Robustness and evolvability: a paradox resolved

Understanding the relationship between robustness and evolvability is key to understand how living things can withstand mutations, while producing ample variation that leads to evolutionary innovations. Mutational robustness and evolvability, a system's ability to produce heritable variation, harbour a paradoxical tension. On one hand, high robustness implies low production of heritable phenotypic variation. On the other hand, both experimental and computational analyses of neutral networks indicate that robustness enhances evolvability. I here resolve this tension using RNA genotypes and their secondary structure phenotypes as a study system. To resolve the tension, one must distinguish between robustness of a genotype and a phenotype. I confirm that genotype (sequence) robustness and evolvability share an antagonistic relationship. In stark contrast, phenotype (structure) robustness promotes structure evolvability. A consequence is that finite populations of sequences with a robust phenotype can access large amounts of phenotypic variation while spreading through a neutral network. Population-level processes and phenotypes rather than individual sequences are key to understand the relationship between robustness and evolvability. My observations may apply to other genetic systems where many connected genotypes produce the same phenotypes.     

Environmental and genetic cues in the evolution of phenotypic polymorphism

Phenotypic polymorphism is a consequence of developmental plasticity, in which the trajectories of developing organisms diverge under the influence of cues. Environmental and genetic phenotype determination are the two main categories of polymorphic development. Even though both may evolve as a response to varied environments, they are traditionally regarded as fundamentally distinct phenomena. They can however be joined into a single framework that emphasizes the parallel roles of environmental and genetic cues in phenotype determination. First, from the point of view of immediate causation, it is common that phenotypic variants can be induced either by environmental or by allelic variation, and this is referred to as gene-environment interchangeability. Second, from the point of view of adaptation, genetic cues in the form of allelic variation at polymorphic loci can play similar roles as environmental cues in providing information to the developmental system about coming selective conditions. Both types of cues can help a developing organism to fit its phenotype to selective circumstances. This perspective of information in environmental and genetic cues can produce testable hypotheses about phenotype determination, and can thus increase our understanding of the evolution of phenotypic polymorphism.     

EXPERIMENTAL EVIDENCE FOR THE EVOLUTION OF INDIRECT GENETIC EFFECTS: CHANGES IN THE INTERACTION EFFECT COEFFICIENT,PSI (Ψ), DUE TO SEXUAL SELECTION

Indirect genetics effects (IGEs)—when the genotype of one individual affects the phenotypic expression of a trait in another—may alter evolutionary trajectories beyond that predicted by standard quantitative genetic theory as a consequence of genotypic evolution of the social environment. For IGEs to occur, the trait of interest must respond to one or more indicator traits in interacting conspecifics. In quantitative genetic models of IGEs, these responses (reaction norms) are termed interaction effect coefficients and are represented by the parameter psi (Ψ). The extent to which Ψ exhibits genetic variation within a population, and may therefore itself evolve, is unknown. Using an experimental evolution approach, we provide evidence for a genetic basis to the phenotypic response caused by IGEs on sexual display traits in Drosophila serrata. We show that evolution of the response is affected by sexual but not natural selection when flies adapt to a novel environment. Our results indicate a further mechanism by which IGEs can alter evolutionary trajectories—the evolution of interaction effects themselves.     

The reliability theory of aging and longevity.

Reliability theory is a general theory about systems failure. It allows researchers to predict the age-related failure kinetics for a system of given architecture (reliability structure) and given reliability of its components. Reliability theory predicts that even those systems that are entirely composed of non-aging elements (with a constant failure rate) will nevertheless deteriorate (fail more often) with age, if these systems are redundant in irreplaceable elements. Aging, therefore, is a direct consequence of systems redundancy. Reliability theory also predicts the late-life mortality deceleration with subsequent leveling-off, as well as the late-life mortality plateaus, as an inevitable consequence of redundancy exhaustion at extreme old ages. The theory explains why mortality rates increase exponentially with age (the Gompertz law) in many species, by taking into account the initial flaws (defects) in newly formed systems. It also explains why organisms "prefer" to die according to the Gompertz law, while technical devices usually fail according to the Weibull (power) law. Theoretical conditions are specified when organisms die according to the Weibull law: organisms should be relatively free of initial flaws and defects. The theory makes it possible to find a general failure law applicable to all adult and extreme old ages, where the Gompertz and the Weibull laws are just special cases of this more general failure law. The theory explains why relative differences in mortality rates of compared populations (within a given species) vanish with age, and mortality convergence is observed due to the exhaustion of initial differences in redundancy levels. Overall, reliability theory has an amazing predictive and explanatory power with a few, very general and realistic assumptions. Therefore, reliability theory seems to be a promising approach for developing a comprehensive theory of aging and longevity integrating mathematical methods with specific biological knowledge.     

Genetic analysis of life-history constraint and evolution in a wild ungulate population

Trade-offs among life-history traits are central to evolutionary theory. In quantitative genetic terms, trade-offs may be manifested as negative genetic covariances relative to the direction of selection on phenotypic traits. Although the expression and selection of ecologically important phenotypic variation are fundamentally multivariate phenomena, the in situ quantification of genetic covariances is challenging. Even for life-history traits, where well-developed theory exists with which to relate phenotypic variation to fitness variation, little evidence exists from in situ studies that negative genetic covariances are an important aspect of the genetic architecture of life-history traits. In fact, the majority of reported estimates of genetic covariances among life-history traits are positive. Here we apply theory of the genetics and selection of life histories in organisms with complex life cycles to provide a framework for quantifying the contribution of multivariate genetically based relationships among traits to evolutionary constraint. We use a Bayesian framework to link pedigree-based inference of the genetic basis of variation in life-history traits to evolutionary demography theory regarding how life histories are selected. Our results suggest that genetic covariances may be acting to constrain the evolution of female life-history traits in a wild population of red deer Cervus elaphus: genetic covariances are estimated to reduce the rate of adaptation by about 40%, relative to predicted evolutionary change in the absence of genetic covariances. Furthermore, multivariate phenotypic (rather than genetic) relationships among female life-history traits do not reveal this constraint.     

The long-term evolution of multilocus traits under frequency-dependent disruptive selection

Frequency-dependent disruptive selection is widely recognized as an important source of genetic variation. Its evolutionary consequences have been extensively studied using phenotypic evolutionary models, based on quantitative genetics, game theory, or adaptive dynamics. However, the genetic assumptions underlying these approaches are highly idealized and, even worse, predict different consequences of frequency-dependent disruptive selection. Population genetic models, by contrast, enable genotypic evolutionary models, but traditionally assume constant fitness values. Only a minority of these models thus addresses frequency-dependent selection, and only a few of these do so in a multilocus context. An inherent limitation of these remaining studies is that they only investigate the short-term maintenance of genetic variation. Consequently, the long-term evolution of multilocus characters under frequency-dependent disruptive selection remains poorly understood. We aim to bridge this gap between phenotypic and genotypic models by studying a multilocus version of Levene's soft-selection model. Individual-based simulations and deterministic approximations based on adaptive dynamics theory provide insights into the underlying evolutionary dynamics. Our analysis uncovers a general pattern of polymorphism formation and collapse, likely to apply to a wide variety of genetic systems: after convergence to a fitness minimum and the subsequent establishment of genetic polymorphism at multiple loci, genetic variation becomes increasingly concentrated on a few loci, until eventually only a single polymorphic locus remains. This evolutionary process combines features observed in quantitative genetics and adaptive dynamics models, and it can be explained as a consequence of changes in the selection regime that are inherent to frequency-dependent disruptive selection. Our findings demonstrate that the potential of frequency-dependent disruptive selection to maintain polygenic variation is considerably smaller than previously expected.     

Widespread correlations between dominance and homozygous effects of mutations: implications for theories of dominance

The dominance of deleterious mutations has important consequences for phenomena such as inbreeding depression, the evolution of diploidy, and levels of natural genetic variation. Kacser and Burns' metabolic theory provides a paradigmatic explanation for why most large-effect mutations are recessive. According to the metabolic theory, the recessivity of large-effect mutations is a consequence of a diminishing-returns relationship between flux through a metabolic pathway and enzymatic activity at any step in the pathway, which in turn is an inevitable consequence of long metabolic pathways. A major line of support for this theory was the demonstration of a negative correlation between homozygous effects and dominance of mutations in Drosophila, consistent with a central prediction of the metabolic theory. Using data on gene deletions in yeast, we show that a negative correlation between homozygous effects and dominance of mutations exists for all major categories of genes analyzed, not just those encoding enzymes. The relationship between dominance and homozygous effects is similar for duplicated and single-copy genes and for genes whose products are members of protein complexes and those that are not. A complete explanation of dominance therefore requires either a generalization of Kacser and Burns' theory to nonenzyme genes or a new theory.     

A heuristic model on the role of plasticity in adaptive evolution: plasticity increases adaptation,population viability and genetic variation

An ongoing new synthesis in evolutionary theory is expanding our view of the sources of heritable variation beyond point mutations of fixed phenotypic effects to include environmentally sensitive changes in gene regulation. This expansion of the paradigm is necessary given ample evidence for a heritable ability to alter gene expression in response to environmental cues. In consequence, single genotypes are often capable of adaptively expressing different phenotypes in different environments, i.e. are adaptively plastic. We present an individual-based heuristic model to compare the adaptive dynamics of populations composed of plastic or non-plastic genotypes under a wide range of scenarios where we modify environmental variation, mutation rate and costs of plasticity. The model shows that adaptive plasticity contributes to the maintenance of genetic variation within populations, reduces bottlenecks when facing rapid environmental changes and confers an overall faster rate of adaptation. In fluctuating environments, plasticity is favoured by selection and maintained in the population. However, if the environment stabilizes and costs of plasticity are high, plasticity is reduced by selection, leading to genetic assimilation, which could result in species diversification. More broadly, our model shows that adaptive plasticity is a common consequence of selection under environmental heterogeneity, and hence a potentially common phenomenon in nature. Thus, taking adaptive plasticity into account substantially extends our view of adaptive evolution.     

Moose body mass variation revisited: disentangling effects of environmental conditions and genetics

Large-scale geographical variation in phenotypic traits within species is often correlated to local environmental conditions and population density. Such phenotypic variation has recently been shown to also be influenced by genetic structuring of populations. In ungulates, large-scale geographical variation in phenotypic traits, such as body mass, has been related to environmental conditions and population density, but little is known about the genetic influences. Research on the genetic structure of moose suggests two distinct genetic lineages in Norway, structured along a north-south gradient. This corresponds with many environmental gradients, thus genetic structuring provides an additional factor affecting geographical phenotypic variation in Norwegian moose. We investigated if genetic structure explained geographical variation in body mass in Norwegian moose while accounting for environmental conditions, age and sex, and if it captured some of the variance in body mass that previously was attributed to environmental factors. Genetic structuring of moose was the most important variable in explaining the geographic variation in body mass within age and sex classes. Several environmental variables also had strong explanatory power, related to habitat diversity, environmental seasonality and winter harshness. The results suggest that environmental conditions, landscape characteristics, and genetic structure should be evaluated together when explaining large-scale patterns in phenotypic characters or life history traits. However, to better understand the role of genetic and environmental effects on phenotypic traits in moose, an extended individual-based study of variation in fitness-related characters is needed, preferably in an area of convergence between different genetic lineages.     

Accounting for the nested nature of genetic variation across levels of organization improves our understanding of biodiversity and community ecology

Recent work has demonstrated that the presence or abundance of specific genotypes, populations, species and phylogenetic clades may influence community and ecosystem properties such as resilience or productivity. Many ecological studies, however, use simple linear models to test for such relationships, including species identity as the predictor variable and some measured trait or function as the response variable without accounting for the nestedness of genetic variation across levels of organization. This omission may lead to incorrect inference about which source of variation influences community and ecosystem properties. Here, we explicitly compare this common approach to alternative ways of modeling variation in trait data, using simulated trait data and empirical results of common‐garden trials using multiple levels of genetic variation within Eucalyptus, Populus and Picea. We show that: 1) when nested variation is ignored, an incorrect conclusion of species effect is drawn in up to 20% of cases; 2) overestimation of the species effect increases – up to 60% in some scenarios – as the nested term explains more of the variation; and 3) the sample sizes needed to overcome these potential problems associated with aggregating nested hierarchical variation may be impractically large. In common‐garden trials, incorporating nested models increased explanatory power twofold for mammal browsing rate in Eucalyptus, threefold for leaf area in Populus, and tenfold for branch number in Picea. Thoroughly measuring intraspecific variation and characterizing hierarchical genetic variation beyond the species level has implications for developing more robust theory in community ecology, managing invaded natural systems, and improving inference in biodiversity–ecosystem functioning research. Synthesis Until recently, ecologists acknowledged the ubiquity of within‐species trait variation, but paid scant attention to how much it affects communities and ecosystems. Here, the authors used simulated trait data and common‐garden studies to demonstrate that we ignore intraspecific trait variation at our peril. In both simulated and experimental systems, in many cases ignoring intraspecific variation led to incorrect statistical inferences and inflated the effect size of species identity. This study shows that ecologists must characterize hierarchically nested genetic and phenotypic variation to fully understand the links between individual traits, community structure and ecosystem functioning.     

Variation and constraint in plant evolution and development

The goal of this short review is to consider the interrelated phenomena of phenotypic variation and genetic constraint with respect to plant diversity. The unique aspects of plants, including sessile habit, modular growth and diverse developmental programs expressed at the phytomer level, merit a specific examination of the genetic basis of their phenotypic variation, and how they experience and escape genetic constraint. Numerous QTL studies with wild and domesticated plants reveal that most phenotypic traits are polygenic but vary in the number and effect of the loci contributing, from a few loci of large effects to many with small effects. Further, somatic mutations, developmental plasticity and epigenetic variation, especially gene methylation, can contribute to increases in phenotypic variation. The flip side of these processes, genetic constraint, can similarly be the result of many factors, including pleiotropy, canalization and genetic redundancy. Genetic constraint is not only a mechanism to prevent change, however, it can also serve to direct evolution along certain paths. Ultimately, genetic constraint often comes full circle and is released through events such as hybridization, genome duplication and epigenetic remodeling. We are just beginning to understand how these processes can operate simultaneously during the evolution of ecologically important traits in plants.     

Phenotypic Variation in Infants,Not Adults,Reflects Genotypic Variation among Chimpanzees and Bonobos

Studies comparing phenotypic variation with neutral genetic variation in modern humans have shown that genetic drift is a main factor of evolutionary diversification among populations. The genetic population history of our closest living relatives, the chimpanzees and bonobos, is now equally well documented, but phenotypic variation among these taxa remains relatively unexplored, and phenotype-genotype correlations are not yet documented. Also, while the adult phenotype is typically used as a reference, it remains to be investigated how phenotype-genotye correlations change during development. Here we address these questions by analyzing phenotypic evolutionary and developmental diversification in the species and subspecies of the genus Pan. Our analyses focus on the morphology of the femoral diaphysis, which represents a functionally constrained element of the locomotor system. Results show that during infancy phenotypic distances between taxa are largely congruent with non-coding (neutral) genotypic distances. Later during ontogeny, however, phenotypic distances deviate from genotypic distances, mainly as an effect of heterochronic shifts between taxon-specific developmental programs. Early phenotypic differences between Pan taxa are thus likely brought about by genetic drift while late differences reflect taxon-specific adaptations.     

Out of the bottleneck: the Diversity Outcross and Collaborative Cross mouse populations in behavioral genetics research

The historical origins of classical laboratory mouse strains have led to a relatively limited range of genetic and phenotypic variation, particularly for the study of behavior. Many recent efforts have resulted in improved diversity and precision of mouse genetic resources for behavioral research, including the Collaborative Cross and Diversity Outcross population. These two populations, derived from an eight way cross of common and wild-derived strains, have high precision and allelic diversity. Behavioral variation in the population is expanded, both qualitatively and quantitatively. Variation that had once been canalized among the various inbred lines has been made amenable to genetic dissection. The genetic attributes of these complementary populations, along with advances in genetic and genomic technologies, makes a systems genetic analyses of behavior more readily tractable, enabling discovery of a greater range of neurobiological phenomena underlying behavioral variation.     

Why are behavioral and immune traits linked?

Through behavior, animals interact with a world where parasites abound. It is easy to understand how behavioral traits can thus have a differential effect on pathogen exposure. Harder to understand is why we observe behavioral traits to be linked to immune defense traits. Is variation in immune traits a consequence of behavior-induced variation in immunological experiences? Or is variation in behavioral traits a function of immune capabilities? Is our immune system a much bigger driver of personality than anticipated? In this review, I provide examples of how behavioral and immune traits co-vary. I then explore the different routes linking behavioral and immune traits, emphasizing on the physiological/hormonal mechanisms that could lead to immune control of behavior. Finally, I discuss why we should aim at understanding more about the mechanisms connecting these phenotypic traits.     

Statistical epistasis is a generic feature of gene regulatory networks

Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation.     

Deciphering the Palimpsest: Studying the Relationship Between Morphological Integration and Phenotypic Covariation

Organisms represent a complex arrangement of anatomical structures and individuated parts that must maintain functional associations through development. This integration of variation between functionally related body parts and the modular organization of development are fundamental determinants of their evolvability. This is because integration results in the expression of coordinated variation that can create preferred directions for evolutionary change, while modularity enables variation in a group of traits or regions to accumulate without deleterious effects on other aspects of the organism. Using our own work on both model systems (e.g., lab mice, avians) and natural populations of rodents and primates, we explore in this paper the relationship between patterns of phenotypic covariation and the developmental determinants of integration that those patterns are assumed to reflect. We show that integration cannot be reliably studied through phenotypic covariance patterns alone and argue that the relationship between phenotypic covariation and integration is obscured in two ways. One is the superimposition of multiple determinants of covariance in complex systems and the other is the dependence of covariation structure on variances in covariance-generating processes. As a consequence, we argue that the direct study of the developmental determinants of integration in model systems is necessary to fully interpret patterns of covariation in natural populations, to link covariation patterns to the processes that generate them, and to understand their significance for evolutionary explanation.     

Panpsychic Organicism: Sewall Wright’s Philosophy for Understanding Complex Genetic Systems

Sewall Wright first encountered the complex systems characteristic of gene combinations while a graduate student at Harvard’s Bussey Institute from 1912 to 1915. In Mendelian breeding experiments, Wright observed a hierarchical dependence of the organism’s phenotype on dynamic networks of genetic interaction and organization. An animal’s physical traits, and thus its autonomy from surrounding environmental constraints, depended greatly on how genes behaved in certain combinations. Wright recognized that while genes are the material determinants of the animal phenotype, operating with great regularity, the special nature of genetic systems contributes to the animal phenotype a degree of spontaneity and novelty, creating unpredictable trait variations by virtue of gene interactions. As a result of his experimentation, as well as his keen interest in the philosophical literature of his day, Wright was inspired to see genetic systems as conscious, living organisms in their own right. Moreover, he decided that since genetic systems maintain ordered stability and cause unpredictable novelty in their organic wholes (the animal phenotype), it would be necessary for biologists to integrate techniques for studying causally ordered phenomena (experimental method) and chance phenomena (correlation method). From 1914 to 1921 Wright developed his “method of path coefficient” (or “path analysis”), a new procedure drawing from both laboratory experimentation and statistical correlation in order to analyze the relative influence of specific genetic interactions on phenotype variation. In this paper I aim to show how Wright’s philosophy for understanding complex genetic systems (panpsychic organicism) logically motivated his 1914–1921 design of path analysis.     

Natural and Unanticipated Modifiers of RNAi Activity in Caenorhabditis elegans

Organisms used as model genomics systems are maintained as isogenic strains, yet evidence of sequence differences between independently maintained wild-type stocks has been substantiated by whole-genome resequencing data and strain-specific phenotypes. Sequence differences may arise from replication errors, transposon mobilization, meiotic gene conversion, or environmental or chemical assault on the genome. Low frequency alleles or mutations with modest effects on phenotypes can contribute to natural variation, and it has proven possible for such sequences to become fixed by adapted evolutionary enrichment and identified by resequencing. Our objective was to identify and analyze single locus genetic defects leading to RNAi resistance in isogenic strains of Caenorhabditis elegans. In so doing, we uncovered a mutation that arose de novo in an existing strain, which initially frustrated our phenotypic analysis. We also report experimental, environmental, and genetic conditions that can complicate phenotypic analysis of RNAi pathway defects. These observations highlight the potential for unanticipated mutations, coupled with genetic and environmental phenomena, to enhance or suppress the effects of known mutations and cause variation between wild-type strains.