The Developmental Basis of Variational Modularity: Insights from Quantitative Genetics,Morphometrics, and Developmental Biology

Groups of correlated characters (variational modules) often are considered to be the result of dissociated local developmental factors, i.e., of a modular genotype–phenotype map. But certain sets of pleiotropic factors can equally well induce modular phenotypic variation—no local developmental factors are necessary for a modular covariance structure. It is thus not possible to infer genetic or developmental modularity from standing variation alone. Yet, only for approximately linear genotype–phenotype maps is the induced covariance structure stable over changes of the phenotypic mean. For larger genetic and phenotypic variation, such as on a macroevolutionary level, developmental effects often are nonlinear and variational modularity remains stable only when it is realized by local dissociated developmental factors with no overlap of pleiotropic ranges. The evo-devo concept of modularity concurs only at this macroevolutionary level with the quantitative notion of variational modularity. Empirical evidence on the genetic and developmental architecture underlying phenotypic variation is inconclusive and partly subject to methodological problems. Many studies seem to indicate modularized phenotypic variation and local clusters of QTL effects, whereas other studies find support for several alternative models of modularity and report continuous distributions of QTL effects. This inconsistency partly results from the neglect of spatial relationships among the measured traits. Given the complex development of higher organisms, a combination of pleiotropic factors and more local developmental effects with a hierarchical, overlapping, and more or less continuous distribution appears most likely.     

The quantitative-genetic and QTL architecture of trait integration and modularity in Brassica rapa across simulated seasonal settings

Within organisms, groups of traits with different functions are frequently modular, such that variation among modules is independent and variation within modules is tightly integrated, or correlated. Here, we investigated patterns of trait integration and modularity in Brassica rapa in response to three simulated seasonal temperature/photoperiod conditions. The goals of this research were to use trait correlations to understand patterns of trait integration and modularity within and among floral, vegetative and phenological traits of B. rapa in each of three treatments, to examine the QTL architecture underlying patterns of trait integration and modularity, and to quantify how variation in temperature and photoperiod affects the correlation structure and QTL architecture of traits. All floral organs of B. rapa were strongly correlated, and contrary to expectations, floral and vegetative traits were also correlated. Extensive QTL co-localization suggests that covariation of these traits is likely due to pleiotropy, although physically linked loci that independently affect individual traits cannot be ruled out. Across treatments, the structure of genotypic and QTL correlations was generally conserved. Any observed variation in genetic architecture arose from genotype × environment interactions (GEIs) and attendant QTL × E in response to temperature but not photoperiod.     

Back to the future: genetic correlations, adaptation and speciation

Genetic correlations can affect the course of phenotypic evolution. Although genetic correlations among traits are a common feature of quantitative genetic analyses, they have played a very minor role in recent linkage-map based analyses of the genetic architecture of quantitative traits. Here, we use our work on host-associated races in pea aphids to illustrate how quantitative trait locus (QTL) mapping can be used to test specific hypotheses about how genetic correlations may facilitate ecological specialization and speciation.     

Quantitative trait loci mapping of floral and leaf morphology traits in Arabidopsis thaliana: evidence for modular genetic architecture

Summary Morphological variation within organisms is integrated and often modular in nature. That is to say, the size and shape of traits tend to vary in a coordinated and structured manner across sets of organs or parts of an organism. The genetic basis of this morphological integration is largely unknown. Here, we report on quantitative trait loci (QTL) analysis of leaf and floral organ size in Arabidopsis thaliana. We evaluate patterns of genetic correlations among traits and perform whole-genome scans using QTL mapping methods. We detected significant genetic variation for the size and shape of each floral and leaf trait in our study. Moreover, we found large positive genetic correlations among sets of either flower or leaf traits, but low and generally nonsignificant genetic correlations between flower and leaf traits. These results support the hypothesis of independent floral and vegetative modules. We consider co-localization of QTL for different traits as support for a pleiotropic basis of morphological integration and modularity. A total of eight QTL affecting flower and three QTL affecting leaf traits were identified. Most QTL affected either floral or leaf traits, providing a general explanation for high correlations within and low correlations between modules. Only two genomic locations affected both flower and leaf growth. These results are discussed in the context of the evolution of modules, pleiotropy, and the putative homologous relationship between leaves and flowers.     

THE GENOMIC ARCHITECTURE OF SEXUAL DIMORPHISM IN THE DIOECIOUS PLANT SILENE LATIFOLIA

Evaluating the genetic architecture of sexual dimorphism can aid our understanding of the extent to which shared genetic control of trait variation versus sex‐specific control impacts the evolutionary dynamics of phenotypic change within each sex. We performed a QTL analysis on Silene latifolia to evaluate the contribution of sex‐specific QTL to phenotypic variation in 46 traits, whether traits involved in trade‐offs had colocalized QTL, and whether the distribution of sex‐specific loci can explain differences between the sexes in their variance/covariance matrices. We used a backcross generation derived from two artificial‐selection lines. We found that sex‐specific QTL explained a significantly greater percent of the variation in sexually dimorphic traits than loci expressed in both sexes. Genetically correlated traits often had colocalized QTL, whose signs were in the expected direction. Lastly, traits with different genetic correlations within the sexes displayed a disproportionately high number of sex‐specific QTL, and more QTL co‐occurred in males than females, suggesting greater trait integration. These results show that sex differences in QTL patterns are congruent with theory on the resolution of sexual conflict and differences based on G ‐matrix results. They also suggest that trade‐offs and trait integration are likely to affect males more than females.     

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.     

Pleiotropic effects on mandibular morphology I. Developmental morphological integration and differential dominance

Pleiotropy refers to a single genetic locus that affects more than one phenotypic trait. Pleiotropic effects of genetic loci are thought to play an important role in evolution, reflecting functional and developmental relationships among phenotypes. In a previous study, we examined pleiotropic effects displayed by quantitative trait loci (QTLs) on murine mandibular morphology in relation to mandibular structure and function. In replicating most of our previous QTLs and increasing our sample size, this study strengthens and extends our earlier results. As in our previous study, we find that QTL effects tend to be restricted to developmentally or functionally related traits. In addition, we examine patterns of differential dominance for pleiotropic QTL effects. Differential dominance occurs when dominance patterns for a single locus vary among traits. We find that multivariate overdominance is a common and substantial phenomenon, and may potentially provide an explanation for the persistence of heterozygosity in natural populations.     

Quantitative genetics of skeletal nonmetric traits in the rhesus macaques on Cayo Santiago. II. Phenotypic,genetic, and environmental correlations between traits

The general lack of phenotypic correlation among skeletal nonmetric traits has been interpreted as indicating a lack of genetic correlation among these traits. Nonmetric traits scored on animals in the skeletal collection of rhesus macaques from Cayo Santiago are used to calculate phenotypic, genetic, and environmental correlations between traits. The results show that even when phenotypic correlations are low, there may be large, significant genetic correlations among these traits. The genetic correlation pattern suggests that genes which affect nonmetric trait variation act primarily at a local level in the cranium, even though there are genes with pleiotropic effects on skeletal nonmetric traits throughout the cranium. Environmental and phenotypic correlations do not show this neighborhood pattern of correlation.     

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.     

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.     

The search for Pleiades in trait constellations: functional integration and phenotypic selection in the complex flowers of Morrenia brachystephana (Apocynaceae)

Pollinator‐mediated natural selection on single traits, such as corolla tube or spur length, has been well documented. However, flower phenotypes are usually complex, and selection is expected to act on several traits that functionally interact rather than on a single isolated trait. Despite the fact that selection on complex phenotypes is expectedly widespread, multivariate selection modelling on such phenotypes still remains under‐explored in plants. Species of the subfamily Asclepiadoideae (Apocynaceae) provide an opportunity to study such complex flower contrivances integrated by fine‐scaled organs from disparate developmental origin. We studied the correlation structure among linear floral traits (i) by testing a priori morphological, functional or developmental hypotheses among traits and (ii) by exploring the organization of flower covariation, considering alternative expectations of modular organization or whole flower integration through conditional dependence analysis (CDA) and integration matrices. The phenotypic selection approach was applied to determine whether floral traits involved in the functioning of the pollination mechanism were affected by natural selection. Floral integration was low, suggesting that flowers are organized in more than just one correlation pleiad; our hypothetical functional correlation matrix was significantly correlated with the empirical matrix, and the CDA revealed three putative modules. Analyses of phenotypic selection showed significant linear and correlational gradients, lending support to expectations of functional interactions between floral traits. Significant correlational selection gradients found involved traits of different floral whorls, providing evidence for the existence of functional integration across developmental domains.     

QTL analysis of floral traits in Louisiana iris hybrids

The formation of hybrid zones between nascent species is a widespread phenomenon. The evolutionary consequences of hybridization are influenced by numerous factors, including the action of natural selection on quantitative trait variation. Here we examine how the genetic basis of floral traits of two species of Louisiana Irises affects the extent of quantitative trait variation in their hybrids. Quantitative trait locus (QTL) mapping was used to assess the size (magnitude) of phenotypic effects of individual QTL, the degree to which QTL for different floral traits are colocalized, and the occurrence of mixed QTL effects. These aspects of quantitative genetic variation would be expected to influence (1) the number of genetic steps (in terms of QTL substitutions) separating the parental species phenotypes; (2) trait correlations; and (3) the potential for transgressive segregation in hybrid populations. Results indicate that some Louisiana Iris floral trait QTL have large effects and QTL for different traits tend to colocalize. Transgressive variation was observed for six of nine traits, despite the fact that mixed QTL effects influence few traits. Overall, our QTL results imply that the genetic basis of floral morphology and color traits might facilitate the maintenance of phenotypic divergence between Iris fulva and Iris brevicaulis, although a great deal of phenotypic variation was observed among hybrids.     

A statistical model for testing the pleiotropic control of phenotypic plasticity for a count trait

The differences of a phenotypic trait produced by a genotype in response to changes in the environment are referred to as phenotypic plasticity. Despite its importance in the maintenance of genetic diversity via genotype-by-environment interactions, little is known about the detailed genetic architecture of this phenomenon, thus limiting our ability to predict the pattern and process of microevolutionary responses to changing environments. In this article, we develop a statistical model for mapping quantitative trait loci (QTL) that control the phenotypic plasticity of a complex trait through differentiated expressions of pleiotropic QTL in different environments. In particular, our model focuses on count traits that represent an important aspect of biological systems, controlled by a network of multiple genes and environmental factors. The model was derived within a multivariate mixture model framework in which QTL genotype-specific mixture components are modeled by a multivariate Poisson distribution for a count trait expressed in multiple clonal replicates. A two-stage hierarchic EM algorithm is implemented to obtain the maximum-likelihood estimates of the Poisson parameters that specify environment-specific genetic effects of a QTL and residual errors. By approximating the number of sylleptic branches on the main stems of poplar hybrids by a Poisson distribution, the new model was applied to map QTL that contribute to the phenotypic plasticity of a count trait. The statistical behavior of the model and its utilization were investigated through simulation studies that mimic the poplar example used. This model will provide insights into how genomes and environments interact to determine the phenotypes of complex count traits.     

The "eyespot module" and eyespots as modules: development, evolution, and integration of a complex phenotype

Organisms are inherently modular, yet modules also evolve in response to selection for functional integration or functional specialization of traits. For serially repeated homologous traits, there is a clear expectation that selection on the function of individual traits will reduce the integration between traits and subdivide a single ancestral module. The eyespots on butterfly wings are one example of serially repeated morphological traits that share a common developmental mechanism but are subject to natural and sexual selection for divergent functions. Here, I test two hypotheses about the organization of the eyespot pattern into independent dorsal-ventral and anterior-posterior modules, using a graphical modeling technique to examine patterns of eyespot covariation among and within wing surfaces in the butterfly Bicyclus anynana. Although there is a hierarchical and complex pattern of integration among eyespots, the results show a surprising mismatch between patterns of eyespot integration and the developmental and evolutionary eyespot units identified in previous empirical studies. These results are discussed in light of the relationships between developmental, functional, and evolutionary modules, and they suggest that developmental sources of independent trait variation are often masked by developmental sources of trait integration.     

Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.     

Trade-offs in the genetic control of functional and nutritional quality traits in UK winter wheat

A complex network of trade-offs exists between wheat quality and nutritional traits. We investigated the correlated relationships among several milling and baking traits as well as mineral density in refined white and whole grain flour. Our aim was to determine their pleiotropic genetic control in a multi-parent population over two trial years with direct application to practical breeding. Co-location of major quantitative trait loci (QTL) and principal component based multi-trait QTL mapping increased the power to detect QTL and revealed pleiotropic effects explaining many complementary and antagonistic trait relationships. High molecular weight glutenin subunit genes explained much of the heritable variation in important dough rheology traits, although additional QTL were detected. Several QTL, including one linked to the TaGW2 gene, controlled grain size and increased flour extraction rate. The semi-dwarf Rht-D1b allele had a positive effect on Hagberg falling number, but reduced grain size, specific weight, grain protein content and flour water absorption. Mineral nutrient concentrations were lower in Rht-D1b lines for many elements, in wholemeal and white flour, but potassium concentration was higher in Rht-D1b lines. The presence of awns increased calcium content without decreasing extraction rate, despite the negative correlation between these traits. QTL were also found that affect the relative concentrations of key mineral nutrients compared to phosphorus which may help increase bioavailability without associated anti-nutritional effects of phytic acid. Taken together these results demonstrate the potential for marker-based selection to optimise trait trade-offs and enhance wheat nutritional value by considering pleiotropic genetic effects across multiple traits.Subject terms: Plant breeding, Quantitative trait, Genetic variation     

Pleiotropic patterns of quantitative trait loci for 70 murine skeletal traits

Quantitative trait locus (QTL) studies of a skeletal trait or a few related skeletal components are becoming commonplace, but as yet there has been no investigation of pleiotropic patterns throughout the skeleton. We present a comprehensive survey of pleiotropic patterns affecting mouse skeletal morphology in an intercross of LG/J and SM/J inbred strains (N = 1040), using QTL analysis on 70 skeletal traits. We identify 798 single-trait QTL, coalescing to 105 loci that affect on average 7-8 traits each. The number of traits affected per locus ranges from only 1 trait to 30 traits. Individual traits average 11 QTL each, ranging from 4 to 20. Skeletal traits are affected by many, small-effect loci. Significant additive genotypic values average 0.23 standard deviation (SD) units. Fifty percent of loci show codominance with heterozygotes having intermediate phenotypic values. When dominance does occur, the LG/J allele tends to be dominant to the SM/J allele (30% vs. 8%). Over- and underdominance are relatively rare (12%). Approximately one-fifth of QTL are sex specific, including many for pelvic traits. Evaluating the pleiotropic relationships of skeletal traits is important in understanding the role of genetic variation in the growth and development of the skeleton.     

Genetic analysis of traits distinguishing outcrossing and self-pollinating forms of currant tomato,Lycopersicon pimpinellifolium (Jusl.) Mill

The evolution of inbreeding is common throughout the angiosperms, although little is known about the developmental and genetic processes involved. Lycopersicon pimpinellifolium (currant tomato) is a self-compatible species with variation in outcrossing rate correlated with floral morphology. Mature flowers from inbreeding and outcrossing populations differ greatly in characters affecting mating behavior (petal, anther, and style lengths); other flower parts (sepals, ovaries) show minimal differences. Analysis of genetic behavior, including quantitative trait locus (QTL) mapping, was performed on representative selfing and outcrossing plants derived from two contrasting natural populations. Six morphological traits were analyzed: flowers per inflorescence; petal, anther, and style lengths; and lengths of the fertile and sterile portions of anthers. All traits were smaller in the selfing parent and had continuous patterns of segregation in the F(2). Phenotypic correlations among traits were all positive, but varied in strength. Quantitative trait locus mapping was done using 48 RFLP markers. Five QTL total were found involving four of the six traits: total anther length, anther sterile length, style length, and flowers per inflorescence. Each of these four traits had a QTL of major (>25%) effect on phenotypic variance.     

A test for selection employing quantitative trait locus and mutation accumulation data

Evolutionary biologists attribute much of the phenotypic diversity observed in nature to the action of natural selection. However, for many phenotypic traits, especially quantitative phenotypic traits, it has been challenging to test for the historical action of selection. An important challenge for biologists studying quantitative traits, therefore, is to distinguish between traits that have evolved under the influence of strong selection and those that have evolved neutrally. Most existing tests for selection employ molecular data, but selection also leaves a mark on the genetic architecture underlying a trait. In particular, the distribution of quantitative trait locus (QTL) effect sizes and the distribution of mutational effects together provide information regarding the history of selection. Despite the increasing availability of QTL and mutation accumulation data, such data have not yet been effectively exploited for this purpose. We present a model of the evolution of QTL and employ it to formulate a test for historical selection. To provide a baseline for neutral evolution of the trait, we estimate the distribution of mutational effects from mutation accumulation experiments. We then apply a maximum-likelihood-based method of inference to estimate the range of selection strengths under which such a distribution of mutations could generate the observed QTL. Our test thus represents the first integration of population genetic theory and QTL data to measure the historical influence of selection.