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，其特征是肝脏、骨骼肌和 脂肪组织对胰岛素的敏感性和胰腺β细胞分泌胰岛素的相对不足。T2D有很大的 基因成分，在过去的十年中，人类基因研究已经确定了400多种关联 不同人群的信号。然而，在大多数情况下，导致这些的特定变异和基因 关联信号未知。T2D信号包括蛋白质产物的功能编码的基因座 由于邻近的基因特征不佳，最接近的已知基因是遥远的，或者似乎有不止一个基因 成为一个看似合理的生物候选人。确定因果变异，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 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。在这里，我们提出了在胰岛、肝脏、脂肪等领域的大规模多学科功能基因组计划 和肌肉细胞，以确定T2D风险相关变体和T2D的作用和机制 它们下游的效应器转录本。在整个项目中，我们利用我们的上一代和正在进行的 基因组数据集以及对变异和基因功能的全基因组和有针对性的筛选。来补充 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，确定 有条件地不同的关联信号，并构建可信的变种集合。我们建议将变体链接到 通过全基因组转录和表观基因组数据的分析，扰动分析，效应转录本 它改变了数千个含有变体的调控元件和效应器转录，数十个 具体的变种和综合的计算分析。接下来，我们建议对数百人进行系统评估 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，取决于检测选择 关于细胞类型。基于这些结果，我们建议对数十到数百个潜在的效应器进行集中研究 转录本，以评估电生理学，糖异生，脂代谢和信号通路，我们 建议深入研究个别基因在特定背景下的作用机制。最后，我们建议 通过将效应器转录放入细胞类型和环境中来分析、集成和可视化所有数据 上下文特定网络，选择网络节点作为药物的候选生物标记物和调制点， 并建立一个框架，以了解变体和转录本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关联信号转化为生物 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.