标题： 以网络为中心的框架，用于增强冠状动脉疾病中的基因-疾病关联研究 显示英文标题

标题： A network-driven framework for enhancing gene-disease association studies in coronary artery disease

摘要： 在过去十年中，全基因组关联研究（GWAS）已成功识别出与复杂疾病相关的许多遗传变异。这些关联有可能揭示复杂疾病的分子机制，并导致新药物靶点的发现。尽管取得了这些进展，但将遗传变异与复杂疾病联系起来的生物通路和机制仍不完全清楚。大多数与性状相关的变异位于非编码区域，被认为通过影响基因表达的调控作用来影响表型。然而，通常不清楚它们调控哪些基因以及在哪些细胞类型中发生这种调控。转录组范围关联研究（TWAS）旨在通过检测由GWAS变异调控的性状相关组织基因表达来弥合这一差距。然而，传统的TWAS方法经常忽视反式调控效应的关键贡献，并未能整合全面的调控网络。在这里，我们提出了一种新的框架，利用组织特异性基因调控网络（GRNs）将顺式和反式遗传调控效应整合到复杂疾病的TWAS框架中。我们使用冠状动脉疾病（CAD）验证了我们的方法，利用来自STARNET项目的数据，该数据提供了约600名心血管疾病患者多组织基因表达和遗传数据。初步结果表明，我们的GRN驱动框架有望发现更多可能与CAD相关的基因和通路。该框架通过利用组织特异性调控见解，扩展了传统TWAS方法，并推进了对复杂疾病遗传结构的理解。 显示英文摘要

摘要： Over the last decade, genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with complex diseases. These associations have the potential to reveal the molecular mechanisms underlying complex diseases and lead to the identification of novel drug targets. Despite these advancements, the biological pathways and mechanisms linking genetic variants to complex diseases are still not fully understood. Most trait-associated variants reside in non-coding regions and are presumed to influence phenotypes through regulatory effects on gene expression. Yet, it is often unclear which genes they regulate and in which cell types this regulation occurs. Transcriptome-wide association studies (TWAS) aim to bridge this gap by detecting trait-associated tissue gene expression regulated by GWAS variants. However, traditional TWAS approaches frequently overlook the critical contributions of trans-regulatory effects and fail to integrate comprehensive regulatory networks. Here, we present a novel framework that leverages tissue-specific gene regulatory networks (GRNs) to integrate cis- and trans-genetic regulatory effects into the TWAS framework for complex diseases. We validate our approach using coronary artery disease (CAD), utilizing data from the STARNET project, which provides multi-tissue gene expression and genetic data from around 600 living patients with cardiovascular disease. Preliminary results demonstrate the potential of our GRN-driven framework to uncover more genes and pathways that may underlie CAD. This framework extends traditional TWAS methodologies by utilizing tissue-specific regulatory insights and advancing the understanding of complex disease genetic architecture.