Molecular diagnosis of neurofibromatosis type 1: 2 years experience
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Our experience of providing an NF1 gene diagnostic mutation detection service as part of the UK Genetic Testing Network (UKGTN) is presented. A total of 169 unrelated individuals suspected of having neurofibromatosis type I (NF1) were referred for NF1 diagnostic testing over a 2 year period. Mutation analysis of the entire NF1 coding region and the flanking splice sites was carried out, and included the use of a combination of FISH, dHPLC and MLPA. Possible disease causing mutations were identified in 109 (64%) cases. These comprised 88 different sequence alterations, of which 57 were novel. Out of the 169 cases referred, there were 102 patients with reliable clinical data, of whom 78 satisfied the NIH diagnostic criteria for NF1. Within this better defined cohort of NF1 patients, NF1 mutations were identified in 61 individuals (78%), showing the importance of clinical selection on overall test sensitivity, and highlighting the problem of full clinical data collection in the audit of routine services. As mutation detection technologies advance, facilitating direct sequencing of all coding and flanking non-coding regions of the NF1 gene, the development of an even more cost-effective, quick and sensitive diagnostic test for future testing of NF1 is discussed.
KeywordsDenaturing High Performance Liquid Chromatography (dHPLC) Fluorescence In-Situ Hybridisation (FISH) Multiplex Ligation-Dependent Probe Amplification (MLPA) Mutation Neurofibromatosis Type 1 (NF1) Polymerase chain reaction (PCR) Polymorphism Sequencing UK Genetic Testing Network (UKGTN)
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We thank all the clinicians for their help in providing the clinical information. We are grateful to the Wales Gene Park for their financial support, and the staff of the Molecular Genetics Laboratory of the All-Wales Genetics Laboratory Service. We thank Dr Nick Thomas for his help with the splice prediction program.We wish to thank Dr Susan Huson for her support in initially setting up a diagnostic service, Linda Selwood for her administrative support and Andrew Wallace for developing MLPA data analysis spreadsheets.
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