Statistical methods for interpretation of genetic variants by gene regulatory networks

通过基因调控网络解释遗传变异的统计方法

• 批准号: 10710939
• 负责人: Zhana Duren
• 金额: $ 35.59万
• 依托单位: CLEMSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-08 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10710939
• 关键词: Address; Affect; Alleles; Cells; Comprehension; Computational Biology; Data; Data Set; Disease; Elements; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genome; Genotype; Genotype-Tissue Expression Project; Goals; Health; Human; Individual; Joints; Linkage Disequilibrium; Maps; Methods; Modeling; Network-based; Persons; Phenotype; Quantitative Trait Loci; Regulator Genes; Regulatory Element; Research; Sampling; Source; Statistical Methods; Untranslated RNA; Variant; causal variant; disease mechanisms study; epigenome; epigenomics; gene regulatory network; genetic variant; human reference genome; population based; precision drugs; precision medicine; response; transcription factor

Project Summary A person’s genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. Interpretation of how these variants cause diseases and understanding the mechanism(s) of their statistical associations to phenotype are crucial problems in computational biology and genetics. The problems are not straightforward to address because over 90% of disease-associated variants are in non-coding regions that have highly specific cellular context regulatory functions and about which we have limited comprehension. The long-term goal of this project is to explain mechanistically how non-coding genetic variants affect cellular context-dependent gene regulatory networks and influence phenotypes. Expression quantitative trait locus (eQTL) mapping and Gene regulatory networks (GRNs) are two common approaches for interpreting regulatory mechanisms of genetic variants. eQTL mapping connects variants in non-coding regions to genes by a population-based association study. GRNs provide information on the cis-regulatory elements that control context-specific expression of target genes, and information about the transcription factors that act on these elements. GRN-based variant interpretation is complementary to eQTL mapping and has the potential to overcome the limitations of eQTL mapping, which are: (1) eQTL mapping is biased for common alleles; (2) eQTL mapping cannot distinguish variants in strong linkage disequilibrium; and (3) the power to detect trans-eQTL is low. Most previous regulatory analysis research based on ENCODE data did not include personal genotyping data, and most eQTL mapping research did not include regulatory information. Joint modelling of eQTLs and GRNs would enable high-accuracy and mechanistic variant interpretation. However, the required dataset for such analysis - matched gene expression, epigenome, and genotyping data from the same individuals - are not available for a large human sample. Available datasets are cross-individual paired genotyping and gene expression data, such as GTEx data, and cross-cellular-contexts paired gene expression and epigenomics data, such as ENCODE data. These two types of paired data are also available at the single cell level. To achieve our long-term goal, we will develop statistical methods to integrate these unmatched datasets (either bulk or single cell) from different sources to (1) infer high accuracy context-specific GRNs to connect variants, transcription factors, cis-regulatory elements, and target genes; and (2) detect trans-eQTLs that regulate target genes. These methods can be extended to interpret disease-associated variants, identify causal variants, and infer personalized drug response to provide guidance for precision medicine. This project is fundamental for precision medicine, and it will increase our understanding of how genetic variants contribute to phenotype.

项目摘要 一个人的基因组通常包含数百万个变异，这些变异代表了这个个体之间的差异 基因组和参考人类基因组。解释这些变异是如何导致疾病和 理解它们与表型的统计关联的机制(S)是 计算生物学和遗传学。这些问题并不容易解决，因为90%以上的人 与疾病相关的变体位于具有高度特异性细胞背景调控的非编码区 功能，我们对它的理解有限。这个项目的长期目标是解释 非编码基因变异如何影响细胞上下文相关的基因调控 网络和影响表型。表达数量性状基因座(EQTL)定位与基因 调控网络(GRN)是解释基因调控机制的两种常用方法 变种。EQTL定位通过基于群体的关联将非编码区的变异与基因联系起来 学习。GRN提供了关于控制靶基因上下文特定表达的顺式调控元件的信息 基因，以及作用于这些元素的转录因子的信息。基于GRN的变体 解释是eQTL定位的补充，有可能克服eQTL的局限性 作图：(1)eQTL作图对常见等位基因有偏见；(2)eQTL作图不能区分 强连锁不平衡变异；(3)检测反式eQTL的能力较低。大多数以前的监管规定 基于ENCODE数据的分析研究不包括个人基因分型数据，以及大多数eQTL定位 研究不包括监管信息。EQTL和GRN的联合建模将实现高精度 机械化的变式解释。然而，这种分析所需的数据集与基因匹配 来自相同个体的表达、表观基因组和基因分型数据--对于大个子人类是不可用的 样本。可用的数据集是跨个体配对的基因分型和基因表达数据，例如GTEx 数据，以及跨细胞背景配对的基因表达和表观基因组学数据，如ENCODE数据。这些 在单个单元格级别上还提供两种类型的配对数据。为了实现长远目标，我们将发展 将来自不同来源的这些不匹配的数据集(批量或单个单元格)整合到(1)的统计方法 推断高精度的上下文特定GRN以连接变体、转录因子、顺式调控元件和 目的基因；以及(2)检测调节目的基因的反式eQTL。这些方法可以扩展到解释 疾病相关变异，识别因果变异，并推断个性化的药物反应，以提供指导 为了精准的医学。这个项目是精准医学的基础，它将增加我们对 基因变异是如何影响表型的。