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

项目摘要 在这个项目中，我们将弥合传统上很大程度上不同的领域的数量遗传学和机械 生物学，以获得对人类遗传变异的调节作用的机械理解。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 在模式生物中开发和验证的原理验证研究和计算方法， 改善与精神健康障碍相关的GWAS基因座的功能解释。我们专注 特别是转录因子作为遗传风险变异的上游调节因子以及 下游网络水平效应的中介。作为目标1，我们将开发扩展方法， 从来自大群人类组织样品的转录组数据建模转录因子活性 在GTEx、PsychENCODE和TOPMed队列中。这些数据将在目标2中用于剖析机制 顺式中潜在的近端遗传调节变体。我们假设转录因子的动力学 活性和结合改变了个体、组织和细胞中遗传调节变体的效应大小 类型，并且通过对这种关系进行建模，我们可以检测调节特定调节变体的TF， 非编码疾病相关基因座。在平行目标3中，我们将绘制网络水平的反式作用遗传变异 TF活性的个体间差异。超越将TF活性视为组织特异性参数 在细胞环境中，我们现在将其视为一个可变的数量性状本身，并通过GWAS/TWAS为 通过推测TF活性，我们绘制了影响每个组织内TF活性的特定多态性。我们预计 在该分析中发现的反式作用基因座不仅对调控的基础生物学， 网络，但也解释GWAS协会复杂的疾病，特别是精神健康。

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.