English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Application of high-throughput sequencing for studying genomic variations in congenital heart disease

MPS-Authors
/persons/resource/persons50136

Dorn,  C.
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

/persons/resource/persons50179

Grunert,  Marcel
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

/persons/resource/persons50492

Sperling,  S.
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (public)

Dorn.pdf
(Publisher version), 593KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Dorn, C., Grunert, M., & Sperling, S. (2014). Application of high-throughput sequencing for studying genomic variations in congenital heart disease. Briefings in Functional Genomics, 13(1), 51-65. doi:Doi 10.1093/Bfgp/Elt040.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0018-D709-C
Abstract
Congenital heart diseases (CHD) represent the most common birth defect in human. The majority of cases are caused by a combination of complex genetic alterations and environmental influences. In the past, many disease-causing mutations have been identified; however, there is still a large proportion of cardiac malformations with unknown precise origin. High-throughput sequencing technologies established during the last years offer novel opportunities to further study the genetic background underlying the disease. In this review, we provide a roadmap for designing and analyzing high-throughput sequencing studies focused on CHD, but also with general applicability to other complex diseases. The three main next-generation sequencing (NGS) platforms including their particular advantages and disadvantages are presented. To identify potentially disease-related genomic variations and genes, different filtering steps and gene prioritization strategies are discussed. In addition, available control datasets based on NGS are summarized. Finally, we provide an overview of current studies already using NGS technologies and showing that these techniques will help to further unravel the complex genetics underlying CHD.