Epistasis

Epistasis has a great influence on the shape of evolutionary landscapes, which leads to profound consequences for evolution and for the evolvability of phenotypic traits.

In this sense, epistasis can be contrasted with genetic dominance, which is an interaction between alleles at the same gene locus.

Increasingly sophisticated computational and evolutionary biology models aim to describe the effects of epistasis on a genome-wide scale and the consequences of this for evolution.

Simply, additive traits were studied early on in the history of genetics, however they are relatively rare, with most genes exhibiting at least some level of epistatic interaction.

[11][12] Positive epistasis between deleterious mutations protects against the negative effects to cause a less severe fitness drop.

[14] Conversely, when two mutations together lead to a less fit phenotype than expected from their effects when alone, it is called negative epistasis.

The opposite situation, when the fitness difference of the double mutant from the wild type is smaller than expected from the effects of the two single mutations, it is called antagonistic epistasis.

[25] Reciprocal sign epistasis also leads to genetic suppression whereby two deleterious mutations are less harmful together than either one on its own, i.e. one compensates for the other.

A clear example of genetic suppression was the demonstration that in the assembly of bacteriophage T4 two deleterious mutations, each causing a deficiency in the level of a different morphogenetic protein, could interact positively.

For example, in a diploid organism, a hypomorphic (or partial loss-of-function) mutant phenotype can be suppressed by knocking out one copy of a gene that acts oppositely in the same pathway.

Proteins are held in their tertiary structure by a distributed, internal network of cooperative interactions (hydrophobic, polar and covalent).

[31] Epistatic interactions occur whenever one mutation alters the local environment of another residue (either by directly contacting it, or by inducing changes in the protein structure).

[34][35] In enzymes, the protein structure orients a few, key amino acids into precise geometries to form an active site to perform chemistry.

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.

Two bacteriophage T4 mutants defective at different locations in the same gene can undergo allelic complementation during a mixed infection.

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.

This makes it more likely that organisms will get stuck at local maxima in the fitness landscape having acquired mutations in the 'wrong' order.

[44] However, of the 120 possible pathways to this 5-mutant variant, only 7% are accessible to evolution as the remainder passed through fitness valleys where the combination of mutations reduces activity.

In contrast, changes in environment (and therefore the shape of the fitness landscape) have been shown to provide escape from local maxima.

[46] These shifting "fitness territories" may act to decelerate evolution and could represent tradeoffs for adaptive traits.

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.

Over time, sexual populations move towards more negative epistasis, or the lowering of fitness by two interacting alleles.

It is thought that negative epistasis allows individuals carrying the interacting deleterious mutations to be removed from the populations efficiently.

[21] However, the evidence for this hypothesis has not always been straightforward and the model proposed by Kondrashov has been criticized for assuming mutation parameters far from real world observations.

Any two locus interactions at a particular gene frequency can be decomposed into eight independent genetic effects using a weighted regression.

The same methodology can be used to investigate the interactions between larger sets of mutations but all combinations have to be produced and assayed.

Many of these rely on machine learning to detect non-additive effects that might be missed by statistical approaches such as linear regression.

[58] For example, multifactor dimensionality reduction (MDR) was designed specifically for nonparametric and model-free detection of combinations of genetic variants that are predictive of a phenotype such as disease status in human populations.

[61] Even more recently, methods that utilize insights from theoretical computer science (the Hadamard transform[62] and compressed sensing[63][64]) or maximum-likelihood inference[65] were shown to distinguish epistatic effects from overall non-linearity in genotype–phenotype map structure,[66] while others used patient survival analysis to identify non-linearity.

An example of epistasis is the interaction between hair colour and baldness. A gene for total baldness would be epistatic to one for blond hair or red hair . The hair-colour genes are hypostatic to the baldness gene. The baldness phenotype supersedes genes for hair colour, and so the effects are non-additive. [ citation needed ]
Example of epistasis in coat colour genetics: If no pigments can be produced the other coat colour genes have no effect on the phenotype, no matter if they are dominant or if the individual is homozygous. Here the genotype "c c" for no pigmentation is epistatic over the other genes. [ 1 ]
Quantitative trait values after two mutations either alone (Ab and aB) or in combination (AB). Bars contained in the grey box indicate the combined trait value under different circumstances of epistasis. Upper panel indicates epistasis between beneficial mutations (blue). [ 11 ] [ 12 ] Lower panel indicates epistasis between deleterious mutations (red). [ 13 ] [ 14 ]
Since, on average, mutations are deleterious, random mutations to an organism cause a decline in fitness. If all mutations are additive, fitness will fall proportionally to mutation number (black line). When deleterious mutations display negative (synergistic) epistasis, they are more deleterious in combination than individually and so fitness falls with the number of mutations at an increasing rate (upper, red line). When mutations display positive (antagonistic) epistasis, effects of mutations are less severe in combination than individually and so fitness falls at a decreasing rate (lower, blue line). [ 13 ] [ 14 ] [ 15 ] [ 16 ]
The top row indicates interactions between two genes that show either ( a ) additive effects, ( b ) positive epistasis or ( c ) reciprocal sign epistasis. 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 ( d ); as 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 ).