5 research outputs found
Gluten-free green banana flour muffins: chemical, physical, antioxidant, digestibility and sensory analysis
Memorias IX Congreso Geológico Venezolano (3)
Memorias IX Congreso Geológico Venezolano (3
Políticas públicas
Amputación de extremidades superiores: caracterización epidemiológicaAnálisis comparado de las políticas de promoción de la salud entre Chile y CataluñaAnálisis de los Avisa para la toma de decisiones en políticas de saludAntecedentes de colelitiasis en pacientes que presentaron colecistitis aguda. ¿Se puede prevenir la urgencia?Asociación entre alcoholemia y traumatismos en Copiapó, 2009Automedicación en la población asistente al Cesfam de Puerto NatalesAutotoma vaginal para detección de VPH para la prevención de cáncer cervicouterino, ChileCalidad de atención programa Auge- cáncer cervicouterino: la perspectiva de los profesionalesCaracterización de los casos de traumatismo encéfalo craneano en la comuna de Til-TilConocimiento de conductores universitarios sobre la alcoholemia permitida para conducir y su equivalencia en bebidas alcohólicasDescripción de la consulta dermatológica pediátrica en el Hospital Roberto del Río (2007-2008)Elementos para un abordaje metodológico de la salud intercultural en la Región Metropolitana de SantiagoEstudio descriptivo de consultas Sapu Cesfam Angachilla, visión tras dos años de registro clínico-electrónicoEstudio descriptivo de ingresos a Conin Valdivia, una revisión de 10 años (1998-2008)Estudio descriptivo de pacientes hospitalizados por absceso y celulitis peritonsilar en el hospital de PurranqueEvaluación de la aceptabilidad y consumo de alimentos del Pacam inscritos en el Cesfam Dr. V.M.FEvaluación de la interacción de medicinas alternativas o complementarias (MAC) en dos centros APSExposición a humo de tabaco ambiental. Signos y síntomas respiratorios bajos: estudio de prevalenciaFactores relacionados con la rotación laboral de médicos en consultorios del Gran SantiagoFibrosis quística como patología GES: una mirada críticaHipersensibilidad dentinaria: comparación de diferentes alternativas terapéuticasImpacto del GES en cáncer mamario: seguimiento a 5 años en un hospital del SSMSImplementación de la política nacional de medicamentos: percepción del profesional químico farmacéuticoLa implementación de políticas públicas cambió mortalidad de los pacientes gran quemado en Chile¿La infertilidad debería ser considerada un problema de salud pública en el Perú?Modelo de monitoreo de una política de protección a la infanciaMortalidad materna en el Hospital Dr. Alfredo van Grieken Coro, Estado Falcón, Venezuela 2005-2009Objetivos de desarrollo del milenio. Modelación de la mortalidad infantil Nicaragua - Costa Rica 1978-2008Percepción de riesgo y beneficio respecto del cigarrillo y su relación con el tabaquismo adolescentePolíticas públicas y salud intercultural: la experiencia de la organización indígena Taiñ adkimnPrevalencia de atipias celulares del cuello uterino en mujeres entre 18 y 24 añosProceso de ser histerectomizada: relatos de experiencias de mujeres en un hospital público de SantiagoProceso de ser histerectomizada: relatos de experiencias de mujeres en un hospital público de SantiagoPrograma Auge y cáncer cervicouterino: calidad de atención percibida por las usuarias del programaResolución quirúrgica por patología adenoamigdalina: ¿Es la población mapuche un grupo de riesgo?Resultados de alcoholemias tanatológicas del Servicio Médico Legal de Copiapó 1999-2009Resultados de la evaluación de los objetivos sanitarios de la década 2000-2010Una mirada a los servicios de salud para adolescentes en Puente Alt
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GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19
Data 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.Copyright © The Author(s) 2023, 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)