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 researchers and PIs invested in consolidating and harmonizing phenotypic data.
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 depression over and above global genomic risk.
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.
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 leverages whole-genome sequencing in a large sample of autism pedigrees to predict clinical phenotypes using rare variants and polygenic risk profiles.
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 risk scores and examined polygenic overlap of suicide with all major psychiatric and medical disorders.