A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics

Abstract

BackgroundGenome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives.MethodsOur tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes.ResultsWe retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants.ConclusionsOur integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0261-8) contains supplementary material, which is available to authorized users.

Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation

“…4A and Table 1). 17–19 CNVs attributable to a single disease gene were included in this analysis. Disease pairs with the lowest scores (≤0.01) included transposition of the great arteries (OMIM 608808) and X-linked, syndromic, Turner-type mental retardation (OMIM 300706) (Patient 72), and the Coffin–Siris syndrome (OMIM 135900) and nonspherocytic hemolytic anemia (OMIM 300908) (Patient 61).…”

Rapid and Accurate Interpretation of Clinical Exomes Using Phenoxome: a Computational Phenotype-driven Approach

“…Therefore, we compared the performance of Phenoxome against other methods using our clinical cohort. A recent comparative study examined the performance of a wide range of phenotype-driven variant prioritization methods, including OMIM Explorer 44 , Phen-Gen, Phevor and PhenIX, on 21 positive clinical exomes, and determined that PhenIX was the most effective 45 . Thus, we benchmarked the performance of PhenIX on the exomes in our cohort and compared the rank positions of the causative variants with Phenoxome.…”

A Practical Guide To Filtering and Prioritizing Genetic Variants

“…OMIM Explorer (63), which uses semantic similarity and an adaptive approach for disease-gene discovery based on patient phenotypes to propose a computationally assisted differential diagnosis informed by a personal genome. This tool is particularly useful and intuitive as it allows the user to visually filter and update variant rankings by interacting with intermediate results (www.omimexplorer.com).…”