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RETRACTION: AKT3-mediated IWS1 phosphorylation promotes the proliferation of EGFR-mutant lung adenocarcinomas through cell cycle-regulated U2AF2 RNA splicing (Retraction of Vol 12, art no 4624, 2021)

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NATURE COMMUNICATIONS 2022年第1期13卷 1页

作者： Laliotis, Georgios I. Chavdoula, Evangelia Paraskevopoulou, Maria D. Kaba, Abdul La Ferlita, Alessandro Singh, Satishkumar Anastas, Vollter Nair, Keith A., II Orlacchio, Arturo Taraslia, Vasiliki Vlachos, Ioannis Capece, Marina Hatzigeorgiou, Artemis Palmieri, Dario Tsatsanis, Christos Alaimo, Salvatore Sehgal, Lalit Carbone, David P. Coppola, Vincenzo Tsichlis, Philip N. Department of Cancer Biology and Genetics The Ohio State University Columbus OH USA The Ohio State University Comprehensive Cancer Center-Arthur G. James Cancer Hospital and Richard J. Solove Research Institute Columbus OH USA School of Medicine University of Crete Heraklion Crete Greece Department of Clinical and Experimental Medicine Bioinformatics Unit University of Catania Catania Italy Tufts Graduate School of Biomedical Sciences Program in Genetics Boston MA USA Department of Medicine Division of Hematology The Ohio State University Columbus OH USA Department of Internal Medicine Division of Medical Oncology The Ohio State University Medical Center Columbus OH USA Institute of Molecular Biology and Biotechnology Foundation for Research and Technology Hellas Heraklion Crete Greece Molecular Oncology Research Institute Tufts Medical Center Boston MA USA DIANA-Lab Hellenic Pasteur Institute Athens Greece

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RETRACTION: Phosphor-IWS1-dependent U2AF2 splicing regulates trafficking of CAR-E-positive intronless gene mRNAs and sensitivity to viral infection (Retraction of Vol 4, art no 1179, 2021)

COMMUNICATIONS BIOLOGY

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COMMUNICATIONS BIOLOGY 2021年第1期4卷 1页

作者： Laliotis, Georgios I. Kenney, Adam D. Chavdoula, Evangelia Orlacchio, Arturo Kaba, Abdul La Ferlita, Alessandro Anastas, Vollter Tsatsanis, Christos Beane, Joal D. Sehgal, Lalit Coppola, Vincenzo Yount, Jacob S. Tsichlis, Philip N. Department of Cancer Biology and Genetics The Ohio State University College of Medicine Columbus Columbus OH 43210 USA The Ohio State University Comprehensive Cancer Center Columbus OH 43210 USA University of Crete School of Medicine Heraklion Crete 71500 Greece Department of Clinical and Experimental Medicine Bioinformatics Unit University of Catania Catania 95131 Italy Tufts Graduate School of Biomedical Sciences Program in Genetics Boston MA 02111 USA Department of Microbial Infection and Immunity and Infectious Diseases Institute The Ohio State University Columbus OH 43210 USA Department of Surgery Division of Surgical Oncology Columbus OH 43210 USA Department of Medicine Division of Hematology The Ohio State University Columbus OH 43210 USA Institute of Molecular Biology and Biotechnology Heraklion Crete 70013 Greece

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Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts

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The Lancet Respiratory Medicine 2020年第7期8卷 696-708页

作者： Moll, Matthew Sakornsakolpat, Phuwanat Shrine, Nick Hobbs, Brian D DeMeo, Dawn L John, Catherine Guyatt, Anna L McGeachie, Michael J Gharib, Sina A Obeidat, Ma'en Lahousse, Lies Wijnant, Sara R A Brusselle, Guy Meyers, Deborah A Bleecker, Eugene R Li, Xingnan Tal-Singer, Ruth Manichaikul, Ani Rich, Stephen S Won, Sungho Kim, Woo Jin Do, Ah Ra Washko, George R Barr, R Graham Psaty, Bruce M Bartz, Traci M Hansel, Nadia N Barnes, Kathleen Hokanson, John E Crapo, James D Lynch, David Bakke, Per Gulsvik, Amund Hall, Ian P Wain, Louise Soler Artigas, María Jackson, Victoria E Strachan, David P Hui, Jennie James, Alan L Kerr, Shona M Polasek, Ozren Vitart, Veronique Marten, Jonathan Rudan, Igor Kähönen, Mika Surakka, Ida Gieger, Christian Karrasch, Stefan Rawal, Rajesh Schulz, Holger Deary, Ian J Harris, Sarah E Enroth, Stefan Gyllensten, Ulf Imboden, Medea Probst-Hensch, Nicole M Lehtimäki, Terho Raitakari, Olli T Langenberg, Claudia Luan, Jian'an Wareham, Nick Zhao, Jing Hua Hayward, Caroline Murray, Alison Porteous, David J Smith, Blair H Jarvelin, Marjo-Riitta Wielscher, Matthias Joshi, Peter K Kentistou, Katherine A Timmers, Paul RHJ Wilson, James F Cook, James P Lind, Lars Mahajan, Anubha Morris, Andrew P Ewert, Ralf Homuth, Georg Stubbe, Beate Weiss, Stefan Zeggini, Eleftheria Weiss, Scott T Silverman, Edwin K Dudbridge, Frank Tobin, Martin D Cho, Michael H Channing Division of Network Medicine Brigham and Women's Hospital Boston MA United States Division of Pulmonary and Critical Care Medicine Brigham and Women's Hospital Boston MA United States Department of Medicine Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Genetic Epidemiology Group Department of Health Sciences University of Leicester Leicester United Kingdom Computational Medicine Core Center for Lung Biology Department of Medicine University of Washington Seattle WA United States Division of Pulmonary Critical Care and Sleep Medicine Department of Medicine University of Washington Seattle WA United States Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle WA United States University of British Columbia Center for Heart Lung Innovation St Paul's Hospital Vancouver BC Canada Department of Epidemiology Erasmus Medical Centre Rotterdam Netherlands Department of Respiratory Medicine Erasmus Medical Centre Rotterdam Netherlands Department of Bioanalysis Faculty of Pharmaceutical Sciences Ghent University Ghent Belgium Department of Respiratory Medicine Ghent University Hospital Ghent Belgium Department of Medicine University of Arizona Tucson AZ United States GlaxoSmithKline Research and Development Collegeville PA United States Center for Public Health Genomics University of Virginia Charlottesville VA United States Department of Public Health Sciences University of Virginia Charlottesville VA United States Department of Public Health Sciences Graduate School of Public Health Seoul National University Seoul South Korea Interdisciplinary Program of Bioinformatics College of National Sciences Seoul National University Seoul South Korea Institute of Health and Environment Seoul National University Seoul South Korea Department of Internal Medicine Kangwon National University Chuncheon South Korea Department of Medicine and Department of Epidemiology Columbia Universit

Background: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. Methods: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV₁ and FEV₁/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV₁/FVC <0·7 and FEV₁ <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth. Findings: The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74–1·88] and non-European (1·42 [1·34–1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56–9·72) in European ancestry and 4·83 (3·45–6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79–0·81] vs 0·76 [0·75–0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The po

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Genome-wide association study of long COVID

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Nature Genetics 2025年 1-16页

作者： Vilma Lammi Tomoko Nakanishi Samuel E. Jones Juha Karjalainen Martin Broberg Hele H. Haapaniemi Matti Pirinen Mari E. K. Niemi Mattia Cordioli Mark J. Daly Andrea Ganna Hanna M. Ollila J. Brent Richards Yukinori Okada Shea J. Andrews Beatriz Cortés Rafael de Cid Susana Iraola-Guzmán Natalia Blay Xavier Farré Heath E. O’Brien Thompson Hannah Patrick J. Short Ana Ochoa-Guzman Michelle Duran-Gomez Brian E. Fulton-Howard Masahiro Kanai Axel Schmidt Daniella Balla Julia Heggemann Sonja Schultz Pari Behzad Markus M. Nöthen Abigail Miller Kerstin U. Ludwig Ruth E. Mitchell George Davey Smith Nicholas J. Timpson Abdou Mousas Evangelos Evangelou Evangelia Ntzani Konstantinos K. Tsilidis Massimo Mangino Alicia Huerta-Chagoya Nasa Sinnott-Armstrong Elizabeth T. Cirulli Francisco Tanudjaja Efren Sandoval Nicole L. Washington Simon White Alexandre Bolze Kelly M. Schiabor Barrett Marc Vaudel Alex S. F. Kwong Amit K. Maiti Minttu M. Marttila Daniel C. Posner Alexis A. Rodriguez Chiara Batini Beatriz Guillen-Guio Olivia C. Leavy Anna L. Guyatt Catherine John Louise V. Wain Francesca Minnai Anna R. Dearman Benedict Hignell Meena Kumari C. A. Robert Warmerdam Lude H. Franke Celia B. Sequeros Søren Brunak David Westergaard Karina Banasik Thomas W. Winkler Iris M. Heid Daniel M. Jordan Ryan C. Thompson Alexander W. Charney Laura G. Sloofman Nicole W. Simons Noam D. Beckmann Raimonds Rešcenko Vita Rovite Peculis Raitis Monta Briviba Janis Klovinš Laura Ansone Lorenzo Miano Flora Peyvandi Francesco Malvestiti Nicola Montano Alessandra Bandera Francesco Bruno Arturo Blasi Jacqueline M. Lane Jakob M. Cherry Ryan K. Chung Laura Carvajal-Silva Kevin Aguilar-Valdés Leslie C. Cerpa Tamara V. Arévalo Eduardo Lamoza Alicia Colombo Ricardo A. Verdugo Erika Frangione Xu Xinyi Jennifer Taher Jordan Lerner-Ellis Lindsay Guare Shefali S. Verma Ekaterina Vergasova Michil Trofimov Layal Shaheen Nikolay Plotnikov Anna Kim Dmitrii Kharitonov Alexei Kamelin Alexander Rakitko Eirini Marouli Pasquale Striano Ummu Afeera Zainulabid Ashutosh Kumar Hajar Fa Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Department of Human Genetics McGill University Montreal Quebec Canada Centre for Clinical Epidemiology Department of Medicine Lady Davis Institute Jewish General Hospital McGill University Montreal Quebec Canada Kyoto-McGill International Collaborative Program in Genomic Medicine Graduate School of Medicine Kyoto University Kyoto Japan Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan Research Fellow Japan Society for the Promotion of Science Tokyo Japan Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Department of Mathematics and Statistics University of Helsinki Helsinki Finland Department of Public Health University of Helsinki Helsinki Finland Broad Institute Cambridge MA USA Analytical and Translational Genetics Unit Massachusetts General Hospital Harvard Medical School Boston MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Twin Research King’s College London London UK 5 Prime Sciences Inc Montreal Quebec Canada Department of Epidemiology Biostatistics and Occupational Health McGill University Montreal Quebec Canada Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Departmen

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association ofFOXP4with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4’s role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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A systems biology approach uncovers novel disease mechanisms in age-related macular degeneration

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Cell Genomics 2023年第6期3卷 100302-100302页

作者： Orozco, Luz D. Owen, Leah A. Hofmann, Jeffrey Stockwell, Amy D. Tao, Jianhua Haller, Susan Mukundan, Vineeth T. Clarke, Christine Lund, Jessica Sridhar, Akshayalakshmi Mayba, Oleg Barr, Julie L. Zavala, Rylee A. Graves, Elijah C. Zhang, Charles Husami, Nadine Finley, Robert Au, Elizabeth Lillvis, John H. Farkas, Michael H. Shakoor, Akbar Sherva, Richard Kim, Ivana K. Kaminker, Joshua S. Townsend, Michael J. Farrer, Lindsay A. Yaspan, Brian L. Chen, Hsu-Hsin DeAngelis, Margaret M. Department of Bioinformatics and Computational Biology Genentech South San Francisco 94080 CA United States Department of Ophthalmology and Visual Sciences University of Utah School of Medicine The University of Utah Salt Lake City 84132 UT United States Department of Population Health Sciences University of Utah School of Medicine The University of Utah Salt Lake City 84132 UT United States Department of Obstetrics and Gynecology University of Utah School of Medicine The University of Utah Salt Lake City 84132 UT United States Department of Ophthalmology Ross Eye Institute Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States Department of Pathology Genentech South San Francisco 94080 CA United States Department of Human Genetics Genentech South San Francisco 94080 CA United States Departments of Microchemistry Proteomics and Lipidomics Genentech South San Francisco 94080 CA United States Department of Human Pathobiology & OMNI Reverse Translation Genentech South San Francisco 94080 CA United States Neuroscience Graduate Program Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States Department of Biochemistry Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States Veterans Administration Western New York Healthcare System Buffalo 14212 NY United States Department of Medicine Biomedical Genetics Boston University School of Medicine Boston 02118 MA United States Retina Service Massachusetts Eye & Ear Department of Ophthalmology Harvard Medical School Boston 02114 MA United States Genetics Genomics and Bioinformatics Graduate Program Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States

Age-related macular degeneration (AMD) is a leading cause of blindness, affecting 200 million people worldwide. To identify genes that could be targeted for treatment, we created a molecular atlas at different stages of AMD. Our resource is comprised of RNA sequencing (RNA-seq) and DNA methylation microarrays from bulk macular retinal pigment epithelium (RPE)/choroid of clinically phenotyped normal and AMD donor eyes (n = 85), single-nucleus RNA-seq (164,399 cells), and single-nucleus assay for transposase-accessible chromatin (ATAC)-seq (125,822 cells) from the retina, RPE, and choroid of 6 AMD and 7 control donors. We identified 23 genome-wide significant loci differentially methylated in AMD, over 1,000 differentially expressed genes across different disease stages, and an AMD Müller state distinct from normal or gliosis. Chromatin accessibility peaks in genome-wide association study (GWAS) loci revealed putative causal genes for AMD, including HTRA1 and C6orf223. Our systems biology approach uncovered molecular mechanisms underlying AMD, including regulators of WNT signaling, FRZB and TLE2, as mechanistic players in disease. © 2023 The Authors

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LOW-FREQUENCY VARIANT IMPUTATION IDENTIFIES NOVEL DISEASE-ASSOCIATED LOCI IN A GENOME-WIDE ASSOCIATION STUDY OF LATE-ONSET ALZHEIMER'S DISEASE

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Alzheimer's & Dementia 2014年第4期10卷 P135-P135页

作者： Brian W. Kunkle Adam C. Naj Kara Hamilton-Nelson William R. Perry Amanda Partch Otto Valladares Jaeyoon Chung Gyungah Jun Mike Schmidt Gary Beecham Li-San Wang Eden Martin Richard Mayeux Jonathan Haines Lindsay A. Farrer Gerard D. Schellenberg Margaret Pericak-Vance University of Miami Hussman Institute for Human Genomics Miami Florida United States University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania United States University of Miami Miami Florida United States University of Pennsylvania Philadelphia Pennsylvania United States Boston University Bioinformatics Graduate Program Boston Massachusetts United States Boston University School of Medicine Boston Massachusetts United States University of Pennsylvania School of Medicine Philadelphia Pennsylvania United States Columbia University New York New York United States Case Western Reserve University Cleveland Ohio United States

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GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts

arXiv

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arXiv 2023年

作者： Wu, Da Yang, Jingye Liu, Cong Hsieh, Tzung-Chien Marchi, Elaine Blair, Justin Krawitz, Peter Weng, Chunhua Chung, Wendy Lyon, Gholson J. Krantz, Ian D. Kalish, Jennifer M. Wang, Kai Raymond G. Perelman Center for Cellular and Molecular Therapeutics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Institute for Genomic Statistics and Bioinformatics University Hospital Bonn Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Department of Human Genetics New York State Institute for Basic Research in Developmental Disabilities Staten IslandNY United States Division of Human Genetics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Pediatrics Boston Children’s Hospital Harvard Medical School BostonMA United States Biology PhD Program The Graduate Center The City University of New York New York United States Department of Genetics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pediatrics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data. © 2023, CC BY.

关键词： Diseases

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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Prior COVID-19 vaccination and reduced risk of cerebrovascular diseases among COVID-19 survivors

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Journal of Medical Virology 2024年第5期96卷 e29648-e29648页

作者： Chen, Sheng-Yin Hsieh, Tina Yi Jin Hung, Yao-Min Oh, Jae Won Chen, Shen-Kai Wang, Shiow-Ing Chang, Renin Wei, James Cheng-Chung Clinical and Translational Epidemiology Unit Massachusetts General Hospital and Harvard Medical School Boston Morocco Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston Morocco Department of Epidemiology Harvard T. H. Chan School of Public Health Boston Morocco Department of Obstetrics & Gynecology Beth Israel Deaconess Medical Center Boston Morocco Department of Bioinformatics Harvard Medical School Boston Morocco Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Neurology Brigham and Women's Hospital Boston Morocco Department of Neurology Harvard Medical School Boston MA United States Department of Education Kaohsiung Chang Gung Memorial Hospital Boston MA United States Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine College of Medicine Chung Shan Medical University Taichung Taiwan Department of Nursing Jen-Teh Junior College of Medicine Nursing and Management Miaoli County Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Recreation and Sports Management Tajen University Pintung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Office of Research and Development Asia University Taichung Taiwan

The effects of COVID-19 vaccination on short-term and long-term cerebrovascular risks among COVID-19 survivors remained unknown. We conducted a national multi-center retrospective cohort study with 151 597 vaccinated and 151 597 unvaccinated COVID-19 patients using the TriNetX database, from January 1, 2020 to December 31, 2023. Patients baseline characteristics were balanced with propensity score matching (PSM). The outcomes were incident cerebrovascular diseases occurred between 1st and 30th days (short-term) after COVID-19 diagnosis. Nine subgroup analyses were conducted to explore potential effect modifications. We performed six sensitivity analyses, including evaluation of outcomes between 1st to 180th days, accounting for competing risk, and incorporating different variant timeline to test the robustness of our results. Kaplan-Meier curves and Log-Rank tests were performed to evaluate survival difference. Cox proportional hazards regressions were adopted to estimate the PSM-adjusted hazard ratios (HR). The overall short-term cerebrovascular risks were lower in the vaccinated group compared to the unvaccinated group (HR: 0.66, 95% CI: 0.56–0.77), specifically cerebral infarction (HR: 0.62, 95% CI: 0.48–0.79), occlusion and stenosis of precerebral arteries (HR: 0.74, 95% CI: 0.53–0.98), other cerebrovascular diseases (HR: 0.57, 95% CI: 0.42–0.77), and sequelae of cerebrovascular disease (HR: 0.39, 95% CI:0.23–0.68). Similarly, the overall cerebrovascular risks were lower in those vaccinated among most subgroups. The long-term outcomes, though slightly attenuated, were consistent (HR: 0.80, 95% CI: 0.73–0.87). Full 2-dose vaccination was associated with a further reduced risk of cerebrovascular diseases (HR: 0.63, 95% CI: 0.50–0.80) compared to unvaccinated patients. Unvaccinated COVID-19 survivors have significantly higher cerebrovascular risks than their vaccinated counterparts. Thus, clinicians are recommended to monitor this population closely for stroke even

关键词： cohort study COVID-19 vaccination strokes

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