Toward a Molecular Understanding of Pleiotropy
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Open Access
- 1 August 2006
- journal article
- research article
- Published by Oxford University Press (OUP) in Genetics
- Vol. 173 (4) , 1885-1891
- https://doi.org/10.1534/genetics.106.060269
Abstract
Pleiotropy refers to the observation of a single gene influencing multiple phenotypic traits. Although pleiotropy is a common phenomenon with broad implications, its molecular basis is unclear. Using functional genomic data of the yeast Saccharomyces cerevisiae, here we show that, compared with genes of low pleiotropy, highly pleiotropic genes participate in more biological processes through distribution of the protein products in more cellular components and involvement in more protein–protein interactions. However, the two groups of genes do not differ in the number of molecular functions or the number of protein domains per gene. Thus, pleiotropy is generally caused by a single molecular function involved in multiple biological processes. We also provide genomewide evidence that the evolutionary conservation of genes and gene sequences positively correlates with the level of gene pleiotropy.Keywords
This publication has 28 references indexed in Scilit:
- Why Do Hubs Tend to Be Essential in Protein Networks?PLoS Genetics, 2006
- The Effect of Multifunctionality on the Rate of Evolution in YeastMolecular Biology and Evolution, 2005
- Significant Impact of Protein Dispensability on the Instantaneous Rate of Protein EvolutionMolecular Biology and Evolution, 2005
- Multifunctional genesMolecular Systems Biology, 2005
- A global view of pleiotropy and phenotypically derived gene function in yeastMolecular Systems Biology, 2005
- From syndrome families to functional genomicsNature Reviews Genetics, 2004
- Evidence for dynamically organized modularity in the yeast protein–protein interaction networkNature, 2004
- Two steps forward, one step back: the pleiotropic effects of favoured allelesProceedings Of The Royal Society B-Biological Sciences, 2004
- Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathwaysNature Biotechnology, 2003
- Comparative assessment of large-scale data sets of protein–protein interactionsNature, 2002