Biology subjects

Haller, B. C.

Publications and source records attributed to Haller, B. C..

18 recordsLinked to original sources

bioRxiv2025

Bridging forward-in-time and coalescent simulations using pyslim

Ancestral Recombination Graphs (ARGs) provide an expressive and compact way to represent genetic variation data generated by simulations embedded within its genealogical history, and it can dramatically speed up simulations. The fact that the ARG records genealogical as well as genomic information opens up the possibility for a number of new analysis and simulation techniques. Here, we aim to introduce the reader to this deep source of information as produced by the forwards simulator SLiM. SLiM records the ARG using the tree sequence format, which can be manipulated using the tskit and pyslim python packages. We first describe the information that SLiM records in the tree sequence, then provide several examples that use the tree sequence as a format to losslessly pass population states between simulators: recapitation of a forwards simulation with coalescent simulation; initiation of a forwards simulation using the results of a coalescent simulation; and parallelization of simulations, first across branches of a phylogenetic tree, and then across the populations of parasites infecting different hosts.

evolutionary biology↗

bioRxiv2025

SimHumanity: Using SLiM 5.0 to run whole-genome simulations of human evolution

The reconstruction of human evolutionary history has undergone repeated advances, each made possible by methodological innovations. In recent decades, genetic and genomic data played a central role in the reconstruction of major evolutionary events such as the out-of-Africa migration, and genetic simulations of human evolutionary history have come to play a major role in testing more specific hypotheses including proposed patterns of migration and admixture with archaic hominins. Increasing computational power has allowed human evolutionary history to be modeled at ever-larger scales, but simulations that encompass the complete human genome, including sex chromosomes and mitochondrial DNA, have been difficult due to the lack of support for whole-genome models in commonly used evolutionary simulation frameworks. With the recent introduction of SLiM 5 such simulations are now straightforward to construct, allowing the easy simulation of humans at whole-genome scale under different demographic models and evolutionary dynamics. We here present three versions of a reusable, customizable, open-source SLiM 5 model for simulating the molecular evolution of the full human genome. We also show some simple analyses of results from the model, to illustrate its utility. We hope this model, which we have nicknamed "SimHumanity" in jest, will facilitate further progress in the field of human evolutionary simulations.

evolutionary biology↗

bioRxiv2025

SLiM 5: Eco-evolutionary simulations across multiple chromosomes and full genomes

Evolutionary simulations of multiple chromosomes, even up to the scale of full-genome simulations, are becoming increasingly important in population genetics and evolutionary ecology. Unfortunately, the popular simulation framework SLiM has always been intrinsically limited to simulations of a single diploid chromosome. Modeling multiple chromosomes of different types, such as sex chromosomes, has always been cumbersome even with scripting, presenting a substantial barrier to the development of full-genome simulations. Here we present SLiM 5, a major extension of SLiMs capabilities for simulating multiple chromosomes. Modeling up to 256 chromosomes is now possible, and each chromosome may belong to any of a wide variety of types - not just autosomes (diploid and haploid), but also sex chromosomes (X, Y, Z, and W), haploid mitochondrial and chloroplast DNA, and more. This new functionality is integrated across all of SLiM, including not only the mechanics of reproduction and inheritance, but also input and output of multi-chromosome data in formats like VCF, and tree-sequence recording across multiple chromosomes. New recipes in the SLiM manual demonstrate these new features, and SLiMs graphical modeling environment, SLiMgui, has been extended in many ways for the visualization of multi-chromosome models. These new features will open new horizons and enable a heightened level of realism for full-genome simulations.

evolutionary biology↗

bioRxiv2025

Gene drive dynamics in plants: the role of seedbanks

Gene drives offer revolutionary potential for the management of problematic plant populations, such as invasive weeds and herbicide-resistant species, by rapidly spreading desired genetic alterations. Two recent studies have provided the first experimental demonstrations of engineered CRISPR gene drive systems in plants (CAIN and ClvR). However, the successful application of such systems in the field will critically depend on an accurate understanding of plant-specific life-history traits, especially seed dormancy, a ubiquitous yet frequently overlooked eco-evolutionary force. In this study, we develop the first comprehensive modeling framework for gene drives in plant populations that incorporates a persistent soil seedbank. We show how the presence of a seedbank can significantly slow gene drive spread but also reduce the genetic load required to achieve population elimination. Furthermore, we show that seedbanks substantially increase the required introduction frequency of threshold-dependent gene drives, which could prevent establishment in some cases, yet also provide an intrinsic biosafety mechanism for confining a highly efficient drive to a target population. Our study highlights the need to incorporate seedbank dynamics into gene drive strategies to ensure realistic predictions and successful field applications.

evolutionary biology↗

bioRxiv2025

Accessible, realistic genome simulation with selection using stdpopsim

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsims core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

evolutionary biology↗

bioRxiv2024

Purifying Selection Shapes the Dynamics of P-element Invasion in Drosophila Populations

BackgroundTransposable elements (TEs) are DNA sequences that can move within a host genome. Many new TE insertions have deleterious effects on their host and are therefore removed by purifying selection. The genomic distribution of TEs thus reflects a balance between new insertions and purifying selection. However, the inference of purifying selection against deleterious TE insertions from the patterns observed in natural populations is challenged by the confounding effects of demographic events, such as population bottlenecks and migration. ResultsWe used Experimental Evolution to study the role of purifying selection during the invasion of the P-element, a highly invasive TE, in replicated Drosophila simulans populations under controlled laboratory conditions. Because the change in P-element copy number over time provides information about the transposition rate and the effect of purifying selection, we repeatedly sequenced the experimental populations to study the P-element invasion dynamics. Based on the empirical data, we used Gaussian Process surrogate models to efficiently explore the parameter space and identify parameter combinations that best reproduce the experimental P-element invasion trajectories. Assuming that beneficial P-element insertions are negligible, we found that, in our experimental populations, 73% (60.9% - 76.1%) of new P-element insertions are under purifying selection with a mean selection coefficient of -0.056 (-0.060 - -0.042), highlighting the central role of selection in shaping P-element invasion dynamics. ConclusionThis study underscores the power of Experimental Evolution as a tool for studying transposable element invasions and highlights the pivotal role of purifying selection in regulating P-element dynamics.

evolutionary biology↗

bioRxiv2024

Population genetics meets ecology: a guide to individual-based simulations in continuous landscapes

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. We additionally discuss natural selection - in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).

evolutionary biology↗

bioRxiv2024

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogens genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIMs capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

bioinformatics↗

bioRxiv2024

Resource-explicit interactions in spatial population models

Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. Though these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. Additionally, models structured in this manner exhibit other desirable characteristics, including more realistic spatial dynamics near the edge of the simulated area, and an efficient route for modeling more complex heterogeneous landscapes.

ecology↗

bioRxiv2022

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

bioRxiv2022

slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes

One of the goals of population genetics is to understand how evolutionary forces shape patterns of genetic variation over time. However, because populations evolve across both time and space, most evolutionary processes also have an important spatial component, acting through phenomena such as isolation by distance, local mate choice, or uneven distribution of resources. This spatial dimension is often neglected, partly due to the lack of tools specifically designed for building and evaluating complex spatio-temporal population genetic models. To address this methodological gap, we present a new framework for simulating spatially-explicit genomic data, implemented in a new R package called slendr (www.slendr.net), which leverages a SLiM simulation back-end script bundled with the package. With this framework, the users can programmatically and visually encode spatial population ranges and their temporal dynamics (i.e., population displacements, expansions, and contractions) either on real Earth landscapes or on abstract custom maps, and schedule splits and gene-flow events between populations using a straightforward declarative language. Additionally, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. Together with its R-idiomatic interface to the tskit library for tree-sequence processing and analysis, slendr opens up the possibility of performing efficient, reproducible simulations of spatio-temporal genomic data entirely within the R environment, leveraging its wealth of libraries for geospatial data analysis, statistics, and visualization. Here, we present the design of the slendr R package and demonstrate its features on several practical example workflows.

genomics↗

bioRxiv2021

Individual-based modeling of genome evolution in haplodiploid organisms

Ants, bees, wasps, bark beetles, and other species have haploid males and diploid females. Although such haplodiploid species play key ecological roles and are threatened by environmental changes, no general framework exists for simulating their genetic evolution. Here, we use the SLiM simulation environment to build a novel model for individual-based forward simulation of genetic evolution in haplodiploid populations. We compare the fates of adaptive and deleterious mutations and find that selection is more effective in haplodiploid species than in diploid species. Our open-source model will help understand the evolution of sociality and how ecologically important species may adapt to changing environments.

evolutionary biology↗

bioRxiv2021

Seeing the Forest for the trees: Assessing genetic offset predictions with Gradient Forest

Gradient Forest (GF) is increasingly being used to forecast climate change impacts, but remains mostly untested for this purpose. We explore its robustness to assumption violations, and relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic "population genetic" model with a single environmentally adapted locus; and (3) a polygenic "quantitative genetic" model with two adaptive traits, each adapting to a different environment. Although we found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. It could also be confounded by neutral demography, genomic architecture, and the nature of the adaptive environment. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of forecasting maladaptation.

evolutionary biology↗

bioRxiv2021

Migration Restores Hybrid Incompatibility Driven By Nuclear-Mitochondrial Sexual Conflict

In the mitochondrial genome, sexual asymmetry in transmission allows the accumulation of male-harming mutations since selection acts only on the effect of the mutation in females. Called the "Mothers Curse", this phenomenon induces a selective pressure for nuclear variants that compensate for this reduction in male fitness. Previous work has demonstrated the existence of these interactions and their potential to act as Dobzhansky-Muller incompatibilities, contributing to reproductive isolation between populations. However, it is not clear how readily they would give rise to and sustain hybrid incompatibilities. Here, we use computer simulations in SLiM 3 to investigate the consequences of sexually antagonistic mitochondrial-nuclear interactions in a subdivided population. We consider distinct migration schemes and vary the chromosomal location, and consequently the transmission pattern, of nuclear restorers. Disrupting these co-evolved interactions results in less-fit males, skewing the sex ratio toward females. Restoration of male fitness depends on both the chromosomal location of nuclear restorer loci and the migration scheme. Our results show that these interactions may act as Dobzhansky-Muller incompatibilities, but their strength is not enough to drive population isolation. Overall, this model shows the varied ways in which populations can respond to migrations disruption of co-evolved mitochondrial-nuclear interactions.

evolutionary biology↗

bioRxiv2020

Simulation of bacterial populations with SLiM

Simulation of genomic data is a key tool in population genetics, yet, to date, there is no forward-in-time simulator of bacterial populations that is both computationally efficient and adaptable to a wide range of scenarios. Here we demonstrate how to simulate bacterial populations with SLiM, a forward-in-time simulator built for eukaryotes. SLiM has gained many users in recent years, due to its speed and power, and has extensive documentation showcasing various scenarios that it can simulate. This paper focuses on a simple demographic scenario, to explore unique aspects of modeling bacteria in SLiMs scripting language. In addition, we illustrate the flexibility of SLiM by simulating the growth of bacteria on a Petri dish with antibiotic. To foster the development of bacterial simulations based upon this recipe, we explain the inner workings of its code. We also validate the simulator, by extensively testing the results of simulations against existing simulators, and against theoretical expectations for some summary statistics. This protocol, with the flexibility and power of SLiM, will enable the community to simulate bacterial populations efficiently under a wide range of evolutionary scenarios.

evolutionary biology↗

bioRxiv2018

SLiM 3: Forward genetic simulations beyond the Wright-Fisher model

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.

evolutionary biology↗

bioRxiv2018

Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes

There is an increasing demand for evolutionary models to incorporate relatively realistic dynamics, ranging from selection at many genomic sites to complex demography, population structure, and ecological interactions. Such models can generally be implemented as individual-based forward simulations, but the large computational overhead of these models often makes simulation of whole chromosome sequences in large populations infeasible. This situation presents an important obstacle to the field that requires conceptual advances to overcome. The recently developed tree-sequence recording method (Kelleher et al., 2018), which stores the genealogical history of all genomes in the simulated population, could provide such an advance. This method has several benefits: (1) it allows neutral mutations to be omitted entirely from forward-time simulations and added later, thereby dramatically improving computational efficiency; (2) it allows neutral burn-in to be constructed extremely efficiently after the fact, using \"recapitation\"; (3) it allows direct examination and analysis of the genealogical trees along the genome; and (4) it provides a compact representation of a populations genealogy that can be analyzed in Python using the msprime package. We have implemented the tree-sequence recording method in SLiM 3 (a free, open-source evolutionary simulation software package) and extended it to allow the recording of non-neutral mutations, greatly broadening the utility of this method. To demonstrate the versatility and performance of this approach, we showcase several practical applications that would have been beyond the reach of previously existing methods, opening up new horizons for the modeling and exploration of evolutionary processes.

evolutionary biology↗

bioRxiv2017

asymptoticMK: A web-based tool for the asymptotic McDonald-Kreitman test

The McDonald-Kreitman (MK) test is a widely used method for quantifying the role of positive selection in molecular evolution. One key shortcoming of this test lies in its sensitivity to the presence of slightly deleterious mutations, which can severely bias its estimates. An asymptotic version of the MK test was recently introduced that addresses this problem by evaluating polymorphism levels for different mutation frequencies separately, and then extrapolating a function fitted to that data. Here we present asymptoticMK, a web-based implementation of this asymptotic McDonald-Kreitman test. Our web service provides a simple R-based interface into which the user can upload the required data (polymorphism and divergence data for the genomic test region and a neutrally evolving reference region). The web service then analyzes the data and provides plots of the test results. This service is free to use, open-source, and available at http://benhaller.com/messerlab/asymptoticMK.html.

evolutionary biology↗