Diabetes risk variants affecting transcription factor-regulated cellular networks

2型糖尿病是一种复杂的疾病，每10个美国人中就有一个受到多种疾病的影响 常见的遗传风险因素。遗传关联研究已经确定了100多个基因座 影响T2D风险，尽管这些基因座如何在糖尿病发病机制中起作用 在很大程度上是未知的。这些风险基因座大多映射到非编码序列，并可能发生改变 特定细胞类型的基因调控过程。翻译这一糖尿病监管的广度 因此，它们的分子机制的变异可以深刻地影响糖尿病 病理生理学，尽管仍然具有挑战性。在这项研究中，我们提出了一种新的方法 通过以下方式确定T2D相关转录因子和受这些因子调控的基因网络 结合统计人类遗传学、表观基因组学、高通量分析和数量性状 基因座(QTL)定位。在这种方法中，我们确定了影响细胞类型的T2D风险变量 转录因子基因的表达，鉴定基因组结合部位和靶点 由这些转录因子调控的基因网络，并广泛地决定了 破坏转录因子调控的糖尿病风险网络的变异。在预赛中 我们已经确定了几个影响细胞类型表达的糖尿病风险变量 转录因子基因的水平，几乎没有人知道与糖尿病有关- 相关路径。在目标1中，我们将结合遗传精细作图和表观基因组注释 以及来自糖尿病相关细胞的eQTL数据，以确定影响细胞的糖尿病风险变量- 转录因子的类型表达。在目标2中，我们将执行5个芯片序列分析 胰岛样本中转录因子结合eQTL数据定位反式 受转录因子调控变异影响的靶基因网络。在《目标3》中，我们将 结合等位基因不平衡作图和胰岛芯片序列数据的电子基序预测 量化破坏转录因子调控网络的变异对全基因组的影响 关于糖尿病风险。这些研究的结果将揭示特定的转录因子 由糖尿病风险变量调节，以及由这些因素调节的基因网络在 反过来影响糖尿病的病理生理学。总而言之，这些研究将提供对关键 转录因子和基因网络参与糖尿病的发病机制。

Type 2 diabetes is a complex disease that affects 1 in 10 Americans and is influenced by many common genetic risk factors. Genetic association studies have identified over 100 loci that influence T2D risk, although how these loci mechanistically contribute to diabetes pathogenesis is largely unknown. The majority of these risk loci map to non-coding sequence, and likely alter gene regulatory processes in specific cell-types. Translating this breadth of diabetes regulatory variation into their molecular mechanisms can thus profoundly inform on diabetes pathophysiology, although remains challenging. In this study we propose a novel approach to identify T2D-relevant transcription factors and gene networks regulated by these factors by combining statistical human genetics, epigenomics, high-throughput assay and quantitative trait locus (QTL) mapping. In this approach we identify T2D risk variants that affect the cell-type expression of a transcription factor gene, characterize the genomic binding sites and target gene network regulated by these transcription factors, and broadly determine the effects of variants disrupting transcription factor-regulated networks on diabetes risk. In preliminary findings we have identified several diabetes risk variants that affect the cell-type expression level of a transcription factor gene, almost none of which have known involvement in diabetes- relevant pathways. In Aim 1 we will combine genetic fine-mapping with epigenomic annotation and eQTL data from diabetes-relevant cells to identify diabetes risk variants that affect the cell- type expression of a transcription factor. In Aim 2 we will perform ChIP-seq assays of five transcription factors in pancreatic islet samples combined with eQTL data to map the trans network of target genes affected by transcription factor regulatory variants. In Aim 3 we will combine allelic imbalance mapping and in silico motif prediction of islet ChIP-seq data to quantify the genome-wide effects of variants disrupting transcription factor-regulated networks on diabetes risk. The results of these studies will reveal specific transcription factors that are regulated by diabetes risk variants, and the gene networks regulated by these factors that in turn impact diabetes pathophysiology. Together these studies will provide insight into critical transcription factors and gene networks involved in diabetes pathogenesis.