Abstract
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 AMD-associated 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 electronic 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.
Highlights
Age-related macular degeneration (AMD) is a multifactorial neurodegenerative disease that is the leading cause of blindness in western individuals over the age of 651–4
As the use of electronic medical record (EMR)-linked DNA bio-repositories expands, improved high-throughput clinical phenotyping (HTCP) algorithms for cohort selection offers an appealing alternative to automate these processes and share clinically linked genotype data across research fields. These methods are of particular importance for chronic, complex diseases like AMD that are associated with a large number of genetic and environmental risk factors
We found that relying on international classification of disease-9 (ICD-9) codes alone for AMD patient selection was not satisfactory
Summary
Age-related macular degeneration (AMD) is a multifactorial neurodegenerative disease that is the leading cause of blindness in western individuals over the age of 651–4. 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)[6,7,8,9]. Studies is the time consuming process of patient recruitment, phenotyping, and DNA collection necessary to build sufficiently powered cohorts This process can be accelerated by implementing electronic medical record (EMR)-linked DNA bio-repositories, which allow multiple unrelated fields of research to share a large, common pool of genetic data coupled to a searchable EMR, significantly facilitating phenotype-genotype comparisons[18,19,20,21,22]. All of the identified studies used inclusion criteria based on international classification of disease-9 (ICD-9) codes and three of these seven studies described validation of their algorithm These studies investigated the epidemiology and treatment response of uveitis, but not genetic associations. We applied our HTCP algorithm to an institutional EMR-linked bio-repository to test our hypothesis
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