Developmental processes regulate craniofacial variation in disease and evolution

Variation in development mediates phenotypic differences observed in evolution and disease. Although the mechanisms underlying phenotypic variation are still largely unknown, recent research suggests that variation in developmental processes may play a key role. Developmental processes mediate genotype–phenotype relationships and consequently play an important role regulating phenotypes. In this review, we provide an example of how shared and interacting developmental processes may explain convergence of phenotypes in spliceosomopathies and ribosomopathies. These data also suggest a shared pathway to disease treatment. We then discuss three major mechanisms that contribute to variation in developmental processes: genetic background (gene–gene interactions), gene–environment interactions, and developmental stochasticity. Finally, we comment on evolutionary alterations to developmental processes, and the evolution of disease buffering mechanisms.     

Network‐based global inference of human disease genes

Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome‐wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome‐wide prioritization of candidate genes for over 5000 human phenotypes, including those with under‐characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.     

Serotonin transporter gene,childhood emotional abuse and cognitive vulnerability to depression

Meta‐analyses evaluating the association between the serotonin transporter polymorphism (5‐HTTLPR) with neuroticism and depression diagnosis as phenotypes have been inconclusive. We examined a gene–environment interaction on a cognitive vulnerability marker of depression, cognitive reactivity (CR) to sad mood. A total of 250 university students of European ancestry were genotyped for the 5‐HTTLPR, including SNP rs25531, a polymorphism of the long allele. Association analysis was performed for neuroticism, CR and depression diagnosis (using a self‐report measure). As an environmental pathogen, self‐reported history of childhood emotional abuse was measured because of its strong relationship with depression. Participants with the homozygous low expressing genotype had high CR if they had experienced childhood emotional maltreatment but low CR if they did not have such experience. This interaction was strongest on the Rumination subscale of the CR measure. The interaction was not significant with neuroticism or depression diagnosis as outcome measures. Our results show that 5‐HTTLPR is related to cognitive vulnerability to depression. Our findings provide evidence for a differential susceptibility genotype rather than a vulnerability genotype, possibly because of the relatively low levels of abuse in our sample. The selection of phenotype and environmental contributor is pivotal in investigating gene–environment interactions in psychiatric disorders.     

Modellvorstellungen zur Genetik multifaktorieller Krankheiten

In contrast to monogenic diseases, a straightforward genotype–phenotype relationship is unlikely for multifactorial diseases because of a number of genetic and nongenetic factors, including genetic heterogeneity, gene–gene and gene–environment interactions, and epigenetic mechanisms. As a consequence, the relative risk of particular genetic variants will generally be small, which implies that large sample sizes are required for their initial identification. No conclusions as to the frequency and diversity of the causative genetic variation can generally be drawn from the prevalence of a disease alone. Homogenization of the genetic background of the study population and the use of simple and clearly defined phenotypes together with “educated guesses” in candidate gene and gene–environment studies appear to be the most promising way to identify the genetic factors underlying multifactorial diseases. Replication of initial disease association findings, particularly for rare variants, should be carried out in populations that are genetically as similar as possible to the original population.     

Lifetime development of behavioural phenotype in the house mouse (<Emphasis Type="Italic">Mus musculus</Emphasis>)

With each trajectory taken during the ontogeny of an individual, the number of optional behavioural phenotypes that can be expressed across its life span is reduced. The initial range of phenotypic plasticity is largely determined by the genetic material/composition of the gametes whereas interacting with the given environment shapes individuals to adapt to/cope with specific demands. In mammalian species, the phenotype is shaped as the foetus grows, depending on the environment in the uterus, which in turn depends on the outer environment the mother experiences during pregnancy. After birth, a complex interaction between innate constitution and environmental conditions shapes individual lifetime trajectories, bringing about a wide range of diversity among individual subjects.In laboratory mice inbreeding has been systematically induced in order to reduce the genetic variability between experimental subjects. In addition, within most laboratories conducting behavioural phenotyping with mice, breeding and housing conditions are highly standardised. Despite such standardisation efforts a considerable amount of variability persists in the behaviour of mice. There is good evidence that phenotypic variation is not merely random but might involve individual specific behavioural patterns consistent over time. In order to understand the mechanisms and the possible adaptive value of the maintenance of individuality we review the emergence of behavioural phenotypes over the course of the life of (laboratory) mice. We present a literature review summarizing developmental stages of behavioural development of mice along with three illustrative case studies. We conclude that the accumulation of environmental differences and experiences lead to a “mouse individuality” that becomes increasingly stable over the lifetime.     

The Evolution of Continuous Variation. III. Joint Transmission of Genotype, Phenotype and Environment

Evolutionary models of continuous traits are developed. The models are based on the ideas that: (1) the phenotype is the result of the interaction between genotype and environment; (2) the phenotype is the object of natural selection; (3) not only the genotype but also environmental variables and even phenotypes can be directly transmitted. The phenotype of an offspring at birth is a linear combination of its genotypic value, the phenotypic values of its parents, and their environmental values, all measured on the phenotypic scale. The genetic effects are additive polygenic, and a mutation contribution to the within family variance is admitted.—The values of the offspring phenotype and environment before selection are each linear combinations of these values at birth, the coefficients defining what we call "development." Selection is mostly stabilizing of the Gaussian type, but directional selection is introduced using a Gaussian fitness function with a large variance and a mean far from the current population.—Assortative mating for both phenotype and environment are considered. The analysis in all cases is made by iteration of the means, variances and covariances of the trivariate random variable (genotype, phenotype, environment) whose changes over time completely specify the evolution. In most cases numerical methods are used. The problems of estimating the relative roles of each of the variates in the parents in determining the variates in the offspring are discussed. The major results concern the relative magnitudes of the variances and correlations of the three variates, genotype, phenotype and environment, in a variety of selective, developmental and assorting situations with complex transmission in which G-(genetic), F-(phenotypic), E-(environment) inheritance mechanisms operate jointly. The transmission rules and development patterns (i.e., interactions between phenotype and environment during development) are of major importance in determining qualitative features of the equilibrium distribution.     

Social competition as a driver of phenotype–environment correlations: implications for ecology and evolution

While it is universally recognised that environmental factors can cause phenotypic trait variation via phenotypic plasticity, the extent to which causal processes operate in the reverse direction has received less consideration. In fact individuals are often active agents in determining the environments, and hence the selective regimes, they experience. There are several important mechanisms by which this can occur, including habitat selection and niche construction, that are expected to result in phenotype–environment correlations (i.e. non-random assortment of phenotypes across heterogeneous environments). Here we highlight an additional mechanism – intraspecific competition for preferred environments – that may be widespread, and has implications for phenotypic evolution that are currently underappreciated. Under this mechanism, variation among individuals in traits determining their competitive ability leads to phenotype–environment correlation; more competitive phenotypes are able to acquire better patches. Based on a concise review of the empirical evidence we argue that competition-induced phenotype–environment correlations are likely to be common in natural populations before highlighting the major implications of this for studies of natural selection and microevolution. We focus particularly on two central issues. First, competition-induced phenotype–environment correlation leads to the expectation that positive feedback loops will amplify phenotypic and fitness variation among competing individuals. As a result of being able to acquire a better environment, winners gain more resources and even better phenotypes – at the expense of losers. The distinction between individual quality and environmental quality that is commonly made by researchers in evolutionary ecology thus becomes untenable. Second, if differences among individuals in competitive ability are underpinned by heritable traits, competition results in both genotype–environment correlations and an expectation of indirect genetic effects (IGEs) on resource-dependent life-history traits. Theory tells us that these IGEs will act as (partial) constraints, reducing the amount of genetic variance available to facilitate evolutionary adaptation. Failure to recognise this will lead to systematic overestimation of the adaptive potential of populations. To understand the importance of these issues for ecological and evolutionary processes in natural populations we therefore need to identify and quantify competition-induced phenotype–environment correlations in our study systems. We conclude that both fundamental and applied research will benefit from an improved understanding of when and how social competition causes non-random distribution of phenotypes, and genotypes, across heterogeneous environments.     

The relative importance of plasticity versus genetic differentiation in explaining between population differences; a meta‐analysis

Both plasticity and genetic differentiation can contribute to phenotypic differences between populations. Using data on non‐fitness traits from reciprocal transplant studies, we show that approximately 60% of traits exhibit co‐gradient variation whereby genetic differences and plasticity‐induced differences between populations are the same sign. In these cases, plasticity is about twice as important as genetic differentiation in explaining phenotypic divergence. In contrast to fitness traits, the amount of genotype by environment interaction is small. Of the 40% of traits that exhibit counter‐gradient variation the majority seem to be hyperplastic whereby non‐native individuals express phenotypes that exceed those of native individuals. In about 20% of cases plasticity causes non‐native phenotypes to diverge from the native phenotype to a greater extent than if plasticity was absent, consistent with maladaptive plasticity. The degree to which genetic differentiation versus plasticity can explain phenotypic divergence varies a lot between species, but our proxies for motility and migration explain little of this variation.     

Evolvability and robustness in a complex signalling circuit

Biological systems at various levels of organisation exhibit robustness, as well as phenotypic variability or evolvability, the ability to evolve novel phenotypes. We still know very little about the relationship between robustness and phenotypic variability at levels of organisation beyond individual macromolecules, and especially for signalling circuits. Here, we examine multiple alternate topologies of the Saccharomyces cerevisiae target-of-rapamycin (TOR) signalling circuit, in order to understand the circuit's robustness and phenotypic variability. We consider each of the topological variants a genotype, a set of alternative interactions between TOR circuit components. Two genotypes are neighbours in genotype space if they can be reached from each other by a single small genetic change. Each genotype (topology) has a signalling phenotype, which we define via the concentration trajectories of key signalling molecules. We find that the circuits we study can produce almost 300 different phenotypes. The number of genotypes with a given phenotype varies very widely among these phenotypes. Some phenotypes have few associated genotypes. Others have many genotypes that form genotype networks extending far through genotype space. A minority of phenotypes accounts for the vast majority of genotypes. Importantly, we find that these phenotypes tend to have large genotype networks, greater robustness and a greater ability to produce novel phenotypes. Thus, over a broad range of phenotypic robustness, robustness facilitates phenotypic variability in our study system. Our observations show parallels to studies on macromolecules, suggesting that similar principles might govern robustness and phenotypic variability in biological systems. Our approach points a way towards mapping genotype spaces in complex circuitry, and it exposes some challenges such mapping faces.     

Unveiling network-based functional features through integration of gene expression into protein networks

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype–phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.     

Developmental and genetic effects on behavioral and life‐history traits in a field cricket

A fundamental goal of evolutionary ecology is to identify the sources underlying trait variation on which selection can act. Phenotypic variation will be determined by both genetic and environmental factors, and adaptive phenotypic plasticity is expected when organisms can adjust their phenotypes to match environmental cues. Much recent research interest has focused on the relative importance of environmental and genetic factors on the expression of behavioral traits, in particular, and how they compare with morphological and life‐history traits. Little research to date examines the effect of development on the expression of heritable variation in behavioral traits, such as boldness and activity. We tested for genotype, environment, and genotype‐by‐environment differences in body mass, development time, boldness, and activity, using developmental density treatments combined with a quantitative genetic design in the sand field cricket (Gryllus firmus). Similar to results from previous work, animals reared at high densities were generally smaller and took longer to mature, and body mass and development time were moderately heritable. In contrast, neither boldness nor activity responded to density treatments, and they were not heritable. The only trait that showed significant genotype‐by‐environment differences was development time. It is possible that adaptive behavioral plasticity is not evident in this species because of the highly variable social environments it naturally experiences. Our results illustrate the importance of validating the assumption that behavioral phenotype reflects genetic patterns and suggest questions about the role of environmental instability in trait variation and heritability.     

Iterative development and the scope for plasticity: contrasts among trait categories in an adaptive radiation

Phenotypic plasticity can influence evolutionary change in a lineage, ranging from facilitation of population persistence in a novel environment to directing the patterns of evolutionary change. As the specific nature of plasticity can impact evolutionary consequences, it is essential to consider how plasticity is manifested if we are to understand the contribution of plasticity to phenotypic evolution. Most morphological traits are developmentally plastic, irreversible, and generally considered to be costly, at least when the resultant phenotype is mis-matched to the environment. At the other extreme, behavioral phenotypes are typically activational (modifiable on very short time scales), and not immediately costly as they are produced by constitutive neural networks. Although patterns of morphological and behavioral plasticity are often compared, patterns of plasticity of life history phenotypes are rarely considered. Here we review patterns of plasticity in these trait categories within and among populations, comprising the adaptive radiation of the threespine stickleback fish Gasterosteus aculeatus. We immediately found it necessary to consider the possibility of iterated development, the concept that behavioral and life history trajectories can be repeatedly reset on activational (usually behavior) or developmental (usually life history) time frames, offering fine tuning of the response to environmental context. Morphology in stickleback is primarily reset only in that developmental trajectories can be altered as environments change over the course of development. As anticipated, the boundaries between the trait categories are not clear and are likely to be linked by shared, underlying physiological and genetic systems.     

How can distinct egg polymorphism be maintained in the rufescent prinia (Prinia rufescens)–plaintive cuckoo (Cacomantis merulinus) interaction—a modeling approach

In avian brood parasitism, both the host and the parasite are expected to develop various conflicting adaptations; hosts develop a defense against parasitism, such as an ability to recognize and reject parasitic eggs that look unlike their own, while parasites evolve egg mimicry to counter this host defense. Hosts may further evolve to generate various egg phenotypes that are not mimicked by parasites. Difference in egg phenotype critically affects the successful reproduction of hosts and parasites. Recent studies have shown that clear polymorphism in egg phenotype is observed in several host–parasite interactions, which suggests that egg polymorphism may be a more universal phenomenon than previously thought. We examined the mechanism for maintaining egg polymorphism in the rufescent prinia (Prinia rufescens) that is parasitized by the plaintive cuckoo (Cacomantis merulinus) from a theoretical viewpoint based on a mathematical model. The prinia has four distinct egg phenotypes: immaculate white, immaculate blue, white with spots, and blue with spots. Only two egg phenotypes, white with spots and blue with spots, are found in the cuckoo population. We show that the observed prinia and cuckoo phenotypes cannot be at an equilibrium and that egg polymorphism can be maintained either at stationary equilibrium or with dynamic, frequency oscillations, depending on the mutation rates of the background color and spottiness. Long‐term monitoring of the prinia–cuckoo interaction over a wide geographic range is needed to test the results of the model analyses.     

Inferring the potentially complex genetic architectures of adaptation,sexual dimorphism and genotype by environment interactions by partitioning of mean phenotypes

Genetic architecture fundamentally affects the way that traits evolve. However, the mapping of genotype to phenotype includes complex interactions with the environment or even the sex of an organism that can modulate the expressed phenotype. Line‐cross analysis is a powerful quantitative genetics method to infer genetic architecture by analysing the mean phenotype value of two diverged strains and a series of subsequent crosses and backcrosses. However, it has been difficult to account for complex interactions with the environment or sex within this framework. We have developed extensions to line‐cross analysis that allow for gene by environment and gene by sex interactions. Using extensive simulation studies and reanalysis of empirical data, we show that our approach can account for both unintended environmental variation when crosses cannot be reared in a common garden and can be used to test for the presence of gene by environment or gene by sex interactions. In analyses that fail to account for environmental variation between crosses, we find that line‐cross analysis has low power and high false‐positive rates. However, we illustrate that accounting for environmental variation allows for the inference of adaptive divergence, and that accounting for sex differences in phenotypes allows practitioners to infer the genetic architecture of sexual dimorphism.     

Evolution and molecular mechanisms of adaptive developmental plasticity

Aside from its selective role in filtering inter-individual variation during evolution by natural selection, the environment also plays an instructive role in producing variation during development. External environmental cues can influence developmental rates and/or trajectories and lead to the production of distinct phenotypes from the same genotype. This can result in a better match between adult phenotype and selective environment and thus represents a potential solution to problems posed by environmental fluctuation. The phenomenon is called adaptive developmental plasticity. The study of developmental plasticity integrates different disciplines (notably ecology and developmental biology) and analyses at all levels of biological organization, from the molecular regulation of changes in organismal development to variation in phenotypes and fitness in natural populations. Here, we focus on recent advances and examples from morphological traits in animals to provide a broad overview covering (i) the evolution of developmental plasticity, as well as its relevance to adaptive evolution, (ii) the ecological significance of alternative environmentally induced phenotypes, and the way the external environment can affect development to produce them, (iii) the molecular mechanisms underlying developmental plasticity, with emphasis on the contribution of genetic, physiological and epigenetic factors, and (iv) current challenges and trends, including the relevance of the environmental sensitivity of development to studies in ecological developmental biology, biomedicine and conservation biology.     

A comparison of genotype-phenotype maps for RNA and proteins

The relationship between the genotype (sequence) and the phenotype (structure) of macromolecules affects their ability to evolve new structures and functions. We here compare the genotype space organization of proteins and RNA molecules to identify differences that may affect this ability. To this end, we computationally study the genotype-phenotype relationship for short RNA and lattice proteins of a reduced monomer alphabet size, to make exhaustive analysis and direct comparison of their genotype spaces feasible. We find that many fewer protein molecules than RNA molecules fold, but they fold into many more structures than RNA. In consequence, protein phenotypes have smaller genotype networks whose member genotypes tend to be more similar than for RNA phenotypes. Neighborhoods in sequence space of a given radius around an RNA molecule contain more novel structures than for protein molecules. We compare this property to evidence from natural RNA and protein molecules, and conclude that RNA genotype space may be more conducive to the evolution of new structure phenotypes.     

Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis

Summary With increasing frequency, epidemiologic studies are addressing hypotheses regarding gene‐environment interaction. In many well‐studied candidate genes and for standard dietary and behavioral epidemiologic exposures, there is often substantial prior information available that may be used to analyze current data as well as for designing a new study. In this article, first, we propose a proper full Bayesian approach for analyzing studies of gene–environment interaction. The Bayesian approach provides a natural way to incorporate uncertainties around the assumption of gene–environment independence, often used in such an analysis. We then consider Bayesian sample size determination criteria for both estimation and hypothesis testing regarding the multiplicative gene–environment interaction parameter. We illustrate our proposed methods using data from a large ongoing case–control study of colorectal cancer investigating the interaction of N‐acetyl transferase type 2 (NAT2) with smoking and red meat consumption. We use the existing data to elicit a design prior and show how to use this information in allocating cases and controls in planning a future study that investigates the same interaction parameters. The Bayesian design and analysis strategies are compared with their corresponding frequentist counterparts.     

A chemical–genetic interaction map of small molecules using high‐throughput imaging in cancer cells

Small molecules often affect multiple targets, elicit off‐target effects, and induce genotype‐specific responses. Chemical genetics, the mapping of the genotype dependence of a small molecule's effects across a broad spectrum of phenotypes can identify novel mechanisms of action. It can also reveal unanticipated effects and could thereby reduce high attrition rates of small molecule development pipelines. Here, we used high‐content screening and image analysis to measure effects of 1,280 pharmacologically active compounds on complex phenotypes in isogenic cancer cell lines which harbor activating or inactivating mutations in key oncogenic signaling pathways. Using multiparametric chemical–genetic interaction analysis, we observed phenotypic gene–drug interactions for more than 193 compounds, with many affecting phenotypes other than cell growth. We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC), which enables exploration of drug mode of action, detection of potential off‐target effects, and the generation of hypotheses on drug combinations and synergism. For example, we demonstrate that MEK inhibitors amplify the viability effect of the clinically used anti‐alcoholism drug disulfiram and show that the EGFR inhibitor tyrphostin AG555 has off‐target activity on the proteasome. Taken together, this study demonstrates how combining multiparametric phenotyping in different genetic backgrounds can be used to predict additional mechanisms of action and to reposition clinically used drugs.     

A unified statistical model for functional mapping of environment-dependent genetic expression and genotype x environment interactions for ontogenetic development

The effects of quantitative trait loci (QTL) on phenotypic development may depend on the environment (QTL x environment interaction), other QTL (genetic epistasis), or both. In this article, we present a new statistical model for characterizing specific QTL that display environment-dependent genetic expressions and genotype x environment interactions for developmental trajectories. Our model was derived within the maximum-likelihood-based mixture model framework, incorporated by biologically meaningful growth equations and environment-dependent genetic effects of QTL, and implemented with the EM algorithm. With this model, we can characterize the dynamic patterns of genetic effects of QTL governing growth curves and estimate the global effect of the underlying QTL during the course of growth and development. In a real example with rice, our model has successfully detected several QTL that produce differences in their genetic expression between two contrasting environments. These detected QTL cause significant genotype x environment interactions for some fundamental aspects of growth trajectories. The model provides the basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments and genetic relationships for growth rates and the timing of life-history events for any organism.