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Correction to: Gene expression identifies patients who develop inflammatory arthritis in a clinically suspect arthralgia cohort

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Arthritis research & therapy 2020年第1期22卷 289页

作者： Ellis Niemantsverdriet Erik B van den Akker Debbie M Boeters Susan J F van den Eeden Annemieke Geluk Annette H M van der Helm-van Mil Department of Rheumatology Leiden University Medical Center PO Box 9600 Leiden 2300 RC The Netherlands. e.niemantsverdriet@lumc.nl. Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands. Pattern Recognition & Bioinformatics Delft University of Technology Delft The Netherlands. Department of Rheumatology Leiden University Medical Center PO Box 9600 Leiden 2300 RC The Netherlands. Department of Infectious Diseases/Immunohematology and Blood Transfusion Leiden University Medical Center Leiden The Netherlands. Department of Rheumatology Erasmus Medical Center Rotterdam The Netherlands.

An amendment to this paper has been published and can be accessed via the original article.

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Hierarchical colorings of cographs

arXiv

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arXiv 2019年

作者： Valdivia, Dulce I. Gei, Manuela Hellmuth, Marc Rosales, Maribel Hernandez Stadler, Peter F. Universidad Autonoma de Aguascalientes Centro de Ciencias Basicas Av. Universidad 940 Aguascalientes20131 Mexico Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics Universitat Leipzig Hartelstrae 16-18 LeipzigD-04107 Institute of Mathematics and Computer Science University of Greifswald Walther-Rathenau-Strae 47 GreifswaldD-17487 Center for Bioinformatics Saarland University Building E 2.1 P.O. Box 151150 SaarbruckenD-66041 CONACYT-Instituto de Matematicas UNAM Juriquilla Blvd. Juriquilla 3001 Juriquilla Queretaro76230 Mexico German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Leipzig Max Planck Institute for Mathematics in the Sciences Inselstrae 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Wahringerstrasse 17 WienA-1090 Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States

Cographs are exactly hereditarily well-colored graphs, i.e., the graphs for which a greedy coloring of every induced subgraph uses only the minimally necessary number of colors (G). In recent work on reciprocal best match graphs so-called hierarchically coloring play an important role. Here we show that greedy colorings are a special case of hierarchical coloring, which also require no more than (G) colors. Copyright © 2019, The Authors. All rights reserved.

关键词： Coloring

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TMOD-34. PATIENT-DERIVED XENOGRAFT AND CELL LINE MODELS FACILITATE NOVEL TREATMENT DISCOVERY IN CENTRAL NERVOUS SYSTEM LYMPHOMAS

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Neuro-Oncology 2020年第SUPPLEMENT_2期22卷 ii235–ii235页

作者： David Meredith Kin Hoe Chow Pei-Lun Kao Yu Zeng Rileen Sinha Aniket Shetty Michael Tolstorukov Tony Tran Smitha Yerrum Ugonma Chukwueke Wenya Linda Bi Omar Arnaout Timothy Smith Keith Ligon Lakshmi Nayak Department of Pathology Brigham and Women’s Hospital Boston MA USA Center for Patient Derived Models Dana-Farber Cancer Institute Boston MA USA Data Science and Bioinformatics Group Dana-Farber Cancer Institute Boston MA USA Dana Farber Cancer Institute Boston MA USA Department of Neurosurgery Brigham and Women’s Hospital Boston MA USA

Lymphomas of the central nervous system (CNSL) are rare tumors with few treatment options; however, recent genomic studies have uncovered several druggable targets. To facilitate novel treatment discovery, we sought to establish patient-derived xenograft (PDX) models and cell lines from patients with newly diagnosed or recurrent CNSL. PDX models were attempted from fresh tumor samples from 18 consented patients for whom rich clinical annotation was available. Orthotopic intracranial injections were performed via stereotactic injection. Cell line generation was attempted from both primary tumors (PDCL) and PDX samples (PDXCL). Characterization included histopathology, targeted exome sequencing, low-pass whole-genome sequencing for copy number evaluation, and expression profiling of matched patient tumors and PDXs. Drug response testing was performed using CellTiterGlo. From 18 samples attempted, 8 PDX models were successfully generated: 2 primary CNS diffuse large B-cell lymphoma (DLBCL), 5 secondary CNS DLBCL, and 1 secondary cutaneous T-cell lymphoma (CTCL). Mean time to moribund was 66 days. All 8 PDXs grew successfully as orthotopic models, and 4 also grew in subrenal capsule. Additionally, 6 PDCL and 2 PDXCL were established (> P3). Models faithfully represented primaries, showing nearly identical histopathology, immunoprofiles, and genomic signatures. To support expanded and rapid preclinical use of these models in drug testing we documented that PDX could be explanted to create short-term cell preparations or long-term cell lines with a 100% and 40% success rate, respectively. Proof of principle testing showed that explanted DLBCL PDX cells and cell lines showed sensitivity to copanlisib and venetoclax, which were not efficacious in the CTCL PDX. These findings showcase a diverse collection of CNSL models demonstrating high fidelity to primary tumors at the genomic and phenotypic levels, emphasizing their utility for preclinical studies and as patient avatars

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Relevant temporal bone anatomy for robotic cochlear implantation: An updated terminology combined with anatomical and clinical terms

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Translational Research in Anatomy 2021年 25卷 100138-100138页

作者： Topsakal, Vedat Kachlik, David Bahşi, Ilhan Carlson, Matthew Isaacson, Brandon Broman, Jonas Tubbs, R. Shane Baud, Robert ten Donkelaar, Hans J. Department of Otorhinolaryngology Head and Neck Surgery University Hospital UZ Brussel Brussels Belgium Vrije Univeristeit Brussel Brussels Health Campus Laarbeeklaan 101 Jette 1090 Belgium Department of Anatomy Second Faculty of Medicine Charles University V Ãšvalu 84 Praha 5 15006 Czech Republic Department of Anatomy Gaziantep University Gaziantep 27310 Turkey Department of Otolaryngology Mayo Clinic 200 1st St SW Rochester 55905 MN United States Department of Otolaryngology University of Texas South Western Dallas 75390 TX United States Department of Biomedical and Clinical Sciences University of LinkÃ¶ping LinkÃ¶ping 58183 Sweden Department of Neurosurgery Tulane School of Medicine 1430 Tulane Avenue New Orleans 70112 LA United States Department of Anatomical Sciences S. Georgeâ€™s University Grenada Anatomy Section of Medicine Faculty of Science and Medicine University of Fribourg Fribourg Switzerland SIB Data Mining Swiss Institute for Bioinformatics Geneva Switzerland Department of Neurology And Donders Institute for Brain Cognition and Behavior Radboud University Medical Center P.O. Box 9101 6500 HB Nijmegen Netherlands Donders Institute for Brain Cognition and Behavior Radboud University Medical Center P.O. Box 9101 6500 HB Nijmegen Netherlands

Background: Translational research refers to scientific findings that have been extensively studied in models and find their way to clinical studies. In a certain sense in translational anatomy a similar application of conventional anatomical knowledge finds its way to clinics in the surgical theatre. With the propagation of endoscopic middle ear surgery there has been a renewed interest in many early anatomical terms, even many years after their discovery, which may threaten the correct and classic eponyms. Moreover, the application of robotic ear surgery executing minimal invasive keyhole accesses requires translation of anatomical knowledge to the sub-millimetric level. Purpose: This review reports relevant anatomy of the temporal bone for robotic cochlear implantation reflecting also on recent revisions of terminology. Results: In 2019, the Terminologia Neuroanatomica (TNA 2017) was accepted at the 19th World Congress of the International Federation of Associations of Anatomists (IFAA) in London as its official terminology for the central and peripheral nervous system and the sense organs. This review follows the terminology of in the TNA, but also compares anatomical and clinical terms to bridge the gap between both approaches, and provides brief descriptions and illustrations of relevant structures for robotic cochlear implantation and elaborates on their significance. With the emergence and more widespread use of the endoscopic ear surgery and image guided ear surgery, visualization of certain anatomical structures has improved. Coinciding, there has been a renewed interest in many early anatomical terms, even many years after their discovery, which may threaten the correct and classic eponyms. Conclusions: Consequently, this review also presents an overview of eponyms associated with specific structures over the years. In Supplementary Tables, the changes in terminology are reported. © 2021 The Authors

关键词： Anatomy Robotic cochlear implantation Temporal bone Terminology

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bioinformatics Pathway Analysis Pipeline for NGS Transcriptome Profile data on Nasopharyngeal Carcinoma

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Digdo Sudigyo Gisti Rahmawati Dicka W. Setiasari Risky H. Poluan Tjeng Wawan Cenggoro Arif Budiarto Alam Ahmad Hidayat Sri R. Indrasari Afiahayati Sofia M. Haryana Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Study Program of Biotechnology Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Medicine Public Health and Nursing Gadjah Mada University Yogyakarta Indonesia 55281 Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Next-Generation Sequencing (NGS)-based genomics data have a huge potential to be used in transcriptomic profiling of Nasopharyngeal Carcinoma (NPC) to study the biosynthesis mechanism behind it. The high dimensionality of NGS data is the main challenge in performing the data analysis to extract useful information. In this workflow pipeline, memory-efficient Linux-based software such as HISAT2 and HTSeq are utilized to process the raw NGS data. Furthermore, Differential Expression Gene (DEG) list can be obtained by performing advanced analysis to the aligned Ribonucleic Acid (RNA) sequence using the edgeR protocol. This DEG list is one of the main inputs of biological pathway analysis that can be done in DAVID and PANTHER web-based software. Both tools generate a different pathway result related to inflammation.

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Author Correction: Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

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Nature genetics 2023年第10期55卷 1778-1779页

作者： Nick Shrine Abril G Izquierdo Jing Chen Richard Packer Robert J Hall Anna L Guyatt Chiara Batini Rebecca J Thompson Chandan Pavuluri Vidhi Malik Brian D Hobbs Matthew Moll Wonji Kim Ruth Tal-Singer Per Bakke Katherine A Fawcett Catherine John Kayesha Coley Noemi Nicole Piga Alfred Pozarickij Kuang Lin Iona Y Millwood Zhengming Chen Liming Li Sara R A Wijnant Lies Lahousse Guy Brusselle Andre G Uitterlinden Ani Manichaikul Elizabeth C Oelsner Stephen S Rich R Graham Barr Shona M Kerr Veronique Vitart Michael R Brown Matthias Wielscher Medea Imboden Ayoung Jeong Traci M Bartz Sina A Gharib Claudia Flexeder Stefan Karrasch Christian Gieger Annette Peters Beate Stubbe Xiaowei Hu Victor E Ortega Deborah A Meyers Eugene R Bleecker Stacey B Gabriel Namrata Gupta Albert Vernon Smith Jian'an Luan Jing-Hua Zhao Ailin F Hansen Arnulf Langhammer Cristen Willer Laxmi Bhatta David Porteous Blair H Smith Archie Campbell Tamar Sofer Jiwon Lee Martha L Daviglus Bing Yu Elise Lim Hanfei Xu George T O'Connor Gaurav Thareja Omar M E Albagha Karsten Suhre Raquel Granell Tariq O Faquih Pieter S Hiemstra Annelies M Slats Benjamin H Mullin Jennie Hui Alan James John Beilby Karina Patasova Pirro Hysi Jukka T Koskela Annah B Wyss Jianping Jin Sinjini Sikdar Mikyeong Lee Sebastian May-Wilson Nicola Pirastu Katherine A Kentistou Peter K Joshi Paul R H J Timmers Alexander T Williams Robert C Free Xueyang Wang John L Morrison Frank D Gilliland Zhanghua Chen Carol A Wang Rachel E Foong Sarah E Harris Adele Taylor Paul Redmond James P Cook Anubha Mahajan Lars Lind Teemu Palviainen Terho Lehtimäki Olli T Raitakari Jaakko Kaprio Taina Rantanen Kirsi H Pietiläinen Simon R Cox Craig E Pennell Graham L Hall W James Gauderman Chris Brightling James F Wilson Tuula Vasankari Tarja Laitinen Veikko Salomaa Dennis O Mook-Kanamori Nicholas J Timpson Eleftheria Zeggini Josée Dupuis Caroline Hayward Ben Brumpton Claudia Langenberg Stefan Weiss Georg Homuth Carsten Oliver Schmidt Nicole Probst-Hensch Marjo-Riitta Jarvelin Alanna C Morrison Ozren Polasek Igor Department of Population Health Sciences University of Leicester Leicester UK. nick.shrine@leicester.ac.uk. Department of Population Health Sciences University of Leicester Leicester UK. Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre University of Nottingham Nottingham UK. Leicester National Institute for Health and Care Research Biomedical Research Centre Glenfield Hospital Leicester UK. Channing Division of Network Medicine Division of Pulmonary and Critical Care Medicine Department of Medicine Brigham and Women's Hospital Boston MA USA. Harvard Medical School Boston MA USA. COPD Foundation Washington DC USA. Department of Clinical Science Unversity of Bergen Bergen Norway. Nuffield Department of Population Health University of Oxford Oxford UK. MRC Population Health Research Unit University of Oxford Oxford UK. Department of Epidemiology and Biostatistics School of Public Health Peking University Health Science Center Beijing China. Department of Respiratory Diseases Ghent Universital Hospital Ghent Belgium. Department of Bioanalysis Faculty of Pharmaceutical Sciences Ghent University Ghent Belgium. Department of Epidemiology Eramus Medical Center Rotterdam The Netherlands. Department of Internal Medicine Eramus Medical Center Rotterdam The Netherlands. Center for Public Health Genomics University of Virginia Charlottesville VA USA. Department of Medicine Columbia University Medical Center New York NY USA. Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK. Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health The University of Texas Health Science Center at Houston Houston TX USA. MRC Centre for Environment and Health Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK. Department of Epidemiology and Public Health Swiss Tropical

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Uncertainty-aware semi-supervised method using large unlabelled and limited labeled COVID-19 data

arXiv

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arXiv 2021年

作者： Alizadehsani, Roohallah Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Gorriz, Juan M. Hussain, Sadiq Arco, Juan E. Sani, Zahra Alizadeh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Author Correction: Cell segmentation-free inference of cell types from in situ transcriptomics data

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Nature communications 2021年第1期12卷 4103页

作者： Jeongbin Park Wonyl Choi Sebastian Tiesmeyer Brian Long Lars E Borm Emma Garren Thuc Nghi Nguyen Bosiljka Tasic Simone Codeluppi Tobias Graf Matthias Schlesner Oliver Stegle Roland Eils Naveed Ishaque Berlin Institute of Health at Charité-Universitätsmedizin Berlin Digital Health Center Kapelle-Ufer 2 10117 Berlin Germany. Faculty of Biosciences Heidelberg University Heidelberg Germany. Division of Computational Genomics and System Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Computer Science Boston University Boston MA USA. Allen Institute for Brain Science Seattle WA USA. Division of molecular neurobiology Department of medical biochemistry and biophysics Karolinska Institutet Stockholm Sweden. Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany. Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany. Berlin Institute of Health at Charité-Universitätsmedizin Berlin Digital Health Center Kapelle-Ufer 2 10117 Berlin Germany. roland.eils@bih-charite.de. Health Data Science Unit Heidelberg University Hospital Heidelberg Germany. roland.eils@bih-charite.de. Berlin Institute of Health at Charité-Universitätsmedizin Berlin Digital Health Center Kapelle-Ufer 2 10117 Berlin Germany. naveed.ishaque@bih-charite.de.

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Sustaining public plant breeding programs across generations

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Crop Science 2025年第3期65卷

作者： Iago Hale Jenny Koebernick Jenna Hershberger Trevor Rife Juan-David Arbelaez Neil Anderson Anton Bekkerman Martin Bohn Fred Bourland Timothy Burke Peng Chee Kate Evans Nathan Fumia Mitchell Feldmann Ksenija Gasic Steve Hague Ana María Heilman-Morales Amanda Hulse Kemp Carlos Iglesias Lukas Mueller Jim Luby Richard Pratt Addie Thompson Richard Vierling Margaret Worthington Margaret Smith Gayle Volk Marnin Wolfe Michael Kantar Department of Agriculture Nutrition and Food Systems University of New Hampshire Durham New Hampshire USA Department of Crop Soil and Environmental Sciences Auburn University Auburn Alabama USA Plant and Environmental Sciences Department Clemson University Clemson South Carolina USA Department of Crop Sciences University of Illinois Urbana Illinois USA Department of Horticultural Science University of Minnesota Saint Paul Minnesota USA Northeast Research and Extension Center (NEREC) University of Arkansas Keiser Arkansas USA Bayer CropScience LLC St. Louis Missouri USA Department of Soil and Crop Sciences University of Georgia Tifton Georgia USA Horticulture Tree Fruit Research and Extension Center Washington State University Wenatchee Washington USA Hawaii Agriculture Research Center Kunia Hawaiʻi USA Department of Plant Sciences University of California Davis Davis California USA Big Data Pipeline Unit North Dakota State University AES Fargo North Dakota USA USDA-ARS Genomics and Bioinformatics Research Unit Raleigh North Carolina USA Department of Horticultural Science North Carolina State University Raleigh North Carolina USA Boyce Thompson Institute Cornell University Ithaca New York USA Department of Plant and Environmental Sciences New Mexico State University Las Cruces New Mexico USA Department of Plant Soil and Microbial Sciences Michigan State University East Lansing Michigan USA AgriLife Research and Extension Center at Vernon Texas A&M University College Station Texas USA Department of Horticulture University of Arkansas System Division of Agriculture Fayetteville Arkansas USA School of Integrative Plant Science Plant Breeding and Genetics Section Cornell University Ithaca New York USA USDA-ARS National Laboratory for Genetic Resources Preservation Fort Collins Colorado USA Department of Tropical Plant & Soil Sciences University of Hawaii at Manoa Honolulu Hawaiʻi USA

Plant breeding in the public sector is a multigenerational process that creates new plant varieties intended to meet current and future needs of society. Many public sector plant breeding programs are over a century old, and they continue to curate plant genetic resources that are far older still. While individual breeders serve as temporary leaders of these programs and the plant genetic resources they maintain, it is only their institutions that have the capacity to provide the necessary generational glue, enabling the accrual of long-term value to both breeders and society. Identifying best practices to ensure mutual benefit to both public sector breeders and their institutions is critical to achieving the smooth leadership transitions necessary for the sustainability and long-term impact of public breeding programs. The findings presented here suggest that the successful passing of the torch in such programs depends not only on strategic institutional support but also, critically, on the routine actions and mindset of the breeders entrusted with their leadership. Plant breeding has long been considered an essential public good. Despite many public breeding programs being over a century old, there are no standard procedures for dealing with inevitable leadership transitions. Here, we report on the findings of a workshop convened to explore the practices required for successful transitions and what current plant breeders and their host institutions can do to prevent germplasm loss and ensure program continuity.

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Whole-genome sequencing of 1,171 elderly admixed individuals from the largest Latin American metropolis (São Paulo, Brazil)

Research Square

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Research Square 2020年

作者： Naslavsky, Michel Scliar, Marilia Yamamoto, Guilherme Wang, Jaqueline Zverinova, Stepanka Karp, Tatiana Nunes, Kelly Ceroni, José Ricardo Carvalho, Diego Simões, Cerlos Eduardo Bozoklian, Daniel Nonaka, Ricardo Silva, Nayane Souza, Andreia Andrade, Heloísa Passos, Marília Castro, Camila Mendes-Junior, Celso Mercuri, Rafael Miller, Thiago Buzzo, José Rego, Fernanda Araújo, Nathalia Magalhaes, Wagner Mingroni-Netto, Regina Borda, Vctor Guio, Heinner Barreto, Maurício Lima-Costa, Maria Horta, Bernardo Tarazona-Santos, Eduardo Meyer, Diogo Galante, Pedro Castelli, Erick Duarte, Yeda Passos-Bueno, Maria Rita Zatz, Mayana Human Genome and Stem Cell Research Center University of São Paulo Department of Genetics and Evolutionary Biology Biosciences Institute University of São Paulo Brazil Universidade Federal de Minas Gerais Brazil University of Sao Paulo Brazil Human Genome and Stem Cell Research Center University of São Paulo Brazil Laboratory of Genome Structure and Ageing European Research Institute for the Biology of Ageing University Medical Center Groningen Netherlands Department of Genetics and Evolutionary Biology University of São Paulo Brazil Molecular Genetics and Bioinformatics Laboratory School of Medicine Sao Paulo State University Brazil Molecular Genetics and Bioinformatics Laboratory School of Medicine Sao Paulo State University Department of Pathology School of Medicine Sao Paulo State University Brazil Faculdade de Filosofia Ciências e Letras de Ribeirão Preto University of São Paulo Brazil Centro de Oncologia Molecular Hospital Sírio-Libanes Department of Biochemistry Institute of Chemistry University of São Paulo Bioinformatics Graduate program University of S Brazil Centro de Oncologia Molecular Hospital Sírio-Libanes Department of Biochemistry Institute of Chemistry University of São Paulo Brazil Centro de Oncologia Molecular Hospital Sírio-Libanes Bolivia Instituto Nacional de Salud Instituto de Saúde Coletiva Universidade Federal da Bahia Center for Data and Knowledge Integration for Health Institute Gonçalo Muniz Fundação Oswaldo Cruz Brazil Instituto de Pesquisas René Rachou Fundação Oswaldo Cruz Programa De Pós-Graduação em Saúde Pública Universidade Federal de Minas Gerais Brazil Universidade Federal de Pelotas Brazil Departamento de Genética Ecologia e Evolução Instituto de Ciências Biológicas Universidade Federal de Minas Gerais Universidad Peruana Cayetano Heredia Lima Peru Medical-Surgical Nursing Department School of Nursing University of São Paulo Epidemiology Department Public Health School University of São Paulo Brazil

As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~ 2 million are absent from large public databases. WGS enabled identifying ~ 2,000 novel mobile element insertions, nearly 5 Mb of genomic segments absent from human genome reference, and over 140 novel alleles from HLA genes. We reclassified and curated nearly four hundred variant's pathogenicity assertions in genes associated with dominantly inherited Mendelian disorders and calculated the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observed that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS. © 2020, CC BY.

关键词： Genome

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