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103 results for “phenotype prediction”
Data from: Phenotypic drought stress prediction of European beech (Fagus sylvatica) by genomic prediction and remote sensing
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Data from: Phenotypically plastic responses to environmental variation are more complex than life history theory predicts
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Data from: Wheat genotypic and phenotypic data for multivariate genomic prediction
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Data from: Phenotype-environment matching predicts both positive and negative effects of intraspecific variation
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Data from: Genetic growth potential, rather than phenotypic size, predicts migration phenotype in Atlantic salmon
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Trait-fitness associations do not predict within-species phenotypic evolution over 2 million years
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Data from: Combining niche-shift and population genetic analyses predicts rapid phenotypic evolution during invasion
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Data from: Population climatic history predicts phenotypic responses in novel environments for Arabidopsis thaliana in North America
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Phenotypic variation in populations of the mosquito vector, <em>Aedes aegypti</em>, and implications for predicting the effects of temperature and climate change on dengue transmission
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Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs
<p><strong>Background: To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. Machine learning (ML) methods in general, and Deep Learning (DL) methods in particular, are a potential way forward because they are able to cope with the nonlinearity of natural systems. However, there are several barriers that exist, including the absence of ML-ready data. We propose to develop a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. A critical part of this framework are data transformation methods that map the heterogeneous input data into formats that are consumable by the ML techniques. The central hypothesis of this research is that deep learning algorithms and biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. Our long term goal is to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches. This pilot project on predicting emergent properties of complex systems and multidimensional interactions is funded by the NSF (Award # 1939945, 1940059, 1940062, 1940330). </strong></p> <p> </p> <p><strong>Results: We have established shared project governance, communication channels, project timeline, and data and computing environment across four universities. We have successfully reached out to three other projects for broader collaboration.</strong></p>
Data from: Stress hormones predict a host superspreader phenotype in the West Nile virus system
Glucocorticoid stress hormones, such as corticosterone (CORT), have profound effects on the behaviour and physiology of organisms, and thus have the potential to alter host competence and the contributions of individuals to population- and community-level pathogen dynamics. For example, CORT could alter the rate of contacts among hosts, pathogens and vectors through its widespread effects on host metabolism and activity levels. CORT could also affect the intensity and duration of pathogen shedding and risk of host mortality during infection. We experimentally manipulated songbird CORT, asking how CORT affected behavioural and physiological responses to a standardized West Nile virus (WNV) challenge. Although all birds became infected after exposure to the virus, only birds with elevated CORT had viral loads at or above the infectious threshold. Moreover, though the rate of mortality was faster in birds with elevated CORT compared with controls, most hosts with elevated CORT survived past the day of peak infectiousness. CORT concentrations just prior to inoculation with WNV and anti-inflammatory cytokine concentrations following viral exposure were predictive of individual duration of infectiousness and the ability to maintain physical performance during infection (i.e. tolerance), revealing putative biomarkers of competence. Collectively, our results suggest that glucocorticoid stress hormones could directly and indirectly mediate the spread of pathogens.
Data from: Is evolution predictable? quantitative genetics under complex genotype-phenotype maps
<p>A fundamental aim of post-genomic 21st century biology is to understand the genotype-phenotype map (GPM) or how specific genetic variation relates to specific phenotypic variation. Quantitative genetics approximates such maps using linear models, and has developed methods to predict the response to selection in a population. The other major field of research concerned with the GPM, developmental evolutionary biology or evo-devo, has found the GPM to be highly nonlinear and complex. Here we quantify how the predictions of quantitative genetics are affected by the complex, nonlinear maps found in developmental biology. We found that the disagreements between predicted and observed responses to selection are common, roughly in a third of generations, systematic and due to nonlinear nature of the genotype-phenotype map. They occur at all time scales, even from one generation to the next. Our results are a step towards integrating the fields studying the GPM.</p>
Data from: Predicting ecological and phenotypic differentiation in the wild: a case of piscivorous fish in a fishless environment
Environmental variation drives ecological and phenotypic change. How predictable is differentiation in response to environmental change? Answering this question requires the development and testing of multifarious a priori predictions in natural systems. We employ this approach using Gobiomorus dormitor populations that have colonized inland blue holes differing in the availability of fish prey. We evaluated predictions of differences in demographics, habitat use, diet, locomotor and trophic morphology, and feeding kinematics and performance between G. dormitor populations inhabiting blue holes with and without fish prey. Populations of G. dormitor independently diverged between prey regimes, with broad agreement between observed differences and a priori predictions. For example, in populations lacking fish prey, we observed male-biased sex ratios, a greater use of shallow-water habitat, and larger population diet breadths as a result of greater individual diet specialization. Furthermore, we found predictable differences in body shape, mouth morphology, suction generation capacity, strike kinematics, and feeding performance on different prey types, consistent with the adaptation of G. dormitor to piscivory when coexisting with fish prey and to feeding on small invertebrates in their absence. The results of the present study suggest great potential in our ability to predict population responses to changing environments, which is an increasingly important capability in a human-dominated, ever-changing world.
Life history predicts flight muscle phenotype and function in birds
<p>1. Functional traits are the essential phenotypes that underlie an organism's life history and ecology. Although biologists have long recognized that intraspecific variation is consequential to an animals' ecology, studies of functional variation are often restricted to species-level comparisons, ignoring critical variation within species. In birds, interspecific comparisons have been foundational in connecting flight muscle phenotypes to species-level ecology, but intraspecific variation has remained largely unexplored.</p> <p>2. We asked how age- and sex-dependent demands on flight muscle function are reconciled in birds. The flight muscle is an essential multifunctional organ, mediating a large range of functions associated with powered flight and thermoregulation. These functions must be balanced over an individual's lifetime.</p> <p>3. We leveraged within- and between-species comparisons in a clade of small passerines (<em>Tarsiger</em> bush-robins) from the eastern edge of the Qinghai-Tibet Plateau. We integrated measurements of flight muscle physiology, morphology, behavior, phenology, and environmental data, analyzing trait data within a context of three widespread, adaptive life-history strategies – sexual dichromatism, age and sex-structured migration, and delayed plumage maturation. This approach provides a framework of the selective forces that shape functional variation within and between species.</p> <p>4. We found more variation in flight muscle traits within species than has been previously described between species of birds under 20 g. This variation was associated with the discovery of mixed muscle fiber types (i.e. both fast glycolytic and fast oxidative fibers), which differ markedly in their physiological and functional attributes. This result is surprising given that the flight muscles of small birds are generally thought to contain only fast oxidative fibers, suggesting a novel ecological context for glycolytic muscle fibers in small birds. Within each species, flight muscle phenotypes varied by age and sex, reflecting the functional demands at different life-history stages and the pressures that individuals face as a result of their multi-class identity (i.e. species, age, and sex).</p> <p>5. Our findings reveal new links between avian physiology, ecology, behavior, and life history, while demonstrating the importance of demographic-dependent selection in shaping functional phenotypic variation. </p>
Data from: Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
Data from: The evolution of vertebrate eye size across an environmental gradient: phenotype does not predict genotype in a Trinidadian killifish
Vertebrates exhibit substantial variation in eye size. Eye size correlates positively with visual capacity and behaviors that enhance fitness, such as predator avoidance. This foreshadows a connection between predation and eye size evolution. Yet, the conditions that favor evolutionary shifts in eye size, besides the well-known role for light availability, are unclear. We tested the influence of predation on the evolution of eye size in Trinidadian killifish, Rivulus hartii. Rivulus are located across a series of communities where they coexist with visually oriented piscivores ('high predation' sites), and no predators ('Rivulus-only' sites). Wild-caught Rivulus from high predation sites generally exhibited a smaller relative eye size than communities that lack predators. Yet, such differences were inconsistent across rivers. Second-generation common garden reared fish revealed repeatable decreases in eye size in Rivulus from high predation sites. We performed additional experiments that tested the importance of light and resources on eye size evolution. Sites that differ in light or resource availability did not differ in eye size. Our results argue that differences in predator-induced mortality underlie genetically-based shifts in vertebrate eye size. We discuss the drivers of eye size evolution in light of the nonparallel trends between the phenotypic and common garden results.
Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
Comorbid-phenome prediction and phenotype risk scores enhance gene discovery for generalized anxiety disorder and posttraumatic stress disorder
<p>GWAS summary statistics. See ReadMe for column descriptions.</p>
No evidence of predicted phenotypic changes after hurricane disturbance in a shade-specialist Caribbean anole
<p>Extreme climatic events (ECE) such as hurricanes have been hypothesized to be a major driving force of natural selection. Recent studies argue that, following strong hurricane disturbance, <em>Anolis</em> lizards in the Caribbean undergo selection for traits such as longer forelimbs or smaller body sizes that improve their clinging ability to their substrates increasing their chances of surviving hurricane wind gusts. Some authors challenge the generalization of this hypothesis arguing that other mechanisms may explain these phenotypic changes or that they may not necessarily be generalizable due to system-specific idiosyncrasies. To address this issue, we compared body size and relative forelimb length of <em>Anolis gundlachi</em>, a trunk-ground anole living in closed canopy forests in Puerto Rico, before, four months after, and 15 months after Hurricanes Irma and Maria in 2017. Overall, our results show no clear evidence of a temporal decrease in body size or increase forelimb length (relative to body size) challenging the generalizability of the clinging ability hypothesis. Understanding how animals adapt to ECE is an emerging field. Still, we are quickly learning that this process is complex and nuanced.</p>
On the predictability of phenotypic divergence in geographic isolation
<p><span>Do related populations that are separated by barriers predictably evolve differences from one another over time, or is such divergence idiosyncratic and unpredictable? We test these </span><span>alternatives by investigating patterns of trait evolution for 54 sister pairs of Andean forest birds that live in similar environments on either side of the arid Marañón Gap, a strong dispersal barrier for humid montane species. We measured divergence in both sexual (song and plumage) and ecological (beak size and beak shape) traits. Sexual traits evolve in a clock-like fashion, with trait divergence positively correlated with genetic distance (r = 0.6 to 0.7). In contrast, divergence in ecological traits is uncorrelated or only loosely correlated with genetic distance (r = 0.0 to 0.3). Thus, for geographically isolated Andean montane forest birds that live in similar environments, divergence is predictable in sexual traits, but not for ecological traits. This means that sexual trait divergence occurs independently of adaptive ecological divergence within the mega-diverse tropical Andean avifauna. Last, we show that variation in genetic divergence across a biogeographic barrier is associated</span><span> with traits that are proxies for species' opportunities for dispersal (low elevation limit and elevational niche breadth), but not with traits that are proxies for species' dispersal abilities (hand-wing index and foraging strata).</span></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.