Individually Measured Endophenotypes to Advance Computational Translation in Mental Health

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 paradigm for SRPDs and inform research and clinical care for years to come.