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493 results for “Multivariate”
Fitting and evaluating univariate and multivariate models of within-lineage evolution
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Code from: Beyond the classroom: Alicia’s multivariate journey
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Data from: Differential drivers of benthic foraminiferal and molluscan community composition from a multivariate record of Early Miocene environmental change
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Data from: Multivariate sexual selection on ejaculate traits under sperm competition
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Data from: Multivariate ratio analysis reveals Trigonoderus pedicellaris Thomson (Hymenoptera, Chalcidoidea, Pteromalidae) as a valid species
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Multivariate stabilizing sexual selection and the evolution of male and female genital morphology in the red flour beetle
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Multivariate analysis of Pleurodeles waltl injection outcomes using FAMD FactoMineR (RStudio 4.3.2) used to probe sources of variability during an Adeno-associated viral (AAV) screen
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Data from: Connecting thermal performance curve variation to the genotype: a multivariate QTL approach
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Data from: Multivariate adaptation but no increase in competitive ability in invasive Geranium carolinianum L. (Geraniaceae)
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Data from: Quantifying (non)parallelism of microbial community change using multivariate vector analysis
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Evolution of multivariate wing allometry in schizophoran flies (Diptera: Schizophora)
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Density-dependent patterns of multivariate selection on sperm motility and morphology in a broadcast spawning mussel
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Multivariate hazard assessment for nonstationary seasonal flood extremes considering climate change
<p>Processed streamflow data used to create the plots shown in the paper ("Multivariate hazard assessment for nonstationary seasonal flood extremes considering climate change" published on JGR_A) for reproducibility</p>
Supplementary material for Roca-Neyra Equids: Late Miocene to Early Pleistocene Hipparion - Equus database for multivariate and statistical analysis for European fossil Equids
<p>We undertake a redescription of the equid sample from the early Pleistocene of Roca – Neyra, France. This locality has been recently calibrated at the Pliocene/Pleistocene boundary (2.6 ± 0.2 Ma) and therefore it is of interest for the first appearance of the genus <i>Equus </i>and last appearance of hipparionine horses. The Roca – Neyra equid sample, re – analyzed herein using morphological, morphometrical and statistical analyses, has revealed the co – occurrence of <i>Plesiohipparion</i> cf. ?<i>P.</i> <i>rocinantis</i> and <i>Equus</i> cf.<i> E. livenzovensis</i>. The analysis undertaken on several European, African and Asian <i>"Hipparion"</i> sensu lato species from late Miocene to early Pleistocene has revealed different remnant <i>Hipparion</i> lineages in the Plio – Pleistocene of Europe: <i>Plesiohipparion</i>, <i>Proboscidippaion</i> and likely <i>Cremohipparion</i>. The discovery of the first European monodactyl horse, <i>Equus</i> cf. <i>E.</i> <i>livenzovensis</i> in itself correlates Roca – Neyra with other 2.6 Ma European localities in Italy, Spain and in the Khapry area (Azov Sea region). The morphological description of the <i>Equus</i> cf. <i>E. livenzovensis</i> lower cheek teeth has highlighted intermediate features between the North American Pliocene species <i>Equus simplicidens</i> and early Pleistocene European <i>Equus stenonis.</i> Our study supports the hypothesis that <i>E. livenzovensis</i> is a plausible evolutionary predecessor for the <i>Equus stenonis</i> group. These observations underscore the importance of Roca – Neyra, as an important locality for the last European hipparions and the first <i>Equus</i> in the early Pleistocene of Europe.</p>
Data from: Multivariate intralocus sexual conflict in seed beetles
Intralocus sexual conflict (IaSC) is pervasive because males and females experience differences in selection but share much of the same genome. Traits with integrated genetic architecture should be reservoirs of sexually antagonistic genetic variation for fitness but explorations of multivariate IaSC are scarce. Previously, we showed that upward artificial selection on male lifespan decreased male fitness but increased female fitness compared with downward selection in the seed beetle Callosobruchus maculatus. Here, we use these selection lines to investigate sex-specific evolution of four functionally integrated traits (metabolic rate, locomotor activity, body mass and lifespan) that collectively define a sexually dimorphic life-history syndrome in many species. Male-limited selection for short lifespan led to correlated evolution in females towards a more male-like multivariate phenotype. Conversely, males selected for long lifespan became more female-like, implying that IaSC results from genetic integration of this suite of traits. However, while lifespan, metabolism and body mass showed correlated evolution in the sexes, activity did not evolve in males but, surprisingly, did so in females. This led to sexual monomorphism in locomotor activity in short-life lines, associated with detrimental effects in females. Our results thus support the general tenet that widespread pleiotropy generates IaSC despite sex-specific genetic architecture.
Data from: Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model
Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding.
Data from: Evolutionary rates for multivariate traits: the role of selection and genetic variation
A fundamental question in evolutionary biology is the relative importance of selection and genetic architecture in determining evolutionary rates. Adaptive evolution can be described by the multivariate breeders' equation, which predicts evolutionary change for a suite of phenotypic traits as a product of directional selection acting on them (β) and the genetic variance–covariance matrix for those traits (G). Despite being empirically challenging to estimate, there are enough published estimates of G and β to allow for synthesis of general patterns across species. We use published estimates to test the hypotheses that there are systematic differences in the rate of evolution among trait types, and that these differences are, in part, due to genetic architecture. We find some evidence that sexually selected traits exhibit faster rates of evolution compared with life-history or morphological traits. This difference does not appear to be related to stronger selection on sexually selected traits. Using numerous proposed approaches to quantifying the shape, size and structure of G, we examine how these parameters relate to one another, and how they vary among taxonomic and trait groupings. Despite considerable variation, they do not explain the observed differences in evolutionary rates.
Data from: Inference of adaptive shifts for multivariate correlated traits
To study the evolution of several quantitative traits, the classical phylogenetic comparative framework consists of a multivariate random process running along the branches of a phylogenetic tree. The Ornstein-Uhlenbeck (OU) process is sometimes preferred to the simple Brownian Motion (BM) as it models stabilizing selection toward an optimum. The optimum for each trait is likely to be changing over the long periods of time spanned by large modern phylogenies. Our goal is to automatically detect the position of these shifts on a phylogenetic tree, while accounting for correlations between traits, which might exist because of structural or evolutionary constraints. We show that, in the presence shifts, phylogenetic Principal Component Analysis (pPCA) fails to decorrelate traits efficiently, so that any method aiming at finding shift needs to deal with correlation simultaneously. We introduce here a simplification of the full multivariate OU model, named scalar OU (scOU), which allows for noncausal correlations and is still computationally tractable. We extend the equivalence between the OU and a BM on a re-scaled tree to our multivariate framework. We describe an Expectation Maximization algorithm that allows for a maximum likelihood estimation of the shift positions, associated with a new model selection criterion, accounting for the identifiability issues for the shift localization on the tree. The method, freely available as an R-package (PhylogeneticEM) is fast, and can deal with missing values. We demonstrate its efficiency and accuracy compared to another state-of-the-art method (l1ou) on a wide range of simulated scenarios, and use this new framework to re-analyze recently gathered datasets on New World Monkeys and Anolis lizards.
Data from: Quantitative genetic variance and multivariate clines in the Ivyleaf morning glory, Ipomoea hederacea
Clinal variation is commonly interpreted as evidence of adaptive differentiation, although clines can also be produced by stochastic forces. Understanding whether clines are adaptive therefore requires comparing clinal variation to background patterns of genetic differentiation at presumably neutral markers. Although this approach has frequently been applied to single traits at a time, we have comparatively fewer examples of how multiple correlated traits vary clinally. Here, we characterize multivariate clines in the Ivyleaf morning glory, examining how suites of traits vary with latitude, with the goal of testing for divergence in trait means that would indicate past evolutionary responses. We couple this with analysis of genetic variance in clinally varying traits in 20 populations to test whether past evolutionary responses have depleted genetic variance, or whether genetic variance declines approaching the range margin. We find evidence of clinal differentiation in five quantitative traits, with little evidence of isolation by distance at neutral loci that would suggest non-adaptive or stochastic mechanisms. Within and across populations, the traits that contribute most to population differentiation and clinal trends in the multivariate phenotype are genetically variable as well, suggesting that a lack of genetic variance will not cause absolute evolutionary constraints. Our data are broadly consistent theoretical predictions of polygenic clines in response to shallow environmental gradients. Ecologically, our results are consistent with past findings of natural selection on flowering phenology, presumably due to season-length variation across the range.
Data from: A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data
Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the K statistic of Blomberg et al. (2003) that is useful for quantifying and evaluating phylogenetic signal in highly-dimensional multivariate data. The method (Kmult) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of Kmult remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on Kmult and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in high-dimensional data. Statistical properties of Kmult were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that Kmult provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders.
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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.