检索结果-内蒙古大学图书馆

Identification of Genetic Variants and Serum Metabolites Associated with Blood-derived Mitochondrial DNA Copy Number

引用

Alzheimer's & Dementia 2022年第S6期18卷

作者： Tong Tong John Farrell Congcong Zhu Zainab Khurshid Eden R. Martin William S. Bush Margaret A. Pericak-Vance Li-San Wang Gerard D. Schellenberg Jonathan L. Haines Kathryn L. Lunetta Lindsay A. Farrer Xiaoling Zhang Disease Sequencing Project Boston University School of Medicine Boston MA USA Boston University Bioinformatics Program Boston MA USA John P. Hussman Institute for Human Genomics University of Miami Miller School of Medicine Miami FL USA Case Western Reserve University School of Medicine Cleveland OH USA John P. Hussman Institute for Human Genomics Miller School of Medicine University of Miami Miami FL USA University of Pennsylvania Perelman School of Medicine Philadelphia PA USA University of Pennsylvania Perelman School of Medicine Path & Lab Med Stellar Chance Philadelphia PA USA Case Western Reserve University School of Medicine Department of Population & Quantitative Health Sciences Cleveland Institute for Computational Biology Cleveland OH USA Boston University School of Public Health Boston MA USA

Background Mitochondrial DNA Copy Number (mtDNA-CN) can be used to estimate mitochondrial function and it varies across tissues and cell types. Blood-derived mtDNA-CN has been studied in aging-related diseases including Alzheimer’s Disease (AD). However, little is known about the genetic contribution to variation of blood-derived mtDNA-CN and how this variation affects the level of serum metabolites. Method We developed a fast computational pipeline to accurately estimate mtDNA-CN using whole genome sequence data from blood samples from European ancestry (EA, 2,885 AD cases and 2,777 controls), African American (AA, 1,118 AD cases and 1,725 controls), and Caribbean Hispanic (CH, 1,022 AD cases and 1,949 controls) participants of the Alzheimer’s Disease Sequencing Project (ADSP). In the combined sample and within each population group. We tested association of blood-derived mtDNA-CN with 17,569,705 variants (minor allele count≥20) genome-wide using a linear mixed effect model including covariates for age, sex, sequencing center, PCR, AD status, polygenic risk score of blood cell count, and principal components of ancestry. We also tested the associations of mtDNA-CN and levels of 175 serum metabolites in 1,521 subjects from Alzheimer’s Disease Neuroimaging Initiative. Result In the total sample, we identified genome-wide significant (GWS) associations with variants from 24 independent loci. The top-ranked SNP (rs1455130415 in NDUFS8 , (p=3.25x10 -42 ) was also GWS in EAs (p=3.96x10 -28 ) and AAs (p=2.20x10 -27 ). SNP rs997412864 (p=5.13x10 -9 ) in GCAT was found associated with mtDNA-CN in EAs, but not in other groups. Association of SNP rs1237233197 (p=1.40x10 -8 ) in NDUFS7 was specific to AAs. Measurements of two long-chain acylcarnitine, C14:2-OH (p=4.91x10 -7 ) and C18:2 (p=1.81x10 -5 ), were negatively associated with mtDNA-CN after Bonferroni correction (P<2.9x10 -4 ). Ornithine, a non-proteinogenic amino acid, was also negatively associated with mtDNA-CN (p=2

关键词：

来源：评论

学校读者我要写书评

暂无评论

Whole exome sequencing identifies clinically relevant mutational signatures in resected hepatocellular carcinoma

Liver Cancer International

引用

Liver Cancer International 2020年第1期1卷

作者： Jason Y. Chan Abner H. Lim Arnoud Boot Elizabeth Lee Cedric C.-Y. Ng Jing Y. Lee Vikneswari Rajasegaran Wei Liu Shane Goh Jing H. Hong Xiaoying Xu Lavina D. Bharwani Chung Y. Chan Alexander Y. F. Chung Peng C. Cheow Chee-Kiat Tan Choon K. Ho Kui H. Liau Winston W. L. Woon Jee K. Low Akhil Chopra Gilberto Lopes Steven G. Rozen Bin T. Teh Alex Y.-C. Chang Division of Medical Oncology National Cancer Centre Singapore Singapore SingHealth Duke-NUS Blood Cancer Centre Singapore Oncology Academic Clinical Program Duke-NUS Medical School Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Dr Jason Y. Chan Division of Medical Oncology National Cancer Centre Singapore 11 Hospital Dr Singapore 169610. Professor Steven G. Rozen Centre for Computational Biology Duke-NUS Medical School Singapore 8 College Rd Singapore 169857. Professor Alex Y. Chang Department of Medical Oncology Tan Tock Seng Hospital Singapore and Department of Oncology Johns Hopkins University 11 Jalan Tan Tock Seng Singapore 308433. Integrated Genomics Platform National Cancer Centre Singapore Singapore Laboratory of Cancer Epigenome National Cancer Centre Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Institute of Molecular and Cell Biology Singapore Department of Medical Oncology Tan Tock Seng Hospital Singapore Department of Hepatopancreatobiliary and Transplant Surgery Singapore General Hospital Singapore Department of Gastroenterology and Hepatology Singapore General Hospital Singapore Department of General Surgery Tan Tock Seng Hospital Singapore LIAU KH Specialist Clinic Mt Elizabeth Novena Hospital Singapore Sylvester Comprehensive Cancer Center University of Miami Coral Gables FL USA Integrated Biostatistics and Bioinformatics Programme Duke-NUS Medical School Singapore Johns Hopkins Singapore Singapore Johns Hopkins University Baltimore MD USA

Background & Aims Most patients develop recurrent disease despite curative surgery for hepatocellular carcinoma (HCC). No standard adjuvant therapy is available and molecular predictors of outcome are poorly understood. Methods We conducted a multicentre pilot study on patients with localized HCC following surgical resection. Patients received up to 6 months of oral gefitinib as adjuvant therapy. Clinical end points included recurrence-free survival (RFS) and overall survival (OS), and exploratory analyses were conducted from whole exome sequencing data. Results A total of 65 patients were screened for the study, of which 40 were eligible. The median age was 63 years (range, 44-80). Most patients (80%) were positive for hepatitis B or C. With a median follow-up of 4.5 years, the median RFS was 24 months. Median OS was not reached. High Child-Pugh score and advanced T-stage (3-4) were independent predictors for both OS and RFS. Mutational signatures for exposure to aristolochic acid (AA), as characterized by a majority of T:A > A:T mutations, were observed in 18 cases (55%). HCC without AA mutagenesis was associated with early recurrences or death within 2 years of surgery ( P = .037). HCC with high T > A mutations was associated with better OS (HR 0.29, 95% CI 0.09-0.90, P = .033). Conversely, HCC with high C > T mutations was associated with worse OS (HR 4.55, 95% CI 1.20-17.31, P = .026). Conclusions Mutational signatures may carry prognostic significance in HCC following curative resection. Patient outcomes with gefitinib as adjuvant therapy after resection for HCC were modest, highlighting the need for urgent research in this area of unmet clinical need. *** number, NCT00282100.

关键词： adjuvant therapy aristolochic acid mutational signatures prognostic biomarker tyrosine kinase inhibitor

来源：评论

学校读者我要写书评

暂无评论

Perspective on recent developments and challenges in regulatory and systems genomics

arXiv

引用

arXiv 2024年

作者： Zeiltinger, Julia Roy, Sushmita Ay, Ferhat Mathelier, Anthony Medina-Rivera, Alejandra Mahony, Shaun Sinha, Saurabh Ernst, Jason Stowers Institute for Medical Research Kansas CityMO64112 United States Department of Pathology & Laboratory Medicine The University of Kansas Medical Center Kansas CityKS66160 United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison Madison53715 United States Wisconsin Institute for Discovery University of Wisconsin-Madison Madison53715 United States Centers for Autoimmunity Inflammation and Cancer Immunotherapy La Jolla Institute for Immunology La Jolla CA United States Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA United States Department of Pediatrics University of California La Jolla San DiegoCA United States Nordic EMBL Partnership University of Oslo Oslo0318 Norway Department of Medical Genetics Institute of Clinical Medicine University of Oslo Oslo University Hospital Oslo Norway Department of Pharmacy University of Oslo Oslo Norway Laboratorio Internacional de Investigación sobre el Genoma Humano Universidad Nacional Autónoma de México Campus Juriquilla Blvd. Juriquilla 3001 Santiago de Querétaro76230 Mexico Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA16802 United States Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Department of Biological Chemistry University of California Los Angeles Los AngelesCA90095 United States Computer Science Department University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Eli and Edythe Broad Center of Regenerative Medicine Stem Cell Research University of California Los Angeles Los AngelesCA90095 United States

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals. © 2024, CC BY-NC-SA.

关键词： Molecular docking

来源：评论

学校读者我要写书评

暂无评论

Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

引用

NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

来源：评论

学校读者我要写书评

暂无评论

Correction to: Comprehensive benchmarking and ensemble approaches for metagenomic classifiers

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The IBEX Imaging Knowledge-Base: A Community Resource Enabling Adoption and Development of Immunofluoresence Imaging Methods

arXiv

引用

arXiv 2024年

作者： Yaniv, Ziv Anidi, Ifeanyichukwu U. Arakkal, Leanne Arroyo-Mejías, Armando J. Beuschel, Rebecca T. Börner, Katy Chu, Colin J. Clark, Beatrice Clatworthy, Menna R. Colautti, Jake Coscia, Fabian Croteau, Joshua Denha, Saven Dever, Rose Dutra, Walderez O. Fritzsche, Sonja Fullam, Spencer Gerner, Michael Y. Gola, Anita Gollob, Kenneth J. Hernandez, Jonathan M. Hor, Jyh Liang Ichise, Hiroshi Jing, Zhixin Jonigk, Danny Kandov, Evelyn Kastenmüller, Wolfgang Koenig, Joshua F.E. Kothurkar, Aanandita Kortekaas, Rosa K. Kreins, Alexandra Y. Lamborn, Ian T. Lin, Yuri Morais, Katia Luciano Pereira Lunich, Aleksandra Luz, Jean C.S. MacDonald, Ryan B. Makranz, Chen Maltez, Vivien I. McDonough, John E. Moriarty, Ryan V. Ocampo-Godinez, Juan M. Olyntho, Vitoria M. Oxenius, Annette Padhan, Kartika Remmert, Kirsten Richoz, Nathan Schrom, Edward C. Shang, Wanjing Shi, Lihong Shih, Rochelle M. Speranza, Emily Stierli, Salome Teichmann, Sarah A. Veres, Tibor Z. Vierhout, Megan Wachter, Brianna T. Williams, Margaret Zangger, Nathan Germain, Ronald N. Radtke, Andrea J. Bioinformatics and Computational Bioscience Branch National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Critical Care Medicine and Pulmonary Branch National Heart Lung and Blood Institute National Institutes of Health BethesdaMD United States Lymphocyte Biology Section Laboratory of Immune System Biology NIAID NIH BethesdaMD United States Department of Intelligent Systems Engineering Indiana University BloomingtonIN United States UCL Institute of Ophthalmology NIHR Moorfields Biomedical Research Centre London United Kingdom Cambridge Institute for Therapeutic Immunology and Infectious Diseases University of Cambridge Department of Medicine Molecular Immunity Unit Laboratory of Molecular Biology Cambridge United Kingdom McMaster Immunology Research Centre Schroeder Allergy and Immunology Research Institute Department of Medicine Faculty of Health Sciences McMaster University HamiltonON Canada Spatial Proteomics Group Berlin Germany Department of Business Development BioLegend Inc. San DiegoCA United States Functional Immunogenomics Unit National Institute of Arthritis and Musculoskeletal and Skin Diseases National Institutes of Health BethesdaMD United States Laboratory of Cell-Cell Interactions Department of Morphology Institute of Biological Sciences Universidade Federal de Minas Gerais MG Belo Horizonte Brazil Division of Rheumatology Rush University Medical Center ChicagoIL United States Department of Immunology University of Washington School of Medicine SeattleWA United States Robin Chemers Neustein Laboratory of Mammalian Cell Biology and Development The Rockefeller University New YorkNY United States Hospital Israelita Albert Einstein SP Sao Paulo Brazil Surgical Oncology Program National Cancer Institute National Institutes of Health BethesdaMD United States Institute of Pathology Aachen Medical University RWTH Aachen Aachen Germany Hannover Germany Würzburg Institute

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, e.g., primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

引用

NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data

Nature computational science

引用

Nature computational science 2023年第9期3卷 805页

作者： Xi Chen Yuan Wang Antonio Cappuccio Wan-Sze Cheng Frederique Ruf Zamojski Venugopalan D Nair Clare M Miller Aliza B Rubenstein German Nudelman Alicja Tadych Chandra L Theesfeld Alexandria Vornholt Mary-Catherine George Felicia Ruffin Michael Dagher Daniel G Chawla Alessandra Soares-Schanoski Rachel R Spurbeck Lishomwa C Ndhlovu Robert Sebra Steven H Kleinstein Andrew G Letizia Irene Ramos Vance G Fowler Christopher W Woods Elena Zaslavsky Olga G Troyanskaya Stuart C Sealfon Center for Computational Biology Flatiron Institute New York NY USA. Lewis-Sigler Institute of Integrative Genomics Princeton University Princeton NJ USA. Department of Computer Science Princeton University Princeton NJ USA. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. Division of Infectious Diseases Department of Medicine Duke University School of Medicine Durham NC USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT USA. Battelle Memorial Institute Columbus OH USA. Division of Infectious Diseases Department of Medicine Weill Cornell Medicine New York NY USA. Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA. Department of Pathology and Department of Immunobiology Yale School of Medicine New Haven CT USA. Naval Medical Research Center Silver Spring MD USA. Precision Immunology Institute Icahn School of Medicine at Mount Sinai New York NY USA. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. elena.zaslavsky@mssm.edu. Center for Computational Biology Flatiron Institute New York NY USA. ogt@genomics.princeton.edu. Lewis-Sigler Institute of Integrative Genomics Princeton University Princeton NJ USA. ogt@genomics.princeton.edu. Department of Computer Science Princeton University Princeton NJ USA. ogt@genomics.princeton.edu. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. stuart.sealfon@mssm.edu.

来源：评论

学校读者我要写书评

暂无评论

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Immunity geneIFITM3variant: relation to cognition and Alzheimer pathology

引用

Alzheimer's & Dementia 2023年第S1期19卷

作者： Jung-Min Pyun Young Ho Park Moon-ku Han Angela Hodges Jae-Won Jang Paula Bice SangYun Kim Andrew J. Saykin Kwangsik Nho Seoul National University Bundang Hospital Seongnam Korea Republic of South Korea Soonchunhyang University Seoul Hospital Seoul Korea Republic of South Korea King’s College London London United Kingdom Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Chuncheon Korea Republic of South Korea Indiana University School of Medicine Indianapolis IN USA Seoul National University Bundang Hospital Seoul National University College of Medicine Seongnam Korea Republic of South Korea Indiana Alzheimer’s Disease Research Center Indiana University School of Medicine Indianapolis IN USA Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis IN USA Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis IN USA Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN USA Indiana Alzheimer’s Disease Research Center Indianapolis IN USA

Background IFITM3, an innate immune response protein and inhibitor of viral infection, was reported to modulate amyloid-β production in Alzheimer’s disease (AD). We aimed to identify single-nucleotide polymorphisms (SNPs) in IFITM3 associated with cognition and AD biomarkers. Method We used genetic, longitudinal cognition and AD biomarker data from Alzheimer’s Disease Neuroimaging Initiative (ADNI; N = 1,565) and AddNeuroMed (N = 633) as discovery and replication samples, respectively. First, we performed gene-based association analysis of SNPs in IFITM3 with cognitive performance. Second, we performed SNP-based association analysis in IFITM3 with cognitive decline and AD biomarkers from amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), and magnetic resonance imaging (MRI). Result Gene-based association analysis showed that IFITM3 was significantly associated with cognitive performance (permutation-corrected p = 1.25×10 −3 ). Particularly, among two SNPs (rs10751647, rs2091850) in IFITM3 significantly associated with cognitive performance, rs10751647 was associated with cognitive decline in ADNI, which was replicated in AddNeuroMed. In addition, rs10751647 was significantly associated with amyloid-β deposition measured by amyloid PET scan, CSF phosphorylated tau levels, and entorhinal cortical thickness measured by MRI scan in ADNI. The association of rs10751647 with entorhinal cortical thickness was replicated in AddNeuroMed. Participants with minor alleles (C) of rs10751647 have less cognitive decline, less amyloid and tau burden, and less brain atrophy. eQTL analysis showed that rs10751647 is associated with IFITM3 expression levels in blood and brain. Conclusion This suggests that rs10751647 in IFITM3 is associated with less vulnerability for cognitive decline and AD biomarkers, providing mechanistic insight regarding involvement of immune activity and infection in AD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：