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6,234 results for “phenotypes”
Data from: Using the generalized index of dissimilarity to detect gene-gene interactions in multi-class phenotypes
To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimensionality reduction (MDR) is an efficient method of examining the interactions between genes in genetic association studies. It commonly assumes a dichotomous classification of the binary phenotypes. Its usual approach to determining the genomic association is to construct a confusion matrix to estimate a classification error, where a binary risk status is determined and assigned to each genotypic multifactor class. While multi-class phenotypes are commonly observed, the current MDR approach does not handle these phenotypes appropriately because the thresholds for the risk statuses may not be clear. In this study, we suggest a new method for estimating gene-gene interactions for multi-class phenotypes. Our approach adopts the index of dissimilarity (IDS) as an evaluation measure. This is analytically equivalent to the common association measure of balanced accuracy (BA) for the binary traits, while it is not required to determine the risk status for the estimation. Moreover, it is easily expandable to the generalized index of dissimilarity (GIDS), which has an explicit form that can handle any number of categories. The performance of the proposed method was compared with those of other approaches via simulation studies in which fifteen genetic models were generated with three class outcomes. A consistently better performance was observed using the proposed method. The effect of a varying number of categories was examined. The proposed method was also illustrated using real genome-wide association studies (GWAS) data from the Korean Association Resource (KARE) project.
Data from: Comparing evolutionary rates for different phenotypic traits on a phylogeny using likelihood
In recent years, likelihood-based approaches have been used with increasing frequency to evaluate macroevolutionary hypotheses of phenotypic evolution under distinct evolutionary processes in a phylogenetic context (e.g., Brownian motion, Ornstein-Uhlenbeck, etc.), and to compare one or more evolutionary rates for the same phenotypic trait along a phylogeny. It is also of interest to determine whether one trait evolves at a faster rate than another trait. However, to date no study has compared phylogenetic evolutionary rates between traits using likelihood, because a formal approach has not yet been proposed. In this article, I describe a new likelihood procedure for comparing evolutionary rates for two or more phenotypic traits on a phylogeny. This approach compares the likelihood of a model where each trait evolves at a distinct evolutionary rate to the likelihood of a model where all traits are constrained to evolve at a common evolutionary rate. The method can also account for within-species measurement error and within-species trait covariation if available. Simulations revealed that the method has appropriate Type I error rates and statistical power. Importantly, when compared with existing approaches based on phylogenetically independent contrasts and methods that compare confidence intervals for model parameters, the likelihood method displays preferable statistical properties for a wide range of simulated conditions. Thus, this likelihood-based method extends the phylogenetic comparative biology toolkit and provides evolutionary biologists with a more powerful means of determining when evolutionary rates differ between phenotypic traits. Finally, I provide an empirical example illustrating the approach by comparing rates of evolution for several phenotypic traits in Plethodon salamanders.
Data from: Detection of human disease conditions by single-cell morpho-rheological phenotyping of blood
Blood is arguably the most important bodily fluid and its analysis provides crucial health status information. A first routine measure to narrow down diagnosis in clinical practice is the differential blood count, determining the frequency of all major blood cells. What is lacking to advance initial blood diagnostics is an unbiased and quick functional assessment of blood that can narrow down the diagnosis and generate specific hypotheses. To address this need, we introduce the continuous, cell-by-cell morpho-rheological (MORE) analysis of diluted whole blood, without labeling, enrichment or separation, at rates of 1,000 cells/sec. In a drop of blood we can identify all major blood cells and characterize their pathological changes in several disease conditions in vitro and in patient samples. This approach takes previous results of mechanical studies on specifically isolated blood cells to the level of application directly in blood and adds a functional dimension to conventional blood analysis.
Data from: The effects of genotype × phenotype interactions on transcriptional response to silver nanoparticle toxicity in organotypic cultures of murine tracheal epithelial cells
The airway epithelium is critical for maintaining innate and adaptive immune responses, and occupational exposures that disrupt its immune homeostasis may initiate and amplify airway inflammation. In our previous study, we demonstrated that silver nanoparticles (AgNP), which are engineered nanomaterials used in multiple applications but primarily in the manufacturing of many antimicrobial products, induce toxicity in organotypic cultures derived from murine tracheal epithelial cells (MTEC), and those differentiated toward a "Type 2 [T2]-Skewed" phenotype experienced an increased sensitivity to AgNP toxicity, suggesting that asthmatics could be a sensitive population to AgNP exposures in occupational settings. However, the mechanistic basis for this genotype × phenotype interaction (G×P) has yet to be defined. In the present study, we conducted transcriptional profiling using RNA-sequencing (RNA-seq) to predict the enrichment of specific canonical pathways and upstream transcriptional regulators to assist in defining a mechanistic basis for G×P effects on AgNP toxicity. Organotypic cultures were derived from MTEC across two genetically inbred mouse strains (A/J and C57BL/6J mice), two phenotypes ("Normal" and "T2-Skewed"), and one AgNP exposure (an acute 24 h exposure) to characterize G×P effects on transcriptional response to AgNP toxicity. The "T2-Skewed" phenotype was marked by increased pro-inflammatory T17 responses to AgNP toxicity, which are significant predictors of neutrophilic/difficult-to-control asthma and suggests that asthmatics could be a sensitive population to AgNP exposures in occupational settings. This study highlights the importance of considering G×P effects when identifying these sensitive populations, whose underlying genetics or diseases could directly modify their response to AgNP exposures.
Heliconiini phenotypic classification
<div> <h3>Summary</h3> <p>This repository contains the <strong>phenotypic classification</strong> for heliconiine butterfly subspecies applied for the analyses presented in this research paper:</p> <p> <strong>“Pérochon et al., 2025 - Müllerian Mimicry in Neotropical Butterflies: One Mimicry Ring to Bring Them All and in the Jungle Bind Them. <em>Global Ecology and Biogeography</em>, 34(9): e70127”</strong> <a href="https://doi.org/10.1111/geb.70127">https://doi.org/10.1111/geb.70127</a></p> <p> </p> </div> <div> <h3>Associated archives</h3> <p><strong>Occurrences data</strong> associated with this classification can be found in this archive: <a href="https://doi.org/10.5281/zenodo.10906853">https://doi.org/10.5281/zenodo.10906853</a>.</p> <p><strong>Distribution maps</strong> built upon the occurrence data and phenotypic classification are available in an other associated archive: <a href="https://doi.org/10.5281/zenodo.10903661">https://doi.org/10.5281/zenodo.10903661</a>.</p> <p><strong>Scripts</strong> to reproduce these maps are available on Github at <a href="https://github.com/EddiePerochon/Heliconiini_Diversity">https://github.com/EddiePerochon/Heliconiini_Diversity</a>.</p> <p> </p> </div> <div> <h3>Contents</h3> <p>This repository contains two folders and three files:</p> <ul> <li> <p><strong>Type_pictures</strong>: 38 pictures of butterfly type taxa used as reference for each phenotypic group. Groups are separated between patterns found only in Heliconiini (types are Heliconiini), and patterns shared with Ithomiini (types are Ithomiini). Credit photos: Chris Jiggins.</p> </li> <li> <p><strong>Groups_shared_with_Ithomiini</strong>: 11 pictures of Heliconiini butterfly illustrating each phenotypic group shared with Ithomiini butterflies. Credits: Chris Jiggins. Type taxa of these pheontypic groups are ithomiine butterflies found in the <strong>Type_pictures</strong> folder and in the dedicated archive <a href="https://doi.org/10.5281/zenodo.5497876">https://doi.org/10.5281/zenodo.5497876</a>.</p> </li> <li> <p><strong>Heliconiini_phenotypic_classification.xlsx</strong>: Phenotypic classification of the 436 subspecies of Heliconiini butterflies + List of type taxa identifying each pattern for both levels used in analyses: split (Main test) and lump (Sensitivity analyses).</p> </li> <li> <p><strong>Plate_Phenotypic_groups_Heliconiini.pdf</strong>: Plate with illustration of all 38 phenotypic groups found in Heliconiini. Credit photos: Chris Jiggins.</p> </li> <li> <p><strong>Plate_Phenotypic_groups_Ithomiini.pdf</strong>: Plate with illustration of all 44 phenotypic groups found in Ithomiini. Credit photos: Chris Jiggins.</p> </li> </ul> </div> <div> <h3> </h3> <h3>Contact</h3> <p>For any questions, contact the corresponding author: <a href="mailto:mael.dore@gmail.com?subject=Heliconiini%20mimicry%20classification">Maël Doré</a></p> <h3> </h3> <h3>How to cite</h3> <p>> Pérochon, E., Rosser, N., Kozak, K., McMillan, W.O., Huertas, B., Mallet, J., Ready, J., Willmott, K., Elias, M. & Doré, M. (2025). Müllerian Mimicry in Neotropical Butterflies: One Mimicry Ring to Bring Them All and in the Jungle Bind Them. <em>Global Ecology and Biogeography</em>, 34(9): e70127. https://doi.org/10.1111/geb.70127</p> <p> </p> </div>
Figures 28-29 from: Zacharczenko B, Wagner D, Hatfield M (2014) A new cryptic Sympistis from eastern North America revealed by novel larval phenotype and host plant association (Lepidoptera, Noctuidae, Oncocnemidinae). ZooKeys 379: 93-107. https://doi.org/10.3897/zookeys.379.5765
Figures 28-29 - Sympistis forbesi pupa 28 ventral 29 lateral.
Figure 1 from: Deans A, Mullins P, Kawada R, Balhoff J (2013) Corrigenda: Mullins PL, Kawada R, Balhoff JP, Deans AR (2012) A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation. ZooKeys 223: 1–38, doi: 10.3897/zookeys.223.3572. ZooKeys 278: 105-113. https://doi.org/10.3897/zookeys.278.5108
Figure 1 - Evaniscus sulcigenis Roman, 1917. Lateral habitus (whole body) of holotype.
Figure 2 from: Deans A, Mullins P, Kawada R, Balhoff J (2013) Corrigenda: Mullins PL, Kawada R, Balhoff JP, Deans AR (2012) A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation. ZooKeys 223: 1–38, doi: 10.3897/zookeys.223.3572. ZooKeys 278: 105-113. https://doi.org/10.3897/zookeys.278.5108
Figure 2 - Evaniscus sulcigenis Roman, 1917. Lateral habitus (mesosma and head) of holotype.
Figure 9 from: Singh G, Aptroot A, Rico VJ, Otte J, Divakar PK, Crespo A, Cáceres ME da S, Lumbsch HT, Schmitt I (2018) Neoprotoparmelia gen. nov. and Maronina (Lecanorales, Protoparmelioideae): species description and generic delimitation using DNA barcodes and phenotypical characters. MycoKeys 44: 19-50. https://doi.org/10.3897/mycokeys.44.29904
Figure 9 Neoprotoparmeliacrassa Elix39818. Scale bar: 1 mm.
Figure 4 from: Singh G, Aptroot A, Rico VJ, Otte J, Divakar PK, Crespo A, Cáceres ME da S, Lumbsch HT, Schmitt I (2018) Neoprotoparmelia gen. nov. and Maronina (Lecanorales, Protoparmelioideae): species description and generic delimitation using DNA barcodes and phenotypical characters. MycoKeys 44: 19-50. https://doi.org/10.3897/mycokeys.44.29904
Figure 4 Neoprotoparmeliaamerisidiata, holotype Lendemer 20995 (NY). Scale bar: 1 mm.
Figure 13 from: Singh G, Aptroot A, Rico VJ, Otte J, Divakar PK, Crespo A, Cáceres ME da S, Lumbsch HT, Schmitt I (2018) Neoprotoparmelia gen. nov. and Maronina (Lecanorales, Protoparmelioideae): species description and generic delimitation using DNA barcodes and phenotypical characters. MycoKeys 44: 19-50. https://doi.org/10.3897/mycokeys.44.29904
Figure 13 Neoprotoparmeliasiamisidiata, v.d. Boom 46872 (Hb. v.d. Boom). Scale bar: 1 mm.
Figure 5 from: Singh G, Aptroot A, Rico VJ, Otte J, Divakar PK, Crespo A, Cáceres ME da S, Lumbsch HT, Schmitt I (2018) Neoprotoparmelia gen. nov. and Maronina (Lecanorales, Protoparmelioideae): species description and generic delimitation using DNA barcodes and phenotypical characters. MycoKeys 44: 19-50. https://doi.org/10.3897/mycokeys.44.29904
Figure 5 Neoprotoparmeliaaustralisidiata, holotype Kantvilas 289/07 (HO 545660). Scale bar: 1 mm.
Figure 10 from: Singh G, Aptroot A, Rico VJ, Otte J, Divakar PK, Crespo A, Cáceres ME da S, Lumbsch HT, Schmitt I (2018) Neoprotoparmelia gen. nov. and Maronina (Lecanorales, Protoparmelioideae): species description and generic delimitation using DNA barcodes and phenotypical characters. MycoKeys 44: 19-50. https://doi.org/10.3897/mycokeys.44.29904
Figure 10 Neoprotoparmeliaisidiata, holotype Aptroot 31494 (BR). Scale bar: 1 mm.
Data for "Hypotheses on the extended phenotype of the mitochondrion"
<p>Mitochondrial genome data associated with the manuscript "Hypotheses on the extended phenotype of the mitochondrion".</p> <p>This is simply a particular RefSeq mitochondrial data release. Note that NCBI does not appear to maintain an archive of past RefSeq mitochondrial data files.<br> </p>
Specific CD4+ T cell phenotypes associate with bacterial control in people who 'resist' infection with Mycobacterium tuberculosis
<p>The repository contains the processed Seurat object generated from SELECT-seq data of ESAT-6/CFP-10-specific T cells sorted from PBMC of household contacts of sputum culture-positive cases of pulmonary TB (four subjects with latent TB infection (LTBI) and three <em>M. tuberculosis </em>'resisters' (RSTR)).</p> <p>Information on how the Seurat object was analyzed can be found in this GitHub respository: https://github.com/ttsunmeng/TB_RSTR_ESAT6CFP10_SelectSeq_analysis_pipeline.</p> <p>The full details of this cohort have been previously published here:</p> <ul> <li>Stein, C.M., et al. Long-term Stability of Resistance to Latent Mycobacterium tuberculosis Infection in Highly Exposed Tuberculosis Household Contacts in Kampala, Uganda. Clin Infect Dis 68, 1705-1712 (2019).</li> <li>Stein, C.M., et al. Resistance and Susceptibility to Mycobacterium tuberculosis Infection and Disease in Tuberculosis Households in Kampala, Uganda. Am J Epidemiol 187, 1477-1489 (2018).</li> </ul>
Gene-based association analysis for back pain-related phenotypes, results
<p>Results of gene-based association analysis of back pain-related phenotypes using imputed genotypes (500K GWAS) and exome sequencing data (200K WES) from the UK Biobank.</p> <p>Back pain-related phenotypes:</p> <p>CBP - chronic back pain</p> <p>IDD - Intervertebral disc disorder defined by the ICD-10 code M51</p> <p>Dorsalgia, defined by the ICD-10 code M54</p> <p>SGIT - linear combination of the above original traits, obtained as described by Svishcheva et al. (doi: 10.3390/genes13101694) </p>
LEADER (Leaf Element Accumulation from Deep Roots): a nondestructive phenotyping platform to estimate rooting depth in the field
<p>Data and code for figure generation for <strong>LEADER (Leaf Element Accumulation from Deep Roots): a nondestructive phenotyping platform to estimate rooting depth in the field</strong></p>
Longitudinal Study of Phenotypic and Developmental Severity in Patients With Dravet Syndrome With SCN1A Gene Mutation
ClinicalTrials.gov study NCT07251673. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Phenotypic Exploration of Autism Spectrum Disorders Retrospective and Prospective Data
ClinicalTrials.gov study NCT04923854. IPD Sharing: NO. Countries: 1. Publications: 0.
The Role of CD4+ Memory Phenotype, Memory, and Effector T Cells in Vaccination and Infection
ClinicalTrials.gov study NCT03453801. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.