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

William P. Bone, Nicole L. Washington, Orion J. Buske, David R. Adams, Joie Davis, David Draper, Elise D. Flynn, Marta Girdea, Rena Godfrey, Gretchen Golas, Catherine Groden, Julius Jacobsen, Sebastian Köhler, Elizabeth M.J. Lee, Amanda E. Links, Thomas C. Markello, Christopher J. Mungall, Michele Nehrebecky, Peter N. Robinson, Murat SincanAriane G. Soldatos, Cynthia J. Tifft, Camilo Toro, Heather Trang, Elise Valkanas, Nicole Vasilevsky, Colleen Wahl, Lynne A. Wolfe, Cornelius F. Boerkoel, Michael Brudno, Melissa A. Haendel, William A. Gahl, Damian Smedley

Research output: Contribution to journalArticle

44 Scopus citations

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, and (iii) phenotypes of protein-protein association neighbors. 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. Conclusion: Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.

Original languageEnglish (US)
Pages (from-to)608-617
Number of pages10
JournalGenetics in Medicine
Volume18
Issue number6
DOIs
StatePublished - Jun 1 2016

Keywords

  • exome sequencing
  • model organisms
  • phenotype
  • semantic comparison
  • undiagnosed diseases

ASJC Scopus subject areas

  • Genetics(clinical)

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    Bone, W. P., Washington, N. L., Buske, O. J., Adams, D. R., Davis, J., Draper, D., Flynn, E. D., Girdea, M., Godfrey, R., Golas, G., Groden, C., Jacobsen, J., Köhler, S., Lee, E. M. J., Links, A. E., Markello, T. C., Mungall, C. J., Nehrebecky, M., Robinson, P. N., ... Smedley, D. (2016). Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genetics in Medicine, 18(6), 608-617. https://doi.org/10.1038/gim.2015.137