A Framework for Automated Gene Selection in Genomic Screening

Abstract

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.