RT Journal Article
SR Electronic
T1 VarSight: Prioritizing Clinically Reported Variants with Binary Classification Algorithms
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 532440
DO 10.1101/532440
A1 James M. Holt
A1 Brandon Wilk
A1 Camille L. Birch
A1 Donna M. Brown
A1 Manavalan Gajapathy
A1 Alexander C. Moss
A1 Nadiya Sosonkina
A1 Melissa A. Wilk
A1 Julie A. Anderson
A1 Jeremy M. Harris
A1 Jacob M. Kelly
A1 Fariba Shaterferdosian
A1 Angelina E. Uno-Antonison
A1 Arthur Weborg
A1 Undiagnosed Diseases Network
A1 Elizabeth A. Worthey
YR 2019
UL http://biorxiv.org/content/early/2019/01/28/532440.abstract
AB Motivation In genomic medicine for rare disease patients, the primary goal is to identify one or more variants that cause their disease. Typically, this is done through filtering and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.Results We tested the application of classification algorithms that ingest variant predictions along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. We treated the classifiers as variant prioritization systems and compared them to another variant prioritization algorithm and two single-measure controls. We showed that these classifiers outperformed the other methods with the best classifier ranking 73% of all reported variants and 97% of reported pathogenic variants in the top 20.Availability The scripts used to generate results presented in this paper are available at https://github.com/HudsonAlpha/VarSight.Contact jholt{at}hudsonalpha.org