Functional Analysis of T2D Associated Non-coding SNPs

 描述(申请人提供)：超过2700万美国人患有2型糖尿病(T2D)。Gwas已经发现了128个与T2D和/或空腹高血糖相关的主要SNPs，但这些变异如何参与T2D的发病机制还知之甚少。在GWAs中发现的变异的功能表征的一个主要挑战是，每个与一个性状直接相关的领先SNP都在LD中，有一系列额外的变异，因此识别这种关联的准确变异(S)需要广泛的计算和实验分析。此外，大多数相关的SNP位于非编码区，在那里推断序列变体的功能后果仍然具有挑战性。最后，当相关的SNP被确定为候选调控变异体时，功能测试往往会因为缺乏合适的实验模型而受到阻碍。为了应对这些挑战，我们组建了一支由基因组学(Frazer)、表观基因组学(REN)和T2D生物学(Sander)领域的高成就研究人员组成的团队。我们建议结合现场计算方法、高通量分子分析和人类胚胎干细胞中的疾病建模来全面注释T2D Gwas数据并测试其基因调控功能的变体。在目标1中，我们将分析5,150个全基因组，寻找T2D和空腹高血糖风险相关基因座的变异。我们估计，这些分析将在感兴趣的区间内识别MAF&GT；1%的~1,000,000个变异。此外，我们将确定在T2D患者中丰富的罕见变异。我们估计，将这些数据与现有的表观基因组数据集交叉将识别出T2D相关组织中假定的调控元件中的约99,000个变异。在目标2中，我们将使用三种高通量分子分析来表征T2D相关细胞类型中的99,000个候选调控变体。首先，我们将进行大规模平行报告分析(MPRA)来测试每个含有SNP的序列元件作为转录增强子的潜力，如果是这样的话，增强子活性是否受到候选变异体的影响。其次，我们将进行高通量体外结合试验(SELEX)，以确定候选变异体是否影响相关转录因子的DNA结合。第三，我们将预测候选人的靶基因 使用高通量染色体构象捕获(Hi-C)分析的突变体。在目标3中，目标1和目标2的结果将被整合，以确定20个与贝塔细胞相关的SNPs的优先顺序，以进行功能验证。关键标准包括：(1)该变异体存在于激活的β细胞增强中，(2)破坏转录因子结合，(3)在Hi-C分析中靶向T2D相关基因。我们将通过(1)对人类β细胞的胚胎干细胞衍生细胞模型进行基因工程，测试顺调控元件的缺失或风险变体的引入如何影响靶基因的表达；(2)对小鼠模型进行基因工程，测试候选增强子/靶基因对是否控制体内的葡萄糖代谢，从而验证这些变体。这项拟议的研究将为T2D病因学中通过GWAS确定的调控变异的基础提供关键的见解。

 DESCRIPTION (provided by applicant): More than 27 million Americans suffer from Type 2 diabetes (T2D). GWAS have identified 128 lead SNPs associated with T2D and/or fasting hyperglycemia, but little is known about how these variants contribute to T2D pathogenesis. A major challenge in functionally characterizing variants found in GWAS is that each lead SNP directly associated with a trait is in LD with a collection of additional variants, and thus identifying the precise variant(s) underlying the association requires extensive computational and experimental analyses. Additionally, the majority of the associated SNPs are located within non-coding regions, where inferring functional consequences of sequence variants remains challenging. Finally, when associated SNPs are identified as candidate regulatory variants, functional testing is frequently hampered by a lack of appropriate experimental models. To address these challenges we have assembled a team of highly accomplished researchers in genomics (Frazer), epigenomics (Ren) and T2D biology (Sander). We propose to combine state-of-the-field computational methods, high throughput molecular assays, and disease modeling in human embryonic stem cells to comprehensively annotates T2D GWAS data and test variants for their gene regulatory function. In Aim 1 we will analyze 5,150 whole-genomes for variants in T2D and fasting hyperglycemia GWAS risk-associated loci. We estimate that these analyses will identify ~1,000,000 variants with a MAF > 1% in the intervals of interest. Additionally, we will identify rare variants that are enriched in T2D patients. We estimate that intersecting these data with existing epigenomic datasets will identify ~99,000 variants in putative regulatory elements in T2D-relevant tissues. In Aim 2 we will use three high throughput molecular assays to characterize these 99,000 candidate regulatory variants in T2D-relevant cell types. First, we will carry out massively parallel reporter assays (MPRA) to test the potentia of each SNP-harboring sequence element to act as a transcriptional enhancer, and if so, whether enhancer activity is affected by the candidate variant. Second, we will carry out a high throughput in vitro binding assay (SELEX) to determine whether the candidate variants affect DNA binding of relevant transcription factors. Third, we will predict target genes of the candidate variants using high throughput chromosome conformation capture (Hi-C) assays. In Aim 3, results from Aim 1 and Aim 2 will be integrated to prioritize 20 beta cell-relevant SNPs for functional validation. Key criteria include: (1) the variant resides in an active beta cell enhance, (2) disrupts transcription factor binding, and (3) targets T2D-relevant genes in Hi-C assays. We will validate these variants by (1) genetic engineering of an embryonic stem cell-derived cell model of human beta cells, testing how deletion of the cis-regulatory element or introduction of the risk variant affects target gene expression; (2) genetic engineering of mouse models, testing whether candidate enhancer/target gene pairs control glucose metabolism in vivo. The proposed study will provide key insights into the underpinnings of regulatory variants identified through GWAS in T2D etiology.