Selective sweeps in SARS-CoV-2 variant competition.

The main mathematical result in this paper is that change of variables in the ordinary differential equation (ODE) for the competition of two infections in a Susceptible-Infected-Removed (SIR) model shows that the fraction of cases due to the new variant satisfies the logistic differential equation, which models selective sweeps. Fitting the logistic to data from the Global Initiative on Sharing All Influenza Data (GISAID) shows that this correctly predicts the rapid turnover from one dominant variant to another. In addition, our fitting gives sensible estimates of the increase in infectivity. These arguments are applicable to any epidemic modeled by SIR equations.

Spatial evolutionary games with weak selection.

Recently, a rigorous mathematical theory has been developed for spatial games with weak selection, i.e., when the payoff differences between strategies are small. The key to the analysis is that when space and time are suitably rescaled, the spatial model converges to the solution of a partial differential equation (PDE). This approach can be used to analyze all [Formula: see text] games, but there are a number of [Formula: see text] games for which the behavior of the limiting PDE is not known. In this paper, we give rules for determining the behavior of a large class of [Formula: see text] games and check their validity using simulation. In words, the effect of space is equivalent to making changes in the payoff matrix, and once this is done, the behavior of the spatial game can be predicted from the behavior of the replicator equation for the modified game. We say predicted here because in some cases the behavior of the spatial game is different from that of the replicator equation for the modified game. For example, if a rock-paper-scissors game has a replicator equation that spirals out to the boundary, space stabilizes the system and produces an equilibrium.

Spatial Moran Models I. Stochastic Tunneling in the Neutral Case.

We consider a multistage cancer model in which cells are arranged in a d-dimensional integer lattice. Starting with all wild-type cells, we prove results about the distribution of the first time when two neutral mutations have accumulated in some cell in dimensions d ≥ 2, extending work done by Komarova [12] for d = 1.

Exact solution for a metapopulation version of Schelling's model.

In 1971, Schelling introduced a model in which families move if they have too many neighbors of the opposite type. In this paper, we will consider a metapopulation version of the model in which a city is divided into N neighborhoods, each of which has L houses. There are ρNL red families and ρNL blue families for some ρ < 1/2. Families are happy if there are ≤ ρ(c)L families of the opposite type in their neighborhood and unhappy otherwise. Each family moves to each vacant house at rates that depend on their happiness at their current location and that of their destination. Our main result is that if neighborhoods are large, then there are critical values ρ(b) < ρ(d) < ρ(c), so that for ρ < ρ(b), the two types are distributed randomly in equilibrium. When ρ > ρ(b), a new segregated equilibrium appears; for ρ(b) < ρ < ρ(d), there is bistability, but when ρ increases past ρ(d) the random state is no longer stable. When ρ(c) is small enough, the random state will again be the stationary distribution when ρ is close to 1/2. If so, this is preceded by a region of bistability.

Chemical evolutionary games.

Inspired by the use of hybrid cellular automata in modeling cancer, we introduce a generalization of evolutionary games in which cells produce and absorb chemicals, and the chemical concentrations dictate the death rates of cells and their fitnesses. Our long term aim is to understand how the details of the interactions in a system with n species and m chemicals translate into the qualitative behavior of the system. Here, we study two simple 2×2 games with two chemicals and revisit the two and three species versions of the one chemical colicin system studied earlier by Durrett and Levin (1997). We find that in the 2×2 examples, the behavior of our new spatial model can be predicted from that of the mean field differential equation using ideas of Durrett and Levin (1994). However, in the three species colicin model, the system with diffusion does not have the coexistence which occurs in the lattices model in which sites interact with only their nearest neighbors.

Fingering in Stochastic Growth Models.

Motivated by the widespread use of hybrid-discrete cellular automata in modeling cancer, two simple growth models are studied on the two dimensional lattice that incorporate a nutrient, assumed to be oxygen. In the first model the oxygen concentration u(x, t) is computed based on the geometry of the growing blob, while in the second one u(x, t) satisfies a reaction-diffusion equation. A threshold θ value exists such that cells give birth at rate ß(u(x, t) - θ)+ and die at rate δ(θ - u(x, t)+. In the first model, a phase transition was found between growth as a solid blob and "fingering" at a threshold θc = 0.5, while in the second case fingering always occurs, i.e., θc = 0.

Multiopinion coevolving voter model with infinitely many phase transitions.

We consider an idealized model in which individuals' changing opinions and their social network coevolve, with disagreements between neighbors in the network resolved either through one imitating the opinion of the other or by reassignment of the discordant edge. Specifically, an interaction between x and one of its neighbors y leads to x imitating y with probability (1-α) and otherwise (i.e., with probability α) x cutting its tie to y in order to instead connect to a randomly chosen individual. Building on previous work about the two-opinion case, we study the multiple-opinion situation, finding that the model has infinitely many phase transitions (in the large graph limit with infinitely many initial opinions). Moreover, the formulas describing the end states of these processes are remarkably simple when expressed as a function of ß=α/(1-α).

Graph fission in an evolving voter model.

We consider a simplified model of a social network in which individuals have one of two opinions (called 0 and 1) and their opinions and the network connections coevolve. Edges are picked at random. If the two connected individuals hold different opinions then, with probability 1 - α, one imitates the opinion of the other; otherwise (i.e., with probability α), the link between them is broken and one of them makes a new connection to an individual chosen at random (i) from those with the same opinion or (ii) from the network as a whole. The evolution of the system stops when there are no longer any discordant edges connecting individuals with different opinions. Letting ρ be the fraction of voters holding the minority opinion after the evolution stops, we are interested in how ρ depends on α and the initial fraction u of voters with opinion 1. In case (i), there is a critical value α(c) which does not depend on u, with ρ ≈ u for α > α(c) and ρ ≈ 0 for α < α(c). In case (ii), the transition point α(c)(u) depends on the initial density u. For α > α(c)(u), ρ ≈ u, but for α < α(c)(u), we have ρ(α,u) = ρ(α,1/2). Using simulations and approximate calculations, we explain why these two nearly identical models have such dramatically different phase transitions.

Evolution of resistance and progression to disease during clonal expansion of cancer.

Inspired by previous work of Iwasa et al. (2006) and Haeno et al. (2007), we consider an exponentially growing population of cancerous cells that will evolve resistance to treatment after one mutation or display a disease phenotype after two or more mutations. We prove results about the distribution of the first time when k mutations have accumulated in some cell, and about the growth of the number of type-k cells. We show that our results can be used to derive the previous results about a tumor grown to a fixed size.

A COSII genetic map of the pepper genome provides a detailed picture of synteny with tomato and new insights into recent chromosome evolution in the genus Capsicum.

We report herein the development of a pepper genetic linkage map which comprises 299 orthologous markers between the pepper and tomato genomes (including 263 conserved ortholog set II or COSII markers). The expected position of additional 288 COSII markers was inferred in the pepper map via pepper-tomato synteny, bringing the total orthologous markers in the pepper genome to 587. While pepper maps have been previously reported, this is the first complete map in the sense that all markers could be placed in 12 linkage groups corresponding to the 12 chromosomes. The map presented herein is relevant to the genomes of cultivated C. annuum and wild C. annuum (as well as related Capsicum species) which differ by a reciprocal chromosome translocation. This map is also unique in that it is largely based on COSII markers, which permits the inference of a detailed syntenic relationship between the pepper and tomato genomes-shedding new light on chromosome evolution in the Solanaceae. Since divergence from their last common ancestor is approximately 20 million years ago, the two genomes have become differentiated by a minimum number of 19 inversions and 6 chromosome translocations, as well as numerous putative single gene transpositions. Nevertheless, the two genomes share 35 conserved syntenic segments (CSSs) within which gene/marker order is well preserved. The high resolution COSII synteny map described herein provides a platform for cross-reference of genetic and genomic information (including the tomato genome sequence) between pepper and tomato and therefore will facilitate both applied and basic research in pepper.

Simple models of genomic variation in human SNP density.

BACKGROUND: Descriptive hierarchical Poisson models and population-genetic coalescent mixture models are used to describe the observed variation in single-nucleotide polymorphism (SNP) density from samples of size two across the human genome. RESULTS: Using empirical estimates of recombination rate across the human genome and the observed SNP density distribution, we produce a maximum likelihood estimate of the genomic heterogeneity in the scaled mutation rate theta. Such models produce significantly better fits to the observed SNP density distribution than those that ignore the empirically observed recombinational heterogeneities. CONCLUSION: Accounting for mutational and recombinational heterogeneities can allow for empirically sound null distributions in genome scans for "outliers", when the alternative hypotheses include fundamentally historical and unobserved phenomena.

Stepping-stone spatial structure causes slow decay of linkage disequilibrium and shifts the site frequency spectrum.

The symmetric island model with D demes and equal migration rates is often chosen for the investigation of the consequences of population subdivision. Here we show that a stepping-stone model has a more pronounced effect on the genealogy of a sample. For samples from a small geographical region commonly used in genetic studies of humans and Drosophila, there is a shift of the frequency spectrum that decreases the number of low-frequency-derived alleles and skews the distribution of statistics of Tajima, Fu and Li, and Fay and Wu. Stepping-stone spatial structure also changes the two-locus sampling distribution and increases both linkage disequilibrium and the probability that two sites are perfectly correlated. This may cause a false prediction of cold spots of recombination and may confuse haplotype tests that compute probabilities on the basis of a homogeneously mixing population.

Bayesian and maximum likelihood estimation of genetic maps.

There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods that makes the Bayesian method applicable to large data sets. We present an extensive simulation study examining the statistical properties of the method and comparing it with the likelihood method implemented in Mapmaker. We show that the Maximum A Posteriori (MAP) estimator of the genetic distances, corresponding to the maximum likelihood estimator, performs better than estimators based on the posterior expectation. We also show that while the performance is similar between Mapmaker and the MCMC-based method in the absence of genotyping errors, the MCMC-based method has a distinct advantage in the presence of genotyping errors. A similar advantage of the Bayesian method was not observed for missing data. We also re-analyse a recently published set of data from the eggplant and show that the use of the MCMC-based method leads to smaller estimates of genetic distances.

Microsatellite mutation models: insights from a comparison of humans and chimpanzees.

Using genomic data from homologous microsatellite loci of pure AC repeats in humans and chimpanzees, several models of microsatellite evolution are tested and compared using likelihood-ratio tests and the Akaike information criterion. A proportional-rate, linear-biased, one-phase model emerges as the best model. A focal length toward which the mutational and/or substitutional process is linearly biased is a crucial feature of microsatellite evolution. We find that two-phase models do not lead to a significantly better fit than their one-phase counterparts. The performance of models based on the fit of their stationary distributions to the empirical distribution of microsatellite lengths in the human genome is consistent with that based on the human-chimp comparison. Microsatellites interrupted by even a single point mutation exhibit a twofold decrease in their mutation rate when compared to pure AC repeats. In general, models that allow chimps to have a larger per-repeat unit slippage rate and/or a shorter focal length compared to humans give a better fit to the human-chimp data as well as the human genomic data.

Subfunctionalization: How often does it occur? How long does it take?

The mechanisms responsible for the preservation of duplicate genes have been debated for more than 70 years. Recently, Lynch and Force have proposed a new explanation: subfunctionalization--after duplication the two gene copies specialize to perform complementary functions. We investigate the probability that subfunctionalization occurs, the amount of time after duplication that it takes for the outcome to be resolved, and the relationship of these quantities to the population size and mutation rates.

Approximating selective sweeps.

The fixation of advantageous mutations in a population has the effect of reducing variation in the DNA sequence near that mutation. Kaplan et al. (1989) used a three-phase simulation model to study the effect of selective sweeps on genealogies. However, most subsequent work has simplified their approach by assuming that the number of individuals with the advantageous allele follows the logistic differential equation. We show that the impact of a selective sweep can be accurately approximated by a random partition created by a stick-breaking process. Our simulation results show that ignoring the randomness when the number of individuals with the advantageous allele is small can lead to substantial errors.

Bayesian estimation of genomic distance.

We present a Bayesian approach to the problem of inferring the number of inversions and translocations separating two species. The main reason for developing this method is that it will allow us to test hypotheses about the underlying mechanisms, such as the distribution of inversion track lengths or rate constancy among lineages. Here, we apply these methods to comparative maps of eggplant and tomato, human and cat, and human and cattle with 170, 269, and 422 markers, respectively. In the first case the most likely number of events is larger than the parsimony value. In the last two cases the parsimony solutions have very small probability.

Competition and species packing in patchy environments.

In models of competition in which space is treated as a continuum, and population size as continuous, there are no limits to the number of species that can coexist. For a finite number of sites, N, the results are different. The answer will, of course, depend on the model used to ask the question. In the Tilman-May-Nowak ordinary differential equation model, the number of species is asymptotically C log N with most species packed in at the upper end of the competitive hierarchy. In contrast, for metapopulation models with discrete individuals and stochastic spatial systems with various competition neighborhoods, we find a traditional species area relationship CN(a), with no species clumping along the phenotypic gradient. The exponent a is larger by a factor of 2 for spatially explicit models. In words, a spatial distribution of competitors allows for greater diversity than a metapopulation model due to the effects of recruitment limitation in their competition.

A simple formula useful for positional cloning.

We derive a formula for the distribution of the length T of the recombination interval containing a target gene and using N gametes in a region where R kilobases correspond to 1 cM. The formula can be used to calculate the number of meiotic events required to narrow a target gene down to a specific interval size and hence should be useful for planning positional cloning experiments. The predictions of this formula agree well with the results from a number of published experiments in Arabidopsis.