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12 results for “quantitative trait evolution”
Data from: Quantitative genetic analysis of floral traits shows current limits but potential evolution in the wild
<p>The vast variation in floral traits across angiosperms is often interpreted as the result of adaptation to pollinators. However, studies in wild populations often find no evidence of pollinator-mediated selection on flowers. Evolutionary theory predicts this could be the outcome of periods of stasis under stable conditions, followed by shorter periods of pollinator change that provide selection for innovative phenotypes. We asked if periods of stasis are caused by stabilizing selection, absence of other forms of selection, or by low trait ability to respond even if selection is present. We studied a plant predominantly pollinated by one bee species across its range. We measured heritability and evolvability of traits, using genome-wide relatedness in a large wild population, and combined this with estimates of selection on the same individuals. We found evidence for both stabilizing selection and low trait heritability as potential explanations for stasis in flowers. The area of the standard petal is under stabilizing selection, but the variability is not heritable. A separate trait, floral weight, presents high heritability, but is not currently under selection. We show how a simple pollination environment coincides with the absence of current prerequisites for adaptive evolutionary change, while heritable variation remains to respond to future selection pressures.</p>
Data from: Quantitative genetic analysis of floral traits shows current limits but potential evolution in the wild
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Data from: Limits to behavioral evolution: the quantitative genetics of a complex trait under directional selection
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Data from: Evolution of quantitative traits under a migration-selection balance: when does skew matter?
Quantitative-genetic models of differentiation under migration-selection balance often rely on the assumption of normally distributed genotypic and phenotypic values. When a population is subdivided into demes with selection toward different local optima, migration between demes may result in asymmetric, or skewed, local distributions. Using a simplified two-habitat model, we derive formulas without a priori assuming a Gaussian distribution of genotypic values, and we find expressions that naturally incorporate higher moments, such as skew. These formulas yield predictions of the expected divergence under migration-selection balance that are more accurate than models assuming Gaussian distributions, which illustrates the importance of incorporating these higher moments to assess the response to selection in heterogeneous environments. We further show with simulations that traits with loci of large effect display the largest skew in their distribution at migration-selection balance.
Data from: Uncovering the genetic signature of quantitative trait evolution with replicated time series data
The genetic architecture of adaptation in natural populations has not yet been resolved: it is not clear to what extent the spread of beneficial mutations (selective sweeps) or the response of many quantitative trait loci drive adaptation to environmental changes. Although much attention has been given to the genomic footprint of selective sweeps, the importance of selection on quantitative traits is still not well studied, as the associated genomic signature is extremely difficult to detect. We propose 'Evolve and Resequence' as a promising tool, to study polygenic adaptation of quantitative traits in evolving populations. Simulating replicated time series data we show that adaptation to a new intermediate trait optimum has three characteristic phases that are reflected on the genomic level: (1) directional frequency changes towards the new trait optimum, (2) plateauing of allele frequencies when the new trait optimum has been reached and (3) subsequent divergence between replicated trajectories ultimately leading to the loss or fixation of alleles while the trait value does not change. We explore these 3 phase characteristics for relevant population genetic parameters to provide expectations for various experimental evolution designs. Remarkably, over a broad range of parameters the trajectories of selected alleles display a pattern across replicates, which differs both from neutrality and directional selection. We conclude that replicated time series data from experimental evolution studies provide a promising framework to study polygenic adaptation from whole-genome population genetics data.
Data from: The evolution of genetic architectures underlying quantitative traits
In the classic view introduced by R. A. Fisher, a quantitative trait is encoded by many loci with small, additive effects. Recent advances in quantitative trait loci mapping have begun to elucidate the genetic architectures underlying vast numbers of phenotypes across diverse taxa, producing observations that sometimes contrast with Fisher's blueprint. Despite these considerable empirical efforts to map the genetic determinants of traits, it remains poorly understood how the genetic architecture of a trait should evolve, or how it depends on the selection pressures on the trait. Here, we develop a simple, population-genetic model for the evolution of genetic architectures. Our model predicts that traits under moderate selection should be encoded by many loci with highly variable effects, whereas traits under either weak or strong selection should be encoded by relatively few loci. We compare these theoretical predictions with qualitative trends in the genetics of human traits, and with systematic data on the genetics of gene expression levels in yeast. Our analysis provides an evolutionary explanation for broad empirical patterns in the genetic basis for traits, and it introduces a single framework that unifies the diversity of observed genetic architectures, ranging from Mendelian to Fisherian.
Data from: The evolution of genetic architectures underlying quantitative traits
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Data from: Uncovering the genetic signature of quantitative trait evolution with replicated time series data
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Data from: Evolution of quantitative traits under a migration-selection balance: when does skew matter?
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Data from: Phylogenetic ANOVA: the Expression Variance and Evolution model for quantitative trait evolution
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Data from: Sex chromosome linked genetic variance and the evolution of sexual dimorphism of quantitative traits
Theory predicts that sex chromsome linkage should reduce intersexual genetic correlations thereby allowing the evolution of sexual dimorphism. Empirical evidence for sex linkage has come largely from crosses and few studies have examined how sexual dimorphism and sex linkage are related within outbred populations. Here we use data on an array of different traits measured on over 10,000 individuals from two pedigreed populations of birds (collared flycatcher and zebra finch) to estimate the amount of sex linked genetic variance (h2z). Out of 17 traits examined, eight showed a non-zero h2Z estimate but only four were significantly different from zero (wing patch size and tarsus length in collared flycatchers, wing length and beak colour in zebra finches). We further tested how sexual dimorphism and the mode of selection operating on the trait relate to the proportion of sex linked genetic variance. Sexually selected traits did not show higher h2Z than morphological traits and there was only a weak positive relationship between h2Z and sexual dimorphism. However, given the relative scarcity of empirical studies it is premature to make conclusions about the role sex chromosome linkage in the evolution of sexual dimorphism.
Data from: Sex chromosome linked genetic variance and the evolution of sexual dimorphism of quantitative traits
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.