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

Detalhes bibliográficos
Autor(a) principal: Michels, Marcus Silva
Data de Publicação: 2019
Outros Autores: Matte, Ursula da Silveira, Fraga, Lucas Rosa, Mancuso, Aline Castello Branco, Braun, Rodrigo Ligabue, Berneira, Elias Figueroa Rodrigues, Siebert, Marina, Sanseverino, Maria Teresa Vieira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/205299
Resumo: 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.
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spelling Michels, Marcus SilvaMatte, Ursula da SilveiraFraga, Lucas RosaMancuso, Aline Castello BrancoBraun, Rodrigo LigabueBerneira, Elias Figueroa RodriguesSiebert, MarinaSanseverino, Maria Teresa Vieira2020-02-01T04:14:29Z20191415-4757http://hdl.handle.net/10183/205299001107880Pathogenic 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.application/pdfengGenetics and molecular biology. Ribeirão Preto. Vol. 42, no. 3 (2019), p. 560-570Fibrose císticaBiomarcadoresPrognósticoRegulador de condutância transmembrana em fibrose císticaBiologia computacionalRNA mensageiroCFTRMissense variantPredictionBioinformaticsCystic fibrosisDetermining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databasesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001107880.pdf.txt001107880.pdf.txtExtracted Texttext/plain50805http://www.lume.ufrgs.br/bitstream/10183/205299/2/001107880.pdf.txt3b4c6692db542f64f95457660cd945e2MD52ORIGINAL001107880.pdfTexto completo (inglês)application/pdf1823614http://www.lume.ufrgs.br/bitstream/10183/205299/1/001107880.pdfb8984bc8536078724cb026651b6d38beMD5110183/2052992023-11-15 04:26:12.927476oai:www.lume.ufrgs.br:10183/205299Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-11-15T06:26:12Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Determining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databases
title Determining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databases
spellingShingle Determining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databases
Michels, Marcus Silva
Fibrose cística
Biomarcadores
Prognóstico
Regulador de condutância transmembrana em fibrose cística
Biologia computacional
RNA mensageiro
CFTR
Missense variant
Prediction
Bioinformatics
Cystic fibrosis
title_short Determining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databases
title_full Determining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databases
title_fullStr Determining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databases
title_full_unstemmed Determining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databases
title_sort Determining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databases
author Michels, Marcus Silva
author_facet Michels, Marcus Silva
Matte, Ursula da Silveira
Fraga, Lucas Rosa
Mancuso, Aline Castello Branco
Braun, Rodrigo Ligabue
Berneira, Elias Figueroa Rodrigues
Siebert, Marina
Sanseverino, Maria Teresa Vieira
author_role author
author2 Matte, Ursula da Silveira
Fraga, Lucas Rosa
Mancuso, Aline Castello Branco
Braun, Rodrigo Ligabue
Berneira, Elias Figueroa Rodrigues
Siebert, Marina
Sanseverino, Maria Teresa Vieira
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Michels, Marcus Silva
Matte, Ursula da Silveira
Fraga, Lucas Rosa
Mancuso, Aline Castello Branco
Braun, Rodrigo Ligabue
Berneira, Elias Figueroa Rodrigues
Siebert, Marina
Sanseverino, Maria Teresa Vieira
dc.subject.por.fl_str_mv Fibrose cística
Biomarcadores
Prognóstico
Regulador de condutância transmembrana em fibrose cística
Biologia computacional
RNA mensageiro
topic Fibrose cística
Biomarcadores
Prognóstico
Regulador de condutância transmembrana em fibrose cística
Biologia computacional
RNA mensageiro
CFTR
Missense variant
Prediction
Bioinformatics
Cystic fibrosis
dc.subject.eng.fl_str_mv CFTR
Missense variant
Prediction
Bioinformatics
Cystic fibrosis
description 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.
publishDate 2019
dc.date.issued.fl_str_mv 2019
dc.date.accessioned.fl_str_mv 2020-02-01T04:14:29Z
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dc.identifier.issn.pt_BR.fl_str_mv 1415-4757
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dc.relation.ispartof.pt_BR.fl_str_mv Genetics and molecular biology. Ribeirão Preto. Vol. 42, no. 3 (2019), p. 560-570
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eu_rights_str_mv openAccess
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