Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Int J Neuropsychopharmacol. 2012 Nov;15(10):1401-11. doi: 10.1017/S1461145711001891. Epub 2012 Jan 16.
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.
重度抑郁症(MDD)在全球范围内造成了相当大的疾病负担,其具有中等程度的遗传性。尽管通过进行大量的关联研究,以及现在的全基因组关联(GWA)研究,努力识别 MDD 的易感性位点取得了一定的成功,但这是有限的,部分原因是抑郁症发病机制的复杂性。一种基于途径的分析策略,用于研究特定生物途径中各种基因的联合效应,已成为研究复杂特征的有力工具。本研究旨在使用 MDD 的 GWA 数据集确定抑郁症的富集途径。对于每个基因,我们分别使用合并和最小 p 值来估计其基因特异性 p 值。京都基因与基因组百科全书(KEGG)和 BioCarta 的经典途径被使用。我们采用了四种基于途径的分析方法(基因集富集分析、超几何检验、平方和检验、平方和统计)。我们使用 Benjamini 和 Hochberg 方法进行了多重检验调整,以报告显著的途径。我们发现了 17 个与抑郁症显著相关的富集途径,这些途径呈现出低到中等程度的串扰。前四个途径是长时程抑郁(p ⩽1×10-5)、钙信号(p ⩽6×10-5)、致心律失常性右心室心肌病(p ⩽1.6×10-4)和细胞黏附分子(p ⩽2.2×10-4)。总之,我们全面的途径分析确定了与神经递质和神经元系统、免疫系统和炎症反应相关的有前途的抑郁症途径,这些途径可能与抑郁症的病理生理机制有关。我们证明了途径富集分析具有通过更深入地解释 GWA 数据来促进我们对复杂特征的理解的潜力。在即将进行的抑郁症 GWA 数据中应用这种综合分析策略,可以验证本研究报告的发现。