Several studies demonstrate that GWAS risk variants may have an association with gene expression or splicing.4,5 In recent years, there has been an increase in the number of publicly available brain-derived QTL data sets, including those released by the UK Brain Expression Consortium and the Genotype-Tissue Expression (GTEx) Consortium used in this study.6,7,8 The progressively increasing sample size, sequencing depth, and tissue resolution of these data sets improves our power to detect QTLs and, as a result, to interpret GWAS results in the context of regulatory effects.9,10 This improved resource availability has been accompanied by advances in statistical tools that permit the systematic, genome-wide integration of QTL and GWAS data. Because different methods have different limitations and assumptions, we adopted a stringent approach of applying the tools Coloc and transcriptome-wide association study (TWAS), then retaining only significant results present from both methods to reduce the likelihood of false-positive results. Coloc is a method that uses a bayesian framework to calculate a probability that 2 traits share a causal variant, whereas TWAS uses prediction models trained on reference QTL data to
