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94 results for “Mendelian”
Collider Bias Correction for Multiple Covariates in GWAS Using Robust Multivariable Mendelian Randomization
<p>This repository contains the data underlying the figures in paper "Collider Bias Correction for Multiple Covariates in GWAS<br>Using Robust Multivariable Mendelian Randomization".</p> <p> </p> <p> </p> <p>The file names and sheet names in the xlsx file indicate the corresponding figures of data. </p> <p><br>The underlying data of manhattan plots and QQ plots are in text file. For other figures, the underlying data are in the spreadsheet.</p> <p>In each file, column names indicate the MVMR method used to obtain the result. </p> <p>For example: </p> <p>In text files:</p> <p>The abbreviation "mPC" refers to metabolomic principle components.</p> <p>beta_no_correction: the SNP effect estimate without bias correction.</p> <p>beta_cml or beta_MVMR_cml: the standard error of SNP effect estimate after the bias correction of MVMR-cML.</p> <p>SE_UVMR_cml: the standard error of SNP effect estimate after the bias correction of UVMR-cML.</p> <p>p_value_Egger or p_value_MVMR_Egger: the p-value of SNP effect estimate after the bias correction of MVMR-Egger regression.</p> <p><br>In the spreadsheet, column names follow the same style. </p> <p>The GWAS data is also available. The column names follows the plink output file. The detailed explanations are available at https://www.cog-genomics.org/plink/2.0/formats#glm_linear</p>
Summary statistics from "Sex-Specific Causal Relations between Steroid Hormones and Obesity—A Mendelian Randomization Study"
<p>GWAMA summary statistics of four steroid hormone levels and one steroid hormone ratio using fixed-effect model.</p> <p>When using this data, please cite: Pott J, Horn K, Zeidler R, et al.. Sex-Specific Causal Relations between Steroid Hormones and Obesity - A Mendelian Randomization Study. <em>Metabolites</em> <strong>2021</strong>, <em>11</em>, 738. https://doi.org/10.3390/metabo11110738</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>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 (number of studies)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>phenotype (phenotyp setting)</li> </ul>
Vitamin B12 deficiency anaemia and gestational diabetes mellitus: a two-sample Mendelian randomization study
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Supplementary table for "Deciphering the Relationship Between Circulating Metabolites and Osteoarthritis: A Comprehensive Genetic Correlation and Mendelian Randomization Studies"
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Supplement to "Endogenous DHEAS is causally linked with lumbar spine bone mineral density and forearm fractures in women - A mendelian randomization study"
<p>Supplemental tables to "Endogenous DHEAS is causally linked with lumbar spine bone mineral density and forearm fractures in women - A mendelian randomization study" by Johan Quester, Maria Nethander, Anna Eriksson and Claes Ohlsson.</p>
Predicting the strength of urban-rural clines in a Mendelian polymorphism along a latitudinal gradient
Cities are emerging as models for addressing the fundamental question of whether populations evolve in parallel to similar environments. Here, we examine the environmental factors that drive the evolution of parallel urban-rural clines in a Mendelian trait—the cyanogenic antiherbivore defense of white clover (Trifolium repens). Previous work suggested urban-rural gradients in frost and snow depth could drive the evolution of reduced hydrogen cyanide (HCN) frequencies in urban populations. Here, we sampled over 700 urban and rural clover populations across 16 cities along a latitudinal transect in eastern North America. In each population, we quantified changes in the frequency of genotypes that produce HCN, and in a subset of the cities we estimated the frequency of the alleles at the two genes (CYP79D15 and Li) that epistatically interact to produce HCN. We then tested the hypothesis that cold climatic conditions are necessary for the evolution of cyanogenesis clines by comparing the strength of clines among cities located along a gradient of winter temperatures and frost exposure. Overall, half of the cities exhibited urban-rural clines in the frequency of HCN, whereby urban populations evolved lower HCN frequencies. Clines did not evolve in cities with the lowest temperatures and greatest snowfall, supporting the hypothesis that snow buffers plants against winter frost and constrains the formation of clines. By contrast, the strongest clines occurred in the warmest cities where snow and frost are rare, suggesting that alternative selective agents are maintaining clines in warmer cities. Some clines were driven by evolution at only CYP79D15, consistent with stronger and more consistent selection on this locus than on Li. Together, our results demonstrate that urban environments often select for similar phenotypes, but different selective agents and targets underlie the evolutionary response in different cities.
Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
<p>Hereditary diseases manifest clinically in certain tissues, however their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in 1,459 hereditary diseases. Overall, we identified 114 cell types affected by 1,140 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechanisms and cellular vulnerability.</p>
Data from: Genetic susceptibility, Mendelian randomization and nomogram model construction of gestational diabetes mellitus
<p>The dataset contains subjects' basic information, including the Identification number of the test sample, fasting plasma glucose (FPG), oral glucose tolerance test 1h plasma glucose (1hPG), oral glucose tolerance test 2h plasma glucose (2hPG), glycated hemoglobin (HbA1c), Systolic blood pressure (SBP), Diastolic blood pressure (DBP), triglyceride (TG), total cholesterol (TC), High-density lipoprotein cholesterol (HDL-c), Low-density lipoprotein cholesterol (LDL-c), and also involves subjects' genetic variant information used for analysis of the association of functional polymorphisms and GDM. The variables including SBP_M, DBP_M, FPG_M, 1hPG_M, 2hPG_M, HbA1c_M, TG_M represent the mean value of SBP, DBP, FPG, 1hPG, 2hPG, HbA1c, TG, which are used for the stratification analysis. This study has obtained the support from the Ethics Committee of Guilin Medical University. All included subjects signed the informed consent.</p>
Investigating the causal association between immune cell phenotypes and allergic diseases and non-allergic asthma using conventional Two-sample and Bayesian weighted Mendelian randomization
<p>Investigating the causal association between immune cell phenotypes and allergic diseases and non-allergic asthma using conventional Two-sample and Bayesian weighted Mendelian randomization</p>
Mendelian gene identification through mouse embryo viability screening
<p>Supplementary files to support a manuscript entitled "Mendelian gene identification through mouse embryo viability screening"</p>
Systematic creation and phenotyping of Mendelian disease models in C. elegans: towards large-scale drug repurposing
<p>Data collected for the eLife OpenAccess paper: Systematic creation and phenotyping of Mendelian disease models in <em>C. elegans</em>: towards large-scale drug repurposing. (doi: 10.7554/eLife.92491.1)</p> <p>Contains: extracted features, calculated stats, normalised z-scores and timerseries data of all the disease model mutants generated. In addition, there is a static .html file that allows for mousing over the clustermaps to easily view differences in strains compared to the N2 wild-type. Dataset also contains, metadata and feature summary/file name information of FDA-library drug screen and the confirmation screen of the hit from this (i.e., all data collected in published in the associated paper). </p>
Predicting the strength of urban-rural clines in a Mendelian polymorphism along a latitudinal gradient
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Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
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Data from: Genetic susceptibility, Mendelian randomization and nomogram model construction of gestational diabetes mellitus
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Data from: Sleep, major depressive disorder and Alzheimer's disease: a Mendelian randomisation study
<div class="WordSection1"> <span><span>Objective</span></span> <p><span>To explore the causal relationships between sleep, major depressive disorder (MDD), and Alzheimer's disease (AD).</span></p> <span><span>Methods</span></span> <p><span>We conducted bi-directional two-sample Mendelian randomisation analyses. Genetic associations were obtained from the largest genome-wide association studies currently available in UK Biobank (N=446,118), the Psychiatric Genomics Consortium (N=18,759), and the International Genomics of Alzheimer's Project (N=63,926). We used the inverse variance weighted Mendelian randomisation method to estimate the causal effects, and the weighted median and MR-Egger for sensitivity analyses to test for pleiotropic effects. </span></p> <span><span>Results</span></span> <p><span>We found that higher risk of AD was significantly associated with being a "morning person" (odds ratio (OR)=1.01, P=0.001), shorter sleep duration (self-reported: β=-0.006, P=1.9×10<sup>-4</sup>; accelerometer-based: β=-0.015, P=6.9×10<sup>-5</sup>), less likely to report long sleep (β=-0.003, P=7.3×10<sup>-7</sup>), earlier timing of the least active 5 hours (β=-0.024, P=1.7×10<sup>-13</sup>), and a smaller number of sleep episodes (β=-0.025, P=5.7×10<sup>-14</sup>) after adjusting for multiple comparisons. We also found that higher risk of AD was associated with lower risk of insomnia (OR=0.99, P=7×10<sup>-13</sup>). However, we did not find evidence that these abnormal sleep patterns were causally related to AD or a significant causal relationship between MDD and risk of AD. </span></p> <span><span>Conclusion</span></span> <p><span>We found that AD may causally influence sleep patterns. However, we did not find evidence supporting a causal role of disturbed sleep patterns in AD or evidence for a causal relationship between MDD and AD risk.</span></p> </div> <p> </p>
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>
Lethal phenotypes in Mendelian disorders - Supplementary Files
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Supplementary Materials of the article:The relationship between circulating inflammatory proteins mediating gut microbiota and postherpetic neuralgia: A Mendelian Randomization study
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Appendix: Association between hemostatic profile and migraine: a Mendelian randomization analysis
<p><b>Objective: </b><span>To assess support for a causal relationship between </span>hemostatic measures and migraine susceptibility using genetic instrumental analysis.</p> <p><b>Methods: </b>Two-sample Mendelian randomization (MR) instrumental leveraging available genome-wide association study (GWAS) summary statistics was applied to hemostatic measures as potential causal for migraine and its subtypes, migraine with aura (MA) and migraine without aura (MO). Twelve blood-based measures of hemostasis were examined, including plasma level or activity of eight hemostatic factors and two <span>fibrinopeptides together with </span>two hemostasis clinical tests<span>.</span></p> <p><b>Results: </b>There were significant instrumental effects between increased coagulation factor VIII activity (FVIII, odds ratio[95% confidence interval]=1.05[1.03, 1.08]/standard deviation (SD), <i>P</i>=6.08×10<sup>-05</sup>), von Willebrand factor level (VWF, 1.05[1.03, 1.08]/SD, <i>P</i>=2.25×10<sup>-06</sup>), and phosphorylated <span>fibrinopeptide A level</span> (1.13[1.07, 1.19]/SD, <i>P</i>=5.44×10<sup>-06</sup>) with migraine susceptibility. When extended to migraine subtypes, FVIII, VWF, and phosphorylated <span>fibrinopeptide A</span> showed slightly stronger effects with MA than overall migraine. Fibrinogen level was inversely linked with MA (0.76[0.64, 0.91]/SD, <i>P</i>=2.32×10<sup>-03</sup>) but not overall migraine. None of the hemostatic factors was linked with MO. In sensitivity analysis, effects for fibrinogen and phosphorylated <span>fibrinopeptide A</span> were robust, while independent effects of FVIII and VWF could not be distinguished, and FVIIII associations were potentially affected by pleiotropy at the <i>ABO</i> locus. Causal effects from migraine to the hemostatic measures were not supported in reverse MR. However, MA was not included due to lack of instruments.</p> <p><b>Conclusions:</b> The current study supports potential causality of increased FVIII, VWF, and phosphorylated <span>fibrinopeptide A,</span> and decreased fibrinogen in migraine susceptibility, especially for MA, and may provide insights into the etiology underlying the relationship between <span>hemostasis</span> and migraine.</p>
Supplementary data for: Maternal testosterone and offspring birth weight: A Mendelian randomization study
<p><span>Evidence showed maternal androgen levels in both healthy/general population and populations with hyperandrogenic disorders were inversely associated with offspring's birth weight. We aimed to investigate the causal effect of maternal testosterone levels in general population on offspring's birth weight and preterm delivery risk using two-sample Mendelian randomization (MR) method.</span></p> <p><span>We obtained independent genetic instruments from a sex-specific genome wide association study with up to 230,454 females of European descent from UK biobank. Genetic instruments with consistent testosterone effects but no aggregate effect on sex-hormone binding globulin were used to perform the main analysis. Summary-level data of offspring's birth weight with offspring's genotype adjusted was obtained from a study with 210,406 females of European descent. Summary-level data of preterm delivery was obtained from the FinnGen study (6,736 cases and 116,219 controls).</span></p> <p><span>For outcome of offspring's birth weight, the numbers of instruments included in final MR analyses were 123, 128 and 91 respectively. For outcome of preterm delivery, the numbers were 115, 120 and 83 respectively. The present data files show the genetic instruments and their effects on exposures and outcomes. Pleiotropic genetic instruments that excluded in the MR analysis were also provided in the files.</span></p>
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Allen Brain Atlas
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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.
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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.
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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.