Disintegrating the fly: A mutational perspective on phenotypic integration and covariation

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

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

Multivariate analysis of morphology, behaviour, growth and developmental timing in hybrids brings new insights into the divergence of sympatric Arctic charr morphs

BACKGROUND

Studying the development of fitness related traits in hybrids from populations diverging in sympatry is a fundamental approach to understand the processes of speciation. However, such traits are often affected by covariance structures that complicate the comprehension of these processes, especially because the interactive relationships between traits of different nature (e.g. morphology, behaviour, life-history) remain largely unknown in this context. In a common garden setup, we conducted an extensive examination of a large suit of traits putatively involved in the divergence of two morphs of Arctic charr (Salvelinus alpinus), and investigated the consequences of potential patterns of trait covariance on the phenotype of their hybrids. These traits were measured along ontogeny and involved growth, yolk sac resorption, developmental timing (hatching and the onset of exogeneous feeding), head morphology and feeding behaviour.

RESULTS

Growth trajectories provided the strongest signal of phenotypic divergence between the two charr. Strikingly, the first-generation hybrids did not show intermediate nor delayed growth but were similar to the smallest morph, suggesting parental biases in the inheritance of growth patterns. However, we did not observe extensive multivariate trait differences between the two morphs and their hybrids. Growth was linked to head morphology (suggesting that morphological variations in early juveniles relate to simple allometric effects) but this was the only strong signal of covariance observed between all the measured traits. Furthermore, we did not report evidence for differences in overall phenotypic variance between morphs, nor for enhanced phenotypic variability in their hybrids.

CONCLUSION

Our study shed light on the multivariate aspect of development in a context of adaptive divergence. The lack of evidence for the integration of most traits into a single covariance structure suggested that phenotypic constraints may not always favour nor impede divergence toward ecological niches differing in numerous physical and ecological variables, as observed in the respective habitats of the two charr. Likewise, the role of hybridization as a disruptive agent of trait covariance may not necessarily be significant in the evolution of populations undergoing resource polymorphism.

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.

Copulatory function and development shape modular architecture of genitalia differently in males and females

Genitalia are multitasking structures whose development is mediated by numerous regulatory pathways. This multifactorial nature provides an avenue for multiple sources of selection. As a result, genitalia tend to evolve as modular systems comprising semi-independent subsets of structures, yet the processes that give rise to those patterns are still poorly understood. Here, we ask what are the relative roles of development and function in shaping modular patterns of genitalia within populations and across species of stink-bugs. We found that male genitalia are less integrated, more modular, and primarily shaped by functional demands. In contrast, females show higher integration, lower modularity, and a predominant role of developmental processes. Further, interactions among parts of each sex are more determinant to modularity than those between the sexes, and patterns of modularity are equivalent between and within species. Our results strongly indicate that genitalia have been subjected to sex-specific selection, although male and female genitalia are homologous and functionally associated. Moreover, modular patterns are seemingly constant in the evolutionary history of stink-bugs, suggesting a scenario of multivariate stabilizing selection within each sex. Our study demonstrates that interactions among genital parts of the same sex may be more fundamental to genital evolution than previously thought.

Differential Regulation of Cryptic Genetic Variation Shapes the Genetic Interactome Underlying Complex Traits

Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets.