Epistatic relationship between genes and alleles

Mendelian Genetics

epistatic relationship between genes and alleles

Few studies constructed multiple mutant alleles for single genes to examine the dynamics of epistatic relations among genes under different. In biochemical genetics, analysis of epistatic relationships can be used to assign Epistasis refers to the behavioral effect of interaction among gene alleles at. Epistasis - the interaction between two or more genes to control a single phenotype Therefore, only one dominant allele at either of the two loci is required to.

Conversely, when deleterious mutations are introduced, proteins often exhibit mutational robustness whereby as stabilising interactions are destroyed the protein still functions until it reaches some stability threshold at which point further destabilising mutations have large, detrimental effects as the protein can no longer fold.

This leads to negative epistasis whereby mutations that have little effect alone have a large, deleterious effect together.

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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.

epistatic relationship between genes and alleles

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.

epistatic relationship between genes and alleles

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.

Linked alleles and Epistatic Interactions

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. This gateway mutation alleviated the negative epistatic interactions of other individually beneficial mutations, allowing them to better function in concert. As a consequence, the global landscape of epistasis for different alleles of the same gene remains largely uninvestigated.

Here we address this issue by combining experimental data with mathematical modeling using flux balance analysis FBA. FBA has been used to investigate the fitness consequence of single-deletion mutants 1920 and epistatic relations between metabolic reactions, genes, and functional modules 21 — The FBA predictions show good agreement with genome-wide experimental studies 25 — One essential advantage of FBA modeling is that it can simulate epistasis between genes based on different genetic mutants.

Using this platform, together with data from a recently published experiment, we were able to show that epistasis can be rewired among genes, and that the sign of epistasis can change dramatically at the global scale, depending on the mutant alleles involved in the processes.

Dynamic epistasis for different alleles of the same gene

Our study provides a genome-wide picture on the dynamic epistatic landscape of various mutant alleles for the same gene. We first used the yeast Saccharomyces cerevisiae metabolic reconstruction iMM 16 to examine the distribution of epistasis under various genetic mutant alleles.

The reconstruction is a genome-scale metabolic model, having metabolic genes associated with 1, reactions. We computed the fitness of the single mutants and double mutants with any possible pairwise allele combination of different genes. These data were used to infer the epistatic relationships among genes. In this table, the effect of allele A can only be observed when allele B is also present: This leads to a situation that is not precisely analogous to that described by Bateson 1.

In Bateson's 1 definition, it is clear that if factor B is epistatic to factor A, we do not expect factor A to also be epistatic to factor B.

This is illustrated by the lack of symmetry in Table 1.

epistatic relationship between genes and alleles

In Table 2the symmetry between the effects at loci A and B means that we cannot say that one of the loci is epistatic to the other. Nevertheless this type of penetrance table has been interpreted as representing a more general form of epistasis between the loci 4albeit of a rather different nature from that originally implied by the term.

Lack of epistasis has classically been represented by penetrance tables such as Table 3 4 — 6. This would appear to represent a different biological phenomenon to that represented by Table 2. Table 3 is usually assumed to correspond to a situation in which the biological pathways involved in disease influenced by the two loci are at some level separate or independent 5.

Epistasis - Wikipedia

Thus the classical heterogeneity model falls within a definition interpretable as epistasis! In Fisher's definition, epistasis refers to a deviation from additivity in the effect of alleles at different loci with respect to their contribution to a quantitative phenotype. This definition is not equivalent to Bateson's definition, as was pointed out in the initial review of Fisher's paper by R. Epistasis in the Fisher 7 sense is closer to the usual concept of statistical interaction: With this definition, the choice of scale becomes important, since factors that are additive with respective to an outcome measured on one scale may not be additive, i.

Lack of epistasis in this model implies that all interaction coefficients are zero. For binary traits, similar models may be applied, but with the outcome of interest usually defined to be pij, the penetrance for genotype i at locus 1 and j at locus 2. The additive and heterogeneity models are usually assumed to represent non-epistatic models and to correspond to a situation in which the biological pathways are at some level separate or independent. However, the biological interpretation of the heterogeneity model when the penetrances pij are not constrained to be 0 or 1 is unclear.

The reason that additive and heterogeneity models for the penetrance are often used interchangeably is that it can be shown that these models give very similar results when used to model familial relative risks of disease 5 It is not clear, however, that this will hold when modelling other outcomes e.

Note that the heterogeneity and multiplicative models can also both be expressed as additive models when transformed to different scales: Although these models are additive and therefore expressible without interaction effects on their appropriate scales, they correspond to models with interaction effects epistasis when transformed to the penetrance scale.

Indeed, the term epistasis has often been used without being precisely defined, so that it is not clear which definition is being assumed in any given situation. Many authors have assumed that epistasis or interaction between loci refers to departure from additivity on the penetrance scale 51718whereas others have assumed instead that it refers to a departure from multiplicativity on the penetrance scale 19 — Moreover, epistasis is sometimes investigated in the context of epistatic variance: The epistatic variance depends not only on the genetic model for the action of two or more loci, but also on population parameters such as multilocus genotype frequencies 2223in the same way that additive and dominance variances at a single locus depend not just on the model of dominance assumed but also on population genotype or allele frequencies.

Confusions of definition and terminology apart, the main problem with the interpretation of epistasis is that the word itself suggests that we are dealing with a biologically interesting phenomenon. If epistasis is detected, the assumption is that this tells us something of interest about the mechanisms and pathways involved in disease—in particular in relation to the biological interaction between implicated proteins.

Indeed, the description in 5 hints strongly for a biological or causal interpretation of the models there defined. However, statistical tests of interaction are limited to testing specific hypotheses concerning precisely defined quantities.

  • Dynamic epistasis for different alleles of the same gene

Unfortunately, as we have seen, there is not a precise correspondence between biological models of epistasis and those that are more statistically motivated. We should like to perform a statistical test and interpret the outcome biologically, but this is in general not permissible.

Statistical interaction does not necessarily imply interaction on the biological or mechanistic level