Prediction and Enhanced Prevention with Psychiatric Genomics:
The Psychiatric Genomics Lab is a computational lab in the Department of Psychiatry at the University of Utah. We work with multiple research sites to map the genetic architecture of severe outcomes like suicide, treatment-resistant depression, and schizophrenia to enhance prediction and prevention efforts.
We currently have special emphasis on examining genetic risk and resilience in emerging adults, and improving polygenic clinical prediction models of suicide death and psychosis.
Imputing genotype data from 4,800 suicide deaths in Utah and 14,810 ancestry-matched controls, we were able to conduct the first major genome-wide association study of suicide death. These analyses included calculation of polygenic ri...
Simons Foundation Award: Polygenic And Rare Variant-Based Genetic Subtyping Of Autism Spectrum Disorders
This research leverages whole-genome sequencing in a large sample of autism pedigrees to predict clinical phenotypes using rare variants and polygenic risk profiles.
Fast Simulation of Polygenic Risk Scores to Estimate PRS Performance under Various Conditions and Thresholds
We've created an extremely efficient pipeline for imputation, QC, and processing of polygenic risk on a massive scale. Fast PRS simulations allow us to select an optimal p-value threshold γ, perform more accurate power analyses, and evaluate the polygenicity of a phenotype.
This research maps genetic risk for numerous health and psychiatric phenotypes to real-world outcomes and behaviors in emerging adults (N = 5,947) across ancestries.
Pathway-based Polygenic Risk for Severe Depression Implicates Drug Metabolism in a Large Han Chinese Cohort
Using low-coverage whole genome sequencing in the CONVERGE cohort (N = 10,640), we derived polygenic risk scores specific to each of over 300 molecular pathways previously shown to be relevant to severe psychopathology. We then identified gene pathways significantly predictive of recurrent depres...
It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. We've developed a network of rese...