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Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria

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Quantum processor-inspired machine learning in the biomedical sciences

arXiv

引用

arXiv 2019年

作者： Li, Richard Y. Gujja, Sharvari Bajaj, Sweta R. Gamel, Omar E. Cilfone, Nicholas Gulcher, Jeffrey R. Lidar, Daniel A. Chittenden, Thomas W. Department of Chemistry University of Southern California Los AngelesCA United States Computational Biology and Bioinformatics Program Department of Biological Sciences University of Southern California Los AngelesCA United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA United States Computational Statistics and Bioinformatics Group Advanced Artificial Intelligence Research Laboratory Genuity Science BostonMA United States Complex Biological Systems Alliance MedfordMA United States Cancer Genetics Group Genuity Science BostonMA United States Department of Electrical Engineering University of Southern California Los AngelesCA United States Department of Physics and Astronomy University of Southern California Los AngelesCA United States Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School BostonMA United States

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data;namely "Ising-type" methods whose objective function is formulated identically to simulated annealing and quantum annealing, as well as a restricted Boltzmann machine. Here we show for the first time the efficacy of multiple Ising-type ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using Ising-type ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences. © 2019, CC BY.

关键词： Machine learning

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Cheminformatics Tools for Analyzing and Designing Optimized Small Molecule Collections and Libraries

SSRN

引用

SSRN 2019年

作者： Moret, Nienke Clark, Nicholas A. Hafner, Marc Wang, Yuan Lounkine, Eugen Medvedovic, Mario Wang, Jinhua Gray, Nathanael Jenkins, Jeremy Sorger, Peter K. HMS LINCS and Druggable Genome Centers Laboratory of Systems Pharmacology Harvard Program in Therapeutic Science Harvard Medical School BostonMA02115 United States Department of Systems Biology Harvard Medical School BostonMA02115 United States Division of Biostatistics and Bioinformatics Department of Environmental Health University of Cincinnati CincinnatiOH45221 United States Novartis Institutes for BioMedical Research Inc. 181 Massachusetts Avenue CambridgeMA02139 United States Department of Cancer Biology Dana-Farber Cancer Institute BostonMA02115 United States Department of Biological Chemistry and Molecular Pharmacology Harvard Medical School 360 Longwood Avenue Longwood Center 2209 BostonMA02115 United States Department of Bioinformatics and Computational Biology Genentech Inc. 1 DNA Way South San FranciscoCA94080 United States Warren Alpert 444 200 Longwood Avenue BostonMA02115 United States

Libraries of well-annotated small molecules have many uses in chemical genetics, drug discovery and drug repurposing. Many such libraries have become available, but few data-driven approaches exist to compare these libraries and design new ones. In this paper, we describe an approach to scoring and creating libraries based on available data, which is often incomplete, on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure, stage of clinical development and user preference. The approach, implemented as R software and a Web-accessible tool also assembles small sets of compounds (typically 2-8) that target a protein of interest while having the lowest possible off target overlap. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them, leading us to design a new LSP-OptimalKinase library that outperforms all previous collections in terms of target coverage and compact size. We also assemble a mechanism of action library that optimally covers 1852 targets of the liganded genome. Using our tools, individual research groups and companies can quickly analyze private compound collections, public libraries can be updated based on the latest data and the confounding effects of off-target activities minimized in chemical genetic studies. © 2019, The Authors. All rights reserved.

关键词： Libraries

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Single‐cell eQTLGen Consortium: A personalized understanding of disease

arXiv

引用

arXiv 2019年

作者： van der Wijst, M.G. de Vries, D.H. Groot, H.E. Trynka, G. Hon, C.C. Nawijn, M.C. Idaghdour, Y. van der Harst, P. Ye, C.J. Powell, J. Theis, F.J. Mahfouz, A. Heinig, M. Franke, L. Department of Genetics Oncode Institute University of Groningen University Medical Center Groningen Groningen Netherlands Department of Cardiology University of Groningen University Medical Center Groningen Groningen Netherlands Wellcome Trust Sanger Institute Open Targets Wellcome Genome Campus Hinxton Cambridge United Kingdom RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Pathology and Medical Biology GRIAC Research institute University of Groningen University Medical Center Groningen Netherlands Program in Biology Public Health Research Center New York University Abu Dhabi United Arab Emirates Institute for Human Genetics Bakar Computational Health Sciences Institute Bakar ImmunoX Initiative Division of Rheumatology Department of Medicine Department of Bioengineering and Therapeutic Sciences Department of Epidemiology and Biostatistics Chan Zuckerberg Biohub University of California San Francisco San Francisco United States Garvan‐Weizmann Centre for Cellular Genomics Garvan Institute Sydney Australia UNSW Cellular Genomics Futures Institute University of New South Wales Sydney Australia Institute of Computational Biology Helmholtz Zentrum München Neuherberg Germany Department of Mathematics Technical University of Munich Garching bei München Germany Leiden Computational Biology Center Leiden University Medical Center Leiden Netherlands Delft Bioinformatics Lab Delft University of Technology Delft Netherlands Institute of Computational Biology Helmholtz Zentrum München Neuherberg Germany Department of Informatics Technical University of Munich Garching bei München Germany

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease‐associated genetic risk factors through so‐called expression quantitative trait locus (eQTL) analysis. Single‐cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. The enormous increase in throughput and reduction in cost per cell now allow this technology to be applied to large‐scale population genetics studies. Therefore, we have founded the single‐cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing disease‐causing genetic variants and identifying the cellular contexts in which they affect gene expression. Ultimately, this information can enable development of personalized medicine. Here, we outline the goals, approach, potential utility and early proofs‐of-concept of the sc‐eQTLGen consortium. We also provide a set of study design considerations for future single‐cell eQTL studies. Copyright © 2019, The Authors. All rights reserved.

关键词： RNA

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Geometry-based data generation

arXiv

引用

arXiv 2018年

作者： Lindenbaum, Ofir Stanley, Jay S. Wolf, Guy Krishnaswamy, Smita Department of Genetics Yale University New HavenCT United States Applied Mathematics Program Yale University New HavenCT United States Computational Biology & Bioinformatics Program Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States

We propose a new type of generative model of high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, and improves several downstream data analysis tasks such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations and reveal intrinsic patterns and mutual-information relationships between genes on a single cell dataset of hematopoiesis. Copyright © 2018, The Authors. All rights reserved.

关键词： Clustering algorithms

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Author Correction: Differential peripheral immune signatures elicited by vegan versus ketogenic diets in humans

引用

Nature medicine 2024年第6期30卷 1785页

作者： Verena M Link Poorani Subramanian Foo Cheung Kyu Lee Han Apollo Stacy Liang Chi Brian A Sellers Galina Koroleva Amber B Courville Shreni Mistry Andrew Burns Richard Apps Kevin D Hall Yasmine Belkaid Metaorganism Immunity Section Laboratory of Host Immunity and Microbiome National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. verena.link@nih.gov. NIH Center for Human Immunology National Institutes of Health Bethesda MD USA. verena.link@nih.gov. Bioinformatics and Computational Biosciences Branch Office of Cyber Infrastructure and Computational Biology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. NIH Center for Human Immunology National Institutes of Health Bethesda MD USA. Center for Cellular Engineering Department of Transfusion Medicine Clinical Center National Institutes of Health Bethesda MD USA. Metaorganism Immunity Section Laboratory of Host Immunity and Microbiome National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. Department of Cardiovascular and Metabolic Sciences Lerner Research Institute Cleveland Clinic Cleveland OH USA. National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD USA. NIAID Microbiome Program National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD USA. kevinh@niddk.nih.gov. Metaorganism Immunity Section Laboratory of Host Immunity and Microbiome National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. ybelkaid@niaid.nih.gov. NIH Center for Human Immunology National Institutes of Health Bethesda MD USA. ybelkaid@niaid.nih.gov.

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Association between known Alzheimer’s disease risk loci andin vivobrain amyloid pathology in a Korean Cohort

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Alzheimer's & Dementia 2023年第S24期19卷

作者： Hyejin Ahn Min Soo Byun Dahyun Yi Gijung Jung Yen-Ning Huang Anthony J. Griswold Margaret A. A Pericak-Vance Yu Kyeong Kim Yun-Sang Lee Chul-Ho Sohn Koung Mi Kang Jun-Young Lee Andrew J. Saykin Kwangsik Nho Dong Young Lee Interdisciplinary program of cognitive science Seoul National University College of Humanities Seoul Korea Republic of (South) HyejinAhn Interdisciplinary program of cognitive science Seoul National University College of Humanities Seoul Korea Republic of (South). Department of Psychiatry Seoul National University College of Medicine Seoul Korea Republic of (South) Seoul National University Dementia Research Center Seoul Korea Republic of (South) Department of Neuropsychiatry Seoul National University Hospital Seoul Korea Republic of (South) Institute of Human Behavioral Medicine Medical Research Center Seoul National University Seoul Korea Republic of (South) Center for Neuroimaging Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis IN USA Indiana Alzheimer Disease Research Center Indiana University School of Medicine Indianapolis IN USA University of Miami Miller School of Medicine Miami FL USA Department of Nuclear Medicine SMG-SNU Boramae Medical Center Seoul Korea Republic of (South) Department of Nuclear Medicine Seoul National University College of Medicine Seoul Korea Republic of (South) Department of Radiology Seoul National University Hospital Seoul Korea Republic of (South) Department of Neuropsychiatry SMG-SNU Boramae Medical Center Seoul Korea Republic of (South) Indiana Alzheimer’s Disease Research Center 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

Background Most genome-wide association studies (GWAS) of Alzheimer’s disease (AD) have been conducted in populations of European ancestry. It is important to determine if known AD-related variants from these studies are associated with AD biomarkers. Therefore, we investigated the association between known AD risk loci and in vivo brain Aβ deposition in older Korean adults. Method Participants (n = 503) from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (KBASE) were analyzed. Older adults who were cognitively normal (CN), had mild cognitive impairment (MCI) or AD dementia (264 CN, 148 MCI, 91 AD) underwent comprehensive assessments including 11 C-PiB-PET/MRI and blood sampling. Among 76 AD-related target single nucleotide polymorphisms (SNPs) with genetic evidence compiled by the AD Sequencing Project (ADSP), 38 SNPs with minor allele frequency (MAF)>1% were extracted from TOPMed-based imputed GWAS genotyping data in KBASE. We employed exploratory univariable logistic regression analysis with Aβ positivity as the dependent variable for each SNP. Then, SNPs with a significance level of p<0.05 were subjected to confirmatory multivariable logistic regression analysis, adjusting for age and sex, with Bonferroni correction. Result In the first step of exploratory analyses, we discovered significant differences in Aβ positivity rates between carriers and noncarriers of minor allele of several loci, including rs16824536 in MME , rs11218343 in SORL1 , rs4985556 in IL34 , rs4277405 in ACE and rs3851179, located in the intergenic region between EED and PICALM . The subsequent confirmatory analysis found an association between these variants and Aβ positivity after adjusting age and sex (Table 1). Notably, the negative association of rs3851179 with Aβ positivity remained significant after correcting for multiple comparisons and after controlling for APOE4 carrier status, age, and sex (OR 0.529, 95% CI 0.345–0.813). Conclusion We id

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The anatomic determinants of conductive hearing loss secondary to tympanic membrane perforation

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Journal of Otology 2017年第3期12卷 125-131页

作者： David J.Carpenter Debara L.Tucci David M.Kaylie Dennis O.Frank-Ito Division of Head and Neck Surgery & Communication Sciences Department of Surgery Duke University Medical Center Durham NC USA Computational Biology & Bioinformatics PhD Program Duke University Durham NC USA Department of Mechanical Engineering and Materials Science Duke University Durham NC USA

Objectives: Recent studies have introduced middle ear volume(MEV) as a novel determinant of perforation-induced conductive hearing loss(CHL) in a mechanism driven by trans-tympanic membrane pressure differences. The primary aims of this preliminary report are to: 1) correlate CHL with perforation size; 2) describe the relationship between CHL and MEV; and 3) compare CHL across a range of cholesteatoma ***: A retrospective pilot study was performed in 31 subjects with audiometry indicative of conductive hearing loss, temporal bone CT scans,and no prior middle ear surgery. Perforation size and MEV were analyzed with respect to CHL in a cohort of 10 perforated ears with no cholesteatoma. CHLs were compared in 3 groups defined by extent of cholesteatoma ***: Ears with large and small perforations showed mean ABG values of 32.0 ± 15.7 dB and 16.0 ± 16.4 dB, respectively. A direct relationship was observed between MEV and CHL for ears with large perforations across all frequencies, whereas this relationship for small perforations was frequency-dependent. Finally, a statistically significant increase in CHL was found across ears with increasing cholesteatoma involvement at 1000 Hz(X^2(2) = 9.786, p = 0.008),2000 Hz(x^2(2) = 8.455, p = 0.015),and 4000 Hz(x^2(2)= 8.253, p = 0.016).Conclusions: These pilot data suggest that greater perforation-induced conductive hearing losses may be associated with larger perforation sizes and cholesteatoma. The correlation between MEV and CHL may require additional study.

关键词： Perforation-induced hearing loss Conductive hearing loss Middle ear volume Three-dimensional volume reconstruction

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Author Correction: Pathway and network analysis of more than 2500 whole cancer genomes

引用

Nature communications 2022年第1期13卷 7566页

作者： Matthew A Reyna David Haan Marta Paczkowska Lieven P C Verbeke Miguel Vazquez Abdullah Kahraman Sergio Pulido-Tamayo Jonathan Barenboim Lina Wadi Priyanka Dhingra Raunak Shrestha Gad Getz Michael S Lawrence Jakob Skou Pedersen Mark A Rubin David A Wheeler Søren Brunak Jose M G Izarzugaza Ekta Khurana Kathleen Marchal Christian von Mering S Cenk Sahinalp Alfonso Valencia Jüri Reimand Joshua M Stuart Benjamin J Raphael Department of Computer Science Princeton University Princeton NJ 08540 USA. Department of Biomedical Informatics Emory University Atlanta GA 30322 USA. Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute University of California Santa Cruz Santa Cruz CA 95060 USA. Computational Biology Program Ontario Institute for Cancer Research Toronto Ontario Canada. Department of Information Technology IDLab Ghent University IMEC Ghent the Netherlands. Department of Plant Biotechnology and Bioinformatics Ghent University Ghent the Netherlands. Barcelona Supercomputing Center (BSC) Barcelona 08034 Spain. Department of Clinical and Molecular Medicine Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway. Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics University of Zurich CH-8057 Zurich Switzerland. Department of Pathology and Molecular Pathology University Hospital Zurich CH-8091 Zurich Switzerland. Department of Physiology and Biophysics Weill Cornell Medicine New York NY 10065 USA. Vancouver Prostate Centre 2660 Oak Street Vancouver BC V6H 3Z6 Canada. The Broad Institute of MIT and Harvard Cambridge MA 02124 USA. Massachusetts General Hospital Center for Cancer Research Charlestown MA 02129 USA. Harvard Medical School 250 Longwood Avenue Boston MA 02115 USA. Massachusetts General Hospital Department of Pathology Boston MA 02114 USA. Department of Molecular Medicine (MOMA) Aarhus University Hospital Aarhus Denmark. Bioinformatics Research Centre (BiRC) Aarhus University Aarhus Denmark. Human Genome Sequencing Center Baylor College of Medicine Houston TX 77030 USA. DTU Bioinformatics Department of Bio and Health Informatics Technical University of Denmark Kemitorvet 2800 Kongens Lyngby Denmark. Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences University of Copenhagen 2200 Copenhagen Denmark. De

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

Research Square

引用

Research Square 2021年

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

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

关键词： RNA

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