7,746 research outputs found
GenOMICC release 3 GWAS summary statistics
No full textHere, we provide an updated analysis of the international GenOMICC study comprising a combination of microarray genotype data from 11,325 critically ill cases in the UK (9,279 cases) and Brazil (2,186), combined with other studies recruiting hospitalised patients with a strong focus on severe and critical disease: ISARIC4C (655 cases) and SCOURGE consortium (5,934 cases). We put these new results in context by completing a meta-analysis of the new GenOMICC GWAS results with previously-published data.Pairo-Castineira, Erola; Rawlik, Konrad; Baillie, Kenneth. (2023). GenOMICC release 3 GWAS summary statistics, [dataset]. University of Edinburgh. Roslin Institute. https://doi.org/10.7488/ds/3418
GWAS summary statistics GenOMICC meta-analysis for hospitalised and critically ill datasets
No full textSUPERSEDED - Here, we provide an updated analysis of the international GenOMICC study comprising a combination
of microarray genotype data from 11,325 critically ill cases in the UK (9,279 cases) and Brazil (2,186),
combined with other studies recruiting hospitalised patients with a strong focus on severe and critical
disease: ISARIC4C (655 cases) and SCOURGE consortium (5,934 cases). We put these
new results in context by completing a meta-analysis of the new GenOMICC GWAS results with
previously-published data
GWAS summary statistics trans-ancestry GenOMICC meta-analysis for hospitalised and critically ill datasets
No full textHere we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously
GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19
No full textData availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability:
Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)
SHui open data research platform
No full textData collected and revised by individual instutions of the Shui-Consortium. Publication by the EU-China Consortium SHui.For each data-file, the author (institution) of the file is given as “operator”.-- At project end, June 30th, 2022.-- For each data-file, the author/data owner for citation is given as “operator” and “contact”.-- Plot data as .csv; catchment data ad libitum.Spatial situation data: Plot data and catchment data available; country, latitude, and longitude coordinates given.-- Temporal situation data: Long-term and single-season data available. Start and end date for each data file given.CC BY-SA. No embargo. The release on the Shui download site and CSIC repository implies expiration of any embargo delivered by the data owner.Project Co-ordinators: Dr. Jose Alfonso Gómez Calero (Instituto de Agricultura Sostenible (IAS-CISC), Dr. Weifeng Xu (Fujian Agriculture and Forest University, FAFU).This data set contains data from the SHui open-data platform for sharing long-term agricultural experiments aimed to optimizing yield and soil and water. Data and additional material are available under https://shui.boku.ac.at/shui/public/startAlphanumeric data measured at hydrologic and agronomical experiments (e.g., plant development, soil properties, hydrology, erosion, management).Further information on the data, project, partners, and publications under https://www.shui-eu.org/EU-China Consortium SHui: European Union Project 773903 and Chinese MOST.Peer reviewe
Enrichment and characterization of a bacteria consortium capable of heterotrophic nitrification and aerobic denitrification at low temperature
No full textNitrogen removal in wastewater treatment plants is usually severely inhibited under cold temperature. The present study proposes bioaugmentation using psychrotolerant heterotrophic nitrification-aerobic denitrification consortium to enhance nitrogen removal at low temperature. A functional consortium has been successfully enriched by stepped increase in DO concentration. Using this consortium, the specific removal rates of ammonia and nitrate at 10 degrees C reached as high as 3.1 mg N/(g SS h) and 9.6 mg N/ (g SS h), respectively. PCR-DGGE and clone library analysis both indicated a significant reduction in bacterial diversity during enrichment. Phylogenetic analysis based on nearly full-length 16S rRNA genes showed that Alphaproteobacteria. Deltaproteobacteria and particularly Bacteroidetes declined while Gammaproteobacteria (all clustered into Pseudomonas sp.) and Betaproteobacteria (mainly Rhodoferax ferrireducens) became dominant in the enriched consortium. It is likely that Pseudomonas spp. played a major role in nitrification and denitrification, while R. ferrireducens and its relatives utilized nitrate as both electron acceptor and nitrogen source. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.</p
Arabic Treebank : Part 2 v 3.1
No full textArabic Treebank: Part 2 (ATB2) v 3.1 , Linguistic Data Consortium (LDC) catalog number LDC2011T09 and isbn 1-58563-590-1, was developed at LDC. It consists of 501 newswire stories from Ummah Press with part-of-speech (POS), morphology, gloss and syntactic treebank annotation in accordance with the Penn Arabic Treebank (PATB) Guidelines developed in 2008 and 2009
Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes (Nature Genetics, (2018), 50, 4, (524-537), 10.1038/s41588-018-0058-3)
No full textIn the HTML version of this article initially published, the author groups ‘AFGen Consortium’, ‘Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium’, ‘International Genomics of Blood Pressure (iGEN-BP) Consortium’, ‘INVENT Consortium’, ‘STARNET’, ‘BioBank Japan Cooperative Hospital Group’, ‘COMPASS Consortium’, ‘EPIC-CVD Consortium’, ‘EPIC-InterAct Consortium’, ‘International Stroke Genetics Consortium (ISGC)’, ‘METASTROKE Consortium’, ‘Neurology Working Group of the CHARGE Consortium’, ‘NINDS Stroke Genetics Network (SiGN)’, ‘UK Young Lacunar DNA Study’ and ‘MEGASTROKE Consortium’ appeared at the end of the author list but should have appeared earlier in the list. In addition, the author group ‘MEGASTROKE Consortium’ was duplicated, and its members were not displayed in the ‘Author information’ section. The errors have been corrected in the HTML version of the article
Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes
No full textOnline Correction for: https://doi.org/10.1038/s41586-020-2493-4 | Erratum for https://bura.brunel.ac.uk/handle/2438/21299In the version of this article initially published, two members of the ENCODE Project Consortium were missing from the author list. Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) are now included in the author list. These errors have been corrected in the online version of the article : 'Expanded encyclopaedias of DNA elements in the human and mouse genomes'.https://www.nature.com/articles/s41586-021-04226-3https://www.nature.com/articles/s41586-021-04226-
Author Correction: Perspectives on ENCODE (Nature, (2020), 583, 7818, (693-698), 10.1038/s41586-020-2449-8)
No full textThe Original Article (https://doi.org/10.1038/s41586-020-2449-8) was published on 29 July 2020.Copyright © The Authors 2022. In this Article, the authors Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) were mistakenly omitted from the ENCODE Project Consortium author list. The original Article has been corrected online
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