Strategic mapping of tissue and population methylation for mental health research

我们的团队已经做好了为GTEx做出实质性贡献的准备，已经开发了许多 现在普遍使用的表观遗传学的实验和统计方法。我们发现了CpG岛海岸， 疾病和人口差异比CPG群岛本身更大的地区，以及 低甲基化的区块。我们还开创了识别组织特异性差异甲基化的努力 区域(t-DMR)和群体可变甲基化区域(VMR)。最后，我们一直是 整合甲基化、SNP和表达数据来定义与疾病相关的表型。特别的 考虑到这一应用的重要性，我们带头努力在人口层面上应用这些方法。在这 应用，我们采取了一种战略方法，主要集中在精神健康相关的大脑区域，但也在 组织变异的一般问题。我们对心理健康的关注是因为所有这些人类疾病 由于临床检查固有的主观性，对理解仍然特别不透明；以及 我们最近开发的将神经元和胶质细胞特异性信号解卷积为总信号的方法的价值 DNA甲基化和差异甲基化，对大脑非常重要，对GTEx也很普遍。 在目标1中，我们将使用全基因组亚硫酸盐测序(WGBS)来识别大脑区域特异性VMRS 以及对三种破坏性精神健康障碍特别重要的四个区域的t-DMR， 精神分裂症、抑郁和成瘾：即前扣带回皮质、额叶皮质、海马体和 伏隔核。我们将研究足够数量的样本(每个样本来自匹配的患者)以确定 T-DMRS和VMRS。确定VMRS需要更大的样本数量，因为从定义上讲，它们是 在一个大脑区域内，不同个体之间是可变的。我们已经表明，t-DMRS是相对较差的指标 但更有可能与群体中的遗传变异有关的是VMRS。我们会 用相同脑区的羟甲基胞嘧啶测序来补充这一分析，因为 这种修饰在大脑中的潜在重要性。我们还将包括另外5个GTEx组织，它们存在于 与资源中的许多其他资源相比，具有合理的同质性。例如，这些将用作控件 为了比较大脑区域之间的可变性和组织之间的可变性，也为了更多的 T-DMR识别。在目标2中，我们将执行捕获亚硫酸盐测序来识别特定于大脑区域的 群体中的VMRS，研究更多的个体(100)，以便与差异甲基化相关 以目标1中确定的VMRS和t-DMRS为目标。我们还将 对VMRS与遗传变异和基因表达的关系进行初步分析 现有的GTEx关于表达和遗传变异的数据。这里提出的工作将提供一个关键的 为研究精神疾病的研究人员提供资源，并将提供一个强大的平台和数据集 跨种群的相关基因表达、甲基化和DNA序列。

Our group is extremely well poised to make a substantial contribution to GTEx, having developed many of experimental and statistical methods for epigenetics now in general use. We discovered CpG island shores, regions of greater disease and population variation than the CpG islands themselves, as well as large hypomethylated blocks. We also pioneered efforts to identify both tissue-specific differentially methylated regions, or t-DMRs, and population variable methylated regions, or VMRs. Finally, we have been pioneers in the integration of methylation, SNP, and expression data to define disease-relevant phenotypes. Of particular importance to this application, we have led efforts to apply these methods on a population level. In this application, we take a strategic approach primarily focused on mental health relevant brain regions, but also on the general question of tissue variation. Our focus on mental health is because of all human diseases these remain particularly opaque to understanding due to the inherent subjectivity of clinical examination; as well as the value of our recently developed methods for deconvoluting the neuron- and glia-specific signals to total DNA methylation and to differential methylation, of great importance for the brain but also to GTEx generally. In Aim 1, we will use whole genome bisulfite sequencing (WGBS) to identify brain region-specific VMRs as well as t-DMRs in four regions of particular importance to three devastating mental health disorders, schizophrenia, depression, and addiction: namely anterior cingulate cortex, frontal cortex, hippocampus, and nucleus accumbens. We will study a sufficient number of samples (30 each from matched patients) to identify both t-DMRs and VMRs. Identification of VMRs requires this larger sample number, since by definition they are variable within a brain region, across individuals. We have shown that t-DMRs are relatively poor indicators of VMRs, but it is the VMRs that are more likely to be related to genetic variation in the population. We will complement this analysis with hydroxymethylcytosine sequencing of the same brain regions because of the potential importance of this modification in the brain. We will also include 5 additional GTEx tissues that exist in reasonable homogeneity compared to many others in the resource. These will serve as controls, for example to compare variability between brain regions to variability across tissues, and also for the purpose of additional t-DMR identification. In Aim 2, we will perform capture bisulfite sequencing to identify brain region-specific VMRs in the population, studying a larger number of individuals (100), in order to relate differential methylation to genotype and gene expression, and targeting the VMRs and t-DMRs identified in Aim 1. We will also perform preliminary analysis of the relationship of VMRs and genetic variation and gene expression, using the existing GTEx data on expression and genetic variation. The work proposed here will provide a critical resource for investigators studying psychiatric disease, and will provide a robust platform and datasets for relating gene expression, methylation, and DNA sequence across populations.