PUBLICATION
Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency
- Authors
- 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., Links, A.E., Markello, T.C., Mungall, C.J., Nehrebecky, M., Robinson, P.N., Sincan, M., Soldatos, A.G., Tifft, C.J., Toro, C., Trang, H., Valkanas, E., Vasilevsky, N., Wahl, C., Wolfe, L.A., Boerkoel, C.F., Brudno, M., Haendel, M.A., Gahl, W.A., Smedley, D.
- ID
- ZDB-PUB-171212-15
- Date
- 2016
- Source
- Genetics in medicine : official journal of the American College of Medical Genetics 18: 608-17 (Journal)
- Registered Authors
- Haendel, Melissa A., Robinson, Peter N.
- Keywords
- none
- MeSH Terms
-
- Mice
- Patients
- Rare Diseases/diagnosis
- Rare Diseases/epidemiology
- Rare Diseases/genetics*
- Rare Diseases/physiopathology*
- Genetic Variation
- Humans
- Zebrafish
- Computational Biology
- Databases, Genetic
- Exome Sequencing/methods*
- Exome/genetics*
- National Institutes of Health (U.S.)
- Disease Models, Animal
- United States
- Genetic Association Studies
- Phenotype
- Animals
- PubMed
- 26562225 Full text @ Genet. Med.
Citation
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., Links, A.E., Markello, T.C., Mungall, C.J., Nehrebecky, M., Robinson, P.N., Sincan, M., Soldatos, A.G., Tifft, C.J., Toro, C., Trang, H., Valkanas, E., Vasilevsky, N., Wahl, C., Wolfe, L.A., Boerkoel, C.F., Brudno, M., Haendel, M.A., Gahl, W.A., Smedley, D. (2016) Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genetics in medicine : official journal of the American College of Medical Genetics. 18:608-17.
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.Genet Med 18 6, 608-617.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping