Integrative analysis of genetic variation and transcription factor networks to elucidate mechanisms of mental health disorders

项目总结 在这个项目中，我们将跨越数量遗传学和机械学这两个传统上截然不同的领域。 生物学，以获得对人类基因变异调节效应的机械论理解。利用 在提供并行全基因组和转录组测序数据的大型人类数据集上，我们将扩展 在模型生物体中开发和验证的原理验证研究和计算方法，以实现 改进了与精神健康障碍相关的GWA基因座的功能解释。我们专注于 特别是转录因子作为遗传风险变异体的上游调节因子以及 下游网络层面效应的中介者。作为目标1，我们将开发扩展方法以允许准确 从大量人类组织样本转录组数据中建立转录因子活性模型 在GTEx、MedicenCode和TOPMed队列中。这些数据将在目标2中用于剖析机制 顺式基因的潜在近端基因调控变异体。我们假设转录因子的动态变化 活性和结合改变了跨个体、组织和细胞的基因调控变异的效应大小 类型，通过对这种关系进行建模，我们可以检测到调控特定调控变体的因子，并 非编码疾病相关基因座。在平行目标3中，我们将绘制网络级别的反式作用遗传变异图 对于转铁蛋白活性的个体间差异。超越将转铁蛋白活性作为组织特异性参数 现在，我们将把它视为一个变量数量性状本身，并由GWAS/TWAS为 推断转铁蛋白活性，我们定位在每个组织中影响转铁蛋白活性的特定多态。我们预料到 在这一分析中发现的反式作用基因座不仅对调控的基础生物学具有重大意义 这不仅有助于解释全球气候变化与复杂疾病，特别是与心理健康之间的联系。

PROJECT SUMMARY In this project we will bridge the traditionally largely distinct fields of quantitative genetics and mechanistic biology to obtain a mechanistic understanding of regulatory effects of genetic variants in humans. Leveraging on large human data sets providing parallel whole genome and transcriptome sequencing data, we will extend proof-of-principle studies and computational approaches developed and validated in model organisms to achieve improved functional interpretation of GWAS loci associated to mental health disorders. We focus specifically on the role of transcription factors as both upstream regulators of genetic risk variants as well as mediators of downstream network-level effects. As Aim 1, we will develop extend methods to allow accurate modeling of transcription factor activity from transcriptome data from large cohorts of human tissue samples in GTEx, PsychENCODE, and TOPMed cohorts. These data will be used in Aim 2 to dissect the mechanisms underlying proximal genetic regulatory variants in cis. We hypothesize that dynamics of transcription factor activity and binding modifies the effect size of genetic regulatory variants across individuals, tissues, and cell types, and that by modeling this relationship we can detect TFs regulating specific regulatory variants and noncoding disease-associated loci. In parallel Aim 3, we will map network-level trans-acting genetic variants for inter-individual variation in TF activity. Going beyond treating TF activity as a tissue-specific parameter of the cellular environment, we will now consider it as a variable quantitative trait itself, and by GWAS/TWAS for inferred TF activity, we map specific polymorphisms that affect TF activity within each tissue. We anticipate that the trans-acting loci discovered in this analysis will be of major interest not only to basic biology of regulatory networks, but also for explaining GWAS associations to complex diseases, and to mental health in particular.