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Research Square 2021年

作者： Freedman, Matthew L. Baca, Sylvan C. Singler, Cassandra Zacharia, Soumya Seo, Ji-Heui Morova, Tunc Hach, Faraz Ding, Yi Schwarz, Tommer Huang, Chia-Chi Flora Kalita, Cynthia Groha, Stefan Pomerantz, Mark M. Wang, Victoria Linder, Simon Sweeney, Christopher J. Zwart, Wilbert Lack, Nathan A. Pasaniuc, Bogdan Takeda, David Y. Gusev, Alexander Department of Medical Oncology Dana-Farber Cancer Institute BostonMA United States Center for Functional Cancer Epigenetics Dana-Farber Cancer Institute BostonMA United States The Eli and Edythe L. Broad Institute CambridgeMA United States Laboratory of Genitourinary Cancer Pathogenesis Center for Cancer Research National Cancer Institute BethesdaMD United States Vancouver Prostate Centre University of British Columbia VancouverBC Canada Bioinformatics Interdepartmental Program UCLA Los AngelesCA United States Division of Genetics Brigham & Women’s Hospital BostonMA United States Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute BostonMA United States Department of Biostatistics Harvard School of Public Health BostonMA United States Division of Oncogenomics Oncode Institute The Netherlands Cancer Institute Amsterdam Netherlands Laboratory of Chemical Biology Institute for Complex Molecular Systems Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Medicine Koç University Istanbul Turkey Department of Computational Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States

Methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs), are widely used to functionally annotate trait-associated variants, but they are limited in identifying context-dependent effects on transcription. To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for nominating variants that impact traits through their effects on chromatin state. CWAS associates the genetic determinants of cistromes (e.g., the genome-wide profiles of transcription factor binding sites or histone modifications) with traits using summary statistics from genome-wide association studies (GWAS). We performed CWASs of prostate cancer and androgen-related traits, using a reference panel of 307 prostate cistromes from 165 individuals. CWAS nominated susceptibility regulatory elements or androgen receptor (AR) binding sites at 52 out of 98 known prostate cancer GWAS loci and implicated an additional 17 novel loci. We functionally validated a subset of our results using CRISPRi and in vitro reporter assays. At 28 of the 52 risk loci, CWAS identified regulatory mechanisms that are not observable via eQTLs, implicating genes with complex or context-specific regulation that are overlooked by current approaches that relying on steady-state transcript measurements. CWAS genes include transcription factors that govern prostate development such as NKX3-1, HOXB13, GATA2, and KLF5. Moreover, CWAS boosts discovery power in modestly sized GWAS, identifying novel genetic associations mediated through AR binding for androgen-related phenotypes, including resistance to prostate cancer therapy. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting context-dependent transcriptional regulation. © 2021, CC BY.

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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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Random-effects substitution models for phylogenetics via scalable gradient approximations

arXiv

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arXiv 2023年

作者： Magee, Andrew F. Holbrook, Andrew J. Pekar, Jonathan E. Caviedes-Solis, Itzue W. Matsen, Fredrick A. Baele, Guy Wertheim, Joel O. Ji, Xiang Lemey, Philippe Suchard, Marc A. Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Biomedical Informatics University of California San Diega La Jolla CA United States Department of Biology Swarthmore College SwarthmorePA United States Howard Hughes Medical Institute SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Department of Medicine University of California San Diego La Jolla CA United States Department of Mathematics Tulane University New OrleansLA United States Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Universtiy of California Los Angeles Los AngelesCA United States

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Comprehensive Functional Characterization of Germline ETV6 Variants Associated with Inherited Predisposition to Acute Lymphoblastic Leukemia in Children

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BLOOD 2016年第22期128卷 1085-1085页

作者： Nishii, Rina Baskin, Rebekah Moriyama, Takaya Hoshitsuki, Keito Metzger, Monika L. Wu, Gang Devidas, Meenakshi Yang, Wenjian Tedrick, Paige Quinn, Emily Gastier-Foster, Julie M. Raetz, Elizabeth Larsen, Eric Martin, Paul L. Bowman, W. Paul Winick, Naomi Takagi, Masatoshi Pui, Ching-Hon Evans, William E. Zhang, Jinghui Relling, Mary V. Hunger, Stephen P. Mullighan, Charles Loh, Mignon L. Nichols, Kim E. Yang, Jun J. Hematological Malignancies Program and Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN Department of Pediatrics and Developmental Biology Tokyo Medical and Dental University Graduate School of Medicine Tokyo Japan Department of Oncology St. Jude Children's Research Hospital Memphis TN School of Pharmacy University of Pittsburgh Pittsburgh PA Department of Computational Biology St. Jude Children's Research Hospital Memphis TN Department of Biostatistics College of Medicine Public Health and Health Professions University of Florida Gainesville FL Department of Pathology and Laboratory Medicine Nationwide Children's Hospital Columbus OH Huntsman Cancer Institute University of Utah Salt Lake City UT Maine Children's Cancer Program Scarborough ME Department of Pediatrics Duke University Durham NC Cook Children's Medical Center Fort Worth TX Pediatric Hematology Oncology University of Texas Southwestern Medical Center Dallas TX Oncology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology and Bioinformatics St. Jude Children's Research Hospital Memphis TN Department of Pediatrics and Center for Childhood Cancer Research Children's Hospital of Philadelphia and University of Pennsylvania Perelman School of Medicine Philadelphia PA Department of Pathology St. Jude Children's Research Hospital Memphis TN Department of Pediatrics Benioff Children's Hospital and Helen Diller Family Comprehensive Cancer Center University of California San Francisco CA

1085

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Technology dictates algorithms: Recent developments in read alignment

arXiv

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arXiv 2020年

作者： Alser, Mohammed Rotman, Jeremy Taraszka, Kodi Shi, Huwenbo Baykal, Pelin Icer Yang, Harry Taegyun Xue, Victor Knyazev, Sergey Singer, Benjamin D. Balliu, Brunilda Koslicki, David Skums, Pavel Zelikovsky, Alex Alkan, Can Mutlu, Onur Mangul, Serghei Computer Science Department ETH Zürich Zürich8092 Switzerland Computer Engineering Department Bilkent University Bilkent Ankara 06800 Turkey Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Department of Epidemiology Harvard T.H. Chan School of Public Health MA02115 United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA02142 United States Department of Computer Science Georgia State University AtlantaGA30302 United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles Los AngelesCA90095 United States Division of Pulmonary and Critical Care Medicine Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Biochemistry & Molecular Genetics Northwestern University Feinberg School of Medicine United States Simpson Querrey Center for Epigenetics Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Computer Science and Engineering Pennsylvania State University University ParkPA16801 United States Biology Department Pennsylvania State University University ParkPA16801 United States The Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16801 United States The Laboratory of Bioinformatics I.M. Sechenov First Moscow State Medical University Moscow119991 Russia Bilkent-Hacettepe Health Sciences and Technologies Program Ankara Turkey Department of Clinical Pharmacy School of Pharmacy University of Southern California Los AngelesCA90089 United States

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to today’s diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies. Copyright © 2020, The Authors. All rights reserved.

关键词： bioinformatics

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Cover Image, Volume 86, Issue 5

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

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

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Front Cover: Design, Synthesis, and Characterization of Novel Styryl Dyes as Fluorescent Probes for Tau Aggregate Detectionin Vitroand in Cells (Chem. Asian J. 6/2024)

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Chemistry – An Asian Journal 2024年第6期19卷

作者： Withsakorn Sangsuwan Kriangsak Faikhruea Kotchakorn Supabowornsathit Don Sangsopon Praewphan Ingrungruanglert Hathaichanok Chuntakaruk Napatsaporn Nuntavanotayan Kittiporn Nakprasit Nipan Israsena Thanyada Rungrotmongkol Pitak Chuawong Tirayut Vilaivan Chanat Aonbangkhen Center of Excellence in Natural Products Chemistry (CENP) Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Department of Chemistry and Center of Excellence for Innovation in Chemistry Faculty of Science Special Research Unit for Advanced Magnetic Resonance (AMR) Kasetsart University Bangkok 10900 Thailand These authors are contributed equally. Organic Synthesis Research Unit Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Stem Cell and Cell Therapy Research Unit and Department of Pharmacology Faculty of Medicine Chulalongkorn University Bangkok 10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok 10330 Thailand Center of Excellence in Structural and Computational Biology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand

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Cell2Sentence: teaching large language models the language of biology 24

Cell2Sentence: teaching large language models the language o...

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Proceedings of the 41st International Conference on Machine Learning

作者： Daniel Levine Syed Asad Rizvi Sacha Lévy Nazreen Pallikkavaliyaveetil David Zhang Xingyu Chen Sina Ghadermarzi Ruiming Wu Zihe Zheng Ivan Vrkic Anna Zhong Daphne Raskin Insu Han Antonio Henrique De Oliveira Fonseca Josue Ortega Caro Amin Karbasi Rahul M. Dhodapkar David Van Dijk Department of Computer Science Yale University New Haven CT School of Engineering Applied Science University of Pennsylvania Philadelphia PA School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT and Wu Tsai Institute Yale University New Haven CT Google and Yale Institute for Foundations of Data Science New Haven CT and Department of Computer Science Yale University New Haven CT and Yale School of Engineering and Applied Science New Haven CT Roski Eye Institute University of Southern California Los Angeles CA and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Yale Institute for Foundations of Data Science New Haven CT and Wu Tsai Institute Yale University New Haven CT and Cardiovascular Research Center Yale School of Medicine New Haven CT and Interdepartmental Program in Computational Biology & Bioinformatics Yale University New Haven CT and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

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

Research Square

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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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