Evolution Of Quantitative Traits Research Articles

A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.

Natural selection often acts on multiple traits simultaneously. For example, the virus HIV-1 faces pressure to evade host immunity while also preserving replicative fitness. While past work has studied selection during HIV-1 evolution, as in other examples where selection acts on multiple traits, it is challenging to quantitatively separate different contributions to fitness. This task is made more difficult because a single mutation can affect both immune escape and replication. Here, we develop an evolutionary model that disentangles the effects of escaping CD8+ T cell-mediated immunity, which we model as a binary trait, from other contributions to fitness. After validation in simulations, we applied this model to study within-host HIV-1 evolution in a clinical data set. We observed strong selection for immune escape, sometimes greatly exceeding past estimates, especially early in infection. Conservative estimates suggest that roughly half of HIV-1 fitness gains during the first months to years of infection can be attributed to T cell escape. Our approach is not limited to HIV-1 or viruses, and could be adapted to study the evolution of quantitative traits in other contexts.

Genetic independence between traits separated by metamorphosis is widespread but varies with biological function.

Why is metamorphosis so pervasive? Does it facilitate the independent (micro)evolution of quantitative traits in distinct life stages, similarly to how it enables some limbs and organs to develop at specific life stages? We tested this hypothesis by measuring the expression of 6400 genes in 41 Drosophila melanogaster inbred lines at larval and adult stages. Only 30% of the genes showed significant genetic correlations between larval and adult expression. By contrast, 46% of the traits showed some level of genetic independence between stages. Gene ontology terms enrichment revealed that across stages correlated traits were often involved in proteins synthesis, insecticide resistance and innate immunity, while a vast number of genes expression traits associated with energy metabolism were independent between life stages. We compared our results to a similar case: genetic constraints between males and females in gonochoric species (i.e. sexual antagonism). We expected selection for the separation between males and females to be higher than between juvenile and adult functions, as gonochorism is a more common strategy in the animal kingdom than metamorphosis. Surprisingly, we found that inter-stage constraints were lower than inter-sexual genetic constraints. Overall, our results show that metamorphosis enables a large part of the transcriptome to evolve independently at different life stages.

Evolutionary game theory and the adaptive dynamics approach: adaptation where individuals interact.

Evolutionary game theory and the adaptive dynamics approach have made invaluable contributions to understanding how gradual evolution leads to adaptation when individuals interact. Here, we review some of the basic tools that have come out of these contributions to model the evolution of quantitative traits in complex populations. We collect together mathematical expressions that describe directional and disruptive selection in class- and group-structured populations in terms of individual fitness, with the aims of bridging different models and interpreting selection. In particular, our review of disruptive selection suggests there are two main paths that can lead to diversity: (i) when individual fitness increases more than linearly with trait expression; (ii) when trait expression simultaneously increases the probability that an individual is in a certain context (e.g. a given age, sex, habitat, size or social environment) and fitness in that context. We provide various examples of these and more broadly argue that population structure lays the ground for the emergence of polymorphism with unique characteristics. Beyond this, we hope that the descriptions of selection we present here help see the tight links among fundamental branches of evolutionary biology, from life history to social evolution through evolutionary ecology, and thus favour further their integration. This article is part of the theme issue 'Half a century of evolutionary games: a synthesis of theory, application and future directions'.

Long-term evolution of quantitative traits in the Drosophila melanogaster species subgroup.

Quantitative genetics aims at untangling the genetic and environmental effects on phenotypic variation. Trait heritability, which summarizes the relative importance of genetic effects, is estimated at the intraspecific level, but theory predicts that heritability could influence long-term evolution of quantitative traits. The phylogenetic signal concept bears resemblance to heritability and it has often been called species-level heritability. Under certain conditions, such as trait neutrality or contribution to phylogenesis, within-species heritability and between-species phylogenetic signal should be correlated. Here, we investigate the potential relationship between these two concepts by examining the evolution of multiple morphological traits for which heritability has been estimated in Drosophila melanogaster. Specifically, we analysed 42 morphological traits in both sexes on a phylogeny inferred from 22 nuclear genes for nine species of the melanogaster subgroup. We used Pagel's λ as a measurement of phylogenetic signal because it is the least influenced by the number of analysed taxa. Pigmentation traits showed the strongest concordance with the phylogeny, but no correlation was found between phylogenetic signal and heritability estimates mined from the literature. We obtained data for multiple climatic variables inferred from the geographical distribution of each species. Phylogenetic regression of quantitative traits on climatic variables showed a significantly positive correlation with heritability. Convergent selection, the response to which depends on the trait heritability, may have led to the null association between phylogenetic signal and heritability for morphological traits in Drosophila. We discuss the possible causes of discrepancy between both statistics and caution against their confusion in evolutionary biology.

Microevolutionary variation in molar morphology of Onychomys leucogaster decoupled from genetic structure.

In neutral models of quantitative trait evolution, both genetic and phenotypic divergence scale as random walks, producing a correlation between the two measures. However, complexity in the genotype-phenotype map may alter the correlation between genotypic and phenotypic divergence, even when both are evolving neutrally or nearly so. Understanding this correlation between phenotypic and genetic variation is critical for accurately interpreting the fossil record. This study compares the geographic structure and scaling of morphological variation of the shape of the first lower molar of 77 individuals of the northern grasshopper mouse Onychomys leucogaster to genome-wide SNP variation in the same sample. We found strong genetic structure but weak or absent morphological structure indicating that the scaling of each type of variation is decoupled from one another. Low PST values relative to FST values are consistent with a lack of morphological divergence in contrast to genetic divergence between groups. This lack of phenotypic structure and the presence of notable within-sample phenotypic variance are consistent with uniform selection or constraints on molar shape across a wide geographic and environmental range. Over time, this kind of decoupling may result in patterns of phenotypic stasis masking underlying genetic patterns.

Multimorph Eco-Evolutionary Dynamics in Structured Populations.

AbstractOur understanding of the evolution of quantitative traits in nature is still limited by the challenge of including realistic trait distributions in the context of frequency-dependent selection and ecological feedbacks. We extend to class-structured populations a recently introduced "oligomorphic approximation," which bridges the gap between adaptive dynamics and quantitative genetics approaches and allows for the joint description of the dynamics of ecological variables and of the moments of multimodal trait distributions. Our theoretical framework allows us to analyze the dynamics of populations composed of several morphs and structured into distinct classes (e.g., age, size, habitats, infection status, and species). We also introduce a new approximation to simplify the eco-evolutionary dynamics using reproductive values. We illustrate the effectiveness of this approach by applying it to the important conceptual case of two-habitat migration-selection models. In particular, we show that our approach allows us to predict both the long-term evolutionary end points and the short-term transient dynamics of the eco-evolutionary process, including fast evolution regimes. We discuss the theoretical and practical implications of our results and sketch perspectives for future work.

Community structure determines the predictability of population collapse.

Early warning signals (EWS) are phenomenological tools that have been proposed as predictors of the collapse of biological systems. Although a growing body of work has shown the utility of EWS based on either statistics derived from abundance data or shifts in phenotypic traits such as body size, so far this work has largely focused on single species populations.However, to predict reliably the future state of ecological systems, which inherently could consist of multiple species, understanding how reliable such signals are in a community context is critical.Here, reconciling quantitative trait evolution and Lotka–Volterra equations, which allow us to track both abundance and mean traits, we simulate the collapse of populations embedded in mutualistic and multi‐trophic predator–prey communities. Using these simulations and warning signals derived from both population‐ and community‐level data, we showed the utility of abundance‐based EWS, as well as metrics derived from stability‐landscape theory (e.g. width and depth of the basin of attraction), were fundamentally linked. Thus, the depth and width of such stability‐landscape curves could be used to identify which species should exhibit the strongest EWS of collapse.The probability a species displays both trait and abundance‐based EWS was dependent on its position in a community, with some species able to act as indicator species. In addition, our results also demonstrated that in general trait‐based EWS were less reliable in comparison with abundance‐based EWS in forecasting species collapses in our simulated communities. Furthermore, community‐level abundance‐based EWS were fairly reliable in comparison with their species‐level counterparts in forecasting species‐level collapses.Our study suggests a holistic framework that combines abundance‐based EWS and metrics derived from stability‐landscape theory that may help in forecasting species loss in a community context.

Bayesian Analyses of Comparative Data with the Ornstein-Uhlenbeck Model: Potential Pitfalls.

The Ornstein–Uhlenbeck (OU) model is widely used in comparative phylogenetic analyses to study the evolution of quantitative traits. It has been applied to various purposes, including the estimation of the strength of selection or ancestral traits, inferring the existence of several selective regimes, or accounting for phylogenetic correlation in regression analyses. Most programs implementing statistical inference under the OU model have resorted to maximum-likelihood (ML) inference until the recent advent of Bayesian methods. A series of issues have been noted for ML inference using the OU model, including parameter nonidentifiability. How these problems translate to a Bayesian framework has not been studied much to date and is the focus of the present article. In particular, I aim to assess the impact of the choice of priors on parameter estimates. I show that complex interactions between parameters may cause the priors for virtually all parameters to impact inference in sometimes unexpected ways, whatever the purpose of inference. I specifically draw attention to the difficulty of setting the prior for the selection strength parameter, a task to be undertaken with much caution. I particularly address investigators who do not have precise prior information, by highlighting the fact that the effect of the prior for one parameter is often only visible through its impact on the estimate of another parameter. Finally, I propose a new parameterization of the OU model that can be helpful when prior information about the parameters is not available. [Bayesian inference; Brownian motion; Ornstein–Uhlenbeck model; phenotypic evolution; phylogenetic comparative methods; prior distribution; quantitative trait evolution.]

On the covariance of phylogenetic quantitative trait evolution models and their matrix condition

Phylogenetic comparative methods (PCMs) use phylogenetic tree and trait data to explore the evolutionary information among a group of related species. Given a phylogenetic tree of extant species, the evolutionary evidence among a group of species can be represented by a squared matrix C obtained by an isomorphic transformation using the shared branch lengths in the tree. The quantitative trait evolution for species along the branch can be described by stochastic processes. Currently, most statistical models for trait evolution are built under the assumption of Gaussian processes, where the trait vector Y follows a multivariate normal distribution with covariance matrix V , transformed from the C matrix and model parameters. This study investigates the effects on parameter estimation in phylogenetic comparative methods that are caused by ill-conditioned phylogenetic tree matrices ( C matrix) and their associated model-adjusted variance–covariance matrices ( V matrix). Several popular models—the Brownian motion model, the Ornstein–Uhlenbeck model, the early burst model, and the phylogenetic mixed model (Pagel’s λ)—are evaluated and compared through an empirical dataset and extensive simulations. Suggestions are provided to users in the community for identifying and overcoming potential issues with their own data.

The effects of introgression across thousands of quantitative traits revealed by gene expression in wild tomatoes.

It is now understood that introgression can serve as powerful evolutionary force, providing genetic variation that can shape the course of trait evolution. Introgression also induces a shared evolutionary history that is not captured by the species phylogeny, potentially complicating evolutionary analyses that use a species tree. Such analyses are often carried out on gene expression data across species, where the measurement of thousands of trait values allows for powerful inferences while controlling for shared phylogeny. Here, we present a Brownian motion model for quantitative trait evolution under the multispecies network coalescent framework, demonstrating that introgression can generate apparently convergent patterns of evolution when averaged across thousands of quantitative traits. We test our theoretical predictions using whole-transcriptome expression data from ovules in the wild tomato genus Solanum. Examining two sub-clades that both have evidence for post-speciation introgression, but that differ substantially in its magnitude, we find patterns of evolution that are consistent with histories of introgression in both the sign and magnitude of ovule gene expression. Additionally, in the sub-clade with a higher rate of introgression, we observe a correlation between local gene tree topology and expression similarity, implicating a role for introgressed cis-regulatory variation in generating these broad-scale patterns. Our results reveal a general role for introgression in shaping patterns of variation across many thousands of quantitative traits, and provide a framework for testing for these effects using simple model-informed predictions.

A variable-rate quantitative trait evolution model using penalized-likelihood.

In recent years it has become increasingly popular to use phylogenetic comparative methods to investigate heterogeneity in the rate or process of quantitative trait evolution across the branches or clades of a phylogenetic tree. Here, I present a new method for modeling variability in the rate of evolution of a continuously-valued character trait on a reconstructed phylogeny. The underlying model of evolution is stochastic diffusion (Brownian motion), but in which the instantaneous diffusion rate (σ2) also evolves by Brownian motion on a logarithmic scale. Unfortunately, it’s not possible to simultaneously estimate the rates of evolution along each edge of the tree and the rate of evolution of σ2 itself using Maximum Likelihood. As such, I propose a penalized-likelihood method in which the penalty term is equal to the log-transformed probability density of the rates under a Brownian model, multiplied by a ‘smoothing’ coefficient, λ, selected by the user. λ determines the magnitude of penalty that’s applied to rate variation between edges. Lower values of λ penalize rate variation relatively little; whereas larger λ values result in minimal rate variation among edges of the tree in the fitted model, eventually converging on a single value of σ2 for all of the branches of the tree. In addition to presenting this model here, I have also implemented it as part of my phytools R package in the function multirateBM. Using different values of the penalty coefficient, λ, I fit the model to simulated data with: Brownian rate variation among edges (the model assumption); uncorrelated rate variation; rate changes that occur in discrete places on the tree; and no rate variation at all among the branches of the phylogeny. I then compare the estimated values of σ2 to their known true values. In addition, I use the method to analyze a simple empirical dataset of body mass evolution in mammals. Finally, I discuss the relationship between the method of this article and other models from the phylogenetic comparative methods and finance literature, as well as some applications and limitations of the approach.

Sperm competition risk affects ejaculate strategy in terms of sperm number but not sperm size in squid.

In polygamous species, the mode of sperm storage in females influences evolution of sperm quantitative and qualitative traits because it provides the arena for sperm competition, cryptic female choice and fertilization processes. In this study, we compared ejaculate traits of two squid species, Heterololigo bleekeri and Loligo reynaudii. Both species show dimorphic sperm traits associated with alternative reproductive tactics where consort and sneaker males transfer sperm to different storage sites within a female (on the oviduct and near the mouth, respectively). Due to differences in reproductive behaviours and sperm placement, sperm competition risk is expected to be higher in sneakers than in consorts of both species and higher overall in L.reynaudii. Our results demonstrate that the instantaneous number of released sperm is adjusted to the expected sperm competition risk via an elaborate sperm package. Consort sperm are similar in size; however, sneaker sperm have a significantly longer flagellum in H.bleekeri than in L.reynaudii, most likely due to intra-tactic conflicts associated with sperm storage conditions.From consideration of the different mating tactics, we suggest that while levels of sperm competition determine quantitative traits, sperm quality traits are determined more by the mode of sperm storage and fertilization.

Did population differences in human pelvic form evolve by drift or selection?

Pelvic dimensions differ, on average, among modern human populations. Some recent studies have reported that population differences in pelvic form evolved mainly by neutral processes, without considerable natural selection. This is a surprising claim given the many important functions of the human pelvis. This article re-evaluates this claim through a review of the medical literature and a new analysis of the data from which these results were obtained. We show that variation in modern pelvic form correlates with the risks of obstructed labour, pelvic floor disorders such as incontinence and prolapse, and numerous orthopaedic disorders that impair walking. Comparative population studies also document adaptations of human body form and pelvic dimensions to climatic conditions. However, these various and partly antagonistic selective forces on the human pelvis are not homogeneous across populations. They depend not only on climatic differences, but also on maternal and foetal body size, pelvic floor tissue properties, diet, lifestyle and the resulting metabolic capacities, as well as on obstetric practices and health care, all of which vary across human populations. Given the relatively rapid evolution of polygenic quantitative traits, we propose that pelvic dimensions have evolved different "compromise solutions" in different populations in response to local selective regimes. The results from a reanalysis of a large published global dataset on human pelvic canal dimensions clearly support this view.

Phylogenetic Curved Optimal Regression for Adaptive Trait Evolution.

Regression analysis using line equations has been broadly applied in studying the evolutionary relationship between the response trait and its covariates. However, the characteristics among closely related species in nature present abundant diversities where the nonlinear relationship between traits have been frequently observed. By treating the evolution of quantitative traits along a phylogenetic tree as a set of continuous stochastic variables, statistical models for describing the dynamics of the optimum of the response trait and its covariates are built herein. Analytical representations for the response trait variables, as well as their optima among a group of related species, are derived. Due to the models’ lack of tractable likelihood, a procedure that implements the Approximate Bayesian Computation (ABC) technique is applied for statistical inference. Simulation results show that the new models perform well where the posterior means of the parameters are close to the true parameters. Empirical analysis supports the new models when analyzing the trait relationship among kangaroo species.

Gene-level quantitative trait mapping in Caenorhabditis elegans.

The Caenorhabditis elegans multiparental experimental evolution (CeMEE) panel is a collection of genome-sequenced, cryopreserved recombinant inbred lines useful for mapping the evolution and genetic basis of quantitative traits. We have expanded the resource with new lines and new populations, and here report the genotype and haplotype composition of CeMEE version 2, including a large set of putative de novo mutations, and updated additive and epistatic mapping simulations. Additive quantitative trait loci explaining 4% of trait variance are detected with >80% power, and the median detection interval approaches single-gene resolution on the highly recombinant chromosome arms. Although CeMEE populations are derived from a long-term evolution experiment, genetic structure is dominated by variation present in the ancestral population.

On the Effect of Asymmetrical Trait Inheritance on Models of Trait Evolution.

Current phylogenetic comparative methods modeling quantitative trait evolution generally assume that, during speciation, phenotypes are inherited identically between the two daughter species. This, however, neglects the fact that species consist of a set of individuals, each bearing its own trait value. Indeed, because descendent populations after speciation are samples of a parent population, we can expect their mean phenotypes to randomly differ from one another potentially generating a “jump” of mean phenotypes due to asymmetrical trait inheritance at cladogenesis. Here, we aim to clarify the effect of asymmetrical trait inheritance at speciation on macroevolutionary analyses, focusing on model testing and parameter estimation using some of the most common models of quantitative trait evolution. We developed an individual-based simulation framework in which the evolution of phenotypes is determined by trait changes at the individual level accumulating across generations, and cladogenesis occurs then by separation of subsets of the individuals into new lineages. Through simulations, we assess the magnitude of phenotypic jumps at cladogenesis under different modes of trait inheritance at speciation. We show that even small jumps can strongly alter both the results of model selection and parameter estimations, potentially affecting the biological interpretation of the estimated mode of evolution of a trait. Our results call for caution when interpreting analyses of trait evolution, while highlighting the importance of testing a wide range of alternative models. In the light of our findings, we propose that future methodological advances in comparative methods should more explicitly model the intraspecific variability around species mean phenotypes and how it is inherited at speciation.

Index to Titles and Authors/Editors of Books Reviewed

Index to Titles and Authors/Editors of Books Reviewed

Invasion implies substitution in ecological communities with class-structured populations

Long-term evolution of quantitative traits is classically and usefully described as the directional change in phenotype due to the recurrent fixation of new mutations. A formal justification for such continual evolution ultimately relies on the “invasion implies substitution”-principle. Here, whenever a mutant allele causing a small phenotypic change can successfully invade a population, the ancestral (or wild-type) allele will be replaced, whereby fostering gradual phenotypic change if the process is repeated. It has been argued that this principle holds in a broad range of situations, including spatially and demographically structured populations experiencing frequency- and density-dependent selection under demographic and environmental fluctuations. However, prior studies have not been able to account for all aspects of population structure, leaving unsettled the conditions under which the “invasion implies substitution”-principle really holds. In this paper, we start by laying out a program to explore and clarify the generality of the “invasion implies substitution”-principle. Particular focus is given on finding an explicit and functionally constant representation of the selection gradient on a quantitative trait. Using geometric singular perturbation methods, we then show that the “invasion implies substitution”-principle generalizes to well-mixed and scalar-valued polymorphic multispecies ecological communities that are structured into finitely many demographic (or physiological) classes. The selection gradient is shown to be constant over the evolutionary timescale and that it depends only on the resident phenotype, individual growth-rates, population steady states and reproductive values, all of which are calculated from the resident dynamics. Our work contributes to the theoretical foundations of evolutionary ecology.

Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia).

Pleiotropy, the control of multiple phenotypes by a single locus, is expected to slow the rate of adaptation by increasing the chance that beneficial alleles also have deleterious effects. However, a prediction arising from classical theory of quantitative trait evolution states that pleiotropic alleles may have a selective advantage when phenotypes are distant from their selective optima. We examine the role of pleiotropy in regulating adaptive differentiation among populations of common ragweed (Ambrosia artemisiifolia); a species that has recently expanded its North American range due to human-mediated habitat change. We employ a phenotype-free approach by using connectivity in gene networks as a proxy for pleiotropy. First, we identify loci bearing footprints of local adaptation, and then use genotype-expression mapping and co-expression networks to infer the connectivity of the genes. Our results indicate that the putatively adaptive loci are highly pleiotropic, as they are more likely than expected to affect the expression of other genes, and they reside in central positions within the gene networks. We propose that the conditionally advantageous alleles at these loci avoid the cost of pleiotropy by having large phenotypic effects that are beneficial when populations are far from their selective optima. We further use evolutionary simulations to show that these patterns are in agreement with a model where populations face novel selective pressures, as expected during a range expansion. Overall, our results suggest that highly connected genes may be targets of positive selection during environmental change, even though they likely experience strong purifying selection in stable selective environments.

Beyond Brownian Motion and the Ornstein-Uhlenbeck Process: Stochastic Diffusion Models for the Evolution of Quantitative Characters

Gaussian processes, such as Brownian motion and the Ornstein-Uhlenbeck process, have been popular models for the evolution of quantitative traits and are widely used in phylogenetic comparative methods. However, they have drawbacks that limit their utility. Here we describe new, non-Gaussian stochastic differential equation (diffusion) models of quantitative trait evolution. We present general methods for deriving new diffusion models and develop new software for fitting non-Gaussian evolutionary models to trait data. The theory of stochastic processes provides a mathematical framework for understanding the properties of current and future phylogenetic comparative methods. Attention to the mathematical details of models of trait evolution and diversification may help avoid some pitfalls when using stochastic processes to model macroevolution.