Gaulton Kyle J
Department of Pediatrics, University of California San Diego, San Diego, CA, 92093, USA.
Curr Diab Rep. 2017 Sep;17(9):72. doi: 10.1007/s11892-017-0908-x.
Deciphering the mechanisms of type 2 diabetes (T2DM) risk loci can greatly inform on disease pathology. This review discusses current knowledge of mechanisms through which genetic variants influence T2DM risk and considerations for future studies.
Over 100 T2DM risk loci to date have been identified. Candidate causal variants at risk loci map predominantly to non-coding sequence. Physiological, epigenomic and gene expression data suggest that variants at many known T2DM risk loci affect pancreatic islet regulation, although variants at other loci also affect protein function and regulatory processes in adipose, pre-adipose, liver, skeletal muscle and brain. The effects of T2DM variants on regulatory activity in these tissues appear largely, but not exclusively, due to altered transcription factor binding. Putative target genes of T2DM variants have been defined at an increasing number of loci and some, such as FTO, may entail several genes and multiple tissues. Gene networks in islets and adipocytes have been implicated in T2DM risk, although the molecular pathways of risk genes remain largely undefined. Efforts to fully define the mechanisms of T2DM risk loci are just beginning. Continued identification of risk mechanisms will benefit from combining genetic fine-mapping with detailed phenotypic association data, high-throughput epigenomics data from diabetes-relevant tissue, functional screening of candidate genes and genome editing of cellular and animal models.
解析2型糖尿病（T2DM）风险位点的机制能够为疾病病理学提供重要信息。本综述讨论了当前关于基因变异影响T2DM风险的机制的知识以及对未来研究的考虑因素。
迄今为止已鉴定出100多个T2DM风险位点。风险位点的候选因果变异主要定位于非编码序列。生理学、表观基因组学和基因表达数据表明，许多已知T2DM风险位点的变异会影响胰岛调节，尽管其他位点的变异也会影响脂肪、前脂肪、肝脏、骨骼肌和大脑中的蛋白质功能和调节过程。T2DM变异对这些组织中调节活性的影响在很大程度上（但并非唯一）是由于转录因子结合的改变。T2DM变异的推定靶基因已在越来越多的位点得到定义，一些基因（如FTO）可能涉及多个基因和多个组织。胰岛和脂肪细胞中的基因网络与T2DM风险有关，尽管风险基因的分子途径在很大程度上仍不明确。全面定义T2DM风险位点机制的工作才刚刚开始。将基因精细定位与详细的表型关联数据、来自糖尿病相关组织的高通量表观基因组学数据、候选基因的功能筛选以及细胞和动物模型的基因组编辑相结合，将有助于持续识别风险机制。
