阿托伐他汀（AT）所致肌毒性是患者停药一个主要原因，常导致心血管事件，目前其遗传机制尚不清楚。研究表明阿托伐他汀内酯（ATL）是引起肌毒性的关键物质，我们认为影响ATL生成增多的基因变异可增加肌毒性风险。为了阐明这个问题，我们前期研究了多个代谢酶基因变异对ATL生成的影响，但这些基因变异解释个体差异不到14%。为了进一步系统地发现新的影响ATL生成和肌毒性风险的基因变异，本课题拟：①用人肝微粒体代谢模型，通过整合全基因组基因型和转录组数据进行基因表达数量性状位点（eQTL）分析，发现影响ATL生成的eQTL，并用不同生物信息学工具揭示其因果关系；②在冠心病患者中，明确关系最强的30个eQTL对ATL循环暴露的影响及对肌毒性的危险度；③靶向ATL代谢网络，从体内、肝组织和细胞模型三个层次阐明eQTL影响生成ATL的环节。研究结果有助阐明eQTL影响肌毒性的代谢机制和提高肌毒性风险预测能力。

Statin-induced myotoxicity (SIM) is one of the principal reasons for atorvastatin (AT) non-adherence and/or discontinuation, contributing to adverse cardiovascular outcomes. To date, the genetic mechanism of SIM is far from well-illustrated. Published data in vitro and in vivo indicated that atorvastatin lactone (ATL) is the key metabolite to induce myotoxicity. We therefore hypothesis that genetic variants increasing the formation of ATL can increase the risk to statin-induced myotoxicity. Based on the hypothesis, we have investigated the impact of candidate genetic variants on the formation of ATL. UGT1A1*6 and UGT2B7*2 were identified to be associated with the formation. However, they can only explain less than 14% variation in the formation of ATL. In the proposal, to further systematically discover novel functional genetic variants contributing to the formation of atorvastatin lactone and the risk to statin-induced myotoxicity, (1) a transcriptome-wide association study integrating genome wide variants and gene expression data will applied to identify expression quantitative trait loci (eQTL) associated with the formation of ATL and different bioinformatic tools will then be applied to reveal the potential causal impact of eQTLs on the formation of ATL. (2) Then, the effect of 30 eQTLs with the strongest causal relationship with the formation of ATL on the circulating explosure of ATL and the risk to SIM in patients with coronary artery disease (CAD) will be evaluated. (3) Last, the molecular mechanism of the eQTL with the highest risk to SIM on the metabolic network of ATL will be illustrated at three levels of patients with CAD, liver tissures, and cell models. The results of this study will give us insight into the metabolic mechanism of eQTLs on SIM and will help clinic optimize the therapy strategy of atorvastatin.