Abstracts

Rationale: Next-generation sequencing facilitated an explosion in availability and number of clinical laboratories offering high-throughput, sequencing-based diagnostic tests. One subgroup of such tests are gene panels. Relatively low-cost sequencing, in combination with rapid gene discovery now enables laboratories to offer gene panels that include hundreds of genes. Competing laboratories commonly offer similarly-marketed gene panels, one example being an “epilepsy gene panel”; however, genes analyzed can vary widely and may or may not include limited-evidence genes. Disease association and inheritance for described genes is freely available, but to characterize each gene within a panel of several genes requires health care providers (HCPs) to perform multiple iterative searches of different databases. These factors contribute to the burden on the HCP to understand and identify the right test for the right patient and better understand the results. Methods: Orphadata (static download) and Gene Ontology (GO) consortium data (SQL server) were used to match genes to their corresponding conditions, inheritance pattern, molecular function, cellular components, and biological process. Results: Using python, clinically relevant information for a user-provided list of genes was automatically collected from Orphadata and GO and applied to a web-browser-readable display. The “Epilepsy Genes: Inheritance and Phenotype Tool” (EGIPT) illustrates how the data retrieved can be displayed; here, a list of genes commonly appearing on epilepsy gene panels in the United States was used. Using EGIPT, users can (1) search for conditions of interest, (2) explore specific genes to see associated phenotypes and links to other resources, (3) search based on inheritance pattern, and (4) highlight genes based on GO terms. Conclusions: The process developed here illustrates an automated method to collect and display clinically relevant information for a set of genes. This process is scalable and can be applied to any genes included in Orphadata and GO. EGIPT illustrates how a browser front-end can display detailed information in a digestible manner. This could be integrated into web ordering systems currently used by diagnostic laboratories. This process may facilitate a greater understanding of the tests being ordered, and results returned, and serve as a way to propagate rapidly increasing information on gene-phenotype relationships Funding: This study was funded by BioMarin Pharmaceutical Inc.