Major depressive disorder (MDD) has caused a substantial burden of disease worldwide with moderate heritability. Despite efforts through conducting numerous association studies and now, genome-wide association (GWA) studies, the success of identifying susceptibility loci for MDD has been limited, which is partially attributed to the complex nature of depression pathogenesis. A pathway-based analytic strategy to investigate the joint effects of various genes within specific biological pathways has emerged as a powerful tool for complex traits. The present study aimed to identify enriched pathways for depression using a GWA dataset for MDD. For each gene, we estimated its gene-wise p value using combined and minimum p value, separately. Canonical pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta were used. We employed four pathway-based analytic approaches (gene set enrichment analysis, hypergeometric test, sum-square statistic, sum-statistic). We adjusted for multiple testing using Benjamini & Hochberg's method to report significant pathways. We found 17 significantly enriched pathways for depression, which presented low-to-intermediate crosstalk. The top four pathways were long-term depression (p⩽1×10-5), calcium signalling (p⩽6×10-5), arrhythmogenic right ventricular cardiomyopathy (p⩽1.6×10-4) and cell adhesion molecules (p⩽2.2×10-4). In conclusion, our comprehensive pathway analyses identified promising pathways for depression that are related to neurotransmitter and neuronal systems, immune system and inflammatory response, which may be involved in the pathophysiological mechanisms underlying depression. We demonstrated that pathway enrichment analysis is promising to facilitate our understanding of complex traits through a deeper interpretation of GWA data. Application of this comprehensive analytic strategy in upcoming GWA data for depression could validate the findings reported in this study.