Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI.
Hum Mutat
; 38(9): 1042-1050, 2017 09.
Article
en En
| MEDLINE
| ID: mdl-28440912
ABSTRACT
Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Variación Genética
/
Biología Computacional
/
Inhibidor p18 de las Quinasas Dependientes de la Ciclina
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Hum Mutat
Asunto de la revista:
GENETICA MEDICA
Año:
2017
Tipo del documento:
Article
País de afiliación:
Italia