Genetic variation in pleiotropy: differential epistasis as a source of variation in the allometric relationship between long bone lengths and body weight

Pleiotropy is an aspect of genetic architecture underlying the phenotypic covariance structure. The presence of genetic variation in pleiotropy is necessary for natural selection to shape patterns of covariation between traits. We examined the contribution of differential epistasis to variation in the intertrait relationship and the nature of this variation. Genetic variation in pleiotropy was revealed by mapping quantitative trait loci (QTLs) affecting the allometry of mouse limb and tail length relative to body weight in the mouse-inbred strain LG/J by SM/J intercross. These relationship QTLs (rQTLs) modify relationships between the traits affected by a common pleiotropic locus. We detected 11 rQTLs, mostly affecting allometry of multiple bones. We further identified epistatic interactions responsible for the observed allometric variation. Forty loci that interact epistatically with the detected rQTLs were identified. We demonstrate how these epistatic interactions differentially affect the body size variance and the covariance of traits with body size. We conclude that epistasis, by differentially affecting both the canalization and mean values of the traits of a pleiotropic domain, causes variation in the covariance structure. Variation in pleiotropy maintains evolvability of the genetic architecture, in particular the evolvability of its modular organization.     

The genetic architecture of domestication in the chicken: effects of pleiotropy and linkage

The extent of pleiotropy and epistasis in quantitative traits remains equivocal. In the case of pleiotropy, multiple quantitative trait loci are often taken to be pleiotropic if their confidence intervals overlap, without formal statistical tests being used to ascertain if these overlapping loci are statistically significantly pleiotropic. Additionally, the degree to which the genetic correlations between phenotypic traits are reflected in these pleiotropic quantitative trait loci is often variable, especially in the case of antagonistic pleiotropy. Similarly, the extent of epistasis in various morphological, behavioural and life-history traits is also debated, with a general problem being the sample sizes required to detect such effects. Domestication involves a large number of trade-offs, which are reflected in numerous behavioural, morphological and life-history traits which have evolved as a consequence of adaptation to selective pressures exerted by humans and captivity. The comparison between wild and domestic animals allows the genetic analysis of the traits that differ between these population types, as well as being a general model of evolution. Using a large F(2) intercross between wild and domesticated chickens, in combination with a dense SNP and microsatellite marker map, both pleiotropy and epistasis were analysed. The majority of traits were found to segregate in 11 tight 'blocks' and reflected the trade-offs associated with domestication. These blocks were shown to have a pleiotropic 'core' surrounded by more loosely linked loci. In contrast, epistatic interactions were almost entirely absent, with only six pairs identified over all traits analysed. These results give insights both into the extent of such blocks in evolution and the development of domestication itself.     

Pleiotropic Effects of methoprene-tolerant(Met), a Gene Involved in Juvenile Hormone Metabolism, on Life History Traits in Drosophila melanogaster

Life history theory assumes that there are alleles with pleiotropic effects on fitness components. Although quantitative genetic data are often consistent with pleiotropy, there are few explicit examples of pleiotropic loci. The Drosophila melanogaster gene Methoprene-tolerant (Met) may be such a locus. The Met gene product, a putative juvenile hormone receptor, facilitates the action of juvenile hormone (JH) and JH analogs; JH affects many life history traits in arthropods. Here we use quantitative complementation to investigate effects of Met mutant and wildtype alleles on female developmental time, onset of reproduction, and fecundity. Whereas the alleles did not differ in their effects on developmental time, we detected allelic variation for the onset of reproduction and for age-specific fecundity. Alleles influenced phenotypic co-variances among traits (developmental time and onset of reproduction; onset of reproduction and both early and late fecundity; early and late fecundity), suggesting that alleles of Met vary in their pleiotropic effects upon life history. Furthermore, the genetic covariance between developmental time and early fecundity attributed to alleles of Met was negative, indicating consistent pleiotropic effects among alleles on these traits. The allelic effects of Met support genetic models where pleiotropy at genes associated with hormone regulation can contribute to the evolution of life history traits.     

Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multitrait GWA studies

Genetic correlations between traits may cause correlated responses to selection. Previous models described the conditions under which genetic correlations are expected to be maintained. Selection, mutation, and migration are all proposed to affect genetic correlations, regardless of whether the underlying genetic architecture consists of pleiotropic or tightly linked loci affecting the traits. Here, we investigate the conditions under which pleiotropy and linkage have different effects on the genetic correlations between traits by explicitly modeling multiple genetic architectures to look at the effects of selection strength, degree of correlational selection, mutation rate, mutational variance, recombination rate, and migration rate. We show that at mutation-selection(-migration) balance, mutation rates differentially affect the equilibrium levels of genetic correlation when architectures are composed of pairs of physically linked loci compared to architectures of pleiotropic loci. Even when there is perfect linkage (no recombination within pairs of linked loci), a lower genetic correlation is maintained than with pleiotropy, with a lower mutation rate leading to a larger decrease. These results imply that the detection of causal loci in multitrait association studies will be affected by the type of underlying architectures, whereby pleiotropic variants are more likely to be underlying multiple detected associations. We also confirm that tighter linkage between nonpleiotropic causal loci maintains higher genetic correlations at the traits and leads to a greater proportion of false positives in association analyses.     

Genotype-Phenotype Maps Maximizing Evolvability: Modularity Revisited

The mechanisms translating genetic to phenotypic variation determine the distribution of heritable phenotypic variance available to selection. Pleiotropy is an aspect of this structure that limits independent variation of characters. Modularization of pleiotropy has been suggested to promote evolvability by restricting genetic covariance among unrelated characters and reducing constraints due to correlated response. However, modularity may also reduce total genetic variation of characters. We study the properties of genotype-phenotype maps that maximize average conditional evolvability, measured as the amount of unconstrained genetic variation in random directions of phenotypic space. In general, maximal evolvability occurs by maximizing genetic variance and minimizing genetic covariance. This does not necessarily require modularity, only patterns of pleiotropy that cancel on average. The detailed structure of the most evolvable genotype-phenotype maps depends on the distribution of molecular variance. When molecular variance is determined by mutation-selection equilibrium either highly pleiotropic or highly modular genotype-phenotype maps can be optimal, depending on the mutation rate and the relative strengths of stabilizing selection on the characters.     

Epistatic pleiotropy and the genetic architecture of covariation within early and late-developing skull trait complexes in mice

The role of epistasis as a source of trait variation is well established, but its role as a source of covariation among traits (i.e., as a source of "epistatic pleiotropy") is rarely considered. In this study we examine the relative importance of epistatic pleiotropy in producing covariation within early and late-developing skull trait complexes in a population of mice derived from an intercross of the Large and Small inbred strains. Significant epistasis was found for several pairwise combinations of the 21 quantitative trait loci (QTL) affecting early developing traits and among the 20 QTL affecting late-developing traits. The majority of the epistatic effects were restricted to single traits but epistatic pleiotropy still contributed significantly to covariances. Because of their proportionally larger effects on variances than on covariances, epistatic effects tended to reduce within-group correlations of traits and reduce their overall degree of integration. The expected contributions of single-locus and two-locus epistatic pleiotropic QTL effects to the genetic covariance between traits were analyzed using a two-locus population genetic model. The model demonstrates that, for single-locus or epistatic pleiotropy to contribute to trait covariances in the study population, both traits must show the same pattern of single-locus or epistatic effects. As a result, a large number of the cases where loci show pleiotropic effects do not contribute to the covariance between traits in this population because the loci show a different pattern of effect on the different traits. In general, covariance patterns produced by single-locus and epistatic pleiotropy predicted by the model agreed well with actual values calculated from the QTL analysis. Nearly all single-locus and epistatic pleiotropic effects contributed positive components to covariances between traits, suggesting that genetic integration in the skull is achieved by a complex combination of pleiotropic effects.     

Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability

Evolvability is the ability to respond to a selective challenge. This requires the capacity to produce the right kind of variation for selection to act upon. To understand evolvability we therefore need to understand the variational properties of biological organisms. Modularity is a variational property, which has been linked to evolvability. If different characters are able to vary independently, selection will be able to optimize each character separately without interference. But although modularity seems like a good design principle for an evolvable organism, it does not therefore follow that it is the only design that can achieve evolvability. In this essay I analyze the effects of modularity and, more generally, pleiotropy on evolvability. Although, pleiotropy causes interference between the adaptation of different characters, it also increases the variational potential of those characters. The most evolvable genetic architectures may often be those with an intermediate level of integration among characters, and in particular those where pleiotropic effects are variable and able to compensate for each other's constraints.     

Pleiotropic effects of beneficial mutations in Escherichia coli

Micromutational models of adaptation have placed considerable weight on antagonistic pleiotropy as a mechanism that prevents mutations of large effect from achieving fixation. However, there are few empirical studies of the distribution of pleiotropic effects, and no studies that have examined this distribution for a large number of adaptive mutations. Here we examine the form and extent of pleiotropy associated with beneficial mutations in Escherichia coli. To do so, we used a collection of independently evolved genotypes, each of which contains a beneficial mutation that confers increased fitness in a glucose-limited environment. To determine the pleiotropic effects of these mutations, we examined the fitnesses of the mutants in five novel resource environments. Our results show that the majority of mutations had significant fitness effects in alternative resources, such that pleiotropy was common. The predominant form of this pleiotropy was positive--that is, most mutations that conferred increased fitness in glucose also conferred increased fitness in novel resources. We did detect some deleterious pleiotropic effects, but they were primarily limited to one of the five resources, and within this resource, to only a subset of mutants. Although pleiotropic effects were generally positive, fitness levels were lower and more variable on resources that differed most in their mechanisms of uptake and catabolism from that of glucose. Positive pleiotropic effects were strongly correlated in magnitude with their direct effects, but no such correlation was found among mutants with deleterious pleiotropic effects. Whereas previous studies of populations evolved on glucose for longer periods of time showed consistent declines on some of the resources used here, our results suggest that deleterious pleiotropic effects were limited to only a subset of the beneficial mutations available.     

DIFFERENTIAL DOMINANCE OF PLEIOTROPIC LOCI FOR MOUSE SKELETAL TRAITS

The term "differential dominance" describes the situation in which the dominance effects at a pleiotropic locus vary between traits. Directional selection on the phenotype can lead to balancing selection on differentially dominant pleiotropic loci. Even without any individual overdominant traits, some linear combination of traits will display overdominance at a locus displaying differential dominance. Multivariate overdominance may be responsible, in part, for high levels of heterozygosity found in natural populations. We examine differential dominance of 70 mouse skeletal traits at 92 quantitative trait loci (QTL). Our results indicate moderate to strong additive and dominance effects at pleiotropic loci, low levels of individual-trait overdominance, and universal multivariate overdominance. Multivariate overdominance affects a range of 6% to 81% of morphospace, with a mean of 32%. Multivariate overdominance tends to affect a larger percentage of morphospace at pleiotropic loci with antagonistic effects on multiple traits (42%). We conclude that multivariate overdominance is common and should be considered in models and in empirical studies of the role of genetic variation in evolvability.     

Systematic analysis of pleiotropy in C. elegans early embryogenesis

Pleiotropy refers to the phenomenon in which a single gene controls several distinct, and seemingly unrelated, phenotypic effects. We use C. elegans early embryogenesis as a model to conduct systematic studies of pleiotropy. We analyze high-throughput RNA interference (RNAi) data from C. elegans and identify "phenotypic signatures", which are sets of cellular defects indicative of certain biological functions. By matching phenotypic profiles to our identified signatures, we assign genes with complex phenotypic profiles to multiple functional classes. Overall, we observe that pleiotropy occurs extensively among genes involved in early embryogenesis, and a small proportion of these genes are highly pleiotropic. We hypothesize that genes involved in early embryogenesis are organized into partially overlapping functional modules, and that pleiotropic genes represent "connectors" between these modules. In support of this hypothesis, we find that highly pleiotropic genes tend to reside in central positions in protein-protein interaction networks, suggesting that pleiotropic genes act as connecting points between different protein complexes or pathways.     

Fine mapping dissects pleiotropic growth quantitative trait locus into linked loci

A recurring issue in studies of quantitative trait loci (QTLs) is whether QTLs that appear to have pleiotropic effects are indeed caused by pleiotropy at single loci or by linked QTLs. Previous work identified a QTL that affected tail length in mice and the lengths of various bones, including the humerus, ulna, femur, tibia, and mandible. The effect of this QTL on tail length has since been found to be due to multiple linked QTLs and so its apparently pleiotropic effects may have been due to linked QTLs with distinct effects. In the present study we examined a line of mice segregating only for a 0.94-Mb chromosomal region known to contain a subset of the QTLs influencing tail length. We measured a number of skeletal dimensions, including the lengths of the skull, mandible, humerus, ulna, femur, tibia, calcaneus, metatarsus, and a tail bone. The QTL region was found to have effects on the size of the mandible and length of the tail bone, with little or no effect on the other traits. Using a randomization approach, we rejected the null hypothesis that the QTL affected all traits equally, thereby demonstrating that the pleiotropic effects reported earlier were due to linked loci with distinct effects. This result underlines the possibility that seemingly pleiotropic effects of QTLs may frequently be due to linked loci and that high-resolution mapping will often be required to distinguish between pleiotropy and linkage.     

Scaling of mutational effects in models for pleiotropy

Mutation-selection models provide a framework to relate the parameters of microevolution to properties of populations. Like all models, these must be subject to test and refinement in light of experiments. The standard mutation-selection model assumes that the effects of a pleiotropic mutation on different characters are uncorrelated. As a consequence of this assumption, mutations of small overall effect are suppressed. For strong enough pleiotropy, the result is a nonvanishing fraction of a population with the "perfect" phenotype. However, experiments on microorganisms and experiments on protein structure and function contradict the assumptions of the standard model, and Kimura's observations of heterogeneity within populations contradict its conclusions. Guided by these observations, we present an alternative model for pleiotropic mutations. The new model allows mutations of small overall effect and thus eliminates the finite fraction of the population with the perfect phenotype.     

What affects the predictability of evolutionary constraints using a G‐matrix? The relative effects of modular pleiotropy and mutational correlation

Phenotypic traits do not always respond to selection independently from each other and often show correlated responses to selection. The structure of a genotype‐phenotype map (GP map) determines trait covariation, which involves variation in the degree and strength of the pleiotropic effects of the underlying genes. It is still unclear, and debated, how much of that structure can be deduced from variational properties of quantitative traits that are inferred from their genetic (co) variance matrix ( G ‐matrix). Here we aim to clarify how the extent of pleiotropy and the correlation among the pleiotropic effects of mutations differentially affect the structure of a G ‐matrix and our ability to detect genetic constraints from its eigen decomposition. We show that the eigenvectors of a G ‐matrix can be predictive of evolutionary constraints when they map to underlying pleiotropic modules with correlated mutational effects. Without mutational correlation, evolutionary constraints caused by the fitness costs associated with increased pleiotropy are harder to infer from evolutionary metrics based on a G ‐matrix's geometric properties because uncorrelated pleiotropic effects do not affect traits' genetic correlations. Correlational selection induces much weaker modular partitioning of traits' genetic correlations in absence then in presence of underlying modular pleiotropy.     

The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms

It was first noticed 100 years ago that mutations tend to affect more than one phenotypic characteristic, a phenomenon that was called 'pleiotropy'. Because pleiotropy was found so frequently, the notion arose that pleiotropy is 'universal'. However, quantitative estimates of pleiotropy have not been available until recently. These estimates show that pleiotropy is highly restricted and are more in line with the notion of variational modularity than with universal pleiotropy. This finding has major implications for the evolvability of complex organisms and the mapping of disease-causing mutations.     

Indirect genetic effects from ecological interactions in Arabidopsis thaliana

Indirect genetic effects arise when genes expressed in one individual affect the expression of traits in other individuals. The importance of indirect genetic effects has been recognized for a diversity of evolutionary processes including kin selection, sexual selection, community structure and multilevel selection, but data regarding their genetic architecture and prevalence throughout the genome remain scarce, especially for interactions between unrelated individuals. Using a set of 411 Bay-0 x Shahdara Arabidopsis recombinant inbred lines grown with Landsberg neighbours, we examined quantitative trait loci (QTL) having direct and indirect effects on size, developmental, and fitness related traits. Using an interval mapping approach, we identified 15 QTL with direct effects and found that 13 of these QTL had significant indirect effects on trait expression in neighbouring plants. These results suggest widespread pleiotropy, as nearly all direct effect QTL have associated pleiotropic indirect effects. Paradoxically, most indirect effects were of the same sign as direct effects, creating a pattern of nearly universal positive pleiotropy that makes most covariances between direct and indirect effects positive. These results are consistent with a complex genetic basis for intraspecific interactions, but suggest that interactions between neighbouring plants are largely positive, rather than negative as would be expected for competition. In addition to their evolutionary and ecological importance, these pleiotropic relationships between DGE and IGE loci have implications for quantitative genetic studies of natural populations as well as experimental design considerations. Additionally, studies that ignore IGEs may over- or underestimate quantitative genetic parameters, as well as the effect of and variance contributed by QTL.     

Phenotypic Integration Without Modularity: Testing Hypotheses About the Distribution of Pleiotropic Quantitative Trait Loci in a Continuous Space

Theories of phenotypic integration have relied heavily on the concept of modularity in order to model the ways in which traits in an organism correlate and covary. Recent investigations suggest that, while some functional and developmental processes may be morphologically and ontogenetically localized, and thus modular in a developmental sense, there is a great deal of overlap among these influences on patterns of integration in the adult form. This can result in blurry boundaries between hypothesized modules constructed to test hypotheses about phenotypic integration. This investigation tests hypotheses about the contribution of pleiotropic quantitative trait loci (QTL) to phenotypic integration in the mouse mandible without using a priori categorical hypotheses about which traits constitute a module. We ask two main questions: (1) Are the effects of pleiotropic QTL localized to highly correlated traits or more spread out among traits than one might expect by chance? (2) Does the pattern of trait influence when all pleiotropic QTL are considered together deviate from what we might expect if QTL affect traits without regard for the correlations among traits? We find that a large proportion of pleiotropic QTL affect traits that are more highly correlated than we expect by chance with the remainder having effects that are distributed as if by chance. Furthermore, the overall distribution of the effects of pleiotropic QTL differs significantly from the null distribution of no association between pleiotropic effects on traits and correlations among traits. The main modular hypothesis used by earlier studies often does not predict the distribution of sets of traits sharing a common QTL. These results suggest that there is a clear tendency for pleiotropic effects of QTL to be localized but that the localization may be best thought of as occurring in a continuous space rather being clustered in discrete modules.     

Pleiotropy and the genomic location of sexually selected genes

Sexual selection drives the evolution of traits involved in the competition for mates. Although considerable research has focused on the evolution of sexually selected traits, their underlying genetic architecture is poorly resolved. Here I address the pleiotropic effects and genomic locations of sexually selected genes. These two important characteristics can impose considerable constraints on evolvability and may influence our understanding of the process of sexual selection. Theoretical models are inconsistent regarding the genomic location of sexually selected genes. Models that do not incorporate pleiotropic effects often predict sex linkage. Conversely, sex linkage is not explicitly predicted by the condition-dependent model (which considers pleiotropic effects). Evidence largely based on reciprocal crosses supports the notion of sex linkage. However, although they infer genetic contribution, reciprocal crosses cannot identify the genes or their pleiotropic effects. By surveying the genome of Drosophila melanogaster, I provide evidence for the genomic location and pleiotropic effects of 63 putatively sexually selected genes. Interestingly, most are pleiotropic (73%), and they are not preferentially sex linked. Their pleiotropic effects include fertility, development, life span, and viability, which may contribute to condition and/or fitness. My findings may also provide evidence for the capture of genetic variation in condition via the pleiotropic effects of sexually selected genes.     

Toward a molecular understanding of pleiotropy

Pleiotropy refers to the observation of a single gene influencing multiple phenotypic traits. Although pleiotropy is a common phenomenon with broad implications, its molecular basis is unclear. Using functional genomic data of the yeast Saccharomyces cerevisiae, here we show that, compared with genes of low pleiotropy, highly pleiotropic genes participate in more biological processes through distribution of the protein products in more cellular components and involvement in more protein-protein interactions. However, the two groups of genes do not differ in the number of molecular functions or the number of protein domains per gene. Thus, pleiotropy is generally caused by a single molecular function involved in multiple biological processes. We also provide genomewide evidence that the evolutionary conservation of genes and gene sequences positively correlates with the level of gene pleiotropy.     

The contribution of pleiotropy to blood pressure and body-mass index variation: the Gubbio Study.

Blood pressure (BP), body-mass index (BMI), and quantitative phenotypes thought to influence BP (e.g., lithium-sodium countertransport activity) were studied in 2,184 households comprising 5,376 people in Gubbio, Italy. Variance-components models were used to partition the variation of these phenotypes into components characterizing the effects of age-related, measured environmental, additive genetic, pleiotropic, unmeasured shared-household, and individual-specific (or random) factors. The goal of the investigation was to estimate the contribution of pleiotropy to variation in BP and BMI in population-based samples. Although our results suggest that numerous significant bivariate genetic correlations exist between BP and some of the traits investigated, they ultimately lead us to reject a prominent role for any individual bivariate pleiotropic system influencing the natural variation of BP. However, because we found evidence that many traits enter into small-impact pleiotropic relationships with BP, we cannot rule out the possibility that pleiotropic genes, when considered collectively, may contribute to BP variation at the population level. Similar results were obtained when BMI was taken as the primary variable of interest. We argue that the small but significant portion of BP variation explained by individual genes displaying bivariate pleiotropic effects is intuitive, in light of the relatively low heritabilities associated with quantitative cardiovascular phenotypes and the low phenotypic correlations between BP, BMI, and many other physiologically linked measures of cardiovascular function. Our results not only bear directly on both the nature of the multifactorial determinants of BP and the maintenance of BP variation in the population at large, but also emphasize the utility of variance-components models in epidemiologic and population genetics research. We discuss the implications of our results for genetic epidemiologists and medical researchers studying hypertension, as well as the limitations of our study and areas for future research.