Pathway-level transcriptional causal mechanism of sleep disordered breathing

摘要 睡眠呼吸障碍（SDB）影响超过20%的老年人，并增加了 多种心脏代谢疾病和死亡率。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- 最近进行了广泛的孟德尔随机化（MR）研究以阐明分子机制 复杂的疾病。然而，这种能力受到测试的基因和组织数量以及小效应的限制 单个基因。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更能鉴定SDB的致病分子机制。在这项研究中，我们将 利用成熟的经典途径和现有方法来计算途径表达评分， 以及基于人群的研究中可用的转录组学和基因组学数据。我们将解决以下问题 具体目的：1）使用通路表达评分来描述组织特异性和跨组织通路活性 并通过执行GWAS确定这些分数的遗传关联; 2）确定因果关联 使用GWAS通路表达的汇总统计量进行MR， 得分和SDB性状。这项研究的结果将推进我们对SDB分子基础的认识， 鉴定SDB生物标志物，并提供新的治疗靶点。本研究也将为解决这一问题提供一种新的思路。 了解其他复杂疾病的因果机制。

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.