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Genet Med. 2015 Oct;17(10):774-81. doi: 10.1038/gim.2014.191. Epub 2015 Jan 15.

Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios.

Author information

1
Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina, USA.
2
Department of Medicine, University of Melbourne, Austin Health and Royal Melbourne Hospital, Melbourne, Australia.
3
Present address: Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
4
Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Ramat Gan, Israel.
5
Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
6
Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel.
7
Present address: Department of Human Genetics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
8
Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA.
9
Pinchas Borenstein Talpiot Medical Leadership Program, Pediatric Neurology Unit, Chaim Sheba Medical Center, Tel HaShomer, Israel.
10
Metabolic Disease Unit, Edmond and Lily Children's Hospital, Sheba Medical Center, Ramat Gan, Israel.
11
Department of Neurobiology, Duke University, Durham, North Carolina, USA.
12
Danek Gertner Institute of Human Genetics, Sheba Medical Center, Ramat Gan, Israel.
13
UCB NewMedicines, Slough, UK.
14
Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK.

Abstract

PURPOSE:

Despite the recognized clinical value of exome-based diagnostics, methods for comprehensive genomic interpretation remain immature. Diagnoses are based on known or presumed pathogenic variants in genes already associated with a similar phenotype. Here, we extend this paradigm by evaluating novel bioinformatics approaches to aid identification of new gene-disease associations.

METHODS:

We analyzed 119 trios to identify both diagnostic genotypes in known genes and candidate genotypes in novel genes. We considered qualifying genotypes based on their population frequency and in silico predicted effects we also characterized the patterns of genotypes enriched among this collection of patients.

RESULTS:

We obtained a genetic diagnosis for 29 (24%) of our patients. We showed that patients carried an excess of damaging de novo mutations in intolerant genes, particularly those shown to be essential in mice (P = 3.4 × 10(-8)). This enrichment is only partially explained by mutations found in known disease-causing genes.

CONCLUSION:

This work indicates that the application of appropriate bioinformatics analyses to clinical sequence data can also help implicate novel disease genes and suggest expanded phenotypes for known disease genes. These analyses further suggest that some cases resolved by whole-exome sequencing will have direct therapeutic implications.

PMID:
25590979
PMCID:
PMC4791490
DOI:
10.1038/gim.2014.191
[Indexed for MEDLINE]
Free PMC Article

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