Mihaela Pavlicev

The road to modularity.

A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components. Modularity has emerged as a rallying point for research in developmental and evolutionary biology (and specifically evo–devo), as well as in molecular systems biology. Here we review the evidence for modularity and models about its origin. Although there is an emerging agreement that organisms have a modular organization, the main open problem is the question of whether modules arise through the action of natural selection or because of biased mutational mechanisms.

Pleiotropic scaling of gene effects and the 'cost of complexity'.

As perceived by Darwin, evolutionary adaptation by the processes of mutation and selection is difficult to understand for complex features that are the product of numerous traits acting in concert, for example the eye or the apparatus of flight. Typically, mutations simultaneously affect multiple phenotypic characters. This phenomenon is known as pleiotropy. The impact of pleiotropy on evolution has for decades been the subject of formal analysis. Some authors have suggested that pleiotropy can impede evolutionary progress (a so-called ‘cost of complexity’). The plausibility of various phenomena attributed to pleiotropy depends on how many traits are affected by each mutation and on our understanding of the correlation between the number of traits affected by each gene substitution and the size of mutational effects on individual traits. Here we show, by studying pleiotropy in mice with the use of quantitative trait loci (QTLs) affecting skeletal characters, that most QTLs affect a relatively small subset of traits and that a substitution at a QTL has an effect on each trait that increases with the total number of traits affected. This suggests that evolution of higher organisms does not suffer a ‘cost of complexity’ because most mutations affect few traits and the size of the effects does not decrease with pleiotropy.

Measuring Morphological Integration Using Eigenvalue Variance

The concept of morphological integration describes the pattern and the amount of correlation between morphological traits. Integration is relevant in evolutionary biology as it imposes constraint on the variation that is exposed to selection, and is at the same time often based on heritable genetic correlations. Several measures have been proposed to assess the amount of integration, many using the distribution of eigenvalues of the correlation matrix. In this paper, we analyze the properties of eigenvalue variance as a much applied measure. We show that eigenvalue variance scales linearly with the square of the mean correlation and propose the standard deviation of the eigenvalues as a suitable alternative that scales linearly with the correlation. We furthermore develop a relative measure that is independent of the number of traits and can thus be readily compared across datasets. We apply this measure to examples of phenotypic correlation matrices and compare our measure to several other methods. The relative standard deviation of the eigenvalues gives similar results as the mean absolute correlation (W.P. Cane, Evol Int J Org Evol 47:844–854, 1993) but is only identical to this measure if the correlation matrix is homogenous. For heterogeneous correlation matrices the mean absolute correlation is consistently smaller than the relative standard deviation of eigenvalues and may thus underestimate integration. Unequal allocation of variance due to variation among correlation coefficients is captured by the relative standard deviation of eigenvalues. We thus suggest that this measure is a better reflection of the overall morphological integration than the average correlation.

GENETIC VARIATION IN PLEIOTROPY: DIFFERENTIAL EPISTASIS AS A SOURCE OF VARIATION IN THE ALLOMETRIC RELATIONSHIP BETWEEN LONG BONE LENGTHS AND BODY WEIGHT

Abstract 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.

A model of developmental evolution: selection, pleiotropy and compensation.

Development and physiology translate genetic variation into phenotypic variation and determine the genotype-phenotype map, such as which gene affects which character (pleiotropy). Any genetic change in this mapping reflects a change in development. Here, we discuss evidence for variation in pleiotropy and propose the selection, pleiotropy and compensation model (SPC) for adaptive evolution. It predicts that adaptive change in one character is associated with deleterious pleiotropy in others and subsequent selection to compensate for these pleiotropic effects. The SPC model provides a unifying perspective for a variety of puzzling phenomena, including developmental systems drift and character homogenization. The model suggests that most adaptive signatures detected in genome scans could be the result of compensatory changes, rather than of progressive character adaptations.

Evolution of adaptive phenotypic variation patterns by direct selection for evolvability

A basic assumption of the Darwinian theory of evolution is that heritable variation arises randomly. In this context, randomness means that mutations arise irrespective of the current adaptive needs imposed by the environment. It is broadly accepted, however, that phenotypic variation is not uniformly distributed among phenotypic traits, some traits tend to covary, while others vary independently, and again others barely vary at all. Furthermore, it is well established that patterns of trait variation differ among species. Specifically, traits that serve different functions tend to be less correlated, as for instance forelimbs and hind limbs in bats and humans, compared with the limbs of quadrupedal mammals. Recently, a novel class of genetic elements has been identified in mouse gene-mapping studies that modify correlations among quantitative traits. These loci are called relationship loci, or relationship Quantitative Trait Loci (rQTL), and affect trait correlations by changing the expression of the existing genetic variation through gene interaction. Here, we present a population genetic model of how natural selection acts on rQTL. Contrary to the usual neo-Darwinian theory, in this model, new heritable phenotypic variation is produced along the selected dimension in response to directional selection. The results predict that selection on rQTL leads to higher correlations among traits that are simultaneously under directional selection. On the other hand, traits that are not simultaneously under directional selection are predicted to evolve lower correlations. These results and the previously demonstrated existence of rQTL variation, show a mechanism by which natural selection can directly enhance the evolvability of complex organisms along lines of adaptive change.

Fast radiation of the subfamily Lacertinae (Reptilia: Lacertidae): History or methodical artefact?

Lacertinae is one of the three lacertid lizard subfamilies with a geographical distribution confined to the Palaearctic. Several past attempts to reconstruct its phylogeny resulted in unresolved bush-like topologies. We address the question of whether the lack of resolution is due to insufficient data or whether this lack reflects a rapid succession of speciation events. We analyzed four partial and one complete gene sequences from mitochondrial and nuclear genomes, totalling roughly 3600 bp. We included 29 species representing all 19 genera suggested in recent revision of Lacertinae [Arnold, E.N., Arribas, O., Carranza, S., 2007. Systematics of the palaearctic and oriental lizard tribe Lacertini (Squamata: Lacertidae: Lacertinae), with descriptions of eight new genera. Zootaxa 1430, 1-86]. The resulting phylogeny, first, corroborates monophyly at the genus level for the suggested genera, as well as the finding that Atlantolacerta andreanskyi, until recently part of Lacertinae, belongs to the subfamily Eremiadinae. Second, we find that increasing the sequence length and combining multiple nuclear and mitochondrial sequences did not resolve the polytomy, suggesting that the inferred topology indicates a multiple cladogenesis within a short geological period, rather than a methodical artefact. Divergence time estimates, based on previous estimates of several node ages, range from 13.9 to 14.9 million years for the radiation event, however with very broad confidence interval. To associate the radiation with a narrower geological time we consider palaeogeographic and palaeoclimatic data, assuming that the Lacertinae probably evolved in Central Europe and W Asia after the collision of Africa and Eurasia. We suggest that this radiation may date to the late Langhian (ca. 14-13.5 million years) when geological events caused abrupt changes in regional water-land distribution and climate, offering a window of distinct conditions.

Identification of Quantitative Trait Loci Affecting Murine Long Bone Length in a Two‐Generation Intercross of LG/J and SM/J Mice

Introduction: Study of mutations with large phenotypic effects has allowed the identification of key players in skeletal development. However, the molecular nature of variation in large, phenotypically normal populations tends to be characterized by smaller phenotypic effects that remain undefined.

The evolution of phenotypic correlations and “developmental memory”

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent nonlinear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can “store” and “recall” multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and “generalize” (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviors follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well‐understood in the context of neural networks. This helps to explain how development facilitates the evolution of high‐fitness phenotypes and how this ability changes over evolutionary time.

On the relationship between ontogenetic and static allometry

Ontogenetic and static allometries describe how a character changes in size when the size of the organism changes during ontogeny and among individuals measured at the same developmental stage, respectively. Understanding the relationship between these two types of allometry is crucial to understanding the evolution of allometry and, more generally, the evolution of shape. However, the effects of ontogenetic allometry on static allometry remain largely unexplored. Here, we first show analytically how individual variation in ontogenetic allometry and body size affect static allometry. Using two longitudinal data sets on ontogenetic and static allometry, we then estimate variances and covariances for the different parameters of the ontogenetic allometry defined in our model and assess their relative contribution to the static allometric slope. The mean ontogenetic allometry is the main parameter that determines the static allometric slope, while the covariance between the ontogenetic allometric slope and body size generates most of the discrepancies between ontogenetic and static allometry. These results suggest that the apparent evolutionary stasis of the static allometric slope is not generated by internal (developmental) constraints but more likely results from external constraints imposed by selection.