Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation.
Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation.
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利用表观基因组和三维基因组组织来解释调控变异。
DOI：
10.1371/journal.pcbi.1011286
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发表时间：
2023-07
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
了解调控变异体对复杂表型的影响是一个重大的挑战，因为这些变异体所针对的基因和途径以及调控变异体工作的细胞类型环境通常是未知的。发生在远端调控序列和基因之间的特定细胞类型的长程调控相互作用为研究调控变异对复杂表型的影响提供了一个强大的框架。然而，这种远程相互作用的高分辨率地图只适用于少数几种细胞类型。此外，确定特定的基因子网络或路径是一组变异体的目标，这是一个重大挑战。我们开发了L-HIC-REG，一种随机森林回归方法，用于预测新细胞类型中的高分辨率接触计数，以及一个基于网络的框架，用于从全基因组关联研究中识别一组变异所针对的候选细胞类型特定基因网络。我们应用我们的方法预测了55种Roadmap表观基因组学作图联合会细胞类型的相互作用，我们使用这些细胞类型来解释NHGRI-EBI GWAS目录中的调节性单核苷酸多态(SNPs)。使用我们的方法，我们对包括精神分裂症、冠状动脉疾病(CAD)和克罗恩病在内的15种不同的表型进行了深入的表征。我们发现了由已知和新的调控SNPs的基因靶点组成的差异连线的子网络。综上所述，我们的互动纲要和相关的基于网络的分析渠道利用远程监管互动来检查复杂表型中监管变异的特定背景影响。单核苷酸多态(SNPs)是人类基因组中的单碱基对变化，可导致包括疾病在内的复杂表型的变化。许多SNP位于基因组的非编码部分，远离基因，因此很难识别受这些变异影响的下游分子通路。长程基因调控，即调控序列元件影响数百个碱基以外的基因表达，已成为非编码SNPs影响基因功能的一种可能机制。HI-C是一种高通量的基因组分析方法，可用于推断三维组织和远程相互作用。然而，收集不同类型和背景的Hi-C数据集是一个巨大的挑战。在我们的研究中，我们使用公开可用的数据集来训练一个模型，以预测多种细胞类型的Hi-C测量。我们使用这些预测来推断SNPs和基因之间的远程相互作用，并结合基于网络的方法来识别被这些SNPs扰乱的潜在下游途径。我们收集的远程相互作用、受影响的途径和分析方法应该是一个有用的资源，可以揭示不同疾病和正常条件下受非编码基因组变异影响的生物途径。
Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn’s disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes. Single nucleotide polymorphisms (SNPs) are single base pair changes in the human genome that can contribute to variations in complex phenotypes, including diseases. Many SNPs lie in non-coding parts of the genome, far away from genes, making it difficult to identify downstream molecular pathways that are affected by these variants. Long-range gene regulation wherein a regulatory sequence element affects the expression of a gene hundreds of kilobases away, has emerged as a possible mechanism by which non-coding SNPs impact the function of a gene. Hi-C is a high-throughput genomic assay that can be used to infer three-dimensional organization and long-range interactions. However, collection of Hi-C datasets across diverse cell types and contexts is a significant challenge. In our study, we used publicly available datasets to train a model to predict Hi-C measurements across multiple cell types. We use these predictions to infer long-range interactions between SNPs and genes and combine with network-based approaches to identify potential downstream pathways disrupted by these SNPs. Our collection of long-range interactions, affected pathways and analysis approach should be a useful resource to uncover biological pathways that are affected by non-coding genomic variation for different disease and normal conditions.
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影响因子：
11.1
作者：
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