The first commonly used definition of independence, originating from additivity, defines the effect of We suggest that epistatic relationships can be divided into several subtypes, or forms, corresponding to Google Scholar. Epistatic Relationships Involving Two Genes Some of the most famous examples of research in which the interaction between two genes was found to. Here we test the hypothesis that complex relationships between a gene pair can be explained by quantity over all effect genes i and define different types of epistasis for the multivariate gene .. View Article; Google Scholar.
The causes of epistasis
Additivity[ edit ] This can be the case when multiple genes act in parallel to achieve the same effect. For example, when an organism is in need of phosphorusmultiple enzymes that break down different phosphorylated components from the environment may act additively to increase the amount of phosphorus available to the organism. However, there inevitably comes a point where phosphorus is no longer the limiting factor for growth and reproduction and so further improvements in phosphorus metabolism have smaller or no effect negative epistasis.
Some sets of mutations within genes have also been specifically found to be additive.
Epistasis - Wikipedia
This interaction may be direct if the genes encode proteins that, for example, are separate components of a multi-component protein such as the ribosomeinhibit each other's activity, or if the protein encoded by one gene modifies the other such as by phosphorylation.
Alternatively the interaction may be indirect, where the genes encode components of a metabolic pathway or networkdevelopmental pathwaysignalling pathway or transcription factor network. For example, the gene encoding the enzyme that synthesizes penicillin is of no use to a fungus without the enzymes that synthesize the necessary precursors in the metabolic pathway.
Epistasis within genes[ edit ] Just as mutations in two separate genes can be non-additive if those genes interact, mutations in two codons within a gene can be non-additive.
In genetics this is sometimes called intragenic complementation when one deleterious mutation can be compensated for by a second mutation within that gene. This occurs when the amino acids within a protein interact. Due to the complexity of protein folding and activity, additive mutations are rare. Proteins are held in their tertiary structure by a distributed, internal network of cooperative interactions hydrophobicpolar and covalent.
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.
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.
The causes of epistasis
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. Fine, so what does a population geneticist mean by epistasis?
RA Fisher used 'epistacy' and later 'epistasis' to describe genetic interactions more generally [ 2 ]. We think that population geneticists hijacked this term over a decade after its coinage just to confuse the classical geneticists. OK, what does a medical doctor mean by epistasis? A thin film on the surface of a urine specimen.
- Q&A: Epistasis
Enough said on that topic. Epistasis seems to mean genetic interaction under both classical and population genetics definitions. Epistasis under the classical definition describes only interactions in which one mutant phenotype is masked or suppressed in the presence of the other mutation.
The population geneticist's definition includes classical epistasis, but also encompasses 'aggravating' or 'synthetic' interactions — where two mutations together yield a surprisingly deleterious phenotype [ 3 ]. OK, you've defined epistasis. But why should I care about it? 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 A brief survey of the epidemiological literature reveals the major difficulties that exist in inferring biological meaning from quantitative data measuring disease risk as outcome 325 The problem is that any given data pattern and statistical model can usually be obtained from a number of completely different underlying mechanisms or models for disease development 326 For instance, five very different causal mechanisms can be shown to all lead to a multiplicative model for the data used in investigating the joint effects of two risk factors Only if the prior biological model can be postulated in some detail is it likely that statistical modelling of this kind will allow insight into the underlying biological mechanisms.
Although the discovery of epistasis may be of limited value for elucidating the underlying biological disease process, allowing for different modes of interaction between potential disease loci can lead to improved power for detection of genetic effects.
Simulation studies 122829 suggest that this improvement in power may be relatively modest. Nevertheless, in analysis of real data for type 1 diabetes 1228type 2 diabetes 30 and inflammatory bowel disease 31increased evidence for linkage at one locus was seen when the interaction with another locus was taken into account. Methods for the detection of epistasis vary according to whether one is performing association or linkage analysis, and according to whether one is dealing with a quantitative or a qualitative in particular a dichotomous trait.
For genetic association studies, standard methods for epidemiological studies may be employed, with genotypes at the various loci considered as risk factors for disease. This provides an overall 4 degree-of-freedom df test for interaction, but the interaction terms could each be tested individually on 1 df by removal from the first model, if required.
Note that this procedure implicitly assumes that the log odds scale is the scale of interest: Quantitative traits can be analysed in a similar way by use of standard multiple linear as opposed to logistic regression: Note that these regression procedures are actually designed for testing epistasis between loci that have been genotyped. If it is believed that these loci are not themselves the etiological variants but rather are in linkage disequilibrium LD with the true disease-causing variants, then epistasis between the surrogate genotyped loci is likely to be diluted compared with epistasis between the true variants, although the extent to which this occurs will depend on the magnitude of the LD.
A related method for analysis of nuclear family data involves a generalization of the genotype relative risk approach proposed by Schaid Conditional logistic regression is used to fit models for the genotype relative risks.
This method can be extended to fit models for genotype relative risks at two unlinked loci by generating not three but fifteen matched pseudocontrols for each case, where the genotype at the two loci for each pseudocontrol consists of one of the two-locus genotypes that could have been, but was not, transmitted to the case.
Two-locus models for the genotype relative risks at the two loci are fitted using conditional logistic regression. Standard statistical software can be used to fit models that involve departure from multiplicativity in the penetrances and hence in the genotype relative risks ; more specialist software or user programming will be required for detecting epistasis defined as departure from additivity in the penetrances.
A variety of related approaches that focus on the issue of association testing but can be used to detect or allow for epistasis in family-based analysis of quantitative traits have also been proposed 34 — Epistasis is relatively easily incorporated into standard non-parametric model-free methods of linkage analysis for quantitative traits.
One popular method is the variance components method, in which the phenotypic covariance between relatives is modelled in terms of variance component parameters and underlying identity-by-descent IBD sharing probabilities at one or more genetic loci, assuming underlying multivariate normality of the trait within pedigrees.
Models that include epistatic in the sense of departure from additive components of variance may be fitted and compared with models that do not contain these components using maximum-likelihood methods implemented in such programs as SOLAR Another popular method of linkage analysis for quantitative traits is the Haseman—Elston method 39 and extensions In this method, the squared difference or product of the phenotype values for a pair of relatives is modelled in a regression framework as a function of the underlying IBD sharing probabilities.
To include epistatic interactions between loci, all that is required is to include products of the IBD sharing probabilities at different loci as predictors in the regression equation For dichotomous traits, model-free methods of linkage analysis typically focus on calculating the likelihood of the observed IBD sharing among pairs of affected relatives.
Likelihoods for the IBD sharing at two or more loci can be calculated 122841 under restricted models such as additive, multiplicative or heterogeneity models for the penetrances, and compared with general unrestricted models for the IBD sharing. In this way, tests for epistasis defined as departure from any of these restricted models can be performed for either linked 2842 or unlinked loci.
Another approach to the detection of loci that act epistatically is to consider the correlation between IBD sharing or linkage statistics at unlinked loci