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

项目总结 2型糖尿病(T2D)是一种高度复杂的异质性疾病，影响着4亿多人 全世界。虽然全基因组关联研究(GWAS)已经确定了500多个基因组基因座， 与T2D相关，这些关联中的大多数都无法描述原因基因和解释分子 疾病病理学的基本机制。为了识别致病基因，有可能利用基因表达 数量性状基因座(EQTL)分析已经评估了遗传变异和基因之间的关联 在胰腺中的表达。以前对胰岛的eQTL分析为我们提供了对 可能导致T2D风险和疾病的因果基因，然而他们还没有检查这些基因是否 基因及其变体效应器与细胞类型相关。此外，基因变异的程度 影响异构体表达在很大程度上是未知的，尽管选择性剪接在 调节β细胞的功能和存活。先进的计算方法已经被开发出来，以准确地 解析大量异质样本中的细胞类型群体，使我们能够绘制出 基因分型、细胞类型比例和基因表达。在本提案的目标1中，我将执行手机 用去卷积方法估计305例全胰腺标本中胰腺细胞类型的比例 420个胰岛(总共725个样本)，并使用这些估计来生成聚焦的细胞类型关联 EQTL图谱。在目标2中，我将在与细胞类型相关的eQTL和Gwas基因座之间执行贝叶斯共定位 以推断影响T2D特征和疾病的细胞机制。确定假定的因果调节变种 在相关的GWAS基因座内，我将进行一项精细的地图研究，其中整合了GWAS的汇总统计数据 通过来自表观基因组数据的功能注释和来自SELEX的转录因子(TF)结合活性- 序列号。在目标3中，我将通过测试它们是否改变转录来从功能上验证精细映射的变体 使用SNATAC-seq对不同类型胰腺细胞的足迹进行因子分析。接下来，使用来自 这一分析以及目标1和目标2中，我将优先列出10个假定的因果监管变体，以询问它们的 用凝胶迁移率改变分析对核因子结合的直接影响。总而言之，这项提议将 使用先进的计算方法来识别改变基因或异构体表达的T2D风险变量 Tf结合，并确定它们发挥作用的特定胰腺细胞类型。这些结果将提供 对T2D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.