Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China.
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Neuropsychopharmacology. 2018 Apr;43(5):1128-1137. doi: 10.1038/npp.2017.241. Epub 2017 Oct 9.
Genomic analyses have identified only a handful of robust risk loci for major depressive disorder (MDD). In addition to the published genome-wide significant genes, it is believed that there are undiscovered 'treasures' underlying the current MDD genome-wide association studies (GWASs) and gene expression data sets, and digging into these data will allow better understanding of the illness and development of new therapeutic approaches. For this purpose, we performed a meta-analytic study combining three MDD GWAS data sets (23andMe, CONVERGE, and PGC), and then conducted independent replications of significant loci in two additional samples. The genome-wide significant variants then underwent explorative analyses on MDD-related phenotypes, cognitive function alterations, and gene expression in brains. In the discovery meta-analysis, a previously unidentified single-nucleotide polymorphism (SNP) rs9540720 in the PCDH9 gene was genome-wide significantly associated with MDD (p=1.69 × 10 in a total of 89 610 cases and 246 603 controls), and the association was further strengthened when additional replication samples were included (p=1.20 × 10 in a total of 136 115 cases and 355 275 controls). The risk SNP was also associated with multiple MDD-related phenotypes and cognitive function impairment in diverse samples. Intriguingly, the risk allele of rs9540720 predicted lower PCDH9 expression, consistent with the diagnostic analysis results that PCDH9 mRNA expression levels in the brain and peripheral blood tissues were reduced in MDD patients compared with healthy controls. These convergent lines of evidence suggest that PCDH9 is likely a novel risk gene for MDD. Our study highlights the necessity and importance of excavating the public data sets to explore risk genes for MDD, and this approach is also applicable to other complex diseases.
基因组分析仅确定了少数几个与重度抑郁症(MDD)相关的稳健风险基因座。除了已发表的全基因组显著基因外,人们相信当前 MDD 全基因组关联研究(GWAS)和基因表达数据集下还存在未被发现的“宝藏”,深入挖掘这些数据将有助于更好地了解疾病并开发新的治疗方法。为此,我们进行了一项荟萃分析研究,将三个 MDD GWAS 数据集(23andMe、CONVERGE 和 PGC)结合起来,然后在另外两个样本中对显著基因座进行独立复制。全基因组显著变异体随后对 MDD 相关表型、认知功能改变和大脑中的基因表达进行了探索性分析。在发现荟萃分析中,先前未被识别的 PCDH9 基因中的单核苷酸多态性(SNP)rs9540720 与 MDD 呈全基因组显著相关(在总共 89610 例病例和 246603 例对照中,p=1.69×10),当纳入额外的复制样本时,相关性进一步增强(在总共 136115 例病例和 355275 例对照中,p=1.20×10)。风险 SNP 还与多种 MDD 相关表型和认知功能障碍在不同样本中相关。有趣的是,rs9540720 的风险等位基因预测 PCDH9 表达降低,这与诊断分析结果一致,即与健康对照相比,MDD 患者的大脑和外周血组织中的 PCDH9 mRNA 表达水平降低。这些趋同的证据表明 PCDH9 可能是 MDD 的一个新的风险基因。我们的研究强调了挖掘公共数据集以探索 MDD 风险基因的必要性和重要性,这种方法也适用于其他复杂疾病。