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The diffusion approximation of the Wright-Fisher model of population genetics leads to partial differentiable equations, the Kolmogorov forward and backward equations, with a leading term that degenerates at the boundary. This degeneracy has the consequence that standard PDE tools do not apply, and solutions lack regularity properties. In this paper, we develop a regularizing blow-up scheme for the iteratively extended global solutions of the backward Kolmogorov equation presented in a previous paper, which are constructed from a known class of solutions, and establish their uniqueness for the stationary case. As the model describes the random genetic drift of several alleles at the same locus from a backward perspective, the occurring singularities result from the loss of an allele. While in an analytical approach, this provides substantial difficulties, from a biological or geometric perspective, this is a natural process that can be analyzed in detail. The presented scheme regularizes the solution via a carefully constructed iterative transformation of the domain. [ABSTRACT FROM PUBLISHER]

Discrete population genetics models with unequal (skewed) fertilities are considered, with an emphasis on skewed versions of Cannings models, conditional branching process models in the spirit of Karlin and McGregor, and compound Poisson models. Three particular classes of models with skewed fertilities are investigated, the Wright-Fisher model, the Dirichlet model, and the Kimura model. For each class the asymptotic behavior as the total population size N tends to infinity is investigated for power law fertilities and for geometric fertilities. This class of models can exhibit a rich variety of sub-linear or even constant effective population sizes. Therefore, the models are not necessarily in the domain of attraction of the Kingman coalescent. For a substantial range of the parameters, discrete-time coalescent processes with simultaneous multiple collisions arise in the limit. [ABSTRACT FROM AUTHOR]

This article is concerned with the long-time behavior of neutral genetic population models with fixed population size. We design an explicit, finite, exact, genealogical tree based representation of stationary populations that holds both for finite and infinite types (or alleles) models. We analyze the decays to the equilibrium of finite populations in terms of the convergence to stationarity of their first common ancestor. We estimate the Lyapunov exponent of the distribution flows with respect to the total variation norm. We give bounds on these exponents only depending on the stability with respect to mutation of a single individual; they are inversely proportional to the population size parameter. [ABSTRACT FROM AUTHOR]

Dale's necessary and sufficient conditions for an array to contain the joint moments for some probability distribution on the unit simplex in R2 are extended to the unit simplex in Rd. These conditions are then used in a computational method, based on linear programming, to evaluate the stationary distribution for the diffusion approximation of the Wright–Fisher model in population genetics. The computational method uses a characterization of the diffusion through an adjoint relation between the diffusion operator and its stationary distribution. Application of this adjoint relation to a set of functions in the domain of the generator leads to one set of constraints for the linear program involving the moments of the stationary distribution. The extension of Dale's conditions on the moments add another set of linear conditions and the linear program is solved to obtain bounds on numerical quantities of interest. Numerical illustrations are given to illustrate the accuracy of the method. [ABSTRACT FROM AUTHOR]