The Variant rs1867277 in FOXE1 Gene Confers Thyroid Cancer Susceptibility through the Recruitment of USF1/USF2 Transcription Factors

In order to identify genetic factors related to thyroid cancer susceptibility, we adopted a candidate gene approach. We studied tag- and putative functional SNPs in genes involved in thyroid cell differentiation and proliferation, and in genes found to be differentially expressed in thyroid carcinoma. A total of 768 SNPs in 97 genes were genotyped in a Spanish series of 615 cases and 525 controls, the former comprising the largest collection of patients with this pathology from a single population studied to date. SNPs in an LD block spanning the entire FOXE1 gene showed the strongest evidence of association with papillary thyroid carcinoma susceptibility. This association was validated in a second stage of the study that included an independent Italian series of 482 patients and 532 controls. The strongest association results were observed for rs1867277 (OR[per-allele] = 1.49; 95%CI = 1.30–1.70; P = 5.9×10−9). Functional assays of rs1867277 (NM_004473.3:c.−283G>A) within the FOXE1 5′ UTR suggested that this variant affects FOXE1 transcription. DNA-binding assays demonstrated that, exclusively, the sequence containing the A allele recruited the USF1/USF2 transcription factors, while both alleles formed a complex in which DREAM/CREB/αCREM participated. Transfection studies showed an allele-dependent transcriptional regulation of FOXE1. We propose a FOXE1 regulation model dependent on the rs1867277 genotype, indicating that this SNP is a causal variant in thyroid cancer susceptibility. Our results constitute the first functional explanation for an association identified by a GWAS and thereby elucidate a mechanism of thyroid cancer susceptibility. They also attest to the efficacy of candidate gene approaches in the GWAS era

Análisis de los indicadores de salud infantil en Argentina, en relación a los objetivos de desarrollo del Milenio trazados por Naciones Unidas

Objetivos: A la luz del 4° Objetivo de Desarrollo del Milenio trazado por Naciones Unidas, este trabajo tuvo como objetivos analizar la epidemiología de la mortalidad de menores de 1 año y menores de 5 años en la Argentina entre los años 1990 y 2011, y describir la cobertura de vacunación antisarampionosa en niños de 1 año entre 1990 y 2009. Método: Se midieron indicadores epidemiológicos con datos nacionales de fuentes secundarias: Tasa de mortalidad infantil y tasa de mortalidad en menores de 5 años; y porcentaje de cobertura de vacunación antisarampionosa en Argentina. Resultados: Los resultados muestran un descenso del 54,2% en la tasa de mortalidad infantil, y del 54,5% en la mortalidad de menores de 5 años. Las causas de muerte más frecuentes durante los primeros 5 años de vida están asociadas principalmente a las afecciones perinatales y anomalías congénitas. En relación a la cobertura de vacunación antisarampionosa, se observa una evolución positiva de ese indicador llegando en el año 2009 al 100% de cobertura. Conclusiones: La mortalidad infantil representa un indicador indiscutible de muertes evitables e innecesarias, que pueden prevenirse mediante acciones sanitarias y políticas sociales orientadas a influir sobre los determinantes sociales y económicos, en ese sentido se torna indispensable avanzar hacia la reducción prevista entre los compromisos que las Naciones Unidas establecieron en el marco de los Objetivos de Desarrollo del Milenio

Mortalidad por Trastornos Mentales y por causas Externas : Análisis de una década en Argentina

Objetivo: analizar las tasas de mortalidad de Trastornos Mentales y del Comportamiento (TMyC) y de las Causas Externas (CE) en las Estadísticas Vitales de Argentina, periodo 2000-2009. Metodología: se utilizó las bases de datos de defunciones, Argentina, años 2000 a 2009, de la Dirección de Estadísticas e Información en Salud Ministerial. Se construyeron tasas generales y específicas. Las estimaciones poblacionales, se obtuvieron a partir del aplicativo AGEINT. Según la distribución de la variable, se utilizó análisis de la varianza o test no paramétricos. Resultados: se observa un comportamiento disímil de la tasa de mortalidad por TMyC según sexo, pero con un comportamiento similar por cada año. Los TMyC y las CE, comprometen a la población adulta del país, con tasas mayores en el grupo de varones. Conclusión: tener en cuenta el impacto de los TMyC y CE, permite diseñar políticas públicas en salud adecuadas a las realidades de cada contexto

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)