A Validated Phenotyping Algorithm for Genetic Association Studies in Age-related Macular Degeneration

Joseph M. Simonett, Mahsa A. Sohrab, Jennifer Pacheco, Loren L. Armstrong, Margarita Rzhetskaya, Maureen Smith, M. Geoffrey Hayes, Amani A. Fawzi
2015 Scientific Reports  
Age-related macular degeneration (AMD), a multifactorial, neurodegenerative disease, is a leading cause of vision loss. With the rapid advancement of DNA sequencing technologies, many AMDassociated genetic polymorphisms have been identified. Currently, the most time consuming steps of these studies are patient recruitment and phenotyping. In this study, we describe the development of an automated algorithm to identify neovascular (wet) AMD, non-neovascular (dry) AMD and control subjects using
more » ... ectronic medical record (EMR)-based criteria. Positive predictive value (91.7%) and negative predictive value (97.5%) were calculated using expert chart review as the gold standard to assess algorithm performance. We applied the algorithm to an EMR-linked DNA bio-repository to study previously identified AMD-associated single nucleotide polymorphisms (SNPs), using case/ control status determined by the algorithm. Risk alleles of three SNPs, rs1061170 (CFH), rs1410996 (CFH), and rs10490924 (ARMS2) were found to be significantly associated with the AMD case/control status as defined by the algorithm. With the rapid growth of EMR-linked DNA biorepositories, patient selection algorithms can greatly increase the efficiency of genetic association study. We have found that stepwise validation of such an algorithm can result in reliable cohort selection and, when coupled within an EMR-linked DNA biorepository, replicates previously published AMD-associated SNPs. Age-related macular degeneration (AMD) is a multifactorial neurodegenerative disease that is the leading cause of blindness in western individuals over the age of 65 1-4 . Clinical presentation of AMD is heterogeneous, with many genetic and environmental risk factors contributing to its pathogenesis 5 . The rate of identification of AMD-associated genetic risk factors, including but not limited to single nucleotide polymorphisms (SNPs) in CFH, ARMS2 and HTRA1 genes, has increased rapidly with the utilization of genome-wide association studies (GWAS) 6-9 . These studies have led to a better understanding of AMD pathophysiology, creation of genetic based prediction models and a plethora of AMD pharmacogenomics studies 8,10-15 . GWAS studies have also identified environmental exposures that interact with AMD genetic risk factors, highlighting the importance of developing accurate criteria for clinical phenotyping in order to discriminate disease and control populations 16,17 . One important barrier to genetic association
doi:10.1038/srep12875 pmid:26255974 pmcid:PMC4530462 fatcat:t7nu5wlrwvezdetaefbm326z6u