Biology subjects

Bertram, J.

Publications and source records attributed to Bertram, J..

14 recordsLinked to original sources

bioRxiv2026

Model-free inference of evolution from allele frequency timeseries using permutation tests

Allele frequency (AF) timeseries allow us to directly observe the dynamics of evolution at a genetic level. However, extracting useful inferences from AF timeseries has proved difficult due to the model uncertainties and noisiness inherent in AF change at fine temporal scales. Here we present three new permutation tests -- which do not assume a model of evolutionary change or a parametric statistical model -- to detect AF timeseries features of evolutionary interest. The features identified by these approaches are: 1) any evolutionary change (as opposed to apparent change due to measurement error); 2) directional selection; 3) fluctuating selection with a propensity to change sign (negative autocorrelation). We are not aware of existing tests for features 1 and 3. Feature 2 is commonly tested using standard evolutionary models such as the Wright-Fisher; we show that the permutation approach has comparable statistical power. We apply our new approaches to AF timeseries data from D. melanogaster and D. pulex.

evolutionary biology↗

bioRxiv2025

Outbreak dynamics of high pathogenicity avian influenza virus H5N1, clade 2.3.4.4b euBB, in black-headed gulls and common terns in Germany in 2023

Since winter 2022/23, high pathogenicity avian influenza virus (HPAIV) H5N1 clade 2.3.4.4b, genotype euBB, has caused extensive mortality among wild birds. This genotype emerged in France in spring 2022 through reassortment between a gull-adapted low-pathogenicity virus and HPAIV H5N1. Phylogeographic and spatiotemporal analyses show that transmission into German breeding colonies of black-headed gulls (Chroicocephalus ridibundus) and common terns (Sterna hirundo) involved multiple independent incursions, likely via black-headed gulls returning from wintering grounds in the Netherlands, Belgium, and France. It spilled over into common terns, which breed in shared colonies with black-headed gulls, and led to high adult mortality in both species in 2023 (at least 8,137 black-headed gulls and 614 common terns; >3% of the breeding population), followed by significant breeding pair declines in 2024 (-15.9% in black-headed gulls, -5.8% in common terns). Increased immunity, at least in common terns, may have contributed to the apparent fade-out of genotype euBB. These findings highlight how integrating ornithological, epidemiological, and virological data can aid our understanding of viral transmission routes and population-level impacts, while also stressing that HPAIV should be added to the growing list of pressures on seabirds, a group that was already the most threatened among all bird taxa globally. Research Highlights- HPAIV H5N1 euBB caused mass mortality in gulls and terns in Germany in 2023 - Multiple independent viral incursions occurred into different parts of Germany - Serology showed survival and rising H5-seroprevalence in common terns - Outbreaks reduced the black-headed gull breeding populations by up to 24%

ecology↗

bioRxiv2025

How sex shapes transcriptome evolution in the songbird brain

Sex differences have long captivated scientists, yet the evolutionary rate of change in sex-biased gene expression has not been directly quantified. To address this issue, we introduce new options in CAGEE (Computational Analysis of Gene Expression Evolution), specifically unbounded Brownian motion and variable evolutionary rates among genes. We applied these features to brain transcriptomes of ten songbird species, half of which convergently evolved obligate cavity-nesting, an element of reproductive ecology linked to sex-specific changes in competitive behavior. We find that the degree of sex-bias - measured as male:female expression ratio for each gene - evolves twice as fast on the Z chromosome versus autosomes, but Z gene expression does not evolve at different rates among sexes. Most Z-linked genes are male-biased in their expression, but not all. These sex-balanced genes are not skewed in their rate of evolution, contrary to the hypothesis that some genes experience selection for balance and consequently evolve more slowly. Finally, the degree of sex-bias in gene expression evolves more quickly along obligate-cavity nesting lineages, suggesting that sex-specific selection may shape the evolution of brain sex differences, or lack thereof. Together, these results provide new insights into the interplay between sex and gene expression evolution.

evolutionary biology↗

bioRxiv2025

Strong amplification of quantitative genetic variation under a balance between mutation and fluctuating stabilizing selection

The observation of high heritability in most quantitative traits has been a long-standing puzzle. There is a general consensus that simple models of quantitative genetic variation, which are mostly founded on the assumption of mutation-selection balance in a constant environment, have failed to explain high heritability. To make matters worse, the reasons for failure are unknown, leaving little to guide future model developments. Here we revisit this puzzle by taking the canonical Latter-Bulmer model and relaxing the assumption of perfect environmental stasis. Instead we assume that the trait optimum changes slowly but steadily in a random walk (specifically, an Ornstein-Uhlenbeck process), similar to standard models used for phylogenetic comparative methods. We show that our model behaves qualitatively differently to its stationary optimum counterpart even though the optimum only changes slowly. This is the result of a feedback between the adaptation rate and selection coefficient fluctuations. The heritability predictions resulting from this feedback are more consistent with observations and also less sensitive to evolutionary parameters than the classical LB model. We derive a simple formula to predict genetic variation under random walk optimum fluctuations which helps to explain some of our counter-intuitive results and which should be useful for understanding the potential influence of fluctuations in future work. Since the feedback driving our results should also occur in more complex models e.g. with multiple traits, we argue that environmental change has been an essential biological ingredient missing in previous mutation-selection balance models of quantitative trait heritability.

evolutionary biology↗

bioRxiv2023

Human deleterious mutation rate implies high fitness variance, with declining mean fitness compensated by rarer beneficial mutations of larger effect

Each new human has an expected Ud = 2-10 new deleterious mutations. Using a novel approach to capture complex linkage disequilibria from high Ud using genome-wide simulations, we confirm that fitness decline due to the fixation of many slightly deleterious mutations can be compensated by rarer beneficial mutations of larger effect. The evolution of increased genome size and complexity have previously been attributed to a similarly asymmetric pattern of fixations, but we propose that the cause might be high Ud rather than the small population size posited as causal by drift barrier theory. High within-population variance in relative fitness is an inevitable consequence of high Ud[~]2-10 combined with inferred human deleterious effect sizes; two individuals will typically differ in fitness by 15-40%. The need to compensate for the deluge of deleterious mutations slows net adaptation (i.e. to the external environment) by [~]13%-55%. The rate of beneficial fixations is more sensitive to changes in the mutation rate than the rate of deleterious fixations is. As a surprising consequence of this, an increase (e.g. 10%) in overall mutation rate leads to faster adaptation; this puts to rest dysgenic fears about increasing mutation rates due to rising paternal age.

evolutionary biology↗

bioRxiv2022

CAGEE: computational analysis of gene expression evolution

Despite the increasing abundance of whole transcriptome data, few methods are available to analyze global gene expression across phylogenies. Here, we present a new software package (CAGEE) for inferring patterns of increases and decreases in gene expression across a phylogenetic tree, as well as the rate at which these changes occur. In contrast to previous methods that treat each gene independently, CAGEE can calculate genome-wide rates of gene expression, along with ancestral states for each gene. The statistical approach developed here makes it possible to infer lineage-specific shifts in rates of evolution across the genome, in addition to possible differences in rates among multiple tissues sampled from the same species. We demonstrate the accuracy and robustness of our method on simulated data, and apply it to a dataset of ovule gene expression collected from multiple self-compatible and self-incompatible species in the genus Solanum to test hypotheses about the evolutionary forces acting during mating system shifts. These comparisons allow us to highlight the power of CAGEE, demonstrating its utility for use in any empirical system and for the analysis of most morphological traits. Our software is available at https://github.com/hahnlab/CAGEE/.

bioinformatics↗

bioRxiv2021

Allele frequency divergence reveals ubiquitous influence of positive selection in Drosophila

Resolving the role of natural selection is a basic objective of evolutionary biology. It is generally difficult to detect the influence of selection because ubiquitous non-selective stochastic change in allele frequencies (genetic drift) degrades evidence of selection. As a result, selection scans typically only identify genomic regions that have undergone episodes of intense selection. Yet it seems likely such episodes are the exception; the norm is more likely to involve subtle, concurrent selective changes at a large number of loci. We develop a new theoretical approach that uncovers a previously undocumented genome-wide signature of selection in the collective divergence of allele frequencies over time. Applying our approach to temporally resolved allele frequency measurements from laboratory and wild Drosophila populations, we quantify the selective contribution to allele frequency divergence and find that selection has substantial effects on much of the genome. We further quantify the magnitude of the total selection coefficient (a measure of the combined effects of direct and linked selection) at a typical polymorphic locus, and find this to be large (of order 1%) even though most mutations are not directly under selection. We find that selective allele frequency divergence is substantial at intermediate allele frequencies, which we argue is most parsimoniously explained by positive -- not purifying -- selection. Thus, in these populations most mutations are far from evolving neutrally in the short term (tens of generations), including mutations with neutral fitness effects, and the result cannot be explained simply as a purging of deleterious mutations. Author summaryNatural selection is the process fundamentally driving evolutionary adaptation; yet the specifics of how natural selection molds the genome are contentious. A prevailing neutralist view holds that the evolution of most mutations is essentially random. Here, we develop new theory that looks past the stochasticity of individual mutations and instead analyzes the behavior of mutations across the genome as a collective. We find that selection has a strong non-random influence on most of the Drosophila genome over short timescales (tens of generations), including the bulk of mutations that are not themselves directly targeted by selection. We show that this likely involves ongoing positive selection.

evolutionary biology↗

bioRxiv2020

Mutation bias can shape adaptation in large asexual populations experiencing clonal interference

The Extended Evolutionary Synthesis invokes a role for development in shaping adaptive evolution, which in population genetics terms corresponds to mutation-biased adaptation. Critics have claimed that clonal interference makes mutation-biased adaptation rare. We consider the behavior of two simultaneously adapting traits, one with larger mutation rate U, the other with larger selection coefficient s, using asexual traveling wave models. We find that adaptation is dominated by whichever trait has the faster rate of adaptation v in isolation, with the other trait subject to evolutionary stalling. Reviewing empirical claims for mutation-biased adaptation, we find that not all occur in the "origin-fixation" regime of population genetics where v is only twice as sensitive to s as to U. In some cases, differences in U are at least ten to twelve times larger than differences in s, as needed to cause mutation-biased adaptation even in the "multiple mutations" regime. Surprisingly, when U > s in the "diffusive-mutation" regime, the required sensitivity ratio is also only two, despite pervasive clonal interference. Given two traits with identical v, the benefit of having higher s is surprisingly small, occurring largely when one trait is at the boundary between the origin-fixation and multiple mutations regimes.

evolutionary biology↗

bioRxiv2019

Evolution rapidly optimizes stability and aggregation in lattice proteins despite pervasive landscape valleys and mazes

Fitness landscapes are widely used to visualize the dynamics and long-term outcomes of evolution. The fitness landscapes of genetic sequences are characterized by high dimensionality and \"ruggedness\" due to sign epistasis. Ascending from low to high fitness on such landscapes can be difficult because adaptive trajectories get stuck at low-fitness local peaks. Compounding matters, recent computational complexity arguments have proposed that extremely long, winding adaptive paths may be required to even reach local peaks, a \"maze-like\" landscape topography. The extent to which peaks and mazes shape the mode and tempo of evolution is poorly understood due to empirical limitations and the abstractness of many landscape models. We develop a biophysically-grounded computational model of protein evolution based on two novel extensions of the classic hydrophobic-polar lattice model of protein folding. First, rather than just considering fold stability we account for the tradeoff between stability and aggregation propensity. Second, we use a \"hydrophobic zipping\" algorithm to kinetically generate ensembles of post-translationally folded structures. Our stability-aggregation fitness landscape exhibits extensive sign epistasis and local peaks galore. We confirm the postulated existence of maze-like topography in our biologically-grounded landscape. Although these landscape features frequently obstruct adaptive ascent to high fitness and virtually eliminate reproducibility of evolutionary outcomes, many adaptive paths do successfully complete the long ascent from low to high fitness. This delicate balance of \"hard but possible\" adaptation could occur more broadly provided that the optimal outcomes possible under a tradeoff are improved by rare constraint-breaking substitutions.

evolutionary biology↗

bioRxiv2019

In vivo identification of GTPase interactors by mitochondrial relocalization and proximity biotinylation

The GTPases of the Ras superfamily regulate cell growth, membrane traffic and the cytoskeleton, and a wide range of diseases are caused by mutations in particular members. They function as switchable landmarks with the active GTP-bound form recruiting to the membrane a specific set of effector proteins. The GTPases are precisely controlled by regulators that promote acquisition of GTP (GEFs) or its hydrolysis to GDP (GAPs). We report here MitoID, a method for identifying effectors and regulators by performing in vivo proximity biotinylation with mitochondrially-localized forms of the GTPases. Applying this to 11 human Rab GTPases identified many known effectors and GAPs, as well as putative novel effectors, with examples of the latter validated for Rab2, Rab5 and Rab9. MitoID can also efficiently identify effectors and GAPs of Rho and Ras family GTPases such as Cdc42, RhoA, Rheb, and N-Ras, and can identify GEFs by use of GDP-bound forms.

cell biology↗

bioRxiv2019

Maximum entropy models elucidate the contribution of metabolic traits to patterns of community assembly

Aim: Maximum entropy (MaxEnt) models promise a novel approach for understanding community assembly and species abundance patterns. One of these models, the \"Maximum Entropy Theory of Ecology\" (METE) reproduces many observed species abundance patterns, but is based on an aggregated representation of community structure that does not resolve species identity or explicitly represent species-specific functional traits. In this paper, METE is compared to \"Very Entropic Growth\" (VEG), a MaxEnt model with a less aggregated representation of community structure that represents species (more correctly, functional types) in terms of their per capita metabolic rates. We examine the contribution of metabolic traits to the patterns of community assembly predicted by VEG and, through aggregation, compare the results with METE predictions in order to gain insight into the biological factors underlying observed patterns of community assembly. Innovation: We formally compare two MaxEnt-based community models, METE and VEG, that differ as to whether or not they represent species-specific functional traits. We empirically test and compare the metabolic predictions of both models, thereby elucidating the role of metabolic traits in patterns of community assembly. Main Conclusions: Our analysis reveals that a key determinant of community metabolic patterns is the \"density of species\" distribution, defined as the intrinsic number of species with metabolic rates in a given range that are available to a community prior to filtering by environmental constraints. Our analysis suggests that appropriate choice of of the density of species in VEG may lead to more realistic predictions than METE, for which this distribution is not defined, and thus opens up new ways to understanding the link between functional traits and patterns of community assembly.

ecology↗

bioRxiv2018

In rapidly adapting asexuals, the orientation of G can reflect selection rather than functional constraints

Genetic covariances represent a combination of pleiotropy and linkage disequilibrium, shaped by the populations history. Observed genetic covariance is most often interpreted in pleiotropic terms. In particular, functional constraints restricting which phenotypes are physically possible can lead to a stable G matrix with high genetic variance in fitness-associated traits and high pleiotropic negative covariance along the phenotypic curve of constraint. In contrast, population genetic models of relative fitness assume endless adaptation without constraint, through a series of selective sweeps that are well described by recent traveling wave models. We describe the implications of such population genetic models for the G matrix when pleiotropy is excluded by design, such that all covariance comes from linkage disequilibrium. The G matrix is far less stable than has previously been found, fluctuating over the timescale of selective sweeps. However, its orientation is relatively stable, corresponding to high genetic variance in fitness-associated traits and strong negative covariance - the same pattern often interpreted in terms of pleiotropic constraints but caused instead by linkage disequilibrium. We find that different mechanisms drive the instabilities along versus perpendicular to the fitness gradient. The origin of linkage disequilibrium is not drift, but small amounts of linkage disequilibrium are instead introduced by mutation and then amplified during competing selective sweeps. This illustrates the need to integrate a broader range of population genetic phenomena into quantitative genetics.

evolutionary biology↗

bioRxiv2017

A central role for boom-bust cycles in maintaining genetic variation in fluctuating environments

The long-running debate about the role of selection in maintaining genetic variation has been given new impetus by the discovery of hundreds of seasonally oscillating polymorphisms in wild Drosophila, possibly stabilized by an alternating summer-winter selection regime. Historically there has been skepticism about the potential of temporal variation to balance polymorphism, because selection must be strong to have a meaningful stabilizing effect -- unless dominance also varies over time ("reversal of dominance"). Here we develop a simplified model of seasonally variable selection that simultaneously incorporates four different stabilizing mechanisms, including two genetic mechanisms ("cumulative overdominance" and reversal of dominance), as well as ecological "storage" ("protection from selection" and boom-bust demography). We use our model to compare the stabilizing effects of these mechanisms. Although reversal of dominance has by far the greatest stabilizing effect, we argue that the three other mechanisms could also stabilize polymorphism under plausible conditions, particularly when all three are present. With many loci subject to diminishing returns epistasis, reversal of dominance stabilizes many alleles of small effect. This makes the combination of the other three mechanisms, which are incapable of stabilizing small effect alleles, a better candidate for stabilizing the detectable frequency oscillations of large effect alleles.

evolutionary biology↗

bioRxiv2017

Density-dependent selection in evolutionary genetics: a lottery model of Grime’s triangle

Selection is commonly described by assigning constant relative fitness values to genotypes. Yet population density is often regulated by crowding. Relative fitness may then depend on density, and selection can change density when it acts on a density-regulating trait. When strong density-dependent selection acts on a density-regulating trait, selection is no longer describable by density-independent relative fitnesses, even in demographically stable populations. These conditions are met in most previous models of density-dependent selection (e.g. \"K-selection\" in the logistic and Lotka-Volterra models), suggesting that density-independent relative fitnesses must be replaced with more ecologically explicit absolute fit-nesses unless selection is weak. Here we show that density-independent relative fitnesses can also accurately describe strong density-dependent selection under some conditions. We develop a novel model of density-regulated population growth with three ecologically intuitive traits: fecundity, mortality, and competitive ability. Our model, unlike the logistic or Lotka-Volterra, incorporates a density-dependent juvenile \"reproductive excess\", which largely decouples density-dependent selection from the regulation of density. We find that density-independent relative fitnesses accurately describe strong selection acting on any one trait, even fecundity, which is both density-regulating and subject to density-dependent selection. Pleiotropic interactions between these traits recovers the familiar K-selection behavior. In such cases, or when the population is maintained far from demographic equilibrium, our model offers a possible alternative to relative fitness.

evolutionary biology↗