Publications

Gaussian process emulation for exploring complex infectious disease models

Published in PLOS Computational Biology, 2025

Detailed individual-based models can capture a high degree of realism, but their complexity often makes them too slow or cumbersome to explore fully. In this work, we explore how Gaussian Process emulation — a statistical method for building fast, accurate surrogate models — can help overcome this challenge. First, we developed an individual-based model that simulates disease spread in a population, accounting for features such as social structure, human mobility, and seasonal variation in infection risk. We then trained a Gaussian Process surrogate model on epidemiological metrics derived from the outputs of this individual-based model, which allowed us to predict these metrics almost instantly across a wide range of parameter values. This approach made it possible to systematically explore which factors drive simulated epidemics. We found that two variables — average infectivity and average mobility — had the greatest influence on whether and how outbreaks occurred. Our results demonstrate that Gaussian Process emulation offers a practical and powerful way to study complex disease systems. While we applied this approach to infectious disease transmission, the underlying method can be useful for analyzing many other types of detailed, simulation-based models.

Recommended citation: Langmüller AM, Chandrasekher KA, Haller BC, Champer SE, Murdock CC, Messer PW. Gaussian process emulation for exploring complex infectious disease models. PLOS Computational Biology 21(12): e1013849 (2025). https://doi.org/10.1371/journal.pcbi.1013849

Digest: Winter Is Coming: Overwintering Selection and the Cost of Insecticide Resistance in Fruit Flies

Published in Evolution, 2025

This Evolution Digest summarizes recent research by Prileson et al. on how winter survival can cause rapid evolutionary change in fruit fly populations. The original study shows that conditions experienced during overwintering can drive rapid adaptation, but that these changes may come with trade-offs — notably, reduced resistance to insecticides.

Recommended citation: Langmüller AM. Digest: Winter is coming: Overwintering selection and the cost of insecticide resistance in fruit flies. Evolution, qpaf256 (2025). https://doi.org/10.1093/evolut/qpaf256

Purifying selection shapes the dynamics of P-element invasion in Drosophila simulans populations

Published in Genome Biology, 2025

Transposable elements (TEs) are mobile DNA sequences that can disrupt genome function. In this study, we investigated how natural selection acts to remove harmful TE insertions by tracking the invasion of the P-element—a highly active TE—in laboratory populations of Drosophila simulans. Using experimental evolution and whole-genome sequencing over time, we monitored how P-element copy numbers changed across generations. We combined this data with machine learning models to estimate the strength of purifying selection acting against new insertions. Our results show that around 73% of new P-element insertions are harmful and are being removed by selection, revealing that purifying selection plays a major role in controlling TE spread. This work demonstrates how experimental evolution, paired with computational modeling, can uncover the evolutionary forces shaping genome architecture.

Recommended citation: Langmüller AM, Haller BC, Nolte V, Schlötterer C. Purifying selection shapes the dynamics of P-element invasion in Drosophila simulans populations. Genome Biol 26, 221 (2025). https://doi.org/10.1186/s13059-025-03688-2

Catching a wave: On the suitability of traveling-wave solutions in epidemiological modeling

Published in Theoretical Population Biology, 2024

Ordinary differential equation models such as the classical SIR model are widely used in epidemiology to study and predict infectious disease dynamics. However, these models typically assume that populations are homogeneously mixed, ignoring possible variations in disease prevalence due to spatial heterogeneity. To address this issue, reaction–diffusion models have been proposed as an alternative approach to modeling spatially continuous populations in which individuals move in a diffusive manner. In this study, we explore the conditions under which such spatial structure must be explicitly considered to accurately predict disease spread, and when the assumption of homogeneous mixing remains adequate. In particular, we derive a critical threshold for the diffusion coefficient below which disease transmission dynamics exhibit spatial heterogeneity. We validate our analytical results with individual-based simulations of disease transmission across a two-dimensional continuous landscape. Using this framework, we further explore how key epidemiological parameters such as the probability of disease establishment, its maximum incidence, and its final epidemic size are affected by incorporating spatial structure into SI, SIS, and SIR models. We discuss the implications of our findings for epidemiological modeling and identify design considerations and limitations for spatial simulation models of disease dynamics.

Recommended citation: Langmüller AM, Hermisson J, Murdock CC, Messer PW. Catching a wave: On the suitability of traveling-wave solutions in epidemiological modeling. Theoretical Population Biology. 2024. doi: 10.1016/j.tpb.2024.12.004

The genomic distribution of transposable elements is driven by spatially variable purifying selection

Published in Nucleic Acids Research, 2023

It is widely accepted that the genomic distribution of transposable elements (TEs) mainly reflects the outcome of purifying selection and insertion bias. Nevertheless, the relative importance of these two evolutionary forces could not be tested thoroughly. Here, we introduce an experimental system, which allows separating purifying selection from TE insertion bias. We used experimental evolution to study the TE insertion patterns in Drosophila simulans founder populations harboring 1040 insertions of an active P-element. After 10 generations at a large population size, we detected strong selection against P-element insertions. The exception were P-element insertions in genomic regions for which a strong insertion bias has been proposed. Because recurrent P-element insertions cannot explain this pattern, we conclude that purifying selection, with variable strength along the chromosomes, is the major determinant of the genomic distribution of P-elements. Genomic regions with relaxed purifying selection against P-element insertions exhibit normal levels of purifying selection against base substitutions. This suggests that different types of purifying selection operate on base substitutions and P-element insertions. Our results highlight the power of experimental evolution to understand basic evolutionary processes, which are difficult to infer from patterns of natural variation alone.

Recommended citation: Langmüller AM, Nolte V, Dolezal M, Schlötterer C. The genomic distribution of transposable elements is driven by spatially variable purifying selection. Nucleic Acids Research. 2023 Aug 10; gkad635. doi: 10.1093/nar/gkad635

Deep cis-regulatory homology of the butterfly wing pattern ground plan

Published in Science, 2022

Butterfly wing patterns derive from a deeply conserved developmental ground plan yet are diverse and evolve rapidly. It is poorly understood how gene regulatory architectures can accommodate both deep homology and adaptive change. To address this, we characterized the cis-regulatory evolution of the color pattern gene WntA in nymphalid butterflies. Comparative assay for transposase-accessible chromatin using sequencing (ATAC-seq) and in vivo deletions spanning 46 cis-regulatory elements across five species revealed deep homology of ground plan–determining sequences, except in monarch butterflies. Furthermore, noncoding deletions displayed both positive and negative regulatory effects that were often broad in nature. Our results provide little support for models predicting rapid enhancer turnover and suggest that deeply ancestral, multifunctional noncoding elements can underlie rapidly evolving trait systems.

Recommended citation: Mazo-Vargas A, Langmüller AM, Wilder A, Van der Burg KRL, Lewis JJ, Messer PW, Zhang L, Martin A, Reed RD. Deep cis-regulatory homology of the butterfly wing pattern ground plan. Science. 2022 Oct 20; 378:304-308. doi: 10.1126/science.abi9407

Fitness effects of CRISPR endonucleases in Drosophila melanogaster populations

Published in eLife, 2022

CRISPR/Cas9 provides a highly efficient and flexible genome editing technology with numerous potential applications ranging from gene therapy to population control. Some proposed applications involve the integration of CRISPR/Cas9 endonucleases into an organism’s genome, which raises questions about potentially harmful effects to the transgenic individuals. One example for which this is particularly relevant are CRISPR-based gene drives conceived for the genetic alteration of entire populations. The performance of such drives can strongly depend on fitness costs experienced by drive carriers, yet relatively little is known about the magnitude and causes of these costs. Here, we assess the fitness effects of genomic CRISPR/Cas9 expression in Drosophila melanogaster cage populations by tracking allele frequencies of four different transgenic constructs that allow us to disentangle direct fitness costs due to the integration, expression, and target-site activity of Cas9, from fitness costs due to potential off-target cleavage. Using a maximum likelihood framework, we find that a model with no direct fitness costs but moderate costs due to off-target effects fits our cage data best. Consistent with this, we do not observe fitness costs for a construct with Cas9HF1, a high-fidelity version of Cas9. We further demonstrate that using Cas9HF1 instead of standard Cas9 in a homing drive achieves similar drive conversion efficiency. These results suggest that gene drives should be designed with high-fidelity endonucleases and may have implications for other applications that involve genomic integration of CRISPR endonucleases.

Recommended citation: Langmüller AM, Champer J, Lapinska S, Xie L, Metzloff M, Champer SE, Liu J, Xu Y, Du J, Clark AG, Messer PW. Fitness effects of CRISPR endonucleases in Drosophila melanogaster populations. Elife. 2022 Sep 22;11:e71809. doi: 10.7554/eLife.71809.

A homing suppression gene drive with multiplexed gRNAs maintains high drive conversion efficiency and avoids functional resistance alleles

Published in G3, 2022

Gene drives are engineered alleles that can bias inheritance in their favor, allowing them to spread throughout a population. They could potentially be used to modify or suppress pest populations, such as mosquitoes that spread diseases. CRISPR/Cas9 homing drives, which copy themselves by homology-directed repair in drive/wild-type heterozygotes, are a powerful form of gene drive, but they are vulnerable to resistance alleles that preserve the function of their target gene. Such resistance alleles can prevent successful population suppression. Here, we constructed a homing suppression drive in Drosophila melanogaster that utilized multiplexed gRNAs to inhibit the formation of functional resistance alleles in its female fertility target gene. The selected gRNA target sites were close together, preventing reduction in drive conversion efficiency. The construct reached a moderate equilibrium frequency in cage populations without apparent formation of resistance alleles. However, a moderate fitness cost prevented elimination of the cage population, showing the importance of using highly efficient drives in a suppression strategy, even if resistance can be addressed. Nevertheless, our results experimentally demonstrate the viability of the multiplexed gRNAs strategy in homing suppression gene drives.

Recommended citation: Yang E, Metzloff M, Langmüller AM, Xu X, Clark AG, Messer PW, Champer J. A homing suppression gene drive with multiplexed gRNAs maintains high drive conversion efficiency and avoids functional resistance alleles. G3 (Bethesda). 2022 May 30;12(6):jkac081. doi: 10.1093/g3journal/jkac081. PMID: 35394026; PMCID: PMC9157102.

Fine Mapping without Phenotyping: Identification of Selection Targets in Secondary Evolve and Resequence Experiments

Published in Genome Biology and Evolution, 2021

Evolve and Resequence (E&R) studies investigate the genomic selection response of populations in an Experimental Evolution setup. Despite the popularity of E&R, empirical studies in sexually reproducing organisms typically suffer from an excess of candidate loci due to linkage disequilibrium, and single gene or SNP resolution is the exception rather than the rule. Recently, so-called “secondary E&R” has been suggested as promising experimental follow-up procedure to confirm putatively selected regions from a primary E&R study. Secondary E&R provides also the opportunity to increase mapping resolution by allowing for additional recombination events, which separate the selection target from neutral hitchhikers. Here, we use computer simulations to assess the effect of different crossing schemes, population size, experimental duration, and number of replicates on the power and resolution of secondary E&R. We find that the crossing scheme and population size are crucial factors determining power and resolution of secondary E&R: A simple crossing scheme with few founder lines consistently outcompetes crossing schemes where evolved populations from a primary E&R experiment are mixed with a complex ancestral founder population. Regardless of the experimental design tested, a population size of at least 4,800 individuals, which is roughly five times larger than population sizes in typical E&R studies, is required to achieve a power of at least 75%. Our study provides an important step toward improved experimental designs aiming to characterize causative SNPs in Experimental Evolution studies.

Recommended citation: Anna Maria Langmüller, Marlies Dolezal, Christian Schlötterer, Fine Mapping without Phenotyping: Identification of Selection Targets in Secondary Evolve and Resequence Experiments, Genome Biology and Evolution, Volume 13, Issue 8, August 2021, evab154, https://doi.org/10.1093/gbe/evab154

Fitness effects for Ace insecticide resistance mutations are determined by ambient temperature

Published in BMC Biology, 2020

Insect pest control programs often use periods of insecticide treatment with intermittent breaks, to prevent fixing of mutations conferring insecticide resistance. Such mutations are typically costly in an insecticide-free environment, and their frequency is determined by the balance between insecticide treatment and cost of resistance. Ace, a key gene in neuronal signaling, is a prominent target of many insecticides and across several species, three amino acid replacements (I161V, G265A, and F330Y) provide resistance against several insecticides. Because temperature disturbs neuronal signaling homeostasis, we reasoned that the cost of insecticide resistance could be modulated by ambient temperature. Experimental evolution of a natural Drosophila simulans population at hot and cold temperature regimes uncovered a surprisingly strong effect of ambient temperature. In the cold temperature regime, the resistance mutations were strongly counter selected (s = − 0.055), but in a hot environment, the fitness costs of resistance mutations were reduced by almost 50% (s = − 0.031). We attribute this unexpected observation to the advantage of the reduced enzymatic activity of resistance mutations in hot environments. We show that fitness costs of insecticide resistance genes are temperature-dependent and suggest that the duration of insecticide-free periods need to be adjusted for different climatic regions to reflect these costs. We suggest that such environment-dependent fitness effects may be more common than previously assumed and pose a major challenge for modeling climate change.

Recommended citation: Langmüller, A.M., Nolte, V., Galagedara, R. et al. Fitness effects for Ace insecticide resistance mutations are determined by ambient temperature. BMC Biol 18, 157 (2020). https://doi.org/10.1186/s12915-020-00882-5

Low concordance of short-term and long-term selection responses in experimental Drosophila populations

Published in Molecular Ecology, 2020

Experimental evolution is becoming a popular approach to study the genomic selection response of evolving populations. Computer simulation studies suggest that the accuracy of the signature increases with the duration of the experiment. Since some assumptions of the computer simulations may be violated, it is important to scrutinize the influence of the experimental duration with real data. Here, we use a highly replicated Evolve and Resequence study in Drosophila simulans to compare the selection targets inferred at different time points. At each time point, approximately the same number of SNPs deviates from neutral expectations, but only 10% of the selected haplotype blocks identified from the full data set can be detected after 20 generations. Those haplotype blocks that emerge already after 20 generations differ from the others by being strongly selected at the beginning of the experiment and display a more parallel selection response. Consistent with previous computer simulations, our results demonstrate that only Evolve and Resequence experiments with a sufficient number of generations can characterize complex adaptive architectures.

Recommended citation: Langmüller, AM, Schlötterer, C. Low concordance of short-term and long-term selection responses in experimental Drosophila populations. Mol Ecol. 2020; 29: 3466– 3475. https://doi.org/10.1111/mec.15579.

Maximum Likelihood Estimation of Fitness Components in Experimental Evolution

Published in Genetics, 2019

Estimating fitness differences between allelic variants is a central goal of experimental evolution. Current methods for inferring such differences from allele frequency time series typically assume that the effects of selection can be described by a fixed selection coefficient. However, fitness is an aggregate of several components including mating success, fecundity, and viability. Distinguishing between these components could be critical in many scenarios. Here, we develop a flexible maximum likelihood framework that can disentangle different components of fitness from genotype frequency data, and estimate them individually in males and females. As a proof-of-principle, we apply our method to experimentally evolved cage populations of Drosophila melanogaster, in which we tracked the relative frequencies of a loss-of-function and wild-type allele of yellow. This X-linked gene produces a recessive yellow phenotype when disrupted and is involved in male courtship ability. We find that the fitness costs of the yellow phenotype take the form of substantially reduced mating preference of wild-type females for yellow males, together with a modest reduction in the viability of yellow males and females. Our framework should be generally applicable to situations where it is important to quantify fitness components of specific genetic variants, including quantitative characterization of the population dynamics of CRISPR gene drives.

Recommended citation: Jingxian Liu, Jackson Champer, Anna Maria Langmüller, Chen Liu, Joan Chung, Riona Reeves, Anisha Luthra, Yoo Lim Lee, Andrew H Vaughn, Andrew G Clark, Philipp W Messer, Maximum Likelihood Estimation of Fitness Components in Experimental Evolution, Genetics, Volume 211, Issue 3, 1 March 2019, Pages 1005–1017, https://doi.org/10.1534/genetics.118.301893

Suitability of Different Mapping Algorithms for Genome-Wide Polymorphism Scans with Pool-Seq Data

Published in G3, 2016

The cost-effectiveness of sequencing pools of individuals (Pool-Seq) provides the basis for the popularity and widespread use of this method for many research questions, ranging from unraveling the genetic basis of complex traits, to the clonal evolution of cancer cells. Because the accuracy of Pool-Seq could be affected by many potential sources of error, several studies have determined, for example, the influence of sequencing technology, the library preparation protocol, and mapping parameters. Nevertheless, the impact of the mapping tools has not yet been evaluated. Using simulated and real Pool-Seq data, we demonstrate a substantial impact of the mapping tools, leading to characteristic false positives in genome-wide scans. The problem of false positives was particularly pronounced when data with different read lengths and insert sizes were compared. Out of 14 evaluated algorithms novoalign, bwa mem and clc4 are most suitable for mapping Pool-Seq data. Nevertheless, no single algorithm is sufficient for avoiding all false positives. We show that the intersection of the results of two mapping algorithms provides a simple, yet effective, strategy to eliminate false positives. We propose that the implementation of a consistent Pool-Seq bioinformatics pipeline, building on the recommendations of this study, can substantially increase the reliability of Pool-Seq results, in particular when libraries generated with different protocols are being compared.

Recommended citation: Kofler R, Langmüller AM, Nouhaud P, Otte KA, Schlötterer C. Suitability of Different Mapping Algorithms for Genome-Wide Polymorphism Scans with Pool-Seq Data. G3 (Bethesda). 2016 Nov 8;6(11):3507-3515. doi: 10.1534/g3.116.034488