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215 results for “summary statistics”
Summary statistics for: Phenome-wide analyses identify an association between the parent-of-origin effects dependent methylome and the rate of aging in humans
<p>Summary statistics of single POE-CpG based and POE-CpG co-methylation module based phenome-wide association analyses for the manuscript "Phenome-wide analyses identify an association between the parent-of-origin effects dependent methylome and the rate of aging in humans"</p>
DIETxPOSOME - Summary statistics from papers obtained from literature mining and machine learning protocols
<p>DIETxPOSOME database concerning literature selection of potentially useful papers retrieved from PubMed search API, concerning contaminants quantification in food items of worldwide highest supply and using FoodMine code (text matching filter) and machine learning (ML) protocols. 11,723 data points were collected from 254 papers from the last two decades in 72 foods to obtain relevant information on 96 contaminants, including heavy metals, polychlorinated biphenyls, dioxins, furans, polycyclic aromatic hydrocarbons (PAHs), pesticides, mycotoxins, and heterocyclic aromatic amines (HAAs).</p> <p> </p>
Genome-wide determinants of mortality and clinical progression in Parkinson's disease - Summary statistics
<p>Summary statistics from "Genome-wide determinants of mortality and clinical progression in Parkinson’s disease".</p>
GWAS Summary Statistics of 7 chronic pain types
<p>Summary Statistics obtained from .fastGWA files after running GWAS analyses using GCTA package for 7 chronic pain types: back, neck, hip, knee, facial and abdominal pain as well as chronic headaches.</p>
Data from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'
<p>Data sets from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'. These include the test statistics from analyses of real and simulated data, and the data used to generate the figures relating to the goodness-of-fit of the generalised extreme value distribution to the GPS test statistics under the null. Please see the enclosed README for more details.</p>
Multivariate Genome-wide association summary statistics for shared aging factor
<p>This dataset contains genome-wide summary statistics (autosomal variants) computed from a multivariate genome-wide association study of five aging-related phenotypes using Genomic Structural Equation Modeling (https://github.com/GenomicSEM/GenomicSEM). The effective sample size is calculated to be 1,958,774. Column descriptions are included in the accompanying README file. </p> <p>The summary statistics are provided on an "AS-IS" basis, without any type of warranty, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose.</p> <p>If investigators use these data, any and all consequences are entirely their responsibility. The user agrees that to cite the appropriate publication in any communications or publications arising directly or indirectly from these data by downloading and and using these data,. The user also agrees to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles and also agree that they will never attempt to identify any participant.</p> <p> </p>
isoMIGA: Expression and Splicing QTL Summary Statistics in Human Microglia
<p>https://github.com/RajLabMSSM/isoMiGA </p> <p>Full associations and top association summary statistics for QTLs mapped in a multi-ethnic meta-analysis of human microglia, as part of the isoform-centric Microglia Genomic Atlas (isoMiGA) project.</p> <p>Sample size = 555 samples from 391 unique donors</p> <p>6 cohorts (Raj MFG, Raj STG, Raj, SVZ, Raj, THA, Roussos, Gaffney) meta-analysed using the linear mixed model random-effects meta-analysis software mmQTL (PMID: 35058635).</p> <p>Each file has the following naming convention:</p> <p>{REFERENCE}_{PHENOTYPE}_{ASSOC}.tsv.gz</p> <p><strong>References</strong></p> <p>The following two transcript references were used for mapping phenotypes:</p> <p>1. GENCODE - GENCODE v38 comprehensive transcripts (https://www.gencodegenes.org/human/release_38.html)</p> <p>2. Union - a union of all GENCODE v38 transcripts with an additional 35,879 novel transcripts identified from long-read RNA-seq in 30 human microglia samples. GTF to be uploaded in Zenodo.</p> <p><strong>Phenotypes</strong></p> <p>The following phenotypes were tested for genetic association:</p> <ul> <li>expression: total gene expression of each gene following voom normalization of read counts.</li> <li>transcript: transcript usage - each transcript expression normalized by TPM divided by the total TPM for that gene</li> <li>leafcutter: junction usage using the Leafcutter framework to count and cluster intron-splicing junction reads. Each phenotype is the relative usage of the intron against the total count of the introns within that cluster.</li> <li>SUPPA_A3: alternate 3' splice site usage, as identified by SUPPA2 from transcript TPMs</li> <li>SUPPA_A5: alternate 5' splice site usage</li> <li>SUPPA_AF: alternate first exon usage</li> <li>SUPPA_AL: alternate last exon usage</li> <li>SUPPA_RI: intron retention usage</li> <li>SUPPA_SE: exon skipping usage</li> </ul> <p>All phenotype matrices were scaled and centred and then quantile normalized.</p> <p><strong>Associations</strong></p> <p>Top associations (top_assoc.tsv.gz) list the SNP-feature pair with the lowest adjusted P-value (qval) for that feature.</p> <p>Full associations (full_assoc.tsv.gz) list all tested SNP-feature pairs.</p> <p><strong>Data dictionary</strong></p> <p>The columns of the two association files only differ by the presence of the qval column in the top associations.</p> <p>feature: the phenotype being tested</p> <p>variant_id: the genetic variant being tested</p> <p>chr: chromosome</p> <p>pos: position (hg38)</p> <p>ref: reference allele</p> <p>alt: alternate allele</p> <p>Allele: the effect allele that the beta is relative to</p> <p>beta_tissue_0: Gaffney cohort beta</p> <p>sd_tissue_0: Gaffney cohort standard error</p> <p>z_tissue_0: Gafney cohort Z-score</p> <p>beta_tissue_1: Roussos cohort beta</p> <p>sd_tissue_1: Roussos cohort standard error</p> <p>z_tissue_1: Roussos cohort Z-core</p> <p>beta_tissue_2: Raj MFG cohort beta</p> <p>sd_tissue_2: Raj MFG cohort standard error</p> <p>z_tissue_2: Raj MFG cohort Z-score</p> <p>beta_tissue_3: Raj STG cohort beta</p> <p>sd_tissue_3: Raj STG cohort standard error</p> <p>z_tissue_3: Raj STG cohort Z-score</p> <p>beta_tissue_4: Raj SVZ cohort beta</p> <p>sd_tissue_4: Raj SVZ cohort standard error</p> <p>z_tissue_4: Raj SVZ cohort Z-score</p> <p>beta_tissue_5: Raj THA cohort beta</p> <p>sd_tissue_5: Raj THA cohort standard error</p> <p>z_tissue_5: Raj THA cohort Z-score</p> <p>fixed_beta: Fixed effect meta-analysis estimate of the beta</p> <p>fixed_sd: Fixed effect meta-analysis standard error of the beta</p> <p>fixed_z: Fixed effect meta-analysis Z-score</p> <p>Random_Z: Random effect meta-analysis Z-score</p> <p>Fixed_P: Fixed effect meta-analysis P-value</p> <p>Random_P: Random effect meta-analysis P-value</p> <p>Fixed_bonf: Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni)</p> <p>Random_bonf: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni)</p> <p>Fixed_FDR: Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR)</p> <p>Random_FDR: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR)</p> <p>qval: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR) and for the number of features tested in the dataset (Storey's q value)</p> <p> </p> <p><br> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Summary statistics of cis-pQTLs for plasma proteins measured using Olink Explore I and II technology in the KARMA cohort.
<p>This data set contains summary statistics for cis regions (+/- 1 Mb around the protein coding gene) for proteins measured by the Olink Explore I and II technology in pre-diagnostic plasma samples from 299 Breast Cancer cases and 299 Breast Cancer free controls from the KARMA cohort. Only proteins detected in at least 25% of individuals are included in this data set. Data set from <a href="https://www.researchsquare.com/article/rs-2749047/v1">Evaluation of Circulating Plasma Proteins in Breast Cancer: A Mendelian Randomization Analysis | Research Square</a></p>
TWIS meta-analyzed summary statistics
Open the record for dataset details and reuse information.
GWAS summary statistics imputation support data and integration with PrediXcan MASHR
<p># GWAS summary statistics imputation, integration with PrediXcan MASHR-M</p> <p> </p> <p>The file `sample_data.tar` contains all necessary files to perform imputation of GWAS summary statistics to the GTEx v8 QTL data set.</p> <p>It includes 1000 Genomes individuals' genotypes as reference panel.</p> <p>The `.tar` archive, upon uncompression, contains the following folder structure:</p> <p>```</p> <p>data<br> |-- coordinate_map<br> |-- gwas<br> |-- liftover<br> |-- models<br> | |-- eqtl<br> | | `-- mashr<br> | `-- sqtl<br> | `-- mashr<br> |-- reference_panel_1000G<br> `-- ucsc</p> <p>```</p> <p> </p> <p>`data/eur_ld.bed.gz` contains definitions of approximately independent LD-regions in hg38 (Berisa-Pickrell regions, lifted over)</p> <p>`data/gtex_v8_eur_filtered_maf0.01_monoallelic_variants.txt.gz` is a snp annotation file, listing all GTEx v8 variants with MAF>0.01 in europeans.</p> <p>`data/coordinate_map` contains precomputed mapping tables that MetaXcan tools can use to convert GWAS' genomic coordinates in GWAS between genome assemblies.</p> <p>`data/gwas` contains a sample GWAS file for the purposes of a tutorial (data obtained from Nikpay et al (Nat Gen 2016) https://www.ncbi.nlm.nih.gov/pubmed/26343387</p> <p>`data/liftover` contains Liftover chains to map coordinates between human genome assemblies (used by full harmonization tools)</p> <p>`data/models` contains PrediXcan MASHR-M models, and cross-tissue S-MultiXcan LD compilation, from eQTL and sQTL.</p> <p>`data/reference_panel_1000G` contains 1000G hg38 genotypes, in parquet format, to be used by imputation tools.</p> <p>`data/ucsc` contains genomic coordinates of rsids in hg17, hg18 and hg19. You can use these to add chromosome and start position information to a GWAS based on its rsids. (column `end` is not used)</p> <p> </p>
MOSTWAS models, TWAS summary statistics, and simulation results for Bhattacharya and Love, 2020
<p>This compressed folder contains three sub-folders that all pertain to models and results generated with Multi-Omic Strategies for Transcriptome-Wide Association Studies (MOSTWAS):</p> <ol> <li><em>MOSTWAS_Models</em> contains compressed folders for MOSTWAS models trains on TCGA breast cancer and ROS/MAP pre-frontal cortex multi-omic data.</li> <li><em>Simulations </em>contains Simulation_Results_MOSTWAS.xlsx that provides full simulation results outlined in Bhattacharya and Love, 2020 (paper accompanying MOSTWAS).</li> <li><em>TWAS_associations</em> contains four Excel files that provide MOSTWAS and local-only TWAS associations for breast cancer-specific survival (using iCOGs GWAS summary statistics), late-onset Alzheimer's disease risk (using IGAP GWAS summary statistics), and major depressive disorder risk (using PGC GWAS and UK Biobank GWAX summary statistics).</li> </ol>
Risk assessment of Ochratoxin A in food - Summary statistics on occurrence and consumption data and exposure assessment results - Final occurrence data
<p><strong>Annex: Summary statistics on occurrence and consumption data and exposure assessment results</strong></p> <p>Table 1 Number of analytical results excluded from the initial dataset during data cleaning, and justification for exclusion</p> <p>Table 2 Occurrence values of OTA (µg/kg) in food as reported in the cleaned database</p> <p>Table 3 Occurrence values of OTA (µg/kg) in food as used for the exposure assessment</p> <p>Table 4 Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment</p> <p>Table 5 Results of chronic dietary exposure assessment on OTA (ng/kg bw per day)</p> <p>Table 6 Main contributing food categories to the mean LB exposure assessments to OTA across European dietary surveys and population groups</p> <p>Table 7 Distribution of LOQ values among the different food categories</p> <p>Table 8 All contributing food categories to the mean LB and UB exposure to OTA across European dietary surveys and population groups</p> <p> </p> <p><strong>FormattedDATA_OchratoxinA.zip: Occurrence data on OTA</strong></p> <p>Contains the occurrence data of OTA on 73,891 food samples presented in the opinion as described in its section 4.7 Occurrence data.</p> <p>The link to the catalogues of controlled terminologies can be found under "Related identifiers”.</p>
Summary statistics of North American plant populations
<p>Summary statistics of North American plant populations calculated by using the microsatellites of invasive and native species from multiple studies. The packages “hierfstat” (Goudet and Jombart, 2015), “vegan” (Oksanen <em>et al.</em>, 2017) and “sp” (Pebesma and Bivand, 2005; Bivand <em>et al.</em>, 2013) were used in RStudio to calculate the averaged heterozygosity over all subpopulations (<em>H</em><sub>S</sub>), the proportion of genetic variation due to differences among populations (<em>F</em><sub>ST</sub>p), the degree of non-random mating within subpopulations (<em>F</em><sub>IS</sub>) and the correlation between geographic and genetic distance (<em>r)</em>. STRUCTURE (Pritchard <em>et al.</em>, 2000) was used to calculate the number of genetic clusters (<em>K)</em> (5 iterations, burnin length 1000, 10000 MCMC reps after burnin). STRUCTURE HARVESTER (Earl & vonHoldt, 2012) was consulted to determine Evanno’s ΔK (Evanno <em>et al.</em>, 2005).</p>
2025 AV summary statistics
<p>Files that are prefixed with "raw" are the METAL GWAS meta-analysis for aortic stenosis, or REGENIE GWAS summary statistics for the quantitative traits.</p> <p>Files that are not prefixed with "raw" are the summary statistics that have been processed with MTAG.</p>
Summary Statistics from "Genome-wide meta-analysis of phytosterols reveals five novel loci and a detrimental effect on coronary atherosclerosis"
<p>Summary statistics of meta GWAS of phytosterols.</p>
Clonal hematopoiesis GWAS summary statistics
<p>Datasets linked to the pre-print titled, "Genome-wide analyses of 200,453 individuals yields new insights into the causes and consequences of clonal hematopoiesis", which is available at <a href="https://doi.org/10.1101/2022.01.06.22268846">https://doi.org/10.1101/2022.01.06.22268846</a></p> <p>To estimate odds ratios from BOLT-LMM betas and standard errors please use the formula provided <a href="https://alkesgroup.broadinstitute.org/BOLT-LMM/BOLT-LMM_manual.html#x1-5500010.2">here</a>. The formula requires the number of controls (= 173,918 for each of the five GWAS) and the numbers of cases (10,203 overall-CH, 5,185 DNMT3A-CH, 2,042 TET2-CH, 4,049 large-CH, and 6,154 small-CH).</p>
Summary statistics from "Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers"
<p>The zipped csv-file is comma-delimited and contains the following columns:</p> <p>identifier: Probe number and associated active substance<br> PROBE_ID.Adult: Probe number<br> logFC.Adult: log fold change of specific probe in LIFE-Adult<br> SE.Adult: Stand error for probe in LIFE-Adult<br> P.Value.Adult: p-value for probe in LIFE-Adult<br> qval.Adult: q-value for probe in LIFE-Adult<br> medi.Adult: associated active substance in in LIFE-Adult<br> P.Value.Heart: p-value for probe in LIFE-Heart<br> logFC.Heart: p-value for probe in LIFE-Heart<br> SE.Heart: Standard error for probe in LIFE-Heart<br> qval.Heart: q-value for probe in LIFE-Heart<br> medi.Heart: associated active substance in LIFE-Heart<br> symbol_INGENUITY: Gene associated with probe according to Ingenuity<br> description_INGENUITY: Description of gene associated with probe according to Ingenuity</p> <p>For LIFE-Adult the results were calculated for all probes associated with the relevant 83 active substances described. For LIFE-Heart results were only calculated for probes that were also available LIFE-Adult.</p>
CAGE and txrevise promoter usage QTL summary statistics
<p>This dataset contains promoter usage QTL summary statistics obtained using <a href="https://github.com/eQTL-Catalogue/qtlmap">QTLmap</a>. QTLmap_summary_CAGE.txt contains summary statistics for CAGE, QTLmap_summary_txrevise-25.txt for regular <a href="https://github.com/kauralasoo/txrevise">txrevise</a> and QTLmap_summary_txrevise-supplemented-20.txt for txrevise with the new generated annotations.</p> <p>This dataset contains the promoter annotations obtained with CAGE as part of the FANTOM5 project, cleaned and reformatted using <a href="https://github.com/andreasvija/cage/blob/master/qtlmap_prep/clean_promoters.R">this code</a> for the purpose of QTL mapping, that were used to obtain the aforementioned summary statistics. They can be found in Cleaned_FANTOM5_promoter_annotations.tsv.</p> <p> </p> <p>QTL summary statistics column descriptions:</p> <p>molecular_trait_object_id - gene</p> <p>molecular_trait_id - top promoter for the gene</p> <p>n_traits - how many promoters were tested for the gene</p> <p>n_variants - how many genetic variants were tested for the promoter</p> <p>variant - top genetic variant for the promoter</p> <p>chromosome - chromosome</p> <p>position - promoter's top genetic variant start position</p> <p>pvalue - nominal p-value of the association between the promoter and genetic variant</p> <p>beta - the fitted linear model's regression coefficent</p> <p>p_perm - empirical p-value calculated using permutations</p> <p>p_beta - estimated empirical p-value based on the beta distribution, this is the recommended p-value column</p>
Normalized linear counts from NanoString autoimmune profiling panel and summary of statistical analyses
<p>Though dependent on genetic anomalies, clinical manifestations of the human autoimmune disease systemic lupus erythematosus (lupus) can be triggered by environmental exposures including inhalation toxicants such as crystalline silica dust (cSiO<sub>2</sub>), tobacco smoke, and ambient air particles. Prednisone, a glucocorticoid (GC), is a keystone therapy for managing lupus flaring and progression, however, long-term use is associated with many adverse side effects. Here, we characterized the dose-dependent immunomodulation and toxicity of prednisone in a preclinical model that emulates onset and progression of cSiO<sub>2</sub>-triggered lupus. Two cohorts of 6-wk-old female NZBWF1 mice were fed either control AIN-93G diet or one of three AIN-93G diets containing prednisone at 5, 15, or 50 mg/kg diet which span human equivalent oral doses (HED) currently considered to be low (PL; 5 mg/d HED), moderate (PM; 14 mg/d HED), or high (PH; 46 mg/d HED), respectively. At 8 wk of age, mice were intranasally instilled with either saline vehicle or 1 mg cSiO<sub>2</sub> once weekly for 4 wk. The experimental plan was to 1) terminate one cohort of mice (n=8/group) 14 wk after the last cSiO<sub>2</sub> instillation for pathology and autoimmunity assessment and 2) to maintain a second cohort (n=9/group) to monitor glomerulonephritis development and survival. Mean blood concentrations of prednisone's chief active metabolite, prednisolone, in mice fed PL, PM, and PH diets were 27, 105, 151 ng/ml, respectively, which are consistent with levels observed in human blood ≤ 12 h after single bolus treatments with equivalent prednisone doses. Results from the first cohort revealed that consumption of PM but not PL diet significantly reduced cSiO<sub>2</sub>-induced pulmonary ectopic lymphoid structure formation, nuclear-specific AAb production, and inflammation/autoimmune gene expression in the lung, splenomegaly, and glomerulonephritis in the kidney. Relative to GC-associated toxicity, PM but not PL diet elicited muscle wasting, but these diets did not affect bone density or cause glucosuria. Importantly, neither PM nor PL diet influenced latency of cSiO<sub>2</sub>-accelerated death. PH-fed mice in both cohorts displayed robust GC-associated toxicity including body weight loss, reduced muscle mass, and hyperglycemia 7 wk after the final cSiO<sub>2</sub> instillation requiring their early removal from the study. Taken together, our results demonstrate that while moderate doses of prednisone can reduce certain pathological endpoints of cSiO<sub>2</sub>-induced autoimmunity in lupus-prone mice, these ameliorative effects come with unwanted GC toxicity and, crucially, none of these three doses extended survival time.</p>
Formatted Public GWAS Summary Statistics for 16 Traits and LD pruned SNP sets
<p>This data set includes 16 files with formatted GWAS summary statistic and a csv file gwas_info.csv. The csv provides the original download link and publication for each study. The data in this repository were created by downloading raw summary statistics for each study and processing them using Joe Marcus' GWAS pipeline (https://github.com/jhmarcus/gwass). The resulting data set have consistent allele orientation and column headers making them convenient for analysis. We use them in an MR analysis of pairs of GWAS traits described in Section 2.3 of Morrison et al (2019) (https://www.biorxiv.org/content/10.1101/682237v3) and here https://jean997.github.io/cause/gwas_pairs.html.</p> <p>New Sep 2022: I have added LD pruned SNP sets for each pair of traits.For each exposure/outcome pair, the set of snps in</p> <p>snps_<exposure>__<outcome>.txt</p> <p>were generated by LD pruning using LD estimated using LD Shrink (available https://zenodo.org/record/1464357/) at a threshold of r^2 < 0.1. LD pruning was performed using the ld_prune function in the cause R package (github.com/jean997/cause). These are the SNP sets used in the analysis in the paper</p>
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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.