A Framework for Automated Gene Selection in Genomic Screening

An efficient framework to identify disease-causing genes is needed to evaluate genomic data for both individuals with an unknown disease etiology and those undergoing genomic screening. Here, we propose a framework for gene selection used in genomic analyses, including screening applications limited to genes with strong or established evidence levels and diagnostic applications that includes genes with less or emerging evidence of disease association. We extracted genes with evidence for gene-disease association from the Human Gene Mutation Database, Online Mendelian Inheritance in Man, and ClinVar to build a diagnostic gene list of 5,973 genes. Next, we applied stringent filters in conjunction with computationally curated evidence (DisGeNET) to create a list limited to 3,600 genes with stronger levels of evidence for disease association. When compared to manual gene curation efforts, including the Clinical Genome Resource, genes with strong or definitive disease associations are included in both gene lists at high percentages, while genes with limited evidence are largely removed. We further confirmed the utility of this approach in the screening of 45 ostensibly healthy genomes. Our approach efficiently creates highly sensitive gene lists for genomic applications, while remaining dynamic and updatable, enabling time savings in gene curation and review. ### Competing Interest Statement Ms. Blout Zawatsky, Dr. Lazo de la Vega, Dr. Lebo, report grants from National Heart Blood and Lung Institute, during the conduct of the study. Dr. Austin-Tse, Dr. Mason-Suares, Dr. Machini, Dr. Hao, and Dr. Yu have nothing to disclose. Dr. Rehm reports grants from NIH, grants from National Heart Blood and Lung Institute, during the conduct of the study; personal fees from Genome Medical, outside the submitted work. Dr. Green reports grants from National Heart Blood and Lung Institute, during the conduct of the study; personal fees from AIA, personal fees from SavvySherpa, personal fees from Verily, personal fees from Wamberg, and is co-founder of Genome Medical, outside the submitted work. ### Funding Statement Funding support was partly provided by grant 5R01HL143295 from the National Heart, Lung, and Blood Institute (LLV, CLZ, RCG, HLR, MSL). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This project has been reviewed and approved by the Mass General Brigham IRB. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The resulting genes lists and information used to create them can be found in the Supplemental Materials. This gene list will be provided on-line for easy access and on-going updates.