Pancreas cell type-specific regulatory variants and T2D disease risk association

项目摘要 2型糖尿病（T2 D）是一种高度复杂和异质性的疾病，影响超过4亿人 国际吧虽然全基因组关联研究（GWAS）已经确定了500多个基因组位点， 与T2 D相关，这些关联中的大多数未能描述致病基因并解释T2 D的分子机制。 疾病病理学的潜在机制。为了确定致病基因，可以利用表达 数量性状基因座（eQTL）分析已经评估了遗传变异和基因 在胰腺中的表达。先前对胰岛的eQTL分析提供了对胰岛功能的有价值的见解。 可能导致T2 D风险和疾病的基因，但他们还没有检查这些基因是否 基因及其变体效应物与细胞类型相关。此外，遗传变异的程度 影响同种型表达在很大程度上是未知的，尽管选择性剪接在 调节β细胞功能和存活。先进的计算方法已经被开发出来， 解析大量异质样品中的细胞类型群体，使我们能够绘制 基因型、细胞类型比例和基因表达。在本提案的目标1中，我将执行蜂窝 去卷积以估计305个全胰腺的大量RNA-seq中胰腺细胞类型的比例， 420个胰岛（总共725个样品），并使用这些估计来产生聚焦的细胞类型相关的 eQTL图谱。在目标2中，我将在细胞类型相关的eQTL和GWAS位点之间进行贝叶斯共定位 推断影响T2 D性状和疾病的细胞机制。识别推定的因果调节变体 在相关的GWAS基因座中，我将进行一个精细的定位研究，整合GWAS汇总统计 具有来自表观基因组数据的功能注释和来自SELEX-2000的转录因子（TF）结合活性， seq.在目标3中，我将通过测试它们是否改变转录来验证精细映射的变体的功能 使用snATAC-seq的不同胰腺细胞类型中的因子足迹。接下来，使用来自 根据这一分析以及目标1和2，我将优先考虑10种假定的因果调节变体，以询问它们的 使用电泳迁移率变动分析对核因子结合的直接影响。总的来说，这项建议将 采用先进的计算方法来识别改变基因或亚型表达的T2 D风险变体， TF结合并决定其功能所在的特定胰腺细胞类型。这些结果将提供 对T2 D风险和疾病的遗传基础和细胞起源的新见解。

PROJECT SUMMARY Type 2 diabetes (T2D) is a highly complex and heterogenous disease, affecting more than 400 million individuals worldwide. While genome-wide association studies (GWAS) have identified more than 500 genomic loci that are associated with T2D, most of these associations fail to delineate the causal genes and explain the molecular mechanisms underlying disease pathology. To identify the causal genes, it is possible to exploit expression quantitative trait locus (eQTL) analyses that have assessed the associations between genetic variation and gene expression in the pancreas. Previous eQTL analyses of pancreatic islets have provided valuable insight into the probable causal genes that contribute to T2D risk and disease, however they have not examined whether these genes and their variant effectors are cell type-associated. Furthermore, the extent to which genetic variation affects isoform expression is largely unknown despite an increasing importance of alternative splicing in regulating beta cell function and survival. Advanced computational methods have been developed to accurately resolve cell type populations within bulk heterogenous samples, allowing us to the map the associations between genotype, cell type proportions, and gene expression. In Aim 1 of this proposal, I will perform cellular deconvolution to estimate the proportions of pancreatic cell types in bulk RNA-seq of 305 whole pancreas and 420 pancreatic islets (725 samples in total) and use these estimations to generate a focused cell-type associated eQTL map. In Aim 2, I will perform Bayesian colocalization between cell type-associated eQTLs and GWAS loci to infer cellular mechanisms affecting T2D traits and disease. To identify putative causal regulatory variants within the associated GWAS loci, I will conduct a fine-mapping study that integrates GWAS summary statistics with functional annotations from epigenomic data and transcription factor (TF) binding activities from SELEX- seq. In Aim 3, I will functionally validate the fine-mapped variants by testing whether or not they alter transcription factor footprints in distinct pancreatic cell types using snATAC-seq. Next, using computational predictions from this analysis and from Aims 1 and 2, I will prioritize 10 putative causal regulatory variants to interrogate their direct effects on nuclear factor binding using electrophoretic mobility shift assay. Altogether, this proposal will employ advanced computational methods to identify T2D risk variants that alter gene or isoform expression and TF binding and determine the specific pancreatic cell types in which they are functional. These results will provide novel insights into the genetic basis and cellular origins of T2D risk and disease.