Modularity has emerged as a central concept for evolutionary biology, providing the field with a theory of organismal structure and variation. This theory has reframed long standing questions and serves as a unified conceptual framework for genetics, developmental biology and multivariate evolution. Research programs in systems biology and quantitative genetics are bridging the gap between these fields. While this synthesis is ongoing, some major themes have emerged and empirical evidence for modularity has become abundant. In this review, we look at modularity from an historical perspective, highlighting its meaning at different levels of biological organization and the different methods that can be used to detect it. We then explore the relationship between quantitative genetic approaches to modularity and developmental genetic studies. We conclude by investigating the dynamic relationship between modularity and the adaptive landscape and how this potentially shapes evolution and can help bridge the gap between micro- and macroevolution.

Modularity is a central concept in modern biology, providing a powerful framework for the study of living organisms on many organizational levels. Two central and related questions can be posed in regard to modularity: How does modularity appear in the first place, and what forces are responsible for keeping and/or changing modular patterns? We approached these questions using a quantitative genetics simulation framework, building on previous results obtained with bivariate systems and extending them to multivariate systems. We developed an individual-based model capable of simulating many traits controlled by many loci with variable pleiotropic relations between them, expressed in populations subject to mutation, recombination, drift, and selection. We used this model to study the problem of the emergence of modularity, and hereby show that drift and stabilizing selection are inefficient at creating modular variational structures. We also demonstrate that directional selection can have marked effects on the modular structure between traits, actively promoting a restructuring of genetic variation in the selected population and potentially facilitating the response to selection. Furthermore, we give examples of complex covariation created by simple regimes of combined directional and stabilizing selection and show that stabilizing selection is important in the maintenance of established covariation patterns. Our results are in full agreement with previous results for two-trait systems and further extend them to include scenarios of greater complexity. Finally, we discuss the evolutionary consequences of modular patterns being molded by directional selection.variational modularity | G-matrix | quantitative genetics | pleiotropy | phenotypic correlations M odularity, the organizational pattern found in many organisms, can be defined as the tendency for some parts to be more associated with each other than with other parts of the same organism. This type of modular organization can manifest at many levels, for example, between the bases of an RNA molecule (1), in the interaction between proteins (2), or in the covariance structure of continuous morphological traits (3). In each case, the type of association is different, but the modular pattern remains (4). Traits measured on a continuous scale that covary with each other can frequently be divided into variational modules. A variational module is characterized by higher correlations between traits in the same module and lower correlations between traits of different modules.Modularity is an important concept in understanding the evolution of many biological systems. At the individual level, tension in producing a full, coherent organism, and having each of its parts performing a separate task subject to different selective pressures and requirements, shapes the association between traits (5). At the same time, the existing pattern of association also influences the response to a given selective pressure: Traits that are associated tend to change together (6, 7). The...

Most biological systems are formed by component parts that are to some degree interrelated. Groups of parts that are more associated among themselves and are relatively autonomous from others are called modules. One of the consequences of modularity is that biological systems usually present an unequal distribution of the genetic variation among traits. Estimating the covariance matrix that describes these systems is a difficult problem due to a number of factors such as poor sample sizes and measurement errors. We show that this problem will be exacerbated whenever matrix inversion is required, as in directional selection reconstruction analysis. We explore the consequences of varying degrees of modularity and signal-to-noise ratio on selection reconstruction. We then present and test the efficiency of available methods for controlling noise in matrix estimates.In our simulations, controlling matrices for noise vastly improves the reconstruction of selection gradients. We also perform an analysis of selection gradients reconstruction over a New World Monkeys skull database to illustrate the impact of noise on such analyses. Noise-controlled estimates render far more plausible interpretations that are in full agreement with previous results. K E Y W O R D S :Adaptation, models/simulations, morphological evolution, pleiotropy, quantitative genetics, selection-natural.The study of biological systems and its component parts, whether molecules, cells, tissues, organisms and its forming parts, and even species and their interactions, is rapidly converging to the central theme of modularity. This refers to the connections among some of the component parts of a biological system (genes or morphological traits, for example) and the lack of such associations among other parts of the same system. (Olson and Miller 1958;Berg 1960;Wagner et al. 2007). The notion that interacting parts are not independent is intuitive and appears early in the history of Biology (see Mayr 1982). Therefore, modularity is quickly becoming one of the central questions in modern biology (Wagner et al. 2007;Klingenberg 2008) and a point of convergence of various specialties and areas (Mathematics, Statistics, Genetics and Genomics, Evolutionary Biology, Ecology, Biochemistry, and Physiology).In biology, several types of modules have been recognized, including (1) functional, consisting of characters or features that act together on performing a task or function and are quasiautonomous in relation to other functional sets; (2) developmental, which corresponds to parts of an embryo that are relatively autonomous with respect to pattern formation and differentiation, or an autonomous signaling cascade; (3) variational, composed of characters that vary together and are relatively independent of other such sets (Wagner et al. 2007).The study of modularity is centered on statistical estimation of association among traits (Olson and Miller 1958;Berg 1960). Whether such association is measured by correlation, covariance, or distance/similarity measures, i...

SummaryCooperation is ubiquitous across the tree of life, from simple microbes to the complex social systems of animals [1]. Individuals cooperate by engaging in costly behaviors that can be exploited by other individuals who benefit by avoiding these associated costs. Thus, if successful exploitation of social partners during cooperative interactions increases relative fitness, then we expect selection to lead to the emergence of a single optimal winning strategy in which individuals maximize their gain from cooperation while minimizing their associated costs [2]. Such social “cheating” appears to be widespread in nature [3], including in several microbial systems [4–11], but despite the fitness advantages favoring social cheating, populations tend to harbor significant variation in social success rather than a single optimal winning strategy. Using the social amoeba Dictyostelium discoideum, we provide a possible explanation for the coexistence of such variation. We find that genotypes typically designated as “cheaters” [12] because they produce a disproportionate number of spores in chimeric fruiting bodies do not actually gain higher fitness as a result of this apparent advantage because they produce smaller, less viable spores than putative “losers.” As a consequence of this trade-off between spore number and viability, genotypes with different spore production strategies, which give the appearance of differential social success, ultimately have similar realized fitness. These findings highlight the limitations of using single fitness proxies in evolutionary studies and suggest that interpreting social trait variation in terms of strategies like cheating or cooperating may be misleading unless these behaviors are considered in the context of the true multidimensional nature of fitness.

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the EvolQG package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification.