Wednesday, March 22, 2023
3/22/2023
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.
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