Targeted PCR-based enrichment and next generation sequencing for diagnostic testing of congenital disorders of glycosylation (CDG)

• Type of Material: Article
• Authors: Melanie A. Jones, Emory University; Shruti Bhide, Emory University; Ephrem Chin, Emory University; Bobby G. Ng, Sanford-Burnham Medical Research Institute; Devin Rhodenizer, Emory University; Victor W. Zhang, Emory University; Jessica J. Sun, Emory University; Alice Tanner, Emory University; Hudson H. Freeze, Sanford-Burnham Medical Research Institute; Madhuri Hegde, Emory University
• Language: English
• Date: 2011-11
• Publisher: Nature Publishing Group: Open Access Hybrid Model Option B
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2013 American College of Medical Genetics and Genomics
• Final Published Version (URL): http://dx.doi.org/10.1097%2FGIM.0b013e318226fbf2
• Title of Journal or Parent Work: Genetics in Medicine
• ISSN: 1098-3600
• Volume: 13
• Issue: 11
• Start Page: 921
• End Page: 932
• Grant/Funding Information: This work was supported by grants from NIH RC1NS 069541-01, MDA G6396330, 1T32MH087977 Training Program in Human Disease Genetics, and The Rocket Williams Fund and a Sanford Professorship to Dr. Freeze. This research was supported in part by PHS Grant (UL1 RR025008, KL2 R0025009, or TL1 RR025010) from the Clinical and Translational Science Award Program, National Institutes of Health, National Center for Research Resources.
• Keywords: bioinformatics; congenital disorders of glycosylation; next generation sequencing; molecular diagnostic testing; target enrichment
• Research Categories: Biology, Genetics

Abstract

Purpose Congenital disorders of glycosylation (CDG) are a heterogeneous group of disorders caused by deficient glycosylation, primarily affecting the N-linked pathway. It is estimated that over 40% of CDG patients lack a confirmatory molecular diagnosis. The purpose of this study was to improve molecular diagnosis for CDG by developing and validating a next generation sequencing (NGS) panel for comprehensive mutation detection in 24 genes known to cause CDG. Methods NGS validation was performed on 12 positive control CDG 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 NGS 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 NGS 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 NGS-derived data in the clinical laboratory, which will be important for the widespread use of this technology.