Translating insights from genome-wide association studies (GWASs) into practical therapies for depression poses a significant challenge. Our objective is to address this challenge by utilizing network-based algorithms on GWAS outcomes for drug repurposing. This approach aims to advance precision medicine, specifically focusing on East Asian populations.Our research involved an initial meta-analysis of GWASs to reveal genetic variants linked to depression, utilizing data from large-scale studies including the Taiwan Biobank (Ntotal: 19389) and BioBank Japan (Ntotal: 178630). We employed a random-effects model through METASOFT. Subsequently, gene analysis was conducted to aggregate genetic marker data at the whole gene level using MAGMA. Drug repurposing was then performed through the NeDRex platform, a network-based model. We validated prioritized drugs using Gene2drug, a pathway-based tool.The top five genes with the highest p-values identified from the meta-analysis were UGT2B10, MTURN, RSPO4, EYS and RLBP1. UGT2B10, MTURN, and RLBP1 were novel genes not previously reported to be related to depression. EYS had been previously identified in a large cross-ancestry meta-analysis. Gene-set analysis revealed enrichment of genes in mitochondrial function. Four drugs (dinoprostone, nimodipine, metformin, and loperamide) were consistently prioritized across TrustRank and closeness centrality algorithms in NeDRex, also significantly identified via Gene2drug.In conclusion, our study leverages GWAS and network-based algorithms for depression therapy. Novel genes were identified, and four drugs showed promise, warranting further clinical investigation. Our findings contribute to precision medicine advancements for East Asian populations.