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215 results for “summary statistics”
Genome-wide association summary statistics for G4 and G6
<p>This repository contains GWAS summary statistics for G4 (g4_GWAS_Sumstats_Cleaned.txt) and G6 (g6_GWAS_Sumstats_Cleaned.txt), which are part of the paper titled "Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants".</p> <p>For methodological details check the paper at: <span><a href="https://gbr01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41591-024-02960-5&data=05%7C02%7Cshafiqur.rahman%40mrc-bsu.cam.ac.uk%7Ceba3de1db9174976852b08dc6f7001a0%7C513def5bdf174107b5523dba009e5990%7C0%7C0%7C638507774058203135%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=c8Nc%2B%2BqUFnfIoFkLqM7S6FS6PbXk8kKvIE8QvvOb2Zw%3D&reserved=0">https://www.nature.com/articles/s41591-024-02960-5</a></span></p> <p>The columns are as follows:</p> <ul> <li>SNP: rs identifier for the SNP</li> <li>CHR: chromosome (GRCh37 build) </li> <li>BP: base pair (GRCh37 build) </li> <li>A1: effect allele </li> <li>A2: reference allele </li> <li>FREQ: effect allele frequency </li> <li>INFO: imputation information</li> <li>P: p-value </li> <li>BETA: effect size of effect allele</li> <li>SE: standard error </li> <li>N: sample size</li> </ul>
Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case)
<p>Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case). The first GWAS meta-analysis combines GWAS summary statistics of invasive melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The second GWAS meta-analysis combines GWAS summary statistics of in situ melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The third GWAS meta-analysis combines GWAS summary statistics of in situ vs invasive (case-case; in situ code 0, invasive code 1) melanoma from UK Biobank (as of August 2022), QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>Columns</p> <p>CHR Chromosome</p> <p>SNP rsid</p> <p>POS Base position HG Build 37</p> <p>A1 effect allele</p> <p>A2 Non-effect allele</p> <p>A1FREQ Allele frequency of effect allele</p> <p>BETA effect estimate of effect allele</p> <p>SE standard error of effect estimate</p> <p>PVAL two-tailed p value</p> <p>DIRECTION the Direction of effect of the SNP in each cohort ( in the order UKBB, FINNGEN, QSKIN, QMEGA 610k, QMEGA OMNI)</p> <p>N sample size</p> <p>See </p>
Full eQTL and caQTL summary statistics from RASQUAL
<p>The summary statistics files contain the following columns:</p> <ol> <li>Feature ID</li> <li>Variant ID</li> <li>Chromosome</li> <li>Variant position</li> <li>Allele frequency (not MAF!)</li> <li>HWE Chi-square statistic</li> <li>Imputation quality score (IA)</li> <li>Chi square statistic (2 x log Likelihood ratio)</li> <li>Effect size (Pi)</li> <li>Sequencing/mapping error rate (Delta)</li> <li>Reference allele mapping bias (Phi)</li> <li>Overdispersion</li> <li>No. of feature SNPs</li> <li>No. of tested SNPs</li> <li>Convergence status (0=success)</li> <li>Squared correlation between prior and posterior genotypes (fSNPs)</li> <li>Squared correlation between prior and posterior genotypes (rSNP)</li> </ol>
Summary statistics of transcript usage QTLs in naive and stimulated macrophages (part 2)
<p>Column names:</p> <ol> <li>phenotype_id</li> <li>pheno_chr</li> <li>pheno_start</li> <li>pheno_end</li> <li>strand - strand of the phenotype</li> <li>n_snps - number of SNPs tested per phenotype</li> <li>distance - distance from the variant to the phenotype</li> <li>snp_id</li> <li>snp_chr</li> <li>snp_start - position of the SNP</li> <li>snp_end - same as snp_start</li> <li>p_nominal - nominal p-value from QTLTools</li> <li>beta - effect size from QTLTools</li> <li>is_lead - is the variant the lead QTL for the phenotype?</li> </ol>
Summary statistics of transcript usage QTLs in naive and stimulated macrophages (part 1)
<p>Column names:</p> <ol> <li>phenotype_id</li> <li>pheno_chr</li> <li>pheno_start</li> <li>pheno_end</li> <li>strand - strand of the phenotype</li> <li>n_snps - number of SNPs tested per phenotype</li> <li>distance - distance from the variant to the phenotype</li> <li>snp_id</li> <li>snp_chr</li> <li>snp_start - position of the SNP</li> <li>snp_end - same as snp_start</li> <li>p_nominal - nominal p-value from QTLTools</li> <li>beta - effect size from QTLTools</li> <li>is_lead - is the variant the lead QTL for the phenotype?</li> </ol>
GCTB SBayesR shrunk sparse linkage disequilibrium matrices for HM3 variants, summary statistics and predictors generated from "Improved polygenic prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al. 2019.
<p>GCTB LD matrices and results for HapMap 3 variants and 2.8M variants, which were used for</p> <p>simulation, cross-validation and across biobank analyses in the manuscript "Improved polygenic</p> <p>prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al.</p> <p>2019.</p> <p>Unzip and see README for further details.</p>
GWAS summary statistics for corneal resistance factor in 72,301 unrelated UK Biobank white-British participants
<p>The dataset contains summary statistics of the genome-wide association study (GWAS) for corneal resistance factor (CRF) of 72,301 unrelated UK-biobank white-British individuals, originally generated by Jiang X, et al (2020<a href="https://doi.org/10.1038/s42003-020-01497-w">)</a> (doi: <a href="https://doi.org/10.1038%2Fs42003-020-01497-w">10.1038/s42003-020-01497-w</a>).</p> <p><strong>Generation of the dataset: </strong></p> <ul> <li><strong>Phynotypic filtering:</strong></li> </ul> <p>The CRF analysis was performed on the average of the left and right eye measurements. Any outliers (i.e. CRF greater than the population mean difference + 3 standard deviations) were removed. Further phenotypic filtering was applied by removing samples linked to, or self-reporting, ocular conditions that could affect the measurements accuracy such as eye surgery, refractive laser surgery, cataract surgery, glaucoma high pressure surgery or laser treatment, corneal graft surgery, eye injury, keratoconus or cornea disorders.</p> <ul> <li><strong>Population selection:</strong></li> </ul> <p>Using the genetic quality control of the UK Biobank, white British participants with imputed data failing heterozygosity or/and missingness, or having a mismatch between self-reported and genotype-derived gender or showing putative sex chromosome aneuploidy as well as individuals who have withdrawn from the study at the time of analysis were removed. <em>This GWAS is restricted to individuals who were <strong>not closely related</strong> (pairwise kinship coefficient greater than 0.025 calculated by KING).</em> Finally, a total of 72,301 individuals were included in the GWAS.</p> <ul> <li><strong>GWAS:</strong></li> </ul> <p>The GWAS was performed using the generalized linear model (--glm) in PLINK2, common and low-frequency (MAF > 0.5%) well-imputed (INFO > 0.6) variants. Covariates fitted in the model were: age, sex, genotyping array, assessment center, genotyping batch, and the 20 first principal components of ancestry provided by the UK Biobank.</p> <ul> <li>UK Biobank application number: 19655</li> </ul> <p> </p> <p>Methods:</p> <p>Please see Jiang X, et al (2020<a href="https://doi.org/10.1038/s42003-020-01497-w">)</a> (doi: <a href="https://doi.org/10.1038%2Fs42003-020-01497-w">10.1038/s42003-020-01497-w</a>) for more details:</p> <p> </p> <p><strong>Header Description:</strong></p> <ul> <li>CHROM: chromosome,</li> <li>POS: hg19/GRCh37 position</li> <li>ID: variant Rsid</li> <li>A1: effective allele, minor allele</li> <li>A2: alternative allele, major allele</li> <li>BETA: effect size</li> <li>SE: standard error</li> <li>P: P value</li> <li>INFO: imputation quality</li> <li>MAF: Minor allele frequency</li> </ul> <p> </p> <p><strong>Some related analysis using this dataset: </strong></p> <p>Jiang X, et al (2020<a href="https://doi.org/10.1038/s42003-020-01497-w">)</a> (doi: <a href="https://doi.org/10.1038%2Fs42003-020-01497-w">10.1038/s42003-020-01497-w</a>): Fine-mapped CRF GWAS loci using FINEMAP and in-sample linkage disequilibrium data </p> <p>Jiang X, et al (2023) (doi: <a href="https://doi.org/10.3389%2Ffgene.2023.1171217">10.3389/fgene.2023.1171217</a>): Two colocalisation analysis using fine-mapped data: 1) CRF - cis-e/sQTL (GTEx v8); (2) CRF-keratoconus</p>
Summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI WGS data set
<p>These are the summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI Whole-Genome Sequencing data set from the European subcohort (n=1,017).</p> <p>Case - control definitions:<br>Ex (file: DeCOI_EUR_Ex_RVAS.tsv.gz): 272 cases (required mechanical ventilation or died because of COVID-19 = WHO score 6-10); 362 controls (not hospitalized = WHO score 1-3)<br>B1 (file: DeCOI_EUR_B1_RVAS.tsv.gz): 655 cases (at least hospitalized = WHO score 4-10); 362 controls (not hospitalized = WHO score 1-3)</p> <p>Column description:<br>CHROM: Chromosome of the respective gene<br>ID: Column of the format: [Ensemble Gene ID].[mask which was used to select variants].[upper alle frequency cut-off]<br>ALLELE0: The reference allele<br>ALLELE1: The alternative allele and the effect allele - here all variants which pass allele frequency and mask filters (see ID-column)<br>A1FREQ: Total frequency of Allele 1<br>A1FREQ_CASES: Frequency of Allele 1 in cases<br>A1FREQ_CONTROLS: Frequency of Allele 1 in controls<br>N: Count of individuals that were used in the analysis<br>N_CASES: Count of cases that were used in the analysis<br>N_CONTROLS: Count of controls that were used in the analysis<br>TEST: Test-mode of regenie - here an additive model was used<br>BETA: Estimated effect size given as beta<br>SE: Standard error of BETA<br>LOG10P: Negative decadic logarithm of the p-value</p> <p>Method:<br>Please refer to our accompanying manuscript for a detailed description. We conducted gene-based collapsing analysis using regenie 3.2.4 in logistic regression mode (without step 1). Covariates used were sex, age, age*age, age*sex and the first 10 principal components derived from common variants.</p> <p>Contact for further information:<br>dac_decoi_hostgenetics@listen.uni-bonn.de</p>
Full summary statistics from eQTL mapping in heterogeneous differentiating cultures (HDCs)
<p>Full summary statistics, and significant gene-variant pairs, for the cis-eQTL mapping results presented in the manuscript: <em>Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures</em>. These include cell-type specific eQTL mapping in 29 cell types, dynamic eQTL mapping in 3 trajectories, and topic eQTL mapping with 10 topics. We additionally include a mash object, which includes posterior effect size estimates and local false sign rate for the analysis of eQTL effects across cell types. See README and manuscript for details.</p>
GWAS summary statistics for 56 NMR metabolites measured in 246,683 UK Biobank participants
<p>GWAS summary statistics for 56 metabolites measured in 246,683 UK Biobank participants using the Nightingale Health platform. GWAS was performed using the https://github.com/AlasooLab/reGSusie workflow.</p>
Data from: Efficient summary statistics for detecting lineage fusion from phylogeographic datasets
<p>Aim: Lineage fusion (merging of two or more populations of a species resulting in a single panmictic group) is a special case of secondary contact. It has the potential to counteract diversification and speciation, or to facilitate it through creation of novel genotypes. Understanding the prevalence of lineage fusion in nature requires reliable detection of it, such that efficient summary statistics are needed. Here we report on simulations that characterized the initial intensity and subsequent decay of signatures of past fusion for 17 summary statistics applicable to DNA sequence haplotype data.</p> <p>Location: Global.</p> <p>Taxon: Diploid out-crossing species.</p> <p>Methods: We considered a range of scenarios that could reveal the impacts of different combinations of read length versus number of loci (arrangement of DNA sequence data), and whether or not pre-fusion populations experienced bottlenecks coinciding with their divergence (historical context of fusion). Post-fusion gene pools were sampled along 10 successive time points representing increasing lag times following merging of sister populations, and summary statistic values were recalculated at each.</p> <p>Results: Many summary statistics were able to detect signatures of complete merging of populations after a sampling lag time of 1.5 Ne generations, but the most informative ones included two neutrality tests and four diversity metrics, with ZnS being particularly powerful. Correlation was relatively low among the two neutrality tests and two of the diversity metrics. There were clear benefits of many short (200-bp × 200) loci over a handful of long (4-kb × 10) loci. Also, only the latter genetic dataset type was showed impacts of bottlenecks during divergence upon the number of informative summary statistics.<br> Main conclusions: This work contributes to identifying cases of lineage fusion, and advances phylogeography by enabling more nuanced reconstructions of how individual species, or multiple members of an ecological community, responded to past environmental change.</p>
Nationwide genomic biobank in Mexico unravels demographic history and complex trait architecture from 6,057 individuals: GWAS summary statistics
<p>Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories as well as complex trait architectures remain hidden due to the lack of Big Data. To fill this gap, the Mexican Biobank project genotyped 1.8 million markers in 6,057 individuals from 32 states and 898 sampling localities across Mexico with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Through a suite of state-of-the-art methods for ancestry deconvolution and inference of identity-by-descent (IBD) segments, we inferred detailed ancestral histories for the last 200 generations in different Mesoamerican regions, unravelling native and colonial/post-colonial demographic dynamics. We observed large variations in runs of homozygosity (ROH) among genomic regions with different ancestral origins reflecting their demographic histories, which also affect the distribution of rare deleterious variants across Mexico. We analysed a range of biomedical complex traits and identified significant genetic and environmental factors explaining their variation, such as ROH found to be significant predictors for trait variation in BMI and triglycerides.<br> ======================================</p> <p>This dataset contains GWAS summary statistics for the Mexico Biobank Project. Summary statistics for 22 binary and quantitative traits are provided from the full cohort of 5721 individuals from across Mexico, and a subset of 1061 individuals inferred to have more than 90% Native American ancestry.</p>
Multi-Omic Approach Associates Blood Methylome with Bronchodilator Drug Response in Pediatric Asthma: Summary statistics
<p>We conducted an epigenome-wide association study of bronchodilator drug response (BDR) in a discovery and validation design. The discovery phase was focused on 221 African American children with asthma. The association between DNA methylation and BDR was conducted using the limma package correcting for age, sex, ancestry, and tissue heterogeneity. Summary statistics include the output from toptable limma function and CpG annotation (based on Illumina EPIC Manifest file v 1.0 B4) organized in the following columns:</p> <ul> <li>Probe: Probe ID</li> <li>Chr: chromosome</li> <li>Pos: genomic position based on GRCh37/hg19</li> <li>Gene: Gene annotation based on Illumina EPIC Manifest file v 1.0 B4</li> <li>logFC: estimate of the log2-fold-change corresponding to the effect or contrast</li> <li>SE: standard error</li> <li>AveExpr: average log2-expression for the probe over all arrays and channels</li> <li>t: moderated t-statistic</li> <li>P.value: raw p-value</li> <li>FDR: adjusted p-value by false discovery rate</li> <li>B: log-odds that the gene is differentially expressed</li> <li>Problem: Flagged potentially problematic probes</li> </ul>
Data and analysis result for "A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics"
<p>Data set and analysis results from our paper "A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics" (pre-print). This file contains GWAS summary statistics of 2,483 traits and 51,399 SNP variants from European individuals, originally downloaded and processed from Pan-UK Biobank (https://pan.ukbb.broadinstitute.org/). Additionally, we include results of 100 pleiotropic factors inferred by our method and tSVD as comparison. Please see README for detailed breakdown.</p>
Summary statistics for "Variants in the TERT gene increase the occurrence of solar lentigines by modifying telomerase expression exclusively in the skin"
<p>Publication: Deecke et al., Variants in the TERT gene increase the occurrence of solar lentigines by modifying telomerase expression exclusively in the skin, manuscript under revision<br> <br> Brief summary:<br> Solar lentigines (SL) are a characteristic of the aging skin. Their occurrence is likely determined by a combination of genetic variants and environmental factors such as chronic sun exposure. However, the genetic factors underlying SL formation remain largely unknown. A recently performed genome-wide association study (GWAS) of SL in two Chinese cohorts reported <em>TERT</em> and <em>OCA2</em> as novel genome-wide significant genetic loci for SL, while only the <em>TERT</em> locus could be validated in a cohort of European descent (Peng et al, 2023). The telomerase reverse transcriptase TERT is known to be involved in the process of maintenance of telomere lengths, but interestingly no causal relationship between telomere length and the SL GWAS results was observed by the investigators.</p> <p>In this study, we quantified the heritability of SL located on the back of the hands and performed a de novo GWAS in 1,137 elderly participants (aged 60 and more) of European descent from the Berlin Aging Study II. We confirmed <em>TERT</em> as the only genome-wide significant locus for SL (top SNP: rs2735940, p= 1.35E-16). In line with the previous Mendelian Randomization (MR) results by Peng et al., our updated MR using substantially larger datasets did not show a causal relationship between the occurrence of SL and telomere lengths measured in blood leukocytes. Importantly, rs2735940 modifies <em>TERT</em> expression in skin tissue (based on GTEx data), but not in any of the other GTEx tissues including blood. Furthermore, colocalization analyses suggested a shared causal effect of rs2735940 on SL occurrence and on <em>TERT</em> expression in skin but not in blood.</p> <p>Thus, we could show that variants in the <em>TERT</em> gene increase the occurrence of SL by modifying <em>TERT</em> expression exclusively in the skin, based on currently available GTEx data. This skin-specific effect was not reported previously and may provide the hitherto missing functional link for the occurrence of SLs via <em>TERT</em> expression and telomere length alterations.</p>
Normalized linear counts from NanoString autoimmune profiling panel and summary of statistical analyses
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Data from: Efficient summary statistics for detecting lineage fusion from phylogeographic datasets
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BayesW time-to-event analysis posterior outputs and summary statistics
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Input data files for RSS-NET analysis of IBD GWAS summary statistics and NK cell regulatory network
<p>Details of these data files are provided in https://suwonglab.github.io/rss-net/ibd2015_nkcell.</p> <p>Contact:<code> xiangzhu[at]psu.edu </code></p>
Summary wind statistics from NEWA WRF mesoscale ensemble
<p>These files contain summary statistics from the NEWA WRF mesoscale ensemble for a Northern European domain.</p> <p>Each netCDF file includes:<br> - Mean wind speed (50, 75, 100 and 150 m AGL)<br> - Surface mean air density<br> - Surface static fields (latitude, longitude, surface elevation and surface roughness)<br> - Wind speed and direction frequency distribution (100 m AGL)</p> <p>The WRF simulation setup and parameterizations correspond to those in the README.ensemble file.</p>
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