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: 10028931
• 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/10028931
• 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; Untranslated RNA; Variant; causal variant; cell type; disorder risk; 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.

项目概要 全基因组关联研究 (GWAS) 确定了数千个与多种基因相关的基因位点 复杂的性状和疾病。尽管如此，破译大多数 GWAS 变异如何与疾病相关 仍然非常具有挑战性，因为这些变体中的大多数都是非编码的。非编码变异可以 影响物理上邻近（顺式）或物理上远离（反式）的基因的调节。安装 有证据表明，反式遗传调控在基因表达的控制中起主导作用， 疾病风险。因此，迫切需要高质量的反式 eQTL 和反式调控网络，以充分 了解与疾病相关的变异如何通过基因网络影响因果基因和途径。 然而，迄今为止，大多数研究仅集中于研究顺式基因调控，因为极端的情况 反式QTL检测困难。因此，缺乏高质量的反式 eQTL 图谱是一个重要的问题。 我们对疾病机制的理解存在差距。在这笔赠款中，我建议通过开发来解决这一差距 强大的统计方法可生成多个人类中高质量、全面的反式 QTL 图谱 组织和细胞类型。我们将利用这些图谱来揭示转基因背后的主要机制 规定。最后，我们将开发新的方法来使用我们的高质量图谱来识别疾病基因 反式 eQTL。