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215 results for “summary statistics”
Genome-wide association summary statistics for varicose veins of lower extremities
<p>The dataset contains summary statistics for the discovery and the replication stages of the large-scale genome-wide associations study for varicose veins of lower extremities. The discovery stage was based on genetic association data provided by the Neale Lab (<a href="https://vk.com/away.php?to=http%3A%2F%2Fwww.nealelab.is%2F&cc_key=">http://www.nealelab.is/</a>) for 337,199 UK biobank individuals. Phenotype “varicose veins of lower extremities” was defined based on International Classification of Disease (ICD-10) billing code “I83” present in the electronic patient record. Data were adjusted for two potential confounders – body mass index and deep venous thrombosis. A replication cohort (N=71,256) was generated by means of reverse meta-analysis of two overlapping datasets: genetic association data for 408,455 UK Biobank participants provided by the Gene ATLAS database (<a href="https://vk.com/away.php?to=http%3A%2F%2Fgeneatlas.roslin.ed.ac.uk%2F&cc_key=">http://geneatlas.roslin.ed.ac.uk/</a>), and the above mentioned data provided by the Neale Lab.</p> <p>Please, note, that in Shadrina et al (PLOS Genetics 2019) we only used "discovery" dataset, while in biorxiv preprint (https://doi.org/10.1101/368365) both discovery and replication datasets were used. </p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. </p> <p><strong>When using downloaded data, please cite corresponding paper and this repository:</strong></p> <ol> <li> <p>Shadrina, A. S., Sharapov, S. Z., Shashkova, T. I. & Tsepilov, Y. A. Varicose veins of lower extremities: Insights from the first large-scale genetic study. <em>PLOS Genet.</em> <strong>15,</strong> e1008110 (2019).</p> </li> <li>Alexandra S. Shadrina, Sodbo Zh. Sharapov, Tatiana I. Shashkova, & Yakov A. Tsepilov. (2018). Genome-wide association summary statistics for varicose veins of lower extremities (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1323484</li> </ol> <p><strong>Funding:</strong></p> <p>The work of ASS was supported by the Russian Science Foundation [Project No 17-75-20223]. <br> The work of YAT was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Programme. <br> The work of SZS was supported by the Institute of Cytology and Genetics [Project No 0324-2018-0017].</p> <p><strong>Column headers - discovery</strong></p> <ol> <li>SNP: SNP rsID</li> <li>b: effect size of effect allele</li> <li>se: standard error of effect size</li> <li>chi2: T^2 value of effect allele</li> <li>Pval: P-value of association (without GC correction)</li> <li>N: sample size</li> <li>Chr: chromosome</li> <li>Pos: position (GRCh37 build)</li> <li>A1: effect allele (coded as "1")</li> <li>A2: reference allele (coded as "0")</li> </ol> <p><strong>Column headers - replication</strong></p> <ol> <li>SNP: SNP rsID</li> <li>A1: effect allele (coded as "1")</li> <li>A2: reference allele (coded as "0")</li> <li>N: Total sample size</li> <li>Z: Z-value of effect allele</li> <li>P: P-value of association (without GC correction)</li> </ol>
A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy: Summary statistics - Health records
<p>Infectious agents contribute significantly to the global burden of diseases, through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens including those from the herpes, retro- and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant. This variant has persisted throughout hominid evolution and could play a key role in regulation of the immune response. Using 121 infection and inflammation related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates haematopoietic pathways, and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation. </p> <p>-------------------------------------------------------------------------------------</p> <p>This dataset contains GWAS summary statistics for infection, inflammation, and allergy related traits in 487,297 individuals</p>
A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy: Summary statistics - Serology
<p>Infectious agents contribute significantly to the global burden of diseases, through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens including those from the herpes, retro- and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant. This variant has persisted throughout hominid evolution and could play a key role in regulation of the immune response. Using 121 infection and inflammation related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates haematopoietic pathways, and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation. </p> <p>-------------------------------------------------------------------------------------</p> <p>This dataset contains GWAS summary statistics for quantitative antibody responses in 9611 individuals and results for a meta-analysis of UK Biobank and CoLaus/PsyCoLaus antibody responses.</p> <p> </p>
Fine-mapped summary statistics for protein coding regions (i.e. cis regions) based on the Olink Explore 1536 and Explore Expansion technologies
<p>This data set contains fine-mapping results performed by SuSie for cis regions ±500kb around the protein coding gene) for protein targets as measured by the Olink Explore 1536 and Explore Expansion technologies in 1,180 individuals from EPIC Norfolk study (https://www.epic-norfolk.org.uk/). Only protein targets where fine-mapping predicted at least one credible set were included in the results. </p>
Accurate and Efficient Estimation of Local Heritability using Summary Statistics and LD Matrix -- Demo datasets for the HEELS tutorials
<p>We introduced a new estimator for local heritability, "HEELS", which attains comparable statistical efficiency as the REML estimator (such as those produced by GCTA and BOLT-REML) but only requires summary-level statistics – Z-scores from marginal association tests and the empirical LD. Our method has been implemented into an open-source Python-based command line tool. </p> <p>The datasets released here can be downloaded to test the two main functions of our software package: 1) estimating local heritability; 2) computing the low-dimensional representation of the LD matrix. They are meant to accompany the HEELS tutorials we have posted onto the wiki pages of our github repository: https://github.com/huilisabrina/HEELS/wiki.</p> <p> </p>
Summary statistics of stimulated fibroblast eQTLs
<p>This dataset comprises summary statistics from an eQTL mapping study of stimulated fibroblast eQTLs demonstrated in the manuscript entitled "Mapping interindividual dynamics of innate immune response at single-cell resolution" by Kumasaka et al.</p>
Curated GWAS summary statistics on African ancestry on 19 blood count traits and glycemic traits (hg38)
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p> </p>
Curated GWAS summary statistics on East Asian ancestry on 19 blood count traits and glycemic traits
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p>Full description of the method used to derive this dataset can be found in </p>
Supplementary data and summary statistics - Genetic influences on circulating retinol and its relationship to human health
<p><strong>Summary statistics from the circulating retinol GWAS</strong></p> <p>See -<em><strong> GWAS_summary_stats_README.txt </strong></em>for details of these files and the header names. METSIM+INTERVAL meta-analyses have a sample size of 17268. The full meta-analysis that includes ATBC+PLCO has a sample size of 22274.</p> <p><strong>Please cite the following if you use any of these data </strong>- Reay, W.R. et al. Genetic influences on circulating retinol and its relationship to human health. Nature Communications (2024).</p> <p>By downloading these summary statistics, investigators agree to the following:</p> <ol> <li>Investigators acknowledge that these data are provided on an “as-is” basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose.</li> <li>Investigators will not cross-post these data or make them available elsewhere – this website is the definitive source for these data without express written permission from the study corresponding authors.</li> <li>Investigators will never attempt to identify any participant who contributed to these data.</li> <li>Any commercial or for-profit use of these data is forbidden unless express permission is sought from the study corresponding authors.</li> <li>Investigators will cite the associated manuscript when using these data.</li> </ol> <p><strong>Supplementary data from the circulating retinol GWAS phenome-wide Mendelian randomisation study</strong></p> <p>1. MR_retinol_as_exp - full output from the MR-pheWAS using circulating retinol as the exposure</p>
Summary statistics data for "Genetic Analyses Support the Contribution of mRNA N6-methyladenosine (m6A) Modification to Human Diseases Heritability"
<p>We included the summary statistics data associated with our manuscript "<strong>Genetic Analyses Support the Contribution of mRNA <em>N</em><sup>6</sup>-methyladenosine (m<sup>6</sup>A) Modification to Human Diseases Heritability". </strong></p> <p>We also included the newly imputed genotype data for the 60 YRI individuals involved in our study, the joint m<sup>6</sup>A peaks tested (locations of the molecular phetnotype in BED12 format) and normalized log odds ratio (enrichment) of these joint peaks (molecular phenotype data). </p>
Full summary statistics of mixQTL for GTEx v8 Small_Intestine_Terminal_Ileum
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Kidney Cortex
<p>The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.</p>
Full summary statistics of mixQTL for GTEx v8 Kidney_Cortex
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Heart_Left_Ventricle
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Brain_Putamen_basal_ganglia
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Brain_Frontal_Cortex_BA9
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Brain_Cerebellum
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Brain_Caudate_basal_ganglia
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Brain_Substantia_nigra
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Cells_Cultured_fibroblasts
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
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.