title: Synthetic genetic interactions allele dependence, uses, and conservation.
abstract: Genetic interactions occur between a pair of genes when the phenotype of the double mutant leads to an unexpected phenotype, one that is not predicted from the phenotypes of the single mutants alone. Here, we focus on genetic enhancements, otherwise known as synthetic genetic interactions, where the double mutant phenotype is more severe than expected. Such interactions are rife in natural populations and underlie complex traits, variable penetrance, variable expressivity, and genetic predisposition. Such interactions can also contribute valuable information for functional genomics analysis. Pairwise synthetic genetic interactions are now being systematically uncovered for some simple model genomes. These data are affording us an unparalleled opportunity to examine, understand and exploit genetic enhancements. Here we focus on some key lessons, insights, and confusions arising from these large-scale datasets. We consider if genome-wide datasets support traditional assumptions about the functional relationships between gene products that underlie genetic enhancements. We argue that the genetic enhancement network of an organism is not uniform in nature and is highly dependent on the nature of the interacting alleles. We consider how such genetic networks can be exploited to inform gene product function. Finally, we consider the extent to which genetic enhancement networks are conserved between species.
authors.affiliation: Molecular Genetics and Integrative & Systems Biology, Faculty of Biomedical and Life Sciences, University of Glasgow, Glasgow, United Kingdom.
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Gray
authors.affiliation: Molecular Genetics and Integrative & Systems Biology, Faculty of Biomedical and Life Sciences, University of Glasgow, Glasgow, United Kingdom.
authors.forename: Sue A
authors.initials: SA
authors.lastname: Krause
mesh_terms: D000818:Animals; D004843:Epistasis, Genetic; D053263:Gene Regulatory Networks; D006801:Humans