Annotating pathogenic non-coding variants in genic regions

Sahar Gelfman (), Quanli Wang, K. Melodi McSweeney, Zhong Ren, Francesca La Carpia, Matt Halvorsen, Kelly Schoch, Fanni Ratzon, Erin L. Heinzen, Michael J. Boland, Slavé Petrovski and David B. Goldstein
Additional contact information
Sahar Gelfman: Columbia University Medical Center
Quanli Wang: Columbia University Medical Center
K. Melodi McSweeney: Columbia University Medical Center
Zhong Ren: Columbia University Medical Center
Francesca La Carpia: Columbia University Medical Center
Matt Halvorsen: Columbia University Medical Center
Kelly Schoch: Duke University Health System
Fanni Ratzon: Lenox Hill Hospital
Erin L. Heinzen: Columbia University Medical Center
Michael J. Boland: Columbia University Medical Center
Slavé Petrovski: Columbia University Medical Center
David B. Goldstein: Columbia University Medical Center

Nature Communications, 2017, vol. 8, issue 1, 1-11

Abstract: Abstract 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.

Date: 2017
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DOI: 10.1038/s41467-017-00141-2

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