Annotating pathogenic non-coding variants in genic regions
AuthorGelfman, S; Wang, Q; McSweeney, KM; Ren, Z; La Carpia, F; Halvorsen, M; Schoch, K; Ratzon, F; Heinzen, EL; Boland, MJ; ...
Source TitleNature Communications
PublisherNATURE PUBLISHING GROUP
University of Melbourne Author/sPetrovski, Slave
AffiliationMedicine and Radiology
Document TypeJournal Article
CitationsGelfman, S., Wang, Q., McSweeney, K. M., Ren, Z., La Carpia, F., Halvorsen, M., Schoch, K., Ratzon, F., Heinzen, E. L., Boland, M. J., Petrovski, S. & Goldstein, D. B. (2017). Annotating pathogenic non-coding variants in genic regions. NATURE COMMUNICATIONS, 8 (1), https://doi.org/10.1038/s41467-017-00141-2.
Access StatusOpen Access
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.
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