Format

Send to

Choose Destination
Genet Med. 2016 Jun;18(6):608-17. doi: 10.1038/gim.2015.137. Epub 2015 Nov 12.

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

Author information

1
Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland, USA.
2
Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
3
Centre for Computational Medicine Hospital for Sick Children, Toronto, Ontario, Canada.
4
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
5
Medical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, USA.
6
Skarnes Faculty group, Wellcome Trust Sanger Institute, Hinxton, UK.
7
Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.
8
Library; and Department of Medical Informatics and Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.

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.

PMID:
26562225
PMCID:
PMC4916229
DOI:
10.1038/gim.2015.137
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center