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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Improved genetic testing for monogenic diabetes using targeted next-generation sequencing.

S Ellard¹, H Lango Allen, E De Franco

¹Institute for Biomedical and Clinical Science, University of Exeter Medical School, Barrack Road, Exeter EX2 5DW, UK. sian.ellard@nhs.net

|June 18, 2013

Summary

A new genetic test using next-generation sequencing simultaneously analyzes all monogenic diabetes genes, improving mutation detection rates for MODY and neonatal diabetes. This comprehensive approach enhances diagnostic accuracy for rare diabetes forms.

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Area of Science:

Genetics
Endocrinology
Molecular Biology

Background:

Current genetic diagnostics for monogenic diabetes are phenotype-dependent, limiting comprehensive gene analysis.
Next-generation sequencing (NGS) offers the potential for simultaneous multi-gene analysis.

Purpose of the Study:

To develop a targeted NGS assay for detecting mutations in all known genes associated with monogenic diabetes (MODY and neonatal diabetes).

Main Methods:

Selected 29 genes linked to neonatal diabetes, MODY, MIDD, and FPLD.
Designed an exon-capture assay covering coding regions and splice sites.
Tested 114 patient samples using NGS, analyzing for various mutation types (substitutions, indels, deletions/duplications).

Main Results:

Successfully identified all known variants in 32 positive controls.
Discovered previously unidentified mutations in 15% of MODY and 18% of neonatal diabetes cases.
A majority of newly identified mutations were in genes not previously analyzed for these patients.

Conclusions:

The developed targeted NGS assay is a sensitive and comprehensive tool for simultaneous analysis of monogenic diabetes genes.
This single test can replace traditional methods like Sanger sequencing and MLPA.
Expanding the number of genes analyzed significantly increases the mutation detection rate.