Multiscore, a gene ranker powered by artificial intelligence and real-world clinical data, shows high sensitivity for the molecular diagnosis of Mendelian disorders in nearly 10,000 exomes and genomes.

Abstract

A challenge for clinical exome and genome sequencing (ES/GS) analysis is correlating the clinical presentation of the cases being tested with known gene-phenotype associations (GPAs). We developed Multiscore, a gene prioritization tool, to facilitate gene-level predictions of phenotypic fit. Multiscore combines data inputs and algorithms to generate similarity subscores that feed a random forest (RF) classifier trained to predict the probability of association between the patient’s clinical features and the gene. The reference GPAs are extracted from: (1) OMIM, (2) patient descriptions in the literature, and (3) GeneDx (GDx) clinical data. We used 9,989 ES/GS cases to assess performance of the tool in combination with genotype filtering. Genotype filters rendered an average of 173 genes with variants requiring clinical review. Multiscore prioritized the reported positive gene with a median rank of 3 and mean rank of 6.35. The average recall (sensitivity) of Multiscore was 33% in the top 1, 69% in the top 5, 83% in the top 10, and 93% in the top 20 ranked genes. Multiscore was able to handle non-exact HPO term matches allowing the use of real-world clinical data. 74 genes lacking OMIM entries were prioritized using only the GDx and literature datasets. Multiscore allows the phenotype review to prioritize the most relevant genes, increasing case throughput and broadening access to diagnoses for patients.