Environmentally induced changes in correlated responses to selection reveal variable pleiotropy across a complex genetic network

Bite Force Performance from wild Derived mice has Undetectable Heritability Despite Having Heritable Morphological Components

Fitness-related traits tend to have low heritabilities. Conversely, morphology tends to be highly heritable. Yet, many fitness-related performance traits such as running speed or bite force depend critically on morphology. Craniofacial morphology correlates with bite performance in several groups including rodents. However, within species, this relationship is less clear, and the genetics of performance, morphology and function are rarely analyzed in combination. Here, we use a half-sib design in outbred wild-derived Mus musculus to study the morphology-bite force relationship and determine whether there is additive genetic (co-)variance for these traits. Results suggest that bite force has undetectable additive genetic variance and heritability in this sample, while morphological traits related mechanically to bite force exhibit varying levels of heritability. The most heritable traits include the length of the mandible which relates to bite force. Despite its correlation with morphology, realized bite force was not heritable, which suggests it is less responsive to selection in comparison to its morphological determinants. We explain this paradox with a non-additive, many-to-one mapping hypothesis of heritable change in complex traits. We furthermore propose that performance traits could evolve if pleiotropic relationships among the determining traits are modified.

Yield of soybean genotypes identified through GGE biplot and path analysis

Genotype × environment (G×E) interaction is an important source of variation in soybean yield, which can significantly influence selection in breeding programs. This study aimed to select superior soybean genotypes for performance and yield stability, from data from multi-environment trials (METs), through GGE biplot analysis that combines the main effects of the genotype (G) plus the genotype-by-environment (G×E) interaction. As well as, through path analysis, determine the direct and indirect influences of yield components on soybean grain yield, as a genotype selection strategy. Eight soybean genotypes from the breeding program of Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) were evaluated in field trials using a randomized block experimental design, in an 8 x 8 factorial scheme with four replications in eight different environments of the Cerrado of Northeastern Brazil during two crop seasons. Phenotypic performance data were measured for the number of days to flowering (NDF), height of first pod insertion (HPI), final plant height (FPH), number of days to maturity (NDM), mass of 100 grains (M100) and grain yield (GY). The results revealed that the variance due to genotype, environment, and G×E interaction was highly significant (P < 0.001) for all traits. The ST820RR, BRS 333RR, BRS SambaíbaRR, M9144RR and M9056RR genotypes exhibited the greatest GY stability in the environments studied. However, only the BRS 333RR genotype, followed by the M9144RR, was able to combine good productive performance with high yield stability. The study also revealed that the HPI and the NDM are traits that should be prioritized in the selection of soybean genotypes due to the direct and indirect effects on the GY.

A neglected conceptual problem regarding phenotypic plasticity's role in adaptive evolution: The importance of genetic covariance and social drive

There is tantalizing evidence that phenotypic plasticity can buffer novel, adaptive genetic variants long enough to permit their evolutionary spread, and this process is often invoked in explanations for rapid adaptive evolution. However, the strength and generality of evidence for it is controversial. We identify a conceptual problem affecting this debate: recombination, segregation, and independent assortment are expected to quickly sever associations between genes controlling novel adaptations and genes contributing to trait plasticity that facilitates the novel adaptations by reducing their indirect fitness costs. To make clearer predictions about this role of plasticity in facilitating genetic adaptation, we describe a testable genetic mechanism that resolves the problem: genetic covariance between new adaptive variants and trait plasticity that facilitates their persistence within populations. We identify genetic architectures that might lead to such a covariance, including genetic coupling via physical linkage and pleiotropy, and illustrate the consequences for adaptation rates using numerical simulations. Such genetic covariances may also arise from the social environment, and we suggest the indirect genetic effects that result could further accentuate the process of adaptation. We call the latter mechanism of adaptation social drive, and identify methods to test it. We suggest that genetic coupling of plasticity and adaptations could promote unusually rapid ‘runaway’ evolution of novel adaptations. The resultant dynamics could facilitate evolutionary rescue, adaptive radiations, the origin of novelties, and other commonly studied processes.

Complex pleiotropic genetic architecture of evolved heat stress and oxidative stress resistance in the nematode Caenorhabditis remanei

The adaptation of complex organisms to changing environments has been a central question in evolutionary quantitative genetics since its inception. The structure of the genotype-phenotype maps is critical because pleiotropic effects can generate widespread correlated responses to selection and potentially restrict the extent of evolutionary change. In this study, we use experimental evolution to dissect the genetic architecture of natural variation for acute heat stress and oxidative stress response in the nematode Caenorhabiditis remanei. Previous work in the classic model nematode Caenorhabiditis elegans has found that abiotic stress response is controlled by a handful of genes of major effect and that mutations in any one of these genes can have widespread pleiotropic effects on multiple stress response traits. Here, we find that acute heat stress response and acute oxidative response in C. remanei are polygenic, complex traits, with hundreds of genomic regions responding to selection. In contrast to expectation from mutation studies, we find that evolved acute heat stress and acute oxidative stress response for the most part display independent genetic bases. This lack of correlation is reflected at the levels of phenotype, gene expression, and in the genomic response to selection. Thus, while these findings support the general view that rapid adaptation can be generated by changes at hundreds to thousands of sites in the genome, the architecture of segregating variation is likely to be determined by the pleiotropic structure of the underlying genetic networks.

Towards a unified study of multiple stressors: divisions and common goals across research disciplines

Anthropogenic environmental changes, or 'stressors', increasingly threaten biodiversity and ecosystem functioning worldwide. Multiple-stressor research is a rapidly expanding field of science that seeks to understand and ultimately predict the interactions between stressors. Reviews and meta-analyses of the primary scientific literature have largely been specific to either freshwater, marine or terrestrial ecology, or ecotoxicology. In this cross-disciplinary study, we review the state of knowledge within and among these disciplines to highlight commonality and division in multiple-stressor research. Our review goes beyond a description of previous research by using quantitative bibliometric analysis to identify the division between disciplines and link previously disconnected research communities. Towards a unified research framework, we discuss the shared goal of increased realism through both ecological and temporal complexity, with the overarching aim of improving predictive power. In a rapidly changing world, advancing our understanding of the cumulative ecological impacts of multiple stressors is critical for biodiversity conservation and ecosystem management. Identifying and overcoming the barriers to interdisciplinary knowledge exchange is necessary in rising to this challenge. Division between ecosystem types and disciplines is largely a human creation. Species and stressors cross these borders and so should the scientists who study them.

Chromosome-Level Assembly of the Caenorhabditis remanei Genome Reveals Conserved Patterns of Nematode Genome Organization

The nematode Caenorhabditis elegans is one of the key model systems in biology, including possessing the first fully assembled animal genome. Whereas C. elegans is a self-reproducing hermaphrodite with fairly limited within-population variation, its relative C. remanei is an outcrossing species with much more extensive genetic variation, making it an ideal parallel model system for evolutionary genetic investigations. Here, we greatly improve on previous assemblies by generating a chromosome-level assembly of the entire C. remanei genome (124.8 Mb of total size) using long-read sequencing and chromatin conformation capture data. Like other fully assembled genomes in the genus, we find that the C. remanei genome displays a high degree of synteny with C. elegans despite multiple within-chromosome rearrangements. Both genomes have high gene density in central regions of chromosomes relative to chromosome ends and the opposite pattern for the accumulation of repetitive elements. C. elegans and C. remanei also show similar patterns of interchromosome interactions, with the central regions of chromosomes appearing to interact with one another more than the distal ends. The new C. remanei genome presented here greatly augments the use of the Caenorhabditis as a platform for comparative genomics and serves as a basis for molecular population genetics within this highly diverse species.

Rapid evolution in response to warming does not affect the toxicity of a pollutant: Insights from experimental evolution in heated mesocosms

While human-induced stressors such as warming and pollutants may co-occur and interact, evolutionary studies typically focus on single stressors. Rapid thermal evolution may help organisms better deal with warming, yet it remains an open question whether thermal evolution changes the toxicity of pollutants under warming. We investigated the effects of exposure to a novel pollutant (zinc oxide nanoparticles, nZnO) and 4°C warming (20°C vs. 24°C) on key life history and physiological traits of the water flea Daphnia magna, a keystone species in aquatic ecosystems. To address the role of thermal evolution, we compared these effects between clones from an experimental evolution trial where animals were kept for two years in outdoor mesocosms at ambient temperatures or ambient +4°C. The nZnO was more toxic at 20°C than at 24°C: only at 20°C, it caused reductions in early fecundity, intrinsic growth rate and metabolic activity. This was due to a higher accumulated zinc burden at 20°C than at 24°C, which was associated with an upregulation of a metallothionein gene at 20°C but not at 24°C. Clones from the heated mesocosms better dealt with warming than clones from the ambient mesocosms, indicating rapid thermal evolution. Notably, rapid thermal evolution did not change the toxicity of nZnO, neither at 20°C nor at 24°C, suggesting no pleiotropy or metabolic trade-offs were at work under the current experimental design. Evaluating whether thermal evolution influences the toxicity of pollutants is important for ecological risk assessment. It provides key information to extrapolate laboratory-derived toxicity estimates of pollutants both in space to warmer regions and in time under future global warming scenarios. In general, studying how the evolution of tolerance to one anthropogenic stressor influence tolerance to other anthropogenic stressors should get more attention in a rapidly changing world where animals increasingly face combinations of stressors.

Environmental and Evolutionary Drivers of the Modular Gene Regulatory Network Underlying Phenotypic Plasticity for Stress Resistance in the Nematode Caenorhabditis remanei

Organisms can cope with stressful environments via a combination of phenotypic plasticity at the individual level and adaptation at the population level. Changes in gene expression can play an important role in both. Significant advances in our understanding of gene regulatory plasticity and evolution have come from comparative studies in the field and laboratory. Experimental evolution provides another powerful path by which to learn about how differential regulation of genes and pathways contributes to both acclimation and adaptation. Here we present results from one such study using the nematode Caenorhabditis remanei We selected one set of lines to withstand heat stress and another oxidative stress. We then compared transcriptional responses to acute heat stress of both and an unselected control to the ancestral population using a weighted gene coexpression network analysis, finding that the transcriptional response is primarily dominated by a plastic response that is shared in the ancestor and all evolved populations. In addition, we identified several modules that respond to artificial selection by (1) changing the baseline level of expression, (2) altering the magnitude of the plastic response, or (3) a combination of the two. Our findings therefore reveal that while patterns of transcriptional response can be perturbed with short bouts of intense selection, the overall ancestral structure of transcriptional plasticity is largely maintained over time.

Mechanisms of Plastic Rescue in Novel Environments

Adaptive phenotypic plasticity provides a mechanism of developmental rescue in novel and rapidly changing environments. Understanding the underlying mechanism of plasticity is important for predicting both the likelihood that a developmental response is adaptive and associated life-history trade-offs that could influence patterns of subsequent evolutionary rescue. Although evolved developmental switches may move organisms toward a new adaptive peak in a novel environment, such mechanisms often result in maladaptive responses. The induction of generalized physiological mechanisms in new environments is relatively more likely to result in adaptive responses to factors such as novel toxins, heat stress, or pathogens. Developmental selection forms of plasticity, which rely on within-individual selective processes, such as shaping of tissue architecture, trial-and-error learning, or acquired immunity, are particularly likely to result in adaptive plasticity in a novel environment. However, both the induction of plastic responses and the ability to be plastic through developmental selection come with significant costs, resulting in delays in reproduction, increased individual investment, and reduced fecundity. Thus, we might expect complex interactions between plastic responses that allow survival in novel environments and subsequent evolutionary responses at the population level.

The genetic variance but not the genetic covariance of life-history traits changes towards the north in a time-constrained insect

Seasonal time constraints are usually stronger at higher than lower latitudes and can exert strong selection on life-history traits and the correlations among these traits. To predict the response of life-history traits to environmental change along a latitudinal gradient, information must be obtained about genetic variance in traits and also genetic correlation between traits, that is the genetic variance-covariance matrix, G. Here, we estimated G for key life-history traits in an obligate univoltine damselfly that faces seasonal time constraints. We exposed populations to simulated native temperatures and photoperiods and common garden environmental conditions in a laboratory set-up. Despite differences in genetic variance in these traits between populations (lower variance at northern latitudes), there was no evidence for latitude-specific covariance of the life-history traits. At simulated native conditions, all populations showed strong genetic and phenotypic correlations between traits that shaped growth and development. The variance-covariance matrix changed considerably when populations were exposed to common garden conditions compared with the simulated natural conditions, showing the importance of environmentally induced changes in multivariate genetic structure. Our results highlight the importance of estimating variance-covariance matrixes in environments that mimic selection pressures and not only trait variances or mean trait values in common garden conditions for understanding the trait evolution across populations and environments.

Advancing behavioural genomics by considering timescale

Animal behavioural traits often covary with gene expression, pointing towards a genomic constraint on organismal responses to environmental cues. This pattern highlights a gap in our understanding of the time course of environmentally responsive gene expression, and moreover, how these dynamics are regulated. Advances in behavioural genomics explore how gene expression dynamics are correlated with behavioural traits that range from stable to highly labile. We consider the idea that certain genomic regulatory mechanisms may predict the timescale of an environmental effect on behaviour. This temporally minded approach could inform both organismal and evolutionary questions ranging from the remediation of early life social trauma to understanding the evolution of trait plasticity.

Selection for long and short sleep duration in Drosophila melanogaster reveals the complex genetic network underlying natural variation in sleep

Why do some individuals need more sleep than others? Forward mutagenesis screens in flies using engineered mutations have established a clear genetic component to sleep duration, revealing mutants that convey very long or short sleep. Whether such extreme long or short sleep could exist in natural populations was unknown. We applied artificial selection for high and low night sleep duration to an outbred population of Drosophila melanogaster for 13 generations. At the end of the selection procedure, night sleep duration diverged by 9.97 hours in the long and short sleeper populations, and 24-hour sleep was reduced to 3.3 hours in the short sleepers. Neither long nor short sleeper lifespan differed appreciably from controls, suggesting little physiological consequences to being an extreme long or short sleeper. Whole genome sequence data from seven generations of selection revealed several hundred thousand changes in allele frequencies at polymorphic loci across the genome. Combining the data from long and short sleeper populations across generations in a logistic regression implicated 126 polymorphisms in 80 candidate genes, and we confirmed three of these genes and a larger genomic region with mutant and chromosomal deficiency tests, respectively. Many of these genes could be connected in a single network based on previously known physical and genetic interactions. Candidate genes have known roles in several classic, highly conserved developmental and signaling pathways—EGFR, Wnt, Hippo, and MAPK. The involvement of highly pleiotropic pathway genes suggests that sleep duration in natural populations can be influenced by a wide variety of biological processes, which may be why the purpose of sleep has been so elusive.

One of the biggest mysteries in biology is the need to sleep. Sleep duration has an underlying genetic basis, suggesting that very long and short sleep times could be bred for experimentally. How far can sleep duration be driven up or down? Here we achieved extremely long and short night sleep duration by subjecting a wild-derived population of Drosophila melanogaster to an experimental breeding program. At the end of the breeding program, long sleepers averaged 9.97 hours more nightly sleep than short sleepers. We analyzed whole-genome sequences from seven generations of the experimental breeding to identify allele frequencies that diverged between long and short sleepers, and verified genes and genomic regions with mutation and deficiency testing. These alleles map to classic developmental and signaling pathways, implicating many diverse processes that potentially affect sleep duration.

Structure and stability of genetic variance-covariance matrices: A Bayesian sparse factor analysis of transcriptional variation in the three-spined stickleback

The genetic variance-covariance matrix (G) is a quantity of central importance in evolutionary biology due to its influence on the rate and direction of multivariate evolution. However, the predictive power of empirically estimated G-matrices is limited for two reasons. First, phenotypes are high-dimensional, whereas traditional statistical methods are tuned to estimate and analyse low-dimensional matrices. Second, the stability of G to environmental effects and over time remains poorly understood. Using Bayesian sparse factor analysis (BSFG) designed to estimate high-dimensional G-matrices, we analysed levels variation and covariation in 10,527 expressed genes in a large (n = 563) half-sib breeding design of three-spined sticklebacks subject to two temperature treatments. We found significant differences in the structure of G between the treatments: heritabilities and evolvabilities were higher in the warm than in the low-temperature treatment, suggesting more and faster opportunity to evolve in warm (stressful) conditions. Furthermore, comparison of G and its phenotypic equivalent P revealed the latter is a poor substitute of the former. Most strikingly, the results suggest that the expected impact of G on evolvability-as well as the similarity among G-matrices-may depend strongly on the number of traits included into analyses. In our results, the inclusion of only few traits in the analyses leads to underestimation in the differences between the G-matrices and their predicted impacts on evolution. While the results highlight the challenges involved in estimating G, they also illustrate that by enabling the estimation of large G-matrices, the BSFG method can improve predicted evolutionary responses to selection.

Experimental Evolution with Caenorhabditis Nematodes

The hermaphroditic nematode Caenorhabditis elegans has been one of the primary model systems in biology since the 1970s, but only within the last two decades has this nematode also become a useful model for experimental evolution. Here, we outline the goals and major foci of experimental evolution with C. elegans and related species, such as C. briggsae and C. remanei, by discussing the principles of experimental design, and highlighting the strengths and limitations of Caenorhabditis as model systems. We then review three exemplars of Caenorhabditis experimental evolution studies, underlining representative evolution experiments that have addressed the: (1) maintenance of genetic variation; (2) role of natural selection during transitions from outcrossing to selfing, as well as the maintenance of mixed breeding modes during evolution; and (3) evolution of phenotypic plasticity and its role in adaptation to variable environments, including host-pathogen coevolution. We conclude by suggesting some future directions for which experimental evolution with Caenorhabditis would be particularly informative.

Pleiotropy, constraint, and modularity in the evolution of life histories: insights from genomic analyses

Multicellular organisms display an enormous range of life history (LH) strategies and present an evolutionary conundrum; despite strong natural selection, LH traits are characterized by high levels of genetic variation. To understand the evolution of life histories and maintenance of this variation, the specific phenotypic effects of segregating alleles and the genetic networks in which they act need to be elucidated. In particular, the extent to which LH evolution is constrained by the pleiotropy of alleles contributing to LH variation is generally unknown. Here, we review recent empirical results that shed light on this question, with an emphasis on studies employing genomic analyses. While genome-scale analyses are increasingly practical and affordable, they face limitations of genetic resolution and statistical power. We describe new research approaches that we believe can produce new insights and evaluate their promise and applicability to different kinds of organisms. Two approaches seem particularly promising: experiments that manipulate selection in multiple dimensions and measure phenotypic and genomic response and analytical approaches that take into account genome-wide associations between markers and phenotypes, rather than applying a traditional marker-by-marker approach.

Environmental effects on the structure of the G-matrix

Genetic correlations between traits determine the multivariate response to selection in the short term, and thereby play a causal role in evolutionary change. Although individual studies have documented environmentally induced changes in genetic correlations, the nature and extent of environmental effects on multivariate genetic architecture across species and environments remain largely uncharacterized. We reviewed the literature for estimates of the genetic variance-covariance (G) matrix in multiple environments, and compared differences in G between environments to the divergence in G between conspecific populations (measured in a common garden). We found that the predicted evolutionary trajectory differed as strongly between environments as it did between populations. Between-environment differences in the underlying structure of G (total genetic variance and the relative magnitude and orientation of genetic correlations) were equal to or greater than between-population differences. Neither environmental novelty, nor the difference in mean phenotype predicted these differences in G. Our results suggest that environmental effects on multivariate genetic architecture may be comparable to the divergence that accumulates over dozens or hundreds of generations between populations. We outline avenues of future research to address the limitations of existing data and characterize the extent to which lability in genetic correlations shapes evolution in changing environments.