About The PG Lab

Dr. Docherty is a quantitative geneticist and a clinician, examining risk and resilience in the context of severe psychiatric outcomes like psychosis and suicide. The PG Lab is comprised of data analysts, geneticists, and clinician scientists focused on improving psychiatric outcomes. We have secured NIMH, Simons Foundation, AFSP, and NARSAD funding to build predictive models and identify genetic subtypes of psychosis, autism, and suicide death.

PG Lab - group photo
Back row (left-to-right): Hilary Coon, PhD.; Leslie Jerominski, M.S.; Danli Chen, Ph.D.; Anna Docherty, PhD.; Andrey A. Shabalin, PhD.; Joseph Kim, Ph.D.; Vincent Koppelmans, Ph.D.; Javier Ballester, M.D. Front row (left-to-right): Claire Larson; Katie Warthen, M.S.; Andrea White, PhD.

Our lab is a largely computational laboratory, examining the genetics of extreme adverse outcomes like suicide, severe and treatment-resistant depression, and schizophrenia. To study this, we leverage the largest genetic datasets available from clinical populations, suicide deaths, and healthy emerging adults. Drs. Docherty and Shabalin have built a pipeline for genetic analysis and polygenic prediction of multiple health outcomes (the “phenome”) in emerging adulthood and beyond.

Whilst collaborating with international consortia, we are also working on large studies at the University of Utah. We examine rich clinical data in healthy emerging adults, psychiatric patients, and individuals who have died by suicide. With comprehensive pedigree and medical record data available from large state databases, the lab models both molecular genetic and environmental mediators of risk to identify behavioral and pharmacological intervention targets.

Genes for Good: A College Student Study

A lot of the risk factors for suicide coincide with starting college; being at the age where one is leaving home for the first time, perhaps entering the military or living independently. Several risk factors can compound with genetic risk. It is PG Lab’s goal to improve prediction of risk and early intervention strategies.

Storyline: AI for Precision Mental Health

With collaborator Chris Gregg (Molecular Biology), the PG Lab will use new AI software, Storyline, to model and validate features of risk encompassed by realtime speech, facial affect, muscle movement, and emotion tracking. Thousands of features can be modeled with genomic data to identify clusters or subgroups of individuals at greater risk for mental health concerns. These projects will unroll in outpatient clinics and will include individuals treated for cancer-related illnesses at the Huntsman Cancer Institute.

Genome-Wide Association and Whole Genome Sequencing Efforts to Study Suicide in Utah

The lab works closely with the centralized Office of the Medical Examiner to analyze genetic samples of individuals who have died by suicide. These studies have recently expanded to cross-ancestry GWAS efforts and whole genome sequencing of enriched extended pedigrees. All data are population-based and evidence ancestry admixture, enhancing sensitivity for discovery and increasing the generalizability of results.