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Mol Genet Metab. 2004 Sep-Oct;83(1-2):74-81.

Genetic screening methods for the detection of mutations responsible for multiple endocrine neoplasia type 1.

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  • 12nd Department of Medicine, Faculty of Medicine, Semmelweis University, Budapest, Hungary.

Abstract

Identification of mutations, which cause genetic diseases can be difficult when the disease is caused by the mutation of a large gene, which contains multiple exons. Detection of these mutations by DNA sequencing can be made more efficient by using mutation detection methods for pre-screening to identify the affected exon and to screen for the presence of already identified mutations in family members. These screening methods include denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), single-strand conformation polymorphism, conformation-sensitive gel electrophoresis (CSGE), heteroduplex analysis and denaturing high-performance liquid chromatography (DHPLC). We discuss the advantages and shortcomings of these methods by reviewing the results of studies screening for mutations causing multiple endocrine neoplasia type 1 (MEN 1) syndrome, an autosomal dominant disorder characterized by endocrine tumours of the anterior pituitary gland, parathyroid glands, and pancreas. MEN 1 is caused by mutations of the MEN1 gene, a tumour suppressor gene, which contains one untranslated exon and nine exons. Previous studies have identified more than 400 germline and somatic mutations spreading across all the encoding sequence, and found no mutational "hot spot" or genotype-phenotype correlation. The wide diversity of mutations in the entire coding region of the MEN1 gene makes mutation screening time-consuming and expensive. We conclude that combination of mutation detection methods with DNA sequencing enhances the efficiency of identifying pathogenic mutations. However, it should be considered that experimental determination of the optimal electrophoresis conditions, such as using perpendicular electrophoresis to optimise DGGE or TGGE, is more useful than computerized algorithms to calculate these parameters.

PMID:
15464422
[PubMed - indexed for MEDLINE]
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