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An ancient origin of the naked grains of maize

Adaptation to novel environments requires genetic variation, which may either predate the novel environment or arise as new mutations. The relative importance of standing genetic variation vs. de novo mutations in adaptation remains a fundamental question in evolutionary biology. Selection during domestication has been long used as a model to understand evolutionary processes, providing information not only on the phenotypes selected but also, in many cases, an understanding of the causal loci. Of the multiple causal loci that have been identified in maize, the selected allele can be found segregating in natural populations, consistent with their origin as standing genetic variation. The sole exception to this pattern is the well-characterized domestication locus tga1, which has long been thought to be an example of selection on a de novo mutation. Here, we use a large dataset of maize and teosinte genomes to reconstruct the origin and evolutionary history of tga1. We first estimated the age of tga1-maize using a genealogy-based method, finding that the allele arose approximately 41,000-49,000 years ago, predating the beginning of maize domestication. We also identify, for the first time, tga1-maize in teosinte populations, indicating the allele can survive in the wild. Finally, we compare observed patterns of haplotype structure and mutational age distributions near tga1 with simulations, finding that patterns near tga1 in maize better resemble those generated under simulated selective sweeps on standing variation. These multiple lines of evidence suggest that maize domestication likely drew upon standing genetic variation at tga1 and cement the importance of standing variation in driving adaptation during domestication.

evolutionary biology↗

On the Colijn-Plazzotta numbering scheme for unlabeled binary rooted trees

Colijn & Plazzotta (Syst. Biol. 67:113-126, 2018) introduced a scheme for bijectively associating the unlabeled binary rooted trees with the positive integers. First, the rank 1 is associated with the 1-leaf tree. Proceeding recursively, ordered pair (k1, k2), k1 [>=] k2 [>=] 1, is then associated with the tree whose left subtree has rank k1 and whose right subtree has rank k2. Following dictionary order on ordered pairs, the tree whose left and right subtrees have the ordered pair of ranks (k1, k2) is assigned rank k1(k1 - 1)/2 + 1 + k2. With this ranking, given a number of leaves n, we determine recursions for an, the smallest rank assigned to some tree with n leaves, and bn, the largest rank assigned to some tree with n leaves. For n equal to a power of 2, the value of an is seen to increase exponentially with 2n for a constant {approx} 1.24602; more generally, we show it is bounded an < 1.5n. The value of bn is seen to increase with [Formula] for a constant {beta} {approx} 1.05653. The great difference in the rates of increase for an and bn indicates that as the index v is incremented, the number of leaves for the tree associated with rank v quickly traverses a wide range of values. We interpret the results in relation to applications in evolutionary biology. Mathematics subject classification05C05, 92B10, 92D15

evolutionary biology↗

Decontextualized learning for interpretable hierarchical representations of visual patterns

SO_SCPLOWUMMARYC_SCPLOWApart from discriminative models for classification and object detection tasks, the application of deep convolutional neural networks to basic research utilizing natural imaging data has been somewhat limited; particularly in cases where a set of interpretable features for downstream analysis is needed, a key requirement for many scientific investigations. We present an algorithm and training paradigm designed specifically to address this: decontextualized hierarchical representation learning (DHRL). By combining a generative model chaining procedure with a ladder network architecture and latent space regularization for inference, DHRL address the limitations of small datasets and encourages a disentangled set of hierarchically organized features. In addition to providing a tractable path for analyzing complex hierarchal patterns using variation inference, this approach is generative and can be directly combined with empirical and theoretical approaches. To highlight the extensibility and usefulness of DHRL, we demonstrate this method in application to a question from evolutionary biology.

evolutionary biology↗

The missing fraction problem as an episodes of selection problem

In evolutionary quantitative genetics, the missing fraction problem refers to the case where phenotypes seen later in life are biased because a non-random subset of those phenotypes are missing from the population due to prior viability selection on correlated traits. As any such missing fraction will bias our estimates of selection, and therefore, responses to selection, it is one potential explanation for the paradox of stasis seen in wild populations. The two components required for the missing fraction problem to arise are: (1) viability selection; and (2) correlation between later-life traits and those important for early-life survival. Although it is plausible that these conditions are widespread in wild populations, this problem has received very little attention since it was first discussed (Grafen 1988; Hadfield 2008). It is impossible to know what phenotypes would have been expressed later in life by individuals who died during an earlier episode of viability selection, which has probably put researchers off. Here we show that we can break the problem down into episodes of selection and recover either (a) true estimates of phenotypic selection for later-life traits, or (b) adjusted estimates of the response to selection, depending on the data available. Implementation of complex statistics should uncover how prevalent the problem may be across many existing datasets (the latter approach). Whereas overall, we hope that viewing the missing fraction problem as an episodes of selection problem increases motivation, and provides justification, for a shift in focus to directly studying early-life viability selection. "...what a correlation with [lifetime reproductive success] or its components tells you depends on the causes of the natural variation in the character. It will not always be easy to discover those causes." -- Grafen (1988) "It is surprising that the problem of missing data has received so little attention given that viability selection is central to evolutionary biology." -- Hadfield (2008)

evolutionary biology↗

The highly rugged yet navigable regulatory landscape of the bacterial transcription factor TetR

Transcription factor binding sites (TFBSs) are important sources of evolutionary innovations. Understanding how evolution navigates the sequence space of such sites can be achieved by mapping TFBS adaptive landscapes. In such a landscape, an individual location corresponds to a TFBS bound by a transcription factor. The elevation at that location corresponds to the strength of transcriptional regulation conveyed by the sequence. We developed an in vivo massively parallel reporter assay to map the landscape of bacterial TFBSs. We applied this assay to the TetR repressor, for which few TFBSs are known. We quantify the strength of transcriptional repression for 17,765 TFBSs and show that the resulting landscape is highly rugged, with 2,092 peaks. Only a few peaks convey stronger repression than the wild type. Non-additive (epistatic) interactions between mutations are frequent. Despite these hallmarks of ruggedness, most high peaks are evolutionarily accessible. They have large basins of attraction and are reached by around 20% of populations evolving on the landscape. Which high peak is reached during evolution is unpredictable and contingent on the mutational path taken. This first in-depth analysis of a prokaryotic gene regulator reveals a landscape that is navigable but much more rugged than the landscapes of eukaryotic regulators. SignificanceUnderstanding how evolution explores the vast space of genotypic possibilities is a fundamental question in evolutionary biology. The mapping of genotypes to quantitative traits (such as phenotypes and fitness) allows us to delineate adaptive landscapes and their topological properties, shedding light on how evolution can navigate such vast spaces. In this study, we focused on mapping a transcription factor binding site (TFBS) landscape to gene expression levels, as changes in gene expression patterns play a crucial role in biological innovation. We developed a massively parallel reporter assay and mapped the first comprehensive in vivo gene regulatory landscape for a bacterial transcriptional regulator, TetR. Surprisingly, this landscape is way more rugged than those observed in eukaryotic regulators. Despite its ruggedness, the landscape remains highly navigable through adaptive evolution. Our study presents the first high-resolution landscape for a bacterial TFBS, offering valuable insights into the evolution of TFBS in vivo. Moreover, it holds promise as a framework for discovering new genetic components for synthetic biological systems.

evolutionary biology↗

Hawkish but helpful: When cultural group selection favors within-group aggression

The origin of cooperation is a central problem in evolutionary biology and social science. Cultural group selection and parochial altruism are popular but controversial evolutionary explanations for large-scale cooperation. Proponents of the cultural group selection hypothesis argue that the human tendency to conform--a consequence of our reliance on social learning--maintained sufficient between-group variation to allow group selection (which favors altruism) to overpower individual selection (which favors selfishness), whereupon large-scale altruism could emerge. Proponents of the parochial altruism hypothesis argue that altruism could emerge in tandem with hostility toward other groups if the combination of the two traits increased success in inter-group contests. Proponents of both hypotheses assume that cooperation is altruistic and that within-group conflict is antithetical to cooperation, implying that group selection for cooperation reduces within-group conflict. Yet within-group conflict need not be antithetical to cooperation. This essay uses a mathematical model to show that selection between groups can lead to greater within-group aggression if within-group aggression enhances the value of individually costly public goods contributions. This model may help to explain cross-cultural associations between warfare, socialization for aggression, aggressive sports, and interpersonal violence among humans. It may also apply to other forms of inter-group conflict among humans. Finally, the model suggests that group selection can lead to disharmony within groups, a caveat to the use of group selection models to inform social policy.

Evolutionary Biology↗

Flexible methods for estimating genetic distances from nucleotide data

O_LIWith the increasing use of massively parallel sequencing approaches in evolutionary biology, the need for fast and accurate methods suitable to investigate genetic structure and evolutionary history are more important than ever. We propose new distance measures for estimating genetic distances between individuals when allelic variation, gene dosage and recombination could compromise standard approaches.\nC_LIO_LIWe present four distance measures based on single nucleotide polymorphisms (SNP) and evaluate them against previously published measures using coalescent-based simulations. Simulations were used to test (i) whether the measures give unbiased and accurate distance estimates, (ii) if they can accurately identify the genomic mixture of hybrid individuals and (iii) if they give precise (low variance) estimates.\nC_LIO_LIThe results showed that the SNP-based GENPOFAD distance we propose appears to work well in the widest circumstances. It was the most accurate method for estimating genetic distances and is also relatively good at estimating the genomic mixture of hybrid individuals.\nC_LIO_LIOur simulations provide benchmarks to compare the performance of different distance measures in specific situations.\nC_LI

Evolutionary Biology↗

Evolutionary history of an Alpine archaeognath(Machilis pallida) - insights from different variant types

Reconstruction of species histories is a central aspect of evolutionary biology. Patterns of genetic variation within and among populations can be leveraged to elucidate evolutionary processes and demographic histories. However, interpreting genetic signatures and unraveling the contributing processes can be challenging, in particular for non-model organisms with complex reproductive modes and genome organization. One way forward is the combined consideration of patterns revealed by different molecular markers (nuclear vs. mitochondrial) and types of variants (common vs. rare) that differ in their age, mode and rate of evolution. Here, we applied this approach to Machilis pallida (Archaeognatha), an Alpine jumping bristletail considered parthenogenetic and triploid. We generated de-novo transcriptome and mitochondrial assemblies to obtain high-density data to investigate patterns of mitochondrial and common and rare nuclear variation in 17 M. pallida individuals sampled across the Alps from all known populations. We find that the different variant types capture distinct aspects of the evolutionary history and discuss the observed patterns in the context of parthenogenesis, polyploidy and survival during glaciation. This study highlights the potential of different variant types to unravel complex evolutionary scenarios and the suitability of M. pallida and the genus Machilis as a study system for the evolution of sexual strategies and polyploidization during environmental change. We also emphasize the need for further research which will be stimulated and facilitated by these newly generated resources and insights.

evolutionary biology↗

Stochastic Phylogenetic Models of Shape

Phylogenetic modeling of morphological shape in two or three dimensions is one of the most challenging statistical problems in evolutionary biology. As shape data are inherently correlated and non-linear, most naive methods for phylogenetic analysis of morphological shape fail to capture the biological realities of evolving shapes. In this study we propose a novel framework for evolutionary analysis of morphological shape which facilitates stochastic character mapping on landmark shapes. Our framework is based on recent advances in mathematical shape analysis and models the evolution of shape as a diffusion process that accounts for the evolutionary correlation between nearby landmarks. The diffusion process we consider is parametrized in terms of meaningful parameters describing the evolutionary rate and the degree of spatial autocorrelation among landmarks. The framework we propose assumes that the phylogenetic tree is fixed and uses a Metropolis-Hastings Markov Chain Monte Carlo sampling scheme for inferring ancestral shapes and parameters of the model. We evaluate the new inference algorithm using simulations and show that the method leads to improved estimates of the shape at the root and well-calibrated credible sets of shapes at internal nodes. In addition, we also compare the diffusion parameter describing the degree of spatial autocorrelation to an existing metric of integration and find that they quantify integration in a shape in a similar way. To illustrate the method, we also apply it to a previously published data set of butterfly wing images.

evolutionary biology↗

Impact of Data Error on Phylogenetic Network Inference from Gene Trees Under the Multispecies Network Coalescent

Phylogenetic network inference has become an essential tool in evolutionary biology, offering a framework to model complex evolutionary events such as hybridization and horizontal gene transfer. However, a critical but often overlooked challenge is the presence of error in empirical datasets, including sequencing errors, misalignments, and inaccuracies in gene tree estimation. This issue is particularly pressing in the context of phylogenetic networks, which can contain an arbitrary number of parameters and are thus highly susceptible to overfitting. Errors in the input data can lead to artificially inflated network complexity, misrepresenting evolutionary history with non-biological reticulations. In this study,we systematically examine how different sources of data error influence network inference and show that many widely used methods are vulnerable to these distortions. We find that inaccuracies in gene tree estimation and sequence alignment degrade the reliability of inferred networks. These issues are exacerbated when the number of reticulations that an algorithm can infer exceeds the true number of reticulations in the phylogenetic network. Our analysis underscores the importance of accounting for data error when applying network inference methods and provides practical recommendations for minimizing its impact. By highlighting the vulnerabilities of different approaches and demonstrating how errors propagate through the inference process, we offer practical recommendations for optimizing data processing pipelines. Our findings emphasize the necessity of integrating realistic error models into species network inference methods to enhance their reliability and applicability to real-world biological datasets.

bioinformatics↗

Reconstructing gene content in the last common ancestor of cellular life: is it possible, should it be done, and are we making any progress?

I review recent literature on the reconstruction of gene repertoire of the Last Universal Common Ancestor of cellular life (LUCA). The form of the phylogenetic record of cellular life on Earth is important to know in order to reconstruct any ancestral state; therefore I also discuss the emerging understanding that this record does not take the form of a tree. I argue that despite this, \"tree-thinking\" remains an essential component in evolutionary thinking and that \"pattern pluralism\" in evolutionary biology can be only epistemological, but not ontological.

Evolutionary Biology↗

Negative frequency-dependent selection is frequently confounding

This preprint has been reviewed and recommended by Peer Community in Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100024). The existence of persistent genetic variation within natural populations presents an evolutionary problem as natural selection and genetic drift tend to erode genetic diversity. Models of balancing selection were developed to account for the high and sometimes extreme levels of polymorphism found in many natural populations. Negative frequency-dependent selection may be the most powerful selective force maintaining balanced natural polymorphisms but it is also commonly misinterpreted. The aim of this review is to clarify the processes underlying negative frequency-dependent selection, describe classes of natural polymorphisms that can and cannot result from these processes, and discuss observational and experimental data that can aid in accurately identifying the processes that generated or are maintain diversity in nature. Finally, I consider the importance of accurately describing the processes affecting genetic diversity within populations as it relates to research progress.

evolutionary biology↗

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 Pagels {lambda} 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.

evolutionary biology↗

Brooding brittle-star is a global hybrid polyploid swarm

The widespread and abundant brooding brittle-star (Amphipholis squamata) is a simultaneous hermaphrodite with a complex mitochondrial phylogeography of multiple divergent overlapping mtDNA lineages and can exhibit high levels of inbreeding or clonality and unusual sperm morphology. We use exon-capture and transcriptome data to show that the nuclear genome comprises multiple (>3) divergent ({pi} > 6%) expressed components spread across the mitochondrial lineages, and encompassing several other genera, including diploid dioecious dimorphic species. We also report a massive sperm genome size in A. squamata, an order of magnitude larger than in the sperm of other brittle-star species, consistent with our genetic measures of elevated and variable ploidy (>6). We propose that A. squamata (and related taxa) is a hybrid polyploid complex with many independent hybrid origins, variable ploidy, and complex patterns of parental subgenomes. We hypothesize that A. squamata has facultative sperm-dependent asexual reproduction, where sperm is required for embryogenesis but the egg and sperm only occasionally undergo fertilisation, a process that has been associated with the formation of polyploid hybrid swarms in other taxa [1]. Unique amongst known marine allopolyploids, the A. squamata complex inhabits an extensive bathymetric as well as geographic range. A. squamata is a much-studied animal amenable to laboratory culture: appreciating it as a hybrid polyploid complex makes it even more interesting to the study of evolutionary biology.

evolutionary biology↗

Shared selection and genetic architecture drive strikingly repeatable evolution in long-term experimental hybrid populations

The degree to which evolution repeats itself has implications regarding the major forces driving evolution and the potential for evolutionary biology to be a predictive (versus solely historical) science. To understand the factors that control evolutionary repeatability, we experimentally evolved four replicate hybrid populations of sunflowers at natural sites for up to 14 years and tracked ancestry across the genome. We found that there was very strong negative selection against introgressed ancestry in several chromosomes, but positive selection for introgressed ancestry in one chromosome. Further, the strength of selection was influenced by recombination rate. High recombination regions had lower selection against introgressed ancestry due to more frequent recombination away from incompatible backgrounds. Strikingly, evolution was highly parallel across replicates, with shared selection driving 88% of variance in introgressed allele frequency change. Parallel evolution was driven by both high levels of sustained linkage in introgressed alleles and strong selection on large-effect quantitative trait loci. This work highlights the repeatability of evolution through hybridization and confirms the central roles that natural selection, genomic architecture, and recombination play in the process.

evolutionary biology↗

Leveraging Comparative Phylogenetics for Evolutionary Medicine: Applications to Comparative Oncology

Comparative phylogenetics provides a wealth of computational tools to understand evolutionary processes and their outcomes. Advances in these methodologies have occurred in parallel with a surge in cross-species genomic and phenotypic data. To date, however, the majority of published studies have focused on classical questions in evolutionary biology, such as speciation and the ecological drivers of trait evolution. Here, we argue that evolutionary medicine in general, and our understanding of the origin and diversification of disease traits in particular, would be greatly expanded by a wider integration of phylogenetic comparative methods (PCMs). We use comparative oncology - the study of cancer across the tree of life - as a case study to demonstrate the power of the approach and show that implementing PCMs can highlight the mode and tempo of the evolutionary changes in intrinsic, species-level disease vulnerabilities.

evolutionary biology↗

A mosaic of independent innovations involving eyes shut are critical for the evolutionary transition from closed to open rhabdoms

A fundamental question in evolutionary biology is how developmental processes are modified to produce morphological innovations while abiding by functional constraints. Here we address this question by investigating the cellular mechanism responsible for the transition between fused and open rhabdoms in ommatidia of apposition compound eyes; a critical step required for the development of visual systems based on neural superposition. Utilizing Drosophila and Tribolium as representatives of fused and open rhabdom morphology respectively, we identified three changes required for this innovation to occur. First, the expression pattern of the extracellular matrix protein Eyes Shut (EYS) was co-opted and expanded from mechanosensory neurons to the photoreceptor cells in taxa with open rhabdoms. Second, EYS homologs in these taxa obtained a novel extension of the amino terminus leading to the internalization of a cleaved signal sequence. This amino terminus extension does not interfere with cleavage or function in mechanosensory neurons, but it does permit specific targeting of the EYS protein to the apical photoreceptor membrane. Finally, a specific interaction evolved between EYS and a subset of Prominin homologs that is required for the development of open, but not fused, rhabdoms. Together, our findings portray a case study wherein the evolution of a set of molecular novelties has precipitated the origin of an adaptive photoreceptor cell arrangement.\n\nAuthor SummaryUnderstanding how adaptive morphologies originate is a central question in evolutionary developmental biology. Once confined largely to arguments about the relative frequencies of protein coding vs. regulatory mutations, numerous studies have since revealed more complex interactions involving alterations in gene expression and novel protein-protein interactions as drivers of novel trait evolution. Our study exploits the genetic amenability of Drosophila and utilizes direct comparisons with Tribolium to define a set of cellular mechanisms necessary for the evolutionary transition from a fused (Tribolium) to an open (Drosophila) rhabdom. Our results depict an evolutionary transition involving both non-coding and coding changes that resulted in a novel visual architecture, permitting a subset of diurnal insects to diversify into niches characterized by low light.

evolutionary biology↗

Cancer Risk and the Somatic Cell Lineage Tree

All the cells of a multicellular organism are the product of cell divisions that trace out a single binary tree, the so-called cell lineage tree. Because cell divisions are accompanied by replication errors, the shape of the cell lineage tree is one of the key determinants of how somatic evolution, which can potentially lead to cancer, proceeds. Cancer initiation usually requires the accumulation of a certain number of driver mutations. By mapping the accumulation of driver mutations into a graph theoretical problem, we show that in leading order of the mutation rate the probability of collecting a given number of driver mutations depends only on the distribution of the lineage lengths (irrespective of any other details of the cell lineage tree), and we derive a simple analytical formula for this probability. Our results are crucial in understanding how natural selection can shape the cell lineage trees of multicellular organisms in order to reduce their lifetime risk of cancer. In particular, our results highlight the significance of the longest cell lineages. Our analytical formula also provides a tool to quantify cancer susceptibility in theoretical models of tissue development and maintenance, as well as for empirical data on cell linage trees. Significance StatementA series of cell divisions starting from a single cell produce and maintain tissues of multicellular organisms. Somatic evolution, including the development of cancer, takes place along the cell lineage tree traced out by these cell divisions. A fundamental question in cancer research is how the lifetime risk of cancer depends on the properties of an arbitrary cell lineage tree. Here we show that for small mutation rates (which is the case in reality) the distribution of the lineage lengths alone determines cancer risk, and that this risk can be described by a simple analytical formula. Our results have far-reaching implications not only for cancer research, but also for evolutionary biology in general.

evolutionary biology↗