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

遗传因素对重性抑郁障碍（MDD）和双相情感障碍（BD）的影响已得到充分证实，遗传度估计值分别为40%至70%（MDD）和高达80%（BD）。分子遗传学候选基因和全基因组关联研究（GWAS）已经确定了许多与其病因有关的易感基因，包括CACNA 1C和NCAN，我们的小组对这些发现做出了重大贡献。WP 5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精神障碍的病因。到目前为止，我们对前1000例有DNA可用的MACS进行了系统的全基因组基因分型。对获得的基因型数据进行插补，并将几个基因（包括NCAN和CACNA 1C）的基因型信息提供给WP 1和WP 6进行进一步分析。此外，对CACNA 1C和NCAN进行了深入的亚硫酸氢盐测序分析。对来自两个具有遗传或环境风险的亚组以及对照组的66名个体进行了全基因组甲基化分析。该分析揭示了先前与BD有关的通路中的差异甲基化模式。使用我们的大型BD GWAS数据，我们进行了额外的生物信息学分析，包括与WP 3和WP 6合作对microRNA编码基因进行全基因组分析。虽然到目前为止，大多数表观遗传和基因表达研究都集中在分类诊断与被忽视的疾病病程、严重程度和药物效果之间的比较，但在第二个资助期，WP 5试图在完整样本中确定此类相关性，并与WP 6一起在300名个体的子样本中进行最广泛的研究（100例MDD患者，100例BD患者，100例健康受试者），也将通过WP 1、WP 3和WP 4进行密集分析。将在基线和随访时评估该子样本的表观基因组全甲基化和表达谱。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2.500 MACS的全基因组基因型数据集。该数据集将使我们能够进一步分析变异累积效应对亚表型的影响。使用来自不同平台的数据，我们将与WP 6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作为两个风险基因CACNA 1C和NCAN的相互作用伙伴或途径的基因。生成的数据将提供给WP 1、WP 3、WP 4和WP 6进行进一步分析。我们的分析结果将阐明疾病特异性因素，以及跨诊断界限的相关因素。

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.