RESUMEN
MutationTaster is a widely used web-based tool that predicts the functional impact of genetic variants. In recent years, the software has undergone significant improvements, leading to the development of MutationTaster2 and MutationTaster2021. The main difference between these two versions is the use of updated reference datasets and an improved algorithm for variant classification. MutationTaster2 utilizes the dbNSFP database, while MutationTaster2021 incorporates gnomAD and ClinVar data. Both versions employ a machine learning approach that combines multiple features to predict variant pathogenicity, including evolutionary conservation, physical properties of amino acid changes, and the potential effect on protein function. The output of MutationTaster is a score indicating the likelihood of a variant being disease causing, with a high score indicating a high likelihood of pathogenicity. Overall, MutationTaster2 and MutationTaster2021 represent valuable tools for researchers and clinicians in the field of genetic variant analysis, providing accurate and efficient predictions of variant pathogenicity.
MutationTaster es una herramienta web ampliamente utilizada que predice el impacto funcional de las variantes genéticas. En los últimos años, el software ha experimentado mejoras significativas, lo que ha llevado al desarrollo de MutationTaster2 y MutationTaster2021. La principal diferencia entre estas dos versiones es el uso de conjuntos de datos de referencia actualizados y un algoritmo mejorado para la clasificación de variantes. MutationTaster2 utiliza la base de datos dbNSFP, mientras que MutationTaster2021 incorpora datos de gnomAD y ClinVar. Ambas versiones emplean un enfoque de aprendizaje automático que combina múltiples características para predecir la patogenicidad variante, incluida la conservación evolutiva, las propiedades físicas de los cambios de aminoácidos y el efecto potencial en la función de la proteína. El resultado de MutationTaster es una puntuación que indica la probabilidad de que una variante cause una enfermedad; una puntuación alta indica una alta probabilidad de patogenicidad. En general, MutationTaster2 y MutationTaster2021 representan herramientas valiosas para investigadores y médicos en el campo del análisis de variantes genéticas, ya que proporcionan predicciones precisas y eficientes de la patogenicidad de variantes.
RESUMEN
BACKGROUND: In this study, the prevalence of different types of mucopolysaccharidoses (MPS) was estimated based on data from the exome aggregation consortium (ExAC) and the genome aggregation database (gnomAD). The population-based allele frequencies were used to identify potential disease-causing variants on each gene related to MPS I to IX (except MPS II). METHODS: We evaluated the canonical transcripts and excluded homozygous, intronic, 3', and 5' UTR variants. Frameshift and in-frame insertions and deletions were evaluated using the SIFT Indel tool. Splice variants were evaluated using SpliceAI and Human Splice Finder 3.0 (HSF). Loss-of-function single nucleotide variants in coding regions were classified as potentially pathogenic, while synonymous variants outside the exon-intron boundaries were deemed non-pathogenic. Missense variants were evaluated by five in silico prediction tools, and only those predicted to be damaging by at least three different algorithms were considered disease-causing. RESULTS: The combined frequencies of selected variants (ranged from 127 in GNS to 259 in IDUA) were used to calculate prevalence based on Hardy-Weinberg's equilibrium. The maximum estimated prevalence ranged from 0.46 per 100,000 for MPSIIID to 7.1 per 100,000 for MPS I. Overall, the estimated prevalence of all types of MPS was higher than what has been published in the literature. This difference may be due to misdiagnoses and/or underdiagnoses, especially of the attenuated forms of MPS. However, overestimation of the number of disease-causing variants by in silico predictors cannot be ruled out. Even so, the disease prevalences are similar to those reported in diagnosis-based prevalence studies. CONCLUSION: We report on an approach to estimate the prevalence of different types of MPS based on publicly available population-based genomic data, which may help health systems to be better prepared to deal with these conditions and provide support to initiatives on diagnosis and management of MPS.