eQTL meta-analysis in adult brain

RNA Sequencing data on the DLFPC region of the brain for 138 samples was generated as part of COGA-INIA collaboration²⁸. We also genotyped the brain samples using the UK Biobank Axiom array. More than 97% of subjects (N = 133) included in this study belonged to European ethnicity. All NSWBB samples were imputed to 1000 Genomes using the cosmopolitan reference panel (Phase 3, version 5, NCBI GRCh37) using SHAPEIT then Impute2⁵⁸ within each array. Only variants with non‐A/T or C/G alleles, missing rates <5%, MAF > 5%, and HWE P ‐values > .0001 were used for imputation. Imputed variants with R2 < .30 were excluded, and genotype probabilities were converted to genotypes if probabilities ≥ .90. All genotyped and imputed variants (4,615,871 SNPs) with missing rates <10%, MAF ≥ 5% and HWE P ‐values >1 × 10⁻⁶ were included in the downstream analyses using MatrixQTL. The gene expression was corrected for the batch, age, sex, RNA integrity number (RIN), Post-mortem Interval (PMI), and alcohol status using the “removeBatchEffect” option from the limma package. The genetically derived PC1 was also added as additional covariate in the eQTL analysis. The eQTL summary statistics from PsychEncode⁴⁹, ROSMAP⁵⁰, and COGA-INIA datasets were processed and munged together at single bp and allele level to remove ambiguity due to dbSNP rsids. The gene labels in all three datasets were also matched to Ensembl ids. The summary statistics were saved in binary format files (BESD) using the SMR (https://cnsgenomics.com/software/smr/#DataManagement). The meta-analysis for eQTLs was performed using the conventional inverse-variance-weightedmeta-analysis assuming all cohorts are independent. SMR “–meta” option was used to perform the meta-analysis in all three datasets.