Reducing Sanger confirmation testing through false positive prediction algorithms.
Genet Med
; 23(7): 1255-1262, 2021 07.
Article
en En
| MEDLINE
| ID: mdl-33767343
ABSTRACT
PURPOSE:
Clinical genome sequencing (cGS) followed by orthogonal confirmatory testing is standard practice. While orthogonal testing significantly improves specificity, it also results in increased turnaround time and cost of testing. The purpose of this study is to evaluate machine learning models trained to identify false positive variants in cGS data to reduce the need for orthogonal testing.METHODS:
We sequenced five reference human genome samples characterized by the Genome in a Bottle Consortium (GIAB) and compared the results with an established set of variants for each genome referred to as a truth set. We then trained machine learning models to identify variants that were labeled as false positives.RESULTS:
After training, the models identified 99.5% of the false positive heterozygous single-nucleotide variants (SNVs) and heterozygous insertions/deletions variants (indels) while reducing confirmatory testing of nonactionable, nonprimary SNVs by 85% and indels by 75%. Employing the algorithm in clinical practice reduced overall orthogonal testing using dideoxynucleotide (Sanger) sequencing by 71%.CONCLUSION:
Our results indicate that a low false positive call rate can be maintained while significantly reducing the need for confirmatory testing. The framework that generated our models and results is publicly available at https//github.com/HudsonAlpha/STEVE .
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Genoma Humano
/
Secuenciación de Nucleótidos de Alto Rendimiento
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Genet Med
Asunto de la revista:
GENETICA MEDICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos