The modular nature of genetic diseases
- PMID: 17204041
- DOI: 10.1111/j.1399-0004.2006.00708.x
The modular nature of genetic diseases
Abstract
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.
Similar articles
-
A text-mining analysis of the human phenome.Eur J Hum Genet. 2006 May;14(5):535-42. doi: 10.1038/sj.ejhg.5201585. Eur J Hum Genet. 2006. PMID: 16493445
-
A new web-based data mining tool for the identification of candidate genes for human genetic disorders.Eur J Hum Genet. 2003 Jan;11(1):57-63. doi: 10.1038/sj.ejhg.5200918. Eur J Hum Genet. 2003. PMID: 12529706
-
Syndrome to gene (S2G): in-silico identification of candidate genes for human diseases.Hum Mutat. 2010 Mar;31(3):229-36. doi: 10.1002/humu.21171. Hum Mutat. 2010. PMID: 20052752
-
Atypical patterns of inheritance.Semin Pediatr Neurol. 2007 Mar;14(1):34-45. doi: 10.1016/j.spen.2006.11.007. Semin Pediatr Neurol. 2007. PMID: 17331882 Review.
-
Psychiatric genetics: the case of single gene disorders.Eur Child Adolesc Psychiatry. 2002 Oct;11(5):201-9. doi: 10.1007/s00787-002-0284-0. Eur Child Adolesc Psychiatry. 2002. PMID: 12469237 Review.
Cited by
-
A Combined Analysis of Genetically Correlated Traits Identifies Genes and Brain Regions for Insomnia.Can J Psychiatry. 2020 Dec;65(12):874-884. doi: 10.1177/0706743720940547. Epub 2020 Jul 10. Can J Psychiatry. 2020. PMID: 32648482 Free PMC article.
-
Improved human disease candidate gene prioritization using mouse phenotype.BMC Bioinformatics. 2007 Oct 16;8:392. doi: 10.1186/1471-2105-8-392. BMC Bioinformatics. 2007. PMID: 17939863 Free PMC article.
-
Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.NPJ Syst Biol Appl. 2019 Mar 1;5:7. doi: 10.1038/s41540-019-0087-2. eCollection 2019. NPJ Syst Biol Appl. 2019. PMID: 30854222 Free PMC article.
-
Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data.BMC Bioinformatics. 2011 Aug 31;12:359. doi: 10.1186/1471-2105-12-359. BMC Bioinformatics. 2011. PMID: 21884587 Free PMC article.
-
DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases.BMC Syst Biol. 2011 Apr 19;5:55. doi: 10.1186/1752-0509-5-55. BMC Syst Biol. 2011. PMID: 21504591 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical