The modular nature of genetic diseases

Clin Genet. 2007 Jan;71(1):1-11. doi: 10.1111/j.1399-0004.2006.00708.x.


Evidence from many sources suggests that similar phenotypes are begotten by functionally related genes. This is most obvious in the case of genetically heterogeneous diseases such as Fanconi anemia, Bardet-Biedl or Usher syndrome, where the various genes work together in a single biological module. Such modules can be a multiprotein complex, a pathway, or a single cellular or subcellular organelle. This observation suggests a number of hypotheses about the human phenome that are now beginning to be explored. First, there is now good evidence from bioinformatic analyses that human genetic diseases can be clustered on the basis of their phenotypic similarities and that such a clustering represents true biological relationships of the genes involved. Second, one may use such phenotypic similarity to predict and then test for the contribution of apparently unrelated genes to the same functional module. This concept is now being systematically tested for several diseases. Most recently, a systematic yeast two-hybrid screen of all known genes for inherited ataxias indicated that they all form part of a single extended protein-protein interaction network. Third, one can use bioinformatics to make predictions about new genes for diseases that form part of the same phenotype cluster. This is done by starting from the known disease genes and then searching for genes that share one or more functional attributes such as gene expression pattern, coevolution, or gene ontology. Ultimately, one may expect that a modular view of disease genes should help the rapid identification of additional disease genes for multifactorial diseases once the first few contributing genes (or environmental factors) have been reliably identified.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Computational Biology / methods*
  • Databases, Genetic
  • Genetic Diseases, Inborn / classification
  • Genetic Diseases, Inborn / genetics*
  • Humans
  • Multifactorial Inheritance / genetics*
  • Phenotype*
  • Software*