Massively parallel identification of functional 3' UTR variants in asthma

描述(申请人提供)：哮喘是一种常见的疾病，具有显著的遗传成分。遗传学研究已经确定了许多与哮喘和其他疾病相关的序列变异。人类基因学界现在面临着从关联变异到因果变异的重大挑战。应对这一挑战需要改进识别基因序列变化如何影响基因功能的策略。大多数与哮喘和其他常见疾病相关的遗传变异都是在非编码区发现的，但我们对非编码变异如何影响基因功能的了解仍然非常有限。该项目致力于极大地提高我们测量和预测3‘非翻译区(3’UTRs)变异如何影响基因调控的能力。3‘UTRs含有顺式调控元件，通过与特定的蛋白质和miRNAs结合来控制mRNA的稳定性和翻译。我们对3‘非编码区功能知之甚少，因此不可能准确预测哪些3’非编码区变异会影响基因功能。我们开发了一种新的大规模并行实验方法(FAST-UTR)来对来自三素数UTRs的序列进行功能标注。FAST-UTR同时测量非常大量的3‘非编码区序列变异对感兴趣细胞的信使核糖核酸水平、信使核糖核酸稳定性和蛋白质生产的影响。我们建议用这种方法来理解3‘UTR序列变异在哮喘中的作用。在目标1中，我们将选择适当的细胞模型，代表已知在哮喘和这些细胞中丰富的miRNAs中重要的关键细胞类型。在目标2中，我们将使用FAST-UTR来研究1000基因组计划和美洲非洲裔人口哮喘联合会(CAAPA)研究中发现的数十万个3‘UTR变异体。CAAPA的研究包括对来自哮喘高发人群的1005个高度多样化的个体的样本进行全基因组测序。在目标3中，我们将使用快速非编码区结果来改进预测任何3‘非编码区变异的效果的方法，并将开发和实施使用快速非编码区精细作图信息来帮助分析顺式eQTL研究、CAAPA研究以及其他GWAS和测序研究的方法。这个项目将展示如何利用大规模并行分析技术来识别功能变异，从而解决当今人类基因学界面临的最紧迫的问题之一。

DESCRIPTION (provided by applicant): Asthma is a common disease with a prominent genetic component. Genetic studies have identified many sequence variants associated with asthma and other diseases. The human genetics community now faces the major challenge of moving from associated variants to causal variants. Addressing this challenge requires improved strategies for identifying how changes in gene sequence affect gene function. Most genetic variants associated with asthma and other common diseases are found in non-coding regions, but our understanding of how non-coding variants affect gene function remains very limited. This project focuses on dramatically advancing our ability to measure and predict how variants in 3' untranslated regions (3' UTRs) affect gene regulation. 3' UTRs contain cis-regulatory elements that control mRNA stability and translation by binding to specific proteins and miRNAs. We understand so little about 3' UTR function that it is impossible to accurately predict which 3' UTR variants affect gene function. We developed a novel massively parallel experimental method for functional annotation of sequences from three-prime UTRs (fast-UTR). Fast-UTR simultaneously measures the effects of very large numbers of 3' UTR sequence variants on mRNA levels, mRNA stability, and protein production in cells of interest. We propose to apply this method to understanding the role of 3' UTR sequence variation in asthma. In aim 1, we will select appropriate cellular models that represent key cell types known to be important in asthma and miRNAs that are abundant in those cells. In aim 2, we will use fast-UTR to study hundreds of thousands of 3' UTR variants found in the 1000 Genomes Project and the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) study. The CAAPA study includes whole genome sequencing of samples from 1005 highly diverse individuals from understudied populations with a high incidence of asthma. In aim 3, we will use fast-UTR results to improve methods for predicting effects of any 3' UTR variant and we will develop and implement methods for using fast-UTR fine-mapping information to help analyze cis-eQTL studies, the CAAPA study, and other GWAS and sequencing studies. This project will demonstrate how massively parallel assay technology can be harnessed to identify functional variants thereby addressing one of the most pressing issues facing the human genetics community today.