Integrative analyses of genetic, epigenetic, transcriptomic, and environmental vulnerability factors of affective disorders

遗传因素对重度抑郁症（MDD）和双相情感障碍（BD）的影响已经得到了很好的证实，遗传率估计分别在40 - 70% （MDD）和高达80% （BD）之间。分子遗传候选和全基因组关联研究（GWAS）已经确定了许多导致其病因的易感基因，包括CACNA1C和NCAN，我们的团队对这些发现做出了重大贡献。WP5的目的是通过使用遗传学、表观遗传学和转录组学方法，确定遗传和环境因素如何影响情感性障碍的病因。到目前为止，我们对前1000个可用DNA的MACS进行了系统的全基因组基因分型。将获得的基因型数据进行输入，并将包括NCAN和CACNA1C在内的多个基因的基因型信息提供给WP1和WP6进行进一步分析。此外，对CACNA1C和NCAN进行了深入的亚硫酸盐测序分析。来自两个具有遗传或环境风险的亚组以及对照组的66名个体完成了全基因组甲基化分析。该分析揭示了之前与双相障碍有关的途径中的差异甲基化模式。利用我们的大BD GWAS数据，我们进行了额外的生物信息学分析，包括与WP3和WP6合作的microRNA编码基因的全基因组分析。虽然到目前为止，大多数表观遗传学和基因表达研究都集中在分类诊断与被忽视的疾病过程、严重程度和药物效果之间的比较，但在第二个资助期，WP5试图在完整样本中确定这种相关性，并在300个个体（100名MDD-患者、100名bd -患者、100名健康受试者）的子样本中最广泛地与WP6一起确定这种相关性，WP1、WP3和WP4也将对其进行深入分析。将在基线和随访时评估该亚样本的全表观基因组甲基化和表达谱。甲基化和表达谱的纵向分析将与疾病过程或生活事件的发生有关。将完成所有2500个MACS的全基因组基因型数据集。该数据集将允许我们进一步分析变异对亚表型的累积效应的影响。利用来自不同平台的数据，我们将与WP6合作，对遗传、表观遗传和表达数据进行综合分析。这包括多标记、多基因和途径分析，以确定尚未检测到的基因型-表型和GxE效应。这些数据将特别用于深入分析CACNA1C和NCAN两个风险基因的相互作用伙伴或通路中的基因。生成的数据将提供给WP1、WP3、WP4和WP6作进一步分析。我们的分析结果将阐明疾病特异性因素，以及跨诊断界限相关的因素。

The contribution of genetic factors to major depressive disorder (MDD) and bipolar disorder (BD) is well established, with heritability estimates ranging between 40 and 70% (MDD) and up to 80% (BD), respectively. Molecular genetic candidate and genome-wide association studies (GWAS) have identified a number of susceptibility genes contributing to their etiology including CACNA1C and NCAN, and our group has contributed substantially to these findings.The aim of WP5 is to identify how genetic and environmental factors impact on the etiology of affective disorders, by using genetic, epigenetic and transcriptomic methods. So far, we performed systematic genome-wide genotyping of the first 1.000 MACS for which DNA was available. The obtained genotype data was imputed and genotype information for several genes, including NCAN and CACNA1C, was provided to WP1 and WP6 for further analyses. In addition, in-depth bisulfite sequencing analysis of CACNA1C and NCAN was performed. The genome-wide methylation analysis was completed for 66 individuals from two sub-groups with genetic or environmental risk as well as from a control group. The analysis revealed patterns of differential methylation in pathways which have previously been implicated in BD. Using our large BD GWAS data, we performed additional bioinformatics analyses including a genome-wide analysis of microRNA coding genes in collaboration with WP3 and WP6. While so far most epigenetic and gene expression studies have concentrated on comparison between categorical diagnosis and neglected course of disorder, severity, and effect of medication, in the second funding period, WP5 seeks to identify such correlations in the complete sample and most extensively together with WP6 in a subsample of 300 individuals (100 MDD-, 100 BD-patients, 100 healthy subjects) which will also be analyzed intensely by WP1, WP3, and WP4. Epigenome-wide methylation and expression profiles will be assessed in this subsample at baseline and follow-up. Longitudinal analyses of the methylation and expression profiles will be performed in relation to the course of illness or the occurrence of life-events. The genome-wide genotype dataset will be completed for all 2.500 MACS. This dataset will allow us to further analyze the impact of the cumulative effect of variation on subphenotypes. Using data from different platforms, we will perform integrative analyses of the genetic, epigenetic and expression data in collaboration with WP6. This includes multimarker, polygenic and pathway analyses to identify yet undetected genotype-phenotype and GxE effects. The data will be used particularly for in-depth analysis of genes which are interaction partners or in pathways of the two risk genes CACNA1C and NCAN. The generated data will be provided to WP1, WP3, WP4 and WP6 for further analyses. The results of our analyses will elucidate disease-specific factors, as well as factors which are of relevance across diagnostic boundaries.