Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency

Abstract

Purpose:Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles.Genet Med 18 6, 608–617.Methods:Using simulated exomes and the National Institutes of Health Undiagnosed Diseases Program (UDP) patient cohort and associated exome sequence, we tested our hypothesis using Exomiser. Exomiser ranks candidate variants based on patient phenotype similarity to (i) known disease–gene phenotypes, (ii) model organism phenotypes of candidate orthologs, and (iii) phenotypes of protein–protein association neighbors.Genet Med 18 6, 608–617.Results:Benchmarking showed Exomiser ranked the causal variant as the top hit in 97% of known disease–gene associations and ranked the correct seeded variant in up to 87% when detectable disease–gene associations were unavailable. Using UDP data, Exomiser ranked the causative variant(s) within the top 10 variants for 11 previously diagnosed variants and achieved a diagnosis for 4 of 23 cases undiagnosed by clinical evaluation.Genet Med 18 6, 608–617.Conclusion:Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.Genet Med 18 6, 608–617.

A Practical Guide To Filtering and Prioritizing Genetic Variants

“…Exomiser (62), which has been shown to improve the prioritization of disease genes from WES variant calls through cross-species phenotype comparisons (www.sanger.ac.uk/science/tools/exomiser). …”

“…HPO plays a key role in the Monarch Initiative (61), which provides tools for genotypephenotype analysis across broad areas of disease (https://monarchinitiative.org). Toolkits that have been built on top of these and other annotations primarily for the discovery of Mendelian genes include: Exomiser (62), which has been shown to improve the prioritization of disease genes from WES variant calls through cross-species phenotype comparisons (www.sanger.ac.uk/science/tools/exomiser). …”

Navigating the Phenotype Frontier: The Monarch Initiative

“…Monarch supports researchers and clinicians using this data with visualization tools, application programming interfaces, and a rich web site (https://monarchinitiative.org). These approaches make it possible to overcome limitations in the data for many applications; including disease diagnostics (Bone et al 2016), drug repurposing, and improved phenotyping; both clinically and in model organisms (e.g., helping identify candidate phenotyping assays based on preliminary phenotyping). Indeed, Monarch's unified data corpus and tools have been applied to diagnosing real patients and plans are underway to scale up their use with larger efforts, including the Undiagnosed Diseases Network (Brownstein et al 2015) and the 100,000 Genomes Project (http://www.genomicsengland.co.uk/the-100000-genomes-project/).…”

CCEPAS: the creation and validation of a fast and sensitive clinical whole exome analysis pipeline based on gene and variant ranking

“…These searches may miss certain gene to phenotype associations due to the lack of frequent updates, synonymous word challenges and the lack of comprehensive search engines. However, new computational algorithms are necessary to be developed to address these challenges [32] . Recently, a number of algorithms that associate a specific phenotype to genes have started to emerge, namely, PHIVE, PhenIX, hiPHIVE, ENDEAVOUR, Phenolyzer, Ingenuity Variant Analysis (Ingenuity) and phenomizer.…”