Plasticity in novel environments induces larger changes in genetic variance than adaptive divergence

2021 ◽  
Author(s):  
Greg M. Walter ◽  
Delia Terranova ◽  
James Clark ◽  
Salvatore Cozzolino ◽  
Antonia Cristaudo ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Phenotypic Plasticity ◽  
Quantitative Genetics ◽  
Leaf Morphology ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Elevational Gradient ◽  
Adaptive Divergence ◽  
Genetic Constraints ◽  
Novel Environments

AbstractGenetic correlations between traits are expected to constrain the rate of adaptation by concentrating genetic variation in certain phenotypic directions, which are unlikely to align with the direction of selection in novel environments. However, if genotypes vary in their response to novel environments, then plasticity could create changes in genetic variation that will determine whether genetic constraints to adaptation arise. We tested this hypothesis by mating two species of closely related, but ecologically distinct, Sicilian daisies (Senecio, Asteraceae) using a quantitative genetics breeding design. We planted seeds of both species across an elevational gradient that included the native habitat of each species and two intermediate elevations, and measured eight leaf morphology and physiology traits on established seedlings. We detected large significant changes in genetic variance across elevation and between species. Elevational changes in genetic variance within species were greater than differences between the two species. Furthermore, changes in genetic variation across elevation aligned with phenotypic plasticity. These results suggest that to understand adaptation to novel environments we need to consider how genetic variance changes in response to environmental variation, and the effect of such changes on genetic constraints to adaptation and the evolution of plasticity.

scholarly journals Artificial selection on male size depletes genetic variance but not covariance of life history traits in the yellow dung fly

2019 ◽  
Author(s):  
WU Blanckenhorn ◽  
V Llaurens ◽  
C Reim ◽  
Y Teuschl ◽  
E Postma
Keyword(s):  
Genetic Variation ◽  
Life History ◽  
Body Size ◽  
Artificial Selection ◽  
Development Time ◽  
Life History Traits ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Male Size ◽  
First Clutch

SUMMARYThe evolutionary potential of organisms depends on the presence of sufficient genetic variation for traits subject to selection, as well as on the genetic covariances among them. While genetic variation ultimately derives from mutation, theory predicts the depletion of genetic (co)variation under consistent directional or stabilizing selection in natural populations. We estimated and compared additive genetic (co)variances for several standard life history traits, including some for which this has never been assessed, before and after 24 generations of artificial selection on male size in the yellow dung fly Scathophaga stercoraria (Diptera: Scathophagidae) using a series of standard half-sib breeding experiments. As predicted, genetic variances (VA), heritabilities (h2) and evolvabilities (IA) of body size, development time, first clutch size, and female age at first clutch were lower after selection. As independent selection lines were crossed prior to testing, we can rule out that this reduction is due to genetic drift. In contrast to the variances, and against expectation, the additive genetic correlations between the sexes for development time and body size remained strong and positive (rA = 0.8–0.9), while the genetic correlation between these traits within the sexes tended to strengthen (but not significantly so). Our study documents that the effect of selection on genetic variance is predictable, whereas that on genetic correlations is not.

scholarly journals Evolution of genetic variance during adaptive radiation

2017 ◽  
Author(s):  
Greg M. Walter ◽  
J. David Aguirre ◽  
Mark W. Blows ◽  
Daniel Ortiz-Barrientos
Keyword(s):  
Natural Selection ◽  
Adaptive Radiation ◽  
Genetic Variance ◽  
Allelic Variation ◽  
Genetic Correlations ◽  
Leaf Traits ◽  
Adaptive Divergence ◽  
Ecotypic Differentiation ◽  
Divergent Natural Selection ◽  
Rapid Divergence

AbstractGenetic correlations between traits can bias adaptation away from optimal phenotypes and constrain the rate of evolution. If genetic correlations between traits limit adaptation to contrasting environments, rapid adaptive divergence across a heterogeneous landscape may be difficult. However, if genetic variance can evolve and align with the direction of natural selection, then abundant allelic variation can promote rapid divergence during adaptive radiation. Here, we explored adaptive divergence among ecotypes of an Australian native wildflower by quantifying divergence in multivariate phenotypes of populations that occupy four contrasting environments. We investigated differences in multivariate genetic variance underlying morphological traits and examined the alignment between divergence in phenotype and divergence in genetic variance. We found that divergence in mean multivariate phenotype has occurred along two major axes represented by different combinations of plant architecture and leaf traits. Ecotypes also showed divergence in the level of genetic variance in individual traits, and the multivariate distribution of genetic variance among traits. Divergence in multivariate phenotypic mean aligned with divergence in genetic variance, with most of the divergence in phenotype among ecotypes associated with a change in trait combinations that had substantial levels of genetic variance in each ecotype. Overall, our results suggest that divergent natural selection acting on high levels of standing genetic variation might fuel ecotypic differentiation during the early stages of adaptive radiation.

scholarly journals Adaptive phenotypic divergence in an annual grass differs across biotic contexts

2018 ◽  
Author(s):  
Anna M O'Brien ◽  
Ruairidh J.H. Sawers ◽  
Sharon Y Strauss ◽  
Jeffrey Ross-Ibarra
Keyword(s):  
Genetic Variation ◽  
Species Interactions ◽  
Genetic Variance ◽  
Phenotypic Expression ◽  
Elevational Gradient ◽  
Annual Grass ◽  
Trait Divergence ◽  
Fitness Consequences ◽  
Historical Role ◽  
Half Sibling

Climate is a powerful force shaping adaptation within species, yet adaptation to climate does not occur in a vacuum: species interactions can filter fitness consequences of genetic variation by altering phenotypic expression of genotypes. We investigated this process using populations of teosinte, a wild annual grass related to maize (Zea mays ssp. mexicana), sampling plants from ten sites along an elevational gradient as well as rhizosphere biota from three of those sites. We grew half-sibling teosinte families in each biota to test whether trait divergence among teosinte populations reflects adaptation or drift, and whether rhizosphere biota affect expression of diverged traits. We further assayed the influence of rhizosphere biota on contemporary additive genetic variation. We found that adaptation across environment shaped divergence of some traits, particularly flowering time and root biomass. We also observed that different rhizosphere biota shifted expressed values of these traits within teosinte populations and families and altered within-population genetic variance and covariance. In sum, our results imply that changes in trait expression and covariance elicited by rhizosphere communities may have played a historical role in teosinte adaptation to environments and that they are likely to continue to play a role in the response to future selection.

THE STUDY OF INTERSEXUAL SELECTION USING QUANTITATIVE GENETICS

Behaviour ◽  
1999 ◽  
Vol 136 (9) ◽  
pp. 1237-1266 ◽  
Author(s):  
Theo C.M. Bakker
Keyword(s):  
Genetic Variation ◽  
Sexual Selection ◽  
Quantitative Genetics ◽  
Genetic Variance ◽  
Female Mate Choice ◽  
Good Genes ◽  
Condition Dependence ◽  
Genetic Covariance ◽  
Mating Preferences ◽  
Sexual Traits

AbstractIn this review, I stress the importance of incorporating Quantitative Genetics (QG) in the study of sexual selection through female mate choice. A short overview of QG principles and methods of estimating genetic variance and covariance is given. The state of knowledge is summarized as to two QG assumptions (genetic variance in female mating preferences and male sexual traits) and one QG prediction (genetic covariance between preferences and preferred traits) of models of sexual selection. A review is given of studies of repeatability of mating preferences because of recent accumulation of data. The general conclusion is that sexual traits and mating preferences show large genetic variation and are genetically correlated. The extensive genetic variation asks for an explanation that goes beyond the ususal explanations of the maintenance of genetic variation in fitness traits. Two models that explain the high genetic variance in sexual traits are treated in detail: modifier selection and condition dependence. There are many unexplored areas of QG research that could stimulate further research in sexual selection like the study of genetic covariance between mating preferences and good genes, of genetic variances and covariances of multiple male traits and multiple females preferences, of genetic variance in condition, and of condition dependence of mating preferences.

scholarly journals Hidden genetic variation in plasticity increases the potential to adapt to novel environments

2020 ◽  
Author(s):  
Greg M. Walter ◽  
James Clark ◽  
Delia Terranova ◽  
Salvatore Cozzolino ◽  
Antonia Cristaudo ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Genetic Variance ◽  
Adaptive Plasticity ◽  
The Novel ◽  
Rapid Adaptation ◽  
Novel Environment ◽  
Slow Adaptation ◽  
Novel Environments ◽  
Plastic Responses ◽  
Native Site

AbstractAdaptive plasticity increases population persistence, but can slow adaptation to changing environments by hiding the effects of different alleles on fitness. However, if plastic responses are no longer adaptive in novel environments, then differences among alleles can emerge and increase genetic variation in fitness that allows rapid adaptation. We tested this hypothesis by transplanting cuttings and seeds of a Sicilian daisy within and outside its native range, and quantifying variation in morphology, physiology, gene expression and fitness. We show that genetic variance in plasticity increases the potential for rapid adaptation to novel environments. Genetic variation in fitness was low across native environments where plasticity effectively tracked familiar environments. In the novel environment however, genetic variation in fitness increased threefold, and correlated with genetic variation in plasticity. Furthermore, genetic variation that can increase fitness in the novel environment had the lowest fitness at the native site, suggesting that adaptation to novel environments relies on genetic variation in plasticity that is selected against in native environments.

scholarly journals Sex- and genotype-effects on nutrient-dependent fitness landscapes in Drosophila melanogaster

2017 ◽  
Author(s):  
M. Florencia Camus ◽  
Kevin Fowler ◽  
Matthew W.D. Piper ◽  
Max Reuter
Keyword(s):  
Drosophila Melanogaster ◽  
Genetic Variation ◽  
Focal Point ◽  
Physiological Adaptation ◽  
Adaptive Divergence ◽  
Male And Female ◽  
Genetic Constraints ◽  
Dietary Requirements ◽  
Metabolic Variation ◽  
Biochemical Analyses

AbstractThe sexes perform different reproductive roles and have evolved sometimes strikingly different phenotypes. One focal point of adaptive divergence occurs in the context of diet and metabolism, and males and females of a range of species have been shown to require different nutrients to maximise their fitness. Biochemical analyses in Drosophila melanogaster have confirmed that dimorphism in dietary requirements is associated with molecular sex-differences in metabolite titres. In addition, they also showed significant within-sex genetic variation in the metabolome. To date however, it is unknown whether this metabolic variation translates into differences in reproductive fitness. The answer to this question is crucial to establish whether genetic variation is selectively neutral or indicative of constraints on sex-specific physiological adaptation and optimisation. Here we assay genetic variation in consumption and metabolic fitness effects by screening male and female fitness of thirty D. melanogaster genotypes across four protein-to-carbohydrate ratios. In addition to confirming sexual dimorphism in consumption and fitness, we find significant genetic variation in male and female dietary requirements. Importantly, these differences are not explained by feeding responses and most likely reflect metabolic variation that, in turn, suggest the presence of genetic constraints on metabolic dimorphism.

scholarly journals Fast and flexible linear mixed models for genome-wide genetics

2018 ◽  
Author(s):  
Daniel E Runcie ◽  
Lorin Crawford
Keyword(s):  
Genetic Variation ◽  
Quantitative Genetics ◽  
Mixed Models ◽  
Genetic Variance ◽  
Linear Mixed Models ◽  
Additive Genetic Variance ◽  
Multiple Sources ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Genome Wide

AbstractLinear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.Author summaryThe goal of quantitative genetics is to characterize the relationship between genetic variation and variation in quantitative traits such as height, productivity, or disease susceptibility. A statistical method known as the linear mixed effect model has been critical to the development of quantitative genetics. First applied to animal breeding, this model now forms the basis of a wide-range of modern genomic analyses including genome-wide associations, polygenic modeling, and genomic prediction. The same model is also widely used in ecology, evolutionary genetics, social sciences, and many other fields. Mixed models are frequently multi-faceted, which is necessary for accurately modeling data that is generated from complex experimental designs. However, most genomic applications use only the simplest form of linear mixed methods because the computational demands for model fitting can be too great. We develop a flexible approach for fitting linear mixed models to genome scale data that greatly reduces their computational burden and provides flexibility for users to choose the best statistical paradigm for their data analysis. We demonstrate improved accuracy for genetic association tests, increased power to discover causal genetic variants, and the ability to provide accurate summaries of model uncertainty using both simulated and real data examples.

scholarly journals Geographical gradients in selection can reveal genetic constraints for evolutionary responses to ocean acidification

2017 ◽  
Vol 13 (2) ◽  
pp. 20160784 ◽  
Author(s):  
Juan Diego Gaitán-Espitia ◽  
Dustin Marshall ◽  
Sam Dupont ◽  
Leonardo D. Bacigalupe ◽  
Levente Bodrossy ◽  
...  
Keyword(s):  
Climate Change ◽  
Genetic Variation ◽  
Ocean Acidification ◽  
Genetic Correlations ◽  
Natural Populations ◽  
Low Ph ◽  
Directional Selection ◽  
Genetic Constraints ◽  
Geographical Regions ◽  
Spatio Temporal

Geographical gradients in selection can shape different genetic architectures in natural populations, reflecting potential genetic constraints for adaptive evolution under climate change. Investigation of natural pH/ p CO 2 variation in upwelling regions reveals different spatio-temporal patterns of natural selection, generating genetic and phenotypic clines in populations, and potentially leading to local adaptation, relevant to understanding effects of ocean acidification (OA). Strong directional selection, associated with intense and continuous upwellings, may have depleted genetic variation in populations within these upwelling regions, favouring increased tolerances to low pH but with an associated cost in other traits. In contrast, diversifying or weak directional selection in populations with seasonal upwellings or outside major upwelling regions may have resulted in higher genetic variances and the lack of genetic correlations among traits. Testing this hypothesis in geographical regions with similar environmental conditions to those predicted under climate change will build insights into how selection may act in the future and how populations may respond to stressors such as OA.

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