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215 results for “summary statistics”
MOSTWAS models, TWAS summary statistics, and simulation results for Bhattacharya and Love, 2020
<p>MOSTWAS models, TWAS results, simulation results, and comparison to BGW-TWAS results</p>
Summary statistics data for "Genetic variation and microRNA targeting of A-to-I RNA editing fine tune human tissue transcriptomes"
<p>edQTL and ASED summary statistics data for the manuscript titled "Genetic variation and microRNA targeting of A-to-I RNA editing fine tune human tissue transcriptomes"</p>
Data from: Evaluating summary statistics used to test for incomplete lineage sorting: mito-nuclear discordance in the reef sponge Callyspongia vaginalis
Conflicting patterns of population differentiation between the mitochondrial and nuclear genomes (mito-nuclear discordance) have become increasingly evident as multilocus datasets have become easier to generate. Incomplete lineage sorting (ILS) of nucDNA is often implicated as the cause of such discordance, stemming from the large effective population size of nucDNA relative to mtDNA. However, selection, sex-biased dispersal, and historical demography can also lead to mito-nuclear discordance. Here we compare patterns of genetic diversity and subdivision for six nuclear protein-coding gene regions to those for mtDNA in a common Caribbean coral reef sponge, Callyspongia vaginalis, along the Florida reef tract. We also evaluated a suite of summary statistics to determine which are effective metrics for comparing empirical and simulated data when testing drivers of mito-nuclear discordance in a statistical framework. While earlier work revealed three divergent and geographically subdivided mtDNA COI haplotypes separated by 2.4% sequence divergence, nuclear alleles were admixed with respect to mitochondrial clade and geography. Bayesian analysis showed substitution rates for the nuclear loci were up to 7 times faster than for mitochondrial COI. Coalescent simulations and neutrality tests suggested that mito-nuclear discordance in C. vaginalis is not the result of ILS in the nucDNA or selection on the mtDNA but is more likely caused by changes in population size. Sperm-mediated gene flow may also influence patterns of population subdivision in the nucDNA.
Summary statistics of eQTLs obtained from single-nuclei RNA-seq in 8 major brain cell-types for mendelian randomisation
<p>This dataset contains <em>cis</em>-eQTL summary statistics for 8 brain cell-types, generated on a snRNA-seq dataset on post-mortem brains from 391 individuals (full), as well as a controls-only (subset of full, 183 individuals).</p> <p>Genotype dosage matrices were obtained with <code>SeqArray</code>, where 0 = homozygous alt, 1 = heterozygous, 2 = homozygous ref https://bioconductor.org/packages/release/bioc/manuals/SeqArray/man/SeqArray.pdf . The eQTL models as applied by <code>MatrixEQTL</code> therefore use "ref" as the effect allele (as implemented in their additive model).</p> <p>The eQTL summary statistics for within the "full" and "controls-only" dataset have been packed into <code>.tar.gz</code> files for each cell-type, where unpacking will yield summary statistics by chromosome. Each file contains the following columns;</p> <p>1. <code>SNP</code> (in rsid format)</p> <p>2. <code>gene</code> (in symbol format)</p> <p>3. <code>t.stat</code> (t-statistic as determined by MatrixEQTL)</p> <p>4. <code>p.value</code> (linear model association p-value)</p> <p>5. <code>FDR</code> (false discovery rate as determined by MatrixEQTL)</p> <p>6. <code>beta</code> (effect size / slope of the linear model)</p> <p>7. <code>chrom</code> (chromosome in "chrN" format)</p> <p>8. <code>position</code> (SNP position, hg38 build)</p> <p>9. <code>effect_allele</code> (this is the "ref" allele as described above)</p> <p>10. <code>other_allele</code> (alternate allele)</p> <p>11. <code>maf</code> (minor allele frequency, as determined by SeqArray on this dataset)</p> <p> </p> <p>In addition, single-cell expression matrices in count format are available in the <code>single-cell_data.tar</code> archive for the full 391 individuals (2,348,438 cells). This archive contains processed single-cell counts as described in our manuscript for the 4 datasets included; "BRYOIS_192" (separated into "MS" and "AD" as per their publication), "MATTHEWS", "ROCHE_MPD92" and "MRC_60". In addition, a cell-level metadata file containing covariates and cell-type labels across all datasets is included (<code>cell_level_metadata.rds</code>). Cell barcodes and individual IDs have been renamed to preserve anonymity.</p> <p> </p> <p><strong>January 2025 update: </strong>Now published at <strong><em>Nature Genetics</em></strong>. <strong>https://www.nature.com/articles/s41588-024-02050-9</strong></p> <p><strong>May 2025 update: </strong>Added the aggregated pseudo "Bulk" eQTLs as seen in Fig 1. d. </p>
INTERVAL eQTL & sQTL summary statistics
<p>Summary statistics for eQTL & sQTL traits computed from the INTERVAL RNA-seq samples (n=4,372) as detailed in the following preprint: <a href="https://www.medrxiv.org/content/10.1101/2023.11.25.23299014v1">https://www.medrxiv.org/content/10.1101/2023.11.25.23299014v1</a></p>
WES CH UKBB 200K Summary statistics
<p>Bi-allelic summary statistics for "Exome-wide evidence of compound heterozygous effects".</p>
Hawkes et al 2025 UKB-WGS-Olink Summary Statistics
<p>Summary statistics for Hawkes et. al 202X "Whole genome sequencing analysis identifies rare, large-effect non-coding variants and regulatory regions associated with circulating protein levels". Single variant summary statistics (minor allele count >=5) for EUR, AFR and SAS ancestries, and aggregates for EUR-only .</p>
GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank (5-fold cross-validation)
<p>This dataset contains GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank.</p> <p>The dataset is designed to enable systematic PRS analyses with 5-fold cross validation. For each phenotype and fold, we provide GWAS summary statistics for the training, validation, and test sets. The validation summary statistics can be used for model selection/tuning. The test summary statistics can be used to evaluate PRS models via pseudo-validation metrics. Association testing for all phenotypes and samples was done with <strong>plink2</strong>.</p> <p> </p> <p>The <strong>phenotypes</strong> included in this dataset are:</p> <ul> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=50">HEIGHT</a>: Standing height (Data-Field: 50)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=21001">BMI</a>: Body mass index (Data-Field: 21001)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=48">WC</a>: Waist circumference (Data-Field: 48)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=49">HC</a>: Hip circumference (Data-Field: 49)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=20022">BW</a>: Birth weight (Data-Field: 20022)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=3062">FVC</a>: Forced vital capacity (Data-Field: 3062)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=3063">FEV1</a>: Forced expiratory volume in 1-second (Data-Field: 3063)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=30760">HDL</a>: HDL cholesterol (Data-Field: 30760)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=30780">LDL</a>: LDL cholesterol (Data-Field: 30780)</li> </ul> <p> </p> <p>To allow users to assess PRS performance as a function of sample size, we also provide <strong>subsampled training GWAS summary statistics</strong>. This is done by taking the training samples and randomly selecting (without replacement) a subset of them for conducting association testing. The training sample sizes are:</p> <ul> <li>N = 5000</li> <li>N = 10000</li> <li>N = 20000</li> <li>N = 40000</li> <li>N = 80000</li> <li>N = 160000</li> <li>Full training set (sample size varies by phenotype).</li> </ul> <p><strong>NOTE</strong>: Due to the smaller overall sample size for the Birth weight phenotype, we do not include training data for the `N=160000` setting.<br><br></p> <p>The <strong>folder structure</strong> of the GWAS data for each phenotype is as follows:</p> <ul> <li><code>train</code> <ul> <li><code>N_5000</code> <ul> <li><code> fold_1</code> <ul> <li><code>chr_1.PHENO1.glm.linear</code></li> <li><code>chr_2.PHENO1.glm.linear</code></li> <li><code>...</code></li> </ul> </li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> <li><code>N_10000</code></li> <li><code>N_20000</code></li> <li><code>N_40000</code></li> <li><code>N_80000</code></li> <li><code>N_160000</code></li> <li><code>full</code></li> </ul> </li> <li><code>validation</code> <ul> <li><code>fold_1</code> <ul> <li><code>chr_1.PHENO1.glm.linear</code></li> <li><code>chr_2.PHENO1.glm.linear</code></li> <li><code>...</code></li> </ul> </li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> <li><code>test</code> <ul> <li><code>fold_1</code></li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> </ul> <p>For more details about the GWAS study, Quality Control (QC) criteria, or other information, please consult our publication:</p> <p>Zabad, S., Gravel, S., & Li, Y. (2023). <strong>Fast and accurate Bayesian polygenic risk modeling with variational inference.</strong> The American Journal of Human Genetics, 110(5), 741–761. <a href="https://doi.org/10.1016/j.ajhg.2023.03.009" rel="nofollow">https://doi.org/10.1016/j.ajhg.2023.03.009</a></p> <p>If you use this data in your work, please cite the publication above.</p> <p> </p>
GWAS summary statistics pertaining to the publication "Mapping of the gene network that regulates glycan clock of ageing"
<p>Dataset pertaining to the publication "Mapping of the gene network that regulates IgG galactosylation". If you use this dataset, please cite the manuscript in order to fairly acknowledge the contribution of all participating studies and their sponsors.</p> <p>Data consists of three gzipped files corresponding to genome-wide association meta-analysis (GWAMA) of three IgG N-glycome traits describing the percentage of galactosylation: G0, G1 and G2. The files are tab-separated and contain genome-wide association meta-analysis data for the discovery studies. Summary data are given for the meta-analyses of over 15 million directly genotyped or imputed single variant polymorphisms corresponding to the HRC (Haplotype Reference Consortium) r1.1 reference panel. Meta-analysis estimates are corrected for inflation of test statistics using genomic control at the individual study level.</p>
Sex-specific summary statistics
<p>Provided are summary statistics for 27 traits using UK Biobank genotype and phenotype data. plink2.00 alpha is used to generate the summary statistics. </p>
GWAS Summary Statistics For Eye Imaging Traits
<p>This repository hosts GWAS summary statistics for eye imaging traits (OCT and fundus image traits) on the UK Biobank cohort.</p> <p>We have a website (<a href="https://www.eyekp.org/">eyekp.org</a>) showing more details with respect to this repository.</p>
Summary Statistics for Genetic determinants and phenotypic consequences of blood T-cell proportions in 207,000 diverse individuals
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Non-linear genetic regulation of the blood plasma proteome - GWAS Summary Statistics
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Global social media statistics research summary May 2024
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Ant Positions, Colony Summary Statistics
<p>x, y coordinates of all ants in genotype A (GS2AAutoDataDistManualNest.mat) and B (GS3BAutoDataDistManualNest.mat)<br> Summary statistics for all colonies of both genotypes (ColonySummaryStatistics_Ulrich.xlsx)</p>
Colocalisation between QTLs and summary statistics from complex traits
<p>Colocalisation between QTLs and summary statistics from complex traits</p>
Preprocessed adult height GWAS summary statistics and estimated LD matrices
<p>This dataset contains preprocessed adult height genome-wide association study (GWAS) summary statistics and estimated linkage disequilibrium (LD) matrices, which were created and analyzed in the following publication.</p> <p>Zhu, Xiang; Stephens, Matthew. Bayesian large-scale multiple regression with summary statistics from genome-wide association studies. Ann. Appl. Stat. 11 (2017), no. 3, 1561--1592. doi:10.1214/17-AOAS1046. https://projecteuclid.org/euclid.aoas/1507168840</p> <p>For more information on this dataset, please see https://stephenslab.github.io/rss/Height2014.</p>
Summary statistics of vegetation composition of Chitanga, Mafai, and Nwamukuku heritage forests in Inhambane province, Mozambique
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Summary statistics BAM+Unitsim
<p>Summary statistics of calibration and mock production using BAM and the Unit N-body simulation.</p>
High-resolution African HLA resource uncovers HLA-DRB1 expression effects underlying vaccine response: summary statistics
<p>How human genetic variation contributes to vaccine immunogenicity and effectiveness is unclear, particularly in infants from Africa. We undertook genome-wide association analyses of eight vaccine antibody responses in 2,499 infants from three African countries and identified significant associations across the human leukocyte antigen (HLA) locus for five antigens spanning pertussis, diphtheria and hepatitis B vaccines. Using high-resolution HLA typing in 1,706 individuals from 11 African populations we constructed a continental imputation resource to fine-map signals of association across the class II HLA observing genetic variation explaining up to 10% of the observed variance in antibody responses. Using follicular helper T-cell assays, <em>in silico</em> binding, and immune cell eQTL datasets we find evidence of <em>HLA-DRB1</em> expression correlating with serological response and inferred protection from pertussis following vaccination. This work improves our understanding of molecular mechanisms underlying HLA associations that should support vaccine design and development across Africa with wider global relevance.</p>
ScienceDex guides
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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.