Next generation diagnostics of heritable connective tissue disorders

Amr Salam, Michael A. Simpson, Kristina L. Stone, Takuya Takeichi, Arti Nanda, Masashi Akiyama, John A. McGrath

Research output: Contribution to journalReview articlepeer-review

10 Citations (Scopus)

Abstract

Finding pathogenic mutations in monogenic diseases represents one of the significant milestones of late 20th century molecular genetics. Mutation data can improve genetic counseling, assist disease modeling and provide a basis for translational research and therapeutics. The logistics of detecting disease mutations, however, has not always been easy or straightforward. Traditional approaches using genetic linkage or candidate gene analysis have often been laborious and expensive, but the advent of next generation sequencing technologies is changing the very nature of modern-day gene discovery and mutation detection. The application of whole-exome and whole-genome sequencing has demonstrated how these new approaches can improve diagnostic sensitivity as well as disclose completely novel and unsuspected disease-gene associations. Use of next generation sequencing in inherited diseases that display genetic heterogeneity is already a cost-effective methodology for mutation detection. Further reductions in sequencing costs and machine run time, as well as improved bioinformatics, are likely to lead to the incorporation of next generation sequencing into routine diagnostics within clinical genetics. In the short term, the impact of next generation sequencing on the genetically diverse and clinically protean heritable connective tissue disorders is likely to mean more comprehensive documentation of individual mutations. Longer term, dissection of bioinformatics data may lead to further insight into individual prognosis and an era of new personal therapeutics.

Original languageEnglish
Pages (from-to)35-40
Number of pages6
JournalMatrix Biology
Volume33
DOIs
Publication statusPublished - 2014 Jan

All Science Journal Classification (ASJC) codes

  • Molecular Biology

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