Pathway-level transcriptional causal mechanism of sleep disordered breathing

摘要 睡眠呼吸紊乱(SDB)影响了超过20%的老年人，并增加了患 多发性心脏代谢性疾病与死亡率。候选基因和全基因组关联研究(GWAS) 已经确定了与SDB相关的基因和途径。然而，因果关系尚不清楚。转录组- 广泛的孟德尔随机化(Mr)研究最近被用来阐明分子机制。 复杂的疾病。然而，这种力量受到测试基因和组织数量以及小影响的限制。 单基因的基因。基于我们的初步数据，我们假设通路水平的转录MR将 比单基因MR更能识别SDB的致病分子机制。在这项研究中，我们将 利用成熟的规范途径和现有方法来计算途径表达分数，如 以及基于人群的研究中可用的转录组和基因组学数据。我们将解决以下问题 具体目标：1)使用通路表达分数来描述组织特异性和跨组织通路活动 并通过执行GWAS来确定这些分数的遗传关联；2)确定因果关联 通过使用途径表达的汇总统计进行MR检查来研究途径活动和SDB之间的关系 分数和SDB特征。这项研究的结果将加深我们对SDB分子基础的认识，有助于 确定SDB生物标记物，并提供新的治疗靶点。这项研究还将提供一种新的方法 了解其他复杂疾病的致病机制。

ABSTRACT Sleep disordered breathing (SDB) affects more than 20% of the older population and increases the risks of multiple cardiometabolic diseases and mortality. Candidate gene and genome-wide association studies (GWASs) have identified genes and pathways associated with SDB. However, the causality is not clear. Transcriptome- wide Mendelian randomization (MR) studies have recently been conducted to elucidate molecular mechanisms of complex diseases. However, the power is limited by the number of tested genes and tissues and small effects of single genes. Building on our preliminary data, we hypothesize that pathway-level transcriptional MR will have better power than single gene MR to identify causal molecular mechanisms for SDB. In this study, we will leverage well-established canonical pathways and existing methods to calculate pathway expression scores, as well as available transcriptomics and genomics data in population-based studies. We will address the following specific aims: 1) To describe tissue-specific and cross-tissue pathway activities using pathway expression scores and identify genetic associations for these scores by performing GWAS; 2) To identify causal associations between pathway activities and SDB by performing MR using GWAS summary statistics of pathway expression scores and SDB traits. Results of this study will advance our knowledge of the molecular basis of SDB, help identify SDB biomarkers, and provide novel treatment targets. This study also will provide a new approach to understand causal mechanisms for other complex diseases.