Massively parallel identification of functional 3' UTR variants in asthma

描述（由申请人提供）：哮喘是一种常见疾病，具有显著的遗传成分。遗传研究已经确定了许多与哮喘和其他疾病相关的序列变异。人类遗传学界现在面临着从相关变异转向因果变异的重大挑战。应对这一挑战需要改进的策略，以确定基因序列的变化如何影响基因功能。大多数与哮喘和其他常见疾病相关的遗传变异都存在于非编码区，但我们对非编码变异如何影响基因功能的理解仍然非常有限。该项目的重点是大大提高我们测量和预测3'非翻译区（3' UTR）变异如何影响基因调控的能力。3'UTR含有顺式调节元件，其通过与特定蛋白质和miRNA结合来控制mRNA稳定性和翻译。我们对3' UTR的功能知之甚少，因此不可能准确预测哪些3' UTR变体影响基因功能。我们开发了一种新的大规模并行实验方法，用于三个非翻译区（fast-UTR）序列的功能注释。Fast-UTR同时测量非常大量的3' UTR序列变体对目标细胞中mRNA水平、mRNA稳定性和蛋白质产生的影响。我们建议应用这种方法来了解3' UTR序列变异在哮喘中的作用。在目标1中，我们将选择合适的细胞模型，这些模型代表已知在哮喘中重要的关键细胞类型和在这些细胞中丰富的miRNAs。在目标2中，我们将使用fast-UTR来研究在1000个基因组计划和美洲非洲血统人群哮喘联盟（CAAPA）研究中发现的数十万个3' UTR变体。CAAPA研究包括对来自哮喘发病率高、研究不足人群的1005名高度多样化个体的样本进行全基因组测序。在目标3中，我们将使用fast-UTR结果来改进预测任何3' UTR变体影响的方法，我们将开发和实施使用fast-UTR精细定位信息来帮助分析cis-eQTL研究，CAAPA研究和其他GWAS和测序研究的方法。该项目将展示如何利用大规模并行检测技术来识别功能变体，从而解决当今人类遗传学界面临的最紧迫问题之一。

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.