The Genetic Architecture of Diabetic Retinopathy

糖尿病视网膜病变的遗传结构 项目摘要/摘要 糖尿病视网膜病变(DR)是成年人视力丧失和可预防的失明的主要原因， 据估计，全球有9300万人。随着全球糖尿病(DM)患病率的上升，全球 糖尿病视网膜病变的患病率也将上升。眼睛非常容易受到糖尿病的损害，因为 眼部环境的微妙结构和复杂的动态平衡控制。长期以来，DR一直是 被认为是一种微血管疾病，由血视网膜屏障的破坏和 视网膜中的新生血管。糖尿病的病程和后遗症的严重程度有很大不同。 在病例之间，这种差异的原因不能用已知的风险因素很好地解释。几个基因组- 糖尿病视网膜病变的广谱关联研究已经完成，虽然数量不多的重要基因座 有报道称，在复制后，只有一个基因座与DR显著相关。这些 研究是在队列研究的协作联盟中进行的，迄今为止规模最大的研究包括 22,279例糖尿病患者和23,977例糖尿病对照组。我们将利用之前的研究，结合 来自百万退伍军人计划(MVP)、BioVU和Emerge Network的资源(估计为77,518 病例，133,295例II型糖尿病对照)，以检验常见基因变异相关的假设 这是迄今为止规模最大的多种族GWA中的DR。目标1.我们将从以下方面结合证据进行关联 使用逆向对上述资源的DR-SNP关系进行逻辑回归分析 方差加权固定效应荟萃分析，包括种族群体内部和跨种族群体。大多数以前的 研究侧重于检测SNP与表型的关联，而没有评估调控的遗传效应或 多基因和因果效应对Dr.AIM的影响。我们将使用Meta分析和 基因-组织表达计划在S-PrediXcan分析中评估DR与 基因预测的基因表达。此外，我们将确定基因预测的基因效应 通过使用共定位分析，不是由于连锁不平衡污染。协同本地化 方法正在不断发展中，我们将监测文献中的最佳实践。目标3.我们将 开发基因组信息预测模型，结合糖尿病视网膜病变多基因风险评分(PR)，DR 我们将利用这一摘要开发一份DR PrS。 统计MVP、GWAS和Pollack等人的数据，然后是BioVU的培训。一项结合Phewas的分析 DR-PRS可以阐明以前未知的DR相关基因与其他疾病之间的关联 特征。我们将把PRS、Phewas结果和当前的临床风险因素结合到基因组中 将在Emerge Network中验证的预测模型。概述的研究将显著增加 DR以前研究的样本量，评估对DR的调节效应，并开发预测模型 这可能对糖尿病视网膜病变患者的临床决策有一定的参考价值。

The Genetic Architecture of Diabetic Retinopathy Project Summary/Abstract Diabetic retinopathy (DR) is the leading cause of vision loss and preventable blindness in adults, afflicting an estimated 93 million individuals worldwide. As the global prevalence of diabetes mellitus (DM) rises, the global prevalence of diabetic retinopathy will also rise. The eye is highly susceptible to damage from DM due to the delicate structures and intricate control of homeostasis in the ocular environment. DR has long been recognized as a microvascular disease triggered by the breakdown of the blood-retinal barrier and neovascularization in the retina. The course of diabetic disease and severity of sequelae vary substantially between cases, and the cause for this variability is not explained well by known risk factors. Several genome- wide association studies (GWAS) of DR have been completed, and while a modest number of significant loci have been reported only a single locus has been significantly associated with DR after a replication. These studies were conducted in collaborative consortia of cohort studies and the largest study to date included 22,279 cases and 23,977 diabetic controls. We will leverage the previous studies in combination with resources from the Million Veteran Program (MVP), BioVU, and the eMERGE Network (an estimated 77,518 cases, 133,295 type II diabetic controls) to test the hypothesis that common genetic variants are associated with DR in the largest multi-ethnic GWAS to date. AIM 1. We will combine evidence for association from logistic regression analysis of DR-SNP relationships across the resources described above using inverse variance-weighted fixed effects meta-analyses, both within and across racial groups. The majority of previous studies focused on detecting SNP-phenotype associations and did not evaluate regulatory genetic effects or polygenic and causal effects for DR. AIM 2. We will use the summary statistics from the meta-analysis and the Gene-Tissue Expression Project in an analysis using S-PrediXcan to evaluate associations between DR and genetically predicted gene expression. Additionally, we will identify genetically predicted gene effects that are not due to linkage disequilibrium contamination through the use of a colocalization analysis. Colocalization methods are under constant development, and we will monitor the literature for best practices. AIM 3. We will develop a genome-informed predictive model, combining a diabetic retinopathy polygenic risk score (PRS), DR PheWAS results, and current clinical risk factors for DR. We will develop a DR PRS utilizing the summary statistics of the MVP GWAS and Pollack et al., followed by training in BioVU. A PheWAS analysis incorporating the DR PRS can elucidate previously unknown association between DR associated loci and other disease traits. We will combine the PRS, PheWAS results, and current clinical risk factors into the genome informed predictive model that will be validated in the eMERGE Network. The outlined study will significantly increase the sample size of previous studies of DR, evaluate regulatory effects on DR, and develop predictive models that may have utility in clinical decision making of DR patients.