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Epistasis is the term that refers to the action of one gene upon another. scientists have performed numerous studies in an attempt to better understand and classify digenic epistatic relationships. .. Polygenic Inheritance and Gene Mapping. For example, the symptoms associated with sickle-cell disease are due to pleiotropic effects. Individuals with sickle-cell disease are. multiple alleles; pleiotropy; polygenic inheritance; quantitative character However, the ratio of phenotypes in the F2 generation is not
We show that epistasis increases the pleiotropic degree of single mutations and provides modularity to the GP map of drug resistance in HIV Moreover, modules of epistatic pleiotropic effects within the GP map match the phenotypic modules of correlated replicative capacity among drug classes.
Epistasis thus increases the evolvability of cross-resistance in HIV by providing more drug- and class-specific pleiotropic profiles to the main effects of the mutations.
What is the difference between pleiotropy and epistasis? How are they similar? - Quora
We discuss the implications for the evolution of cross-resistance in HIV. Introduction A central goal of evolutionary biology is to understand how genetic variation maps onto phenotypic variation, and ultimately fitness. The genes affecting such traits often affect other traits as well, and are thus pleiotropic.
The way they affect trait variation also often depends on their interactions with other genes. There is thus pleiotropy and epistasis in the GP map Hansen ; Phillips The way pleiotropic and epistatic gene effects are organized within the GP map is expected to play a capital role in the capacity of living organisms to adapt and evolve Wagner and Altenberg ; Hansen ; Wagner et al.
In particular, the modular clustering of pleiotropic effects among sets of traits allows phenotypic modules to respond to selection independently from each other, potentially increasing the evolvability of the organism Wagner and Altenberg ; Wagner et al.
This class contains four terms corresponding to the additive and dominance effects of each locus i. The first two terms,correspond to the additive genetic covariance while the next two terms,correspond to the dominance genetic covariance between the traits.
We refer to this class collectively as single-locus effects. These are the covariance terms that result from the familiar single-locus pleiotropic effects. Note that a locus must show the same type of effect on both traits i.
Pleiotropy - Wikipedia
If the sign of the effect of a locus on the two traits is the same, its contribution to the covariance will be positive and if the sign of effects is opposite, its contribution will be negative.
We refer to the former as positive pleiotropy and to the latter as negative pleiotropy. Thus, one can see that the sign of pleiotropy is not related to the sign of the QTL effects, only to the sign of the product of their effects. The second class Equation 4line 2 results from cases where the pair of loci has an epistatic effect on both traits.
There are four terms in this class, corresponding to the four types of epistasis. Note that, as with single-locus effects, both traits must show the same pattern of epistasis for epistasis to contribute to the genetic covariance between the traits. Together, these four terms correspond to the epistatic genetic covariance between traits. From Equation 4 we can see that single-locus additive effects have the greatest impact on the covariance when examined on a per term basis.
Dominance and epistatic effects have a smaller impact, with dd epistasis having a smaller effect than the forms of epistasis that contain an additive component in the interaction.
The contribution of pleiotropic loci involved in multiple epistatic interactions to the covariance between traits was also derived using this same genetic model. These kinds of effects occur when a single locus affects a pair of traits, but its effect on each trait depends on its interaction with a different locus. For example, if locus A interacts with locus B to affect trait X while locus A interacts with locus C to affect trait Y we would consider locus A to be pleiotropic since it affects both X and Y, but its pleiotropic effect is modified by two other loci.
These types of effects could contribute considerable complexity to the genetic architecture of pleiotropy, but they do not contribute to the covariance between traits in the population studied here. As a result, we do not present the derivation of these covariances or discuss these effects here. All mice were bred under conditions previously described Cheverud et al.
Altogether, these 76 loci defined a total of 55 intervals between loci with an average interval length of Precise descriptions of these traits, as well as a calculation of the amount of measurement error involved, are given in Leamy et al. After appropriate adjustment of all skull traits for potential effects of sex, dam, block, and litter size see Leamy et al. We used the interval mapping approach described by Haley and Knottexcept that multivariate canonical correlation rather than regression techniques were used to allow us to simultaneously analyze all traits in each group and thus identify QTL pleiotropically affecting more than one of these traits.
This canonical correlation approach to QTL mapping has been previously described for example, Workman et al. Genotypic index values were imputed every 2 cM between flanking microsatellite markers, using the previously calculated recombination percentages on each chromosome see Cheverud et al.
Canonical correlation of the index values with the skull traits was then performed at each position 2 cM apart on all chromosomes. A whole-genome scan for the skull traits in each of the separate groups was then run as before, but for the interaction of sex with the additive and dominance genotypic index values but also partialing the main effects due to genotypic values and sex.
Chromosomewise and experimentwise threshold LOD values again were estimated by permutation tests, and chromosomes with significant LOD values were assumed to contain a QTL whose effects differed in the two sexes.
No significant sex effects on the expression of these QTL were found, however, so sexes were combined in all subsequent analyses. Once the positions of all QTL on each chromosome were established, we ran multivariate regressions of the traits on the additive and dominance genotypic index values for the QTL at that site to test for significance for each trait and to estimate additive a and dominance d genotypic values.
For each chromosome with two QTL, tests of significance were done with canonical correlation where effects of the other QTL were partialed out, and then regressions were run that included the genotypic index values at the site of the QTL not being analyzed.