scholarly journals Extensive genic and allelic heterogeneity underlying inherited retinal dystrophies in Mexican patients molecularly analyzed by next‐generation sequencing

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Juan C. Zenteno ◽  
Leopoldo A. García‐Montaño ◽  
Marisa Cruz‐Aguilar ◽  
Josué Ronquillo ◽  
Agustín Rodas‐Serrano ◽  
...  
2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Isabella Bernardis ◽  
Laura Chiesi ◽  
Elena Tenedini ◽  
Lucia Artuso ◽  
Antonio Percesepe ◽  
...  

To assess the clinical utility of targeted Next-Generation Sequencing (NGS) for the diagnosis of Inherited Retinal Dystrophies (IRDs), a total of 109 subjects were enrolled in the study, including 88 IRD affected probands and 21 healthy relatives. Clinical diagnoses included Retinitis Pigmentosa (RP), Leber Congenital Amaurosis (LCA), Stargardt Disease (STGD), Best Macular Dystrophy (BMD), Usher Syndrome (USH), and other IRDs with undefined clinical diagnosis. Participants underwent a complete ophthalmologic examination followed by genetic counseling. A custom AmpliSeq™ panel of 72 IRD-related genes was designed for the analysis and tested using Ion semiconductor Next-Generation Sequencing (NGS). Potential disease-causing mutations were identified in 59.1% of probands, comprising mutations in 16 genes. The highest diagnostic yields were achieved for BMD, LCA, USH, and STGD patients, whereas RP confirmed its high genetic heterogeneity. Causative mutations were identified in 17.6% of probands with undefined diagnosis. Revision of the initial diagnosis was performed for 9.6% of genetically diagnosed patients. This study demonstrates that NGS represents a comprehensive cost-effective approach for IRDs molecular diagnosis. The identification of the genetic alterations underlying the phenotype enabled the clinicians to achieve a more accurate diagnosis. The results emphasize the importance of molecular diagnosis coupled with clinic information to unravel the extensive phenotypic heterogeneity of these diseases.


2017 ◽  
Vol 1 (Special Issue) ◽  
pp. 91-91
Author(s):  
Rajeshwari Patil ◽  
Poornachandra Poornachandra ◽  
Nallathambi Jeyabalan ◽  
Arkasubhra Ghosh ◽  
Anuprita Ghosh

2020 ◽  
Vol 8 (11) ◽  
Author(s):  
Alessandra Carnevale ◽  
Sandra Rosas‐Madrigal ◽  
Rigoberto Rosendo‐Gutiérrez ◽  
Enrique López‐Mora ◽  
Sandra Romero‐Hidalgo ◽  
...  

2020 ◽  
Vol 83 ◽  
pp. 102423 ◽  
Author(s):  
Laura Villarreal-Martínez ◽  
Marisol Ibarra-Ramirez ◽  
Geovana Calvo-Anguiano ◽  
José de Jesús Lugo-Trampe ◽  
Hilda Luna-Záizar ◽  
...  

2017 ◽  
Vol 55 (2) ◽  
pp. 114-121 ◽  
Author(s):  
Jamie M Ellingford ◽  
Bradley Horn ◽  
Christopher Campbell ◽  
Gavin Arno ◽  
Stephanie Barton ◽  
...  

BackgroundDiagnostic use of gene panel next-generation sequencing (NGS) techniques is commonplace for individuals with inherited retinal dystrophies (IRDs), a highly genetically heterogeneous group of disorders. However, these techniques have often failed to capture the complete spectrum of genomic variation causing IRD, including CNVs. This study assessed the applicability of introducing CNV surveillance into first-tier diagnostic gene panel NGS services for IRD.MethodsThree read-depth algorithms were applied to gene panel NGS data sets for 550 referred individuals, and informatics strategies used for quality assurance and CNV filtering. CNV events were confirmed and reported to referring clinicians through an accredited diagnostic laboratory.ResultsWe confirmed the presence of 33 deletions and 11 duplications, determining these findings to contribute to the confirmed or provisional molecular diagnosis of IRD for 25 individuals. We show that at least 7% of individuals referred for diagnostic testing for IRD have a CNV within genes relevant to their clinical diagnosis, and determined a positive predictive value of 79% for the employed CNV filtering techniques.ConclusionIncorporation of CNV analysis increases diagnostic yield of gene panel NGS diagnostic tests for IRD, increases clarity in diagnostic reporting and expands the spectrum of known disease-causing mutations.


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