Orphan Diseases, Bioinformatics and Drug Discovery

Chapter
Part of the Translational Bioinformatics book series (TRBIO, volume 2)

Abstract

In general, a rare or orphan disease is any disease that affects a small percentage of the population. Since a majority of the known orphan diseases are genetic, they are present throughout the life of affected individuals. Many of the orphan diseases appear early in life and approximately 30% of children with orphan diseases die before the age of 5. The bulk of genes and pathways underlying these diseases remain unknown and pose a major gap in orphan disease research. In spite of technological advances and opportunities available to understand the causes of orphan diseases and for developing innovative medical approaches, most of the current efforts are focused either on a single or related group of orphan diseases. Relatively few studies have attempted global analysis of all orphan diseases. Constructing networks that underlie biological processes and pathways associated with orphan diseases and orphan drugs facilitate identification of the functional units that respond to genetic perturbations and potentially affect disease risk or therapeutics. Analysis of these biological networks can also identify common pathways or processes for multiple orphan diseases that are biologically related. Comprehensive understanding of such molecular bases may provide opportunities for novel interventions that are beneficial for an array of related orphan diseases. In this chapter, we review some of the current bioinformatic analytical options available for orphan disease and drug research including computational approaches for candidate gene prioritization. We also discuss strategies and present examples and case studies of common drugs being repositioned for treatment of orphan diseases.

Keywords

Rare Disease Exome Sequencing Orphan Drug Drug Discovery Process Orphan Disease 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiUSA
  2. 2.Division of Biomedical InformaticsCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  3. 3.School of Computing Sciences and InformaticsUniversity of Cincinnati College of Engineering and Applied ScienceCincinnatiUSA
  4. 4.Genome Informatics Core Laboratory, Division of Biomedical InformaticsCincinnati Children’s Hospital Medical CenterCincinnatiUSA

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