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Zhou X, Zhou L, Qian F, Chen J, Zhang Y, Yu Z, Zhang J, Yang Y, Li Y, Song C, Wang Y, Shang D, Dong L, Zhu J, Li C, Wang Q. TFTG: A comprehensive database for human transcription factors and their targets. Comput Struct Biotechnol J 2024; 23:1877-1885. [PMID: 38707542 PMCID: PMC11068477 DOI: 10.1016/j.csbj.2024.04.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 05/07/2024] Open
Abstract
Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.
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Affiliation(s)
- Xinyuan Zhou
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- College of Artificial Intelligence and Big Data For Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Liwei Zhou
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Fengcui Qian
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Jiaxin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yuexin Zhang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Zhengmin Yu
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yongsan Yang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yanyu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chao Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Yuezhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Desi Shang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Longlong Dong
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chunquan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
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2
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Hosseinzadeh S, Rafat SA, Javanmard A, Fang L. Identification of candidate genes associated with milk production and mastitis based on transcriptome-wide association study. Anim Genet 2024; 55:430-439. [PMID: 38594914 DOI: 10.1111/age.13422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 02/10/2024] [Accepted: 03/18/2024] [Indexed: 04/11/2024]
Abstract
Genetic research for the assessment of mastitis and milk production traits simultaneously has a long history. The main issue that arises in this context is the known existence of a positive correlation between the risk of mastitis and lactation performance due to selection. The transcriptome-wide association study (TWAS) approach endeavors to combine the expression quantitative trait loci and genome-wide association study summary statistics to decode complex traits or diseases. Accordingly, we used the farmgtex project results as a complete bovine database for mastitis and milk production. The results of colocalization and TWAS approaches were used for the detection of functional associated candidate genes with milk production and mastitis traits on multiple tissue-based transcriptome records. Also, we used the david database for gene ontology to identify significant terms and associated genes. For the identification of interaction networks, the genemania and string databases were used. Also, the available z-scores in TWAS results were used for the calculation of the correlation between tissues. Therefore, the present results confirm that LYNX1, DGAT1, C14H8orf33, and LY6E were identified as significant genes associated with milk production in eight, six, five, and five tissues, respectively. Also, FBXL6 was detected as a significant gene associated with mastitis trait. CLN3 and ZNF34 genes emerged via both the colocalization and TWAS approaches as significant genes for milk production trait. It is expected that TWAS and colocalization can improve our perception of the potential health status control mechanism in high-yielding dairy cows.
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Affiliation(s)
- Sevda Hosseinzadeh
- Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
| | - Seyed Abbas Rafat
- Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
| | - Arash Javanmard
- Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
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3
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Li MD, Liu Q, Shi X, Wang Y, Zhu Z, Guan Y, He J, Han H, Mao Y, Ma Y, Yuan W, Yao J, Yang Z. Integrative analysis of genetics, epigenetics and RNA expression data reveal three susceptibility loci for smoking behavior in Chinese Han population. Mol Psychiatry 2024:10.1038/s41380-024-02599-1. [PMID: 38789676 DOI: 10.1038/s41380-024-02599-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 04/18/2024] [Accepted: 05/03/2024] [Indexed: 05/26/2024]
Abstract
Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking.
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Affiliation(s)
- Ming D Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Research Center for Air Pollution and Health, Zhejiang University, Hangzhou, China.
| | - Qiang Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoqiang Shi
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yan Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhouhai Zhu
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Ying Guan
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Jingmin He
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- College of Biological Sciences, Shanxi Agricultural University, Taigu, Shanxi, China
| | - Haijun Han
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ying Mao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yunlong Ma
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenji Yuan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianhua Yao
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Zhongli Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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4
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Wen C, Margolis M, Dai R, Zhang P, Przytycki PF, Vo DD, Bhattacharya A, Matoba N, Tang M, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker RL, Chatzinakos C, Clarke D, Pratt H, Peters MA, Gerstein M, Daskalakis NP, Weng Z, Jaffe AE, Kleinman JE, Hyde TM, Weinberger DR, Bray NJ, Sestan N, Geschwind DH, Roeder K, Gusev A, Pasaniuc B, Stein JL, Love MI, Pollard KS, Liu C, Gandal MJ. Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain. Science 2024; 384:eadh0829. [PMID: 38781368 DOI: 10.1126/science.adh0829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/07/2024] [Indexed: 05/25/2024]
Abstract
Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.
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Affiliation(s)
- Cindy Wen
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael Margolis
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Pan Zhang
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Pawel F Przytycki
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA
| | - Daniel D Vo
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Miao Tang
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Chuan Jiao
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team Krebs, 75014 Paris, France
| | - Minsoo Kim
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ellen Tsai
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Celine Hoh
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rebecca L Walker
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Christos Chatzinakos
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- McLean Hospital, Belmont, MA 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Declan Clarke
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Henry Pratt
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Mette A Peters
- CNS Data Coordination Group, Sage Bionetworks, Seattle, WA 98109, USA
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Nikolaos P Daskalakis
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- McLean Hospital, Belmont, MA 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Neumora Therapeutics, Watertown, MA 02472, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Nicholas J Bray
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University School of Medicine, Cardiff CF24 4HQ, UK
| | - Nenad Sestan
- Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kathryn Roeder
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Alexander Gusev
- Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard Medical School, Boston, MA 02215, USA
- Division of Genetics, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - Bogdan Pasaniuc
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Katherine S Pollard
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA
- Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
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5
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Zeng B, Bendl J, Deng C, Lee D, Misir R, Reach SM, Kleopoulos SP, Auluck P, Marenco S, Lewis DA, Haroutunian V, Ahituv N, Fullard JF, Hoffman GE, Roussos P. Genetic regulation of cell type-specific chromatin accessibility shapes brain disease etiology. Science 2024; 384:eadh4265. [PMID: 38781378 DOI: 10.1126/science.adh4265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 12/20/2023] [Indexed: 05/25/2024]
Abstract
Nucleotide variants in cell type-specific gene regulatory elements in the human brain are risk factors for human disease. We measured chromatin accessibility in 1932 aliquots of sorted neurons and non-neurons from 616 human postmortem brains and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci (caQTLs). Only 10.4% of caQTLs are shared between neurons and non-neurons, which supports cell type-specific genetic regulation of the brain regulome. Incorporating allele-specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms that underlie disease risk. Using massively parallel reporter assays in induced excitatory neurons, we screened 19,893 brain QTLs and identified the functional impact of 476 regulatory variants. Combined, this comprehensive resource captures variation in the human brain regulome and provides insights into disease etiology.
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Affiliation(s)
- Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chengyu Deng
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ruth Misir
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah M Reach
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Steven P Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pavan Auluck
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD 20892, USA
| | - Stefano Marenco
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD 20892, USA
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Vahram Haroutunian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY 10468, USA
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6
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Zappa M, Golino M, Verdecchia P, Angeli F. Genetics of Hypertension: From Monogenic Analysis to GETomics. J Cardiovasc Dev Dis 2024; 11:154. [PMID: 38786976 PMCID: PMC11121881 DOI: 10.3390/jcdd11050154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/26/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024] Open
Abstract
Arterial hypertension is the most frequent cardiovascular risk factor all over the world, and it is one of the leading drivers of the risk of cardiovascular events and death. It is a complex trait influenced by heritable and environmental factors. To date, the World Health Organization estimates that 1.28 billion adults aged 30-79 years worldwide have arterial hypertension (defined by European guidelines as office systolic blood pressure ≥ 140 mmHg or office diastolic blood pressure ≥ 90 mmHg), and 7.1 million die from this disease. The molecular genetic basis of primary arterial hypertension is the subject of intense research and has recently yielded remarkable progress. In this review, we will discuss the genetics of arterial hypertension. Recent studies have identified over 900 independent loci associated with blood pressure regulation across the genome. Comprehending these mechanisms not only could shed light on the pathogenesis of the disease but also hold the potential for assessing the risk of developing arterial hypertension in the future. In addition, these findings may pave the way for novel drug development and personalized therapeutic strategies.
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Affiliation(s)
- Martina Zappa
- Department of Medicine and Surgery, University of Insubria, 21100 Varese, Italy
| | - Michele Golino
- Department of Medicine and Surgery, University of Insubria, 21100 Varese, Italy
- Pauley Heart Center, Virginia Commonwealth University, Richmond, VA 23223, USA
| | - Paolo Verdecchia
- Fondazione Umbra Cuore e Ipertensione-ONLUS, 06100 Perugia, Italy
- Division of Cardiology, Hospital S. Maria della Misericordia, 06100 Perugia, Italy
| | - Fabio Angeli
- Department of Medicine and Technological Innovation (DiMIT), University of Insubria, 21100 Varese, Italy
- Department of Medicine and Cardiopulmonary Rehabilitation, Maugeri Care and Research Institutes, IRCCS, 21049 Tradate, Italy
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7
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Barry T, Mason K, Roeder K, Katsevich E. Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection. Genome Biol 2024; 25:124. [PMID: 38760839 PMCID: PMC11100084 DOI: 10.1186/s13059-024-03254-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 04/19/2024] [Indexed: 05/19/2024] Open
Abstract
Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
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Affiliation(s)
- Timothy Barry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA.
| | - Kaishu Mason
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA
| | - Eugene Katsevich
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA.
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8
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Xiang G, He X, Giardine BM, Isaac KJ, Taylor DJ, McCoy RC, Jansen C, Keller CA, Wixom AQ, Cockburn A, Miller A, Qi Q, He Y, Li Y, Lichtenberg J, Heuston EF, Anderson SM, Luan J, Vermunt MW, Yue F, Sauria MEG, Schatz MC, Taylor J, Gottgens B, Hughes JR, Higgs DR, Weiss MJ, Cheng Y, Blobel GA, Bodine DM, Zhang Y, Li Q, Mahony S, Hardison RC. Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.02.535219. [PMID: 37066352 PMCID: PMC10103973 DOI: 10.1101/2023.04.02.535219] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Knowledge of locations and activities of cis-regulatory elements (CREs) is needed to decipher basic mechanisms of gene regulation and to understand the impact of genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining epigenetic states and identifying cCREs in blood cell types to generate regulatory maps that are comparable between species, using integrative modeling of eight epigenetic features jointly in human and mouse in our Validated Systematic Integration (VISION) Project. The resulting catalogs of cCREs are useful resources for further studies of gene regulation in blood cells, indicated by high overlap with known functional elements and strong enrichment for human genetic variants associated with blood cell phenotypes. The contribution of each epigenetic state in cCREs to gene regulation, inferred from a multivariate regression, was used to estimate epigenetic state Regulatory Potential (esRP) scores for each cCRE in each cell type, which were used to categorize dynamic changes in cCREs. Groups of cCREs displaying similar patterns of regulatory activity in human and mouse cell types, obtained by joint clustering on esRP scores, harbored distinctive transcription factor binding motifs that were similar between species. An interspecies comparison of cCREs revealed both conserved and species-specific patterns of epigenetic evolution. Finally, we showed that comparisons of the epigenetic landscape between species can reveal elements with similar roles in regulation, even in the absence of genomic sequence alignment.
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9
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Chen K, Nan J, Xiong X. Genetic regulation of m 6A RNA methylation and its contribution in human complex diseases. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-024-2609-8. [PMID: 38764000 DOI: 10.1007/s11427-024-2609-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/02/2024] [Indexed: 05/21/2024]
Abstract
N6-methyladenosine (m6A) has been established as the most prevalent chemical modification in message RNA (mRNA), playing an essential role in determining the fate of RNA molecules. Dysregulation of m6A has been revealed to lead to abnormal physiological conditions and cause various types of human diseases. Recent studies have delineated the genetic regulatory maps for m6A methylation by mapping the quantitative trait loci of m6A (m6A-QTLs), thereby building up the regulatory circuits linking genetic variants, m6A, and human complex traits. Here, we review the recent discoveries concerning the genetic regulatory maps of m6A, describing the methodological and technical details of m6A-QTL identification, and introducing the key findings of the cis- and trans-acting drivers of m6A. We further delve into the tissue- and ethnicity-specificity of m6A-QTL, the association with other molecular phenotypes in light of genetic regulation, the regulators underlying m6A genetics, and importantly, the functional roles of m6A in mediating human complex diseases. Lastly, we discuss potential research avenues that can accelerate the translation of m6A genetics studies toward the development of therapies for human genetic diseases.
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Affiliation(s)
- Kexuan Chen
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China
- State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 311121, China
| | - Jiuhong Nan
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China
- State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 311121, China
| | - Xushen Xiong
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China.
- State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 311121, China.
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10
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Koido M, Tomizuka K, Terao C. Fundamentals for predicting transcriptional regulations from DNA sequence patterns. J Hum Genet 2024:10.1038/s10038-024-01256-3. [PMID: 38730006 DOI: 10.1038/s10038-024-01256-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/10/2024] [Accepted: 04/25/2024] [Indexed: 05/12/2024]
Abstract
Cell-type-specific regulatory elements, cataloged through extensive experiments and bioinformatics in large-scale consortiums, have enabled enrichment analyses of genetic associations that primarily utilize positional information of the regulatory elements. These analyses have identified cell types and pathways genetically associated with human complex traits. However, our understanding of detailed allelic effects on these elements' activities and on-off states remains incomplete, hampering the interpretation of human genetic study results. This review introduces machine learning methods to learn sequence-dependent transcriptional regulation mechanisms from DNA sequences for predicting such allelic effects (not associations). We provide a concise history of machine-learning-based approaches, the requirements, and the key computational processes, focusing on primers in machine learning. Convolution and self-attention, pivotal in modern deep-learning models, are explained through geometrical interpretations using dot products. This facilitates understanding of the concept and why these have been used for machine learning for DNA sequences. These will inspire further research in this genetics and genomics field.
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Affiliation(s)
- Masaru Koido
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan.
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.
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11
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024:S0168-9525(24)00095-7. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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12
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Moeckel C, Mouratidis I, Chantzi N, Uzun Y, Georgakopoulos-Soares I. Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights. Bioessays 2024:e2300210. [PMID: 38715516 DOI: 10.1002/bies.202300210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.
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Affiliation(s)
- Camille Moeckel
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Ioannis Mouratidis
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Nikol Chantzi
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Yasin Uzun
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
- Department of Pediatrics, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Ilias Georgakopoulos-Soares
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
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13
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Teyssonnière EM, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. Proc Natl Acad Sci U S A 2024; 121:e2319211121. [PMID: 38696467 PMCID: PMC11087752 DOI: 10.1073/pnas.2319211121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/25/2024] [Indexed: 05/04/2024] Open
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.
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Affiliation(s)
- Elie Marcel Teyssonnière
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Pauline Trébulle
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
| | - Julia Muenzner
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Victor Loegler
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Daniela Ludwig
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Fatma Amari
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Anne Friedrich
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Jing Hou
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Markus Ralser
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Max Planck Institute for Molecular Genetics, Berlin14195, Germany
| | - Joseph Schacherer
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
- Institut Universitaire de France, Paris75000, France
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14
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Rosean S, Sosa EA, O'Shea D, Raj SM, Seoighe C, Greally JM. Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization. BMC Bioinformatics 2024; 25:179. [PMID: 38714913 PMCID: PMC11075237 DOI: 10.1186/s12859-024-05794-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype. RESULTS In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation . CONCLUSIONS GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.
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Affiliation(s)
- Samuel Rosean
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Eric A Sosa
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Dónal O'Shea
- School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - Srilakshmi M Raj
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Cathal Seoighe
- School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - John M Greally
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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15
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Wahbeh MH, Boyd RJ, Yovo C, Rike B, McCallion AS, Avramopoulos D. A functional schizophrenia-associated genetic variant near the TSNARE1 and ADGRB1 genes. HGG ADVANCES 2024; 5:100303. [PMID: 38702885 DOI: 10.1016/j.xhgg.2024.100303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/01/2024] [Accepted: 04/23/2024] [Indexed: 05/06/2024] Open
Abstract
Recent collaborative genome-wide association studies (GWAS) have identified >200 independent loci contributing to risk for schizophrenia (SCZ). The genes closest to these loci have diverse functions, supporting the potential involvement of multiple relevant biological processes, yet there is no direct evidence that individual variants are functional or directly linked to specific genes. Nevertheless, overlap with certain epigenetic marks suggest that most GWAS-implicated variants are regulatory. Based on the strength of association with SCZ and the presence of regulatory epigenetic marks, we chose one such variant near TSNARE1 and ADGRB1, rs4129585, to test for functional potential and assay differences that may drive the pathogenicity of the risk allele. We observed that the variant-containing sequence drives reporter expression in relevant neuronal populations in zebrafish. Next, we introduced each allele into human induced pluripotent cells and differentiated four isogenic clones homozygous for the risk allele and five clones homozygous for the non-risk allele into neural progenitor cells. Employing RNA sequencing, we found that the two alleles yield significant transcriptional differences in the expression of 109 genes at a false discovery rate (FDR) of <0.05 and 259 genes at a FDR of <0.1. We demonstrate that these genes are highly interconnected in pathways enriched for synaptic proteins, axon guidance, and regulation of synapse assembly. Exploration of genes near rs4129585 suggests that this variant does not regulate TSNARE1 transcripts, as previously thought, but may regulate the neighboring ADGRB1, a regulator of synaptogenesis. Our results suggest that rs4129585 is a functional common variant that functions in specific pathways likely involved in SCZ risk.
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Affiliation(s)
- Marah H Wahbeh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Rachel J Boyd
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Christian Yovo
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Bailey Rike
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Andrew S McCallion
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Dimitrios Avramopoulos
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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16
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Head ST, Dezem F, Todor A, Yang J, Plummer J, Gayther S, Kar S, Schildkraut J, Epstein MP. Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia. Am J Hum Genet 2024:S0002-9297(24)00127-7. [PMID: 38723630 DOI: 10.1016/j.ajhg.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 04/11/2024] [Accepted: 04/16/2024] [Indexed: 05/21/2024] Open
Abstract
Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases.
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Affiliation(s)
- S Taylor Head
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Felipe Dezem
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Andrei Todor
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jingjing Yang
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jasmine Plummer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Simon Gayther
- Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Siddhartha Kar
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Joellen Schildkraut
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Michael P Epstein
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
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17
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Mannens CCA, Hu L, Lönnerberg P, Schipper M, Reagor CC, Li X, He X, Barker RA, Sundström E, Posthuma D, Linnarsson S. Chromatin accessibility during human first-trimester neurodevelopment. Nature 2024:10.1038/s41586-024-07234-1. [PMID: 38693260 DOI: 10.1038/s41586-024-07234-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/02/2024] [Indexed: 05/03/2024]
Abstract
The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.
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Affiliation(s)
- Camiel C A Mannens
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Lijuan Hu
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Peter Lönnerberg
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Marijn Schipper
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Caleb C Reagor
- Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, New York, NY, USA
| | - Xiaofei Li
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Xiaoling He
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Roger A Barker
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Erik Sundström
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden.
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18
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Lincoln MR, Connally N, Axisa PP, Gasperi C, Mitrovic M, van Heel D, Wijmenga C, Withoff S, Jonkers IH, Padyukov L, Rich SS, Graham RR, Gaffney PM, Langefeld CD, Vyse TJ, Hafler DA, Chun S, Sunyaev SR, Cotsapas C. Genetic mapping across autoimmune diseases reveals shared associations and mechanisms. Nat Genet 2024; 56:838-845. [PMID: 38741015 DOI: 10.1038/s41588-024-01732-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/21/2024] [Indexed: 05/16/2024]
Abstract
Autoimmune and inflammatory diseases are polygenic disorders of the immune system. Many genomic loci harbor risk alleles for several diseases, but the limited resolution of genetic mapping prevents determining whether the same allele is responsible, indicating a shared underlying mechanism. Here, using a collection of 129,058 cases and controls across 6 diseases, we show that ~40% of overlapping associations are due to the same allele. We improve fine-mapping resolution for shared alleles twofold by combining cases and controls across diseases, allowing us to identify more expression quantitative trait loci driven by the shared alleles. The patterns indicate widespread sharing of pathogenic mechanisms but not a single global autoimmune mechanism. Our approach can be applied to any set of traits and is particularly valuable as sample collections become depleted.
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Affiliation(s)
- Matthew R Lincoln
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Division of Neurology at the Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Noah Connally
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Pierre-Paul Axisa
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | | | - Mitja Mitrovic
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia
| | - David van Heel
- Blizard Institute, Queen Mary University of London, London, UK
| | - Cisca Wijmenga
- Department of Genetics at the University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sebo Withoff
- Department of Genetics at the University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Iris H Jonkers
- Department of Genetics at the University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Leonid Padyukov
- Division of Rheumatology at the Department of Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Robert R Graham
- Maze Therapeutics, South San Francisco, CA, USA
- Genentech, South San Francisco, CA, USA
| | - Patrick M Gaffney
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Timothy J Vyse
- Department of Medical and Molecular Genetics, Kings College London, London, UK
| | - David A Hafler
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Sung Chun
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shamil R Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chris Cotsapas
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
- Vesalius Therapeutics, Cambridge, MA, USA.
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19
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Paterson AH, Queitsch C. Genome organization and botanical diversity. THE PLANT CELL 2024; 36:1186-1204. [PMID: 38382084 PMCID: PMC11062460 DOI: 10.1093/plcell/koae045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024]
Abstract
The rich diversity of angiosperms, both the planet's dominant flora and the cornerstone of agriculture, is integrally intertwined with a distinctive evolutionary history. Here, we explore the interplay between angiosperm genome organization and botanical diversity, empowered by genomic approaches ranging from genetic linkage mapping to analysis of gene regulation. Commonality in the genetic hardware of plants has enabled robust comparative genomics that has provided a broad picture of angiosperm evolution and implicated both general processes and specific elements in contributing to botanical diversity. We argue that the hardware of plant genomes-both in content and in dynamics-has been shaped by selection for rather substantial differences in gene regulation between plants and animals such as maize and human, organisms of comparable genome size and gene number. Their distinctive genome content and dynamics may reflect in part the indeterminate development of plants that puts strikingly different demands on gene regulation than in animals. Repeated polyploidization of plant genomes and multiplication of individual genes together with extensive rearrangement and differential retention provide rich raw material for selection of morphological and/or physiological variations conferring fitness in specific niches, whether natural or artificial. These findings exemplify the burgeoning information available to employ in increasing knowledge of plant biology and in modifying selected plants to better meet human needs.
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Affiliation(s)
- Andrew H Paterson
- Plant Genome Mapping Laboratory, University of Georgia, Athens, GA, USA
| | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
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20
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Zhang T, Ambrodji A, Huang H, Bouchonville KJ, Etheridge AS, Schmidt RE, Bembenek BM, Temesgen ZB, Wang Z, Innocenti F, Stroka D, Diasio RB, Largiadèr CR, Offer SM. Germline cis variant determines epigenetic regulation of the anti-cancer drug metabolism gene dihydropyrimidine dehydrogenase ( DPYD). eLife 2024; 13:RP94075. [PMID: 38686795 PMCID: PMC11060711 DOI: 10.7554/elife.94075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024] Open
Abstract
Enhancers are critical for regulating tissue-specific gene expression, and genetic variants within enhancer regions have been suggested to contribute to various cancer-related processes, including therapeutic resistance. However, the precise mechanisms remain elusive. Using a well-defined drug-gene pair, we identified an enhancer region for dihydropyrimidine dehydrogenase (DPD, DPYD gene) expression that is relevant to the metabolism of the anti-cancer drug 5-fluorouracil (5-FU). Using reporter systems, CRISPR genome-edited cell models, and human liver specimens, we demonstrated in vitro and vivo that genotype status for the common germline variant (rs4294451; 27% global minor allele frequency) located within this novel enhancer controls DPYD transcription and alters resistance to 5-FU. The variant genotype increases recruitment of the transcription factor CEBPB to the enhancer and alters the level of direct interactions between the enhancer and DPYD promoter. Our data provide insight into the regulatory mechanisms controlling sensitivity and resistance to 5-FU.
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Affiliation(s)
- Ting Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Alisa Ambrodji
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of BernBernSwitzerland
- Graduate School for Cellular and Biomedical Sciences, University of BernBernSwitzerland
| | - Huixing Huang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Kelly J Bouchonville
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Amy S Etheridge
- Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel HillChapel HillUnited States
| | - Remington E Schmidt
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Brianna M Bembenek
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Zoey B Temesgen
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Zhiquan Wang
- Division of Hematology, Department of Medicine, Mayo ClinicRochesterUnited States
| | - Federico Innocenti
- Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel HillChapel HillUnited States
| | - Deborah Stroka
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Robert B Diasio
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Carlo R Largiadèr
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Steven M Offer
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
- Department of Pathology, University of Iowa Carver College of Medicine, University of IowaIowa CityUnited States
- Holden Comprehensive Cancer Center, University of Iowa Carver College of Medicine, University of IowaIowa CityUnited States
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21
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Jung J, Wu Q. Identification of bone mineral density associated genes with shared genetic architectures across multiple tissues: Functional insights for EPDR1, PKDCC, and SPTBN1. PLoS One 2024; 19:e0300535. [PMID: 38683846 PMCID: PMC11057974 DOI: 10.1371/journal.pone.0300535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 02/28/2024] [Indexed: 05/02/2024] Open
Abstract
Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes, EPDR1, PKDCC, and SPTBN1, which are highly expressed in muscle tissue and previously unlinked to bone metabolism. About 90% and 85% of filtered Single-Nucleotide Polymorphisms were in the intronic and intergenic regions for the threshold at P≤5×10-8 and P≤5×10-100, respectively. EPDR1 was highly expressed in multiple tissues, including muscles, adrenal glands, blood vessels, and the thyroid. SPTBN1 was highly expressed in all 30 tissue types except blood, while PKDCC was highly expressed in all 30 tissue types except the brain, pancreas, and skin. Our study provides a framework for using GWAS findings to highlight functional evidence of crosstalk between multiple tissues based on shared genetic architecture between muscle and bone. Further research should focus on functional validation, multi-omics data integration, gene-environment interactions, and clinical relevance in musculoskeletal disorders.
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Affiliation(s)
- Jongyun Jung
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Qing Wu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America
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22
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Hudgins AD, Zhou S, Arey RN, Rosenfeld MG, Murphy CT, Suh Y. A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveal modulators of memory function. Neuron 2024:S0896-6273(24)00247-2. [PMID: 38692279 DOI: 10.1016/j.neuron.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 10/18/2023] [Accepted: 04/08/2024] [Indexed: 05/03/2024]
Abstract
Genome-wide association studies (GWASs) have uncovered over 75 genomic loci associated with risk for late-onset Alzheimer's disease (LOAD), but identification of the underlying causal genes remains challenging. Studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects. Here, we searched for genetic contributions to neuronal dysfunction in LOAD using an integrative systems approach that incorporated multi-evidence-based gene mapping and network-analysis-based prioritization. A systematic perturbation screening of candidate risk genes in Caenorhabditis elegans (C. elegans) revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) alters short-/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.
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Affiliation(s)
- Adam D Hudgins
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
| | - Shiyi Zhou
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Rachel N Arey
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Michael G Rosenfeld
- Department of Medicine, School of Medicine, University of California, La Jolla, CA, USA; Howard Hughes Medical Institute, University of California, La Jolla, CA, USA
| | - Coleen T Murphy
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA; LSI Genomics, Princeton University, Princeton, NJ, USA.
| | - Yousin Suh
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA; Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY, USA.
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23
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Liu L, He S, Jia L, Yao H, Zhou D, Guo X, Miao L. Correlation analysis of serum TLR4 protein levels and TLR4 gene polymorphisms in gouty arthritis patients. PLoS One 2024; 19:e0300582. [PMID: 38652726 PMCID: PMC11037531 DOI: 10.1371/journal.pone.0300582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/29/2024] [Indexed: 04/25/2024] Open
Abstract
OBJECTIVE The Toll-like receptor (TLR) 4-mediated nuclear factor kappa B (NF-κB) signaling pathway regulates the production of inflammatory factors and plays a key role in the pathogenesis of gouty arthritis. The aim of the present study was to investigate the link among TLR4 gene polymorphisms at various loci, protein expression, and gouty arthritis susceptibility. METHODS Between 2016 and 2021, a case-control study was used to collect a total of 1207 study subjects, including 317 male patients with gouty arthritis (gout group) and 890 healthy males (control group). The association between gout susceptibility and different genetic models was analyzed by typing three loci of the TLR4 gene (rs2149356, rs2737191, and rs10759932) using a multiplex point mutation rapid assay, and the association between protein expression and gout was confirmed by measuring TLR4 protein concentrations using enzyme-linked immunosorbent assays (ELISAs). RESULTS In a codominant models AA and AG, the rs2737191 polymorphism in the gout group increased the risk of gout compared to the AA genotype (OR = 2.249, 95%CI 1.010~5.008), and the risk of gout was higher for those carrying the G allele compared to the A allele (OR = 2.227, 95%CI 1.006~4.932). TLR4 protein expression was different between the two groups with different locus genotypes. The differences in TLR4 protein expression between the gout group and control group were statistically significant between the following genotypes: the GG and GT genotypes of the rs2149356 polymorphism; the AA and AG genotypes of the rs2737191 polymorphism; and the TT and TC genotypes of the rs10759932 polymorphism(P<0.05). The TLR4 protein level in the gout group (19.19±3.09 ng/ml) was significantly higher than that in the control group (15.85±4.75 ng/ml). CONCLUSION The AG genotype of the TLR4 gene rs2737191 polymorphism may be correlated with the development of gouty arthritis. The level of TLR4 protein expression is significantly higher in patients with gouty arthritis than in controls, and there is a correlation between high TLR4 protein expression and the development of gouty arthritis.
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Affiliation(s)
- Lu Liu
- The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
- School of Public Health, Xinjiang Medical University, Xinjiang, China
| | - Shuang He
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China
| | - Lin Jia
- The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
| | - Hua Yao
- The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
| | - Dan Zhou
- The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
| | - Xiaobin Guo
- The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
| | - Lei Miao
- School of Public Health, Xinjiang Medical University, Xinjiang, China
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24
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Yin KF, Chen T, Gu XJ, Su WM, Jiang Z, Lu SJ, Cao B, Chi LY, Gao X, Chen YP. Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for sarcopenia. J Cachexia Sarcopenia Muscle 2024. [PMID: 38644354 DOI: 10.1002/jcsm.13479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 02/27/2024] [Accepted: 03/07/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND There are no effective pharmacological treatments for sarcopenia. We aim to identify potential therapeutic targets for sarcopenia by integrating various publicly available datasets. METHODS We integrated druggable genome data, cis-eQTL/cis-pQTL from human blood and skeletal muscle tissue, and GWAS summary data of sarcopenia-related traits to analyse the potential causal relationships between drug target genes and sarcopenia using the Mendelian Randomization (MR) method. Sensitivity analyses and Bayesian colocalization were employed to validate the causal relationships. We also assessed the side effects or additional indications of the identified drug targets using a phenome-wide MR (Phe-MR) approach and investigated actionable drugs for target genes using available databases. RESULTS MR analysis identified 17 druggable genes with potential causation to sarcopenia in human blood or skeletal muscle tissue. Six of them (HP, HLA-DRA, MAP 3K3, MFGE8, COL15A1, and AURKA) were further confirmed by Bayesian colocalization (PPH4 > 90%). The up-regulation of HP [higher ALM (beta: 0.012, 95% CI: 0.007-0.018, P = 1.2*10-5) and higher grip strength (OR: 0.96, 95% CI: 0.94-0.98, P = 4.2*10-5)], MAP 3K3 [higher ALM (beta: 0.24, 95% CI: 0.21-0.26, P = 1.8*10-94), higher grip strength (OR: 0.82, 95% CI: 0.75-0.90, P = 2.1*10-5), and faster walking pace (beta: 0.03, 95% CI: 0.02-0.05, P = 8.5*10-6)], and MFGE8 [higher ALM (muscle eQTL, beta: 0.09, 95% CI: 0.06-0.11, P = 6.1*10-13; blood pQTL, beta: 0.05, 95% CI: 0.03-0.07, P = 3.8*10-09)], as well as the down-regulation of HLA-DRA [lower ALM (beta: -0.09, 95% CI: -0.11 to -0.08, P = 5.4*10-36) and lower grip strength (OR: 1.13, 95% CI: 1.07-1.20, P = 1.8*10-5)] and COL15A1 [higher ALM (muscle eQTL, beta: -0.07, 95% CI: -0.10 to -0.04, P = 3.4*10-07; blood pQTL, beta: -0.05, 95% CI: -0.06 to -0.03, P = 1.6*10-07)], decreased the risk of sarcopenia. AURKA in blood (beta: -0.16, 95% CI: -0.22 to -0.09, P = 2.1*10-06) and skeletal muscle (beta: 0.03, 95% CI: 0.02 to 0.05, P = 5.3*10-05) tissues showed an inverse relationship with sarcopenia risk. The Phe-MR indicated that the six potential therapeutic targets for sarcopenia had no significant adverse effects. Drug repurposing analysis supported zinc supplementation and collagenase clostridium histolyticum might be potential therapeutics for sarcopenia by activating HP and inhibiting COL15A1, respectively. CONCLUSIONS Our research indicated MAP 3K3, MFGE8, COL15A1, HP, and HLA-DRA may serve as promising targets for sarcopenia, while the effectiveness of zinc supplementation and collagenase clostridium histolyticum for sarcopenia requires further validation.
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Affiliation(s)
- Kang-Fu Yin
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
- Institute of Brain Science and Brain-Inspired Technology, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
- Institute of Brain Science and Brain-Inspired Technology, West China Hospital, Sichuan University, Chengdu, China
| | - Xiao-Jing Gu
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Wei-Ming Su
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
- Institute of Brain Science and Brain-Inspired Technology, West China Hospital, Sichuan University, Chengdu, China
| | - Zheng Jiang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
- Institute of Brain Science and Brain-Inspired Technology, West China Hospital, Sichuan University, Chengdu, China
| | - Si-Jia Lu
- Department of Respiratory, The Fourth People's Hospital of Chengdu, Mental Health Center of Chengdu, Chengdu, China
| | - Bei Cao
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
- Institute of Brain Science and Brain-Inspired Technology, West China Hospital, Sichuan University, Chengdu, China
| | - Li-Yi Chi
- Department of Neurology, First Affiliated Hospital of Air Force Military Medical University, Xi'an, China
| | - Xia Gao
- Department of Geriatrics, Dazhou Central Hospital, Dazhou, China
| | - Yong-Ping Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
- Institute of Brain Science and Brain-Inspired Technology, West China Hospital, Sichuan University, Chengdu, China
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25
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Barry T, Mason K, Roeder K, Katsevich E. Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.15.540875. [PMID: 38659821 PMCID: PMC11042176 DOI: 10.1101/2023.05.15.540875] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
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26
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McCallum-Loudeac J, Moody E, Williams J, Johnstone G, Sircombe KJ, Clarkson AN, Wilson MJ. Deletion of a conserved genomic region associated with adolescent idiopathic scoliosis leads to vertebral rotation in mice. Hum Mol Genet 2024; 33:787-801. [PMID: 38280229 PMCID: PMC11031364 DOI: 10.1093/hmg/ddae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 12/15/2023] [Accepted: 01/12/2024] [Indexed: 01/29/2024] Open
Abstract
Adolescent idiopathic scoliosis (AIS) is the most common form of scoliosis, in which spinal curvature develops in adolescence, and 90% of patients are female. Scoliosis is a debilitating disease that often requires bracing or surgery in severe cases. AIS affects 2%-5.2% of the population; however, the biological origin of the disease remains poorly understood. In this study, we aimed to determine the function of a highly conserved genomic region previously linked to AIS using a mouse model generated by CRISPR-CAS9 gene editing to knockout this area of the genome to understand better its contribution to AIS, which we named AIS_CRMΔ. We also investigated the upstream factors that regulate the activity of this enhancer in vivo, whether the spatial expression of the LBX1 protein would change with the loss of AIS-CRM function, and whether any phenotype would arise after deletion of this region. We found a significant increase in mRNA expression in the developing neural tube at E10.5, and E12.5, for not only Lbx1 but also other neighboring genes. Adult knockout mice showed vertebral rotation and proprioceptive deficits, also observed in human AIS patients. In conclusion, our study sheds light on the elusive biological origins of AIS, by targeting and investigating a highly conserved genomic region linked to AIS in humans. These findings provide valuable insights into the function of the investigated region and contribute to our understanding of the underlying causes of this debilitating disease.
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Affiliation(s)
- Jeremy McCallum-Loudeac
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand
| | - Edward Moody
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand
| | - Jack Williams
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand
| | - Georgia Johnstone
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand
| | - Kathleen J Sircombe
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand
| | - Andrew N Clarkson
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand
| | - Megan J Wilson
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand
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Hughes EP, Syage AR, Mehrabad EM, Lane TE, Spike BT, Tantin D. OCA-B promotes autoimmune demyelination through control of stem-like CD4 + T cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.29.569210. [PMID: 38076925 PMCID: PMC10705450 DOI: 10.1101/2023.11.29.569210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Stem-like T cell populations can selectively contribute to autoimmunity, but the activities that promote and sustain these populations are incompletely understood. Here, we show that T cell-intrinsic loss of the transcription cofactor OCA-B protects mice from experimental autoimmune encephalomyelitis (EAE) while preserving responses to CNS infection. In adoptive transfer EAE models driven by multiple antigen encounters, OCA-B deletion nearly eliminates CNS infiltration, proinflammatory cytokine production and clinical disease. OCA-B-expressing CD4 + T cells within the CNS of mice with EAE comprise a minority of the population but display a memory phenotype and preferentially confer disease. In a relapsing-remitting EAE model, OCA-B T cell deficiency specifically protects mice from relapse. During remission, OCA-B promotes the expression of Tcf7 , Slamf6 , and Sell in proliferating T cell populations. At relapse, OCA-B loss results in both the accumulation of an immunomodulatory CD4 + T cell population expressing Ccr9 and Bach2 , and the loss of pro-inflammatory gene expression from Th17 cells. These results identify OCA-B as a driver of pathogenic stem-like T cells.
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He Y, Tang Y, Wen S, Dong L, Li F, Deng Y, Tao Z. LINC00998 Modulating M2 Macrophage Activation in Allergic Rhinitis by Stabilizing BOB.1 mRNA. J Inflamm Res 2024; 17:2309-2326. [PMID: 38638161 PMCID: PMC11026101 DOI: 10.2147/jir.s444692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 04/09/2024] [Indexed: 04/20/2024] Open
Abstract
Background Allergic rhinitis (AR) is globally recognized as a considerable threat to human health with a rising prevalence and a substantial medical and socioeconomic burden. Numerous studies have emphasized the significance of long noncoding RNAs (lncRNAs) in allergic responses. Hence, this research dealt with exploring the involvement of the lncRNA LINC00998 in the mechanism of AR. Methods LINC00998 expression was assessed by qRT-PCR in peripheral blood mononuclear cells acquired from individuals with AR. Additionally, the potential relationship between LINC00998 and macrophage polarization was observed in vitro. Then we constructed AR mice model and macrophage polarization models using THP-1 cells as well as primary human macrophages to verify the M2 shift in AR and the low expression level of LINC00998 in M2 macrophages. We used gain- and loss-of-function experiments to explore the modification of LINC00998 in macrophage polarization. Furthermore, we explored the underlying mechanism of LINC00998 mediates through qRT-PCR, flow cytometry, and Western blot. Results The analysis revealed a significant decrease in LINC00998 expression in the samples obtained from patients with AR. LINC00998 is markedly increased in M1 macrophages whereas decreased in M2 macrophages in vitro. Furthermore, suppression of LINC00998 caused a remarkable enhancement in M2 polarization, whereas its overexpression led to its attenuation. Knockdown of LINC00998 led to a remarkable downregulation of BOB.1 mRNA and protein, while overexpression of LINC00998 upregulated their expression. Moreover, it was found that BOB.1 modulated macrophage polarization through the PU.1/IL-1β axis. Meanwhile, the modulation of LINC00098 overexpression on macrophage polarization and PU.1/ IL-1β can be reversed by BOB.1 siRNA. Conclusion This research revealed the lncRNA LINC00998 altered M2 macrophage polarization by regulating the BOB.1/PU.1/IL-1β axis, which open up new avenues for studying the pathogenesis of AR.
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Affiliation(s)
- Yan He
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Yulei Tang
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Silu Wen
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Lin Dong
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Fen Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
- Research Institute of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Yuqing Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Zezhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
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Gedik H, Peterson R, Chatzinakos C, Dozmorov MG, Vladimirov V, Riley BP, Bacanu SA. A novel multi-omics mendelian randomization method for gene set enrichment and its application to psychiatric disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.14.24305811. [PMID: 38699366 PMCID: PMC11065030 DOI: 10.1101/2024.04.14.24305811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.
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Kim Y, Jeong M, Koh IG, Kim C, Lee H, Kim JH, Yurko R, Kim IB, Park J, Werling DM, Sanders SJ, An JY. CWAS-Plus: Estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305828. [PMID: 38699372 PMCID: PMC11065022 DOI: 10.1101/2024.04.15.24305828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Variants in cis-regulatory elements link the noncoding genome to human brain pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS) employs both whole-genome sequencing and user-provided functional data to enhance noncoding variant analysis, with a faster and more efficient execution of the CWAS workflow. Here, we used single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type specific enhancers and promoters. Examining autism spectrum disorder whole-genome sequencing data (n = 7,280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease whole-genome sequencing data (n = 1,087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale whole-genome sequencing data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/ , respectively. KEY POINTS CWAS-Plus efficiently identifies noncoding associations in WGS data, supporting user-friendly categorization and burden enrichment tests.CWAS-Plus integrates various functional datasets, emphasizing cell-type-specific noncoding associations.CWAS-Plus provides a novel approach for multiple testing correction, enhancing the reliability of the results.Autism spectrum disorder risk noncoding variants are identified as enriched with transcription factors, suggesting their role in the pathology.Rare variant analysis with Alzheimer's disease samples reveals strong association with microglia, supporting the reliability of the results produced by CWAS-Plus.
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Jeong R, Bulyk ML. Chromatin accessibility variation provides insights into missing regulation underlying immune-mediated diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589213. [PMID: 38659802 PMCID: PMC11042205 DOI: 10.1101/2024.04.12.589213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Most genetic loci associated with complex traits and diseases through genome-wide association studies (GWAS) are noncoding, suggesting that the causal variants likely have gene regulatory effects. However, only a small number of loci have been linked to expression quantitative trait loci (eQTLs) detected currently. To better understand the potential reasons for many trait-associated loci lacking eQTL colocalization, we investigated whether chromatin accessibility QTLs (caQTLs) in lymphoblastoid cell lines (LCLs) explain immune-mediated disease associations that eQTLs in LCLs did not. The power to detect caQTLs was greater than that of eQTLs and was less affected by the distance from the transcription start site of the associated gene. Meta-analyzing LCL eQTL data to increase the sample size to over a thousand led to additional loci with eQTL colocalization, demonstrating that insufficient statistical power is still likely to be a factor. Moreover, further eQTL colocalization loci were uncovered by surveying eQTLs of other immune cell types. Altogether, insufficient power and context-specificity of eQTLs both contribute to the 'missing regulation.'
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Affiliation(s)
- Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
| | - Martha L. Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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32
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Pandey GK, Vadlamudi S, Currin KW, Moxley AH, Nicholas JC, McAfee JC, Broadaway KA, Mohlke KL. Liver regulatory mechanisms of noncoding variants at lipid and metabolic trait loci. HGG ADVANCES 2024; 5:100275. [PMID: 38297830 PMCID: PMC10881423 DOI: 10.1016/j.xhgg.2024.100275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
Genome-wide association studies (GWASs) have identified hundreds of risk loci for liver disease and lipid-related metabolic traits, although identifying their target genes and molecular mechanisms remains challenging. We predicted target genes at GWAS signals by integrating them with molecular quantitative trait loci for liver gene expression (eQTL) and liver chromatin accessibility QTL (caQTL). We predicted specific regulatory caQTL variants at four GWAS signals located near EFHD1, LITAF, ZNF329, and GPR180. Using transcriptional reporter assays, we determined that caQTL variants rs13395911, rs11644920, rs34003091, and rs9556404 exhibit allelic differences in regulatory activity. We also performed a protein binding assay for rs13395911 and found that FOXA2 differentially interacts with the alleles of rs13395911. For variants rs13395911 and rs11644920 in putative enhancer regulatory elements, we used CRISPRi to demonstrate that repression of the enhancers altered the expression of the predicted target and/or nearby genes. Repression of the element at rs13395911 reduced the expression of EFHD1, and repression of the element at rs11644920 reduced the expression of LITAF, SNN, and TXNDC11. Finally, we showed that EFHD1 is a metabolically active gene in HepG2 cells. Together, these results provide key steps to connect genetic variants with cellular mechanisms and help elucidate the causes of liver disease.
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Affiliation(s)
- Gautam K Pandey
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | | | - Kevin W Currin
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Anne H Moxley
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Jayna C Nicholas
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Jessica C McAfee
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
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Xiong Z, Thach TQ, Zhang YD, Sham PC. Improved estimation of functional enrichment in SNP heritability using feasible generalized least squares. HGG ADVANCES 2024; 5:100272. [PMID: 38327050 PMCID: PMC10901842 DOI: 10.1016/j.xhgg.2024.100272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/09/2024] Open
Abstract
Functional enrichment results typically implicate tissue or cell-type-specific biological pathways in disease pathogenesis and as therapeutic targets. We propose generalized linkage disequilibrium score regression (g-LDSC) that requires only genome-wide association studies (GWASs) summary-level data to estimate functional enrichment. The method adopts the same assumptions and regression model formulation as stratified linkage disequilibrium score regression (s-LDSC). Although s-LDSC only partially uses LD information, our method uses the whole LD matrix, which accounts for possible correlated error structure via a feasible generalized least-squares estimation. We demonstrate through simulation studies under various scenarios that g-LDSC provides more precise estimates of functional enrichment than s-LDSC, regardless of model misspecification. In an application to GWAS summary statistics of 15 traits from the UK Biobank, estimates of functional enrichment using g-LDSC were lower and more realistic than those obtained from s-LDSC. In addition, g-LDSC detected more significantly enriched functional annotations among 24 functional annotations for the 15 traits than s-LDSC (118 vs. 51).
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Affiliation(s)
- Zewei Xiong
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Thuan-Quoc Thach
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yan Dora Zhang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China; Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.
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Guo X, Chatterjee N, Dutta D. Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability. HGG ADVANCES 2024; 5:100283. [PMID: 38491773 PMCID: PMC10999697 DOI: 10.1016/j.xhgg.2024.100283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/09/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024] Open
Abstract
Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is "activated." For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.
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Affiliation(s)
- Xinyu Guo
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90007, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Diptavo Dutta
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD 20850, USA.
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Hansen TJ, Fong SL, Day JK, Capra JA, Hodges E. Human gene regulatory evolution is driven by the divergence of regulatory element function in both cis and trans. CELL GENOMICS 2024; 4:100536. [PMID: 38604126 PMCID: PMC11019363 DOI: 10.1016/j.xgen.2024.100536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/03/2024] [Accepted: 03/10/2024] [Indexed: 04/13/2024]
Abstract
Gene regulatory divergence between species can result from cis-acting local changes to regulatory element DNA sequences or global trans-acting changes to the regulatory environment. Understanding how these mechanisms drive regulatory evolution has been limited by challenges in identifying trans-acting changes. We present a comprehensive approach to directly identify cis- and trans-divergent regulatory elements between human and rhesus macaque lymphoblastoid cells using assay for transposase-accessible chromatin coupled to self-transcribing active regulatory region (ATAC-STARR) sequencing. In addition to thousands of cis changes, we discover an unexpected number (∼10,000) of trans changes and show that cis and trans elements exhibit distinct patterns of sequence divergence and function. We further identify differentially expressed transcription factors that underlie ∼37% of trans differences and trace how cis changes can produce cascades of trans changes. Overall, we find that most divergent elements (67%) experienced changes in both cis and trans, revealing a substantial role for trans divergence-alone and together with cis changes-in regulatory differences between species.
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Affiliation(s)
- Tyler J Hansen
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Sarah L Fong
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jessica K Day
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - John A Capra
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94143, USA.
| | - Emily Hodges
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Vanderbilt Ingram Cancer Center, Nashville, TN 37232, USA.
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Wang L, Babushkin N, Liu Z, Liu X. Trans-eQTL mapping in gene sets identifies network effects of genetic variants. CELL GENOMICS 2024; 4:100538. [PMID: 38565144 PMCID: PMC11019359 DOI: 10.1016/j.xgen.2024.100538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/08/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.
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Affiliation(s)
- Lili Wang
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Nikita Babushkin
- Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Xuanyao Liu
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
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Kalnapenkis A, Jõeloo M, Lepik K, Kukuškina V, Kals M, Alasoo K, Mägi R, Esko T, Võsa U. Genetic determinants of plasma protein levels in the Estonian population. Sci Rep 2024; 14:7694. [PMID: 38565889 PMCID: PMC10987560 DOI: 10.1038/s41598-024-57966-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.
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Affiliation(s)
- Anette Kalnapenkis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.
| | - Maarja Jõeloo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Kaido Lepik
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Viktorija Kukuškina
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
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38
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Mackay TFC, Anholt RRH. Pleiotropy, epistasis and the genetic architecture of quantitative traits. Nat Rev Genet 2024:10.1038/s41576-024-00711-3. [PMID: 38565962 DOI: 10.1038/s41576-024-00711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2024] [Indexed: 04/04/2024]
Abstract
Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.
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Affiliation(s)
- Trudy F C Mackay
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
| | - Robert R H Anholt
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
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39
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 DOI: 10.1038/s41588-024-01682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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40
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Kiltschewskij DJ, Reay WR, Geaghan MP, Atkins JR, Xavier A, Zhang X, Watkeys OJ, Carr VJ, Scott RJ, Green MJ, Cairns MJ. Alteration of DNA Methylation and Epigenetic Scores Associated With Features of Schizophrenia and Common Variant Genetic Risk. Biol Psychiatry 2024; 95:647-661. [PMID: 37480976 DOI: 10.1016/j.biopsych.2023.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND Unpacking molecular perturbations associated with features of schizophrenia is a critical step toward understanding phenotypic heterogeneity in this disorder. Recent epigenome-wide association studies have uncovered pervasive dysregulation of DNA methylation in schizophrenia; however, clinical features of the disorder that account for a large proportion of phenotypic variability are relatively underexplored. METHODS We comprehensively analyzed patterns of DNA methylation in a cohort of 381 individuals with schizophrenia from the deeply phenotyped Australian Schizophrenia Research Bank. Epigenetic changes were investigated in association with cognitive status, age of onset, treatment resistance, Global Assessment of Functioning scores, and common variant polygenic risk scores for schizophrenia. We subsequently explored alterations within genes previously associated with psychiatric illness, phenome-wide epigenetic covariance, and epigenetic scores. RESULTS Epigenome-wide association studies of the 5 primary traits identified 662 suggestively significant (p < 6.72 × 10-5) differentially methylated probes, with a further 432 revealed after controlling for schizophrenia polygenic risk on the remaining 4 traits. Interestingly, we uncovered many probes within genes associated with a variety of psychiatric conditions as well as significant epigenetic covariance with phenotypes and exposures including acute myocardial infarction, C-reactive protein, and lung cancer. Epigenetic scores for treatment-resistant schizophrenia strikingly exhibited association with clozapine administration, while epigenetic proxies of plasma protein expression, such as CCL17, MMP10, and PRG2, were associated with several features of schizophrenia. CONCLUSIONS Our findings collectively provide novel evidence suggesting that several features of schizophrenia are associated with alteration of DNA methylation, which may contribute to interindividual phenotypic variation in affected individuals.
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Affiliation(s)
- Dylan J Kiltschewskij
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Michael P Geaghan
- Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
| | - Joshua R Atkins
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia
| | - Alexandre Xavier
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Xiajie Zhang
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Oliver J Watkeys
- School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Vaughan J Carr
- School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia; Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Melissa J Green
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia.
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41
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Yao D, Tycko J, Oh JW, Bounds LR, Gosai SJ, Lataniotis L, Mackay-Smith A, Doughty BR, Gabdank I, Schmidt H, Guerrero-Altamirano T, Siklenka K, Guo K, White AD, Youngworth I, Andreeva K, Ren X, Barrera A, Luo Y, Yardımcı GG, Tewhey R, Kundaje A, Greenleaf WJ, Sabeti PC, Leslie C, Pritykin Y, Moore JE, Beer MA, Gersbach CA, Reddy TE, Shen Y, Engreitz JM, Bassik MC, Reilly SK. Multicenter integrated analysis of noncoding CRISPRi screens. Nat Methods 2024; 21:723-734. [PMID: 38504114 PMCID: PMC11009116 DOI: 10.1038/s41592-024-02216-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 02/18/2024] [Indexed: 03/21/2024]
Abstract
The ENCODE Consortium's efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE-gene links in K562 cells, we established guidelines for screening endogenous noncoding elements with CRISPR interference (CRISPRi), including accurate detection of CREs that exhibit variable, often low, transcriptional effects. Benchmarking five screen analysis tools, we find that CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity single guide RNAs. We uncover a subtle DNA strand bias for CRISPRi in transcribed regions with implications for screen design and analysis. Together, we provide an accessible data resource, predesigned single guide RNAs for targeting 3,275,697 ENCODE SCREEN candidate CREs with CRISPRi and screening guidelines to accelerate functional characterization of the noncoding genome.
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Affiliation(s)
- David Yao
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Josh Tycko
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
| | - Jin Woo Oh
- Departments of Biomedical Engineering and Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Lexi R Bounds
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Sager J Gosai
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Center for System Biology, Harvard University, Cambridge, MA, USA
- Harvard Graduate Program in Biological and Biomedical Science, Boston, MA, USA
| | - Lazaros Lataniotis
- Department of Neurology, Institute for Human Genetics, University of California, San Franscisco, San Francisco, CA, USA
| | - Ava Mackay-Smith
- University Program in Genetics and Genomics, Duke University School of Medicine, Durham, NC, USA
| | | | - Idan Gabdank
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tania Guerrero-Altamirano
- University Program in Genetics and Genomics, Duke University School of Medicine, Durham, NC, USA
- Department of Biology, Duke University, Durham, NC, USA
| | - Keith Siklenka
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA
| | - Katherine Guo
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Alexander D White
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | | | - Kalina Andreeva
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Xingjie Ren
- Department of Neurology, Institute for Human Genetics, University of California, San Franscisco, San Francisco, CA, USA
| | - Alejandro Barrera
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA
| | - Yunhai Luo
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | | | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Applied Physics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Pardis C Sabeti
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Center for System Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Christina Leslie
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yuri Pritykin
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Jill E Moore
- Program in Bioinformatics and Integrative Biology, RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Michael A Beer
- Departments of Biomedical Engineering and Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Charles A Gersbach
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Timothy E Reddy
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA
| | - Yin Shen
- Department of Neurology, Institute for Human Genetics, University of California, San Franscisco, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jesse M Engreitz
- Department of Genetics, Stanford University, Stanford, CA, USA
- BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford, CA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Steven K Reilly
- Department of Genetics, Yale University, New Haven, CT, USA.
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42
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Zhang E, Sun Q, Zhang C, Ma H, Zhang J, Ding Y, Wang G, Jin C, Jin C, Fu Y, Yan C, Zhu M, Wang C, Dai J, Jin G, Hu Z, Shen H, Ma H. Comprehensive functional interrogation of susceptibility loci in GWASs identified KIAA0391 as a novel oncogenic driver via regulating pyroptosis in NSCLC. Cancer Lett 2024; 585:216646. [PMID: 38262497 DOI: 10.1016/j.canlet.2024.216646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/23/2023] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
Approximately 51 non-small-cell lung cancer (NSCLC) risk loci have been identified by genome-wide association studies (GWASs). We conducted a high throughput RNA-interference (RNAi) screening to identify the candidate causal genes in NSCLC risk loci. KIAA0391 at 14q13.1 had the highest score and could promote proliferation and metastasis of NSCLC in vitro and in vivo. We next prioritized rs3783313 as a causal variant at 14q13.1, by integrating a large-scale population study consisting of 27,120 lung cancer cases and 27,355 controls, functional annotation, and expression quantitative trait locus (eQTL) analysis. Then we found that rs3783313 could facilitate a promoter-enhancer interaction to upregulate KIAA0391 expression by affecting the affinity of transcription factor NFYA. Mechanistically, KIAA0391 knockdown dramatically influenced pyroptosis-related pathways and increased the expression of CASP1. And KIAA0391 transcriptionally repressed CASP1 by binding to SMAD2 and induced an anti-pyroptosis phenotype, promoting tumorigenesis of NSCLC, which provides new insights and potential target for NSCLC.
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Affiliation(s)
- Erbao Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Qi Sun
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chang Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou Second People's Hospital, Changzhou Medical Center, Nanjing Medical University, Nanjing 211166, China
| | - Huimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Jing Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Yue Ding
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guoqing Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chen Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chenying Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Yating Fu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Caiwang Yan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Cheng Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100142, China.
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100142, China.
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43
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Hatton AA, Cheng FF, Lin T, Shen RJ, Chen J, Zheng Z, Qu J, Lyu F, Harris SE, Cox SR, Jin ZB, Martin NG, Fan D, Montgomery GW, Yang J, Wray NR, Marioni RE, Visscher PM, McRae AF. Genetic control of DNA methylation is largely shared across European and East Asian populations. Nat Commun 2024; 15:2713. [PMID: 38548728 PMCID: PMC10978881 DOI: 10.1038/s41467-024-47005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 03/15/2024] [Indexed: 04/01/2024] Open
Abstract
DNA methylation is an ideal trait to study the extent of the shared genetic control across ancestries, effectively providing hundreds of thousands of model molecular traits with large QTL effect sizes. We investigate cis DNAm QTLs in three European (n = 3701) and two East Asian (n = 2099) cohorts to quantify the similarities and differences in the genetic architecture across populations. We observe 80,394 associated mQTLs (62.2% of DNAm probes with significant mQTL) to be significant in both ancestries, while 28,925 mQTLs (22.4%) are identified in only a single ancestry. mQTL effect sizes are highly conserved across populations, with differences in mQTL discovery likely due to differences in allele frequency of associated variants and differing linkage disequilibrium between causal variants and assayed SNPs. This study highlights the overall similarity of genetic control across ancestries and the value of ancestral diversity in increasing the power to detect associations and enhancing fine mapping resolution.
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Affiliation(s)
- Alesha A Hatton
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Fei-Fei Cheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- School of Life Sciences, Westlake University, Hangzhou, 310030, Zhejiang, China
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ren-Juan Shen
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, 100008, Beijing, China
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Jie Chen
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jia Qu
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Fan Lyu
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Sarah E Harris
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Zi-Bing Jin
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, 100008, Beijing, China
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Nicholas G Martin
- Queensland Institute of Medical Research Berghofer, Brisbane, QLD, 4006, Australia
| | - Dongsheng Fan
- Department of Neurology, Peking University Third Hospital, 100191, Beijing, China
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, 310030, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024, Zhejiang, China
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
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44
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Kadagandla S, Kapoor A. Identification of candidate causal cis -regulatory variants underlying electrocardiographic QT interval GWAS loci. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.13.584880. [PMID: 38585875 PMCID: PMC10996567 DOI: 10.1101/2024.03.13.584880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Identifying causal variants among tens or hundreds of associated variants at each locus mapped by genome-wide association studies (GWAS) of complex traits is a challenge. As vast majority of GWAS variants are noncoding, sequence variation at cis -regulatory elements affecting transcriptional expression of specific genes is a widely accepted molecular hypothesis. Following this cis -regulatory hypothesis and combining it with the observation that nucleosome-free open chromatin is a universal hallmark of all types of cis -regulatory elements, we aimed to identify candidate causal regulatory variants underlying electrocardiographic QT interval GWAS loci. At a dozen loci, selected for higher effect sizes and a better understanding of the likely causal gene, we identified and included all common variants in high linkage disequilibrium with the GWAS variants as candidate variants. Using ENCODE DNase-seq and ATAC-seq from multiple human adult cardiac left ventricle tissue samples, we generated genome-wide maps of open chromatin regions marking putative regulatory elements. QT interval associated candidate variants were filtered for overlap with cardiac left ventricle open chromatin regions to identify candidate causal cis -regulatory variants, which were further assessed for colocalizing with a known cardiac GTEx expression quantitative trait locus variant as additional evidence for their causal role. Together, these efforts have generated a comprehensive set of candidate causal variants that are expected to be enriched for cis -regulatory potential and thereby, explaining the observed genetic associations.
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Yeyeodu S, Hanafi D, Webb K, Laurie NA, Kimbro KS. Population-enriched innate immune variants may identify candidate gene targets at the intersection of cancer and cardio-metabolic disease. Front Endocrinol (Lausanne) 2024; 14:1286979. [PMID: 38577257 PMCID: PMC10991756 DOI: 10.3389/fendo.2023.1286979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/07/2023] [Indexed: 04/06/2024] Open
Abstract
Both cancer and cardio-metabolic disease disparities exist among specific populations in the US. For example, African Americans experience the highest rates of breast and prostate cancer mortality and the highest incidence of obesity. Native and Hispanic Americans experience the highest rates of liver cancer mortality. At the same time, Pacific Islanders have the highest death rate attributed to type 2 diabetes (T2D), and Asian Americans experience the highest incidence of non-alcoholic fatty liver disease (NAFLD) and cancers induced by infectious agents. Notably, the pathologic progression of both cancer and cardio-metabolic diseases involves innate immunity and mechanisms of inflammation. Innate immunity in individuals is established through genetic inheritance and external stimuli to respond to environmental threats and stresses such as pathogen exposure. Further, individual genomes contain characteristic genetic markers associated with one or more geographic ancestries (ethnic groups), including protective innate immune genetic programming optimized for survival in their corresponding ancestral environment(s). This perspective explores evidence related to our working hypothesis that genetic variations in innate immune genes, particularly those that are commonly found but unevenly distributed between populations, are associated with disparities between populations in both cancer and cardio-metabolic diseases. Identifying conventional and unconventional innate immune genes that fit this profile may provide critical insights into the underlying mechanisms that connect these two families of complex diseases and offer novel targets for precision-based treatment of cancer and/or cardio-metabolic disease.
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Affiliation(s)
- Susan Yeyeodu
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
- Charles River Discovery Services, Morrisville, NC, United States
| | - Donia Hanafi
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
| | - Kenisha Webb
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA, United States
| | - Nikia A. Laurie
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
| | - K. Sean Kimbro
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA, United States
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46
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Lin Y, Li J, Gu Y, Jin L, Bai J, Zhang J, Wang Y, Liu P, Long K, He M, Li D, Liu C, Han Z, Zhang Y, Li X, Zeng B, Lu L, Kong F, Sun Y, Fan Y, Wang X, Wang T, Jiang A, Ma J, Shen L, Zhu L, Jiang Y, Tang G, Fan X, Liu Q, Li H, Wang J, Chen L, Ge L, Li X, Tang Q, Li M. Haplotype-resolved 3D chromatin architecture of the hybrid pig. Genome Res 2024; 34:310-325. [PMID: 38479837 PMCID: PMC10984390 DOI: 10.1101/gr.278101.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 02/15/2024] [Indexed: 03/22/2024]
Abstract
In diploid mammals, allele-specific three-dimensional (3D) genome architecture may lead to imbalanced gene expression. Through ultradeep in situ Hi-C sequencing of three representative somatic tissues (liver, skeletal muscle, and brain) from hybrid pigs generated by reciprocal crosses of phenotypically and physiologically divergent Berkshire and Tibetan pigs, we uncover extensive chromatin reorganization between homologous chromosomes across multiple scales. Haplotype-based interrogation of multi-omic data revealed the tissue dependence of 3D chromatin conformation, suggesting that parent-of-origin-specific conformation may drive gene imprinting. We quantify the effects of genetic variations and histone modifications on allelic differences of long-range promoter-enhancer contacts, which likely contribute to the phenotypic differences between the parental pig breeds. We also observe the fine structure of somatically paired homologous chromosomes in the pig genome, which has a functional implication genome-wide. This work illustrates how allele-specific chromatin architecture facilitates concomitant shifts in allele-biased gene expression, as well as the possible consequential phenotypic changes in mammals.
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Affiliation(s)
- Yu Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Jing Li
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China;
| | - Yiren Gu
- College of Animal and Veterinary Sciences, Southwest Minzu University, Chengdu 610041, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China
| | - Long Jin
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Jingyi Bai
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Jiaman Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Yujie Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Pengliang Liu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Keren Long
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Mengnan He
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Diyan Li
- School of Pharmacy, Chengdu University, Chengdu 610106, China
| | - Can Liu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Ziyin Han
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Yu Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Xiaokai Li
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Bo Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Lu Lu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Fanli Kong
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Ying Sun
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
- Institute of Geriatric Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China
| | - Yongliang Fan
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Xun Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Tao Wang
- School of Pharmacy, Chengdu University, Chengdu 610106, China
| | - An'an Jiang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Jideng Ma
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Linyuan Shen
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Li Zhu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Yanzhi Jiang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Guoqing Tang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Xiaolan Fan
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Qingyou Liu
- Animal Molecular Design and Precise Breeding Key Laboratory of Guangdong Province, School of Life Science and Engineering, Foshan University, Foshan 528225, China
| | - Hua Li
- Animal Molecular Design and Precise Breeding Key Laboratory of Guangdong Province, School of Life Science and Engineering, Foshan University, Foshan 528225, China
| | - Jinyong Wang
- Pig Industry Sciences Key Laboratory of Ministry of Agriculture and Rural Affairs, Chongqing Academy of Animal Sciences, Chongqing 402460, China
- National Center of Technology Innovation for Pigs, Chongqing 402460, China
| | - Li Chen
- Pig Industry Sciences Key Laboratory of Ministry of Agriculture and Rural Affairs, Chongqing Academy of Animal Sciences, Chongqing 402460, China
- National Center of Technology Innovation for Pigs, Chongqing 402460, China
| | - Liangpeng Ge
- Pig Industry Sciences Key Laboratory of Ministry of Agriculture and Rural Affairs, Chongqing Academy of Animal Sciences, Chongqing 402460, China
- National Center of Technology Innovation for Pigs, Chongqing 402460, China
| | - Xuewei Li
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Qianzi Tang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China;
| | - Mingzhou Li
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China;
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Felício D, Alves-Ferreira M, Santos M, Quintas M, Lopes AM, Lemos C, Pinto N, Martins S. Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease. Brief Funct Genomics 2024; 23:138-149. [PMID: 37254524 DOI: 10.1093/bfgp/elad020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/13/2023] [Accepted: 05/09/2023] [Indexed: 06/01/2023] Open
Abstract
Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3' UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.
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Affiliation(s)
- Daniela Felício
- Instituto de Investigação e Inovação em Saúde (i3S), Porto 4200-135, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto 4200-135, Portugal
- Instituto Ciências Biomédicas Abel Salazar (ICBAS), Universidade do Porto, Porto 4050-313, Portugal
| | - Miguel Alves-Ferreira
- Instituto de Investigação e Inovação em Saúde (i3S), Porto 4200-135, Portugal
- Instituto Ciências Biomédicas Abel Salazar (ICBAS), Universidade do Porto, Porto 4050-313, Portugal
- Unit for Genetic and Epidemiological Research in Neurological Diseases (UnIGENe), Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, Porto 4200-135, Portugal
- Centre for Predictive and Preventive Genetics (CGPP), Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, Porto 4200-135, Portugal
| | - Mariana Santos
- Instituto de Investigação e Inovação em Saúde (i3S), Porto 4200-135, Portugal
- Unit for Genetic and Epidemiological Research in Neurological Diseases (UnIGENe), Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, Porto 4200-135, Portugal
| | - Marlene Quintas
- Instituto de Investigação e Inovação em Saúde (i3S), Porto 4200-135, Portugal
- Instituto Ciências Biomédicas Abel Salazar (ICBAS), Universidade do Porto, Porto 4050-313, Portugal
- Unit for Genetic and Epidemiological Research in Neurological Diseases (UnIGENe), Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, Porto 4200-135, Portugal
| | - Alexandra M Lopes
- Instituto de Investigação e Inovação em Saúde (i3S), Porto 4200-135, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto 4200-135, Portugal
- Centre for Predictive and Preventive Genetics (CGPP), Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, Porto 4200-135, Portugal
| | - Carolina Lemos
- Instituto de Investigação e Inovação em Saúde (i3S), Porto 4200-135, Portugal
- Instituto Ciências Biomédicas Abel Salazar (ICBAS), Universidade do Porto, Porto 4050-313, Portugal
- Unit for Genetic and Epidemiological Research in Neurological Diseases (UnIGENe), Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, Porto 4200-135, Portugal
| | - Nádia Pinto
- Instituto de Investigação e Inovação em Saúde (i3S), Porto 4200-135, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto 4200-135, Portugal
- Centro de Matemática da Universidade do Porto (CMUP), Porto 4169-007, Portugal
| | - Sandra Martins
- Instituto de Investigação e Inovação em Saúde (i3S), Porto 4200-135, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto 4200-135, Portugal
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48
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Melton HJ, Zhang Z, Wu C. SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations. Hum Mol Genet 2024; 33:624-635. [PMID: 38129112 PMCID: PMC10954367 DOI: 10.1093/hmg/ddad205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/24/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.
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Affiliation(s)
- Hunter J Melton
- Department of Statistics, Florida State University, 214 Rogers Building, 117 N. Woodward Avenue, Tallahassee, FL 32306, United States
| | - Zichen Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Unit 1689, Houston, TX 77030, United States
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Unit 1689, Houston, TX 77030, United States
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49
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Ridnik M, Abberbock E, Alipov V, Lhermann SZ, Kaufman S, Lubman M, Poulat F, Gonen N. Two redundant transcription factor binding sites in a single enhancer are essential for mammalian sex determination. Nucleic Acids Res 2024:gkae178. [PMID: 38499491 DOI: 10.1093/nar/gkae178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/25/2024] [Accepted: 02/29/2024] [Indexed: 03/20/2024] Open
Abstract
Male development in mammals depends on the activity of the two SOX gene: Sry and Sox9, in the embryonic testis. As deletion of Enhancer 13 (Enh13) of the Sox9 gene results in XY male-to-female sex reversal, we explored the critical elements necessary for its function and hence, for testis and male development. Here, we demonstrate that while microdeletions of individual transcription factor binding sites (TFBS) in Enh13 lead to normal testicular development, combined microdeletions of just two SRY/SOX binding motifs can alone fully abolish Enh13 activity leading to XY male-to-female sex reversal. This suggests that for proper male development to occur, these few nucleotides of non-coding DNA must be intact. Interestingly, we show that depending on the nature of these TFBS mutations, dramatically different phenotypic outcomes can occur, providing a molecular explanation for the distinct clinical outcomes observed in patients harboring different variants in the same enhancer.
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Affiliation(s)
- Meshi Ridnik
- The Mina and Everard Goodman Faculty of Life Sciences and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Elisheva Abberbock
- The Mina and Everard Goodman Faculty of Life Sciences and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Veronica Alipov
- The Mina and Everard Goodman Faculty of Life Sciences and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Shelly Ziv Lhermann
- The Mina and Everard Goodman Faculty of Life Sciences and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Shoham Kaufman
- The Mina and Everard Goodman Faculty of Life Sciences and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Maor Lubman
- The Mina and Everard Goodman Faculty of Life Sciences and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Francis Poulat
- Group "Development and Pathology of the Gonad". Department of Genetics, Cell Biology and Development, Institute of Human Genetics, CNRS-University of Montpellier UMR9002, Montpellier, France
| | - Nitzan Gonen
- The Mina and Everard Goodman Faculty of Life Sciences and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
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50
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Zhang T, Ambrodji A, Huang H, Bouchonville KJ, Etheridge AS, Schmidt RE, Bembenek BM, Temesgen ZB, Wang Z, Innocenti F, Stroka D, Diasio RB, Largiadèr CR, Offer SM. Germline cis variant determines epigenetic regulation of the anti-cancer drug metabolism gene dihydropyrimidine dehydrogenase ( DPYD). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565230. [PMID: 37961517 PMCID: PMC10635067 DOI: 10.1101/2023.11.01.565230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Enhancers are critical for regulating tissue-specific gene expression, and genetic variants within enhancer regions have been suggested to contribute to various cancer-related processes, including therapeutic resistance. However, the precise mechanisms remain elusive. Using a well-defined drug-gene pair, we identified an enhancer region for dihydropyrimidine dehydrogenase (DPD, DPYD gene) expression that is relevant to the metabolism of the anti-cancer drug 5-fluorouracil (5-FU). Using reporter systems, CRISPR genome edited cell models, and human liver specimens, we demonstrated in vitro and vivo that genotype status for the common germline variant (rs4294451; 27% global minor allele frequency) located within this novel enhancer controls DPYD transcription and alters resistance to 5-FU. The variant genotype increases recruitment of the transcription factor CEBPB to the enhancer and alters the level of direct interactions between the enhancer and DPYD promoter. Our data provide insight into the regulatory mechanisms controlling sensitivity and resistance to 5-FU.
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Affiliation(s)
- Ting Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Alisa Ambrodji
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Freiestrasse 1, CH-3010 Bern, Switzerland
| | - Huixing Huang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Kelly J. Bouchonville
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Amy S. Etheridge
- Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Remington E. Schmidt
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Brianna M. Bembenek
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Zoey B. Temesgen
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Zhiquan Wang
- Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN 55905 USA
| | - Federico Innocenti
- Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Deborah Stroka
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Robert B. Diasio
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Carlo R. Largiadèr
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
| | - Steven M. Offer
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
- Department of Pathology, University of Iowa Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
- Holden Comprehensive Cancer Center, University of Iowa Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
- Lead contact
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