Communicated by Rachel Karchin
A Classification Model Relative to Splicing for Variants of Unknown Clinical Significance: Application to the CFTR Gene
Article first published online: 5 APR 2013
© 2013 Wiley Periodicals, Inc.
Volume 34, Issue 5, pages 774–784, May 2013
How to Cite
Raynal, C., Baux, D., Theze, C., Bareil, C., Taulan, M., Roux, A.-F., Claustres, M., Tuffery-Giraud, S. and des Georges, M. (2013), A Classification Model Relative to Splicing for Variants of Unknown Clinical Significance: Application to the CFTR Gene. Hum. Mutat., 34: 774–784. doi: 10.1002/humu.22291
Additional Supporting Information may be found in the online version of this article.
- Issue published online: 11 APR 2013
- Article first published online: 5 APR 2013
- Accepted manuscript online: 5 FEB 2013 07:26AM EST
- Manuscript Accepted: 29 JAN 2013
- Manuscript Received: 24 SEP 2012
- decision flowchart;
Molecular diagnosis of cystic fibrosis and cystic fibrosis transmembrane regulator (CFTR)-related disorders led to the worldwide identification of nearly 1,900 sequence variations in the CFTR gene that consist mainly of private point mutations and small insertions/deletions. Establishing their effect on the function of the encoded protein and therefore their involvement in the disease is still challenging and directly impacts genetic counseling. In this context, we built a decision tree following the international guidelines for the classification of variants of unknown clinical significance (VUCS) in the CFTR gene specifically focused on their consequences on splicing. We applied general and specific criteria, including comprehensive review of literature and databases, familial genetics data, and thorough in silico studies. This model was tested on 15 intronic and exonic VUCS identified in our cohort. Six variants were classified as probably nonpathogenic considering their impact on splicing and eight as probably pathogenic, which include two apparent missense mutations. We assessed the validity of our method by performing minigenes studies and confirmed that 93% (14/15) were correctly classified. We provide in this study a high-performance method that can play a full role in interpreting the results of molecular diagnosis in emergency context, when functional studies are not achievable.