# Individually Measured Endophenotypes to Advance Computational Translation in Mental Health

> **NIH NIH DP2** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2024 · $1,413,000

## Abstract

Stress-related psychiatric disorders (SRPDs) are now the leading disease burden worldwide and
diagnoses are increasing in both adolescents and adults. High rates of trauma in the US, especially for women
and marginalized racial, ethnic, and sexual minority groups, ensures continued high rates and health
disparities in SRPDs. Provided the biological complexity, symptom heterogeneity, and high comorbidity of
these disorders, the search for biomarkers has been difficult. Additionally, current gold-standards for clinical
research entails comparing a clinical group to a control group. However, this design is less than ideal for
SRPDs as the risk factors (genetic and environmental), potential biomarkers, and collection of symptoms are
on a continuous spectrum; not a dichotomy like other conditions. Biologically, and statistically, SRPD
biomarkers and symptoms should be treated as continuous variables. The last couple decades have produced
convincing evidence regarding the impact of adverse childhood experiences (ACE) on the epigenome and
brain structure in regions regulating stress, mood, reward, and cognition - the very pillars of SRPD symptom
clusters. An individual’s ACE history, genome, epigenome, and brain metrics should be used in combination to
reveal biocomposites of SRPDs. The objective of this New Innovator Award is to test my newly developed
model, The GEAN Model of mental health (Genetics, Epigenetics, ACE, and Neurobiology) designed to identify
SRPD biocomposite clusters based on continuous individual-level endophenotypes to better understand risk
and heterogeneity SRPDs. Using advanced computational approaches, we will 1) identify epigenetic sites and
brain regions most responsive to the environment, 2) identify genetic and endophenotype clusters that mediate
the relationship between ACE history and SRPD symptoms in a prospective cohort, and 3) validate this same
model in a separate clinical cohort. This project is innovative because it is truly transdisciplinary and bridges
the fields of psychiatry /psychology, (epi)genomics, and neuroscience with powerful computational models to
address the biological underpinnings of SRPDs. The proposed studies directly tackle many of the issues
around the sheer complexity of SRPDs by integrating trauma and multiple biological measures, using a
multidimensional phenotype measure, and using person-centered analyses in both prospective and clinical
cohorts. Importantly, these studies address the challenge of diversity by employing state-of-art mobile imaging
methods for at-home data collection. Further, by including a genetically informed design, we specifically tackle
the challenges of genetic-confounding. The results of these studies represent major acceleration within the
field of mental health and will inform better predictions of long-term outcomes following ACEs, biocomposites of
SRPD risk and diagnoses, and treatment targets for precision medicine. We anticipate that the results will
create a new ...

## Key facts

- **NIH application ID:** 10909679
- **Project number:** 1DP2MH140150-01
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** Candace Renee Lewis
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,413,000
- **Award type:** 1
- **Project period:** 2024-09-10 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10909679

## Citation

> US National Institutes of Health, RePORTER application 10909679, Individually Measured Endophenotypes to Advance Computational Translation in Mental Health (1DP2MH140150-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10909679. Licensed CC0.

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