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.

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, a

SUBMITTER: Bone WP 

PROVIDER: S-EPMC4916229 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature