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260 results for “Biobanks”
Scalable mixed model approaches for set-based association studies on large-scale categorical data analysis and its application to 450k exome sequencing data in UK Biobank
<p>The ongoing release of large-scale sequencing data in the UK Biobank allows for identifying associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a novel method for rare-variant association tests, POLMM-GENE, in which a proportional odds logistic mixed model was used to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole exome-sequencing data for 5 ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.</p>
GWAS of J13 coded pneumonia in UK Biobank.
<p>GWAS of J13 coded pneumonia in UK Biobank using regenie. Details in the manuscript [to be updated when manuscript accepted].</p>
LTPI GWAS summary generated from UK Biobank application
<p>This repository contains a series of GWAS summary data files, each pertaining to a specific disease. The files are named according to the disease they relate to and are available in two versions: LTPI and PHECODE. The naming convention for each file follows the pattern [Disease]_[AnalysisType].txt.gz, where [Disease] is replaced with the specific disease name (e.g., Bowel_cancer, Breast_cancer) and [AnalysisType] indicates the type of analysis, either LTPI or PHECODE.</p>
GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank - Part AC
<p>GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank.</p> <p>Part <strong>AC </strong>of AA, AB, AC, AD and AE</p>
GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank - Parts AD and AE
<p>GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank.</p> <p>Part <strong>AD </strong>of AA, AB, AC, AD and AE</p>
Alzheimer's disease meta-analysis of Kunkle et al GWAS and UK Biobank GWAX
<p>This dataset comprises summary statistics from a meta-analysis of: (1) GWAS for family history of Alzheimer’s disease (GWAX) using the UK Biobank, and (2) the Kunkle et al. (2019) GWAS of diagnosed Alzheimer’s disease. Further analyses of these data are described in an upcoming publication.</p>
DICOM converted Slide Microscopy images for the Cancer Moonshot Biobank initiative collections
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=cmb_aml&collection_id=cmb_crc&collection_id=cmb_gec&collection_id=cmb_lca&collection_id=cmb_mel&collection_id=cmb_mml&collection_id=cmb_pca">here</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> <div> <div>The <a href="https://moonshotbiobank.cancer.gov/">Cancer Moonshot Biobank</a> (CMB) is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving <a href="https://www.cancer.gov/publications/dictionaries/cancer-terms/def/standard-of-care">standard of care cancer treatment</a> at multiple <a href="https://ncorp.cancer.gov/">NCI Community Oncology Research Program (NCORP)</a> sites.</div> <div> <p>CMB program is organized into multiple cancer-specific collections. Digital pathology images for each of those collections were converted into DICOM representation by the IDC team and are shared via IDC.</p> <p>1. <a href="https://doi.org/10.7937/PCTE-6M66">CMB-AML</a> (acute myeloid leukemia cancer)<br>2. <a href="https://doi.org/10.7937/DJG7-GZ87">CMB-CRC</a> (colorectal cancer)<br>3. <a href="https://doi.org/10.7937/E7KH-R486">CMB-GEC</a> (gastroesophageal cancer)<br>4. <a href="https://doi.org/10.7937/3CX3-S132">CMB-LCA</a> (lung cancer)<br>5. <a href="https://doi.org/10.7937/GWSP-WH72">CMB-MEL</a> (melanoma)<br>6. <a href="https://doi.org/10.7937/SZKB-SW39">CMB-MEL</a> (multiple myeloma)<br>7. <a>CMB-PCA</a> (prostate cancer)</p> </div> </div> <p>Digital pathology images, augmented with the metadata describing their content, were converted into DICOM Whole Slide Microscopy (SM) representation [2,3] using custom open source scripts and tools as described in [4]. </p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <p>For each of the collections, the following manifest files are provided:</p> <ol> <li><code><collection_id>-idc_v19-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code><collection_id>-idc_v19-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code><collection_id>-idc_v19-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. <em>Radiographics</em> <strong>43,</strong> (2023).</p> <p>[2] National Electrical Manufacturers Association (NEMA). <em>DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD</em>. at <https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8></p> <p>[3] Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., John Lafrate, A., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N. & Lennerz, J. K. Implementing the DICOM standard for digital pathology. <em>J. Pathol. Inform.</em> <strong>9,</strong> 37 (2018).</p> <p>[4] Clunie, D., Fedorov, A. & Herrmann, M. D. <em>ImagingDataCommons/idc-wsi-conversion: Initial release</em>. (Zenodo, 2023). doi:10.5281/ZENODO.8240154</p>
Data for "Distinct explanations underlie gene-environment interactions in the UK Biobank."
<p>Raw output of scripts run from this repository: https://github.com/arundurvasula/GxE</p>
Retina BioBank: Retina Biomarkers for a Deeper Understanding of Vitreoretinal and Systemic Diseases
ClinicalTrials.gov study NCT06796673. IPD Sharing: NO. Countries: 1. Publications: 15.
Northern Colorado COVID-19 Biobank
ClinicalTrials.gov study NCT04603677. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Utrecht Coronary Biobank (UCORBIO)
ClinicalTrials.gov study NCT02304744. IPD Sharing: Not stated. Countries: 1. Publications: 3.
INfectious DIsease REgistry BIObank
ClinicalTrials.gov study NCT06418048. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Establishment of Biobank of Nasal Epithelium Samples From Healthy Volunteers
ClinicalTrials.gov study NCT06120244. IPD Sharing: YES. Countries: 1. Publications: 2.
Establishment of the Human Intestinal and Salivary Microbiota Biobank - Kidney Diseases
ClinicalTrials.gov study NCT04689074. IPD Sharing: NO. Countries: 1. Publications: 5.
Clinical Database and Biobank of Patients With Gynecologic Neoplasms
ClinicalTrials.gov study NCT01267851. IPD Sharing: Not stated. Countries: 1. Publications: 1.
University of Michigan "Nephrology Research BioBank"
ClinicalTrials.gov study NCT00381121. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Establishment of the Human Intestinal and Salivary Microbiota Biobank - Gastrointestinal Diseases
ClinicalTrials.gov study NCT04698148. IPD Sharing: NO. Countries: 1. Publications: 5.
Clinical Course of Interstitial Lung Diseases: European IPF Registry and Biobank
ClinicalTrials.gov study NCT02951416. IPD Sharing: UNDECIDED. Countries: 5. Publications: 9.
Prospective Biobanking Study in Cancer Patients Aiming at Better Understand the Link Between the Molecular Alterations of the Tumor Itself, Its Microenvironment and Immune Response (SCANDARE)
ClinicalTrials.gov study NCT03017573. IPD Sharing: YES. Countries: 1. Publications: 1.
Advanced Imaging Holistic Omics Biobank
ClinicalTrials.gov study NCT06523270. IPD Sharing: Not stated. Countries: 1. Publications: 3.
ScienceDex guides
Understand access before you commit
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.