Determining the pathogenicity of CFTR missense variants: Multiple comparisons of in silico predictors and variant annotation databases.
Marcus Silva MichelsUrsula MatteLucas Rosa FragaAline Castello Branco MancusoRodrigo Ligabue-BraunElias Figueroa Rodrigues BerneiraMarina SiebertMaria Teresa Vieira SanseverinoPublished in: Genetics and molecular biology (2019)
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.
Keyphrases
- cystic fibrosis
- copy number
- pseudomonas aeruginosa
- lung function
- intellectual disability
- biofilm formation
- genome wide
- decision making
- autism spectrum disorder
- mental health
- physical activity
- rna seq
- staphylococcus aureus
- deep learning
- hepatitis c virus
- chronic obstructive pulmonary disease
- smoking cessation
- molecular dynamics simulations
- structural basis
- candida albicans
- hiv testing
- muscular dystrophy
- duchenne muscular dystrophy