A candidate gene study is the study of a particular gene and its associated phenotypes.  This approach is most often used when a gene is suspected or known to be related to a specific disease or phenotype and can be a powerful tool for studying the genetic components of complex traits.  This method has been applied to the following areas:

  • Gene-disease research
  • Genetic association studies
  • Biomarker and drug target selection

This approach is often compared to genome wide association studies (GWAS), but has that advantage of not requiring the full sequencing of a genome.  Candidate gene studies are more useful, and cost effective, when studying a particular gene (or set of genes) in a large study sample.

This method comes at the disadvantage of requiring existing knowledge of the gene and its associated biological systems.  A bottleneck situation often occurs due to not having enough existing
Candidate gene

Information desired for a successful candidate gene study. (2)

knowledge to perform a effective candidate gene study.  The figure to the right shows a few types of information desired for a successful candidate gene study.  With help from genomics, progress is being made, such as the development of digital candidate gene approach (DigiCGA). 

Another common criticism is the inability for researchers to reproduce results along with its inability to include all possible causative genes.


Digital candidate gene approach (DigiCGA) is a web-resource based candidate gene identification approach.  This approach objectively extracts, filters, (re)assembles, or (re)analyzes all possible resources from the public web databases in accordance with the principles of biological ontology and complex statistical methods in order to identify the potential candidate genes of specific interest. 

A major challenge for DigiCGA is the lack of detailed information on molecular science with respect to biological traits available in public web databases.  Without access to this information DigiCGA will remain useful but will not live up to its full potential.


  1. Zhu, Mengjin.  Candidate Gene Identification Approach: Progess and ChallengesInt J Biol Sci. 2007, 3(7): 420-427.
  2. OncoTypeDX

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