MUTATIONAL CONTRIBUTIONS TO GENETIC VARIANCE-COVARIANCE MATRICES: AN EXPERIMENTAL APPROACH USING INDUCED MUTATIONS IN ARABIDOPSIS THALIANA

Developmental bias in the evolution and plasticity of beetle horn shape

The degree to which developmental systems bias the phenotypic effects of environmental and genetic variation, and how these biases affect evolution, is subject to much debate. Here, we assess whether developmental variability in beetle horn shape aligns with the phenotypic effects of plasticity and evolutionary divergence, yielding three salient results. First, we find that most pathways previously shown to regulate horn length also affect shape. Second, we find that the phenotypic effects of manipulating divergent developmental pathways are correlated with each other as well as multivariate fluctuating asymmetry-a measure of developmental variability. Third, these effects further aligned with thermal plasticity, population differences and macroevolutionary divergence between sister taxa and more distantly related species. Collectively, our results support the hypothesis that changes in horn shape-whether brought about by environmentally plastic responses, functional manipulations or evolutionary divergences-converge along 'developmental lines of least resistance', i.e. are biased by the developmental system underpinning horn shape.

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.

Genetic and environmental canalization are not associated among altitudinally varying populations of Drosophila melanogaster

Organisms are exposed to environmental and mutational effects influencing both mean and variance of phenotypes. Potentially deleterious effects arising from this variation can be reduced by the evolution of buffering (canalizing) mechanisms, ultimately reducing phenotypic variability. There has been interest regarding the conditions enabling the evolution of canalization. Under some models, the circumstances under which genetic canalization evolves are limited despite apparent empirical evidence for it. It has been argued that genetic canalization evolves as a correlated response to environmental canalization (congruence model). Yet, empirical evidence has not consistently supported predictions of a correlation between genetic and environmental canalization. In a recent study, a population of Drosophila adapted to high altitude showed evidence of genetic decanalization relative to those from low altitudes. Using strains derived from these populations, we tested if they varied for multiple aspects of environmental canalization We observed the expected differences in wing size, shape, cell (trichome) density and mutational defects between high- and low-altitude populations. However, we observed little evidence for a relationship between measures of environmental canalization with population or with defect frequency. Our results do not support the predicted association between genetic and environmental canalization.

Rapid reversal of a potentially constraining genetic covariance between leaf and flower traits in Silene latifolia

Genetic covariance between two traits generates correlated responses to selection, and may either enhance or constrain adaptation. Silene latifolia exhibits potentially constraining genetic covariance between specific leaf area (SLA) and flower number in males. Flower number is likely to increase via fecundity selection but the correlated increase in SLA increases mortality, and SLA is under selection to decrease in dry habitats. We selected on trait combinations in two selection lines for four generations to test whether genetic covariance could be reduced without significantly altering trait means. In one selection line, the genetic covariance changed sign and eigenstructure changed significantly, while in the other selection line eigenstructure remained similar to the control line. Changes in genetic variance-covariance structure are therefore possible without the introduction of new alleles, and the responses we observed suggest that founder effects and changes in frequency of alleles of major effect may be acting to produce the changes.

Protein deprivation facilitates the independent evolution of behavior and morphology

Ecological conditions such as nutrition can change genetic covariances between traits and accelerate or slow down trait evolution. As adaptive trait correlations can become maladaptive following rapid environmental change, poor or stressful environments are expected to weaken genetic covariances, thereby increasing the opportunity for independent evolution of traits. Here, we demonstrate the differences in genetic covariance among multiple behavioral and morphological traits (exploration, aggression, and body weight) between southern field crickets (Gryllus bimaculatus) raised in favorable (free-choice) versus stressful (protein-deprived) nutritional environments. We also quantify the extent to which differences in genetic covariance structures contribute to the potential for the independent evolution of these traits. We demonstrate that protein-deprived environments tend to increase the potential for traits to evolve independently, which is caused by genetic covariances that are significantly weaker for crickets raised on protein-deprived versus free-choice diets. The weakening effects of stressful environments on genetic covariances tended to be stronger in males than in females. The weakening of the genetic covariance between traits under stressful nutritional environments was expected to facilitate the opportunity for adaptive evolution across generations. Therefore, the multivariate gene-by-environment interactions revealed here may facilitate behavioral and morphological adaptations to rapid environmental change.

Induced mutations alter patterns of quantitative variation, phenotypic integration, and plasticity to elevated CO2 in Arabidopsis thaliana

A key step toward predicting responses to climate change is characterizing genetic variation in populations. While short-term responses will likely be shaped by currently available genetic variation, longer-term evolutionary responses will depend on the supply of novel variation by, ultimately, mutation. Studying mutational contributions to phenotypic variation can provide insights into the extent of potential variation on which selection may operate in future human-altered environments. Here we used the chemical mutagen ethyl methanesulfonate (EMS) to explore mutational contributions to phenotypic variation, integration, and plasticity to elevated carbon dioxide (eCO₂) in three accessions of Arabidopsis thaliana. We found that (1) mutagenesis increased broad-sense heritabilities and variation in plasticity to eCO₂ (genotype by environment interactions); (2) mutational effects varied among the three genetic backgrounds; (3) induced mutations had non-random (biased) effects on patterns of phenotypic integration. To our knowledge, this is the first study to address the effects of chemically induced mutations on phenotypic plasticity to eCO₂ in a model plant. We discuss our results in light of emerging insights from theoretical and empirical quantitative genetics, suggest potential avenues of research, and identify approaches that may help advance our understanding of climate-driven evolution in plants.

Developmental Bias and Evolution: A Regulatory Network Perspective

Phenotypic variation is generated by the processes of development, with some variants arising more readily than others-a phenomenon known as "developmental bias." Developmental bias and natural selection have often been portrayed as alternative explanations, but this is a false dichotomy: developmental bias can evolve through natural selection, and bias and selection jointly influence phenotypic evolution. Here, we briefly review the evidence for developmental bias and illustrate how it is studied empirically. We describe recent theory on regulatory networks that explains why the influence of genetic and environmental perturbation on phenotypes is typically not uniform, and may even be biased toward adaptive phenotypic variation. We show how bias produced by developmental processes constitutes an evolving property able to impose direction on adaptive evolution and influence patterns of taxonomic and phenotypic diversity. Taking these considerations together, we argue that it is not sufficient to accommodate developmental bias into evolutionary theory merely as a constraint on evolutionary adaptation. The influence of natural selection in shaping developmental bias, and conversely, the influence of developmental bias in shaping subsequent opportunities for adaptation, requires mechanistic models of development to be expanded and incorporated into evolutionary theory. A regulatory network perspective on phenotypic evolution thus helps to integrate the generation of phenotypic variation with natural selection, leaving evolutionary biology better placed to explain how organisms adapt and diversify.

Uneven Distribution of Mutational Variance Across the Transcriptome of Drosophila serrata Revealed by High-Dimensional Analysis of Gene Expression

There are essentially an infinite number of traits that could be measured on any organism, and almost all individual traits display genetic variation, yet substantial genetic variance in a large number of independent traits is not plausible under basic models of selection and mutation. One mechanism that may be invoked to explain the observed levels of genetic variance in individual traits is that pleiotropy results in fewer dimensions of phenotypic space with substantial genetic variance. Multivariate genetic analyses of small sets of functionally related traits have shown that standing genetic variance is often concentrated in relatively few dimensions. It is unknown if a similar concentration of genetic variance occurs at a phenome-wide scale when many traits of disparate function are considered, or if the genetic variance generated by new mutations is also unevenly distributed across phenotypic space. Here, we used a Bayesian sparse factor model to characterize the distribution of mutational variance of 3385 gene expression traits of Drosophila serrata after 27 generations of mutation accumulation, and found that 46% of the estimated mutational variance was concentrated in just 21 dimensions with significant mutational heritability. We show that the extent of concentration of mutational variance into such a small subspace has the potential to substantially bias the response to selection of these traits.

How does mutation affect the distribution of phenotypes?

The potential for mutational processes to influence patterns of neutral or adaptive phenotypic evolution is not well understood. If mutations are directionally biased, shifting trait means in a particular direction, or if mutation generates more variance in some directions of multivariate trait space than others, mutation itself might be a source of bias in phenotypic evolution. Here, we use mutagenesis to investigate the affect of mutation on trait mean and (co)variances in zebrafish, Danio rerio. Mutation altered the relationship between age and both prolonged swimming speed and body shape. These observations suggest that mutational effects on ontogeny or aging have the potential to generate variance across the phenome. Mutations had a far greater effect in males than females, although whether this is a reflection of sex-specific ontogeny or aging remains to be determined. In males, mutations generated positive covariance between swimming speed, size, and body shape suggesting the potential for mutation to affect the evolutionary covariation of these traits. Overall, our observations suggest that mutation does not generate equal variance in all directions of phenotypic space or in each sex, and that pervasive variation in ontogeny or aging within a cohort could affect the variation available to evolution.

Artificial Selection to Increase the Phenotypic Variance in gmax Fails

Stabilizing selection is important in evolutionary theories of the maintenance of genetic variance and has been invoked as the key process determining macroevolutionary patterns of trait evolution. However, manipulative evidence for the extent of stabilizing selection, particularly on multivariate traits, is lacking. We used artificial disruptive selection in Drosophila serrata as a tool to determine the relative strength of stabilizing selection experienced by multivariate trait combinations with contrasting levels of genetic and mutational variance. Contrary to expectation, when disruptive selection was applied to the major axis of standing genetic variance, g_max, we observed a significant and repeatable decrease in its phenotypic variance. In contrast, the multivariate trait combination predicted to be under strong stabilizing selection showed a significant and repeatable increase in its phenotypic variance. Correlated responses were observed in all selection treatments, and viability selection operating on extreme phenotypes of traits genetically correlated with those directly selected on limited our ability to increase their phenotypic range. Our manipulation revealed that multivariate trait combinations were subject to stabilizing selection; however, we did not observe a direct relationship between the strength of stabilizing selection and the levels of standing genetic variance in multivariate trait combinations. Contrasting patterns of allele frequencies underlying traits with high versus low levels of standing genetic variance may be implicated in determining the response to artificial selection in multivariate trait combinations.

Trait Correlations in the Genomics Era

Thinking about the evolutionary causes and consequences of trait correlations has been dominated by quantitative genetics theory that is focused on hypothetical loci. Since this theory was initially developed, technology has enabled the identification of specific genetic variants that contribute to trait correlations. Here, we review studies of the genetic basis of trait correlations to ask: What has this new information taught us? We find that causal variants can be pleiotropic and/or linked in different ways, indicating that pleiotropy and linkage are not alternative genetic mechanisms. Further, many trait correlations have a polygenic basis, suggesting that both pleiotropy and linkage likely contribute. We discuss implications of these findings for the evolutionary causes and consequences of trait correlations.

The effect of induced mutations on quantitative traits in Arabidopsis thaliana: Natural versus artificial conditions

Mutations are the ultimate source of all genetic variations. New mutations are expected to affect quantitative traits differently depending on the extent to which traits contribute to fitness and the environment in which they are tested. The dogma is that the preponderance of mutations affecting fitness will be skewed toward deleterious while their effects on nonfitness traits will be bidirectionally distributed. There are mixed views on the role of stress in modulating these effects. We quantify mutation effects by inducing mutations in Arabidopsis thaliana (Columbia accession) using the chemical ethylmethane sulfonate. We measured the effects of new mutations relative to a premutation founder for fitness components under both natural (field) and artificial (growth room) conditions. Additionally, we measured three other quantitative traits, not expected to contribute directly to fitness, under artificial conditions. We found that induced mutations were equally as likely to increase as decrease a trait when that trait was not closely related to fitness (traits that were neither survivorship nor reproduction). We also found that new mutations were more likely to decrease fitness or fitness‐related traits under more stressful field conditions than under relatively benign artificial conditions. In the benign condition, the effect of new mutations on fitness components was similar to traits not as closely related to fitness. These results highlight the importance of measuring the effects of new mutations on fitness and other traits under a range of conditions.

Epistasis and Pleiotropy Affect the Modularity of the Genotype-Phenotype Map of Cross-Resistance in HIV-1

The genotype–phenotype (GP) map is a central concept in evolutionary biology as it describes the mapping of molecular genetic variation onto phenotypic trait variation. Our understanding of that mapping remains partial, especially when trying to link functional clustering of pleiotropic gene effects with patterns of phenotypic trait co-variation. Only on rare occasions have studies been able to fully explore that link and tend to show poor correspondence between modular structures within the GP map and among phenotypes. By dissecting the structure of the GP map of the replicative capacity of HIV-1 in 15 drug environments, we provide a detailed view of that mapping from mutational pleiotropic variation to phenotypic co-variation, including epistatic effects of a set of amino-acid substitutions in the reverse transcriptase and protease genes. We show that epistasis increases the pleiotropic degree of single mutations and provides modularity to the GP map of drug resistance in HIV-1. Moreover, modules of epistatic pleiotropic effects within the GP map match the phenotypic modules of correlated replicative capacity among drug classes. Epistasis thus increases the evolvability of cross-resistance in HIV by providing more drug- and class-specific pleiotropic profiles to the main effects of the mutations. We discuss the implications for the evolution of cross-resistance in HIV.

Does your gene need a background check? How genetic background impacts the analysis of mutations, genes, and evolution

The premise of genetic analysis is that a causal link exists between phenotypic and allelic variation. However, it has long been documented that mutant phenotypes are not a simple result of a single DNA lesion, but are instead due to interactions of the focal allele with other genes and the environment. Although an experimentally rigorous approach focused on individual mutations and isogenic control strains has facilitated amazing progress within genetics and related fields, a glimpse back suggests that a vast complexity has been omitted from our current understanding of allelic effects. Armed with traditional genetic analyses and the foundational knowledge they have provided, we argue that the time and tools are ripe to return to the underexplored aspects of gene function and embrace the context-dependent nature of genetic effects. We assert that a broad understanding of genetic effects and the evolutionary dynamics of alleles requires identifying how mutational outcomes depend upon the 'wild type' genetic background. Furthermore, we discuss how best to exploit genetic background effects to broaden genetic research programs.

Multivariate phenotypic divergence due to the fixation of beneficial mutations in experimentally evolved lineages of a filamentous fungus

The potential for evolutionary change is limited by the availability of genetic variation. Mutations are the ultimate source of new alleles, yet there have been few experimental investigations of the role of novel mutations in multivariate phenotypic evolution. Here, we evaluated the degree of multivariate phenotypic divergence observed in a long-term evolution experiment whereby replicate lineages of the filamentous fungus Aspergillus nidulans were derived from a single genotype and allowed to fix novel (beneficial) mutations while maintained at two different population sizes. We asked three fundamental questions regarding phenotypic divergence following approximately 800 generations of adaptation: (1) whether divergence was limited by mutational supply, (2) whether divergence proceeded in relatively many (few) multivariate directions, and (3) to what degree phenotypic divergence scaled with changes in fitness (i.e. adaptation). We found no evidence that mutational supply limited phenotypic divergence. Divergence also occurred in all possible phenotypic directions, implying that pleiotropy was either weak or sufficiently variable among new mutations so as not to constrain the direction of multivariate evolution. The degree of total phenotypic divergence from the common ancestor was positively correlated with the extent of adaptation. These results are discussed in the context of the evolution of complex phenotypes through the input of adaptive mutations.

Switch between life history strategies due to changes in glycolytic enzyme gene dosage in Saccharomyces cerevisiae

Adaptation is the process whereby a population or species becomes better fitted to its habitat through modifications of various life history traits which can be positively or negatively correlated. The molecular factors underlying these covariations remain to be elucidated. Using Saccharomyces cerevisiae as a model system, we have investigated the effects on life history traits of varying the dosage of genes involved in the transformation of resources into energy. Changing gene dosage for each of three glycolytic enzyme genes (hexokinase 2, phosphoglucose isomerase, and fructose-1,6-bisphosphate aldolase) resulted in variation in enzyme activities, glucose consumption rate, and life history traits (growth rate, carrying capacity, and cell size). However, the range of effects depended on which enzyme was expressed differently. Most interestingly, these changes revealed a genetic trade-off between carrying capacity and cell size, supporting the discovery of two extreme life history strategies already described in yeast populations: the "ants," which have lower glycolytic gene dosage, take up glucose slowly, and have a small cell size but reach a high carrying capacity, and the "grasshoppers," which have higher glycolytic gene dosage, consume glucose more rapidly, and allocate it to a larger cell size but reach a lower carrying capacity. These results demonstrate antagonist pleiotropy for glycolytic genes and show that altered dosage of a single gene drives a switch between two life history strategies in yeast.

Nonlinear selection and the evolution of variances and covariances for continuous characters in an anole

The pattern of genetic variances and covariances among characters, summarized in the additive genetic variance-covariance matrix, G, determines how a population will respond to linear natural selection. However, G itself also evolves in response to selection. In particular, we expect that, over time, G will evolve correspondence with the pattern of multivariate nonlinear natural selection. In this study, we substitute the phenotypic variance-covariance matrix (P) for G to determine if the pattern of multivariate nonlinear selection in a natural population of Anolis cristatellus, an arboreal lizard from Puerto Rico, has influenced the evolution of genetic variances and covariances in this species. Although results varied among our estimates of P and fitness, and among our analytic techniques, we find significant evidence for congruence between nonlinear selection and P, suggesting that natural selection may have influenced the evolution of genetic constraint in this species.

Survival of the currently fittest: genetics of rainbow trout survival across time and space

As a fitness trait, survival is assumed to exhibit low heritability due to strong selection eroding genetic variation and/or spatio-temporal variation in mortality agents reducing genetic and increasing residual variation. The latter phenomenon in particular may contribute to low heritability in multigeneration data, even if certain cohorts exhibit significant genetic variation. Analysis of survival data from 10 year classes of rainbow trout reared at three test stations showed that treating survival as a single trait across all generations resulted in low heritability (h2 = 0.08-0.17). However, when heritabilities were estimated from homogeneous generation and test station-specific cohorts, a wide range of heritability values was revealed (h2 = 0.04-0.71). Of 64 genetic correlations between different cohorts, 20 were positive, but 16 were significantly negative, confirming that genetic architecture of survival is not stable across generations and environments. These results reveal the existence of hidden genetic variation for survival and demonstrate that treating survival as one trait over several generations may not reveal its true genetic architecture. Negative genetic correlations between cohorts indicate that overall survival has limited potential to predict general resistance, and care should be taken when using it as selection criterion.

Is evolvability evolvable?

In recent years, biologists have increasingly been asking whether the ability to evolve--the evolvability--of biological systems, itself evolves, and whether this phenomenon is the result of natural selection or a by-product of other evolutionary processes. The concept of evolvability, and the increasing theoretical and empirical literature that refers to it, may constitute one of several pillars on which an extended evolutionary synthesis will take shape during the next few years, although much work remains to be done on how evolvability comes about.

The G matrix under fluctuating correlational mutation and selection

Theoretical quantitative genetics provides a framework for reconstructing past selection and predicting future patterns of phenotypic differentiation. However, the usefulness of the equations of quantitative genetics for evolutionary inference relies on the evolutionary stability of the additive genetic variance-covariance matrix (G matrix). A fruitful new approach for exploring the evolutionary dynamics of G involves the use of individual-based computer simulations. Previous studies have focused on the evolution of the eigenstructure of G. An alternative approach employed in this paper uses the multivariate response-to-selection equation to evaluate the stability of G. In this approach, I measure similarity by the correlation between response-to-selection vectors due to random selection gradients. I analyze the dynamics of G under several conditions of correlational mutation and selection. As found in a previous study, the eigenstructure of G is stabilized by correlational mutation and selection. However, over broad conditions, instability of G did not result in a decreased consistency of the response to selection. I also analyze the stability of G when the correlation coefficients of correlational mutation and selection and the effective population size change through time. To my knowledge, no prior study has used computer simulations to investigate the stability of G when correlational mutation and selection fluctuate. Under these conditions, the eigenstructure of G is unstable under some simulation conditions. Different results are obtained if G matrix stability is assessed by eigenanalysis or by the response to random selection gradients. In this case, the response to selection is most consistent when certain aspects of the eigenstructure of G are least stable and vice versa.

Shared quantitative trait loci underlying the genetic correlation between continuous traits

We review genetic correlations among quantitative traits in light of their underlying quantitative trait loci (QTL). We derive an expectation of genetic correlation from the effects of underlying loci and test whether published genetic correlations can be explained by the QTL underlying the traits. While genetically correlated traits shared more QTL (33%) on average than uncorrelated traits (11%), the actual number of shared QTL shared was small. QTL usually predicted the sign of the correlation with good accuracy, but the quantitative prediction was poor. Approximately 25% of trait pairs in the data set had at least one QTL with antagonistic effects. Yet a significant minority (20%) of such trait pairs have net positive genetic correlations due to such antagonistic QTL 'hidden' within positive genetic correlations. We review the evidence on whether shared QTL represent single pleiotropic loci or closely linked monotropic genes, and argue that strict pleiotropy can be viewed as one end of a continuum of recombination rates where r=0. QTL studies of genetic correlation will likely be insufficient to predict evolutionary trajectories over long time spans in large panmictic populations, but will provide important insights into the trade-offs involved in population and species divergence.

Contributions of genomics to life-history theory

Life-history theory seeks to understand the factors that produce variation in life histories that are found both among and within species. At the organismal level there is a well developed mathematical framework, and an important focus of the current research is determining the biological underpinnings of this framework, with particular attention to the causal mechanisms that underlie trade-offs. Genomic approaches are proving useful in addressing this issue.

The effect of temperature and wing morphology on quantitative genetic variation in the cricket Gryllus firmus, with an appendix examining the statistical properties of the Jackknife-manova method of matrix comparison

We investigated the effect of temperature and wing morphology on the quantitative genetic variances and covariances of five size-related traits in the sand cricket, Gryllus firmus. Micropterous and macropterous crickets were reared in the laboratory at 24, 28 and 32 degrees C. Quantitative genetic parameters were estimated using a nested full-sib family design, and (co)variance matrices were compared using the T method, Flury hierarchy and Jackknife-manova method. The results revealed that the mean phenotypic value of each trait varied significantly among temperatures and wing morphs, but temperature reaction norms were not similar across all traits. Micropterous individuals were always smaller than macropterous individuals while expressing more phenotypic variation, a finding discussed in terms of canalization and life-history trade-offs. We observed little variation between the matrices of among-family (co)variation corresponding to each combination of temperature and wing morphology, with only one matrix of six differing in structure from the others. The implications of this result are discussed with respect to the prediction of evolutionary trajectories.

Constraints on the evolution of males and sexual dimorphism: field estimates of genetic architecture of reproductive traits in three populations of gynodioecious Fragaria virginiana

To understand how genetic constraints may limit the evolution of males and sexual dimorphism in a gynodioecious species, I conducted a quantitative genetic experiment in a gynodioecious wild strawberry, Fragaria virginiana. I estimated and compared genetic parameters (narrow-sense heritabilities, between-trait and between-sex genetic correlations, as well as phenotypic and genetic variance-covariance matrices) in the two sex morphs from three populations grown in a common field garden. I measured pollen and ovule production per flower, petal size, fruit set, and flower number. My major findings are as follows. (1) The presence of a phenotypic trade-off between pollen production and fruit set in hermaphrodites reflects a negative genetic correlation in the narrow sense that is statistically significant when pooled across populations. (2) The main constraints on the evolution of males are low genetic variation for pollen per flower and strong positive correlations associated with ovule number (e.g., between pollen and ovules in hermaphrodites, and between ovules in hermaphrodites and females). (3) Traits with the lowest levels of sexual dimorphism (ovule number and flower number) have the highest between-sex genetic correlations suggesting that overlap in the expression of genes in the sex morphs constrains their independent evolution. (4) There are significant differences in G matrices between sex morphs but not among populations. However, evidence that male-female trait correlations in hermaphrodites were lower in populations with higher frequencies of females may indicate subtle changes in genetic architecture.

Flowering time plasticity in Arabidopsis thaliana: a reanalysis of Westerman & Lawrence (1970)

Environmental variation in temperature can have dramatic effects on plant morphology, phenology, and fitness, and for this reason it is important to understand the evolutionary dynamics of phenotypic plasticity in response to temperature. We investigated constraints on the evolution of phenotypic plasticity in response to a temperature gradient in the model plant Arabidopsis thaliana by applying modern analytical tools to the classic data of Westerman & Lawrence (1970). We found significant evidence for two types of constraints. First, we detected numerous significant genetic correlations between plastic responses to temperature and the mean value of a trait across all environments, which differed qualitatively in pattern between the set of ecotypes and the set of mutant lines in the original sample. Secondly, we detected significant costs of flowering time plasticity in two of the three experimental environments, and a net pattern of selection against flowering time plasticity in the experiment overall. Thus, when explored with contemporary methods, the prescient work of Westerman & Lawrence (1970) provides new insights about evolutionary constraints on the evolution of plasticity.

Comparing G Matrices: Are Common Principal Components Informative?

Common principal components (CPC) analysis is a technique for assessing whether variance-covariance matrices from different populations have similar structure. One potential application is to compare additive genetic variance-covariance matrices, G. In this article, the conditions under which G matrices are expected to have common PCs are derived for a two-locus, two-allele model and the model of constrained pleiotropy. The theory demonstrates that whether G matrices are expected to have common PCs is largely determined by whether pleiotropic effects have a modular organization. If two (or more) populations have modules and these modules have the same direction, the G matrices have a common PC, regardless of allele frequencies. In the absence of modules, common PCs exist only for very restricted combinations of allele frequencies. Together, these two results imply that, when populations are evolving, common PCs are expected only when the populations have modules in common. These results have two implications: (1) In general, G matrices will not have common PCs, and (2) when they do, these PCs indicate common modular organization. The interpretation of common PCs identified for estimates of G matrices is discussed in light of these results.

Evolvability and genetic constraint in Dalechampia blossoms: genetic correlations and conditional evolvability

The short-term evolvability of a character is closely related to its level of additive genetic variation. However, a large component of the variation in any one character may be pleiotropically linked to other characters under the influence of different selective factors. Therefore, the organization of the organism into quasi-independent modules may be an important prerequisite for evolvability. In this paper we propose to study character evolvability in terms of conditional genetic variation. By estimating the amount of genetic variation in a character, y, that is independent of other characters, x, we can assess the evolvability of y when there is stabilizing selection on x. We suggest that systematic use of conditioning may help build a picture of modular organization and quasi-independent evolvability. As an illustration, we use this approach to assess the evolvability of floral characters in Dalechampia scandens (Euphorbiaceae). Although our study population had relatively low levels of genetic variation at the outset, we find evidence that conditioning may lead to substantial further reduction in the genetic variation available for independent adaptation. This provides additional evidence that the D. scandens blossom is constrained in its short-term evolvability.

Genetic constraints on the evolution of mate recognition under natural selection

Field populations of Drosophila serrata display reproductive character displacement in cuticular hydrocarbons (CHCs) when sympatric with Drosophila birchii. We have previously shown that the naturally occurring pattern of reproductive character displacement can be experimentally replicated by exposing field allopatric populations of D. serrata to experimental sympatry with D. birchii. Here, we tested whether the repeated evolution of reproductive character displacement in natural and experimental populations was a consequence of genetic constraints on the evolution of CHCs. The genetic variance-covariance (G) matrices for CHCs were determined for populations of D. serrata that had evolved in either the presence or absence of D. birchii under field and experimental conditions. Natural selection on mate recognition under both field and experimental sympatric conditions increased the genetic variance in CHCs consistent with a response to selection based on rare alleles. A close association between G eigenstructure and the eigenstructure of the phenotypic divergence (D) matrix in natural and experimental populations suggested that G matrix eigenstructure may have determined the direction in which reproductive character displacement evolved during the reinforcement of mate recognition.

Modularity, individuality, and evo-devo in butterfly wings

Modularity in animal development is thought to have facilitated morphological diversification, but independent change of those traits integrated within a module might be restricted. Correlations among traits describe potential developmental constraints on evolution. These have often been postulated to explain patterns of morphological variation and have been examined theoretically but seldom analyzed experimentally. Here, we use artificial selection to explore the modular organization of butterfly wing patterns and the extent to which their evolution is constrained by the genetic correlations among repeated pattern elements. We show that, in Bicyclus anynana butterflies, despite the evidence that all eyespots are developmentally coupled, the response to selection for increased size of one individual eyespot can proceed in a manner largely independent from selection imposed on another eyespot. We argue that among-eyespot correlations are unlikely to have constrained the evolutionary diversification of butterfly wing patterns but might be important when only limited time is available for adaptive evolution to occur. The ease with which we have been able to produce independent responses to artificial selection on different eyespots may be linked to a legacy of natural selection favoring individuality. Our results are discussed within the context of the evolution of modularity and individuality of serially repeated morphological traits.

An analysis of G matrix variation in two closely related cricket species, Gryllus firmus and G. pennsylvanicus

An important issue in evolutionary biology is understanding the pattern of G matrix variation in natural populations. We estimated four G matrices based on the morphological traits of two cricket species, Gryllus firmus and G. pennsylvanicus, each reared in two environments. We used three matrix comparison approaches, including the Flury hierarchy, to improve our ability to perceive all aspects of matrix variation. Our results demonstrate that different methods perceive different aspects of the matrices, which suggests that, until more is known about these methods, future studies should use several different statistical approaches. We also found that the differences in G matrices within a species can be larger than the differences between species. We conclude that the expression of the genetic architecture can vary with the environment and that future studies should compare G matrices across several environments. We also conclude that G matrices can be conserved at the level of closely related species.

Phenotypic plasticity is the major determinant of changes in phenotypic integration in Arabidopsis

•  The way in which novel genetic variation affects the patterns of phenotypic integration in natural populations is addressed here. •  An experimental study is presented of the variability in integration caused by interpopulation hybridization and consequent genetic reshuffling, as well as by changes in the physical environment in the model system Arabidopsis thaliana (Brassicaceae). •  Our results show a basic invariance of sets of covarying traits in A. thaliana, with changes in nutrient availability as the principal factor accounting for major departures from the general pattern and where differences in the genetic background of the recombinant lines are less important. In A. thaliana, the relationships among vegetative and reproductive traits form distinct clusters in multivariate space. A high degree of congruence was found between differences in the multivariate mean phenotype and the pattern of phenotypic integration, as expected on the basis of recent theoretical models. •  This relationship might indicate strong selective constraints acting on the specialized life history of these populations, which are spring ephemerals inhabiting ruderal habitats and prone to competition avoidance.

Inbreeding changes the shape of the genetic covariance matrix in Drosophila melanogaster

The pattern of genetic covariation among traits (the G matrix) plays a central role in determining the pattern of evolutionary change from both natural selection and random genetic drift. Here we measure the effect of genetic drift on the shape of the G matrix using a large data set on the inheritance of wing characteristics in Drosophila melanogaster. Fifty-two inbred lines with a total of 4680 parent-offspring families were generated by one generation of brother-sister mating and compared to an outbred control population of 1945 families. In keeping with the theoretical expectation for a correlated set of additively determined traits, the average G matrix of the inbred lines remained proportional to the outbred control G matrix with a proportionality constant approximately equal to (1 - F), where F is the inbreeding coefficient. Further, the pattern of covariance among the means of the inbred lines induced by inbreeding was also proportional to the within-line G matrix of the control population with a constant very close to the expectation of 2F. Although the average G of the inbred lines did not show change in overall structure relative to the outbred controls, separate analysis revealed a great deal of variation among inbred lines around this expectation, including changes in the sign of genetic correlations. Since any given line can be quite different from the outbred control, it is likely that in nature unreplicated drift will lead to changes in the G matrix. Thus, the shape of G is malleable under genetic drift, and the evolutionary response of any particular population is likely to depend on the specifics of its evolutionary history.