Analysis of functional genetic variants in RNA processing and expression

项目摘要 该项目的目标是在转录后水平对基因变异进行功能注释， RNA表达的调节，这扩展并补充了ENCODE目前的重点 数据分析近年来，在建立一个目录， 在疾病基因组或跨人群中的遗传变异。下一个巨大的挑战是 鉴定致病变异并阐明其在生物学和疾病中潜在功能 流程.为此，研究工作已经指向研究位于 蛋白质编码区、启动子区和剪接位点区，因为它们对基因表达有明显的影响。 表情然而，许多新发现的疾病相关变异存在于其他基因组中。 非编码区，如内含子，可赋予相关基因调控功能。 这些变异的机制一直难以破译。预计其中许多人 可能在转录后水平发挥作用，从而影响mRNA表达。在人类中， 在转录后阶段有无数的过程介导RNA表达，例如剪接， 编辑、多聚腺苷酸化和mRNA衰变。转录后调节是非常通用的， 但受到严格调控，影响了大多数人类基因尽管重要，但如何准确地 在这些过程中识别功能性遗传变异仍然是该领域的关键问题。到 为了解决这个问题，大量收集的ENCODE表达和蛋白质结合数据 是一种无价的资源我们将开发新的方法，充分利用 ENCODE和其他公开可用的数据集，并辅以进一步的生物信息学 预测和实验验证。这项工作将使以前没有达到的水平， 理解RNA表达的转录后调节中的遗传变异， 提供了新的手段来解决遗传变异的功能注释的迫切任务。

Project Summary The goal of this project is to functionally annotate genetic variants in post-transcriptional regulation of RNA expression, which extends and complements the current focus of ENCODE data analysis. Recently, tremendous success has been achieved in constructing a catalog of genetic variants in disease genomes or across population. The next great challenge is to identify causal variants and elucidate their potential function in biological and disease processes. To this end, research efforts have been directed to studying variants located in protein-coding, promoter, and splice site regions due to their apparent impacts on gene expression. However, many of the newly identified disease-associated variants reside in other non-coding regions, such as introns, that may confer regulatory function to the related gene. The mechanisms of these variants have been hard to decipher. It is expected that many of them may function at the post-transcriptional level, thus affecting mRNA expression. In human, a myriad of processes mediate RNA expression at the post-transcriptional stage, such as splicing, editing, polyadenylation and mRNA decay. Post-transcriptional regulation is extremely versatile, yet closely regulated, affecting most human genes. Despite the importance, how to accurately identify functional genetic variants in these processes remains a key question in the field. To address this question, the large collection of ENCODE expression and protein-binding data represent an invaluable resource. We will develop novel methodologies to make full use of the ENCODE and other publicly available data sets, complemented by further bioinformatic prediction and experimental validations. This work will allow a previously unattained level of understanding of genetic variants in post-transcriptional regulation of RNA expression and provide new means to tackle the imperative task of functional annotations of genetic variants.