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186 results for “Quantitative genetics”
Data from: Is evolution predictable? quantitative genetics under complex genotype-phenotype maps
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Efficient weighting methods for genomic best linear unbiased prediction (BLUP) adaption to the genetic architectures of quantitative traits
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Molecular and quantitative genetic variation within and between populations of the declining grassland species Saxifraga granulata
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Data from: A quantitative genetic approach to assess the evolutionary potential of a coastal marine fish to ocean acidification
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Data from: Quantitative genetic analyses of male color pattern and female mate choice in a pair of cichlid fishes of Lake Malawi, East Africa
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Data from: The influence of genetic drift and selection on quantitative traits in a plant pathogenic fungus
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Quantitative genetics of eggshell colouration
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Quantitative genetics of phosphorus content in the freshwater herbivore, Daphnia pulicaria
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Data from: Colour ornamentation in the blue tit: quantitative genetic (co)variances across sexes
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Data from: Assessing the potential for assisted gene flow using past introduction of Norway spruce in Southern Sweden: local adaptation and genetic basis of quantitative traits in trees
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Data from: An assessment of the reliability of quantitative genetics estimates in study systems with high rate of extra-pair reproduction and low recruitment
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Data from: The quantitative genetics of physiological and morphological traits in an invasive terrestrial snail: additive versus non-additive genetic variation
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Data from: Quantitative genetics of temperature performance curves of Neurospora crassa
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Data from: Sexual conflict and interacting phenotypes: a quantitative genetic analysis of fecundity and copula duration in Drosophila melanogaster
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Data from: The roles of genetic drift and natural selection in quantitative trait divergence along an altitudinal gradient in Arabidopsis thaliana
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Genetics of quantitative traits with dominance under stabilizing and directional selection in partially selfing species
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Quantitative genetics of wing morphology in the parasitoid wasp Nasonia vitripennis: hosts increase sibling similarity
<p>The central aim of evolutionary biology is to understand patterns of genetic variation between species and within populations. To quantify the genetic variation underlying intraspecific differences, estimating quantitative genetic parameters of traits is essential. In Pterygota, wing morphology is an important trait affecting flight ability. Moreover, gregarious parasitoids such as Nasonia vitripennis oviposit multiple eggs in the same host, and siblings thus share a common environment during their development. Here we estimate the genetic parameters of wing morphology in the outbred HVRx population of N. vit-ripennis, using a sire-dam model adapted to haplodiploids and disentangled additive genetic effects and host effects. The results show that the wing size traits have low heritability (h2~0.1), while most wing shape traits have roughly twice the heritability compared to wing size traits. However, the estimates in-creased to h2~0.6 for wing size traits when omitt ing the host effect from the statistical model, while no meaningful increases were observed for wing shape traits. Overall, host effects contributed ~50% of the variation in wing size traits. This indicates that hosts have a large effect on wing size traits, about five-fold more than genetics. Moreover, bivariate analyses were conducted to derive the genetic relationships among traits. Overall, we demonstrate the evo-lutionary potential for morphological traits in the N. vitripennis HVRx outbred population and report the host effects on wing morphology. Our findings can contribute to a further dissection of the genetics underlying wing morphology in N. vitripennis, with relevance for gregarious parasitoids and possible other insects as well.</p>
Quantitative genetic architecture of adaptive phenology traits in the deciduous tree, Populus trichocarpa (Torr. & Gray)
<p><span>In a warming climate, the ability to accurately predict and track shifting environmental conditions will be fundamental for plant survival. Environmental cues define the transitions between growth and dormancy as plants synchronise development with favourable environmental conditions, however these cues are predicted to change under future climate projections which may have profound impacts on tree survival and growth. Here, we use a quantitative genetic approach to estimate the genetic basis of spring and autumn phenology in <i>Populus trichocarpa</i> to determine this species capacity for climate adaptation. We measured bud burst, leaf coloration, and leaf senescence traits across two years (2017- 2018) and combine these observations with measures of lifetime growth to determine how genetic correlations between phenology and growth may facilitate or constrain adaptation. Timing of transitions differed between years, although we found strong cross year genetic correlations in all traits, suggesting that genotypes respond in consistent ways to seasonal cues. Spring and autumn phenology were correlated with lifetime growth, where genotypes that burst leaves early and shed them late had the highest lifetime growth. We also identified substantial heritable variation in the timing of all phenological transitions (h<sup>2</sup> = 0.5-0.8) and in lifetime growth (h<sup>2</sup> = 0.8). The combination of abundant additive variation and favourable genetic correlations in phenology traits suggests that cultivated varieties of <i>P. Trichocarpa</i> have the capability to create populations which may adapt their phenology to climatic changes without negative impacts on growth.</span></p>
Data from: Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models
Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. 'Animal models', which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations. However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non-randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology. We first illustrate how the occurrence of non-random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce 'genetic group' methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents. We summarize the conceptual foundations of genetic group animal models and provide extensive, step-by-step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods. Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow. We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets.
Data from: The evolution of hominoid cranial diversity: a quantitative genetic approach
Hominoid cranial evolution is characterized by substantial phenotypic diversity, yet the cause of this variability has rarely been explored. Quantitative genetic techniques for investigating evolutionary processes underlying morphological divergence are dependent on the availability of good ancestral models, a problem in hominoids where the fossil record is fragmentary and poorly understood. Here, we use a maximum likelihood approach based on a Brownian motion model of evolutionary change to estimate nested hypothetical ancestral forms from 15 extant hominoid taxa. These ancestors were then used to calculate rates of evolution along each branch of a phylogenetic tree using Lande's generalized genetic distance. Our results show that hominoid cranial evolution is characterized by strong stabilizing selection. Only two instances of directional selection were detected; the divergence of Homo from its last common ancestor with Pan, and the divergence of the lesser apes from their last common ancestor with the great apes. In these two cases, selection gradients reconstructed to identify the specific traits undergoing selection indicated that selection on basicranial flexion, cranial vault expansion and facial retraction characterizes the divergence of Homo, whereas the divergence of the lesser apes was defined by selection on neurocranial size reduction.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.