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Targeted Exome Sequencing Identified Novel USH2A Mutations in Usher Syndrome Families

Overview of attention for article published in PLOS ONE, May 2013
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Title
Targeted Exome Sequencing Identified Novel USH2A Mutations in Usher Syndrome Families
Published in
PLOS ONE, May 2013
DOI 10.1371/journal.pone.0063832
Pubmed ID
Authors

Xiu-Feng Huang, Ping Xiang, Jie Chen, Dong-Jun Xing, Na Huang, Qingjie Min, Feng Gu, Yi Tong, Chi-Pui Pang, Jia Qu, Zi-Bing Jin

Abstract

Usher syndrome (USH) is a leading cause of deaf-blindness in autosomal recessive trait. Phenotypic and genetic heterogeneities in USH make molecular diagnosis much difficult. This is a pilot study aiming to develop an approach based on next-generation sequencing to determine the genetic defects in patients with USH or allied diseases precisely and effectively. Eight affected patients and twelve unaffected relatives from five unrelated Chinese USH families, including 2 pseudo-dominant ones, were recruited. A total of 144 known genes of inherited retinal diseases were selected for deep exome resequencing. Through systematic data analysis using established bioinformatics pipeline and segregation analysis, a number of genetic variants were released. Eleven mutations, eight of them were novel, in the USH2A gene were identified. Biparental mutations in USH2A were revealed in 2 families with pseudo-dominant inheritance. A proband was found to have triple mutations, two of them were supposed to locate in the same chromosome. In conclusion, this study revealed the genetic defects in the USH2A gene and demonstrated the robustness of targeted exome sequencing to precisely and rapidly determine genetic defects. The methodology provides a reliable strategy for routine gene diagnosis of USH.

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Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Unknown 55 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 18%
Other 9 16%
Student > Doctoral Student 5 9%
Professor > Associate Professor 5 9%
Student > Master 5 9%
Other 11 19%
Unknown 12 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 30%
Medicine and Dentistry 11 19%
Biochemistry, Genetics and Molecular Biology 10 18%
Computer Science 2 4%
Neuroscience 2 4%
Other 0 0%
Unknown 15 26%