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Developing a quick, cost-effective genetic screen for enamel disease

Hany, Ummey and Nikolopoulos, Georgios and Smith, Claire and Watson, Christopher M. and Inglehearn, Chris and Mighell, Alan (2020) Developing a quick, cost-effective genetic screen for enamel disease. University of Leeds. [Dataset] https://doi.org/10.5518/854

This item is part of the Leeds Doctoral College Showcase: Online Poster Conference 2020 - Prize winning posters collection.

Dataset description

Amelogenesis imperfecta (AI) refers to a group of rare, inherited disorders characterised by abnormal enamel formation. According to the AI Leiden Open Variant Database (LOVD) hosted by Leeds University (http://dna2.leeds.ac.uk/LOVD/), there are 19 genes involved in non-syndromic AI that account for >90% of the known AI-causing mutations. Conventionally the identification of inherited gene mutations in a family would be done through family studies. However, with technological improvements and decreasing costs, next generation sequencing (NGS) technology has become the gold standard in the genetic research. Single molecule molecular inversion probe (smMIP) is an NGS based DNA sequencing approach that can selectively target and analyse thousands of genomic positions in a single reaction. It is superior in terms of cost, throughput, scalability, sensitivity, and specificity and can process hundreds of patients simultaneously. To identify mutations in AI patients, an smMIP method was adapted and validated that can be used as a first point of screening for all the future patients. The aim is to make diagnosis quicker for patients with known mutations and to provide extra resources to focus on the discovery of novel gene mutations.

Additional information: Prize winning poster in the Leeds Doctoral College Showcase: Online Poster Conference 2020
Subjects: A000 - Medicine & dentistry
Divisions: Faculty of Medicine and Health > School of Dentistry
Faculty of Medicine and Health > School of Medicine
Date deposited: 03 Aug 2020 15:06
URI: https://archive.researchdata.leeds.ac.uk/id/eprint/721

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