Bridging the gap between type 2 diabetes GWAS and therapeutic targets

2型糖尿病（T2D）是一种异质性疾病，其特征在于肝脏、骨骼肌和骨骼肌的抵抗。 脂肪组织对胰岛素的依赖性以及胰腺β细胞胰岛素分泌的相对不足。T2D具有显著的 遗传成分，在过去的十年中，人类遗传研究已经确定了400多个关联 在不同人群中的信号。然而，在大多数情况下，负责这些的特定变体和基因 关联信号是未知的。T2D信号包括编码蛋白质产物的功能的基因座 附近的基因的特征很差，最近的已知基因是遥远的，或者不止一个基因似乎 可能是生物学上的候选人确定致病变异，受影响的调控基因网络， DNA序列的变化，以及这种变化导致疾病的机制是实现基因突变的关键步骤。 了解T2D的遗传结构，验证潜在的药物靶点，并开发新的治疗方法 战略布局在此，我们提出了大规模的多学科功能基因组学项目，在胰岛，肝脏，脂肪， 和肌肉细胞，以确定T2D风险相关变异的贡献和机制， 它们的下游效应子转录本。在整个项目中，我们利用我们以前和正在进行的一代 基因组数据集和基因组范围的和针对变体和基因功能的靶向筛选。以补充 这些努力，我们将首先收集全基因组阵列和基于测序的关联研究结果， 有条件区分关联信号并构建可信的变体集。我们建议将变体与 通过全基因组转录组学和表观基因组学数据分析、干扰分析 改变数千个含有变异的调控元件和效应转录物， 具体的变体和综合计算分析。接下来，我们提出系统评估数百个 通过使用全基因组和靶向筛选胰岛素分泌、脂质 积累、线粒体功能、葡萄糖摄取和分化状态，测定选择取决于 细胞类型。基于这些结果，我们建议对数十至数百个潜在的效应物进行重点研究。 转录，以评估电生理学，胚胎发生，脂质代谢和信号转导途径，我们 建议彻底调查个别基因的特定作用机制。最后提出 通过将效应子转录物置于细胞类型和环境中来分析、整合和可视化所有数据， 上下文特定的网络，选择网络节点作为候选生物标志物和药物的调制点， 并建立一个框架，以了解变异和转录对个体的组织特异性贡献， 疾病异质性这些目标的成功完成将把T2D相关信号转化为生物学信号。 洞察力和治疗目标。

Type 2 diabetes (T2D) is a heterogeneous disorder characterized by resistance of hepatic, skeletal muscle and adipose tissues to insulin and a relative deficiency of insulin secretion by pancreatic β cells. T2D has a substantial genetic component, and over the past decade human genetic studies have identified over 400 association signals across diverse populations. However, in most cases the specific variants and genes responsible for these association signals are not known. T2D signals include loci for which functions of the protein products encoded by nearby genes are poorly characterized, the closest known gene is distant, or more than one gene appears to be a plausible biological candidate. Identifying the causal variants, the regulatory gene networks affected by the change in DNA sequence, and the mechanisms by which such variation leads to disease are critical steps toward understanding the genetic architecture of T2D, validating potential drug targets, and developing novel therapeutic strategies. Here, we propose large-scale multi-disciplinary functional genomics projects in islet, liver, adipose and muscle cells to determine the contributions and mechanisms underlying T2D risk-associated variants and their downstream effector transcripts. Throughout the project, we leverage our prior and ongoing generation of genomic data sets and genome-wide and targeted screens for function of variants and genes. To complement these efforts, we will first collect genome-wide array and sequencing-based association study results, identify conditionally distinct association signals and construct credible sets of variants. We propose to link variants to effector transcripts through analyses of genome-wide transcriptomic and epigenomic data, perturbation assays that alter thousands of variant-containing regulatory elements and effector transcripts, perturbations of tens of specific variants, and integrative computational analyses. Next, we propose systematic evaluation of hundreds of potential effector transcripts through use of genome-wide and targeted screens of insulin secretion, lipid accumulation, mitochondrial function, glucose uptake, and differentiation state, with assay selection depending on cell type. Based on these results, we propose focused studies on tens to hundreds of potential effector transcripts to evaluate electrophysiology, gluconeogenesis, lipid metabolism and signaling pathways, and we propose thorough investigation the context-specific mechanism of action of individual genes. Finally, we propose to analyze, integrate, and visualize all data by placing effector transcripts into cell-type and environmental context-specific networks, selecting network nodes as candidate biomarkers and modulation points for drugs, and building a framework to understand the tissue-specific contribution of variants and transcripts to individual disease heterogeneity. Successful completion of these aims will translate T2D association signals into biological insights and therapeutic targets.