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32 results for “Statistical genetics”
GWAS Summary Statistics from "Sex and statin-related genetic associations at the PCSK9 gene locus – results of genome-wide association meta-analysis"
<p>GWAMA summary statistics of PCSK9 levels stratified by sex and statin useage in Europeans.</p> <p>When using this data, please cite:</p> <p>Pott, J., Kheirkhah, A., Gadin, J.R. <em>et al.</em> Sex and statin-related genetic associations at the <em>PCSK9</em> gene locus: results of genome-wide association meta-analysis. <em>Biol Sex Differ</em> <strong>15</strong>, 26 (2024). https://doi.org/10.1186/s13293-024-00602-6</p> <p>All txt files contain the following columns:</p> <ul> <li>markername (unique SNP ID)</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>EA (effect allele)</li> <li>OA (other allele)</li> <li>EAF (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (in case of double-stratified data: number of studies; in case of single-stratified data: 2, as it is a meta-analysis of the two double-stratified data sets)</li> <li>beta (effect estimate)</li> <li>SE (standard error)</li> <li>pval (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>invalidAssoc (TRUE/FALSE flag if this variant was excluded in our analysis)</li> <li>reason4exclusion (reason why this SNP was excluded)</li> <li>phenotype (phenotyp setting)</li> </ul>
Genome-wide association summary statistics of chronic musculoskeletal pain at four anatomic sites and their genetically independent components
<p>The dataset contains results of a genome-wide association study of distinct chronic musculoskeletal pain conditions: back pain, knee pain, neck pain, and hip pain. Additionally, there are genome-wide association summary statistics for four genetically independent components of pain conditions, listed above. For more details, please, read the paper XXX.</p> <p>All files contain association summary statistics for genome-wide association meta-analysis of the 265,000 white British individuals from the UK Biobank and additional 191,580 individuals of European Ancestry from the UK biobank (total N = 456,580). Cases and controls were defined based on questionnaire responses. First, participants responded to “Pain type(s) experienced in the last months” followed by questions inquiring if the specific pain had been present for more than 3 months. Those who reported back, neck or shoulder, hip, or knee pain lasting more than 3 months were considered chronic back, neck/shoulder, hip, and knee pain cases, respectively. Participants reporting no such pain lasting longer than 3 months were considered controls (regardless of whether they had another regional chronic pain, such as abdominal pain, or not). Individuals who preferred not to answer were excluded from the study. Besides this, we excluded individuals who reported more than 3 months of pain all over the body.</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 utilization 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. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.</p> <p><strong>When using downloaded data, please cite the corresponding paper and this repository:</strong></p> <ol> <li>Tsepilov et al 2020</li> </ol> <p><strong>Funding:</strong></p> <p>The work of YSA and SZS was supported by the Russian Ministry of Education and Science under the 5-100 Excellence Programme and by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project 0324-2019-0040). The work of YAT, ASSh, and EEE was supported by the Russian Foundation for Basic Research (project 19-015-00151). The contribution of LСK was funded by PolyOmica. Dr. Suri was supported by VA Career Development Award # 1IK2RX001515 from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development (RR&D) Service. Dr. Suri is a Staff Physician at the VA Puget Sound Health Care System. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.</p> <p><strong>List of files:</strong></p> <ol> <li>Back_output_done.csv: GWAS summary statistics for the chronic back pain</li> <li>gpc1_output_done.csv: GWAS summary statistics for the GIP1</li> <li>gpc2_output_done.csv: GWAS summary statistics for the GIP2</li> <li>gpc3_output_done.csv: GWAS summary statistics for the GIP3</li> <li>gpc4_output_done.csv: GWAS summary statistics for the GIP4</li> <li>Hip_output_done.csv: GWAS summary statistics for the chronic hip pain</li> <li>Knee_output_done.csv: GWAS summary statistics for the chronic knee pain</li> <li>Neck_output_done.csv: GWAS summary statistics for the chronic neck pain</li> </ol> <p><strong>Column headers:</strong></p> <ol> <li>gwas_id: uninformative field</li> <li>rs_id: dbSNP rsID (GRCh37 build) </li> <li>snp_num: uninformative field</li> <li>chr: chromosome (GRCh37 build) </li> <li>bp: position (GRCh37 build) </li> <li>ea: effect allele (coded as "1")</li> <li>ra: reference allele (coded as "0")</li> <li>eaf: effect allele frequency</li> <li>af_ref: uninformative field</li> <li>beta: effect size of effect allele</li> <li>se: standard error of effect size</li> <li>p: P-value of association (without GC correction)</li> <li>n:Total sample size</li> <li>z: Z-statistic of association</li> <li>info: uninformative field</li> <li>af_outlier: uninformative field</li> <li>pz_outlier: uninformative field</li> </ol>
GWAS Summary Statistics for Publication: Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data
<p>This dataset contains summary statistics for genome-wide association studies (GWAS) conducted on genomic features derived from off-target reads in whole-exome sequencing (WES) data. The study utilized tools like Seeing Beyond the Target (SBT) and ImReP to construct novel phenotypic features from unmapped reads in ~50,000 participants in the UK Biobank. Features include mitochondrial DNA (mtDNA) copy number, ribosomal DNA (rDNA) copy number (5S, 18S, 28S), immune repertoire metrics (e.g., T-cell receptor alpha diversity), and microvial genome load (viral and fungal).</p> <p>Summary statistics can be used for replication studies, meta-analyses, or further exploration of these phenotypes.</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>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
Summary statistics from "Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease"
<p>GWAMA summary statistics of four steroid hormone levels using fixed-effect model and GWAS summary statistics of four other steroid hormones.</p> <p>When using this data, please cite: Pott J, Bae YJ, Horn K, et al.. Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease. <em>J Clin Endocrinol Metab</em> <strong>2019</strong> Nov 1;104(11):5008-5023. doi: 10.1210/jc.2019-00757</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>effect_allele</li> <li>other_allele</li> <li>effect_allele_freq</li> <li>min_info (minimal info score across all used studies)</li> <li>n (sample size per SNP)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>CochransQ (only in GWAMA; SNP heterogeneity across studies)</li> <li>pCochransQ (only in GWAMA; p-value of Cochrans Q value)</li> </ul>
Summary statistics for association tests between human and Plasmodium falciparum genetic variants in 3,346 severe malaria cases from The Gambia and Kenya
<p>This dataset contains summary statistics for association tests between human and<br> <em>Plasmodium falciparum</em> malaria parasite genetic variants, using data from 3,346 severe malaria cases from The Gambia and Kenya. These results underlie the analysis described in our paper:</p> <p><strong>"Malaria protection due to sickle haemoglobin depends on parasite genotype"</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy Nguyen, Sónia M. Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor, David J. Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>:: <a href="https://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a><br> <br> The genotype data underlying these summary statistics has also been deposited on Zenodo<br> (<a href="https://zenodo.org/record/4973477">doi:10.5281/zenodo.4973477</a>). The <a href="https://www.well.ox.ac.uk/~gav/hptest)">HPTEST software</a> used to generate these results has also been deposited (<a href="https://doi.org/10.5281/zenodo.5685580">doi:10.5281/zenodo.5685580</a>). Please see the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a> for a full list of datasets which have been released with this manuscript.</p> <p><strong>Data contents.</strong></p> <p>The dataset consists of a single <a href="http://sqlite.org">sqlite database file</a> containing the results, and an accompanying README file in markdown and html format. Please see the README file for full details of the data contents.</p> <p> </p>
Summary Statistics from "Genetically regulated gene expression and proteins revealed discordant effects" (LWAS of biomarker)
<p>Summary statistics of 92 blood protein levels. The corresponding publication is currently under revision.</p> <p> The zipped txt file is tab-delimited and contains the following columns:</p> <ul> <li>protein: protein name abbreviation</li> <li>cytoband: genomic region</li> <li>gene: gene name abbreviation</li> <li>setting: either "combined" (adj. for sex & age) or sex-stratified ("males", "females"; adj. for age)</li> <li>variant_id_hg19: SNP ID according to hg19</li> <li>variant_id_hg38: SNP ID according to hg19</li> <li>chr: chromosome</li> <li>pos_hg19: base position according to hg19</li> <li>pos_hg38: base position according to hg19</li> <li>effect_allele: also known as counted allele in additive model</li> <li>other_allele: not-counted allele</li> <li>eaf: effect allele frequency</li> <li>maf: minor allele frequency</li> <li>info: imputation info score</li> <li>n_samples: number of samples</li> <li>beta: effect estimate</li> <li>se: standard error</li> <li>zscore: Z-statistic</li> <li>pvalue: p-value</li> <li>FDR: FDR by gene and setting</li> <li>BBFDR: hierarchical FDR by setting</li> <li>hierFDR: TRUE if SNP is significant after hierarchical FDR</li> </ul>
CONTENT -- Multi-context genetic modeling TWAS and eAssociation summary statistics
<p>We provide the summary statistics of running CONTENT, the context-by-context approach, and UTMOST on over 22 phenotypes. The phenotypes are listed in the manuscript, and their respective studies and sample size can be found in a table under the supplementary section of the manuscript. All 3 methods were trained on GTEx v7 as well as CLUES, a single-cell RNA sequencing dataset of PBMCs. The data include the gene name, model, cross-validated R^2, prediction pvalue, TWAS p value, TWAS Z score, and a column titled "hFDR" indicating whether the association was statistically significant while employing hierarchical FDR. The benefits of employing such an approach for all methods can be found in the manuscript.</p> <p> </p> <p>We also include the eAssociations that we obtain by training prediction models on GTEx and CLUES alone. For the CxC and UTMOST approaches, these files contain the gene, context, pvalue and adjusted R^2. For CONTENT, these include the gene, context and pvalue and adjusted R^2 for each CONTENT model--the column names are described like a regression of y~x, rsq_y_x, so rsq_observed_full is the adjusted R^2 from regressing the observed expression onto the cross-validated full model predictions. In cases where the R^2 is higher from the specific or shared models, it's best to use either of those rather than the full model for out of sample prediction.</p>
Fig. 2. Statistical parsimony cladogram network representing relationships among the 45 haplotypes for a 615 in Genetic diversity of Halyomorpha halys (Hemiptera, Pentatomidae) in Korea and comparison with COI sequence datasets from East Asia, Europe, and North America
Fig. 2. Statistical parsimony cladogram network representing relationships among the 45 haplotypes for a 615 bp fragment of the COI gene of Halyomorpha halys. Each circle is labeled with haplotype number, and the size of each circle is proportional to the frequency of each haplotype [H3 (n = 353); H1 (n = 285); H22 (n = 43); H8 (n = 34); H33 (n = 23); H2 (n = 16); H32 (n = 8); H7, H9–H13, and H43 (n = 3); H6, H14, H34, H39, and H40 (n = 2); H4–H5, H12, H15–H21, H23, H30–H31, H35–H38, H41, H42, and H44–H51 (n = 1)]. Differing colors indicate countries in which samples were collected.
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>
Data from: Intraspecific DNA contamination distorts subtle population structure in a marine fish: decontamination of herring samples before restriction-site associated (RAD) sequencing and its effects on population genetic statistics
Wild specimens are often collected in challenging field conditions, where samples may be contaminated with the DNA of conspecific individuals. This contamination can result in false genotype calls, which are difficult to detect, but may also cause inaccurate estimates of heterozygosity, allele frequencies, and genetic differentiation. Marine broadcast spawners are especially problematic, because population genetic differentiation is low and samples are often collected in bulk and sometimes from active spawning aggregations. Here, we used contaminated and clean Pacific herring (Clupea pallasi) samples to test (i) the efficacy of bleach decontamination, (ii) the effect of decontamination on RAD genotypes, and (iii) the consequences of contaminated samples on population genetic analyses. We collected fin tissue samples from actively spawning (and thus contaminated) wild herring and non-spawning (uncontaminated) herring. Samples were soaked for 10 minutes in bleach or left untreated, and extracted DNA was used to prepare DNA libraries using a restriction-site associated DNA (RAD) approach. Our results demonstrate that intraspecific DNA contamination affects patterns of individual and population variability, causes an excess of heterozygotes, and biases estimates of population structure. Bleach decontamination was effective at removing intraspecific DNA contamination and compatible with RAD sequencing, producing high-quality sequences, reproducible genotypes, and low levels of missing data. Although sperm contamination may be specific to broadcast spawners, intraspecific contamination of samples may be common and difficult to detect from high-throughput sequencing data, and can impact downstream analyses.
Data from: Intraspecific DNA contamination distorts subtle population structure in a marine fish: decontamination of herring samples before restriction-site associated (RAD) sequencing and its effects on population genetic statistics
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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>
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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Simulation and empirical data for "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations"
<p>Simulation data and empirical data used to generate figures from "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations". Can be used with code provided on the associated github to regenerate the figures. </p>
Data from: Genetic sex assignment in wild populations using GBS data: a statistical threshold approach
Establishing the sex of individuals in wild systems can be challenging and often requires genetic testing. Genotyping-by-sequencing (GBS) and other reduced representation DNA sequencing (RRS) protocols (e.g., RADseq, ddRAD) have enabled the analysis of genetic data on an unprecedented scale. Here, we present a novel approach for the discovery and statistical validation of sex-specific loci in GBS datasets. We used GBS to genotype 166 New Zealand fur seals (NZFS, Arctocephalus forsteri) of known sex. We retained monomorphic loci as potential sex-specific markers in the locus discovery phase. We then used (i) a sex-specific locus threshold (SSLT) to identify significantly male-specific loci within our dataset and (ii) a significant sex-assignment threshold (SSAT) to confidently assign sex in silico the presence or absence of significantly male-specific loci to individuals in our dataset treated as unknowns (98.9% accuracy for females; 95.8% for males, estimated via cross-validation). Furthermore, we assigned sex to 86 individuals of true unknown sex using our SSAT, and assessed the effect of SSLT adjustments on these assignments. From 90 verified sex-specific loci, we developed a panel of three sex-specific PCR primers that we used to ascertain sex independently of our GBS data, which we show amplify reliably in at least three other pinniped species. Using monomorphic loci normally discarded from large SNP datasets is an effective way to identify robust sex-linked markers for non-model species. Our novel pipeline can be used to identify and statistically validate monomorphic and polymorphic sex-specific markers across a range of species and RRS datasets.
Data from: Genetic sex assignment in wild populations using GBS data: a statistical threshold approach
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