The Experts below are selected from a list of 60081 Experts worldwide ranked by ideXlab platform
Michael A Lenoir - One of the best experts on this subject based on the ideXlab platform.
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the effects of migration and assortative mating on admixture linkage disequilibrium
Genetics, 2017Co-Authors: Noah Zaitlen, Celeste Eng, Scott Huntsman, Melissa L Spear, Marquitta J White, Angel C Y Mak, Adam Davis, Kelly Meade, Emerita Briginobuenaventura, Michael A LenoirAbstract:Statistical Models in medical and population genetics typically assume that individuals assort randomly in a population. While this simplifies Model complexity, it contradicts an increasing body of evidence of nonrandom mating in human populations. Specifically, it has been shown that assortative mating is significantly affected by genomic ancestry. In this work, we examine the effects of ancestry-assortative mating on the linkage disequilibrium between local ancestry tracks of individuals in an admixed population. To accomplish this, we develop an extension to the Wright-Fisher Model that allows for ancestry-based assortative mating. We show that ancestry-assortment perturbs the distribution of local ancestry linkage disequilibrium (LAD) and the variance of ancestry in a population as a function of the number of generations since admixture. This assortment effect can induce errors in demographic inference of admixed populations when methods assume random mating. We derive closed form formulae for LAD under an assortative-mating Model with and without migration. We observe that LAD depends on the correlation of global ancestry of couples in each generation, the migration rate of each of the ancestral populations, the initial proportions of ancestral populations, and the number of generations since admixture. We also present the first direct evidence of ancestry-assortment in African Americans and examine LAD in simulated and real admixed population data of African Americans. We find that demographic inference under the assumption of random mating significantly underestimates the number of generations since admixture, and that accounting for assortative mating using the patterns of LAD results in estimates that more closely agrees with the historical narrative.
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the effects of migration and assortative mating on admixture linkage disequilibrium
bioRxiv, 2016Co-Authors: Noah Zaitlen, Celeste Eng, Scott Huntsman, Melissa L Spear, Marquitta J White, Angel C Y Mak, Adam Davis, Kelly Meade, Emerita Briginobuenaventura, Michael A LenoirAbstract:Statistical Models in medical and population genetics typically assume that individuals assort randomly in a population. While this simplifies Model complexity, it contradicts an increasing body of evidence of non-random mating in human populations. Specifically, it has been shown that assortative mating is significantly affected by genomic ancestry. In this work we examine the effects of ancestry-assortative mating on the linkage disequilibrium between local ancestry tracks of individuals in an admixed population. To accomplish this, we develop an extension to the Wright-Fisher Model that allows for ancestry based assortative mating. We show that ancestry-assortment perturbs the distribution of local ancestry linkage disequilibrium (LAD) and the variance of ancestry in a population as a function of the number of generations since admixture. This assortment effect can induce errors in demographic inference of admixed populations when methods assume random mating. We derive closed form formulae for LAD under an assortative-mating Model with and without migration. We observe that LAD depends on the correlation of global ancestry of couples in each generation, the migration rate of each of the ancestral populations, the initial proportions of ancestral populations, and the number of generations since admixture. We also present the first evidence of ancestry-assortment in African Americans and examine LAD in simulated and real admixed population data of African Americans. We find that demographic inference under the assumption of random mating significantly underestimates the number of generations since admixture, and that accounting for assortative mating using the patterns of LAD results in estimates that more closely agrees with the historical narrative.
Nathan Ross - One of the best experts on this subject based on the ideXlab platform.
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stein s method for the poisson dirichlet distribution and the ewens sampling formula with applications to wright fisher Models
Annals of Applied Probability, 2021Co-Authors: Han L Gan, Nathan RossAbstract:We provide a general theorem bounding the error in the approximation of a random measure of interest—for example, the empirical population measure of types in a Wright–Fisher Model—and a Dirichlet process, which is a measure having Poisson–Dirichlet distributed atoms with i.i.d. labels from a diffuse distribution. The implicit metric of the approximation theorem captures the sizes and locations of the masses, and so also yields bounds on the approximation between the masses of the measure of interest and the Poisson–Dirichlet distribution. We apply the result to bound the error in the approximation of the stationary distribution of types in the finite Wright–Fisher Model with infinite-alleles mutation structure (not necessarily parent independent) by the Poisson–Dirichlet distribution. An important consequence of our result is an explicit upper bound on the total variation distance between the random partition generated by sampling from a finite Wright–Fisher stationary distribution, and the Ewens sampling formula. The bound is small if the sample size n is much smaller than N1/6log(N)−1/2, where N is the total population size. Our analysis requires a result of separate interest, giving an explicit bound on the second moment of the number of types of a finite Wright–Fisher stationary distribution. The general approximation result follows from a new development of Stein’s method for the Dirichlet process, which follows by viewing the Dirichlet process as the stationary distribution of a Fleming–Viot process, and then applying Barbour’s generator approach.
David B Saakian - One of the best experts on this subject based on the ideXlab platform.
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allele fixation probability in a moran Model with fluctuating fitness landscapes
Physical Review E, 2019Co-Authors: David B Saakian, Tatiana Yakushkina, Eugene V KooninAbstract:: Evolution on changing fitness landscapes (seascapes) is an important problem in evolutionary biology. We consider the Moran Model of finite population evolution with selection in a randomly changing, dynamic environment. In the Model, each individual has one of the two alleles, wild type or mutant. We calculate the fixation probability by making a proper ansatz for the logarithm of fixation probabilities. This method has been used previously to solve the analogous problem for the Wright-Fisher Model. The fixation probability is related to the solution of a third-order algebraic equation (for the logarithm of fixation probability). We consider the strong interference of landscape fluctuations, sampling, and selection when the fixation process cannot be described by the mean fitness. Such an effect appears if the mutant allele has a higher fitness in one landscape and a lower fitness in another, compared with the wild type, and the product of effective population size and fitness is large. We provide a generalization of the Kimura formula for the fixation probability that applies to these cases. When the mutant allele has a fitness (dis-)advantage in both landscapes, the fixation probability is described by the mean fitness.
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solution of classical evolutionary Models in the limit when the diffusion approximation breaks down
Physical Review E, 2016Co-Authors: David B SaakianAbstract:The discrete time mathematical Models of evolution (the discrete time Eigen Model, the Moran Model, and the Wright-Fisher Model) have many applications in complex biological systems. The discrete time Eigen Model rather realistically describes the serial passage experiments in biology. Nevertheless, the dynamics of the discrete time Eigen Model is solved in this paper. The 90% of results in population genetics are connected with the diffusion approximation of the Wright-Fisher and Moran Models. We considered the discrete time Eigen Model of asexual virus evolution and the Wright-Fisher Model from population genetics. We look at the logarithm of probabilities and apply the Hamilton-Jacobi equation for the Models. We define exact dynamics for the population distribution for the discrete time Eigen Model. For the Wright-Fisher Model, we express the exact steady state solution and fixation probability via the solution of some nonlocal equation then give the series expansion of the solution via degrees of selection and mutation rates. The diffusion theories result in the zeroth order approximation in our approach. The numeric confirms that our method works in the case of strong selection, whereas the diffusion method fails there. Although the diffusion method is exact for the mean first arrival time, it provides incorrect approximation for the dynamics of the tail of distribution.
Philipp W Messer - One of the best experts on this subject based on the ideXlab platform.
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slim 3 forward genetic simulations beyond the wright fisher Model
Molecular Biology and Evolution, 2019Co-Authors: Benjamin C Haller, Philipp W MesserAbstract:With the desire to Model population genetic processes under increasingly realistic scenarios, forward genetic simulations have become a critical part of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation framework is one of the most powerful and widely used tools in this area. However, its foundation in the Wright-Fisher Model has been found to pose an obstacle to implementing many types of Models; it is difficult to adapt the Wright-Fisher Model, with its many assumptions, to Modeling ecologically realistic scenarios such as explicit space, overlapping generations, individual variation in reproduction, density-dependent population regulation, individual variation in dispersal or migration, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival and hard selection, emergent sex ratios, and so forth. In response to this need, we here introduce SLiM 3, which contains two key advancements aimed at abolishing these limitations. First, the new non-Wright-Fisher or "nonWF" Model type provides a much more flexible foundation that allows the easy implementation of all of the above scenarios and many more. Second, SLiM 3 adds support for continuous space, including spatial interactions and spatial maps of environmental variables. We provide a conceptual overview of these new features, and present several example Models to illustrate their use.
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slim 3 forward genetic simulations beyond the wright fisher Model
bioRxiv, 2018Co-Authors: Benjamin C Haller, Philipp W MesserAbstract:With the desire to Model population genetic processes under increasingly realistic scenarios, forward genetic simulations have become a critical part of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation framework is one of the most powerful and widely used tools in this area. However, its foundation in the Wright-Fisher Model has been found to pose an obstacle to implementing many types of Models; it is difficult to adapt the Wright-Fisher Model, with its many assumptions, to Modeling ecologically realistic scenarios such as explicit space, overlapping generations, individual variation in reproduction, density-dependent population regulation, individual variation in dispersal or migration, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival and hard selection, emergent sex ratios, and so forth. In response to this need, we here introduce SLiM 3, which contains two key advancements aimed at abolishing these limitations. First, the new non-Wright-Fisher or "nonWF" Model type provides a much more flexible foundation that allows the easy implementation of all of the above scenarios and many more. Second, SLiM 3 adds support for continuous space, including spatial interactions and spatial maps of environmental variables. We provide a conceptual overview of these new features, and present several example Models to illustrate their use. These two key features allow SLiM 3 Models to go beyond the Wright-Fisher Model, opening up new horizons for forward genetic Modeling.
Simon Boitard - One of the best experts on this subject based on the ideXlab platform.
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inference of selection from genetic time series using various parametric approximations to the wright fisher Model
G3: Genes Genomes Genetics, 2019Co-Authors: Cyriel Paris, Bertrand Servin, Simon BoitardAbstract:Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to Model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they Model the transition probabilities of the Markov chain. Using the Wright-Fisher Model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this Model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and their estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta with spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher Model in terms of selection inference, for a computational cost that does not increase with population size. We further evaluated this Model for population sizes not accessible to the Wright-Fisher Model and illustrated its performance on a dataset of two divergently selected chicken populations.
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inference of selection from genetic time series using various parametric approximations to the wright fisher Model
bioRxiv, 2019Co-Authors: Cyriel Paris, Bertrand Servin, Simon BoitardAbstract:Abstract Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to Model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they Model the transition probabilities of the Markoiv chain. Using the Wright-Fisher Model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this Model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta-with-Spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher Model in terms of selection inference, for a computational cost that does not increase with population size. We further evaluate this Model for population sizes not accessible to the Wright-Fisher Model and illustrate its performance on a dataset of two divergently selected chicken populations.
