↓ Skip to main content

Molecular diagnostics for hereditary hearing loss in children

Overview of attention for article published in Expert Review of Molecular Diagnostics, June 2017
Altmetric Badge

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
32 Mendeley
Title
Molecular diagnostics for hereditary hearing loss in children
Published in
Expert Review of Molecular Diagnostics, June 2017
DOI 10.1080/14737159.2017.1340834
Pubmed ID
Authors

Manou Sommen, Wim Wuyts, Guy Van Camp

Abstract

Hearing loss (HL) is the most common birth defect in industrialized countries with far-reaching social, psychological and cognitive implications. It is an extremely heterogeneous disease, complicating molecular testing. The introduction of next-generation sequencing (NGS) has resulted in great progress in diagnostics allowing to study all known HL genes in a single assay. The diagnostic yield is currently still limited, but has the potential to increase substantially. Areas covered: In this review the utility of NGS and the problems for comprehensive molecular testing for HL are evaluated and discussed. Expert commentary: Different publications have proven the appropriateness of NGS for molecular testing of heterogeneous diseases such as HL. However, several problems still exist, such as pseudogenic background of some genes and problematic copy number variant analysis on targeted NGS data. Another main challenge for the future will be the establishment of population specific mutation-spectra to achieve accurate personalized comprehensive molecular testing for HL.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 19%
Student > Ph. D. Student 6 19%
Student > Bachelor 5 16%
Student > Doctoral Student 2 6%
Other 2 6%
Other 3 9%
Unknown 8 25%
Readers by discipline Count As %
Medicine and Dentistry 12 38%
Biochemistry, Genetics and Molecular Biology 4 13%
Agricultural and Biological Sciences 3 9%
Computer Science 1 3%
Neuroscience 1 3%
Other 0 0%
Unknown 11 34%