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

• Published in: Genetics in medicine Vol. 18; no. 6; pp. 608 - 617
• Main Authors: Bone, William P., Washington, Nicole L., Buske, Orion J., Adams, David R., Davis, Joie, Draper, David, Flynn, Elise D., Girdea, Marta, Godfrey, Rena, Golas, Gretchen, Groden, Catherine, Jacobsen, Julius, Köhler, Sebastian, Lee, Elizabeth M.J., Links, Amanda E., Markello, Thomas C., Mungall, Christopher J., Nehrebecky, Michele, Robinson, Peter N., Sincan, Murat, Soldatos, Ariane G., Tifft, Cynthia J., Toro, Camilo, Trang, Heather, Valkanas, Elise, Vasilevsky, Nicole, Wahl, Colleen, Wolfe, Lynne A., Boerkoel, Cornelius F., Brudno, Michael, Haendel, Melissa A., Gahl, William A., Smedley, Damian
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.06.2016; Nature Publishing Group US; Elsevier Limited; American College of Medical Genetics and Genomics - Nature Publishing Group; Nature Publishing Group
• Subjects: 631/114; 631/208/1516; 631/208/514/1948; 692/700/139; Animals; BASIC BIOLOGICAL SCIENCES; Biomedicine; Computational Biology; Databases, Genetic; Disease Models, Animal; Exome - genetics; exome sequencing; Exome Sequencing - methods; Genetic Association Studies; Genetic Variation; Genetics & Heredity; Human Genetics; Humans; Laboratory Medicine; Mice; model organisms; National Institutes of Health (U.S.); Original; original-research-article; Patients; Phenotype; Rare Diseases - diagnosis; Rare Diseases - epidemiology; Rare Diseases - genetics; Rare Diseases - physiopathology; semantic comparison; undiagnosed diseases; United States; Zebrafish; phenotype; undiagnosed diseases; exome sequencing; semantic comparison; model organisms
• ISSN: 1098-3600; 1530-0366; 1530-0366
• DOI: 10.1038/gim.2015.137

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. 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. 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. Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.