Statistical Methods to Study the Genetic Basis and Mechanisms of Trans Gene Regulation

研究转基因调控的遗传基础和机制的统计方法

• 批准号: 10627970
• 负责人: Xuanyao Liu
• 金额: $ 40.5万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-07 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10627970
• 关键词: Address; Affect; Complex; Disease; Distant; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic study; Grant; Link; Maps; Methods; Molecular; Pathway interactions; Play; Quantitative Trait Loci; Regulation; Role; Statistical Methods; Transgenes; Untranslated RNA; Variant; causal variant; cell type; disorder risk; gene network; genome wide association study; genomic locus; human tissue; novel; trait

Project Summary Genome wide association studies (GWAS) identified thousands of genetic loci associated with a variety of complex traits and diseases. Nonetheless, deciphering how most GWAS variants are linked to diseases remains exceptionally challenging, as the majority of these variants are noncoding. Noncoding variation can affect the regulation of genes that are either physically nearby (in cis), or physically distant (in trans). Mounting evidence suggest that genetic regulation in trans plays a dominant role in the control of gene expression and disease risk. Thus, high-quality trans-eQTL and trans regulatory networks are critically needed to fully understand how disease-associated variants flow through gene networks to affect causal genes and pathways. However, most studies to date have solely focused on studying genetic regulation in cis owing to the extreme difficulty in detecting trans-QTLs. Therefore, the lack of high-quality trans-eQTL maps represents a significant gap in our understanding of disease mechanisms. In this grant, I propose to address this gap by developing powerful statistical methods to produce high-quality, comprehensive maps of trans-QTLs in multiple human tissues and cell types. We will use these maps to uncover major mechanisms that underlie trans genetic regulation. Finally, we will develop novel methods to identify disease genes using our high-quality maps of trans-eQTLs.

项目摘要 全基因组关联研究(GWAS)发现了数千个与多种基因相关的遗传位点 复杂的特征和疾病。尽管如此，破译大多数GWA变异是如何与疾病联系在一起的 仍然极具挑战性，因为这些变体中的大多数都是非编码的。非编码变体可以 影响物理上邻近(顺式)或物理上遥远(反式)的基因的调节。安装 有证据表明，反式基因的遗传调控在基因表达和基因调控中起主导作用。 疾病风险。因此，迫切需要高质量的跨eQTL和跨调控网络来充分 了解与疾病相关的变异如何通过基因网络影响致病基因和途径。 然而，到目前为止，由于极端的原因，大多数研究都只专注于研究顺式基因的遗传调控。 检测反式QTL的难度较大。因此，缺乏高质量的跨eQTL图谱是一个重要的 我们对疾病机制的理解存在差距。在这笔赠款中，我建议通过发展 强大的统计方法在多个人类中产生高质量、全面的反式QTL图谱 组织和细胞类型。我们将使用这些图谱来揭示转基因背后的主要机制 监管。最后，我们将开发新的方法来识别疾病基因，使用我们的高质量地图 反式eQTL。