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161 results for “LD”
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>
CAESAR-Lisflood 1.9j LD
<p>Updated version of CAESAR-Lisflood 1.9j to incorporate leaky wooden dams.</p> <p>Source code, batch mode and standalone versions of the software are available, as well as accompanying datasets and figures to support EGUSPHERE-2024-2132.</p> <p>Peer reviewed paper available here:</p> <p><strong>Wolstenholme, JM.</strong>, Skinner, CJ., Milan, DJ., Thomas, RE. & Parsons., DR. 2025. Hydro-geomorphological modelling of leaky wooden dam efficacy from reach to catchment scale with CAESAR-Lisflood 1.9j, <em>Geoscientific Model Development</em>. 18, 1395-1411. <a href="https://doi.org/10.5194/gmd-18-1395-2025" target="_blank" rel="noopener">https://doi.org/10.5194/gmd-18-1395-2025</a></p>
LD Estimated from 1k Genomes CEU Population
<p>These data contain estimated pairwise r^2 for variants with allele frequency greater than 0.05 in the 1000 Genomes CEU population. They were estimated using LDshrink (https://github.com/stephenslab/LDshrink). R^2 is only reported when the estimate is greater than 0.1.</p> <p> </p> <p>For each chromosome there are two files:</p> <p>chr<chr>_AF0.5_0.1.RDS is an R object containing a data frame with three columns: rowsnp, colsnp, and r2</p> <p>chr<chr>_AF0.5_snpdata.RDS is an R object containing a data frame with information for every SNP meeting the allele frequency cutoff.</p>
GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank - Part AA - START HERE
<p>GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank.</p> <p>Part <strong>AA </strong>of AA, AB, AC, AD and AE.</p> <p><strong>TO JOIN AND UNZIP THESE MATRICES</strong></p> <p><strong>Download all parts to one folder from:</strong></p> <p> PartAA - 10.5281/zenodo.3375373</p> <p> PartAB - 10.5281/zenodo.3376357</p> <p> Part AC - 10.5281/zenodo.3376456</p> <p> Parts AD and AE - 10.5281/zenodo.3376628</p> <p><strong>Use cat to join </strong></p> <p>cat <a href="https://zenodo.org/api/files/8ffd1abc-07ef-4b8a-a6cf-779a73ee03c2/ukb_50k_bigset_2.8M.zip.partaa?versionId=102ded84-352a-494d-8da3-cebb63a20aee">ukb_50k_bigset_2.8M.zip.part* > ukb_50k_bigset_2.8M.zip </a></p> <p>Then unzip. See README for further details.</p> <p>unzip <a href="https://zenodo.org/api/files/8ffd1abc-07ef-4b8a-a6cf-779a73ee03c2/ukb_50k_bigset_2.8M.zip.partaa?versionId=102ded84-352a-494d-8da3-cebb63a20aee">ukb_50k_bigset_2.8M.zip </a></p>
PHEWAS LD SNPs
<p>List of SNPs associated with multiple phenotypes in PHEWAS catalog in strong LD (r^2 > 0.8) with <em>Prakriti d</em>ifferentiating SNPs in North and Vadu cohorts </p>
GWAS LD SNPs
<p>List of SNPs associated with disease/traits in GWAS catalog1.0.2 in strong LD (r^2 > 0.8) with Prakriti Differentiating SNPs in North and Vadu cohorts</p>
all SNPs and genes assigned to each LD block
<p><strong>Downloadable data files</strong></p> <p>The DistiLD resource integrates information on:</p> <ul> <li>Associations between Single Nucleotide Polymorphisms (SNPs) and diseases from genome-wide association studies (GWAS)</li> <li>Links between SNPs and genes based on linkage disequilibrium (LD) data from <a href="http://www.hapmap.org/">HapMap</a></li> </ul> <p>...</p> <p> </p> <p>The second file contains <a href="http://distild.jensenlab.org/lds.tsv.gz">all SNPs and genes assigned to each LD block</a> [114MB] in the following format:</p> <ol> <li>Linkage disequilibrium (LD) block</li> <li>Reference SNP (rs) number of SNPs in LD block</li> <li>Ensembl genes in LD block</li> </ol>
fastPET-LD
<p>In this challenge, we provide 2 training datasets of 68 cases each: the first one was acquired at Sheba medical center (Israel) nuclear medicine department with a very-short exposure of 30s pbp, while the second is the same data followed by a denoising step implemented by a fully convolutional Dnn architecture trained under perceptual loss [1,2]. The purpose of this challenge is the detection of “hot spots”, that is locations that have an elevated standard uptake value (SUV) and potential clinical significance. Corresponding CT scans are also provided. The ground truth, common to both datasets, was generated by Dr. Liran Domachevsky, chair of nuclear medicine at Sheba medical center. It consists of a 3-D segmentation map of the hot spots as well as an Excel file containing the position and size of a 3D cuboid bounding box for each hot spot.</p> <p>Out of 68 cases, 55 with ground truth, for training, while 13 cases, the test set, are provided without ground truth.</p>
Carbidopa-Levodopa (CD-LD) ER Alone or in Combination With CD-LD IR to IPX066 Followed by IPX066 Extension Safety Study
ClinicalTrials.gov study NCT01411137. IPD Sharing: NO. Countries: 1. Publications: 1.
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of jimsonweed
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from jimsonweed (Datura stramonium) fruits and seeds using Laser Desorption Low-Temperature Plasma ionization. The data indicate the non-uniform distribution of tropane alkaloids.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry 2019 91 (4), 2734-2743</p> <p>DOI: 10.1021/acs.analchem.8b04406</p>
LD information used in MUSSEL for EUR, AFR, AMR, EAS, and SAS
<p>Estimated LD block matrices and other LD information used in MUSSEL for EUR, AFR, AMR, EAS, and SAS for approximately 2.0 million SNPs in HapMap 3 plus MEGA. Given the limited storage space on Zenodo, we have deposited the LD information generated based on 1000 Genomes LD reference panel and provided the link to download the LD files below using:</p> <p>(1) 1000 Genomes LD reference panel:</p> <p>EUR: <a href="https://www.dropbox.com/s/wvxh4yqthm8m7uf/EUR.zip?dl=0">https://www.dropbox.com/s/wvxh4yqthm8m7uf/EUR.zip?dl=0</a> (~6.73G, unzip by: tar -zxvf EUR.tar.gz) </p> <p>AFR: <a href="https://www.dropbox.com/s/iwqg65uieevfzj2/AFR.zip?dl=0">https://www.dropbox.com/s/iwqg65uieevfzj2/AFR.zip?dl=0</a> (~7.69G, unzip by: tar -zxvf AFR.tar.gz)</p> <p>AMR: <a href="https://www.dropbox.com/s/mev5zyf4x6m076q/AMR.zip?dl=0">https://www.dropbox.com/s/mev5zyf4x6m076q/AMR.zip?dl=0</a> (~8.80G, unzip by: tar -zxvf AMR.tar.gz)</p> <p>EAS: <a href="https://www.dropbox.com/s/o28mlovtakv5n7v/EAS.zip?dl=0">https://www.dropbox.com/s/o28mlovtakv5n7v/EAS.zip?dl=0</a> (~5.63G, unzip by: tar -zxvf EAS.tar.gz)</p> <p>SAS: <a href="https://www.dropbox.com/s/idp02rgl8xv379b/SAS.zip?dl=0">https://www.dropbox.com/s/idp02rgl8xv379b/SAS.zip?dl=0</a> (~2.60G, unzip by: tar -zxvf SAS.tar.gz)</p> <p> </p> <p>(2) UK Biobank reference panel:</p> <p>EUR: <a href="https://www.dropbox.com/scl/fi/09yd12dest1tqxkt8p8ch/EUR.zip?rlkey=774vb1e5d6hfnyucilx160cyo&dl=0">https://www.dropbox.com/scl/fi/09yd12dest1tqxkt8p8ch/EUR.zip?rlkey=774vb1e5d6hfnyucilx160cyo&dl=0</a> (~13.15G, unzip by: tar -zxvf EUR.tar.gz) </p> <p>AFR: <a href="https://www.dropbox.com/scl/fi/jfymih83anr2vuevmfqok/AFR.zip?rlkey=r1lxpn1fnbk98ssf8f8ji4xkk&dl=0">https://www.dropbox.com/scl/fi/jfymih83anr2vuevmfqok/AFR.zip?rlkey=r1lxpn1fnbk98ssf8f8ji4xkk&dl=0</a> (~11.59G, unzip by: tar -zxvf AFR.tar.gz)</p> <p>AMR: <a href="https://www.dropbox.com/s/2ba4tsbhz03rg83/AMR.zip?dl=0">https://www.dropbox.com/s/2ba4tsbhz03rg83/AMR.zip?dl=0</a> (~4.88G, unzip by: tar -zxvf AMR.tar.gz)</p> <p>EAS: <a href="https://www.dropbox.com/s/uofu788707dp4xv/EAS.zip?dl=0">https://www.dropbox.com/s/uofu788707dp4xv/EAS.zip?dl=0</a> (~4.27G, unzip by: tar -zxvf EAS.tar.gz)</p> <p>SAS: <a href="https://www.dropbox.com/scl/fi/o635c86ylthbl3omfetbu/SAS.zip?rlkey=ot396toxl0phaiae15cnpbeyn&dl=0">https://www.dropbox.com/scl/fi/o635c86ylthbl3omfetbu/SAS.zip?rlkey=ot396toxl0phaiae15cnpbeyn&dl=0</a> (~11.44G, unzip by: tar -zxvf SAS.tar.gz)</p>
LD Proj12 James Brattengeier
low poly train station from the 1960s Source: Objaverse 1.0 / Sketchfab
Supplementary material 1 from: Vaníčková L, Břízová R, Pompeiano A, Ferreira LL, de Aquino NC, Tavares RF, Rodriguez LD, Mendonça AL, Canal NA, do Nascimento RR (2015) Characterisation of the chemical profiles of Brazilian and Andean morphotypes belonging to the Anastrepha fraterculus complex (Diptera, Tephritidae). In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 193-209. https://doi.org/10.3897/zookeys.540.9649
Table S1. Anastrepha fraterculus male and female characteristic.: Explanation note: Anastrepha fraterculus male (m) and female (f) characteristic cuticular hydrocarbons identified by principal component analyses.
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>
GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank - Part AC
<p>GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank.</p> <p>Part <strong>AC </strong>of AA, AB, AC, AD and AE</p>
GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank - Parts AD and AE
<p>GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank.</p> <p>Part <strong>AD </strong>of AA, AB, AC, AD and AE</p>
LD data from CEU hg18
<p>LD data from CEU hg18, based on <a>ftp://ftp.ncbi.nlm.nih.gov/hapmap/ld_data/latest</a></p> <p> </p> <p>Algo for computing:</p> <p> </p> <p>import pandas as pd</p> <p>rule ld_blocks:<br> input:<br> expand("blocks/{chromosome}_blocks.gz", chromosome=["chr{}".format(c) for c in list(range(1, 23)) + ["X"]])</p> <p>rule get_blocks:<br> output:<br> "blocks/{chromosome}_blocks.gz"<br> run:</p> <p> c = wildcards.chromosome<br> f = r"ftp://ftp.ncbi.nlm.nih.gov/hapmap/ld_data/latest/ld_{}_CEU.txt.gz".format(c)<br> print(f)<br> df = pd.read_csv(f,<br> sep=" ", header=None, usecols=[0, 1, 8])<br> print(df.head())</p> <p> df.columns = "Start End Block".split()<br> ls = df.groupby("Block").agg({"Start": "first", "End": "last", "Block": ["first", "count"]}).reset_index(drop=True)<br> ls.columns = "Start End LDBlock Count".split()<br> lengths = ls["End"] - ls["Start"]<br> ls.insert(ls.shape[1], "Length", lengths)<br> ls.insert(0, "Chromosome", c)</p> <p> ls.to_csv(output[0], sep="\t", index=None)<br> </p>
EOLspecies (eolspecies LD): EOLspecies (59) in DwCA
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Pleurotomariidae (pleurotomariidae LD): Pleurotomariidae (151) DwCA
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Spiders (araneae LD): Spiders (58) in DwCA
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