FTEC: a coalescent simulator for modeling faster than exponential growth.
Ontology highlight
ABSTRACT:
SUBMITTER: Reppell M
PROVIDER: S-EPMC3338023 | biostudies-literature | 2012 May
REPOSITORIES: biostudies-literature
Publications
FTEC: a coalescent simulator for modeling faster than exponential growth.
Reppell Mark M Boehnke Michael M Zöllner Sebastian S
Bioinformatics (Oxford, England) 20120321 9
<h4>Summary</h4>Recent genetic studies as well as recorded history point to massive growth in human population sizes during the recent past. To model and understand this growth accurately we introduce FTEC, an easy-to-use coalescent simulation program capable of simulating haplotype samples drawn from a population that has undergone faster than exponential growth. Samples drawn from a population that has undergone faster than exponential growth show an excess of very rare variation and more rapi ...[more]
Similar Datasets
Project description:One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth-death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R0 close to 1, but substantial differences remain for large R0. In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.
| S-EPMC4426635 | biostudies-literature
Project description:Current human sequencing projects observe an abundance of extremely rare genetic variation, suggesting recent acceleration of population growth. To better understand the impact of such accelerating growth on the quantity and nature of genetic variation, we present a new class of models capable of incorporating faster than exponential growth in a coalescent framework. Our work shows that such accelerated growth affects only the population size in the recent past and thus large samples are required to detect the models' effects on patterns of variation. When we compare models with fixed initial growth rate, models with accelerating growth achieve very large current population sizes and large samples from these populations contain more variation than samples from populations with constant growth. This increase is driven almost entirely by an increase in singleton variation. Moreover, linkage disequilibrium decays faster in populations with accelerating growth. When we instead condition on current population size, models with accelerating growth result in less overall variation and slower linkage disequilibrium decay compared to models with exponential growth. We also find that pairwise linkage disequilibrium of very rare variants contains information about growth rates in the recent past. Finally, we demonstrate that models of accelerating growth may substantially change estimates of present-day effective population sizes and growth times.
| S-EPMC3948808 | biostudies-other
Project description:MotivationEfficient simulation of population genetic samples under a given demographic model is a prerequisite for many analyses. Coalescent theory provides an efficient framework for such simulations, but simulating longer regions and higher recombination rates remains challenging. Simulators based on a Markovian approximation to the coalescent scale well, but do not support simulation of selection. Gene conversion is not supported by any published coalescent simulators that support selection.ResultsWe describe cosi2, an efficient simulator that supports both exact and approximate coalescent simulation with positive selection. cosi2 improves on the speed of existing exact simulators, and permits further speedup in approximate mode while retaining support for selection. cosi2 supports a wide range of demographic scenarios, including recombination hot spots, gene conversion, population size changes, population structure and migration. cosi2 implements coalescent machinery efficiently by tracking only a small subset of the Ancestral Recombination Graph, sampling only relevant recombination events, and using augmented skip lists to represent tracked genetic segments. To preserve support for selection in approximate mode, the Markov approximation is implemented not by moving along the chromosome but by performing a standard backwards-in-time coalescent simulation while restricting coalescence to node pairs with overlapping or near-overlapping genetic material. We describe the algorithms used by cosi2 and present comparisons with existing selection simulators.Availability and implementationA free C++ implementation of cosi2 is available at http://broadinstitute.org/mpg/cosi2.
| S-EPMC4296154 | biostudies-literature
Project description:Humans grossly underestimate exponential growth, but are at the same time overconfident in their (poor) judgement. The so-called 'exponential growth bias' is of new relevance in the context of COVID-19, because it explains why humans have fundamental difficulties to grasp the magnitude of a spreading epidemic. Here, we addressed the question, whether logarithmic scaling and contextual framing of epidemiological data affect the anticipation of exponential growth. Our findings show that underestimations were most pronounced when growth curves were linearly scaled and framed in the context of a more advanced epidemic progression. For logarithmic scaling, estimates were much more accurate, on target for growth rates around 31%, and not affected by contextual framing. We conclude that the logarithmic depiction is conducive for detecting exponential growth during an early phase as well as resurgences of exponential growth.
| S-EPMC8080009 | biostudies-literature
Project description:BACKGROUND: Recombination plays an important role in the maintenance of genetic diversity in many types of organisms, especially diploid eukaryotes. Recombination can be studied and used to map diseases. However, recombination adds a great deal of complexity to the genetic information. This renders estimation of evolutionary parameters more difficult. After the coalescent process was formulated, models capable of describing recombination using graphs, such as ancestral recombination graphs (ARG) were also developed. There are two typical models based on which to simulate ARG: back-in-time model such as ms and spatial model including Wiuf&Hein's, SMC, SMC', and MaCS. RESULTS: In this study, a new method of modeling coalescence with recombination, Spatial Coalescent simulator (SC), was developed, which considerably improved the algorithm described by Wiuf and Hein. The present algorithm constructs ARG spatially along the sequence, but it does not produce any redundant branches which are inevitable in Wiuf and Hein's algorithm. Interestingly, the distribution of ARG generated by the present new algorithm is identical to that generated by a typical back-in-time model adopted by ms, an algorithm commonly used to model coalescence. It is here demonstrated that the existing approximate methods such as the sequentially Markov coalescent (SMC), a related method called SMC', and Markovian coalescent simulator (MaCS) can be viewed as special cases of the present method. Using simulation analysis, the time to the most common ancestor (TMRCA) in the local trees of ARGs generated by the present algorithm was found to be closer to that produced by ms than time produced by MaCS. Sample-consistent ARGs can be generated using the present method. This may significantly reduce the computational burden. CONCLUSION: In summary, the present method and algorithm may facilitate the estimation and description of recombination in population genomics and evolutionary biology.
| S-EPMC4137079 | biostudies-other
Project description:In 2017-2018, a group of international development funding agencies launched the Crops to End Hunger initiative to modernize public plant breeding in lower-income countries. To inform that initiative, USAID asked the International Food Policy Research Institute and the United States Department of Agriculture's Economic Research Service to estimate the impacts of faster productivity growth for 20 food crops on income and other indicators in 106 countries in developing regions in 2030. We first estimated the value of production in 2015 for each crop using data from FAO. We then used the IMPACT and GLOBE economic models to estimate changes in the value of production and changes in economy-wide income under scenarios of faster crop productivity growth, assuming that increased investment will raise annual rates of yield growth by 25% above baseline growth rates over the period 2015-2030. We found that faster productivity growth in rice, wheat and maize increased economy-wide income in the selected countries in 2030 by 59 billion USD, 27 billion USD and 21 billion USD respectively, followed by banana and yams with increases of 9 billion USD each. While these amounts represent small shares of total GDP, they are 2-15 times current public R&D spending on food crops in developing countries. Income increased most in South Asia and Sub-Saharan Africa. Faster productivity growth in rice and wheat reduced the population at risk of hunger by 11 million people and 6 million people respectively, followed by plantain and cassava with reductions of about 2 million people each. Changes in adequacy ratios were relatively large for carbohydrates (already in surplus) and relatively small for micronutrients. In general, we found that impacts of faster productivity growth vary widely across crops, regions and outcome indicators, highlighting the importance of identifying the potentially diverse objectives of different decision makers and recognizing possible tradeoffs between objectives.
| S-EPMC8049331 | biostudies-literature
Project description:Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks.
| S-EPMC3102079 | biostudies-other
Project description:Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics and evolutionary biology. Kingman's coalescent process enables inference of past population dynamics directly from molecular sequence data, and researchers have developed a number of flexible coalescent-based models for Bayesian nonparametric estimation of the effective population size as a function of time. Major goals of demographic reconstruction include identifying driving factors of effective population size, and understanding the association between the effective population size and such factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates that exploit Gaussian Markov random fields to achieve temporal smoothing of effective population size trajectories. To approximate the posterior distribution, we adapt efficient Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. Incorporating covariates into the demographic inference framework enables the modeling of associations between the effective population size and covariates while accounting for uncertainty in population histories. Furthermore, it can lead to more precise estimates of population dynamics. We apply our model to four examples. We reconstruct the demographic history of raccoon rabies in North America and find a significant association with the spatiotemporal spread of the outbreak. Next, we examine the effective population size trajectory of the DENV-4 virus in Puerto Rico along with viral isolate count data and find similar cyclic patterns. We compare the population history of the HIV-1 CRF02_AG clade in Cameroon with HIV incidence and prevalence data and find that the effective population size is more reflective of incidence rate. Finally, we explore the hypothesis that the population dynamics of musk ox during the Late Quaternary period were related to climate change. [Coalescent; effective population size; Gaussian Markov random fields; phylodynamics; phylogenetics; population genetics.
| S-EPMC5066065 | biostudies-literature
Project description:The physiological parameters characterizing human capacities (the ability to move, reproduce or perform tasks) evolve with ageing: performance is limited at birth, increases to a maximum and then decreases back to zero at the day of death. Physical and intellectual skills follow such a pattern. Here, we investigate the development of sport and chess performances during the lifetime at two different scales: the individual athletes' careers and the world record by age class in 25 Olympic sports events and in grandmaster chess players. For all data sets, a biphasic development of growth and decline is described by a simple model that accounts for 91.7% of the variance at the individual level and 98.5% of the variance at the species one. The age of performance peak is computed at 26.1 years old for the events studied (26.0 years old for track and field, 21.0 years old for swimming and 31.4 years old for chess). The two processes (growth and decline) are exponential and start at age zero. Both were previously demonstrated to happen in other human and non-human biological functions that evolve with age. They occur at the individual and species levels with a similar pattern, suggesting a scale invariance property.
| S-EPMC3682058 | biostudies-literature
Project description:Exponential-family Random Graph Models (ERGMs) have long been at the forefront of the analysis of relational data. The exponential-family form allows complex network dependencies to be represented. Models in this class are interpretable, flexible and have a strong theoretical foundation. The availability of powerful user-friendly open-source software allows broad accessibility and use. However, ERGMs sometimes suffer from a serious condition known as near-degeneracy, in which the model exhibits unrealistic probabilistic behavior or a severe lack-of-fit to real network data. Recently, Fellows and Handcock (2017) proposed a new model class, the Tapered ERGM, which circumvents the issue of near-degeneracy while maintaining the desirable features of ERGMs. However, the question of how to determine the proper amount of tapering needed for any model was heretofore left unanswered. This paper develops a new methodology for how to determine the necessary level of tapering and as such provides a new approach to inference for the Tapered ERGM class. Noting that a Tapered ERGM can always be made non-degenerate, we offer data-driven approaches for determining the amount of tapering necessary. The mean-value parameter estimates are unaffected by tapering, and we show that the natural parameter estimates are numerically weakly varying by the level of tapering. We then apply the Tapered ERGM to two published networks to demonstrate its effectiveness in cases where typical ERGMs fail and present the case for Tapered ERGMs replacing ERGMs entirely.
| S-EPMC10441622 | biostudies-literature