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Proteins: Structure, Function, and bioinformatics 2018年第5期86卷

作者： J. C. Gaines S. Acebes A. Virrueta M. Butler L. Regan C. S. O'Hern Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 Integrated Graduate Program in Physical and Engineering Biology (IGPPEB) Yale University New Haven Connecticut 06520 Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 Department of Physics and Astronomy University of Southern California Los Angeles California 90007 Department of Molecular Biophysics & Biochemistry Yale University New Haven Connecticut 06520 Department of Chemistry Yale University New Haven Connecticut 06520 Department of Physics Yale University New Haven Connecticut 06520 Department of Applied Physics Yale University New Haven Connecticut 06520

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Correction to: Comprehensive benchmarking and ensemble approaches for metagenomic classifiers

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Genome biology 2019年第1期20卷 72页

作者： Alexa B R McIntyre Rachid Ounit Ebrahim Afshinnekoo Robert J Prill Elizabeth Hénaff Noah Alexander Samuel S Minot David Danko Jonathan Foox Sofia Ahsanuddin Scott Tighe Nur A Hasan Poorani Subramanian Kelly Moffat Shawn Levy Stefano Lonardi Nick Greenfield Rita R Colwell Gail L Rosen Christopher E Mason Tri-Institutional Program in Computational Biology and Medicine New York NY USA. Department of Physiology and Biophysics Weill Cornell Medicine New York NY 10021 USA. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York NY 10021 USA. Department of Computer Science and Engineering University of California Riverside CA 92521 USA. School of Medicine New York Medical College Valhalla NY 10595 USA. Accelerated Discovery Lab IBM Almaden Research Center San Jose CA 95120 USA. One Codex Reference Genomics San Francisco CA 94103 USA. University of Vermont Burlington VT 05405 USA. CosmosID Inc Rockville MD 20850 USA. Center for Bioinformatics and Computational Biology University of Maryland Institute for Advanced Computer Studies (UMIACS) College Park MD 20742 USA. HudsonAlpha Institute for Biotechnology Huntsville AL 35806 USA. Johns Hopkins University Bloomberg School of Public Health Baltimore MD USA. Department of Electrical and Computer Engineering Drexel University Philadelphia PA 19104 USA. gail.l.rosen@***. Department of Physiology and Biophysics Weill Cornell Medicine New York NY 10021 USA. chm2042@med.cornell.edu. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York NY 10021 USA. chm2042@med.cornell.edu. The Feil Family Brain and Mind Research Institute New York NY 10065 USA. chm2042@med.cornell.edu.

Following publication of the original article [1], the authors would like to highlight the following two corrections.

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Comprehensive reanalysis for CNVs in ES data from unsolved rare disease cases results in new diagnoses

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npj Genomic Medicine 2024年第1期9卷 49页

作者： Demidov, German Yaldiz, Burcu Garcia-Pelaez, José de Boer, Elke Schuermans, Nika Van de Vondel, Liedewei Paramonov, Ida Johansson, Lennart F. Musacchia, Francesco Benetti, Elisa Bullich, Gemma Sablauskas, Karolis Beltran, Sergi Gilissen, Christian Hoischen, Alexander Ossowski, Stephan de Voer, Richarda Lohmann, Katja Oliveira, Carla Topf, Ana Vissers, Lisenka E. L. M. Zguro, Kristina Zara, Federico Zaganas, Ioannis Yépez, Vicente A. Wirth, Brunhilde Webb, David Verdin, Hannah Van Nieuwenhove, Erika van Montfrans, Joris van Heurck, Roxane van der Veken, Lars Valle, Laura Uva, Paolo Udd, Bjarne Trimouille, Aurelien Thompson, Rachel Tartaglia, Marco Sznajer, Yves Striano, Pasquale Steinke-Lange, Verena Spilioti, Martha Scudieri, Paolo Schröck, Evelin Schon, Katherine Scala, Marcello Savarese, Marco Santen, Gijs W. E. Rump, Andreas Ruivenkamp, Claudia A. L. Roos, Andreas Rooryck, Caroline Riva, Antonella Renieri, Alessandra Ratnaike, Thiloka Radio, Francesca Clementina Privitera, Flavia De la Paz, Manuel Posada Poppe, Bruce Polavarapu, Kiran Platzer, Konrad Peterlin, Borut Pérez-Dueñas, Belén Pauly, Martje Parrini, Elena Olimpio, Catarina Reghan, Mary O. de Benito, Daniel Natera Osorio, Andrés Nascimento Munell, Francina Münchau, Alexander Moortgat, Stéphanie Monestier, Olivier Molnar, Maria Judit Meunier, Colombine Mertes, Christian Mei, Davide Maystadt, Isabelle May, Patrick Maver, Ales Mathioudakis, Lambros Masclaux, Frédéric Mary, Sandrine Delgado, Beatriz Martínez Marcé-Grau, Anna Malaichamy, Sivasankar Macaya, Alfons Luknárová, Rebeka Martín, Estrella López Lochmüller, Hanns Leitão, Elsa Lederer, Damien Leavis, Helen Laner, Andreas Lacombe, Didier Krass, Leon Korff, Christian Kokosali, Evgenia Karakaya, Mert Karadurmus, Deniz Kapaki, Elisabeth Johari, Mridul Iacomino, Michele Huibers, Manon Horvath, Rita Holinski-Feder, Elke Hoffer, Mariette J. V. Herzog, Rebecca Hemelsoet, Dimitri Bouveret, Eva Hammar Haimel, Matthias Hackman, Peter Guerrini, Renzo Grimbacher, Bodo Greally, Marie Ghani, Hamidah Gagneur, Julien Institute of Medical Genetics and Applied Genomics University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics (IBMI) University of Tübingen Tübingen Germany Department of Human Genetics Radboud University Medical Center Nijmegen Netherlands Department of Clinical Genetics Maastricht University Medical Center Maastricht Netherlands i3S - Instituto de Investigação e Inovação em Saúde Rua Alfredo Allen 208 Porto 4200-135 Portugal IPATIMUP - Institute of Molecular Pathology and Immunology University of Porto Porto Portugal Faculty of Medicine University of Porto Porto Portugal Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Department of Clinical Genetics Erasmus MC University Medical Center Rotterdam Netherlands Center for Medical Genetics Ghent University Hospital Ghent Belgium Translational Neurosciences Faculty of Medicine and Health Sciences University of Antwerp Antwerp Belgium Laboratory of Neuromuscular Pathology Institute Born-Bunge University of Antwerp Antwerp Belgium Centro Nacional de Análisis Genómico (CNAG) C/Baldiri Reixac 4 Barcelona 08028 Spain Universitat de Barcelona (UB) Barcelona Spain University of Groningen University Medical Center Groningen Department of Genetics Groningen Netherlands Center for Human Technologies Italian Institute of Technology (IIT) Genova Italy Telethon Institute for Genetics and Medicine Pozzuoli (Napoli) 80078 Italy Department of Medical Biotechnologies Med Biotech Hub and Competence Center University of Siena Siena 53100 Italy Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania Departament de Genètica Microbiologia i Estadística Facultat de Biologia Universitat de Barcelona (UB) Barcelona Spain Radboud Institute for Molecular Life Sciences Nijmegen Netherlands Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI) Radboud University Medical Center Nijmegen N

We report the results of a comprehensive copy number variant (CNV) reanalysis of 9171 exome sequencing datasets from 5757 families affected by a rare disease (RD). The data reanalysed was extremely heterogeneous, having been generated using 28 different enrichment kits by 42 different research groups across Europe partnering in the Solve-RD project. Each research group had previously undertaken their own analysis of the data but failed to identify disease-causing variants. We applied three CNV calling algorithms to maximise sensitivity, and rare CNVs overlapping genes of interest, provided by four partner European Reference Networks, were taken forward for interpretation by clinical experts. This reanalysis has resulted in a molecular diagnosis being provided to 51 families in this sample, with ClinCNV performing the best of the three algorithms. We also identified partially explanatory pathogenic CNVs in a further 34 individuals. This work illustrates the value of reanalysing ES cold cases for CNVs. © The Author(s) 2024.

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Transcript expression-aware annotation improves rare variant interpretation (vol 581, pg 452, 2020)

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NATURE 2021年第7846期590卷 E54-E54页

作者： Cummings, Beryl B. Karczewski, Konrad J. Kosmicki, Jack A. Seaby, Eleanor G. Watts, Nicholas A. Singer-Berk, Moriel Mudge, Jonathan M. Karjalainen, Juha Satterstrom, F. Kyle O'Donnell-Luria, Anne H. Poterba, Timothy Seed, Cotton Solomonson, Matthew Alfoldi, Jessica Daly, Mark J. MacArthur, Daniel G. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Analytical and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Genomic Informatics Group University Hospital Southampton Southampton UK Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton Cambridge UK Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA National Heart & Lung Institute and MRC London Institute of Medical Sciences Imperial College London London UK Cardiovascular Research Centre Royal Brompton & Harefield Hospitals NHS Trust London UK Cardiac Arrhythmia Service and Cardiovascular Research Center Massachusetts General Hospital Boston MA USA Estonian Genome Center Institute of Genomics University of Tartu Tartu Estonia Department of Medicine Harvard Medical School Boston MA USA Cardiac Arrhythmia Service Massachusetts General Hospital Boston MA USA Division of General Internal Medicine Massachusetts General Hospital Boston MA USA Institute for Molecular Medicine FIMM University of Helsinki Helsinki Finland Department of Public Health Faculty of Medicine University of Helsinki Helsinki Finland Department of Genetics Harvard Medical School Boston MA USA Data Sciences Platform Broad Institute of MIT and Harvard Cambridge MA USA Genomics Platform Broad Institute of MIT and Harvard Cambridge MA USA Wellcome Sanger Institute Hinxton Cambridgeshire UK Unidad de Investigaci

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Genetically-Defined Diffuse Large B-Cell Lymphoma Subsets Arise By Distinct Pathogenetic Mechanisms and Predict Outcome

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BLOOD 2017年第Sup1期130卷 38-38页

作者： Chapuy, Bjoern Stewart, Chip Dunford, Andrew Kim, Jaegil Kamburow, Atanas Redd, Robert A. Lawrence, Michael Roemer, Margaretha G. M. Li, Amy Ziepert, Marita Staiger, Annette M. Wala, Jeremiah Ducar, Matthew D. Leshchiner, Ignaty Rheinbay, Esther Taylor-Weiner, Amaro Coughlin, Caroline Hess, Julian Pedamallu, Chandra Livitz, Dimitri Rosebrock, Daniel Rosenberg, Mara Tracy, Adam Horn, Heike van Hummelen, Paul Feldman, Andrew L. Link, Brian K. Novak, Anne J. Cerhan, James R. Habermann, Thomas M. Siebert, Reiner Rosenwald, Andreas Thorner, Aaron R. Meyerson, Matthew Golub, Todd R. Beroukhim, Rameen Wulf, Gerald Ott, German Rodig, Scott J. Monti, Stefano Neuberg, Donna S. Loeffler, Markus Pfreundschuh, Michael Trumper, Lorenz Getz, Gad Shipp, Margaret A. Dana-Farber Cancer Institute Boston MA Broad Institute Cambridge MA Section of Computational Biomedicine Boston University School of Medicine Boston MA Bioinformatics Program Boston University Boston MA Institute for Medical Informatics Statistics and Epidemiology University of Leipzig Leipzig Germany Department of Clinical Pathology Robert-Bosch-Krankenhaus Stuttgart Germany Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology Stuttgart and University of Tuebingen Stuttgart Germany Center for Cancer Genome Discovery Dana-Farber Cancer Institute Boston MA Department of Laboratory Medicine and Pathology Mayo Clinic Rochester MN University of Iowa Hospitals and Clinics Iowa City IA Division of Hematology Mayo Clinic Rochester MN Department of Health Sciences Research Mayo Clinic Rochester MN Department of Human Genetics University of Ulm Ulm Germany Institute of Pathology University of Wuerzburg and Comprehensive Cancer Center Mainfranken Wuerzburg Germany Department of Medical Oncology Dana Farber Cancer Institute Boston MA Department of Hematology and Oncology University Hospital of Göttingen Göttingen Germany Brigham and Women's Hospital Boston MA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Boston MA Leipzig University Faculty of Medicine IMISE Leipzig DEU Department for hematology and oncology Saarland University Medical School Homburg Germany Hematology and Medical Oncology University Hospital Göttingen Göttingen Germany Cancer Program Broad Institute of MIT and Harvard Cambridge MA

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease that largely arises from antigen-exposed B-cells that transit through the germinal center (GC). DLBCL is further classified into transcriptional subtypes - activated B-cell (ABC) and GC B-cell (GCB). ABC DLBCLs exhibit genetic alterations in NF-kB modifiers and proximal components of the B-cell receptor (BCR) pathway and perturbed terminal B-cell differentiation. GCB DLBCLs have reported alterations in chromatin-modifying enzymes, PI3K signaling and Gα-migration pathway components and frequent translocations of BCL2 .Despite the recognized molecular heterogeneity in DLBCL, previously published genomic analyses have largely focused on single types of alterations - mutations, copy number alterations (CNAs) or structural variants (SVs) - in smaller data sets with more limited clinical annotation. An unbiased comprehensive analysis of all three alteration types is needed to define discrete, clinically annotated subtypes of *** performed whole exome sequencing (WES) of 304 newly diagnosed DLBCLs, using an expanded bait set that captures recurrent SVs; 85% of study patients were uniformly treated with R-CHOP and had long-term follow-up. Somatic alterations (mutations, CNAs and SVs) and their clonality were determined with analytical pipelines developed at the Broad Institute. Notably, half of our DLBCL cohort lacked patient-matched normal samples, prompting the successful development of new methods to analyze tumor-only WES data. Significantly mutated genes (SMGs) were identified with MutSig2CV andrecurrent CNAs were defined with GISTIC2.0 .With increased sample size and improved methodology, we identified 158 SMGs, CNAs and SVs; the 98 SMGs included ones previously unreported in DLBCL but described in other lymphoid malignancies or cancer. Additional insights into the putative biological function of newly identified alterations were obtained by overlaying the predicted protein changes onto their 3-dime

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A structural variation reference for medical and population genetics (vol 581, pg 444, 2020)

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NATURE 2021年第7846期590卷 E55-E55页

作者： Collins, Ryan L. Brand, Harrison Karczewski, Konrad J. Zhao, Xuefang Alfoldi, Jessica Francioli, Laurent C. Khera, Amit V. Lowther, Chelsea Gauthier, Laura D. Wang, Harold Watts, Nicholas A. Solomonson, Matthew O'Donnell-Luria, Anne Baumann, Alexander Munshi, Ruchi Walker, Mark Whelan, Christopher W. Huang, Yongqing Brookings, Ted Sharpe, Ted Stone, Matthew R. Valkanas, Elise Fu, Jack Tiao, Grace Laricchia, Kristen M. Ruano-Rubio, Valentin Stevens, Christine Gupta, Namrata Cusick, Caroline Margolin, Lauren Taylor, Kent D. Lin, Henry J. Rich, Stephen S. Post, Wendy S. Chen, Yii-Der Ida Rotter, Jerome I. Nusbaum, Chad Philippakis, Anthony Lander, Eric Gabriel, Stacey Neale, Benjamin M. Kathiresan, Sekar Daly, Mark J. Banks, Eric MacArthur, Daniel G. Talkowski, Michael E. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Division of Medical Sciences Harvard Medical School Boston MA USA Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA Analytical and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Department of Medicine Harvard Medical School Boston MA USA Data Science Platform Broad Institute of MIT and Harvard Cambridge MA USA Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton Cambridge UK Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA National Heart & Lung Institute and MRC London Institute of Medical Sciences Imperial College London London UK Cardiovascular Research Centre Royal Brompton & Harefield Hospitals NHS Trust London UK Cardiac Arrhythmia Service Cardiovascular Research Center Massachusetts General Hospital Boston MA USA Estonian Genome Center Institute of Genomics University of Tartu Tartu Estonia Diabetes Unit Massachusetts General Hospital Boston MA USA Program in Metabolism Broad Institute of MIT and Harvard Cambridge MA USA Division of Cardiology Massachusetts General Hospital Boston MA USA Cardiovascular Disease Initiative Broad Institute of MIT and Harvard Cambridge MA USA Division of General Internal Medicine Massachusetts General Hospital Boston MA USA Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Department of Public Health Faculty of Medicine University

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Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

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Science (New York, N.Y.) 2025年第6734期387卷 eadp4753页

作者： Liu, Hongbo Abedini, Amin Ha, Eunji Ma, Ziyuan Sheng, Xin Dumoulin, Bernhard Qiu, Chengxiang Aranyi, Tamas Li, Shen Dittrich, Nicole Chen, Hua-Chang Tao, Ran Tarng, Der-Cherng Hsieh, Feng-Jen Chen, Shih-Ann Yang, Shun-Fa Lee, Mei-Yueh Kwok, Pui-Yan Wu, Jer-Yuarn Chen, Chien-Hsiun Khan, Atlas Limdi, Nita A. Wei, Wei-Qi Walunas, Theresa L. Karlson, Elizabeth W. Kenny, Eimear E. Luo, Yuan Kottyan, Leah Connolly, John J. Jarvik, Gail P. Weng, Chunhua Shang, Ning Cole, Joanne B. Mercader, Josep M. Mandla, Ravi Majarian, Timothy D. Florez, Jose C. Haas, Mary E. Lotta, Luca A. Drivas, Theodore G. Vy, Ha My T Nadkarni, Girish N. Wiley, Laura K. Wilson, Melissa P. Gignoux, Christopher R. Rasheed, Humaira Thomas, Laurent F. Åsvold, Bjørn Olav Brumpton, Ben M. Hallan, Stein I. Hveem, Kristian Zheng, Jie Hellwege, Jacklyn N. Zawistowski, Matthew Zöllner, Sebastian Franceschini, Nora Hu, Hailong Zhou, Jianfu Kiryluk, Krzysztof Ritchie, Marylyn D. Palmer, Matthew Edwards, Todd L. Voight, Benjamin F. Hung, Adriana M. Susztak, Katalin Department of Medicine Renal Electrolyte and Hypertension Division University of Pennsylvania Philadelphia PA United States Institute of Diabetes Obesity and Metabolism University of Pennsylvania Philadelphia PA United States Department of Genetics University of Pennsylvania Philadelphia PA United States Penn-CHOP Kidney Innovation Center University of Pennsylvania Philadelphia PA United States Liangzhu Laboratory Zhejiang University 1369 West Wenyi Road Hangzhou Zhejiang China Department of Nephrology Children' Hospital Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou Zhejiang China Institute of Molecular Life Sciences HUN-REN Research Center for Natural Sciences Budapest Hungary Department of Molecular Biology Semmelweis University Budapest Hungary Department of Medicine Federal University of São Paulo São Paulo Brazil Department of Biostatistics Vanderbilt University Medical Center Nashville TN United States Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN United States Institute of Clinical Medicine School of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Division of Nephrology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Institute of Biomedical Sciences Academia Sinica ROC Taipei Taiwan Cardiovascular Center Taichung Veterans General Hospital Taichung Taiwan ROC National Chung Hsing University Taichung Taiwan ROC Heart Rhythm Center Division of Cardiology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Department of Internal Medicine College of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan ROC Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan ROC Division of Endocrinology and Metabolism Department of Internal Medicine Kaohsiung Medical Universit

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

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Network-based method to infer the contributions of proteins to the etiology of drug side effects

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Frontiers of Electrical and Electronic Engineering in China 2015年第3期10卷 124-134页

作者： Rui Li (1) Ting Chen (1) (2) Shao Li (1) 1.MOE Key Laboratory of Bioinformatics and Bioinformatics Division TNLIST Department of Automation Tsinghua University Beijing 100084 China 2.Program in Computational Biology and Bioinformatics University of Southern California Los Angeles CA 90089 USA

Studying the molecular mechanisms that underlie the relationship between drugs and the side effects they produce is critical for drug discovery and drug development. Currently, however, computational methods are still unavailable to assess drug-protein interactions with the aim of globally inferring the contributions of various classes of proteins toward the etiology of side effects. In this work, we integrated data reflecting drug-side effect relationships, drug- target relationships, and protein-protein interactions to develop a novel network-based probabilistic model, SidePro, to evaluate the contributions of proteins toward the etiology of side effects. For a given side effect, the method applies an expectation--maximization algorithm and a diffusion kernel-based approach to estimate each protein＇s contribution. We applied this method to a wide range of side effects and validated the results using cross-validation and records from the Side Effect Resource database. We also studied a specific side effect, nephrotoxicity, which is known to be associated with the irrational use of the Chinese herbal compound triptolide, a diterpenoid epoxide in the Thunder of God Vine, Tripterygium wilfordii （Lei-Gong-Teng）. Using triptolide as an example, we scored the target proteins of triptolide using our model and investigated the high-scoring proteins and their related biological processes. The results demonstrated that our model could differentiate between the potential side effect targets and therapeutic targets of triptolide. Overall, the proposed model could accurately pinpoint the molecular mechanisms of drug side effects, thus making contribution to safe and effective drug development.

关键词： network pharmacology drug targets side effects triptolide

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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Incorporation of protein binding effects into likelihood ratio test for exome sequencing data

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BMC proceedings 2016年第SUPPL 7期10卷 275-281页

作者： Dongni Zhang Hongzhu Cui Dmitry Korkin Zheyang Wu Mathematics Department Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA. Computer Science Department Bioinformatics and Computational Biology Program Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA.

Statistical association studies are an important tool in detecting novel disease genes. However, for sequencing data, association studies confront the challenge of low power because of relatively small data sample size and rare variants. Incorporating biological information that reflects disease mechanism is likely to strengthen the association evidence of disease genes, and thus increase the power of association studies. In this paper, we annotate non-synonymous single-nucleotide variants according to protein binding sites (BSs) by using a more accurate BS prediction method. We then incorporate this information into association study through a statistical framework of likelihood ratio test (LRT) based on weighted burden score of single-nucleotide variants (SNVs). The strategy is applied to Genetic Analysis Workshop 19 exome-sequencing data for detecting novel genes associated to hypotension. The SNV-weighting LRT idea is empirically verified by the simulated phenotypes (336 cases and 1607 controls), and the weights based on BS annotation are applied to the real phenotypes (394 cases and 1457 controls). Such strategy of weighting the prior information on protein functional sites is shown to be superior to the unweighted LRT and serves as a good complement to the existing association tests. Several putative genes are reported; some of them are functionally related to hypertension according to the previous evidence in the literature.

关键词： Likelihood Ratio Test Real Data Analysis Burden Score Data Sample Size Genomic Inflation Factor

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