We used four methods to identify genes associated with the three latent genomic factors. First, we used multi-marker analysis of genomic annotation (MAGMA; version 1.08)30, in which genome-wide SNPs were mapped to 18,235 protein-coding genes from Ensembl v102, and SNPs within each gene were jointly tested for association with each factor. We evaluated Bonferroni corrected significance adjusted for the number of genes (one sided p < 2.74x10−6). Second, we used Hi-C-coupled MAGMA (H-MAGMA; version XX)33, which builds on MAGMA by leveraging chromatin interaction profiles from human brain tissue. We evaluated Bonferroni corrected two-sided p-value thresholds, adjusted for multiple testing within each analysis (two-sided ps < 1.72x10−7 for residual-PAU and 1.62x10−7 for all other phenotypes). Next, we used MetaXcan31 to conduct a Transcriptome-Wide Association Study (TWAS) using genetically regulated expression models from GTEx v851. This analysis leveraged GWAS summary statistics to estimate gene-trait associations. Within-tissue Bonferroni correction was applied to identify statistically significant TWAS genes. Finally, we used summary-data-based Mendelian randomization (SMR)32 to 1) test the extent to which gene expression mediated the relationship between SNPs and the phenotype and 2)
