Epistasis and functional genomics

rdf:langString Epistasis and functional genomics

rdf:langString Epistasis refers to genetic interactions in which the mutation of one gene masks the phenotypic effects of a mutation at another locus. Systematic analysis of these epistatic interactions can provide insight into the structure and function of genetic pathways. Examining the phenotypes resulting from pairs of mutations helps in understanding how the function of these genes intersects. Genetic interactions are generally classified as either Positive/Alleviating or Negative/Aggravating. Fitness epistasis (an interaction between non-allelic genes) is positive (in other words, diminishing, antagonistic or buffering) when a loss of function mutation of two given genes results in exceeding the fitness predicted from individual effects of deleterious mutations, and it is negative (that is, reinforcing, synergistic or aggravating) when it decreases fitness. Ryszard Korona and Lukas Jasnos showed that the epistatic effect is usually positive in Saccharomyces cerevisiae. Usually, even in case of positive interactions double mutant has smaller fitness than single mutants. The positive interactions occur often when both genes lie within the same pathway Conversely, negative interactions are characterized by an even stronger defect than would be expected in the case of two single mutations, and in the most extreme cases (synthetic sick/lethal) the double mutation is lethal. This aggravated phenotype arises when genes in compensatory pathways are both knocked out. High-throughput methods of analyzing these types of interactions have been useful in expanding our knowledge of genetic interactions. Synthetic genetic arrays (SGA), diploid based synthetic lethality analysis on microarrays (dSLAM), and epistatic miniarray profiles (E-MAP) are three important methods which have been developed for the systematic analysis and mapping of genetic interactions. This systematic approach to studying epistasis on a genome wide scale has significant implications for functional genomics. By identifying the negative and positive interactions between an unknown gene and a set genes within a known pathway, these methods can elucidate the function of previously uncharacterized genes within the context of a metabolic or developmental pathway.