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Pathogenic variants in the Cystic Fibrosis Transmembrane Conductance Regulator gene ( CFTR ) are responsible for cystic fibrosis (CF), the commonest monogenic autosomal recessive disease, and  CFTR -related disorders in infants and youth. Diagnosis of such diseases relies on clinical, functional, and molecular studies. To date, over 2,000 variants have been described on  CFTR  (~40% missense). Since few of them have confirmed pathogenicity,  in silico  analysis could help molecular diagnosis and genetic counseling. Here, the pathogenicity of 779  CFTR  missense variants was predicted by consensus predictor PredictSNP and compared to annotations on CFTR2 and ClinVar. Sensitivity and specificity analysis was divided into modeling and validation phases using just variants annotated on CFTR2 and/or ClinVar that were not in the validation datasets of the analyzed predictors. After validation phase, MAPP and PhDSNP achieved maximum specificity but low sensitivity. Otherwise, SNAP had maximum sensitivity but null specificity. PredictSNP, PolyPhen-1, PolyPhen-2, SIFT, nsSNPAnalyzer had either low sensitivity or specificity, or both. Results showed that most predictors were not reliable when analyzing  CFTR  missense variants, ratifying the importance of clinical information when asserting the pathogenicity of  CFTR  missense variants. Our results should contribute to clarify decision making when classifying the pathogenicity of  CFTR  missense variants.

Determining the pathogenicity of CFTR missense variants: Multiple comparisons of in silico predictors and variant annotation databases