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Abstract Next-generation sequencing (NGS) has altered clinical genetic testing by widening the access to molecular diagnosis of genetically determined rare diseases. However, physicians may face difficulties selecting the best diagnostic approach. Our goal is to estimate the rate of possible molecular diagnoses missed by different targeted gene panels using data from a cohort of patients with rare genetic diseases diagnosed with exome sequencing (ES). For this purpose, we simulated a comparison between different targeted gene panels and ES: the list of genes harboring clinically relevant variants from 158 patients was used to estimate the theoretical rate of diagnoses missed by NGS panels from 53 different NGS panels from eight different laboratories. Panels presented a mean rate of missed diagnoses of 64% (range 14%-100%) compared to ES, representing an average predicted sensitivity of 36%. Metabolic abnormalities represented the group with highest mean of missed diagnoses (86%), while seizure represented the group with lowest mean (46%). Focused gene panels are restricted in covering select sets of genes implicated in specific diseases and they may miss molecular diagnoses of rare diseases compared to ES. However, their role in genetic diagnosis remains important especially for well-known genetic diseases with established genetic locus heterogeneity.

Index code	Element
ti	article title
au	author
kw	article keywords
subject	subject (title words, abstract and keywords)
ab	abstract
ta	journal short title (e.g. Cad. Saúde Pública)
journal_title	journal full title (e.g. Cadernos de Saúde Pública)
la	publication language code (e.g. pt - Portuguese, es - Spanish)
type	document type
pid	publication identifier
publication_year	publication year of publication
sponsor	sponsor
aff_country	country code of the author's affiliation
aff_institution	author affiliation institution
volume	article volume
issue	article issue
elocation	elocation
doi	DOI number
issn	journal ISSN
in	SciELO colection code (e.g. scl - Brasil, col - Colômbia)
use_license	article usage license code

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