The analyses presented in this manuscript performed multi-omic integration analyses separately for AUD and DPW and only later looked at the overlap. We specifically employed this approach as it minimizes the bias in results due to large sample size differences between the two GWASs. Hence, the summary statistics from the correlated meta-analysis of DPW and AUD GWAS will be primarily driven by DPW signals due to very large sample size for DPW GWAS and smaller standard errors. As a result, the multi-omics analysis using these summary statistics will predominantly prioritize genes associated with the DPW phenotype. In fact, we used the Multi-Trait Analysis of GWAS (MTAG) method to meta-analyze the summary statistics from DPW and AUD GWASs. Although, this method is robust to correlated multi-trait meta-analysis, the SMR analysis using the MTAG results was primarily driven by the DPW variants. To reduce this bias, we also focused on SMR results from MTAG using stricter threshold (GWAS AUD 5 × 10−5; e/mQTL P 5 × 10−8; SMR P < 20%; Heidi P > 0.05). The results from this threshold were similar
