Determining the pathogenicity of CFTR missense variants : multiple comparisons of in silico predictors and variant annotation databases
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , |
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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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 |
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2020-02-01T04:14:29Z |
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1415-4757 |
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001107880 |
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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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info:eu-repo/semantics/openAccess |
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