Research program

Precision Mental Health

Can genetic, clinical, and patient-reported information help identify an effective treatment sooner?

COMPASS and related studies examine genetic, clinical, and patient-reported information as possible predictors of mental health treatment response.

Conceptual study design with illustrative response patterns. Models are tested in a separate dataset when suitable data are available.

COMPASS leadership

Amy Bohnert (contact), Srijan Sen, and Lars Fritsche are the principal investigators. Lars leads the genetics and EHR data work, including polygenic scores, pharmacogenetics, and analyses of diagnoses and medication histories.

COMPASS is supported by the National Institute of Mental Health (U01MH136025).

Why treatment response is difficult to predict

Finding an effective treatment can take time, and response varies widely from one person to another. COMPASS asks whether combining genetic, clinical, and patient-reported data can help predict those differences. A useful model must also perform well in people and settings that were not part of its development.

How COMPASS studies treatment response

Combine several kinds of data

Genetic and clinical data, patient reports, and digital measures provide different information about treatment and recovery.

Define treatment response carefully

We define treatment response to match the clinical question instead of treating every kind of response as the same outcome.

Validate in a separate sample

When a separate dataset is available, we use it to test models built in the first sample.

Follow outcomes over time

Repeated follow-up measures show how symptoms and treatment response change over time.

Our team

Ryan Welch, Shivalika Pathania, and Tai Yang.

Current work

Projects and collaborations

Precision mental health

Active

COMPASS

COMPASS studies how genetic, clinical, and patient-reported information relates to mental health treatment response. Its full name is Comprehensive Mobile Precision Approach for Scalable Solutions in Mental Health Treatment.

National Institute of Mental Health · U01 · U01MH136025 · 2024-present

Publications

Selected papers

These papers describe the COMPASS study and ways to collect patient-reported information alongside electronic health records (EHRs) and genetic data. They also describe associations between genetic liability to depression and diagnoses in clinical records.

All publications →

† Shared first authorship. ‡ Shared senior authorship. ^@ Corresponding author.

2026

Precision approaches for scalable digital and clinic-based interventions in mental health

Bohnert A. S. B., Fritsche L., Sen S.

Biol Psychiatry Cogn Neurosci Neuroimaging

Why it matters: Digital tools can widen access, but low engagement and modest average effects mean that scale alone is not enough. Written by the three COMPASS co-leads, the perspective asks whether mobile, genetic, and clinical data can help tailor both conventional and digital treatment.

DOI PubMed

2023

Epidemiologic Questionnaire (EPI-Q) - a scalable, app-based health survey linked to electronic health record and genotype data

Salvatore M., Clark-Boucher D., Fritsche L. G., Ortlieb J., Houghtby J., Driscoll A., Caldwell-Larkins B., Smith J. A., Brummett C. M., Kheterpal S., Lisabeth L., Mukherjee B.

Epidemiol Health

Why it matters: Clinical records include diagnoses, visits, and prescriptions but omit many aspects of a patient's experience. EPI-Q links standardized self-reports—including mood, depression, anxiety, stress, pain, and substance use—with genotype and EHR data. Its 10% response rate also means that participation bias must be considered.

DOI PubMed PMC

2022

Polygenic Liability to Depression Is Associated With Multiple Medical Conditions in the Electronic Health Record: Phenome-wide Association Study of 46,782 Individuals

Fang Y., Fritsche L. G., Mukherjee B., Sen S., Richmond-Rakerd L. S.

Biol Psychiatry

Why it matters: In 46,782 MGI participants of European ancestry, a depression polygenic score was associated with diagnoses well beyond depression, including after patients with recorded depression were removed. The result informs COMPASS study design, but broad population associations should not be treated as predictions of an individual's response to treatment.

DOI PubMed PMC

Updates

News and talks

June 2026 · Research story

U-M researchers aim to shorten the path to effective mental health care

COMPASS participants contribute repeated reports, genetic samples, and health record data to study why mental health treatment responses differ.

July 2024 · Funding

U-M announces NIMH-backed COMPASS precision mental health grant

A $17.9 million NIMH award supports COMPASS, a collaboration studying differences in mental health treatment response.