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bioRxiv2014

DISSECT: an assignment-free Bayesian discovery method for species delimitation under the multispecies coalescent

MotivationThe multispecies coalescent model provides a formal framework for the assignment of individual organisms to species, where the species are modeled as the branches of the species tree. None of the available approaches so far have simultaneously co-estimated all the relevant parameters in the model, without restricting the parameter space by requiring a guide tree and/or prior assignment of individuals to clusters or species.\n\nResultsWe present DISSECT, which explores the full space of possible clusterings of individuals and species tree topologies in a Bayesian framework. It uses an approximation to avoid the need for reversible-jump MCMC, in the form of a prior that is a modification of the birth-death prior for the species tree. It incorporates a spike near zero in the density for node heights. The model has two extra parameters: one controls the degree of approximation, and the second controls the prior distribution on the numbers of species. It is implemented as part of BEAST and requires only a few changes from a standard *BEAST analysis. The method is evaluated on simulated data and demonstrated on an empirical data set. The method is shown to be insensitive to the degree of approximation, but quite sensitive to the second parameter, suggesting that large numbers of sequences are needed to draw firm conclusions.\n\nAvailabilityhttp://code.google.com/p/beast-mcmc/, http://www.indriid.com/dissectinbeast.html\n\nContactart@gjones.name, www.indriid.com\n\nSupplementary informationSupplementary material is available.

Evolutionary Biology↗

bioRxiv2014

Phylogenetic tree shapes resolve disease transmission patterns

Whole genome sequencing is becoming popular as a tool for understanding outbreaks of communicable diseases, with phylogenetic trees being used to identify individual transmission events or to characterize outbreak-level overall transmission dynamics. Existing methods to infer transmission dynamics from sequence data rely on well-characterised infectious periods, epidemiological and clinical meta-data which may not always be available, and typically require computationally intensive analysis focussing on the branch lengths in phylogenetic trees. We sought to determine whether the topological structures of phylogenetic trees contain signatures of the overall transmission patterns underyling an outbreak. Here we use simulated outbreaks to train and then test computational classifiers. We test the method on data from two real-world outbreaks. We find that different transmission patterns result in quantitatively different phylogenetic tree shapes. We describe five topological features that summarize a phylogenys structure and find that computational classifiers based on these are capable of predicting an outbreaks transmission dynamics. The method is robust to variations in the transmission parameters and network types, and recapitulates known epidemiology of previously characterized real-world outbreaks. We conclude that there are simple structural properties of phylogenetic trees which, when combined, can distinguish communicable disease outbreaks with a super-spreader, homogeneous transmission, and chains of transmission. This is possible using genome data alone, and can be done during an outbreak. We discuss the implications for management of outbreaks.

Evolutionary Biology↗

bioRxiv2014

The Role of Migration in the Evolution of Phenotypic Switching

Stochastic switching is an example of phenotypic bet-hedging, where an individual can switch between different phenotypic states in a fluctuating environment. Although the evolution of stochastic switching has been studied when the environment varies temporally, there has been little theoretical work on the evolution of phenotypic switching under both spatially and temporally fluctuating selection pressures. Here we use a population genetic model to explore the interaction of temporal and spatial variation in the evolutionary dynamics of phenotypic switching. We find that spatial variation in selection is important; when selection pressures are similar across space, migration can decrease the rate of switching, but when selection pressures differ spatially, increasing migration between demes can facilitate the evolution of higher rates of switching. These results may help explain the diverse array of non-genetic contributions to phenotypic variability and phenotypic inheritance observed in both wild and experimental populations.

Evolutionary Biology↗

bioRxiv2014

Response to a population bottleneck can be used to infer recessive selection

Here we present the first genome wide statistical test for recessive selection. This test uses explicitly non-equilibrium demographic differences between populations to infer the mode of selection. By analyzing the transient response to a population bottleneck and subsequent re-expansion, we qualitatively distinguish between alleles under additive and recessive selection. We analyze the response of the average number of deleterious mutations per haploid individual and describe time dependence of this quantity. We introduce a statistic, BR, to compare the number of mutations in different populations and detail its functional dependence on the strength of selection and the intensity of the population bottleneck. This test can be used to detect the predominant mode of selection on the genome wide or regional level, as well as among a sufficiently large set of medically or functionally relevant alleles.

Evolutionary Biology↗

bioRxiv2014

Towards a new history and geography of human genes informed by ancient DNA

Genetic information contains a record of the history of our species, and technological advances have transformed our ability to access this record. Many studies have used genome-wide data from populations today to learn about the peopling of the globe and subsequent adaptation to local conditions. Implicit in this research is the assumption that the geographic locations of people today are informative about the geographic locations of their ancestors in the distant past. However, it is now clear that long-range migration, admixture and population replacement have been the rule rather than the exception in human history. In light of this, we argue that it is time to critically re-evaluate current views of the peopling of the globe and the importance of natural selection in determining the geographic distribution of phenotypes. We specifically highlight the transformative potential of ancient DNA. By accessing the genetic make-up of populations living at archaeologically-known times and places, ancient DNA makes it possible to directly track migrations and responses to natural selection.

Evolutionary Biology↗

bioRxiv2014

3D mapping of disease in ant societies reveals a strategy of a specialized parasite

Despite the widely held position that the social insects have evolved effective ways to limit infectious disease spread, many pathogens and parasites do attack insect societies. Maintaining a disease-free nest environment is an important evolutionary feature, but since workers have to leave the nest to forage they are routinely exposed to disease. Here we show that despite effective social immunity, in which workers act collectively to reduce disease inside the nest, 100% of studied ant colonies of Camponotus rufipes in a Brazilian Rainforest were infected by the specialized fungal parasite Ophiocordyceps unilateralis s.l. Not only is disease present for all colonies but long-term dynamics over 20 months revealed disease is a permanent feature. Using 3D maps, we showed the parasite optimizes its transmission by controlling workers behavior to die on the doorstep of the colony, where susceptible foragers are predictable in time and space. Therefore, despite social immunity, specialized diseases of ants have evolved effective strategies to exploit insect societies.

Evolutionary Biology↗

bioRxiv2014

Geometry shapes evolution of early multicellularity

Organisms have increased in complexity through a series of major evolutionary transitions, in which formerly autonomous entities become parts of a novel higher-level entity. One intriguing feature of the higher-level entity after some major transitions is a division of reproductive labor among its lower-level units. Although it can have clear benefits once established, it is unknown how such reproductive division of labor originates. We consider a recent evolution experiment on the yeast Saccharomyces cerevisiae as a unique platform to address the issue of reproductive differentiation during an evolutionary transition in individuality. In the experiment, independent yeast lineages evolved a multicellular \"snowflake-like\" cluster form in response to gravity selection. Shortly after the evolution of clusters, the yeast evolved higher rates of cell death. While cell death enables clusters to split apart and form new groups, it also reduces their performance in the face of gravity selection. To understand the selective value of increased cell death, we create a mathematical model of the cellular arrangement within snowflake yeast clusters. The model reveals that the mechanism of cell death and the geometry of the snowflake interact in complex, evolutionarily important ways. We find that the organization of snowflake yeast imposes powerful limitations on the available space for new cell growth. By dying more frequently, cells in clusters avoid encountering space limitations, and, paradoxically, reach higher numbers. In addition, selection for particular group sizes can explain the increased rate of apoptosis both in terms of total cell number and total numbers of collectives. Thus, by considering the geometry of a primitive multicellular organism we can gain insight into the initial emergence of reproductive division of labor during an evolutionary transition in individuality.

Evolutionary Biology↗

bioRxiv2014

An evolutionary resolution of manipulation conflict

Individuals can manipulate the behavior of social partners. However, manipulation may conflict with the fitness interests of the manipulated individuals. Manipulated individuals can then be favored to resist manipulation, possibly reducing or eliminating the manipulated behavior in the long run. I use a mathematical model to show that conflicts where manipulation and resistance coevolve can disappear as a result of the coevolutionary process. I find that while manipulated individuals are selected to resist, they can simultaneously be favored to express the manipulated behavior at higher efficiency (i.e., providing increasing fitness effects to recipients of the manipulated behavior). Efficiency can increase to a point at which selection for resistance disappears. This process yields an efficient social behavior that is induced by social partners, and over which the inducing and induced individuals are no longer in conflict. A necessary factor is costly inefficiency. I develop the model to address the evolution of advanced eusociality via maternal manipulation (AEMM). The model predicts AEMM to be particularly likely in taxa with ancestrally imperfect resistance to maternal manipulation. Costly inefficiency occurs if the cost of delayed dispersal is larger than the benefit of exploiting the maternal patch. I discuss broader implications of the process.

Evolutionary Biology↗

bioRxiv2014

Multilocus Species Trees Show the Recent Adaptive Radiation of the Mimetic Heliconius Butterflies

Mullerian mimicry among Neotropical Heliconiini butterflies is an excellent example of natural selection, and is associated with the diversification of a large continental-scale radiation. Some of the processes driving the evolution of mimicry rings are likely to generate incongruent phylogenetic signals across the assemblage, and thus pose a challenge for systematics. We use a dataset of 22 mitochondrial and nuclear markers from 92% of species in the tribe to re-examine the phylogeny of Heliconiini with both supermatrix and multi-species coalescent approaches, characterise the patterns of conflicting signal and compare the performance of various methodological approaches to reflect the heterogeneity across the data. Despite the large extent of reticulate signal and strong conflict between markers, nearly identical topologies are consistently recovered by most of the analyses, although the supermatrix approach fails to reflect the underlying variation in the history of individual loci. The first comprehensive, time-calibrated phylogeny of this group is used to test the hypotheses of a diversification rate increase driven by the dramatic environmental changes in the Amazonia over the past 23 million years, or changes caused by diversity-dependent effects on the rate of diversification. We find that the tribe Heliconiini had doubled its rate of speciation around 11 Ma and that the presently most speciose genus Heliconius started diversifying rapidly at 10 Ma, likely in response to the recent drastic changes in topography of the region. Our study provides comprehensive evidence for a rapid adaptive radiation among an important insect radiation in the most biodiverse region of the planet.

Evolutionary Biology↗

bioRxiv2014

Protected polymorphisms and evolutionary stability of patch-selection strategies in stochastic environments

We consider consider a population living in a patchy environment that varies stochastically in space and time. The population is composed of two morphs (that is, individuals of the same species with different genotypes). In terms of survival and reproductive success, the associated phenotypes differ only in their habitat selection strategies. We compute invasion rates corresponding to the rates at which the abundance of an initially rare morph increases in the presence of the other morph established at equilibrium. If both morphs have positive invasion rates when rare, then there is an equilibrium distribution such that the two morphs coexist; that is, there is a protected polymorphism for habitat selection. Alternatively, if one morph has a negative invasion rate when rare, then it is asymptotically displaced by the other morph under all initial conditions where both morphs are present. We refine the characterization of an evolutionary stable strategy for habitat selection from [Schreiber, 2012] in a mathematically rigorous manner. We provide a necessary and sufficient condition for the existence of an ESS that uses all patches and determine when using a single patch is an ESS. We also provide an explicit formula for the ESS when there are two habitat types. We show that adding environmental stochasticity results in an ESS that, when compared to the ESS for the corresponding model without stochasticity, spends less time in patches with larger carrying capacities and possibly makes use of sink patches, thereby practicing a spatial form of bet hedging.

Evolutionary Biology↗

bioRxiv2014

Unravelling the diversity behind Ophiocordyceps unilateralis complex: Three new species of Zombie-Ant fungus from Brazilian Amazon

In tropical forests, one of the most common relationships between parasites and insects is that between the fungus Ophiocordyceps (Ophiocordycipitaceae, Hypocreales, Ascomycota) and ants, especially within the tribe Camponotini. These fungi have the ability to penetrate the exoskeleton of the ant and to manipulate the behavior of the host, making it leave the nest and ascend understorey shrubs, to die biting onto the vegetation: hence, the term zombie-ant fungi to describe this behavioral changes on the host. It is posited that this behavioral change aids spore dispersal and thus increases the chances of infection. Despite their undoubted importance for ecosystem functioning, these fungal pathogens are still poorly documented, especially regarding their diversity, ecology and evolutionary relationships. Here, we describe three new and host-specific species of the genus Ophiocordyceps on Camponotus ants from the central Amazonian region of Brazil which can readily be separated using classic taxonomic criteria, in particular ascospore morphology. In addition, we also employed molecular techniques to show for the first time the phylogenetic relationships between these taxa and closely related species within the Ophiocordyceps unilateralis complex, as well as with other members of the family Ophiocordycipitaceae.

Evolutionary Biology↗

bioRxiv2014

An experimentally informed evolutionary model improves phylogenetic fit to divergent lactamase homologs

Phylogenetic analyses of molecular data require a quantitative model for how sequences evolve. Traditionally, the details of the site-specific selection that governs sequence evolution are not known a priori, making it challenging to create evolutionary models that adequately capture the heterogeneity of selection at different sites. However, recent advances in high-throughput experiments have made it possible to quantify the effects of all single mutations on gene function. I have previously shown that such high-throughput experiments can be combined with knowledge of underlying mutation rates to create a parameter-free evolutionary model that describes the phylogeny of influenza nucleoprotein far better than commonly used existing models. Here I extend this work by showing that published experimental data on TEM-1 beta-lactamase (Firnberg et al, 2014) can be combined with a few mutation rate parameters to create an evolutionary model that describes beta-lactamase phylogenies much than most common existing models. This experimentally informed evolutionary model is superior even for homologs that are substantially diverged (about 35% divergence at the protein level) from the TEM-1 parent that was the subject of the experimental study. These results suggest that experimental measurements can inform phylogenetic evolutionary models that are applicable to homologs that span a substantial range of sequence divergence.

Evolutionary Biology↗

bioRxiv2014

Intermediate Migration Yields Optimal Adaptation in Structured, Asexual Populations

Most evolving populations are subdivided into multiple subpopulations connected to each other by varying levels of gene flow. However, how population structure and gene flow (i.e., migration) affect adaptive evolution is not well understood. Here, we studied the impact of migration on asexually reproducing evolving computer programs (digital organisms). We found that digital organisms evolve the highest fitness values at intermediate migration rates, and we tested three hypotheses that could potentially explain this observation: (i) migration promotes passage through fitness valleys, (ii) migration increases genetic variation, and (iii) migration reduces clonal interference through a process called leapfrogging. We found that migration had no appreciable effect on the number of fitness valleys crossed and that genetic variation declined monotonously with increasing migration rates, instead of peaking at the optimal migration rate. However, the number of leapfrogging events, in which a superior beneficial mutation emerges on a genetic background that predates the previously best genotype in the population, did peak at the optimal migration rate. We thus conclude that in structured, asexual populations intermediate migration rates allow for optimal exploration of multiple, distinct fitness peaks, and thus yield the highest long-term adaptive success.

Evolutionary Biology↗

bioRxiv2014

Estimating Phylogeny from microRNA Data: A Critical Appraisal

Recent progress in resolving the tree of life continues to expose relationships that resist resolution, which drives the search for novel sources of information to solve these difficult phylogenetic problems. A recent example, the presence and absence of microRNA families, has been vigorously promoted as an ideal source of phylogenetic data and has been applied to several perennial phylogenetic problems. The utility of such data for phylogenetic inference hinges critically both on developing stochastic models that provide a reasonable description of the process that give rise to these data, and also on the careful validation of those models in real inference scenarios. Remarkably, however, the statistical behavior and phylogenetic utility of microRNA data have not yet been rigorously characterized. Here we explore the behavior and performance of microRNA presence/absence data under a variety of evolutionary models and reexamine datasets from several previous studies. We find that highly heterogeneous rates of microRNA gain and loss, pervasive secondary loss, and sampling error collectively render microRNA-based inference of phylogeny difficult. Moreover, our reanalyses fundamentally alter the conclusions for four of the five studies that we reexamined. Our results indicate that the capacity of miRNA data to resolve the tree of life has been overstated, and we urge caution in their application and interpretation.

Evolutionary Biology↗

bioRxiv2014

Direct Reciprocity Under Uncertainty Does Not Explain One-shot Cooperation, but Demonstrates the Benefits of a Norm Psychology

Humans in many societies cooperate in economic experiments at much higher levels than would be expected if their goal was maximizing economic returns, even when their interactions are anonymous and one-shot. This is a puzzle because paying a cost to benefit another in one-shot interactions gives no direct or indirect benefits to the cooperator. This paper explores the logic of two competing evolutionary hypotheses to explain this behavior. The \"norm psychology\" hypothesis holds that a player's choice of strategy reflects socially-learned cultural norms. Its premise is that over the course of human evolutionary history, cultural norms varied considerably across human societies and through a process of gene-culture co-evolution, humans evolved mechanisms to learn and adopt the norms that are successful in their particular society. The \"mismatch\" hypothesis holds that pro-social preferences evolved genetically in our hunter-gatherer past where one-shot anonymous interactions were rare and these preferences are misapplied in modern laboratory settings. I compare these hypotheses by adopting a well-known model of the mismatch hypothesis and show that selection for one-shot cooperation in the model is an artifact of agents being constrained to only two strategies: Tit-for-Tat and Always Defect. Allowing for repentant and forgiving strategies reverses selection away from one-shot cooperation under all environmental parameters. Direct reciprocity does not necessarily lead to cooperation, but instead generates many different normative equilibria depending on a groups idiosyncratic evolutionary history. Therefore, an agent whose behavior is evoked solely from non-cultural environmental cues will be disadvantaged relative to an agent who learns the locally successful norms. Cooperation in one-shot laboratory experiments is thus more easily explained as the result of a psychology evolved for learning social norms than as a genetic mismatch.

Evolutionary Biology↗

bioRxiv2014

Gradual divergence and diversification of mammalian duplicate gene functions

Gene duplication provides raw material for the evolution of functional innovation. We recently developed a phylogenetic method to classify the evolutionary processes underlying the retention and functional evolution of duplicate genes by quantifying divergence of their gene expression profiles. Here, we apply our method to pairs of duplicate genes in eight mammalian genomes, using data from 11 distinct tissues to construct spatial gene expression profiles. We find that young mammalian duplicates are often functionally conserved, and that functional divergence gradually increases with evolutionary distance between species. Examination of expression patterns in genes with conserved and new functions supports the \"out-of-testes\" hypothesis, in which new genes arise with testis-specific functions and acquire functions in other tissues over time. While new functions tend to be tissue-specific, there is no bias toward expression in any particular tissue. Thus, duplicate genes acquire a diversity of functions outside of the testes, possibly contributing to the origin of a multitude of complex phenotypes during mammalian evolution.

Evolutionary Biology↗

bioRxiv2014

Behavioral variation in Drosophila melanogaster: no evidence for common alleles of large- effect at the foraging gene in a population from North Carolina, USA

Thirty four years ago, it was postulated that natural populations of Drosophila melanogaster are comprised of two behavioral morphs termed \"rover\" and \"sitter\", and that this variation is caused mainly by large-effect alleles at a single locus. Since that time, considerable data has been amassed that compares the behavior and physiology of these morphs. Contrary to common assertions, however, published support for the existence of common large effect alleles in nature is quite limited. To further investigate, we quantified the foraging behavior of 36 natural strains, performed a genome-wide association study, and described patterns of molecular evolution at the foraging locus. Though there was significant variation in foraging behavior among genotypes, this variation was continuously distributed and not significantly associated with genetic variation at the foraging gene. Patterns of molecular population genetic variation at this gene also provide no support for the hypothesis that for is a target of long term balancing selection We propose that additional data is required to support a hypothesis of common alleles of large effect on foraging behavior in nature. Genome-wide association does support a role for natural variation at several other loci, including the sulfateless gene, though these associations should be considered preliminary until validated with a larger sample size.

Evolutionary Biology↗

bioRxiv2014

Phylogenetic ANOVA: The Expression Variance and Evolution (EVE) model for quantitative trait evolution

A number of methods have been developed for modeling the evolution of a quantitative trait on a phylogeny. These methods have received renewed interest in the context of genome-wide studies of gene expression, in which the expression levels of many genes can be modeled as quantitative traits. We here develop a new method for joint analyses of quantitative traits within and between-species, the Expression Variance and Evolution (EVE) model. The model parameterizes the ratio of population to evolutionary expression variance, facilitating a wide variety of analyses, including a test for lineage-specific shifts in expression level, and a phylogenetic ANOVA that can detect genes with increased or decreased ratios of expression divergence to diversity, analogous to the famous HKA test used to detect selection at the DNA level. We use simulations to explore the properties of these tests under a variety of circumstances and show that the phylogenetic ANOVA is more accurate than the standard ANOVA (no accounting for phylogeny) sometimes used in transcriptomics. We then apply the EVE model to a mammalian phylogeny of 15 species typed for expression levels in liver tissue. We identify genes with high expression divergence between-species as candidates for expression level adaptation, and genes with high expression diversity within-species as candidates for expression level conservation and/or plasticity. Using the test for lineage-specific expression shifts, we identify several candidate genes for expression level adaptation on the catarrhine and human lineages, including genes putatively related to dietary changes in humans. We compare these results to those reported previously using a model which ignores expression variance within-species, uncovering important differences in performance. We demonstrate the necessity for a phylogenetic model in comparative expression studies and show the utility of the EVE model to detect expression divergence, diversity, and branch-specific shifts.

Evolutionary Biology↗