Comparing Polygenic Risk Scores Across Traits

To compare performance across PRS approaches, we used publicly available GWAS summary statistics to calculate PRSs for a variety of traits for subjects in the Mayo Bipolar Biobank dataset, including: SCZ (Consortium et al., 2014 (link)), BD (Stahl et al., 2019 (link)), major depressive disorder (MDD) (Wray et al., 2018 (link)), attention deficit and hyperactivity disorder (ADHD) (Demontis et al., 2019 (link)), anxiety disorders (Otowa et al., 2016 (link)), post-traumatic stress disorder (PTSD) (Duncan et al., 2018 (link)), obsessive compulsive disorder (OCD)(International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS), 2018 (link)), anorexia nervosa (AN) (Bulik et al., 2017 (link)), insomnia (Lane et al., 2019 (link)), and educational attainment (EA) (J. J. Lee et al., 2018 (link)). We used PRSice2 (Choi & O’Reilly, 2019 (link)) to compute the PRSs using the same settings described for the simulations. Some smaller p-value thresholds were not applicable for GWAS without genome-wide significant variants. We used the various PRS approaches to test for association of each PRS with BD case-control status (N cases = 968; N controls = 777) to compare the performances of the methods in a well-studied phenotype. We additionally repeated these analyses using the history of psychosis during mania in BD cases (N with manic psychosis = 336; N without psychosis = 309) as the phenotype. We recently demonstrated that psychosis during mania is associated with polygenic risk of schizophrenia (Markota et al., 2018 (link)). No large GWAS exists for this phenotype, thus, PRS approaches can be quite useful here to elucidate potential differences in genetic background between bipolar cases with and without psychosis, and the genetic overlap of this phenotype with other psychiatric traits in addition to SCZ. We used logistic regression to test for association of each PRS with BD or psychosis status after controlling for the first four principal components of the genotype data to adjust for population stratification. P-values for the Opt-Perm method were calculated using up to 100,000 permutations. We estimated the percent of variation of the binary phenotypes explained by each PRS using Nagelkerke’s R². For the Opt-Perm approach, we followed the standard approach of reporting the Nagelkerke’s R² estimate for the best p-value threshold, which is a biased overestimate of the true R².