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Author Correction: T cell-encoded CD80 and 4-1BBL induce auto- and transcostimulation, resulting in potent tumor rejection

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Nature medicine 2024年第6期30卷 1788页

作者： Matthias T Stephan Vladimir Ponomarev Renier J Brentjens Alex H Chang Konstantin V Dobrenkov Glenn Heller Michel Sadelain Center for Cell Engineering Molecular Pharmacology and Chemistry Program Memorial Sloan-Kettering Cancer Center (MSKCC) New York 10021 New York USA. Immunology Graduate Program Weill Graduate School of Medical Sciences Cornell University New York 10021 New York USA. Department of Radiology MSKCC New York 10021 New York USA. Department of Medicine MSKCC New York 10021 New York USA. Department of Epidemiology and Biostatistics MSKCC New York 10021 New York USA. Center for Cell Engineering Molecular Pharmacology and Chemistry Program Memorial Sloan-Kettering Cancer Center (MSKCC) New York 10021 New York USA. m-sadelain@***. Immunology Graduate Program Weill Graduate School of Medical Sciences Cornell University New York 10021 New York USA. m-sadelain@***. Department of Medicine MSKCC New York 10021 New York USA. m-sadelain@***.

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THU636 Prevalence Of Metabolic Syndrome In A Sample Of Hispanic Patients With Polycystic Ovary Syndrome

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Journal of the Endocrine Society 2023年第SUPPLEMENT_1期7卷

作者： Isabel C Arroyo Loida Gonzalez-Rodriguez Beatriz Diaz Rios Yanni Rivera-Rodriguez Adriana Garcia-Irizarry Hilary Cabrera-Martinez Lorena Gonzalez-sepulveda Cynthia Perez-Cardona Josefina Romaguera Ricardo Azziz Endocrinology Diabetes and Metabolism Division Medicine Department-University of Puerto Rico School of Medicine San Juan PR USA University of Puerto Rico - Rio Piedras Campus San Juan PR USA MD Program – University of Puerto Rico School of Medicine San Juan PR USA Biostatistics Epidemiology and Research Design Core Hispanic Alliance for Clinical and Translational Research – University of Puerto Rico Medical Sciences Campus San Juan PR USA Department of Biostatistics and Epidemiology Graduate School of Public Health – University of Puerto Rico Medical Sciences Campus San Juan PR USA Department of Obstetrics and Gynecology – University of Puerto Rico School of Medicine San Juan PR USA Heersink School of Medicine University of Alabama at Birmingham Birmingham AL USA

Disclosure: I.C. Arroyo: None. L. Gonzalez-Rodriguez: None. B. Diaz Rios: None. Y. Rivera-Rodriguez: None. A. Garcia-Irizarry: None. H. Cabrera-Martinez: None. L. Gonzalez-sepulveda: None. C. Perez-Cardona: None. J. Romaguera: None. R. Azziz, MD, MBA, MPH: Advisory Board Member; Self; Arora Forge. Consulting Fee; Self; Rani Therapeutics, Spruce Biosciences, Fortress Biotech, Core Access Surgical Technologies. Grant Recipient; Self; Ferring Pharmaceuticals. Stock Owner; Self; Martin Imaging.

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Fluctuating risk of acute kidney injury-related mortality for four weeks after exposure to air pollution: A multi-country time-series study in 6 countries

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Environment International 2024年 183卷 108367页

作者： Min, Jieun Kang, Duk-Hee Kang, Cinoo Bell, Michelle L. Kim, Ho Yang, Juyeon Gasparrini, Antonio Lavigne, Eric Hashizume, Masahiro Kim, Yoonhee Fook Sheng Ng, Chris Honda, Yasushi das Neves Pereira da Silva, Susana Madureira, Joana Leon Guo, Yue Pan, Shih-Chun Armstrong, Ben Sera, Francesco Masselot, Pierre Schwartz, Joel Maria Vicedo-Cabrera, Ana Pyo Lee, Jung Al-Aly, Ziyad Won Lee, Jung Kwag, Youngrin Ha, Eunhee Lee, Whanhee Department of Environmental Medicine College of Medicine Ewha Womans University Seoul Korea Republic of Graduate Program in System Health Science and Engineering College of Medicine Ewha Womans University Seoul Korea Republic of Division of Nephrology Department of Internal Medicine Ewha Womans University School of Medicine Ewha Medical Research Center Seoul Korea Republic of Department of Public Health Sciences Graduate School of Public Health Seoul National University Seoul Korea Republic of Yale School of the Environment Yale University New HavenCT United States Biostatistics Collaboration Unit Yonsei University College of Medicine Seoul Korea Republic of Lab Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom School of Epidemiology & Public Health Faculty of Medicine University of Ottawa Ottawa Canada Department of Global Health Policy Graduate School of Medicine The University of Tokyo Tokyo Japan Department of Global Environmental Health Graduate School of Medicine University of Tokyo Tokyo Japan School of Tropical Medicine and Global Health Nagasaki University Nagasaki Japan Center for Climate Change Adaptation National Institute for Environmental Studies Tsukuba Japan Department of Epidemiology Instituto Nacional de Saúde Dr. Ricardo Jorge Lisbon Portugal EPIUnit-Institute of Public Health University of Porto Porto Portugal Porto Portugal College of Medicine and NTU Hospital Taipei Taiwan National Institute of Environmental Health Science National Health Research Institutes Zhunan Taiwan Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Department of Statistics Computer Science and Applications "G. Parenti" University of Florence Florence Italy Department of Environmental Health Harvard T.H. Chan School of Public Health BostonMA United States Institute of Social and Preventive Medicine Universi

Background: Recent studies have reported that air pollution is related to kidney diseases. However, the global evidence on the risk of death from acute kidney injury (AKI) owing to air pollution is limited. Therefore, we investigated the association between short-term exposure to air pollution—particulate matter ≤ 2.5 μm (PM2.5), ozone (O3), and nitrogen dioxide (NO2)—and AKI-related mortality using a multi-country dataset. Methods: This study included 41,379 AKI-related deaths in 136 locations in six countries during 1987–2018. A novel case time-series design was applied to each air pollutant during 0–28 lag days to estimate the association between air pollution and AKI-related deaths. Moreover, we calculated AKI deaths attributable to non-compliance with the World Health Organization (WHO) air quality guidelines. Results: The relative risks (95% confidence interval) of AKI-related deaths are 1.052 (1.003, 1.103), 1.022 (0.994, 1.050), and 1.022 (0.982, 1.063) for 5, 10, and 10 µg/m3 increase in lag 0–28 days of PM2.5, warm-season O3, and NO2, respectively. The lag-distributed association showed that the risk appeared immediately on the day of exposure to air pollution, gradually decreased, and then increased again reaching the peak approximately 20 days after exposure to PM2.5 and O3. We also found that 1.9%, 6.3%, and 5.2% of AKI deaths were attributed to PM2.5, warm-season O3, and NO2 concentrations above the WHO guidelines. Conclusions: This study provides evidence that public health policies to reduce air pollution may alleviate the burden of death from AKI and suggests the need to investigate the several pathways between air pollution and AKI death. © 2023 The Author(s)

关键词： Air quality

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Polygenic risk modeling for prediction of epithelial ovarian cancer risk (vol 30, pg 349, 2021)

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EUROPEAN JOURNAL OF HUMAN GENETICS 2022年第5期30卷 630-631页

作者： Dareng, Eileen O. Tyrer, Jonathan P. Barnes, Daniel R. Jones, Michelle R. Yang, Xin Aben, Katja K. H. Adank, Muriel A. Agata, Simona Andrulis, Irene L. Anton-Culver, Hoda Antonenkova, Natalia N. Aravantinos, Gerasimos Arun, Banu K. Augustinsson, Annelie Balmana, Judith Bandera, Elisa V. Barkardottir, Rosa B. Barrowdale, Daniel Beckmann, Matthias W. Beeghly-Fadiel, Alicia Benitez, Javier Bermisheva, Marina Bernardini, Marcus Q. Bjorge, Line Black, Amanda Bogdanova, Natalia V. Bonanni, Bernardo Borg, Ake Brenton, James D. Budzilowska, Agnieszka Butzow, Ralf Buys, Saundra S. Cai, Hui Caligo, Maria A. Campbell, Ian Cannioto, Rikki Cassingham, Hayley Chang-Claude, Jenny Chanock, Stephen J. Chen, Kexin Chiew, Yoke-Eng Chung, Wendy K. Claes, Kathleen B. M. Colonna, Sarah Cook, Linda S. Couch, Fergus J. Daly, Mary B. Dao, Fanny Davies, Eleanor de la Hoya, Miguel de Putter, Robin Dennis, Joe DePersia, Allison Devilee, Peter Diez, Orland Ding, Yuan Chun Doherty, Jennifer A. Domchek, Susan M. Dork, Thilo du Bois, Andreas Durst, Matthias Eccles, Diana M. Eliassen, Heather A. Engel, Christoph Evans, Gareth D. Fasching, Peter A. Flanagan, James M. Fortner, Renee T. Machackova, Eva Friedman, Eitan Ganz, Patricia A. Garber, Judy Gensini, Francesca Giles, Graham G. Glendon, Gord Godwin, Andrew K. Goodman, Marc T. Greene, Mark H. Gronwald, Jacek Hahnen, Eric Haiman, Christopher A. Hakansson, Niclas Hamann, Ute Hansen, Thomas V. O. Harris, Holly R. Hartman, Mikael Heitz, Florian Hildebrandt, Michelle A. T. Hogdall, Estrid Hogdall, Claus K. Hopper, John L. Huang, Ruea-Yea Huff, Chad Hulick, Peter J. Huntsman, David G. Imyanitov, Evgeny N. Isaacs, Claudine Jakubowska, Anna James, Paul A. Janavicius, Ramunas Jensen, Allan Johannsson, Oskar Th. John, Esther M. Jones, Michael E. Kang, Daehee Karlan, Beth Y. Karnezis, Anthony Kelemen, Linda E. Khusnutdinova, Elza Kiemeney, Lambertus A. Kim, Byoung-Gie Kjaer, Susanne K. Komenaka, Ian Kupryjanczyk, Jolanta Kurian, Allison W. Kwong, Ava Lambrechts, Diether Larson, Melissa C. Lazaro, Conxi L University of Cambridge Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care Cambridge UK University of Cambridge Centre for Cancer Genetic Epidemiology Department of Oncology Cambridge UK Center for Bioinformatics and Functional Genomics Cedars-Sinai Medical Center Los Angeles CA USA Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen The Netherlands Netherlands Comprehensive Cancer Organisation Utrecht The Netherlands The Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital Family Cancer Clinic Amsterdam The Netherlands Veneto Institute of Oncology IOV—IRCCS Immunology and Molecular Oncology Unit Padua Italy Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Fred A. Litwin Center for Cancer Genetics Toronto ON Canada University of Toronto Department of Molecular Genetics Toronto ON Canada University of California Irvine Department of Epidemiology Genetic Epidemiology Research Institute Irvine CA USA N.N. Alexandrov Research Institute of Oncology and Medical Radiology Minsk Belarus ‘Agii Anargiri’ Cancer Hospital Athens Greece University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology Houston TX USA Lund University Department of Cancer Epidemiology Clinical Sciences Lund Sweden Vall d’Hebron Institute of Oncology Hereditary cancer Genetics Group Barcelona Spain University Hospital of Vall d’Hebron Department of Medical Oncology Barcelona Spain Rutgers Cancer Institute of New Jersey Cancer Prevention and Control Program New Brunswick NJ USA Landspitali University Hospital Department of Pathology Reykjavik Iceland University of Iceland BMC (Biomedical Centre) Faculty of Medicine Reykjavik Iceland University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg Department of Gynecology and Obstetrics Comprehensive Cancer Center ER-EMN Erlangen Germany Vanderbilt University School of Medicine Division of Epidemiology Department

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

关键词： Clinical genetics Genetic markers Risk factors Biomedicine general Human Genetics Bioinformatics Gene Expression Cytogenetics

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EVALUATION OF SOFTWARE IMPACT DESIGNED FOR BIOMEDICAL RESEARCH: ARE WE MEASURING WHAT'S MEANINGFUL?

arXiv

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arXiv 2023年

作者： Afiaz, Awan Ivanov, Andrey A. Chamberlin, John Hanauer, David Savonen, Candace L. Goldman, Mary J. Morgan, Martin Reich, Michael Getka, Alexander Holmes, Aaron Pati, Sarthak Knight, Dan Boutros, Paul C. Bakas, Spyridon Caporaso, J. Gregory Del Fiol, Guilherme Hochheiser, Harry Haas, Brian Schloss, Patrick D. Eddy, James A. Albrecht, Jake Fedorov, Andrey Waldron, Levi Hoffman, Ava M. Bradshaw, Richard L. Leek, Jeffrey T. Wright, Carrie Department of Biostatistics University of Washington SeattleWA United States Biostatistics Program Public Health Sciences Division Fred Hutchinson Cancer Center SeattleWA United States Department of Pharmacology and Chemical Biology Emory University School of Medicine Emory University AtlantaGA United States Department of Biomedical Informatics University of Utah Salt Lake CityUT United States Department of Learning Health Sciences University of Michigan Medical School Ann ArborMI United States University of California Santa Cruz Santa CruzCA United States Roswell Park Comprehensive Cancer Center BuffaloNY United States University of California San Diego La Jolla CA United States University of Pennsylvania PhiladelphiaPA United States Jonsson Comprehensive Cancer Center University of California Los AngelesCA United States Institute for Precision Health University of California Los AngelesCA United States Department of Human Genetics University of California Los AngelesCA United States Department of Urology University of California Los AngelesCA United States Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Methods Development Laboratory Broad Institute CambridgeMA United States Department of Microbiology and Immunology University of Michigan Ann ArborMI United States Sage Bionetworks SeattleWA United States Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Epidemiology and Biostatistics City University of New York Graduate School of Public Health and Health Policy New YorkNY United States

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that su

关键词： Diseases

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Mechanical transmission of dengue virus by Aedes aegypti may influence disease transmission dynamics during outbreaks

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eBioMedicine 2023年 94卷 104723-104723页

作者： Li, Hsing-Han Su, Matthew P. Wu, Shih-Cheng Tsou, Hsiao-Hui Chang, Meng-Chun Cheng, Yu-Chieh Tsai, Kuen-Nan Wang, Hsin-Wei Chen, Guan-Hua Tang, Cheng-Kang Chung, Pei-Jung Tsai, Wan-Ting Huang, Li-Rung Yueh, Yueh Andrew Chen, Hsin-Wei Pan, Chao-Ying Akbari, Omar S. Chang, Hsiao-Han Yu, Guann-Yi Marshall, John M. Chen, Chun-Hong National Mosquito-Borne Disease Control Research Center NHRI Miaoli 350401 Taiwan National Institute of Infectious Diseases and Vaccinology NHRI Miaoli 350401 Taiwan Division of Biological Sciences Section of Cell and Developmental Biology University of California San Diego La Jolla 92093 CA United States Graduate School of Science Nagoya University Nagoya 464-8602 Japan Institute for Advanced Research Nagoya University Nagoya 464-8601 Japan Department of Clinical Laboratory Sciences and Medical Biotechnology College of Medicine National Taiwan University Taipei 10048 Taiwan Department of Laboratory Medicine National Taiwan University Hospital College of Medicine National Taiwan University Taipei 10021 Taiwan Institute of Population Health Sciences National Health Research Institutes Zhunan Miaoli 350401 Taiwan Graduate Institute of Biostatistics College of Public Health China Medical University Taichung 40402 Taiwan Department of Life Science & Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Program of Plant Protection and Health Academy of Circular Economy National Chung Hsing University Taichung 402202 Taiwan Institute of Molecular and Genomic Medicine NHRI Miaoli 350401 Taiwan Institute of Biotechnology and Pharmaceutical Research NHRI Miaoli 350401 Taiwan Department of Health Kaohsiung City Government Kaohsiung 800852 Taiwan Divisions of Biostatistics and Epidemiology School of Public Health University of California Berkeley 94720 CA United States

Background: Dengue virus outbreaks are increasing in number and severity worldwide. Viral transmission is assumed to require a minimum time period of viral replication within the mosquito midgut. It is unknown if alternative transmission periods not requiring replication are possible. Methods: We used a mouse model of dengue virus transmission to investigate the potential of mechanical transmission of dengue virus. We investigated minimal viral titres necessary for development of symptoms in bitten mice and used resulting parameters to inform a new model of dengue virus transmission within a susceptible population. Findings: Naïve mice bitten by mosquitoes immediately after they took partial blood meals from dengue infected mice showed symptoms of dengue virus, followed by mortality. Incorporation of mechanical transmission into mathematical models of dengue virus transmission suggest that this supplemental transmission route could result in larger outbreaks which peak sooner. Interpretation: The potential of dengue transmission routes independent of midgut viral replication has implications for vector control strategies that target mosquito lifespan and suggest the possibility of similar mechanical transmission routes in other disease-carrying mosquitoes. Funding: This study was funded by grants from the National Health Research Institutes, Taiwan (04D2-MMMOST02), the Human Frontier Science program (RGP0033/2021), the National Institutes of Health (1R01AI143698-01A1, R01AI151004 and DP2AI152071) and the Ministry of Science and Technology, Taiwan (MOST104-2321-B-400-016). © 2023 The Authors

关键词： Aedes aegypti mosquito Animal models of dengue virus Dengue transmission Mathematical modelling of disease outbreak

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Violence study of healthcare workers and systems in the Caribbean:ViSHWaS-Caribbean study

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World Journal of Methodology 2024年第3期14卷 106-118页

作者： Reshon Hadmon Daniella Myriam Pierre Akshat Banga Jacques W Clerville Hans Mautong Precious Akinsanya Rajat Das Gupta Sama Soliman Tolupe M Hunjah Bamidele A Hunjah Hafeez Hamza Ruman Khurshid Qasba Faisal A Nawaz Salim Surani Rahul Kashyap Department of Medicine St.George University School of MedicineGrenada FZ818West Indies Department of Research Global Remote Research Scholar ProgramSt PaulMN 55101United States Department of Public Health Cuny Graduate School of Public Health and Health PolicyNew YorkNY 10027United States Department of Internal Medicine Sawai Man Singh Medical CollegeJaipur 302004India School of Public Health Georgia State UniversityAtlantaGA 30302United States Department of Sociology and Demography School of HealthUniversidad de Especialidades Espiritu SantoSamborondon 092301Ecuador Department of Medicine Holy Name Medical CenterTeaneckNJ 07666United States Department of Epidemiology and Biostatistics University of South CarolinaColumbiaSC 29208United States Department of Medicine Pavlov First State Medical University of St PetersburgSankt-Peterburg 197022Russia Department of Medicine University of SuffolkIpswich IP41QJUnited Kingdom Department of Medicine Unicaf University of ZambiaLusaka 20842Zambia Department of Pharmacy School of PharmacyGirne 99428Cyprus Department of Medicine Sher-i-KashmirInstitute of Medical ScienceSrinagar 190011India Department of Psychiatry Al Amal Psychiatric HospitalEmirates Health ServicesDubaiUnited Arab Emirates Department of Medicine and Pharmacology Texas A and M UniversityCollege StationTX 77843United States Anesthesiology and Critical Care Mayo ClinicRochesterMN 55905United States Department of Research Wellspan HealthYorkPA 17403United States

BACKGROUND Violence against healthcare workers(HCWs)in the Caribbean continues to prevail yet remains *** aim is to determine the cause,traits,and consequences of violence on HCWs in the *** To determine the cause,traits,and consequences of violence on HCWs in the *** This research adopted an online cross-sectional survey approach,spanning over eight weeks(between June 6th and August 9th,2022).The survey was generated using Research Electronic Data Capture forms and followed a snowballing strategy to contact individuals using emails,social media,text messages,*** regression analysis was performed to evaluate the variables that influence violence,including gender,age,years of experience,institution type,and night shift *** The survey was completed by 225 *** comprised 61%.Over 51%of respondents belonged to the 21 to 35 age ***(n=61),Haiti(n=50),and Grenada(n=31)had the most *** HCWs(49%)worked for government academic institutions,followed by community hospitals(23%).Medical students(32%),followed by attending physicians(22%),and others(16%)comprised the most common cadre of *** 39%of the participants reported experiencing violence themselves,and 18%reported violence against colleague(s).Verbal violence(48%),emotional abuse(24%),and physical misconduct(14%)were the most common types of *** 63%of respondents identified patients or their relatives as the most frequent *** logistic regression analyses demonstrated that female gender(OR=2.08;95%CI:1.16-3.76,P=0.014)and higher frequency of night shifts(OR=2.22;95%CI:1.08-4.58,P=0.030)were associated with significantly higher odds of experiencing *** than 50%of HCWs felt less motivated and had decreased job satisfaction post-violent *** A large proportion of HCWS in the Caribbean are exposed to violence,yet the phenomenon remains *** a result,HCWs’job s

关键词： ViSHWaS Healthcare workers Violence Survey Workplace violence Caribbean Cross-sectional study

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Correction: Gender differences among patients with drug resistant tuberculosis and HIV co-infection in Uganda: a countrywide retrospective cohort study

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BMC infectious diseases 2023年第1期23卷 44页

作者： Joseph Baruch Baluku David Mukasa Felix Bongomin Anna M Stadelman Edwin Nuwagira Sabine Haller Kauthrah Ntabadde Stavia Turyahabwe Division of Pulmonology Kiruddu National Referral Hospital Kampala Uganda. bbjoe18@***. Makerere University Lung Institute PO Box 26343 Kampala Uganda. bbjoe18@***. Complex Diseases and Genome Epidemiology Graduate School of Public Health Seoul National University Seoul South Korea. Department of Medical Microbiology & Immunology Faculty of Medicine Gulu University Gulu Uganda. Division of Epidemiology and Community Health School of Public Health University of Minnesota Minneapolis MN USA. Infectious Diseases Unit Department of Medicine Mbarara University of Science and Technology Mbarara Uganda. Department of Public and Global Health Epidemiology Biostatistics & Prevention Institute University of Zurich Zurich Switzerland. MRC/UVRI & LSHTM Uganda Research Unit Entebbe Uganda. National Tuberculosis and Leprosy Control Program Ministry of Health Kampala Uganda.

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Distinguishing genetic correlation from causation across 52 diseases and complex traits (vol 50, pg 1728, 2018)

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NATURE GENETICS 2018年第12期50卷 1753-1753页

作者： O'Connor, Luke J. Price, Alkes L. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Graduate School of Arts and Sciences Cambridge MA USA Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA USA Program in Medical and Population Genetics Broad Institute Cambridge MA USA

Mendelian randomization, a method to infer causal relationships, is confounded by genetic correlations reflecting shared etiology. We developed a model in which a latent causal variable mediates the genetic correlation; trait 1 is partially genetically causal for trait 2 if it is strongly genetically correlated with the latent causal variable, quantified using the genetic causality proportion. We fit this model using mixed fourth moments \({\it{E}}({\it{\alpha }}_1^2{\it{\alpha }}_1{\it{\alpha }}_2)\) and \({\it{E}}\left( {{\it{\alpha }}_2^2{\it{\alpha }}_1{\it{\alpha }}_2} \right)\) of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large \({\it{\alpha }}_1^2\)) will have correlated effects on trait 2 (large α₁α₂), but not vice versa. In simulations, our method avoided false positives due to genetic correlations, unlike Mendelian randomization. Across 52 traits (average n = 331,000), we identified 30 causal relationships with high genetic causality proportion estimates. Novel findings included a causal effect of low-density lipoprotein on bone mineral density, consistent with clinical trials of statins in osteoporosis.

关键词： Genetics Genome-wide association studies

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HLA allele-calling using multi-ancestry whole-exome sequencing from the UK Biobank identifies 129 novel associations in 11 autoimmune diseases

Communications Biology

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Communications Biology 2023年第1期6卷 1113页

作者： Butler-Laporte, Guillaume Farjoun, Joseph Nakanishi, Tomoko Lu, Tianyuan Abner, Erik Chen, Yiheng Hultström, Michael Metspalu, Andres Milani, Lili Mägi, Reedik Nelis, Mari Hudjashov, Georgi Yoshiji, Satoshi Ilboudo, Yann Liang, Kevin Y. H. Su, Chen-Yang Willet, Julian D. S. Esko, Tõnu Zhou, Sirui Forgetta, Vincenzo Taliun, Daniel Richards, J. Brent Department of Epidemiology Biostatistics and Occupational Health McGill University Montréal QC Canada Lady Davis Institute Jewish General Hospital McGill University Montréal QC Canada Wellcome Trust Centre for Human Genetics University of Oxford Oxford United Kingdom Kyoto-McGill International Collaborative School in Genomic Medicine Graduate School of Medicine Kyoto University Kyoto Japan Department of Human Genetics McGill University Montréal QC Canada Research Fellow Japan Society for the Promotion of Science Tokyo Japan 5 Prime Sciences Inc Montreal QC Canada Estonian Genome Center Institute of Genomics University of Tartu Tartu Estonia Integrative Physiology Department of Medical Cell Biology Uppsala University Uppsala Sweden Anaesthesiology and Intensive Care Medicine Department of Surgical Sciences Uppsala University Uppsala Sweden Department of Twin Research King’s College London London United Kingdom Infectious Diseases and Immunity in Global Health Program Research Institute of the McGill University Health Centre Montréal Québec Canada

The human leukocyte antigen (HLA) region on chromosome 6 is strongly associated with many immune-mediated and infection-related diseases. Due to its highly polymorphic nature and complex linkage disequilibrium patterns, traditional genetic association studies of single nucleotide polymorphisms do not perform well in this region. Instead, the field has adopted the assessment of the association of HLA alleles (i.e., entire HLA gene haplotypes) with disease. Often based on genotyping arrays, these association studies impute HLA alleles, decreasing accuracy and thus statistical power for rare alleles and in non-European ancestries. Here, we use whole-exome sequencing (WES) from 454,824 UK Biobank (UKB) participants to directly call HLA alleles using the HLA-HD algorithm. We show this method is more accurate than imputing HLA alleles and harness the improved statistical power to identify 360 associations for 11 auto-immune phenotypes (at least 129 likely novel), leading to better insights into the specific coding polymorphisms that underlie these diseases. We show that HLA alleles with synonymous variants, often overlooked in HLA studies, can significantly influence these phenotypes. Lastly, we show that HLA sequencing may improve polygenic risk scores accuracy across ancestries. These findings allow better characterization of the role of the HLA region in human disease. © 2023, The Author(s).

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