2型糖尿病发病是环境因素与遗传因素相互作用的结果，已被鉴定与其相关的基因超过400个，这些基因也与肥胖、脂代谢紊乱和心血管病等密切相关。然而，目前检测出来的许多基因突变是低频率、罕见的变异，它们对总体疾病发生风险的作用尚未完全阐明，也缺乏相对应的靶向药物。 本研究主要是在前期研究基础上，期望能通过对个体基因组、蛋白质组、代谢组和临床表型的信息等进行多组学数据深度挖掘 (OWAS-DM)，利用GWAS关联的孟德尔遗传-蛋白组量化性状基因座 (MR-pQTL)，MR-mQTL (代谢组)，MR-eQTL (表型组) 共定位分析及生物信息学分析等多组学分析策略，发现2型糖尿病发生发展的潜在机制及标志物。并利用cMAP及分子对接等人工智能虚拟筛选技术结合基于实验的亲和质谱、表面等离子共振及高内涵等筛选技术，发展针对2型糖尿病靶标的高通量药物筛选技术，为糖尿病患者的个体化精准诊断和治疗提供新的手段。翻译成英文

This study is based on previous research and expects to find the potential mechanism and biomarkers of the onset and development of type 2 diabetes through deep mining of multi-omics data such as individual genomes, proteomes, metabolomes, and clinical phenotypes (OWAS-DM), quantitative trait loci (MR-pQTL, MR-mQTL, MR-eQTL) associated with GWAS, and bioinformatics analysis. In addition, artificial intelligence virtual screening techniques such as cMAP and molecular docking, combined with experimental affinity mass spectrometry, surface plasmon resonance, and high-content screening techniques are used to develop high-throughput drug screening techniques for type 2 diabetes targets, providing new means for individualized and precise diagnosis and treatment of diabetes patients.