Integrating genome-scale data to reveal causal mechanisms in type 2 diabetes

 描述（由申请人提供）：2型糖尿病已成为全球健康的主要威胁之一。在工业化和新兴经济体中，糖尿病患病率的迅速上升证明了预防的失败，糖尿病患者的高并发症率突出了当前治疗方法的不足。我们对糖尿病发展机制的理解存在重大差距，这阻碍了新的预防和治疗策略的创新。人类遗传学提供了一种越来越强大的方法来解决这些缺陷，并提供对疾病的机械见解，从而产生与健康相关的益处。该提案旨在利用人类基因发现工作的信息，这些工作近年来已经确定了基因组中100多个区域，这些区域含有影响T2 D风险的DNA序列变异。在将这些发现转化为机械见解方面取得了有限的进展，但最近的几项技术和分析进展已经改变了这种情况，我们计划利用的正是这些。我们的第一个目标是找到驱动这些区域风险关联的特定DNA序列变化。非常大的遗传数据集的聚集，特别是当来自一系列种族群体时，使得有可能定义这些变异的子集可能驱动T2 D风险效应。我们将采用作为大型国际财团的一部分收集的广泛的遗传数据集，并应用现有的和新的方法来获得这些T2 D风险变体的最精确定位。在确定了这些变体之后，第二个目标是了解它们干扰的细胞过程。最近，从关键的糖尿病相关人体组织（包括胰岛）生成详细的基因组功能图谱已成为可能。这些图谱定义了调节细胞活性的关键元素。我们将使用这些地图来突出包含T2 D因果变异的特定元素，并启动实验研究来测试出现的功能假设。第三个目标是将这些T2 D相关的功能元件与介导其效应的特定基因、蛋白质、网络和途径联系起来。我们将从各种现有的和新的公共和专有来源汇总数据，每个来源都提供了区域基因与T2 D发展相关性的补充线索。大多数药物作用于特定的蛋白质靶点，这些努力将产生与人类疾病直接相关的新蛋白质靶点。这项建议的一个基本特点是，它依赖于已经收集的大量数据集，或在某些情况下，正在利用现有资金生成的数据集。我们在此请求的资金将支持这些数据的进一步整合，并使其能够传播到更广泛的研究社区，特别是通过AMP-T2 DGENES联盟门户网站。

 DESCRIPTION (provided by applicant): Type 2 diabetes has emerged as one of the leading threats to global health. The rapid rise in diabetes prevalence in both industrialized and emerging economies bears testament to the failures of prevention, and high rates of complication in those with diabetes highlight the inadequacies of current therapeutic approaches. Major gaps in our understanding of the mechanisms responsible for the development of diabetes represent obstacles to innovation with respect to novel preventative and therapeutic strategies. Human genetics provides an increasingly-powerful approach for addressing these deficiencies and providing mechanistic insights into disease that can result in health-related benefits. This proposal seeks to use information from human genetic discovery efforts that have, in recent years, identified over 100 regions of the genome which harbor DNA sequence variants influencing T2D-risk. There has been limited progress in turning these discoveries into mechanistic insights but several recent technological and analytical advances have transformed the situation, and it is these that we plan to exploit. Our first aim is to home i on the specific DNA sequence changes driving the risk-associations in these regions. The aggregation of very large genetic datasets, particularly when derived from a range of ethnic groups, makes it possible to define the subset of these variants likely to be driving the T2D-risk effect. We will take extensive genetic data sets collected as part of large international consortia and apply existing and novel approaches to derive the most precise localization of these T2D-risk variants yet obtained. Having identified these variants, the second aim is to understand the cellular processes they perturb. Recently, it has become possible to generate detailed functional maps of the genome from key diabetes- relevant human tissues, including the pancreatic islet. These maps define elements crucial for regulating cellular activity. We will use these maps to highlight the specific elements that contain T2D-causal variants, and initiate experimental studies to test the functional hypotheses that emerge. The third aim seeks to connect these T2D-associated functional elements to the specific genes, proteins, networks and pathways that mediate their effects. We will aggregate data from a variety of existing and novel public and proprietary sources, each of which provides complementary clues to the relevance of the regional genes to T2D development. Most medicines act on specific protein targets, and these efforts will result in novel protein targets that are directly implicated in human disease. An essential feature of this proposal is that it relies on extensive data sets that have already been collected, or, in some cases, are being generated with existing funding. The funding we request here will support the further integration of these data, and also enable its dissemination to the wider research community, most particularly via the AMP-T2DGENES consortium portal.