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

Abstract

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.

CFTR Modulators: The Changing Face of Cystic Fibrosis in the Era of Precision Medicine

“…To date, 432 of these variants have been annotated in the Clinical and Functional Translation of CFTR (CFTR2 Database), of which 352 variants have confirmed disease liability and 46 variants have demonstrated variable clinical consequences. Many other variants are still uncharacterized, and in silico tools may be useful to predict the cellular and molecular consequences caused by these alterations (Michels et al, 2019;Pereira et al, 2019), while they are not confirmed in cell models. Some variants may also be pathogenic when two or more mutations are in cis, i.e., complex allele, thus contributing to the variable clinical phenotypes (Lucarelli et al, 2010;Diana et al, 2016;Pereira et al, 2019) and responsiveness to CFTR modulator therapies.…”

Genome interpretation using in silico predictors of variant impact

“…The performance of variant impact prediction methods is hard to assess unambiguously. Independent studies (Chan et al 2007 ; Dong et al 2015 ; Ghosh et al 2017 ; Gunning et al 2020 ; Leong et al 2015 ; Li et al 2018 ; Livesey and Marsh 2020 ; Michels et al 2019 ; Miosge et al 2015 ; Suybeng et al 2020 ; Tian et al 2019 ; Yadegari and Majidzadeh 2019 ) have compared a limited number of methods each, using specific sets of variants and evaluation tests, but ignoring potential training circularities, and making cutoff assumptions that may not fit each method equally well. In contrast, efforts to systematically and objectively assess variant impact prediction methods come from the Critical Assessment of Genome Interpretation (CAGI) community.…”

Conflito de interpretação e desequilíbrio de ligação em variantes en-contradas nos genes BRCA1 e BRCA2

“…Além disso, as variantes encontradas no ClinVar, podem ter suas interpretações complementadas por meio da análise in silico de outras ferramentas, para um melhor entendimento do comportamento de estruturas moleculares dentro de um sistema de componentes interativo (Michels et al, 2019).…”