# GWAS of the RDoC Cognitive Systems Domain: Modeling the Latent Genetic Architecture of Working Memory

> **NIH NIH R03** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $165,000

## Abstract

Project Summary/Abstract
GWAS of the RDoC Cognitive Systems Domain: Modeling the Latent Genetic Architecture of Working Memory
This application is in response to NIMH PAR-17-158, “Secondary Data Analyses to Explore NIMH Research Domain
Criteria RDoC (R03).” The proposed two-year study will use existing genome-wide association study (GWAS) data from the
Cognitive Genomics Consortium (COGENT) to investigate the latent molecular genetic architecture of working memory.
Working memory is a core Construct of the RDoC Cognitive Systems Domain, defined as the active maintenance and flexible
updating of goal/task relevant information in a form that has limited capacity and resists interference. Limited working
memory capacity is a fundamental aspect of the cognitive impairments prevalent in many neuropsychiatric disorders. Most of
the variability underlying differences in general working memory capacity can be traced back to inherited genetic factors.
However, exactly how our DNA shapes the working memory system has yet to be established. As such, our objective is to
identify the spectrum of genome-wide allelic variation underlying working memory – from individual loci to genes to polygenic
risk scores to functional biological pathways – determined to be causal, not merely correlational, in relation to working memory
performance. To accomplish our goals for the study, we will implement a new multivariate GWAS method, genomic structural
equation modeling (Genomic SEM; Grotzinger, et al. Nat Hum Behav 2019), to conduct a common factor GWAS of working
memory to identify genome-wide significant loci with effects on a genetically-derived general latent working memory factor
(“Gwm”). We have individual-level GWAS data on 24,000 participants in COGENT who have contributed 100,000
performance-based working memory datapoints from objective clinical and laboratory tasks such as digit span, spatial span,
letter-number sequencing, and N-back. At genome-wide scale, we will then establish the range of shared genetic architecture
between working memory and correlated CNS phenotypes, and expect widespread coheritability to emerge. At the molecular
level, Mendelian randomization will determine the direction of causality underlying significant pleiotropy between working
memory and correlated CNS phenotypes such as ADHD, autism, and schizophrenia. Finally, to prioritize working memory
loci for follow-up studies, functional mapping and annotation tools will characterize the biology of causal mechanisms
associated with working memory. To our knowledge, this will be the most comprehensive and statistically powerful GWAS of
working memory. The results are to be openly and rapidly shared with the research community. By deciphering the causal
pathways through which allelic variation either perturbs or protects the working memory system, which in turn can disrupt or
support the development and function of neural systems underlying working memory, we can leverage this existing RDoC...

## Key facts

- **NIH application ID:** 10040385
- **Project number:** 1R03MH123787-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Joey William Trampush
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $165,000
- **Award type:** 1
- **Project period:** 2020-06-15 → 2024-06-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10040385

## Citation

> US National Institutes of Health, RePORTER application 10040385, GWAS of the RDoC Cognitive Systems Domain: Modeling the Latent Genetic Architecture of Working Memory (1R03MH123787-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10040385. Licensed CC0.

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