defines epistasis as a genetic term describing the 'interaction between chemical characterization, such as for DNA replication mutants or mutants affecting. Epistasis (i.e. gene–gene interaction) has long been recognized as an A CNV is a segment of DNA that is present at a variable number of. Finally I examined the dynamic changes of epistatic relations among genes ( ) A DNA integrity network in the yeast Saccharomyces cerevisiae. Cell.
The statistical detection of epistasis usually does not map onto or easily relate to the biological interactions between genetic variations through their combined influence on gene expression or through their interactions at the gene product i.
Recently, greater high-dimensional data on protein—protein interaction PPI and gene expression profiles have been collected that enumerates sets of biological interactions. To better align statistical and molecular models of epistasis, we present an example of how to incorporate the PPI information into the statistical analysis of interactions between copy number variations CNVs.
Two CNV pairs provided sufficient genotype variation to search for epistatic effects on gene expression.
This study demonstrates that using PPI data can assist in targeting statistical hypothesis testing to biological plausible epistatic interaction that reflects molecular mechanisms. Epistasis has long been used as a general term to describe the complex interactions among genetic loci 3. Although we would expect a phenotypic effect to arise from the functional interruption of the relationship between two genes, the translation of the statistical interactions to the functional interactions is not straightforward and has been extensively debated 4.
The common genetic variants identified by the genome-wide association studies GWAS using additive genetic models without considering dominance or potential epistatic associations only explain a small fraction of the heritability 5. Although it has been suggested that the epistatic associations could be responsible for a portion of the unexplained heritability of in GWAS 56the challenge remains to identify how to conduct genome-wide epistatic studies with prior biological knowledge 7 to connect the statistical evidence to a testable molecular model.
Developing databases that curate and annotate information about all the molecular interactions within a cell e. It would also greatly facilitate faster validation of the putative molecular epistatic mechanisms.
Given the current state of this type of molecular interaction knowledge, we have chosen to focus on demonstrating the use of a protein—protein interaction PPI database on the identification of epistasis. The physical interactions between proteins are important for a wide range of biological functions—e. With the advancement of molecular technologies, there are now several biochemical and biophysical high-throughput methods e. Combining the high-throughput affinity purification, mass spectrometry and computational algorithms 1011or using protein-fragment complementation arrays 12the global maps of PPI have been constructed in the budding yeast Saccharomyces cerevisiae.
This leads to negative epistasis whereby mutations that have little effect alone have a large, deleterious effect together. For example, removing any member of the catalytic triad of many enzymes will reduce activity to levels low enough that the organism is no longer viable. This is sometimes called allelic complementation, or interallelic complementation. It may be caused by several mechanisms, for example transvectionwhere an enhancer from one allele acts in trans to activate transcription from the promoter of the second allele.
Similarly, at the protein level, proteins that function as dimers may form a heterodimer composed of one protein from each alternate gene and may display different properties to the homodimer of one or both variants.
Evolutionary consequences[ edit ] Fitness landscapes and evolvability[ edit ] The top row indicates interactions between two genes that are either additive ashow positive epistasis b or reciprocal sign epistasis c. Below are fitness landscapes which display greater and greater levels of global epistasis between large numbers of genes. Purely additive interactions lead to a single smooth peak das increasing numbers of genes exhibit epistasis, the landscape becomes more rugged e and when all genes interact epistatically the landscape becomes so rugged that mutations have seemingly random effects f.
This is because magnitude epistasis positive and negative simply affects how beneficial mutations are together, however sign epistasis affects whether mutation combinations are beneficial or deleterious. It is frequently used as a visual metaphor for understanding evolution as the process of moving uphill from one genotype to the next, nearby, fitter genotype.
The landscape is perfectly smooth, with only one peak global maximum and all sequences can evolve uphill to it by the accumulation of beneficial mutations in any order. Conversely, if mutations interact with one another by epistasis, the fitness landscape becomes rugged as the effect of a mutation depends on the genetic background of other mutations.
This is referred to as a rugged fitness landscape and has profound implications for the evolutionary optimisation of organisms. If mutations are deleterious in one combination but beneficial in another, the fittest genotypes can only be accessed by accumulating mutations in one specific order. This makes it more likely that organisms will get stuck at local maxima in the fitness landscape having acquired mutations in the 'wrong' order.
In contrast, changes in environment and therefore the shape of the fitness landscape have been shown to provide escape from local maxima.
Epistasis - Wikipedia
This gateway mutation alleviated the negative epistatic interactions of other individually beneficial mutations, allowing them to better function in concert. Complex environments or selections may therefore bypass local maxima found in models assuming simple positive selection. High epistasis is usually considered a constraining factor on evolution, and improvements in a highly epistatic trait are considered to have lower evolvability. This is because, in any given genetic background, very few mutations will be beneficial, even though many mutations may need to occur to eventually improve the trait.
The lack of a smooth landscape makes it harder for evolution to access fitness peaks. In highly rugged landscapes, fitness valleys block access to some genes, and even if ridges exist that allow access, these may be rare or prohibitively long.
Rugged, epistatic fitness landscapes also affect the trajectories of evolution. When a mutation has a large number of epistatic effects, each accumulated mutation drastically changes the set of available beneficial mutations.
Therefore, the evolutionary trajectory followed depends highly on which early mutations were accepted.
Thus, repeats of evolution from the same starting point tend to diverge to different local maxima rather than converge on a single global maximum as they would in a smooth, additive landscape.
Experimentally, this idea has been tested in using digital simulations of asexual and sexual populations. Over time, sexual populations move towards more negative epistasis, or the lowering of fitness by two interacting alleles.