We created PGSs for DSM-NicDep, FTND, ICD-TUD, and CPD in the EUR subset of the NESARC-III sample. NESARC-III was genotyped using the Affymetrix Axiom® Exome Array (Zhang et al., 2022), which limited our ability to impute SNPs due to a lack of appreciable nonexonic coverage and resulted in some regions with low SNP densities. Details of quality control and imputation are available in the Supplemental Materials. We used PRS-CS (Ge, Chen, Ni, Feng, & Smoller, 2019), a Bayesian method that uses continuous shrinkage before weight SNP effect sizes, and used the “auto” function, which allows the global scaling parameter to be automatically learned from the data. We then used the “score” function in Plink 1.9 (Chang et al., 2015) to create PGS for DSM-NicDep, FTND, ICD-TUD, and CPD in NESARC-III. We used logistic regression models to estimate associations between the PGS and endorsement of the 11 individual diagnostic criteria for DSM-5 TUD and 4 of the 6 FTND criteria (excluding CPD and smoking when ill) in NESARC-III (N = 12,482; DSM-TUD N cases = 4,205). Linear regression models were used
