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Short-Term Effects of a Therapeutic Diet on Biochemical Parameters in Hemodialysis Patients: A Randomized Crossover Trial

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Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation 2023年第6期33卷 731-739页

作者： Wan-Chuan Tsai Shih-Ping Hsu Yen-Ling Chiu Hon-Yen Wu Chia-Chin Luan Ju-Yeh Yang Mei-Fen Pai Cheng-Jui Lin Wan-Yu Lin Wen-Huei Sun Yu-Sen Peng Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan. School of Life Science National Taiwan Normal University Taipei City Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan. Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan Faculty of Medicine School of Medicine National Yang-Ming University Taipei City Taiwan. Dietary Department Far Eastern Memorial Hospital New Taipei City Taiwan. Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Division of Nephrology Department of Internal Medicine Mackay Memorial Hospital Taipei City Taiwan Department of Medicine Mackay Medical College Taipei City Taiwan Mackay Junior College of Medicine Nursing and Management Taipei City Taiwan. Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan. Dietary Department Far Eastern Memorial Hospital New Taipei City Taiwan. Electronic address: 830636231@mail.femh.org.tw. Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan Department of Electrical Engineering Yuan Ze University Taoyuan City Taiwan. Electronic address: taan70@***.

OBJECTIVE:Although unhealthy diets exacerbate nutritional and metabolic derangements in patients with end-stage kidney disease (ESKD), how therapeutic diets that possess a variety of different dietary strategies acutely modify diverse biochemical parameters related to cardiovascular disease remains underexplored. METHODS:Thirty-three adults with end-stage kidney disease undergoing thrice-weekly hemodialysis participated in a randomized crossover trial comparing a therapeutic diet with their usual diets for 7 days, separated by a 4-week washout period. The therapeutic diet was characterized by adequate calorie and protein amounts, natural food ingredients with a low phosphorus-to-protein ratio, higher portions of plant-based food, and high fiber content. The primary outcome measure was the mean difference in the change-from-baseline intact fibroblast growth factor 23 (FGF23) level between the 2 diets. The other outcomes of interest included changes in mineral parameters, uremic toxins, and high-sensitivity C-reactive protein (hs-CRP) levels. RESULTS:Compared with the usual diet, the therapeutic diet lowered intact FGF23 levels (P = .001), decreased serum phosphate levels (P < .001), reduced intact parathyroid hormone (PTH) levels (P = .003), lowered C-terminal FGF23 levels (P = .03), increased serum calcium levels (P = .01), and tended to lower total indoxyl sulfate levels (P = .07) but had no significant effect on hs-CRP levels. Among these changes, reduction in serum phosphate level achieved in 2 days, modifications of intact PTH and calcium levels in 5 days, and reductions in intact and C-terminal FGF23 levels in 7 days of therapeutic diet intervention. CONCLUSION:Within the 1-week intervention period, the dialysis-specific therapeutic diet rapidly reversed mineral abnormalities and tended to decrease total indoxyl sulfate levels in patients undergoing hemodialysis but had no effect on inflammation. Future studies to assess the long-term effects of such therapeuti

关键词： FGF23 dialysis inflammation therapeutic diet uremic toxin

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Author Correction: A shared disease-associated oligodendrocyte signature among multiple CNS pathologies

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Nature neuroscience 2023年 2023 Jan 31页

作者： Mor Kenigsbuch Pierre Bost Shahar Halevi Yuzhou Chang Shuo Chen Qin Ma Renana Hajbi Benno Schwikowski Bernd Bodenmiller Hongjun Fu Michal Schwartz Ido Amit Department of Neurobiology Weizmann Institute of Science Rehovot Israel. Department of Immunology Weizmann Institute of Science Rehovot Israel. Systems Biology Group Center for Bioinformatics Biostatistics and Integrative Biology Institut Pasteur CNRS Paris France. Sorbonne Université Complexité du vivant Paris France. University of Zurich Zurich Switzerland. Department of Biomedical Informatics College of Medicine Ohio State University Columbus OH USA. Biomedical Sciences Graduate Program Ohio State University Columbus OH USA. Department of Neuroscience College of Medicine Ohio State University Columbus OH USA. Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel. Department of Neurobiology Weizmann Institute of Science Rehovot Israel. michal.schwartz@weizmann.ac.il. Department of Immunology Weizmann Institute of Science Rehovot Israel. ido.amit@weizmann.ac.il.

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The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

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四川生理科学杂志 2022年第7期44卷 1299-1299页

作者： Jonathan E Pekar Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093 USA Department of Mathematics University of California San Diego La Jolla CA 92093 USA Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA New York City Public Health Laboratory New York City Department of Health and Mental Hygiene New York NY 11101 USA Department of Microbiology Institute for Viral Diseases Biosafety Center College of Medicine Korea University Seoul South Korea BK21 Graduate Program Department of Biomedical Sciences Korea University College of Medicine Seoul 02841 Republic of Korea National Public Health Laboratory National Centre for Infectious Diseases Singapore Malaysia Genome and Vaccine Institute Jalan Bangi 43000 Kajang Selangor Malaysia Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Microbiology and Immunology School of Medicine Tulane University New Orleans LA 70112 USA Zalgen Labs Frederick MD 21703 USA Global Virus Network (GVN) Baltimore MD 21201 USA Sydney Institute for Infectious Diseases School of Life and Environmental Sciences and School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia Institute of Evolutionary Biology University of Edinburgh King’s Buildings Edinburgh EH9 3FL UK Department of Biomathematics David Geffen School of Medicine Un

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October-8 December), while the separate introduction of lineage A likely occurred within weeks of this event.

关键词： epidemiology likely acute

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Association of Fine Motor Loss and Allodynia in Fibromyalgia: An fNIRS Study (vol 50, pg 664, 2017)

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JOURNAL OF MOTOR BEHAVIOR 2018年第6期50卷 X-X页

作者： Eken, A. Gokcay, D. Yilmas, C. Baskak, B. Baltaci, A. Kara, M. Biomedical Engineering Department Düzce University Düzce Turkey Medical Informatics Department Informatics Institute Middle East Technical University Ankara Turkey Neuroscience Graduate Program Bilkent University Ankara Turkey Department of Psychiatry Ankara University Faculty of Medicine Ankara Turkey Ankara University Brain Research Center Ankara Turkey Department of Physical and Rehabilitation Medicine Yenimahalle Research Hospital Yıldırım Beyazıt University Ankara Turkey Department of Physical and Rehabilitation Medicine Hacettepe University Medical School Ankara Turkey

Recent studies showed that fine motor control dysfunction was observed in fibromyalgia (FM) syndrome as well as allodynia. However, brain signatures of this association still remain unclear. In this study, finger tapping task (FTT) and median nerve stimulation (MNS) were applied to both hands of 15 FM patients and healthy controls (HC) to understand this relationship. Hemodynamic activity was measured simultaneously using functional near-infrared spectroscopy (fNIRS). Experiments were analyzed separately by using 2x2 repeated measures ANOVA. Results for the FTT experiment revealed that HC showed higher activity than FM patients in bilateral superior parietal gyrus (SPG), left supramarginal gyrus (SMG) and right somatosensory cortex (SI). Furthermore, right-hand FTT resulted in higher activity than left-hand FTT in left SPG, left SI and right motor cortex (MI). In the MNS experiment, FM patients showed higher activity than HC in bilateral SPG, right SMG, right SI and right middle frontal gyrus (MFG). Negative correlation was observed in left SPG between FTT and MNS activities. Besides, MNS activity in left SPG was negatively correlated with left-hand pain *** study revealed that left SPG might be an important indicator to associate fine motor loss and allodynia in FM.

关键词： fNIRS Fibromyalgia Fine Motor Loss

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Erratum for Wang et al., "Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin"

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Microbiology spectrum 2022年第1期10卷 e0261021页

作者： Hsin-Yao Wang Chia-Ru Chung Chao-Jung Chen Ko-Pei Lu Yi-Ju Tseng Tzu-Hao Chang Min-Hsien Wu Wan-Ting Huang Ting-Wei Lin Tsui-Ping Liu Tzong-Yi Lee Jorng-Tzong Horng Jang-Jih Lu Department of Laboratory Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Ph.D. Program in Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Computer Science and Information Engineering National Central Universitygrid.37589.30 Taoyuan City Taiwan. Graduate Institute of Integrated Medicine China Medical Universitygrid.254145.3 Taichung Taiwan. Proteomics Core Laboratory China Medical Universitygrid.254145.3 Hospital Taichung Taiwan. Graduate Program in Biomedical Information Yuan-Ze University Taoyuan City Taiwan. Department of Information Management Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei City Taiwan. Graduate Institute of Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Division of Haematology/Oncology Department of Internal Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Biosensor Group Biomedical Engineering Research Center Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Pathology Kaohsiung Chang Gung Memorial Hospitalgrid.413801.f and Chang Gung University College of Medicine Kaohsiung Taiwan. School of Life and Health Sciences The Chinese University of Hong Kong Shenzhen China. Warshel Institute for Computational Biology The Chinese University of Hong Kong Shenzhen China. Department of Bioinformatics and Medical Engineering Asia University Taichung City Taiwan. School of Medicine Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Medical Biotechnology and Laboratory Science Chang Gung Universitygrid.145695.a Taoyuan City Taiwan.

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Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

arXiv

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arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Adverse events following the first, second and third doses of a COVID-19 vaccine in hemodialysis patients

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Renal failure 2023年第1期45卷 2172432页

作者： Mei-Fen Pai Kuei-Ting Tung Shih-Ping Hsu Yu-Sen Peng Wan-Yu Lin Ju-Yeh Yang Hon-Yen Wu Yen-Ling Chiu Kai-Hsiang Shu Wan-Chuan Tsai Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei City Taiwan. School of Life Science National Taiwan Normal University Taipei City Taiwan. Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan. Department of Electrical Engineering Yuan Ze University Taoyuan City Taiwan. Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan. Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan. Faculty of Medicine School of Medicine National Yang-Ming University Taipei City Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan.

BACKGROUND:This study aimed to identify adverse events following the first three doses of COVID-19 vaccines in hemodialysis (HD) patients. Risk factors associated with postvaccination adverse events were explored. METHODS:Postvaccination adverse events in 438 HD patients who received 3 doses of COVID-19 vaccines were prospectively assessed. The adverse events among three doses were compared using generalized linear mixed models. Factors associated with adverse events were assessed with multivariate analyses. RESULTS:The vast majority of participants received Oxford/AstraZeneca ChAdOx1 as their first two doses and Moderna mRNA-1273 as their third dose. Overall, 79%, 50% and 84% of the participants experienced at least one adverse event after their first, second, and third doses, respectively. These adverse events were mostly minor, short-lived and less than 5% reported daily activities being affected. Compared with the first dose, the second dose caused a lower rate of adverse events. Compared with the first dose, the third dose elicited a higher rate of injection site reactions and a lower rate of systemic reactions. Multivariate analyses showed that every 10-year increase of age (odds ratio 0.67, 95% confidence intervals 0.57-0.79) was associated with decreased risk of adverse events, while female sex (2.82, 1.90-4.18) and arteriovenous fistula (1.73, 1.05-2.84) were associated with increased risk of adverse events. Compared with Oxford/AstraZeneca ChAdOx1, Moderna mRNA-1273 was associated with an increased risk of injection site reactions. CONCLUSIONS:COVID-19 vaccination was well tolerated in HD patients. Age, sex, dialysis vascular access and vaccine types were associated with postvaccination adverse events.

关键词： COVID-19 ChAdOx1 Hemodialysis adverse event mRNA-1273 vaccination

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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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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