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Human Genetics

, Volume 106, Issue 6, pp 663–668 | Cite as

Application and evaluation of denaturing HPLC for molecular genetic analysis in tuberous sclerosis

  • Alistair C. Jones
  • Julian R. Sampson
  • Bastian Hoogendoorn
  • David Cohen
  • Jeremy P. Cheadle
Original Investigation

Abstract.

Tuberous sclerosis (TSC) is an autosomal dominant disorder characterised by the development of hamartomas in multiple tissues and organs. TSC exhibits locus heterogeneity with genes at 9q34 (TSC1) and 16p13.3 (TSC2) that have 21 and 41 coding exons, respectively. The mutational spectrum at both loci is wide and previous studies have shown that 60%–70% of cases are sporadic and represent new mutations. We have formatted denaturing high performance liquid chromatography (DHPLC) for rapid screening of all coding exons of TSC1 and TSC2. DHPLC analysis detected likely disease-causing mutations in 103 of 150 unrelated cases (68%), compared with 92/150 (61%) and 87/150 (58%) for single-strand conformation polymorphism analysis (SSCP) and conventional heteroduplex analysis (HA), respectively. Capital, consumable and labour costs were determined for each exon screening procedure. Estimated costs per patient sample depended on throughput, particularly for DHPLC, where a high proportion of costs are fixed, and were £257, £216 and £242 for DHPLC, SSCP and HA, respectively, assuming a throughput of 252 samples per year, or £354, £233 and £259, assuming a throughput of 126 samples per year. DHPLC had the advantages of increased sensitivity and reduced labour costs when compared with more traditional approaches to exon screening but, unless expensive DHPLC equipment is being efficiently utilised for a very high proportion of the time available, overall costs are slightly higher.

Keywords

Tuberous Sclerosis Tuberous Sclerosis Complex Denature High Performance Liquid Chromatography TSC2 Gene Heteroduplex Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2000

Authors and Affiliations

  • Alistair C. Jones
    • 1
  • Julian R. Sampson
    • 1
  • Bastian Hoogendoorn
    • 2
  • David Cohen
    • 3
  • Jeremy P. Cheadle
    • 1
  1. 1.Institute of Medical Genetics, University of Wales College of Medicine, Heath Park, Cardiff CF14 4XN, UKUK
  2. 2.Division of Psychological Medicine, University of Wales College of Medicine, Heath Park, Cardiff CF14 4XN, UKUK
  3. 3.Health Economics Unit, School of Business Studies, University of Glamorgan, Pontypridd, Rhondda CF62 4XW, UKUK

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