Comparative quantitative genetics: evolution of the G matrix

The Role of (Co)variation in Shaping the Response to Selection in New World Leaf-Nosed Bats

AbstractUnderstanding and predicting the evolutionary responses of complex morphological traits to selection remains a major challenge in evolutionary biology. Because traits are genetically correlated, selection on a particular trait produces both direct effects on the distribution of that trait and indirect effects on other traits in the population. The correlations between traits can strongly impact evolutionary responses to selection and may thus impose constraints on adaptation. Here, we used museum specimens and comparative quantitative genetic approaches to investigate whether the covariation among cranial traits facilitated or constrained the response to selection during the major dietary transitions in one of the world's most ecologically diverse mammalian families-the phyllostomid bats. We reconstructed the set of net selection gradients that would have acted on each cranial trait during the major transitions to feeding specializations and decomposed the selection responses into their direct and indirect components. We found that for all transitions, most traits capturing craniofacial length evolved toward adaptive directions owing to direct selection. Additionally, we showed instances of dietary transitions in which the complex interaction between the patterns of covariation among traits and the strength and direction of selection either constrained or facilitated evolution. Our work highlights the importance of considering the within-species covariation estimates to quantify evolvability and to disentangle the relative contribution of variational constraints versus selective causes for observed patterns.

Polygenic response of sex chromosomes to sexual antagonism

Sexual antagonism occurs when males and females differ in their phenotypic fitness optima but are constrained in their evolution to these optima because of their shared genome. The sex chromosomes, which have distinct evolutionary "interests" relative to the autosomes, are theorized to play an important role in sexually antagonistic conflict. However, the evolutionary responses of sex chromosomes and autosomes have usually been considered independently, that is, via contrasting the response of a gene located on either an X chromosome or an autosome. Here, we study the coevolutionary response of the X chromosome and autosomes to sexually antagonistic selection acting on a polygenic phenotype. We model a phenotype initially under stabilizing selection around a single optimum, followed by a sudden divergence of the male and female optima. We find that, in the absence of dosage compensation, the X chromosome promotes evolution toward the female optimum, inducing coevolutionary male-biased responses on the autosomes. Dosage compensation obscures the female-biased interests of the X, causing it to contribute equally to male and female phenotypic change. We further demonstrate that fluctuations in an adaptive landscape can generate prolonged intragenomic conflict and accentuate the differential responses of the X and autosomes to this conflict.

Evolutionary rescue from climate change: male indirect genetic effects on lay-dates and their consequences for population persistence

Changes in avian breeding phenology are among the most apparent responses to climate change in free-ranging populations. A key question is whether populations will be able to keep up with the expected rates of environmental change. There is a large body of research on the mechanisms by which avian lay-dates track temperature change and the consequences of (mal)adaptation on population persistence. Often overlooked is the role of males, which can influence the lay-date of their mate through their effect on the prelaying environment. We explore how social plasticity causing male indirect genetic effects can help or hinder population persistence when female genes underpinning lay-date and male genes influencing female's timing of reproduction both respond to climate-mediated selection. We extend quantitative genetic moving optimum models to predict the consequences of social plasticity on the maximum sustainable rate of temperature change, and evaluate our model using a combination of simulated data and empirical estimates from the literature. Our results suggest that predictions for population persistence may be biased if indirect genetic effects and cross-sex genetic correlations are not considered and that the extent of this bias depends on sex differences in how environmental change affects the optimal timing of reproduction. Our model highlights that more empirical work is needed to understand sex-specific effects of environmental change on phenology and the fitness consequences for population dynamics. While we discuss our results exclusively in the context of avian breeding phenology, the approach we take here can be generalized to many different contexts and types of social interaction.

Does the definition of a novel environment affect the ability to detect cryptic genetic variation?

Anthropogenic change exposes populations to environments that have been rare or entirely absent from their evolutionary past. Such novel environments are hypothesized to release cryptic genetic variation, a hidden store of variance that can fuel evolution. However, support for this hypothesis is mixed. One possible reason is a lack of clarity in what is meant by 'novel environment', an umbrella term encompassing conditions with potentially contrasting effects on the exposure or concealment of cryptic variation. Here, we use a meta-analysis approach to investigate changes in the total genetic variance of multivariate traits in ancestral versus novel environments. To determine whether the definition of a novel environment could explain the mixed support for a release of cryptic genetic variation, we compared absolute novel environments, those not represented in a population's evolutionary past, to extreme novel environments, those involving frequency or magnitude changes to environments present in a population's ancestry. Despite sufficient statistical power, we detected no broad-scale pattern of increased genetic variance in novel environments, and finding the type of novel environment did not explain any significant variation in effect sizes. When effect sizes were partitioned by experimental design, we found increased genetic variation in studies based on broad-sense measures of variance, and decreased variation in narrow-sense studies, in support of previous research. Therefore, the source of genetic variance, not the definition of a novel environment, was key to understanding environment-dependant genetic variation, highlighting non-additive genetic variance as an important component of cryptic genetic variation and avenue for future research.

Innovation and elaboration on the avian tree of life

Widely documented, megaevolutionary jumps in phenotypic diversity continue to perplex researchers because it remains unclear whether these marked changes can emerge from microevolutionary processes. Here, we tackle this question using new approaches for modeling multivariate traits to evaluate the magnitude and distribution of elaboration and innovation in the evolution of bird beaks. We find that elaboration, evolution along the major axis of phenotypic change, is common at both macro- and megaevolutionary scales, whereas innovation, evolution away from the major axis of phenotypic change, is more prominent at megaevolutionary scales. The major axis of phenotypic change among species beak shapes at megaevolutionary scales is an emergent property of innovation across clades. Our analyses suggest that the reorientation of phenotypes via innovation is a ubiquitous route for divergence that can arise through gradual change alone, opening up further avenues for evolution to explore.

Detecting natural selection in trait-trait coevolution

No phenotypic trait evolves independently of all other traits, but the cause of trait-trait coevolution is poorly understood. While the coevolution could arise simply from pleiotropic mutations that simultaneously affect the traits concerned, it could also result from multivariate natural selection favoring certain trait relationships. To gain a general mechanistic understanding of trait-trait coevolution, we examine the evolution of 220 cell morphology traits across 16 natural strains of the yeast Saccharomyces cerevisiae and the evolution of 24 wing morphology traits across 110 fly species of the family Drosophilidae, along with the variations of these traits among gene deletion or mutation accumulation lines (a.k.a. mutants). For numerous trait pairs, the phenotypic correlation among evolutionary lineages differs significantly from that among mutants. Specifically, we find hundreds of cases where the evolutionary correlation between traits is strengthened or reversed relative to the mutational correlation, which, according to our population genetic simulation, is likely caused by multivariate selection. Furthermore, we detect selection for enhanced modularity of the yeast traits analyzed. Together, these results demonstrate that trait-trait coevolution is shaped by natural selection and suggest that the pleiotropic structure of mutation is not optimal. Because the morphological traits analyzed here are chosen largely because of their measurability and thereby are not expected to be biased with regard to natural selection, our conclusion is likely general.

Functional morphological integration related to feeding biomechanics in the hominine skull

Quantifying and characterizing the pattern of trait covariances is crucial for understanding how population-level patterns of integration might constrain or facilitate craniofacial evolution related to the feeding system. This study addresses an important gap in our knowledge by investigating magnitudes and patterns of morphological integration of biomechanically informative traits in the skulls of Homo sapiens, Pan troglodytes, and Gorilla gorilla. We predicted a lower magnitude of integration among human biomechanical traits since humans eat a softer, less biomechanically challenging diet than apes. Indeed, compared to African apes, the magnitudes of integration were lower in H. sapiens skulls for form data (raw dimensions) but were similar or higher for shape data (raw dimensions scaled by geometric mean). Patterns of morphological integration were generally similar, but not identical, across the three species, particularly for the form data compared to the shape data. Traits that load heavily on the primary axis of variation in morphospace are generally associated with size and/or shape of the temporalis and masseter muscles and with dimensions related to the constrained lever model of jaw biomechanics. Given the conserved nature of morphological integration, skull adaptations for food processing in African apes and humans may have been constrained to occur along certain paths of high evolvability. The conserved pattern of functional integration also indicates that extant hominine species can operate as reasonable analogues for extinct hominins in studies that require population-level patterns of trait variance/covariance.

Selection and the direction of phenotypic evolution

Predicting adaptive phenotypic evolution depends on invariable selection gradients and on the stability of the genetic covariances between the component traits of the multivariate phenotype. We describe the evolution of six traits of locomotion behavior and body size in the nematode Caenorhabditis elegans for 50 generations of adaptation to a novel environment. We show that the direction of adaptive multivariate phenotypic evolution can be predicted from the ancestral selection differentials, particularly when the traits were measured in the new environment. Interestingly, the evolution of individual traits does not always occur in the direction of selection, nor are trait responses to selection always homogeneous among replicate populations. These observations are explained because the phenotypic dimension with most of the ancestral standing genetic variation only partially aligns with the phenotypic dimension under directional selection. These findings validate selection theory and suggest that the direction of multivariate adaptive phenotypic evolution is predictable for tens of generations.

Exact expressions and numerical evaluation of average evolvability measures for characterizing and comparing [Formula: see text] matrices

Theory predicts that the additive genetic covariance ([Formula: see text]) matrix determines a population's short-term (in)ability to respond to directional selection-evolvability in the Hansen-Houle sense-which is typically quantified and compared via certain scalar indices called evolvability measures. Often, interest is in obtaining the averages of these measures across all possible selection gradients, but explicit formulae for most of these average measures have not been known. Previous authors relied either on approximations by the delta method, whose accuracy is generally unknown, or Monte Carlo evaluations (including the random skewers analysis), which necessarily involve random fluctuations. This study presents new, exact expressions for the average conditional evolvability, average autonomy, average respondability, average flexibility, average response difference, and average response correlation, utilizing their mathematical structures as ratios of quadratic forms. The new expressions are infinite series involving top-order zonal and invariant polynomials of matrix arguments, and can be numerically evaluated as their partial sums with, for some measures, known error bounds. Whenever these partial sums numerically converge within reasonable computational time and memory, they will replace the previous approximate methods. In addition, new expressions are derived for the average measures under a general normal distribution for the selection gradient, extending the applicability of these measures into a substantially broader class of selection regimes.

Evolutionary repeatability of emergent properties of ecological communities

Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Multivariate selection and the making and breaking of mutational pleiotropy

The role of mutations have been subject to many controversies since the formation of the Modern Synthesis of evolution in the early 1940ties. Geneticists in the early half of the twentieth century tended to view mutations as a limiting factor in evolutionary change. In contrast, natural selection was largely viewed as a “sieve” whose main role was to sort out the unfit but which could not create anything novel alone. This view gradually changed with the development of mathematical population genetics theory, increased appreciation of standing genetic variation and the discovery of more complex forms of selection, including balancing selection. Short-term evolutionary responses to selection are mainly influenced by standing genetic variation, and are predictable to some degree using information about the genetic variance–covariance matrix (G) and the strength and form of selection (e. g. the vector of selection gradients, β). However, predicting long-term evolution is more challenging, and requires information about the nature and supply of novel mutations, summarized by the mutational variance–covariance matrix (M). Recently, there has been increased attention to the role of mutations in general and M in particular. Some evolutionary biologists argue that evolution is largely mutation-driven and claim that mutation bias frequently results in mutation-biased adaptation. Strong similarities between G and M have also raised questions about the non-randomness of mutations. Moreover, novel mutations are typically not isotropic in their phenotypic effects and mutational pleiotropy is common. Here I discuss the evolutionary origin and consequences of mutational pleiotropy and how multivariate selection directly shapes G and indirectly M through changed epistatic relationships. I illustrate these ideas by reviewing recent literature and models about correlational selection, evolution of G and M, sexual selection and the fitness consequences of sexual antagonism.

Solving the grand challenge of phenotypic integration: allometry across scales

Phenotypic integration is a concept related to the cascade of trait relationships from the lowest organizational levels, i.e. genes, to the highest, i.e. whole-organism traits. However, the cause-and-effect linkages between traits are notoriously difficult to determine. In particular, we still lack a mathematical framework to model the relationships involved in the integration of phenotypic traits. Here, we argue that allometric models developed in ecology offer testable mathematical equations of trait relationships across scales. We first show that allometric relationships are pervasive in biology at different organizational scales and in different taxa. We then present mechanistic models that explain the origin of allometric relationships. In addition, we emphasized that recent studies showed that natural variation does exist for allometric parameters, suggesting a role for genetic variability, selection and evolution. Consequently, we advocate that it is time to examine the genetic determinism of allometries, as well as to question in more detail the role of genome size in subsequent scaling relationships. More broadly, a possible-but so far neglected-solution to understand phenotypic integration is to examine allometric relationships at different organizational levels (cell, tissue, organ, organism) and in contrasted species.

A method to predict the response to directional selection using a Kalman filter

Predicting evolution remains challenging. The field of quantitative genetics provides predictions for the response to directional selection through the breeder's equation, but these predictions can have errors. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. Previous research showed that the expected value of these prediction errors is often not zero, so predictions are systematically biased. Here, we propose that this bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a method to predict evolution, which is built on three key innovations. First, the method predicts change as the breeder's equation plus a bias term. Second, the method combines information from the breeder's equation and from the record of past changes in the mean to predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a learning algorithm on the record of past changes. We compare the method to the breeder's equation in two artificial selection experiments, one using the wing of the fruit fly and another using simulations that include a complex mapping of genotypes to phenotypes. The proposed method outperforms the breeder's equation, particularly when traits under selection are omitted from the analysis, when data are noisy, and when additive genetic variance is estimated inaccurately or not estimated at all. The proposed method is easy to apply, requiring only the trait means over past generations.

Age-dependent genetic architecture across ontogeny of body size in sticklebacks

Heritable variation in traits under natural selection is a prerequisite for evolutionary response. While it is recognized that trait heritability may vary spatially and temporally depending on which environmental conditions traits are expressed under, less is known about the possibility that genetic variance contributing to the expected selection response in a given trait may vary at different stages of ontogeny. Specifically, whether different loci underlie the expression of a trait throughout development and thus providing an additional source of variation for selection to act on in the wild, is unclear. Here we show that body size, an important life-history trait, is heritable throughout ontogeny in the nine-spined stickleback (Pungitius pungitius). Nevertheless, both analyses of quantitative trait loci and genetic correlations across ages show that different chromosomes/loci contribute to this heritability in different ontogenic time-points. This suggests that body size can respond to selection at different stages of ontogeny but that this response is determined by different loci at different points of development. Hence, our study provides important results regarding our understanding of the genetics of ontogeny and opens an interesting avenue of research for studying age-specific genetic architecture as a source of non-parallel evolution.

Conserved and Taxon-Specific Patterns of Phenotypic Modularity in the Mammalian Dentition

Previous genotype:phenotype mapping of the mouse and primate dentition revealed the presence of pre- and post-canine modules in mice and anthropoid primates, as well as molar and premolar submodules in anthropoid primates. We estimated phenotypic correlation matrices for species that sample broadly across Mammalia to test the hypothesis that these modules exist across a broader range of taxa and thereby represent a conserved mammalian trait. We calculated phenotypic correlation matrices from linear dental measurements of 419 individual specimens representing 5 species from 4 mammalian orders: Artiodactyla (Odocoileus hemionus), Carnivora (Canis latrans and Ursus americanus), Didelphimorphia (Didelphis virginiana), and Primates (Colobus guereza). Our results based on hierarchical clustering indicate a generally higher correlation within incisors and among post-canine teeth. However, the post-canine phenotypic correlation matrices do not consistently exhibit the premolar and molar submodularity observed in anthropoid primates. Additionally, we find evidence of sex differences in the Odocoileus phenotypic correlation matrices: Males of this species exhibit overall higher inter-trait correlations compared to females. Our overall findings support the interpretation that incisors and post-canine dentition represent different phenotypic modules, and that this architecture may be a conserved trait for mammals.

Allometry, sexual selection and evolutionary lines of least resistance shaped the evolution of exaggerated sexual traits within the genus Tyrannus

Variational properties hold a fundamental role in shaping biological evolution, exerting control over the magnitude and direction of evolutionary change elicited by microevolutionary processes that sort variation, such as selection or drift. We studied the genus Tyrannus as a model for examining the conditions and drivers that facilitate the repeated evolution of exaggerated, secondary sexual traits in the face of significant functional limitations. In particular, we explore the role of allometry, sexual selection and their interaction, on the diversification of tail morphology in the genus, assessing whether and how they promoted or constrained phenotypic evolution. Non-deep-forked species tend to show reduced sexual dimorphism and moderate allometric variation in tail shape. The exaggerated and functionally constrained long feathers of deep-forked species, T. savana and T. forficatus, which show both marked sexual dimorphism and allometric tail shape variation, independently diverged from the rest of the genus following the same direction of main interspecific variation accrued during the evolution of non-deep-forked species. Moreover, the latter direction is also aligned with axes summarising sexual dimorphism and allometric variation on deep-forked species, a feature lacking in the rest of the species. Thus, exaggerated tail morphologies are interpreted as the result of amplified divergence through reorientation and co-option of allometric variation by sexual selection, repeatedly driving morphology along a historically favoured direction of cladogenetic evolution.

Indirect Genetic Effects: A Cross-disciplinary Perspective on Empirical Studies

Indirect genetic effects (IGE) occur when an individual's phenotype is influenced by genetic variation in conspecifics. Opportunities for IGE are ubiquitous, and, when present, IGE have profound implications for behavioral, evolutionary, agricultural, and biomedical genetics. Despite their importance, the empirical study of IGE lags behind the development of theory. In large part, this lag can be attributed to the fact that measuring IGE, and deconvoluting them from the direct genetic effects of an individual's own genotype, is subject to many potential pitfalls. In this Perspective, we describe current challenges that empiricists across all disciplines will encounter in measuring and understanding IGE. Using ideas and examples spanning evolutionary, agricultural, and biomedical genetics, we also describe potential solutions to these challenges, focusing on opportunities provided by recent advances in genomic, monitoring, and phenotyping technologies. We hope that this cross-disciplinary assessment will advance the goal of understanding the pervasive effects of conspecific interactions in biology.

Life histories as mosaics: plastic and genetic components differ among traits that underpin life-history strategies

Life‐history phenotypes emerge from clusters of traits that are the product of genes and phenotypic plasticity. If the impact of the environment differs substantially between traits, then life histories might not evolve as a cohesive whole. We quantified the sensitivity of components of the life history to food availability, a key environmental difference in the habitat occupied by contrasting ecotypes, for 36 traits in fast‐ and slow‐reproducing Trinidadian guppies. Our dataset included six putatively independent origins of the slow‐reproducing, derived ecotype. Traits varied substantially in plastic and genetic control. Twelve traits were influenced only by food availability (body lengths, body weights), five only by genetic differentiation (interbirth intervals, offspring sizes), 10 by both (litter sizes, reproductive timing), and nine by neither (fat contents, reproductive allotment). Ecotype‐by‐food interactions were negligible. The response to low food was aligned with the genetic difference between high‐ and low‐food environments, suggesting that plasticity was adaptive. The heterogeneity among traits in environmental sensitivity and genetic differentiation reveals that the components of the life history may not evolve in concert. Ecotypes may instead represent mosaics of trait groups that differ in their rate of evolution.

Morphological integration and cranial modularity in six genera of echimyid rodents (Rodentia: Echimyidae)

Rodents of the family Echimyidae present a wide variety of life histories and ecomorphological adaptations. This study evaluated morphological integration patterns, modularity, and evolutionary flexibility in six Echimyid genera representing ecomorphological extremes within the family. The relationships between traits were evaluated by comparing estimated covariance and correlations matrices of populations. The presence of modules was investigated by comparing the patterns of integration between traits and using hypothetical matrices based on shared development/function and masticatory stress. The results point to a common covariance and correlation pattern among the six echimyid genera, suggesting a conserved pattern of covariation (associations among traits) throughout the evolution of this group. The overall magnitude of integration, however, varied greatly. We also found a high degree of modularity in all six echimyid genera. Finally, we observed a clear association between flexibility, i.e., the ability of a species to respond to the direction of selection, with the overall magnitude of integration and degree of modularization. The results of this study provide hypotheses concerning the underlying effects of the association among traits, which may have facilitated or constrained the evolution of morphological variation in the diverse family Echimyidae.

Covariation between the cranium and the cervical vertebrae in hominids

The analysis of patterns of integration is crucial for the reconstruction and understanding of how morphological changes occur in a taxonomic group throughout evolution. These patterns are relatively constant; however, both patterns and the magnitudes of integration may vary across species. These differences may indicate morphological diversification, in some cases related to functional adaptations to the biomechanics of organisms. In this study, we analyze patterns of integration between two functional and developmental structures, the cranium and the cervical spine in hominids, and we quantify the amount of divergence of each anatomical element through phylogeny. We applied these methods to three-dimensional data from 168 adult hominid individuals, summing a total of more than 1000 cervical vertebrae. We found the atlas (C1) and axis (C2) display the lowest covariation with the cranium in hominids (Homo sapiens, Pan troglodytes, Pan paniscus, Gorilla gorilla, Gorilla beringei, Pongo pygmaeus). H. sapiens show a relatively different pattern of craniocervical correlation compared with chimpanzees and gorillas, especially in variables implicated in maintaining the balance of the head. Finally, the atlas and axis show lower magnitude of shape change during evolution than the rest of the cervical vertebrae, especially those located in the middle of the subaxial cervical spine. Overall, results suggest that differences in the pattern of craniocervical correlation between humans and gorillas and chimpanzees could reflect the postural differences between these groups. Also, the stronger craniocervical integration and larger magnitude of shape change during evolution shown by the middle cervical vertebrae suggests that they have been selected to play an active role in maintaining head balance.

Stepwise Covariance-Free Common Principal Components (CF-CPC) With an Application to Neuroscience

Finding the common principal component (CPC) for ultra-high dimensional data is a multivariate technique used to discover the latent structure of covariance matrices of shared variables measured in two or more k conditions. Common eigenvectors are assumed for the covariance matrix of all conditions, only the eigenvalues being specific to each condition. Stepwise CPC computes a limited number of these CPCs, as the name indicates, sequentially and is, therefore, less time-consuming. This method becomes unfeasible when the number of variables p is ultra-high since storing k covariance matrices requires O(k p 2) memory. Many dimensionality reduction algorithms have been improved to avoid explicit covariance calculation and storage (covariance-free). Here we propose a covariance-free stepwise CPC, which only requires O(k n) memory, where n is the total number of examples. Thus for n < < p, the new algorithm shows apparent advantages. It computes components quickly, with low consumption of machine resources. We validate our method CFCPC with the classical Iris data. We then show that CFCPC allows extracting the shared anatomical structure of EEG and MEG source spectra across a frequency range of 0.01-40 Hz.

Statistics of eigenvalue dispersion indices: Quantifying the magnitude of phenotypic integration

Analysis of trait covariation plays a pivotal role in the study of phenotypic evolution. The magnitude of covariation is often quantified with statistics based on dispersion of eigenvalues of a covariance or correlation matrix-eigenvalue dispersion indices. This study clarifies the statistical justifications of these statistics and elaborates on their sampling properties. The relative eigenvalue variance of a covariance matrix is known in the statistical literature a test statistic for sphericity, and thus is an appropriate measure of eccentricity of variation. The same of a correlation matrix is equal to the average squared correlation, which has a straightforward interpretation as a measure of integration. Here, expressions for the mean and variance of these statistics are analytically derived under multivariate normality, clarifying the effects of sample size N, number of variables p, and parameters on sampling bias and error. Simulations confirm that approximations involved are reasonably accurate with a moderate sample size (N ≥ 16-64). Importantly, sampling properties of these indices are not adversely affected by a high p:N ratio, promising their utility in high-dimensional phenotypic analyses. They can furthermore be applied to shape variables and phylogenetically structured data with appropriate modifications.

Axes of multivariate sexual signal divergence among incipient species: Concordance with selection, genetic variation and phenotypic plasticity

Sexual signalling traits are often observed to diverge rapidly among populations, thereby playing a potentially key early role in the evolution of reproductive isolation. While often assumed to reflect divergent sexual selection among populations, patterns of sexual trait diversification might sometimes be biased along axes of standing additive genetic variation and covariation among trait components. Additionally, theory predicts that environmentally induced phenotypic variation might facilitate rapid trait evolution, suggesting that patterns of divergence between populations should mirror phenotypic plasticity within populations. Here, we evaluate the concordance between observed axes of multivariate sexual trait divergence and predicted divergence based on (1) interpopulation variation in sexual selection, (2) additive genetic variances and (3) temperature-related phenotypic plasticity in male courtship song among geographically isolated populations of the Hawaiian swordtail cricket, Laupala cerasina, which exhibit sexual isolation due acoustic signalling traits. The major axis of multivariate divergence, d_max , accounted for 76% of variation among population male song trait means and was moderately correlated with interpopulation differences in directional sexual selection based on female preferences. However, the majority of additive genetic variance was largely oriented away from the direction of divergence, suggesting that standing genetic variation may not play a dominant role in the patterning of signal divergence. In contrast, the axis of phenotypic plasticity strongly mirrored patterns of interpopulation phenotypic divergence, which is consistent with a role for temperature-related plasticity in facilitating instead of inhibiting male song evolution and sexual isolation in these incipient species. We propose potential mechanisms by which sexual selection might interact with phenotypic plasticity to facilitate the rapid acoustic diversification observed in this species and clade.

Connecting the data landscape of long-term ecological studies: The SPI-Birds data hub

The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long-term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad-scale global issues (e.g. climate change). To solve data integration issues and enable a new scale of ecological and evolutionary research based on long-term studies of birds, we have created the SPI-Birds Network and Database (www.spibirds.org)-a large-scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI-Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting. SPI-Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community-derived data and meta-data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta-data language). The encouraging community involvement stems from SPI-Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI-Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community-specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much-needed large-scale ecological data integration.

Morphometric variance, evolutionary constraints and their change through time in Late Devonian Palmatolepis conodonts

Phenotypic variation is the raw material of evolution. Standing variation can facilitate response to selection along "lines of least evolutionary resistance", but selection itself might alter the structure of the variance. Shape was quantified using 2D geometric morphometrics in Palmatolepis conodonts through the Late Devonian period. Patterns of variance were characterized along the record by the variance-covariance matrix (P-matrix) and its first axis (Pmax). The Late Frasnian was marked by environmental oscillations culminating with the Frasnian/Famennian mass extinction. A shape response was associated with these fluctuations, together with a deflection of the Pmax and the P-matrix. Thereafter, along the Famennian, Palmatolepis mean shape shifted from broad elements with a large platform to slender elements devoid of platform. This shift in shape was associated with a reorientation of Pmax and the P-matrix, due to profound changes in the functioning of the elements selecting for new types of variants. Both cases provide empirical evidences that moving adaptive optimum can reorient phenotypic variation, boosting response to environmental changes. On such time scales, the question seems thus not to be whether the P-matrix is stable, but how it is varying in response to changes in selection regimes and shifts in adaptive optimum. This article is protected by copyright. All rights reserved.

Multi-project and Multi-profile joint Non-negative Matrix Factorization for cancer omic datasets

MOTIVATION

The integration of multi-omic data using machine learning methods has been focused on solving relevant tasks such as predicting sensitivity to a drug or subtyping patients. Recent integration methods, such as joint Non-negative Matrix Factorization (jNMF), have allowed researchers to exploit the information in the data to unravel the biological processes of multi-omic datasets.

RESULTS

We present a novel method called Multi-project and Multi-profile joint Non-negative Matrix Factorization (M&M-jNMF) capable of integrating data from different sources, such as experimental and observational multi-omic data. The method can generate co-clusters between observations, predict profiles and relate latent variables. We applied the method to integrate low-grade glioma omic profiles from The Cancer Genome Atlas (TCGA) and Cell Line Encyclopedia (CCLE) projects. The method allowed us to find gene clusters mainly enriched in cancer-associated terms. We identified groups of patients and cell lines similar to each other by comparing biological processes. We predicted the drug profile for patients, and we identified genetic signatures for resistant and sensitive tumors to a specific drug.

AVAILABILITY AND IMPLEMENTATION

Source code repository is publicly available at https://bitbucket.org/dsalazarb/mmjnmf/ - Zenodo DOI: 10.5281/zenodo.5150920.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Common evolutionary patterns in the human nasal region across a worldwide sample

OBJECTIVES

Variation in the external nasal region among human populations has long been proposed in the literature to reflect adaptations to facilitate thermoregulation, air conditioning, and moisture retention in local climates and environments. More specifically, adaptations in populations living in colder climates have often been assumed due to correlational relationships found between variation in the nasal region and climatic variables. Here, we test this hypothesis by applying a quantitative genetics approach based on the Lande model to assess whether variation in the nasal region can be explained by random neutral processes (e.g., genetic drift) or if non-random forces (i.e., adaptation) have contributed significantly to its diversity.

MATERIALS AND METHODS

A mixed-sex sample representing 28 population groups from Howells' craniometric dataset were analyzed (n = 2504). Twenty standard measurements were chosen to reflect the external skeletal morphology of the nasal region. We apply statistical tests developed from evolutionary quantitative genetics theory to analyze patterns of within- and between-population divergence under a null hypothesis of genetic drift.

RESULTS

This study finds a rejection of genetic drift in all analyses, across tests that involve all 28 populations, exclusively cold-climate populations, and with cold-climate populations excluded, indicating that non-random evolutionary forces have contributed significantly to variation in the nasal region overall.

DISCUSSION

These results show that nasal region adaptation is not exclusive to cold-climate populations, which have often been implicated in the literature to drive nasal variation, instead suggesting that the propensity for adaptation in the nasal region is shared among all human populations.

The contribution of mutation and selection to multivariate quantitative genetic variance in an outbred population of Drosophila serrata

Genetic variance is not equal for all multivariate combinations of traits. This inequality, in which some combinations of traits have abundant genetic variation while others have very little, biases the rate and direction of multivariate phenotypic evolution. However, we still understand little about what causes genetic variance to differ among trait combinations. Here, we investigate the relative roles of mutation and selection in determining the genetic variance of multivariate phenotypes. We accumulated mutations in an outbred population of Drosophila serrata and analyzed wing shape and size traits for over 35,000 flies to simultaneously estimate the additive genetic and additive mutational (co)variances. This experimental design allowed us to gain insight into the phenotypic effects of mutation as they arise and come under selection in naturally outbred populations. Multivariate phenotypes associated with more (less) genetic variance were also associated with more (less) mutational variance, suggesting that differences in mutational input contribute to differences in genetic variance. However, mutational correlations between traits were stronger than genetic correlations, and most mutational variance was associated with only one multivariate trait combination, while genetic variance was relatively more equal across multivariate traits. Therefore, selection is implicated in breaking down trait covariance and resulting in a different pattern of genetic variance among multivariate combinations of traits than that predicted by mutation and drift. Overall, while low mutational input might slow evolution of some multivariate phenotypes, stabilizing selection appears to reduce the strength of evolutionary bias introduced by pleiotropic mutation.

Hormonal pleiotropy structures genetic covariance

Quantitative genetic theory proposes that phenotypic evolution is shaped by G, the matrix of genetic variances and covariances among traits. In species with separate sexes, the evolution of sexual dimorphism is also shaped by B, the matrix of between‐sex genetic variances and covariances. Despite considerable focus on estimating these matrices, their underlying biological mechanisms are largely speculative. We experimentally tested the hypothesis that G and B are structured by hormonal pleiotropy, which occurs when one hormone influences multiple phenotypes. Using juvenile brown anole lizards (Anolis sagrei) bred in a paternal half‐sibling design, we elevated the steroid hormone testosterone with slow‐release implants while administering empty implants to siblings as a control. We quantified the effects of this manipulation on the genetic architecture of a suite of sexually dimorphic traits, including body size (males are larger than females) and the area, hue, saturation, and brightness of the dewlap (a colorful ornament that is larger in males than in females). Testosterone masculinized females by increasing body size and dewlap area, hue, and saturation, while reducing dewlap brightness. Control females and males differed significantly in G, but treatment of females with testosterone rendered G statistically indistinguishable from males. Whereas B was characterized by low between‐sex genetic correlations when estimated between control females and males, these same correlations increased significantly when estimated between testosterone females and either control or testosterone males. The full G matrix (including B) for testosterone females and either control or testosterone males was significantly less permissive of sexually dimorphic evolution than was G estimated between control females and males, suggesting that natural sex differences in testosterone help decouple genetic variance between the sexes. Our results confirm that hormonal pleiotropy structures genetic covariance, implying that hormones play an important yet overlooked role in mediating evolutionary responses to selection.

Correlational selection in the age of genomics

Ecologists and evolutionary biologists are well aware that natural and sexual selection do not operate on traits in isolation, but instead act on combinations of traits. This long-recognized and pervasive phenomenon is known as multivariate selection, or-in the particular case where it favours correlations between interacting traits-correlational selection. Despite broad acknowledgement of correlational selection, the relevant theory has often been overlooked in genomic research. Here, we discuss theory and empirical findings from ecological, quantitative genetic and genomic research, linking key insights from different fields. Correlational selection can operate on both discrete trait combinations and quantitative characters, with profound implications for genomic architecture, linkage, pleiotropy, evolvability, modularity, phenotypic integration and phenotypic plasticity. We synthesize current knowledge and discuss promising research approaches that will enable us to understand how correlational selection shapes genomic architecture, thereby linking quantitative genetic approaches with emerging genomic methods. We suggest that research on correlational selection has great potential to integrate multiple fields in evolutionary biology, including developmental and functional biology, ecology, quantitative genetics, phenotypic polymorphisms, hybrid zones and speciation processes.

The role of ontogenetic allometry and nonallometric flower shape variation in species-level adaptive diversification - Calceolaria polyrhiza (Calceolariaceae) as a case study

Organism shape changes predictably during ontogeny, resulting in specific patterns of ontogenetic allometry. In several plant and animal lineages, among-species variation in the shape of mature organisms mirrors variation along their growth trajectories. Hence, ontogenetic allometry is an important bias in evolution. This bias should be stronger at reduced evolutionary time scales, in which among-trait correlations had less time to evolve. Nevertheless, it was shown that adaptation of organism shape frequently involved departures from the ancestral ontogenetic allometry. Moreover, only a moderate fraction of shape variation is correlated with size during ontogeny. Hence, nonallometric variation in shape (NAVSh) is likely to contribute to adaptation, even at reduced evolutionary time scales. We explored the contributions of allometric variation in shape (AVSh), NAVSh, and size variation to adaptive evolution in the angiosperm species Calceolaria polyrhiza. This strongly relies on oil-collecting bees for pollination and experienced transitions in the size of pollinators during the last 2 Ma. Using geometric morphometrics, we described corolla morphology in several populations across its distribution range. Variation in corolla shape was decomposed into an allometric and a nonallometric component, and corolla size was estimated. We then looked for the correlation between these aspects of morphology and the pollinator. Our results suggest that adaptation to pollinators with different sizes relied on NAVSh, which resulted from shifts in the allometric slope and from shape changes that occurred early in flower development. We conclude that NAVSh can contribute to adaptation in flowering plants, even at the species-level.

Structure of the G-matrix in relation to phenotypic contributions to fitness

Classical theory in population genetics includes derivation of the stationary distribution of allele frequencies under balance between selection, genetic drift, and mutation. Here we investigate the simplest generalization of these single locus models to quantitative genetics with many loci, assuming simple additive effects on a set of phenotypes and a linear approximation to the fitness function. Genetic effects and pleiotropy are simulated by a prescribed stochastic model. Our goal is to analyze the structure of the G-matrix at stasis when the model is not very close to being neutral. The smallest eigenvalue of the G-matrix is practically zero by Fisher's fundamental theorem for natural selection and the fitness function is approximately a linear function of the corresponding eigenvector. Evolution of genetic trade-offs is closely linked to the fitness function. If a single locus never codes for more than two traits, then additive genetic covariance between two phenotype components always has the opposite sign of the product of their coefficients in the fitness function under no mutation, a pattern that is likely to occur frequently also in more complex models. In our major examples only 1-2 percent of the loci are over-dominant for fitness, but they still account for practically all dominance variance in fitness as well as all contributions to the G-matrix. These analyses show that the structure of the G-matrix reveals important information about the contribution of different traits to fitness.

On the importance of evolving phenotype distributions on evolutionary diversification

Evolutionary branching occurs when a population with a unimodal phenotype distribution diversifies into a multimodally distributed population consisting of two or more strains. Branching results from frequency-dependent selection, which is caused by interactions between individuals. For example, a population performing a social task may diversify into a cooperator strain and a defector strain. Branching can also occur in multi-dimensional phenotype spaces, such as when two tasks are performed simultaneously. In such cases, the strains may diverge in different directions: possible outcomes include division of labor (with each population performing one of the tasks) or the diversification into a strain that performs both tasks and another that performs neither. Here we show that the shape of the population's phenotypic distribution plays a role in determining the direction of branching. Furthermore, we show that the shape of the distribution is, in turn, contingent on the direction of approach to the evolutionary branching point. This results in a distribution-selection feedback that is not captured in analytical models of evolutionary branching, which assume monomorphic populations. Finally, we show that this feedback can influence long-term evolutionary dynamics and promote the evolution of division of labor.

Evolution, genes, and inter‐disciplinary personality research

Most commentaries welcomed an evolutionary genetic approach to personality, but several raised concerns about our integrative model. In response, we clarify the scientific status of evolutionary genetic theory and explain the plausibility and value of our evolutionary genetic model of personality, despite some shortcomings with the currently available theories and data. We also have a closer look at mate choice for personality traits, point to promising ways to assess evolutionarily relevant environmental factors and defend higher‐order personality domains and the g‐factor as the best units for evolutionary genetic analyses. Finally, we discuss which extensions of and alternatives to our model appear most fruitful, and end with a call for more inter‐disciplinary personality research grounded in evolutionary theory. Copyright © 2007 John Wiley & Sons, Ltd.

Population Divergence along a Genetic Line of Least Resistance in the Tree Species Eucalyptus globulus

The evolutionary response to selection depends on the distribution of genetic variation in traits under selection within populations, as defined by the additive genetic variance-covariance matrix (G). The structure and evolutionary stability of G will thus influence the course of phenotypic evolution. However, there are few studies assessing the stability of G and its relationship with population divergence within foundation tree species. We compared the G-matrices of Mainland and Island population groups of the forest tree Eucalyptus globulus, and determined the extent to which population divergence aligned with within-population genetic (co)variation. Four key wood property traits exhibiting signals of divergent selection were studied—wood density, extractive content, and lignin content and composition. The comparison of G-matrices of the mainland and island populations indicated that the G-eigenstructure was relatively well preserved at an intra-specific level. Population divergence tended to occur along a major direction of genetic variation in G. The observed conservatism of G, the moderate evolutionary timescale, and close relationship between genetic architecture and population trajectories suggest that genetic constraints may have influenced the evolution and diversification of the E. globulus populations for the traits studied. However, alternative scenarios, including selection aligning genetic architecture and population divergence, are discussed.

Many Options, Few Solutions: Over 60 My Snakes Converged on a Few Optimal Venom Formulations

Gene expression changes contribute to complex trait variations in both individuals and populations. However, the evolution of gene expression underlying complex traits over macroevolutionary timescales remains poorly understood. Snake venoms are proteinaceous cocktails where the expression of each toxin can be quantified and mapped to a distinct genomic locus and traced for millions of years. Using a phylogenetic generalized linear mixed model, we analyzed expression data of toxin genes from 52 snake species spanning the 3 venomous snake families and estimated phylogenetic covariance, which acts as a measure of evolutionary constraint. We find that evolution of toxin combinations is not constrained. However, although all combinations are in principle possible, the actual dimensionality of phylomorphic space is low, with envenomation strategies focused around only four major toxin families: metalloproteases, three-finger toxins, serine proteases, and phospholipases A2. Although most extant snakes prioritize either a single or a combination of major toxin families, they are repeatedly recruited and lost. We find that over macroevolutionary timescales, the venom phenotypes were not shaped by phylogenetic constraints, which include important microevolutionary constraints such as epistasis and pleiotropy, but more likely by ecological filtering that permits a small number of optimal solutions. As a result, phenotypic optima were repeatedly attained by distantly related species. These results indicate that venoms evolve by selection on biochemistry of prey envenomation, which permit diversity through parallelism, and impose strong limits, since only a few of the theoretically possible strategies seem to work well and are observed in extant snakes.

Ecomorphological specialization leads to loss of evolvability in primate limbs

Primate limb morphology is often described as either generalized, that is, suited to a range of locomotor and positional behaviors, or specialized for unique locomotor behaviors such as brachiation or bipedalism. The evolution of highly specialized limb morphology may result in loss of evolvability, that is, in a decreased capacity of the locomotor skeleton to evolve in response to selection towards alternative ecomorphological niches. Using evolutionary simulations, I show that the highly specialized limb anatomy of hominoids is associated with a significant loss of evolvability, defined as the number of generations to reach alternative adaptive peaks, and in parallel an increased risk of extinction, particularly in simulated evolution toward generalized quadrupedal limb proportions. Loss of evolvability in apes and humans correlates with three factors: (1) decreased correlation among limb bone lengths (i.e., integration), which slows the rate of change along lines of least evolutionary resistance; (2) limb specialization, which places apes and humans in relatively remote areas of morphospace; and (3) increased skeletal size as a proxy for body size. Thus, locomotor over-specialization can lead to evolutionary dead-ends that significantly increase the probability of hominoid populations going extinct before evolving new adaptive morphologies.

The Evolution of Variance Control

Genetically identical individuals can be phenotypically variable, even in constant environmental conditions. The ubiquity of this phenomenon, known as 'intra-genotypic variability', is increasingly evident and the relevant mechanistic underpinnings are beginning to be understood. In parallel, theory has delineated a number of formal expectations for contexts in which such a feature would be adaptive. Here, we review empirical evidence across biological systems and theoretical expectations, including nonlinear averaging and bet hedging. We synthesize existing results to illustrate the dependence of selection outcomes both on trait characteristics, features of environmental variability, and species' demographic context. We conclude by discussing ways to bridge the gap between empirical evidence of intra-genotypic variability, studies demonstrating its genetic component, and evidence that it is adaptive.

Temperature and Rainfall Are Separate Agents of Selection Shaping Population Differentiation in a Forest Tree

Research highlights: We present evidence indicating that covariation of functional traits among populations of a forest tree is not due to genetic constraints, but rather selective covariance arising from local adaptation to different facets of the climate, namely rainfall and temperature. Background and Aims: Traits frequently covary among natural populations. Such covariation can be caused by pleiotropy and/or linkage disequilibrium, but also may arise when the traits are genetically independent as a direct consequence of natural selection, drift, mutation and/or gene flow. Of particular interest are cases of selective covariance, where natural selection directly generates among-population covariance in a set of genetically independent traits. We here studied the causes of population-level covariation in two key traits in the Australian tree Eucalyptus pauciflora. Materials and Methods: We studied covariation in seedling lignotuber size and vegetative juvenility using 37 populations sampled from throughout the geographic and ecological ranges of E. pauciflora on the island of Tasmania. We integrated evidence from multiple sources: (i) comparison of patterns of trait covariation within and among populations; (ii) climate-trait modelling using machine-learning algorithms; and (iii) selection analysis linking trait variation to field growth in an arid environment. Results: We showed strong covariation among populations compared with the weak genetic correlation within populations for the focal traits. Population differentiation in these genetically independent traits was correlated with different home-site climate variables (lignotuber size with temperature; vegetative juvenility with rainfall), which spatially covaried. The role of selection in shaping the population differentiation in lignotuber size was supported by its relationship with fitness measured in the field. Conclusions: Our study highlights the multi-trait nature of adaptation likely to occur as tree species respond to spatial and temporal changes in climate.

Comparative analysis of the multivariate genetic architecture of morphological traits in three species of Gomphocerine grasshoppers

Evolutionary change is the change in trait values across generations, and usually occurs in multidimensional trait space rather than along isolated traits. Genetic covariation influences the magnitude and direction of evolutionary change and can be statistically summarized by the additive genetic (co)variance matrix, G. While G can affect the response to selection, it is exposed to evolutionary change by selection and genetic drift, but the magnitude and speed of these changes are poorly understood. We use comparative G matrix analyses to assess evolution of the shape and orientation of G over longer timescales in three species of Gomphocerine grasshoppers. We estimate 10 × 10 G matrices for five morphological traits expressed in both sexes. We find low-to-moderate heritabilities (average 0.36), mostly large cross-sex correlations (average 0.54) and moderate between-trait correlations (average 0.34). G matrices differ significantly among species with wing length contributing most to these differences. Wing length is the trait that is most divergent among species, suggesting it has been under selection during species divergence. The more distantly related species, Pseudochorthippus parallelus, was the most different in the shape of G. Projection of contemporary genetic variation into the divergence space D illustrates that the major axis of genetic variation in Gomphocerippus rufus is aligned with divergence from Chorthippus biguttulus, while the major axis of genetic variation in neither of the species is aligned with the divergence between Pseudochorthippus parallelus and the other two species. Our results demonstrate significant differences in G matrices with a phylogenetic signal in the differentiation.

Phenotypic plasticity, global change, and the speed of adaptive evolution

The role phenotypic plasticity might play in adaptation to the ongoing climate changes is unclear. Plasticity allows for the production of a diversity of intra-generational responses, whose inter-generational evolutionary consequences are difficult to predict. In this article, we review theory and empirical studies addressing this question in insects by considering three scenarios. The first scenario corresponds to adaptive plasticity that should lead to slow or no evolution. The second scenario is the case of non-adaptive phenotypic plasticity to new environmental conditions that should lead either to extinction or, on the contrary, to rapid evolutionary change. The third scenario deals with how plasticity alters the variance selection acts upon. These scenarios are then discussed by highlighting examples of empirical studies on insects. We conclude that more studies are needed to better understand the relationship between phenotypic plasticity and evolutionary processes in insects.