Targeted polymerase chain reaction-based enrichment and next generation sequencing for diagnostic testing of congenital disorders of glycosylation

• Published in: Genetics in medicine Vol. 13; no. 11; pp. 921 - 932
• Main Authors: Jones, Melanie A, Bhide, Shruti, Chin, Ephrem, Ng, Bobby G, Rhodenizer, Devin, Zhang, Victor W, Sun, Jessica J, Tanner, Alice, Freeze, Hudson H, Hegde, Madhuri R
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.11.2011; Elsevier Limited
• Subjects: 631/208/2489/144; 631/208/514/2254; 692/700/139/1512; Base Sequence; Biomedical and Life Sciences; Biomedicine; Congenital diseases; Congenital Disorders of Glycosylation - diagnosis; Congenital Disorders of Glycosylation - genetics; DNA Mutational Analysis - methods; Genes; Genetic Predisposition to Disease - genetics; Human Genetics; Humans; Laboratory Medicine; Mutation; Polymerase Chain Reaction - methods; Reproducibility of Results; Sensitivity and Specificity; Sequence Analysis, DNA - methods; Time Factors; target enrichment; next generation sequencing; molecular diagnostic testing; bioinformatics; congenital disorders of glycosylation
• ISSN: 1098-3600; 1530-0366
• DOI: 10.1097/GIM.0b013e318226fbf2

Purpose: Congenital disorders of glycosylation are a heterogeneous group of disorders caused by deficient glycosylation, primarily affecting the N-linked pathway. It is estimated that more than 40% of congenital disorders of glycosylation patients lack a confirmatory molecular diagnosis. The purpose of this study was to improve molecular diagnosis for congenital disorders of glycosylation by developing and validating a next generation sequencing panel for comprehensive mutation detection in 24 genes known to cause congenital disorders of glycosylation. Methods: Next generation sequencing validation was performed on 12 positive control congenital disorders of glycosylation patients. These samples were blinded as to the disease-causing mutations. Both RainDance and Fluidigm platforms were used for sequence enrichment and targeted amplification. The SOLiD platform was used for sequencing the amplified products. Bioinformatic analysis was performed using NextGENe® software. Results: The disease-causing mutations were identified by next generation sequencing for all 12 positive controls. Additional variants were also detected in three controls that are known or predicted to impair gene function and may contribute to the clinical phenotype. Conclusions: We conclude that development of next generation sequencing panels in the diagnostic laboratory where multiple genes are implicated in a disorder is more cost-effective and will result in improved and faster patient diagnosis compared with a gene-by-gene approach. Recommendations are also provided for data analysis from the next generation sequencing-derived data in the clinical laboratory, which will be important for the widespread use of this technology.