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7 results for “G-matrix”
G-matrix stability of clinally diverging populations of an annual weed
<p>How phenotypic and genetic divergence among populations is influenced by the genetic architecture of those traits, and how microevolutionary changes in turn affect the within-population patterns of genetic variation, are of major interest to evolutionary biology. Work on <em>Ipomoea hederacea</em>, an annual vine, has found genetic clines in the means of a suite of ecologically important traits, including flowering time, growth rate, seed mass, and corolla width. Here we investigate the genetic (co)variances of these clinally varying traits in two northern range-edge and two central populations of <em>Ipomoea hederacea </em>to evaluate the influence of the genetic architecture on divergence across the range. We find 1) limited evidence for clear differentiation between Northern and Southern populations in the structure of <strong>G</strong>, suggesting overall stability of <strong>G</strong> across the range despite mean trait divergence and 2) that the axes of greatest variation (g<sub>max</sub>) were unaligned with the axis of greatest multivariate divergence. Together these results indicate the role of the quantitative genetic architecture in constraining evolutionary response and divergence among populations across the geographic range.</p>
G-matrix stability of clinally diverging populations of an annual weed
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Data from: What affects the predictability of evolutionary constraints using a G-matrix? The relative effects of modular pleiotropy and mutational correlation
Phenotypic traits do not always respond to selection independently from each other and often show correlated responses to selection. The structure of a genotype-phenotype map (GP map) determines trait covariation, which involves variation in the degree and strength of the pleiotropic effects of the underlying genes. It is still unclear, and debated, how much of that structure can be deduced from variational properties of quantitative traits that are inferred from their genetic (co)variance matrix (G-matrix). Here we aim to clarify how the extent of pleiotropy and the correlation among the pleiotropic effects of mutations differentially affect the structure of a G-matrix and our ability to detect genetic constraints from its eigen decomposition. We show that the eigenvectors of a G-matrix can be predictive of evolutionary constraints when they map to underlying pleiotropic modules with correlated mutational effects. Without mutational correlation, evolutionary constraints caused by the fitness costs associated with increased pleiotropy are harder to infer from evolutionary metrics based on a G-matrix's geometric properties because uncorrelated pleiotropic effects do not affect traits' genetic correlations. Correlational selection induces much weaker modular partitioning of traits' genetic correlations in absence then in presence of underlying modular pleiotropy.
Data from: 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. While 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.
Data from: What affects the predictability of evolutionary constraints using a G-matrix? The relative effects of modular pleiotropy and mutational correlation
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Data from: Environmental effects on the structure of the G-matrix
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Data from: Rapid and unpredictable changes of the G-matrix in a natural bird population over 25 years
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