects in conjunction with variations in symptom clusters, refining the diagnostic categories based on biological
evidence.

## Key facts

- **NIH application ID:** 9867740
- **Project number:** 5R01MH094524-10
- **Recipient organization:** GEORGIA STATE UNIVERSITY
- **Principal Investigator:** VINCE D CALHOUN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $629,153
- **Award type:** 5
- **Project period:** 2012-03-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9867740

## Citation

> US National Institutes of Health, RePORTER application 9867740, Mining the Genomewide Scan: Genetic Profiles of Structural Loss in Schizophrenia (5R01MH094524-10). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9867740. Licensed CC0.

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