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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Zhu, X; Petrovski, S; Xie, P; Ruzzo, EK; Lu, Y-F; McSweeney, KM; Ben-Zeev, B; Nissenkorn, A; Anikster, Y; Oz-Levi, D; Dhindsa, RS; Hitomi, Y; Schoch, K; Spillmann, RC; Heimer, G; Marek-Yagel, D; Tzadok, M; Han, Y; Worley, G; Goldstein, J; Jiang, Y-H; Lancet, D; Pras, E; Shashi, V; McHale, D; Need, AC; Goldstein, DB

Published Date

  • October 2015

Published In

Volume / Issue

  • 17 / 10

Start / End Page

  • 774 - 781

PubMed ID

  • 25590979

Pubmed Central ID

  • PMC4791490

Electronic International Standard Serial Number (EISSN)

  • 1530-0366

Digital Object Identifier (DOI)

  • 10.1038/gim.2014.191


  • eng

Conference Location

  • United States