Annotating pathogenic non-coding variants in genic regions.

Published

Journal Article

Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. High TraP scores single out extremely rare variants with lower minor allele frequencies than missense variants. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP analysis of 843 exomes from epilepsy family trios identifies synonymous variants in known epilepsy genes, thus pinpointing risk factors of disease from non-coding sequence data. TraP outperforms leading methods in identifying non-coding variants that are pathogenic and is therefore a valuable tool for use in gene discovery and the interpretation of personal genomes.While non-coding synonymous and intronic variants are often not under strong selective constraint, they can be pathogenic through affecting splicing or transcription. Here, the authors develop a score that uses sequence context alterations to predict pathogenicity of synonymous and non-coding genetic variants, and provide a web server of pre-computed scores.

Full Text

Cited Authors

  • Gelfman, S; Wang, Q; McSweeney, KM; Ren, Z; La Carpia, F; Halvorsen, M; Schoch, K; Ratzon, F; Heinzen, EL; Boland, MJ; Petrovski, S; Goldstein, DB

Published Date

  • August 9, 2017

Published In

Volume / Issue

  • 8 / 1

Start / End Page

  • 236 -

PubMed ID

  • 28794409

Pubmed Central ID

  • 28794409

Electronic International Standard Serial Number (EISSN)

  • 2041-1723

International Standard Serial Number (ISSN)

  • 2041-1723

Digital Object Identifier (DOI)

  • 10.1038/s41467-017-00141-2

Language

  • eng