1
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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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2
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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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3
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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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4
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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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5
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Abnizova I, Stapel C, Boekhorst RT, Lee JTH, Hemberg M. Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation. BMC Biol 2024; 22:78. [PMID: 38600550 PMCID: PMC11005181 DOI: 10.1186/s12915-024-01869-2] [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: 08/23/2023] [Accepted: 03/14/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging. RESULTS Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters. CONCLUSION Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.
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Affiliation(s)
- Irina Abnizova
- Epigenetics Programme, Babraham Institute, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Carine Stapel
- Epigenetics Programme, Babraham Institute, Cambridge, UK
| | | | | | - Martin Hemberg
- Wellcome Sanger Institute, Hinxton, UK.
- The Gene Lay Institute of Immunology and Inflammation Brigham & Women's Hospital and Harvard Medical School, Boston, USA.
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6
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Lin S, Wu S, Zhao W, Fang Z, Kang H, Liu X, Pan S, Yu F, Bao Y, Jia P. TargetGene: a comprehensive database of cell-type-specific target genes for genetic variants. Nucleic Acids Res 2024; 52:D1072-D1081. [PMID: 37870478 PMCID: PMC10767789 DOI: 10.1093/nar/gkad901] [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: 08/15/2023] [Revised: 09/27/2023] [Accepted: 10/09/2023] [Indexed: 10/24/2023] Open
Abstract
Annotating genetic variants to their target genes is of great importance in unraveling the causal variants and genetic mechanisms that underlie complex diseases. However, disease-associated genetic variants are often located in non-coding regions and manifest context-specific effects, making it challenging to accurately identify the target genes and regulatory mechanisms. Here, we present TargetGene (https://ngdc.cncb.ac.cn/targetgene/), a comprehensive database reporting target genes for human genetic variants from various aspects. Specifically, we collected a comprehensive catalog of multi-omics data at the single-cell and bulk levels and from various human tissues, cell types and developmental stages. To facilitate the identification of Single Nucleotide Polymorphism (SNP)-to-gene connections, we have implemented multiple analytical tools based on chromatin co-accessibility, 3D interaction, enhancer activities and quantitative trait loci, among others. We applied the pipeline to evaluate variants from nearly 1300 Genome-wide association studies (GWAS) and assembled a comprehensive atlas of multiscale regulation of genetic variants. TargetGene is equipped with user-friendly web interfaces that enable intuitive searching, navigation and browsing through the results. Overall, TargetGene provides a unique resource to empower researchers to study the regulatory mechanisms of genetic variants in complex human traits.
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Affiliation(s)
- Shiqi Lin
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Song Wu
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Wei Zhao
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhanjie Fang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongen Kang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinxuan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fudong Yu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Yiming Bao
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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7
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Shi W, Ye J, Shi Z, Pan C, Zhang Q, Lin Y, Liang D, Liu Y, Lin X, Zheng Y. Single-cell chromatin accessibility and transcriptomic characterization of Behcet's disease. Commun Biol 2023; 6:1048. [PMID: 37848613 PMCID: PMC10582193 DOI: 10.1038/s42003-023-05420-x] [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: 04/06/2023] [Accepted: 10/04/2023] [Indexed: 10/19/2023] Open
Abstract
Behect's disease is a chronic vasculitis characterized by complex multi-organ immune aberrations. However, a comprehensive understanding of the gene-regulatory profile of peripheral autoimmunity and the diverse immune responses across distinct cell types in Behcet's disease (BD) is still lacking. Here, we present a multi-omic single-cell study of 424,817 cells in BD patients and non-BD individuals. This study maps chromatin accessibility and gene expression in the same biological samples, unraveling vast cellular heterogeneity. We identify widespread cell-type-specific, disease-associated active and pro-inflammatory immunity in both transcript and epigenomic aspects. Notably, integrative multi-omic analysis reveals putative TF regulators that might contribute to chromatin accessibility and gene expression in BD. Moreover, we predicted gene-regulatory networks within nominated TF activators, including AP-1, NF-kB, and ETS transcript factor families, which may regulate cellular interaction and govern inflammation. Our study illustrates the epigenetic and transcriptional landscape in BD peripheral blood and expands understanding of potential epigenomic immunopathology in this disease.
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Affiliation(s)
- Wen Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China
- Research Unit of Ocular Development and Regeneration, Chinese Academy of Medical Sciences, 100085, Beijing, China
| | - Jinguo Ye
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China
| | - Zhuoxing Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China
| | - Caineng Pan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China
| | - Qikai Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China
| | - Yuheng Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China
| | - Dan Liang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China.
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China.
- Research Unit of Ocular Development and Regeneration, Chinese Academy of Medical Sciences, 100085, Beijing, China.
| | - Xianchai Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China.
- Research Unit of Ocular Development and Regeneration, Chinese Academy of Medical Sciences, 100085, Beijing, China.
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China.
- Research Unit of Ocular Development and Regeneration, Chinese Academy of Medical Sciences, 100085, Beijing, China.
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8
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Li Y, Xu M, Xiang BL, Li X, Zhang DF, Zhao H, Bi R, Yao YG. Functional genomics identify causal variant underlying the protective CTSH locus for Alzheimer's disease. Neuropsychopharmacology 2023; 48:1555-1566. [PMID: 36739351 PMCID: PMC10516988 DOI: 10.1038/s41386-023-01542-2] [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: 10/07/2022] [Revised: 12/30/2022] [Accepted: 01/25/2023] [Indexed: 02/06/2023]
Abstract
Alzheimer's disease (AD) is the most prevalent age-related neurodegenerative disease, which has a high heritability of up to 79%. Exploring the genetic basis is essential for understanding the pathogenic mechanisms underlying AD development. Recent genome-wide association studies (GWASs) reported an AD-associated signal in the Cathepsin H (CTSH) gene in European populations. However, the exact functional/causal variant(s), and the genetic regulating mechanism of CTSH in AD remain to be determined. In this study, we carried out a comprehensive study to characterize the role of CTSH variants in the pathogenesis of AD. We identified rs2289702 in CTSH as the most significant functional variant that is associated with a protective effect against AD. The genetic association between rs2289702 and AD was validated in independent cohorts of the Han Chinese population. The CTSH mRNA expression level was significantly increased in AD patients and AD animal models, and the protective allele T of rs2289702 was associated with a decreased expression level of CTSH through the disruption of the binding affinity of transcription factors. Human microglia cells with CTSH knockout showed a significantly increased phagocytosis of Aβ peptides. Our study identified CTSH as being involved in AD genetic susceptibility and uncovered the genetic regulating mechanism of CTSH in pathogenesis of AD.
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Affiliation(s)
- Yu Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
| | - Min Xu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
| | - Bo-Lin Xiang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
| | - Xiao Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
| | - Deng-Feng Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, Yunnan, China
| | - Hui Zhao
- KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Yunnan, 650204, Kunming, China
- Key Laboratory for Regenerative Medicine, Ministry of Education, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Branch of CAS Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Rui Bi
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, Yunnan, China.
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, Yunnan, China.
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, Yunnan, China.
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, Yunnan, China.
- KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Yunnan, 650204, Kunming, China.
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
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9
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Wu Y, Qi T, Wray NR, Visscher PM, Zeng J, Yang J. Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes. CELL GENOMICS 2023; 3:100344. [PMID: 37601976 PMCID: PMC10435383 DOI: 10.1016/j.xgen.2023.100344] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 04/04/2023] [Accepted: 05/23/2023] [Indexed: 08/22/2023]
Abstract
Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements underpinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we introduce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n = 20,833-766,345) and xQTL data from seven omics layers (n = 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mechanisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular intermediates to GWAS signals.
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Affiliation(s)
- Yang Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Ting Qi
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, 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
| | - Peter M. Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
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10
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Jeong R, Bulyk ML. Blood cell traits' GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants. CELL GENOMICS 2023; 3:100327. [PMID: 37492098 PMCID: PMC10363807 DOI: 10.1016/j.xgen.2023.100327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/10/2023] [Accepted: 04/25/2023] [Indexed: 07/27/2023]
Abstract
Genome-wide association studies (GWASs) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretation difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell trait GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.
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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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11
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Kumasaka N, Rostom R, Huang N, Polanski K, Meyer KB, Patel S, Boyd R, Gomez C, Barnett SN, Panousis NI, Schwartzentruber J, Ghoussaini M, Lyons PA, Calero-Nieto FJ, Göttgens B, Barnes JL, Worlock KB, Yoshida M, Nikolić MZ, Stephenson E, Reynolds G, Haniffa M, Marioni JC, Stegle O, Hagai T, Teichmann SA. Mapping interindividual dynamics of innate immune response at single-cell resolution. Nat Genet 2023; 55:1066-1075. [PMID: 37308670 PMCID: PMC10260404 DOI: 10.1038/s41588-023-01421-y] [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: 08/28/2021] [Accepted: 04/27/2023] [Indexed: 06/14/2023]
Abstract
Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution.
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Affiliation(s)
- Natsuhiko Kumasaka
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Medical Support Center of Japan Environment and Children's Study (JECS), National Center for Child Health and Development, Tokyo, Japan
| | - Raghd Rostom
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Ni Huang
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | - Kerstin B Meyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Sharad Patel
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Rachel Boyd
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Celine Gomez
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Sam N Barnett
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | - Jeremy Schwartzentruber
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Maya Ghoussaini
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Paul A Lyons
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | | | - Berthold Göttgens
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Josephine L Barnes
- UCL Respiratory, Division of Medicine, University College London, London, UK
| | - Kaylee B Worlock
- UCL Respiratory, Division of Medicine, University College London, London, UK
| | - Masahiro Yoshida
- UCL Respiratory, Division of Medicine, University College London, London, UK
| | - Marko Z Nikolić
- UCL Respiratory, Division of Medicine, University College London, London, UK
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Emily Stephenson
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Gary Reynolds
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Muzlifah Haniffa
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- Department of Dermatology, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - John C Marioni
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Oliver Stegle
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Tzachi Hagai
- Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK.
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12
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Ziyani C, Delaneau O, Ribeiro DM. Multimodal single cell analysis infers widespread enhancer co-activity in a lymphoblastoid cell line. Commun Biol 2023; 6:563. [PMID: 37237005 DOI: 10.1038/s42003-023-04954-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
Non-coding regulatory elements such as enhancers are key in controlling the cell-type specificity and spatio-temporal expression of genes. To drive stable and precise gene transcription robust to genetic variation and environmental stress, genes are often targeted by multiple enhancers with redundant action. However, it is unknown whether enhancers targeting the same gene display simultaneous activity or whether some enhancer combinations are more often co-active than others. Here, we take advantage of recent developments in single cell technology that permit assessing chromatin status (scATAC-seq) and gene expression (scRNA-seq) in the same single cells to correlate gene expression to the activity of multiple enhancers. Measuring activity patterns across 24,844 human lymphoblastoid single cells, we find that the majority of enhancers associated with the same gene display significant correlation in their chromatin profiles. For 6944 expressed genes associated with enhancers, we predict 89,885 significant enhancer-enhancer associations between nearby enhancers. We find that associated enhancers share similar transcription factor binding profiles and that gene essentiality is linked with higher enhancer co-activity. We provide a set of predicted enhancer-enhancer associations based on correlation derived from a single cell line, which can be further investigated for functional relevance.
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Affiliation(s)
- Chaymae Ziyani
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Olivier Delaneau
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Diogo M Ribeiro
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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13
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Kerimov N, Tambets R, Hayhurst JD, Rahu I, Kolberg P, Raudvere U, Kuzmin I, Chowdhary A, Vija A, Teras HJ, Kanai M, Ulirsch J, Ryten M, Hardy J, Guelfi S, Trabzuni D, Kim-Hellmuth S, Rayner W, Finucane H, Peterson H, Mosaku A, Parkinson H, Alasoo K. Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.06.535816. [PMID: 37066341 PMCID: PMC10104061 DOI: 10.1101/2023.04.06.535816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Splicing quantitative trait loci (QTLs) have been implicated as a common mechanism underlying complex trait associations. However, utilising splicing QTLs in target discovery and prioritisation has been challenging due to extensive data normalisation which often renders the direction of the genetic effect as well as its magnitude difficult to interpret. This is further complicated by the fact that strong expression QTLs often manifest as weak splicing QTLs and vice versa, making it difficult to uniquely identify the underlying molecular mechanism at each locus. We find that these ambiguities can be mitigated by visualising the association between the genotype and average RNA sequencing read coverage in the region. Here, we generate these QTL coverage plots for 1.7 million molecular QTL associations in the eQTL Catalogue identified with five quantification methods. We illustrate the utility of these QTL coverage plots by performing colocalisation between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. We find that while visually confirmed splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. All our association summary statistics and QTL coverage plots are freely available at https://www.ebi.ac.uk/eqtl/.
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Affiliation(s)
- Nurlan Kerimov
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ralf Tambets
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - James D Hayhurst
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ida Rahu
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Peep Kolberg
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Uku Raudvere
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Ivan Kuzmin
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Anshika Chowdhary
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Andreas Vija
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Hans J Teras
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jacob Ulirsch
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mina Ryten
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London
| | - John Hardy
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London
| | - Sebastian Guelfi
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London
| | - Daniah Trabzuni
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London
| | - Sarah Kim-Hellmuth
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany
| | - Will Rayner
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Hilary Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hedi Peterson
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Abayomi Mosaku
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Helen Parkinson
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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14
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Jeong R, Bulyk ML. Colocalization of blood cell traits GWAS associations and variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.29.534582. [PMID: 37034747 PMCID: PMC10081269 DOI: 10.1101/2023.03.29.534582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Genome-wide association studies (GWAS) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretations difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell traits GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.
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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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15
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van Mierlo G, Pushkarev O, Kribelbauer JF, Deplancke B. Chromatin modules and their implication in genomic organization and gene regulation. Trends Genet 2023; 39:140-153. [PMID: 36549923 DOI: 10.1016/j.tig.2022.11.003] [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: 06/08/2022] [Revised: 11/04/2022] [Accepted: 11/27/2022] [Indexed: 12/24/2022]
Abstract
Regulation of gene expression is a complex but highly guided process. While genomic technologies and computational approaches have allowed high-throughput mapping of cis-regulatory elements (CREs) and their interactions in 3D, their precise role in regulating gene expression remains obscure. Recent complementary observations revealed that interactions between CREs frequently result in the formation of small-scale functional modules within topologically associating domains. Such chromatin modules likely emerge from a complex interplay between regulatory machineries assembled at CREs, including site-specific binding of transcription factors. Here, we review the methods that allow identifying chromatin modules, summarize possible mechanisms that steer CRE interactions within these modules, and discuss outstanding challenges to uncover how chromatin modules fit in our current understanding of the functional 3D genome.
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Affiliation(s)
- Guido van Mierlo
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Olga Pushkarev
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Judith F Kribelbauer
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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16
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Okamoto J, Wang L, Yin X, Luca F, Pique-Regi R, Helms A, Im HK, Morrison J, Wen X. Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits. Am J Hum Genet 2023; 110:44-57. [PMID: 36608684 PMCID: PMC9892769 DOI: 10.1016/j.ajhg.2022.12.002] [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: 09/01/2022] [Accepted: 12/06/2022] [Indexed: 01/07/2023] Open
Abstract
Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.
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Affiliation(s)
- Jeffrey Okamoto
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Lijia Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Adam Helms
- University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Jean Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
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17
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Koido M, Hon CC, Koyama S, Kawaji H, Murakawa Y, Ishigaki K, Ito K, Sese J, Parrish NF, Kamatani Y, Carninci P, Terao C. Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning. Nat Biomed Eng 2022:10.1038/s41551-022-00961-8. [PMID: 36411359 DOI: 10.1038/s41551-022-00961-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 10/12/2022] [Indexed: 11/22/2022]
Abstract
Gene transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). As the transcription of ncRNAs, especially of enhancer RNAs, is often low and cell type specific, how the levels of RNA transcription depend on genotype remains largely unexplored. Here we report the development and utility of a machine-learning model (MENTR) that reliably links genome sequence and ncRNA expression at the cell type level. Effects on ncRNA transcription predicted by the model were concordant with estimates from published studies in a cell-type-dependent manner, regardless of allele frequency and genetic linkage. Among 41,223 variants from genome-wide association studies, the model identified 7,775 enhancer RNAs and 3,548 long ncRNAs causally associated with complex traits across 348 major human primary cells and tissues, such as rare variants plausibly altering the transcription of enhancer RNAs to influence the risks of Crohn's disease and asthma. The model may aid the discovery of causal variants and the generation of testable hypotheses for biological mechanisms driving complex traits.
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Affiliation(s)
- Masaru Koido
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Division of Molecular Pathology, Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.,Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Chung-Chau Hon
- Laboratory for Genome Information Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Satoshi Koyama
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Hideya Kawaji
- Preventive Medicine and Applied Genomics Unit, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Research Center for Genome & Medical Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Yasuhiro Murakawa
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy.,Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Jun Sese
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Aomi, Koto-ku, Tokyo, Japan.,Humanome Lab Inc., Tokyo, Japan
| | - Nicholas F Parrish
- Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Cluster for Pioneering Research and RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Piero Carninci
- Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory for Single Cell Technologies, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Human Technopole, Milan, Italy
| | - 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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18
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Ito S, Das ND, Umehara T, Koseki H. Factors and Mechanisms That Influence Chromatin-Mediated Enhancer–Promoter Interactions and Transcriptional Regulation. Cancers (Basel) 2022; 14:cancers14215404. [PMID: 36358822 PMCID: PMC9659172 DOI: 10.3390/cancers14215404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/28/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Abstract
Simple Summary The physical interactions between enhancers and promoters create chromatin conformations involved in gene regulation. In cancer cells, the chromatin conformations can be altered with uncontrolled deposition of histone marks resulting in varied gene expression. Although it is not entirely comprehensive how chromatin-mediated enhancer–promoter (E–P) interactions with various histone marks can affect gene expression, this proximity has been observed in multiple systems at multiple loci and is thought to be essential to control gene expression. In this review, we focus on emerging views of chromatin conformations associated with the E–P interactions and factors that establish or maintain such interactions, which may regulate gene expression. Abstract Eukaryotic gene expression is regulated through chromatin conformation, in which enhancers and promoters physically interact (E–P interactions). How such chromatin-mediated E–P interactions affect gene expression is not yet fully understood, but the roles of histone acetylation and methylation, pioneer transcription factors, and architectural proteins such as CCCTC binding factor (CTCF) and cohesin have recently attracted attention. Moreover, accumulated data suggest that E–P interactions are mechanistically involved in biophysical events, including liquid–liquid phase separation, and in biological events, including cancers. In this review, we discuss various mechanisms that regulate eukaryotic gene expression, focusing on emerging views regarding chromatin conformations that are involved in E–P interactions and factors that establish and maintain them.
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Affiliation(s)
- Shinsuke Ito
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Correspondence: ; Tel.: +81-45-503-9690
| | - Nando Dulal Das
- Laboratory for Epigenetics Drug Discovery, RIKEN Center for Biosystems Dynamics Research, Yokohama 230-0045, Japan
| | - Takashi Umehara
- Laboratory for Epigenetics Drug Discovery, RIKEN Center for Biosystems Dynamics Research, Yokohama 230-0045, Japan
| | - Haruhiko Koseki
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Immune Regulation, Advanced Research Departments, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
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19
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Ishigaki K, Sakaue S, Terao C, Luo Y, Sonehara K, Yamaguchi K, Amariuta T, Too CL, Laufer VA, Scott IC, Viatte S, Takahashi M, Ohmura K, Murasawa A, Hashimoto M, Ito H, Hammoudeh M, Emadi SA, Masri BK, Halabi H, Badsha H, Uthman IW, Wu X, Lin L, Li T, Plant D, Barton A, Orozco G, Verstappen SMM, Bowes J, MacGregor AJ, Honda S, Koido M, Tomizuka K, Kamatani Y, Tanaka H, Tanaka E, Suzuki A, Maeda Y, Yamamoto K, Miyawaki S, Xie G, Zhang J, Amos CI, Keystone E, Wolbink G, van der Horst-Bruinsma I, Cui J, Liao KP, Carroll RJ, Lee HS, Bang SY, Siminovitch KA, de Vries N, Alfredsson L, Rantapää-Dahlqvist S, Karlson EW, Bae SC, Kimberly RP, Edberg JC, Mariette X, Huizinga T, Dieudé P, Schneider M, Kerick M, Denny JC, Matsuda K, Matsuo K, Mimori T, Matsuda F, Fujio K, Tanaka Y, Kumanogoh A, Traylor M, Lewis CM, Eyre S, Xu H, Saxena R, Arayssi T, Kochi Y, Ikari K, Harigai M, Gregersen PK, Yamamoto K, Louis Bridges S, Padyukov L, Martin J, Klareskog L, Okada Y, Raychaudhuri S. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nat Genet 2022; 54:1640-1651. [PMID: 36333501 PMCID: PMC10165422 DOI: 10.1038/s41588-022-01213-w] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 09/26/2022] [Indexed: 11/06/2022]
Abstract
Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.
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Affiliation(s)
- Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - 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
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
| | - Kensuke Yamaguchi
- Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tiffany Amariuta
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Chun Lai Too
- Immunogenetics Unit, Allergy and Immunology Research Center, Institute for Medical Research, National Institutes of Health Complex, Ministry of Health, Kuala Lumpur, Malaysia
- Department of Medicine, Division of Rheumatology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Vincent A Laufer
- Department of Clinical Immunology and Rheumatology, University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA
- Department of Pathology, Michigan Medicine, Ann Arbor, MI, USA
| | - Ian C Scott
- Haywood Academic Rheumatology Centre, Haywood Hospital, Midlands Partnership NHS Foundation Trust, Burslem, UK
- Primary Care Centre Versus Arthritis, School of Medicine, Keele University, Keele, UK
| | - Sebastien Viatte
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Meiko Takahashi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akira Murasawa
- Department of Rheumatology, Niigata Rheumatic Center, Niigata, Japan
| | - Motomu Hashimoto
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Clinical Immunology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hiromu Ito
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Orthopaedic Surgery, Kurashiki Central Hospital, Kurashiki, Japan
| | - Mohammed Hammoudeh
- Rheumatology Division, Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
| | - Samar Al Emadi
- Rheumatology Division, Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
| | - Basel K Masri
- Department of Internal Medicine, Jordan Hospital, Amman, Jordan
| | - Hussein Halabi
- Section of Rheumatology, Department of Internal Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia
| | - Humeira Badsha
- Dr. Humeira Badsha Medical Center, Emirates Hospital, Dubai, United Arab Emirates
| | - Imad W Uthman
- Department of Rheumatology, American University of Beirut, Beirut, Lebanon
| | - Xin Wu
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
| | - Li Lin
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
| | - Ting Li
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
| | - Darren Plant
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Anne Barton
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Suzanne M M Verstappen
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
| | - John Bowes
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | | | - Suguru Honda
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Department of Rheumatology, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Masaru Koido
- 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
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Hiroaki Tanaka
- The First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health Japan, Kitakyushu, Japan
| | - Eiichi Tanaka
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Department of Rheumatology, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yuichi Maeda
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Satoru Miyawaki
- Department of Neurosurgery, Faculty of Medicine, the University of Tokyo, Tokyo, Japan
| | - Gang Xie
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
| | - Jinyi Zhang
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Gertjan Wolbink
- Department of Rheumatology, Amsterdam Rheumatology and Immunology Center (ARC), Reade, Amsterdam, the Netherlands
| | - Irene van der Horst-Bruinsma
- Department of Rheumatology & Clinical Immunology/ARC, Amsterdam Institute for Infection and Immunity, Amsterdam UMC location Vrije Universiteit, Amsterdam, the Netherlands
| | - Jing Cui
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Katherine P Liao
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
- Hanyang University Institute for Rheumatology Research, Seoul, Korea
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
- Hanyang University Institute for Rheumatology Research, Seoul, Korea
| | - Katherine A Siminovitch
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Departments of Medicine and Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Niek de Vries
- Department of Rheumatology & Clinical Immunology/ARC, Amsterdam Institute for Infection and Immunity, Amsterdam UMC location AMC/University of Amsterdam, Amsterdam, the Netherlands
| | - Lars Alfredsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Elizabeth W Karlson
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
- Hanyang University Institute for Rheumatology Research, Seoul, Korea
| | - Robert P Kimberly
- Center for Clinical and Translational Science, Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jeffrey C Edberg
- Center for Clinical and Translational Science, Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Xavier Mariette
- Department of Rheumatology, Université Paris-Saclay, Assistance Pubique - Hôpitaux de Paris, Hôpital Bicêtre, INSERM UMR1184, Le Kremlin Bicêtre, France
| | - Tom Huizinga
- Leiden University Medical Center, Leiden, the Netherlands
| | - Philippe Dieudé
- University of Paris Cité, Inserm, PHERE, F-75018, Paris, France
- Department of Rheumatology, Hôpital Bichat, APHP, Paris, France
| | - Matthias Schneider
- Department of Rheumatology & Hiller Research Unit Rheumatology, UKD, Heinrich-Heine University, Düsseldorf, Germany
| | - Martin Kerick
- Institute of Parasitology and Biomedicine Lopez-Neyra, CSIC, Granada, Spain
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
- All of Us Research Program, Office of the Director, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Koichi Matsuda
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Department of Preventive Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan
- Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tsuneyo Mimori
- Department of Rheumatology and Clinical immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshiya Tanaka
- The First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health Japan, Kitakyushu, Japan
| | - Atsushi Kumanogoh
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Matthew Traylor
- Department of Medical & Molecular Genetics, King's College London, London, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
- Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Cathryn M Lewis
- Department of Medical & Molecular Genetics, King's College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Stephen Eyre
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Huji Xu
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
- School of Clinical Medicine Tsinghua University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Tsinghua University, Beijing, China
| | - Richa Saxena
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Thurayya Arayssi
- Department of Internal Medicine, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Yuta Kochi
- Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Katsunori Ikari
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Department of Orthopedic Surgery, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
- Division of Multidisciplinary Management of Rheumatic Diseases, Tokyo Women's Medical University, Tokyo, Japan
| | - Masayoshi Harigai
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Division of Rheumatology, Department of Internal Medicine, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Peter K Gregersen
- Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - S Louis Bridges
- Department of Medicine, Hospital for Special Surgery, New York, NY, USA
- Division of Rheumatology, Weill Cornell Medicine, New York, NY, USA
| | - Leonid Padyukov
- Department of Medicine, Division of Rheumatology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Javier Martin
- Institute of Parasitology and Biomedicine Lopez-Neyra, CSIC, Granada, Spain
| | - Lars Klareskog
- Department of Medicine, Division of Rheumatology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan.
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
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20
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Chou SP, Alexander AK, Rice EJ, Choate LA, Danko CG. Genetic dissection of the RNA polymerase II transcription cycle. eLife 2022; 11:78458. [PMID: 35775732 PMCID: PMC9286732 DOI: 10.7554/elife.78458] [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: 03/08/2022] [Accepted: 06/30/2022] [Indexed: 11/20/2022] Open
Abstract
How DNA sequence affects the dynamics and position of RNA Polymerase II (Pol II) during transcription remains poorly understood. Here, we used naturally occurring genetic variation in F1 hybrid mice to explore how DNA sequence differences affect the genome-wide distribution of Pol II. We measured the position and orientation of Pol II in eight organs collected from heterozygous F1 hybrid mice using ChRO-seq. Our data revealed a strong genetic basis for the precise coordinates of transcription initiation and promoter proximal pause, allowing us to redefine molecular models of core transcriptional processes. Our results implicate DNA sequence, including both known and novel DNA sequence motifs, as key determinants of the position of Pol II initiation and pause. We report evidence that initiation site selection follows a stochastic process similar to Brownian motion along the DNA template. We found widespread differences in the position of transcription termination, which impact the primary structure and stability of mature mRNA. Finally, we report evidence that allelic changes in transcription often affect mRNA and ncRNA expression across broad genomic domains. Collectively, we reveal how DNA sequences shape core transcriptional processes at single nucleotide resolution in mammals.
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Affiliation(s)
- Shao-Pei Chou
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
| | - Adriana K Alexander
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
| | - Edward J Rice
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
| | - Lauren A Choate
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
| | - Charles G Danko
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
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21
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Llimos G, Gardeux V, Koch U, Kribelbauer JF, Hafner A, Alpern D, Pezoldt J, Litovchenko M, Russeil J, Dainese R, Moia R, Mahmoud AM, Rossi D, Gaidano G, Plass C, Lutsik P, Gerhauser C, Waszak SM, Boettiger A, Radtke F, Deplancke B. A leukemia-protective germline variant mediates chromatin module formation via transcription factor nucleation. Nat Commun 2022; 13:2042. [PMID: 35440565 PMCID: PMC9018852 DOI: 10.1038/s41467-022-29625-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 03/24/2022] [Indexed: 12/13/2022] Open
Abstract
Non-coding variants coordinate transcription factor (TF) binding and chromatin mark enrichment changes over regions spanning >100 kb. These molecularly coordinated regions are named “variable chromatin modules” (VCMs), providing a conceptual framework of how regulatory variation might shape complex traits. To better understand the molecular mechanisms underlying VCM formation, here, we mechanistically dissect a VCM-modulating noncoding variant that is associated with reduced chronic lymphocytic leukemia (CLL) predisposition and disease progression. This common, germline variant constitutes a 5-bp indel that controls the activity of an AXIN2 gene-linked VCM by creating a MEF2 binding site, which, upon binding, activates a super-enhancer-like regulatory element. This triggers a large change in TF binding activity and chromatin state at an enhancer cluster spanning >150 kb, coinciding with subtle, long-range chromatin compaction and robust AXIN2 up-regulation. Our results support a model in which the indel acts as an AXIN2 VCM-activating TF nucleation event, which modulates CLL pathology. Non-coding variants can regulate transcription factor binding and gene expression at variable chromatin modules. Here, the authors show that a germline variant induces transcription factor nucleation through chromatin compaction leading to AXIN2 up-regulation and is associated to better prognosis in chronic lymphocytic leukaemia.
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Affiliation(s)
- Gerard Llimos
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Vincent Gardeux
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ute Koch
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Judith F Kribelbauer
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Antonina Hafner
- Department of Developmental Biology, Stanford University, Stanford, CA, USA
| | - Daniel Alpern
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joern Pezoldt
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Maria Litovchenko
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Cancer Research UK Lung Cancer Centre of Excellence, University College London (UCL) Cancer Institute, Cancer Genome Evolution Research Group, London, UK
| | - Julie Russeil
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Riccardo Dainese
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Riccardo Moia
- Division of Hematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy
| | - Abdurraouf Mokhtar Mahmoud
- Division of Hematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy
| | - Davide Rossi
- Oncology Institute of Southern Switzerland, Università della Svizzera italiana, Bellinzona, Switzerland.,Institute of Oncology Research, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Gianluca Gaidano
- Division of Hematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy
| | - Christoph Plass
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Pavlo Lutsik
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Clarissa Gerhauser
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian M Waszak
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo and Oslo University Hospital, Oslo, Norway.,Department of Pediatric Research, Division of Paediatric and Adolescent Medicine, Rikshospitalet, Oslo University Hospital, Oslo, Norway
| | - Alistair Boettiger
- Department of Developmental Biology, Stanford University, Stanford, CA, USA
| | - Freddy Radtke
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bart Deplancke
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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22
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Hait TA, Elkon R, Shamir R. CT-FOCS: a novel method for inferring cell type-specific enhancer–promoter maps. Nucleic Acids Res 2022; 50:e55. [PMID: 35100425 PMCID: PMC9178001 DOI: 10.1093/nar/gkac048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 01/09/2022] [Accepted: 01/15/2022] [Indexed: 11/13/2022] Open
Abstract
Spatiotemporal gene expression patterns are governed to a large extent by the activity of enhancer elements, which engage in physical contacts with their target genes. Identification of enhancer–promoter (EP) links that are functional only in a specific subset of cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS (cell type FOCS), a statistical inference method that uses linear mixed effect models to infer EP links that show marked activity only in a single or a small subset of cell types out of a large panel of probed cell types. Analyzing 808 samples from FANTOM5, covering 472 cell lines, primary cells and tissues, CT-FOCS inferred such EP links more accurately than recent state-of-the-art methods. Furthermore, we show that strictly cell type-specific EP links are very uncommon in the human genome.
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Affiliation(s)
- Tom Aharon Hait
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ron Shamir
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
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23
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Jiang L, Liang J, Huang W, Ma J, Park KH, Wu Z, Chen P, Zhu H, Ma JJ, Cai W, Paul C, Niu L, Fan GC, Wang HS, Kanisicak O, Xu M, Wang Y. CRISPR activation of endogenous genes reprograms fibroblasts into cardiovascular progenitor cells for myocardial infarction therapy. Mol Ther 2022; 30:54-74. [PMID: 34678511 PMCID: PMC8753567 DOI: 10.1016/j.ymthe.2021.10.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/27/2021] [Accepted: 10/18/2021] [Indexed: 01/07/2023] Open
Abstract
Fibroblasts can be reprogrammed into cardiovascular progenitor cells (CPCs) using transgenic approaches, although the underlying mechanism remains unclear. We determined whether activation of endogenous genes such as Gata4, Nkx2.5, and Tbx5 can rapidly establish autoregulatory loops and initiate CPC generation in adult extracardiac fibroblasts using a CRISPR activation system. The induced fibroblasts (>80%) showed phenotypic changes as indicated by an Nkx2.5 cardiac enhancer reporter. The progenitor characteristics were confirmed by colony formation and expression of cardiovascular genes. Cardiac sphere induction segregated the early and late reprogrammed cells that can generate functional cardiomyocytes and vascular cells in vitro. Therefore, they were termed CRISPR-induced CPCs (ciCPCs). Transcriptomic analysis showed that cell cycle and heart development pathways were important to accelerate CPC formation during the early reprogramming stage. The CRISPR system opened the silenced chromatin locus, thereby allowing transcriptional factors to access their own promoters and eventually forming a positive feedback loop. The regenerative potential of ciCPCs was assessed after implantation in mouse myocardial infarction models. The engrafted ciCPCs differentiated into cardiovascular cells in vivo but also significantly improved contractile function and scar formation. In conclusion, multiplex gene activation was sufficient to drive CPC reprogramming, providing a new cell source for regenerative therapeutics.
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Affiliation(s)
- Lin Jiang
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Jialiang Liang
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA.
| | - Wei Huang
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Jianyong Ma
- Department of Pharmacology and Systems Physiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Ki Ho Park
- Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Zhichao Wu
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Peng Chen
- Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Hua Zhu
- Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Jian-Jie Ma
- Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Wenfeng Cai
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Christian Paul
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Liang Niu
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Guo-Chang Fan
- Department of Pharmacology and Systems Physiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Hong-Sheng Wang
- Department of Pharmacology and Systems Physiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Onur Kanisicak
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Meifeng Xu
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Yigang Wang
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA.
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24
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Claussnitzer M, Susztak K. Gaining insight into metabolic diseases from human genetic discoveries. Trends Genet 2021; 37:1081-1094. [PMID: 34315631 PMCID: PMC8578350 DOI: 10.1016/j.tig.2021.07.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 06/29/2021] [Accepted: 07/05/2021] [Indexed: 12/30/2022]
Abstract
Human large-scale genetic association studies have identified sequence variations at thousands of genetic risk loci that are more common in patients with diverse metabolic disease compared with healthy controls. While these genetic associations have been replicated in multiple large cohorts and sometimes can explain up to 50% of heritability, the molecular and cellular mechanisms affected by common genetic variation associated with metabolic disease remains mostly unknown. A variety of new genome-wide data types, in conjunction with novel biostatistical and computational analytical methodologies and foundational experimental technologies, are paving the way for a principled approach to systematic variant-to-function (V2F) studies for metabolic diseases, turning associated regions into causal variants, cell types and states of action, effector genes, and cellular and physiological mechanisms. Identification of new target genes and cellular programs for metabolic risk loci will improve mechanistic understanding of disease biology and identification of novel therapeutic strategies.
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Affiliation(s)
- Melina Claussnitzer
- Beth Israel Deaconess Medical Center, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Katalin Susztak
- Department of Medicine and Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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25
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Shetty A, Seo JH, Bell CA, O’Connor EP, Pomerantz MM, Freedman ML, Gusev A. Allele-specific epigenetic activity in prostate cancer and normal prostate tissue implicates prostate cancer risk mechanisms. Am J Hum Genet 2021; 108:2071-2085. [PMID: 34699744 DOI: 10.1016/j.ajhg.2021.09.008] [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: 06/15/2021] [Accepted: 09/15/2021] [Indexed: 11/26/2022] Open
Abstract
Genome-wide association studies (GWASs) of prostate cancer have identified >250 significant risk loci, but the causal variants and mechanisms for these loci remain largely unknown. Here, we sought to identify and characterize risk-harboring regulatory elements by integrating epigenomes from primary prostate tumor and normal tissues of 27 individuals across the H3K27ac, H3K4me3, and H3K4me2 histone marks and FOXA1 and HOXB13 transcription factors. We identified 7,371 peaks with significant allele specificity (allele-specific quantitative trait locus [asQTL] peaks). Showcasing their relevance to prostate cancer risk, H3K27ac T-asQTL peaks were the single annotation most enriched for prostate cancer GWAS heritability (40×), significantly higher than corresponding non-asQTL H3K27ac peaks (14×) or coding regions (14×). Surprisingly, fine-mapped GWAS risk variants were most significantly enriched for asQTL peaks observed in tumors, including asQTL peaks that were differentially imbalanced with respect to tumor-normal states. These data pinpointed putative causal regulatory elements at 20 GWAS loci, of which 11 were detected only in the tumor samples. More broadly, tumor-specific asQTLs were enriched for expression QTLs in benign tissues as well as accessible regions found in stem cells, supporting a hypothesis where some germline variants become reactivated during or after transformation and can be captured by epigenomic profiling of the tumor. Our study demonstrates the power of allele specificity in chromatin signals to uncover GWAS mechanisms, highlights the relevance of tumor-specific regulation in the context of cancer risk, and prioritizes multiple loci for experimental follow-up.
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26
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Ishigaki K. Beyond GWAS: from simple associations to functional insights. Semin Immunopathol 2021; 44:3-14. [PMID: 34605948 DOI: 10.1007/s00281-021-00894-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/08/2021] [Indexed: 12/31/2022]
Abstract
Each human, when born, has slightly different DNA sequences, which make each of us unique. The variations in DNA sequences are called genetic variants. The primary aim of genome-wide association study (GWAS) is to detect associations between genetic variants and human phenotypes. Since GWAS focuses on germ-line variants, there is no reverse causation. Therefore, GWAS is one of the few tools that can assess the causality of human diseases. In the past 10 years, many large-scale GWAS have been conducted. Although the primary outputs of GWAS are just a series of statistics, its downstream analyses provided many insights beyond simple associations: the causal mechanisms for autoimmune diseases and shared etiology between diseases. Moreover, GWAS downstream analyses generated scores potentially helpful in predicting clinical outcomes of each patient. This review focuses on GWAS for autoimmune diseases and introduces significant achievements of its downstream analyses. We also provide future directions that potentially overcome current limitations. We restrict our discussion to common autoimmune diseases (e.g., rheumatoid arthritis) since rare Mendelian diseases possess distinct genetic etiologies and are not tested by GWAS.
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Affiliation(s)
- Kazuyoshi Ishigaki
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan.
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27
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Ray-Jones H, Spivakov M. Transcriptional enhancers and their communication with gene promoters. Cell Mol Life Sci 2021; 78:6453-6485. [PMID: 34414474 PMCID: PMC8558291 DOI: 10.1007/s00018-021-03903-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/08/2021] [Accepted: 07/19/2021] [Indexed: 12/13/2022]
Abstract
Transcriptional enhancers play a key role in the initiation and maintenance of gene expression programmes, particularly in metazoa. How these elements control their target genes in the right place and time is one of the most pertinent questions in functional genomics, with wide implications for most areas of biology. Here, we synthesise classic and recent evidence on the regulatory logic of enhancers, including the principles of enhancer organisation, factors that facilitate and delimit enhancer-promoter communication, and the joint effects of multiple enhancers. We show how modern approaches building on classic insights have begun to unravel the complexity of enhancer-promoter relationships, paving the way towards a quantitative understanding of gene control.
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Affiliation(s)
- Helen Ray-Jones
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, W12 0NN, UK
| | - Mikhail Spivakov
- MRC London Institute of Medical Sciences, London, W12 0NN, UK.
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, W12 0NN, UK.
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28
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Elmentaite R, Kumasaka N, Roberts K, Fleming A, Dann E, King HW, Kleshchevnikov V, Dabrowska M, Pritchard S, Bolt L, Vieira SF, Mamanova L, Huang N, Perrone F, Goh Kai'En I, Lisgo SN, Katan M, Leonard S, Oliver TRW, Hook CE, Nayak K, Campos LS, Domínguez Conde C, Stephenson E, Engelbert J, Botting RA, Polanski K, van Dongen S, Patel M, Morgan MD, Marioni JC, Bayraktar OA, Meyer KB, He X, Barker RA, Uhlig HH, Mahbubani KT, Saeb-Parsy K, Zilbauer M, Clatworthy MR, Haniffa M, James KR, Teichmann SA. Cells of the human intestinal tract mapped across space and time. Nature 2021; 597:250-255. [PMID: 34497389 PMCID: PMC8426186 DOI: 10.1038/s41586-021-03852-1] [Citation(s) in RCA: 232] [Impact Index Per Article: 77.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 07/26/2021] [Indexed: 12/12/2022]
Abstract
The cellular landscape of the human intestinal tract is dynamic throughout life, developing in utero and changing in response to functional requirements and environmental exposures. Here, to comprehensively map cell lineages, we use single-cell RNA sequencing and antigen receptor analysis of almost half a million cells from up to 5 anatomical regions in the developing and up to 11 distinct anatomical regions in the healthy paediatric and adult human gut. This reveals the existence of transcriptionally distinct BEST4 epithelial cells throughout the human intestinal tract. Furthermore, we implicate IgG sensing as a function of intestinal tuft cells. We describe neural cell populations in the developing enteric nervous system, and predict cell-type-specific expression of genes associated with Hirschsprung's disease. Finally, using a systems approach, we identify key cell players that drive the formation of secondary lymphoid tissue in early human development. We show that these programs are adopted in inflammatory bowel disease to recruit and retain immune cells at the site of inflammation. This catalogue of intestinal cells will provide new insights into cellular programs in development, homeostasis and disease.
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Affiliation(s)
- Rasa Elmentaite
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Kenny Roberts
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aaron Fleming
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Emma Dann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Hamish W King
- Centre for Immunobiology, Blizard Institute, Queen Mary University of London, London, UK
| | | | | | | | - Liam Bolt
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sara F Vieira
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Lira Mamanova
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Ni Huang
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Issac Goh Kai'En
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Steven N Lisgo
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Matilda Katan
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Steven Leonard
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Thomas R W Oliver
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - C Elizabeth Hook
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Komal Nayak
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Lia S Campos
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Emily Stephenson
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Justin Engelbert
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachel A Botting
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | | | - Minal Patel
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Michael D Morgan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - John C Marioni
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Kerstin B Meyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Xiaoling He
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences and 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 and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Holm H Uhlig
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Department of Paediatrics, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Krishnaa T Mahbubani
- Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Kourosh Saeb-Parsy
- Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Matthias Zilbauer
- Department of Paediatrics, University of Cambridge, Cambridge, UK
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Cambridge University Hospitals Trust, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, Anne McLaren Laboratory, University of Cambridge, Cambridge, UK
| | - Menna R Clatworthy
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Muzlifah Haniffa
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Kylie R James
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
- Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia.
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
- Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK.
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29
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Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, Carnero-Montoro E, Lawson DJ, Burrows K, Suderman M, Bretherick AD, Richardson TG, Klughammer J, Iotchkova V, Sharp G, Al Khleifat A, Shatunov A, Iacoangeli A, McArdle WL, Ho KM, Kumar A, Söderhäll C, Soriano-Tárraga C, Giralt-Steinhauer E, Kazmi N, Mason D, McRae AF, Corcoran DL, Sugden K, Kasela S, Cardona A, Day FR, Cugliari G, Viberti C, Guarrera S, Lerro M, Gupta R, Bollepalli S, Mandaviya P, Zeng Y, Clarke TK, Walker RM, Schmoll V, Czamara D, Ruiz-Arenas C, Rezwan FI, Marioni RE, Lin T, Awaloff Y, Germain M, Aïssi D, Zwamborn R, van Eijk K, Dekker A, van Dongen J, Hottenga JJ, Willemsen G, Xu CJ, Barturen G, Català-Moll F, Kerick M, Wang C, Melton P, Elliott HR, Shin J, Bernard M, Yet I, Smart M, Gorrie-Stone T, Shaw C, Al Chalabi A, Ring SM, Pershagen G, Melén E, Jiménez-Conde J, Roquer J, Lawlor DA, Wright J, Martin NG, Montgomery GW, Moffitt TE, Poulton R, Esko T, Milani L, Metspalu A, Perry JRB, Ong KK, Wareham NJ, Matullo G, Sacerdote C, Panico S, Caspi A, Arseneault L, Gagnon F, Ollikainen M, Kaprio J, Felix JF, Rivadeneira F, Tiemeier H, van IJzendoorn MH, Uitterlinden AG, Jaddoe VWV, Haley C, McIntosh AM, Evans KL, Murray A, Räikkönen K, Lahti J, Nohr EA, Sørensen TIA, Hansen T, Morgen CS, Binder EB, Lucae S, Gonzalez JR, Bustamante M, Sunyer J, Holloway JW, Karmaus W, Zhang H, Deary IJ, Wray NR, Starr JM, Beekman M, van Heemst D, Slagboom PE, Morange PE, Trégouët DA, Veldink JH, Davies GE, de Geus EJC, Boomsma DI, Vonk JM, Brunekreef B, Koppelman GH, Alarcón-Riquelme ME, Huang RC, Pennell CE, van Meurs J, Ikram MA, Hughes AD, Tillin T, Chaturvedi N, Pausova Z, Paus T, Spector TD, Kumari M, Schalkwyk LC, Visscher PM, Davey Smith G, Bock C, Gaunt TR, Bell JT, Heijmans BT, Mill J, Relton CL. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet 2021; 53:1311-1321. [PMID: 34493871 PMCID: PMC7612069 DOI: 10.1038/s41588-021-00923-x] [Citation(s) in RCA: 179] [Impact Index Per Article: 59.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 07/12/2021] [Indexed: 12/25/2022]
Abstract
Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.
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Affiliation(s)
- Josine L Min
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eilis Hannon
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Koen F Dekkers
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | | | - René Luijk
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Elena Carnero-Montoro
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
| | - Daniel J Lawson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Andrew D Bretherick
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Johanna Klughammer
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | | | - Gemma Sharp
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Aleksey Shatunov
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Alfredo Iacoangeli
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Biostatistics and Health Informatics, King's College London, London, UK
| | - Wendy L McArdle
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Karen M Ho
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ashish Kumar
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Chronic Disease Epidemiology unit, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Cilla Söderhäll
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Carolina Soriano-Tárraga
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Eva Giralt-Steinhauer
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Nabila Kazmi
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Karen Sugden
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Silva Kasela
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Alexia Cardona
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Felix R Day
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giovanni Cugliari
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Clara Viberti
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Simonetta Guarrera
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Michael Lerro
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Richa Gupta
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sailalitha Bollepalli
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Pooja Mandaviya
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Yanni Zeng
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Toni-Kim Clarke
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Vanessa Schmoll
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Carlos Ruiz-Arenas
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Faisal I Rezwan
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tian Lin
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Yvonne Awaloff
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Marine Germain
- INSERM UMR_S 1219, Bordeaux Population Health Center, University of Bordeaux, Bordeaux, France
| | - Dylan Aïssi
- Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
| | - Ramona Zwamborn
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Kristel van Eijk
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Annelot Dekker
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Cheng-Jian Xu
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC Research Institute Groningen, Groningen, the Netherlands
- CiiM and TWINCORE, Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany
| | - Guillermo Barturen
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
| | - Francesc Català-Moll
- Chromatin and Disease Group, Cancer Epigenetics and Biology Programme, Bellvitge Biomedical Research Institute, Barcelona, Spain
| | - Martin Kerick
- Instituto de Parasitología y Biomedicina López Neyra, CSIC, Granada, Spain
| | - Carol Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Phillip Melton
- Menzies Institute for Medical Research, College of Health and Medicine, University of Tasmania, Hobart, Australia
- School of Global Population Health, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Hannah R Elliott
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jean Shin
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Manon Bernard
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Idil Yet
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Bioinformatics, Institute of Health Sciences, Hacettepe University, Ankara, Turkey
| | - Melissa Smart
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | | | - Chris Shaw
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Neurology, King's College Hospital, London, UK
| | - Ammar Al Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Neurology, King's College Hospital, London, UK
- United Kingdom Dementia Research Institute, King's College London, London, UK
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Göran Pershagen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Jordi Jiménez-Conde
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Jaume Roquer
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford, UK
| | | | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Terrie E Moffitt
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University Medical School, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - John R B Perry
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giuseppe Matullo
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Carlotta Sacerdote
- Italian Institute for Genomic Medicine, Turin, Italy
- Piemonte Centre for Cancer Prevention, Turin, Italy
| | - Salvatore Panico
- Dipartimento Di Medicina Clinica E Chirurgia, Federico II University, Naples, Italy
| | - Avshalom Caspi
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University Medical School, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Louise Arseneault
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - France Gagnon
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Miina Ollikainen
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Marinus H van IJzendoorn
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Department of Clinical, Educational and Health Psychology, Division on Psychology and Language Sciences, Faculty of Brain Sciences, University College London, London, UK
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Chris Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Alison Murray
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ellen A Nohr
- Research Unit for Gynaecology and Obstetrics, Institute of Clinical research, University of Southern Denmark, Odense, Denmark
- Centre of Women's, Family and Child Health, University of South-Eastern Norway, Kongsberg, Norway
| | - Thorkild I A Sørensen
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health (Section of Epidemiology), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Camilla S Morgen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Susanne Lucae
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Juan Ramon Gonzalez
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
- Center for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jordi Sunyer
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
- Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Wilfried Karmaus
- Division of Epidemiology, Biostatistics, and Environmental Health Sciences, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Hongmei Zhang
- Division of Epidemiology, Biostatistics, and Environmental Health Sciences, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Diana van Heemst
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | | | | | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Eco J C de Geus
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Judith M Vonk
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, GRIAC Research Institute Groningen, Groningen, the Netherlands
| | - Bert Brunekreef
- Institute for Risk Assessment Sciences, Universiteit Utrecht, Utrecht, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerard H Koppelman
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC Research Institute Groningen, Groningen, the Netherlands
| | - Marta E Alarcón-Riquelme
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Rae-Chi Huang
- Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | | | | | | - Zdenka Pausova
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Tomas Paus
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, Canada
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Jonathan Mill
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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Janowski M, Milewska M, Zare P, Pękowska A. Chromatin Alterations in Neurological Disorders and Strategies of (Epi)Genome Rescue. Pharmaceuticals (Basel) 2021; 14:765. [PMID: 34451862 PMCID: PMC8399958 DOI: 10.3390/ph14080765] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/23/2021] [Accepted: 07/24/2021] [Indexed: 12/26/2022] Open
Abstract
Neurological disorders (NDs) comprise a heterogeneous group of conditions that affect the function of the nervous system. Often incurable, NDs have profound and detrimental consequences on the affected individuals' lives. NDs have complex etiologies but commonly feature altered gene expression and dysfunctions of the essential chromatin-modifying factors. Hence, compounds that target DNA and histone modification pathways, the so-called epidrugs, constitute promising tools to treat NDs. Yet, targeting the entire epigenome might reveal insufficient to modify a chosen gene expression or even unnecessary and detrimental to the patients' health. New technologies hold a promise to expand the clinical toolkit in the fight against NDs. (Epi)genome engineering using designer nucleases, including CRISPR-Cas9 and TALENs, can potentially help restore the correct gene expression patterns by targeting a defined gene or pathway, both genetically and epigenetically, with minimal off-target activity. Here, we review the implication of epigenetic machinery in NDs. We outline syndromes caused by mutations in chromatin-modifying enzymes and discuss the functional consequences of mutations in regulatory DNA in NDs. We review the approaches that allow modifying the (epi)genome, including tools based on TALENs and CRISPR-Cas9 technologies, and we highlight how these new strategies could potentially change clinical practices in the treatment of NDs.
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Affiliation(s)
| | | | | | - Aleksandra Pękowska
- Dioscuri Centre for Chromatin Biology and Epigenomics, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteur Street, 02-093 Warsaw, Poland; (M.J.); (M.M.); (P.Z.)
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31
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Baglaenko Y, Macfarlane D, Marson A, Nigrovic PA, Raychaudhuri S. Genome editing to define the function of risk loci and variants in rheumatic disease. Nat Rev Rheumatol 2021; 17:462-474. [PMID: 34188205 PMCID: PMC10782829 DOI: 10.1038/s41584-021-00637-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2021] [Indexed: 02/06/2023]
Abstract
Discoveries in human genetic studies have revolutionized our understanding of complex rheumatic and autoimmune diseases, including the identification of hundreds of genetic loci and single nucleotide polymorphisms that potentially predispose individuals to disease. However, in most cases, the exact disease-causing variants and their mechanisms of action remain unresolved. Functional follow-up of these findings is most challenging for genomic variants that are in non-coding genomic regions, where the large majority of common disease-associated variants are located, and/or that probably affect disease progression via cell type-specific gene regulation. To deliver on the therapeutic promise of human genetic studies, defining the mechanisms of action of these alleles is essential. Genome editing technology, such as CRISPR-Cas, has created a vast toolbox for targeted genetic and epigenetic modifications that presents unprecedented opportunities to decipher disease-causing loci, genes and variants in autoimmunity. In this Review, we discuss the past 5-10 years of progress in resolving the mechanisms underlying rheumatic disease-associated alleles, with an emphasis on how genomic editing techniques can enable targeted dissection and mechanistic studies of causal autoimmune risk variants.
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Affiliation(s)
- Yuriy Baglaenko
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Dana Macfarlane
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexander Marson
- Gladstone Institutes, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, CA, USA
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, University of California, San Francisco, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Peter A Nigrovic
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Immunology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK.
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32
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Abood A, Farber CR. Using "-omics" Data to Inform Genome-wide Association Studies (GWASs) in the Osteoporosis Field. Curr Osteoporos Rep 2021; 19:369-380. [PMID: 34125409 PMCID: PMC8767463 DOI: 10.1007/s11914-021-00684-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/22/2021] [Indexed: 01/12/2023]
Abstract
PURPOSE OF REVIEW Osteoporosis constitutes a major societal health problem. Genome-wide association studies (GWASs) have identified over 1100 loci influencing bone mineral density (BMD); however, few of the causal genes have been identified. Here, we review approaches that use "-omics" data and genetic- and systems genetics-based analytical strategies to facilitate causal gene discovery. RECENT FINDINGS The bone field is beginning to adopt approaches that are commonplace in other disease disciplines. The slower progress has been due in part to the lack of large-scale "omics" data on bone and bone cells. This is however changing, and approaches such as eQTL colocalization, transcriptome-wide association studies (TWASs), network, and integrative approaches are beginning to provide significant insight into the genes responsible for BMD GWAS associations. The use of "-omics" data to inform BMD GWASs has increased in recent years, leading to the identification of novel regulators of BMD in humans. The ultimate goal will be to use this information to develop more effective therapies to treat and ultimately prevent osteoporosis.
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Affiliation(s)
- Abdullah Abood
- Center for Public Health Genomics, University of Virginia, 800717, Charlottesville, VA, 22908, USA
- Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Charles R Farber
- Center for Public Health Genomics, University of Virginia, 800717, Charlottesville, VA, 22908, USA.
- Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA.
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA.
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33
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Liang D, Elwell AL, Aygün N, Krupa O, Wolter JM, Kyere FA, Lafferty MJ, Cheek KE, Courtney KP, Yusupova M, Garrett ME, Ashley-Koch A, Crawford GE, Love MI, de la Torre-Ubieta L, Geschwind DH, Stein JL. Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation. Nat Neurosci 2021; 24:941-953. [PMID: 34017130 PMCID: PMC8254789 DOI: 10.1038/s41593-021-00858-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 04/15/2021] [Indexed: 02/03/2023]
Abstract
Common genetic risk for neuropsychiatric disorders is enriched in regulatory elements active during cortical neurogenesis. However, it remains poorly understood as to how these variants influence gene regulation. To model the functional impact of common genetic variation on the noncoding genome during human cortical development, we performed the assay for transposase accessible chromatin using sequencing (ATAC-seq) and analyzed chromatin accessibility quantitative trait loci (QTL) in cultured human neural progenitor cells and their differentiated neuronal progeny from 87 donors. We identified significant genetic effects on 988/1,839 neuron/progenitor regulatory elements, with highly cell-type and temporally specific effects. A subset (roughly 30%) of chromatin accessibility-QTL were also associated with changes in gene expression. Motif-disrupting alleles of transcriptional activators generally led to decreases in chromatin accessibility, whereas motif-disrupting alleles of repressors led to increases in chromatin accessibility. By integrating cell-type-specific chromatin accessibility-QTL and brain-relevant genome-wide association data, we were able to fine-map and identify regulatory mechanisms underlying noncoding neuropsychiatric disorder risk loci.
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Affiliation(s)
- Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Angela L Elwell
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Justin M Wolter
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Felix A Kyere
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael J Lafferty
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kerry E Cheek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kenan P Courtney
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Marianna Yusupova
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Melanie E Garrett
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Allison Ashley-Koch
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Gregory E Crawford
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Luis de la Torre-Ubieta
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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34
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Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv. Cell Syst 2021; 10:298-306.e4. [PMID: 32213349 DOI: 10.1016/j.cels.2020.02.009] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 11/15/2019] [Accepted: 02/25/2020] [Indexed: 12/17/2022]
Abstract
The assay for transposase-accessible chromatin using sequencing (ATAC-seq) has become the preferred method for mapping chromatin accessibility due to its time and input material efficiency. However, it can be difficult to evaluate data quality and identify sources of technical bias across samples. Here, we present ataqv, a computational toolkit for efficiently measuring, visualizing, and comparing quality control (QC) results across samples and experiments. We use ataqv to analyze 2,009 public ATAC-seq datasets; their QC metrics display a 10-fold range. Tn5 dosage experiments and statistical modeling show that technical variation in the ratio of Tn5 transposase to nuclei and sequencing flowcell density induces systematic bias in ATAC-seq data by changing the enrichment of reads across functional genomic annotations including promoters, enhancers, and transcription-factor-bound regions, with the notable exception of CTCF. ataqv can be integrated into existing computational pipelines and is freely available at https://github.com/ParkerLab/ataqv/.
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35
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A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat Genet 2021; 53:861-868. [PMID: 34083789 PMCID: PMC7610960 DOI: 10.1038/s41588-021-00875-2] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 04/23/2021] [Indexed: 02/05/2023]
Abstract
Microglia, the tissue-resident macrophages of the central nervous system (CNS), play critical roles in immune defense, development and homeostasis. However, isolating microglia from humans in large numbers is challenging. Here, we profiled gene expression variation in primary human microglia isolated from 141 patients undergoing neurosurgery. Using single-cell and bulk RNA sequencing, we identify how age, sex and clinical pathology influence microglia gene expression and which genetic variants have microglia-specific functions using expression quantitative trait loci (eQTL) mapping. We follow up one of our findings using a human induced pluripotent stem cell-based macrophage model to fine-map a candidate causal variant for Alzheimer's disease at the BIN1 locus. Our study provides a population-scale transcriptional map of a critically important cell for human CNS development and disease.
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36
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Devenish LP, Mhlanga MM, Negishi Y. Immune Regulation in Time and Space: The Role of Local- and Long-Range Genomic Interactions in Regulating Immune Responses. Front Immunol 2021; 12:662565. [PMID: 34046034 PMCID: PMC8144502 DOI: 10.3389/fimmu.2021.662565] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/26/2021] [Indexed: 12/27/2022] Open
Abstract
Mammals face and overcome an onslaught of endogenous and exogenous challenges in order to survive. Typical immune cells and barrier cells, such as epithelia, must respond rapidly and effectively to encountered pathogens and aberrant cells to prevent invasion and eliminate pathogenic species before they become overgrown and cause harm. On the other hand, inappropriate initiation and failed termination of immune cell effector function in the absence of pathogens or aberrant tissue gives rise to a number of chronic, auto-immune, and neoplastic diseases. Therefore, the fine control of immune effector functions to provide for a rapid, robust response to challenge is essential. Importantly, immune cells are heterogeneous due to various factors relating to cytokine exposure and cell-cell interaction. For instance, tissue-resident macrophages and T cells are phenotypically, transcriptionally, and functionally distinct from their circulating counterparts. Indeed, even the same cell types in the same environment show distinct transcription patterns at the single cell level due to cellular noise, despite being robust in concert. Additionally, immune cells must remain quiescent in a naive state to avoid autoimmunity or chronic inflammatory states but must respond robustly upon activation regardless of their microenvironment or cellular noise. In recent years, accruing evidence from next-generation sequencing, chromatin capture techniques, and high-resolution imaging has shown that local- and long-range genome architecture plays an important role in coordinating rapid and robust transcriptional responses. Here, we discuss the local- and long-range genome architecture of immune cells and the resultant changes upon pathogen or antigen exposure. Furthermore, we argue that genome structures contribute functionally to rapid and robust responses under noisy and distinct cellular environments and propose a model to explain this phenomenon.
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Affiliation(s)
- Liam P Devenish
- Division of Chemical, Systems, and Synthetic Biology, Department of Integrative Biomedical Sciences, Institute of Infectious Disease & Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Musa M Mhlanga
- Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands.,Epigenomics & Single Cell Biophysics Group, Department of Cell Biology, Radboud University, Nijmegen, Netherlands.,Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
| | - Yutaka Negishi
- Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands.,Epigenomics & Single Cell Biophysics Group, Department of Cell Biology, Radboud University, Nijmegen, Netherlands.,Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
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37
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Ong SYQ, Flyamer IM, Bickmore WA, Biddie SC. From bedside to bench: regulation of host factors in SARS-CoV-2 infection. Exp Mol Med 2021; 53:483-494. [PMID: 33828231 PMCID: PMC8024942 DOI: 10.1038/s12276-021-00595-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/01/2021] [Accepted: 02/15/2021] [Indexed: 01/08/2023] Open
Abstract
The zoonotic coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2), which causes COVID-19 (coronavirus disease-2019), has resulted in a pandemic. This has led to an urgent need to understand the molecular determinants of SARS-CoV-2 infection, factors associated with COVID-19 heterogeneity and severity, and therapeutic options for these patients. In this review, we discuss the role of host factors in SARS-CoV-2 infection and describe variations in host factor expression as mechanisms underlying the symptoms and severity of COVID-19. We focus on two host factors, angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2), implicated in SARS-CoV-2 infection. We also discuss genetic variants associated with COVID-19 severity revealed in selected patients and based on genome-wide association studies (GWASs). Furthermore, we highlight important advances in cell and chromatin biology, such as single-cell RNA and chromatin sequencing and chromosomal conformation assays, as methods that may aid in the discovery of viral-host interactions in COVID-19. Understanding how regulation of host factor genes varies in physiological and pathological states might explain the heterogeneity observed in SARS-CoV-2 infection, help identify pathways for therapeutic development, and identify patients most likely to progress to severe COVID-19.
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Affiliation(s)
| | - Ilya M Flyamer
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Wendy A Bickmore
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Simon C Biddie
- Intensive Care Medicine, NHS Lothian, Edinburgh, UK. .,MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, EH4 2XU, UK.
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38
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Bejjani F, Tolza C, Boulanger M, Downes D, Romero R, Maqbool M, Zine El Aabidine A, Andrau JC, Lebre S, Brehelin L, Parrinello H, Rohmer M, Kaoma T, Vallar L, Hughes J, Zibara K, Lecellier CH, Piechaczyk M, Jariel-Encontre I. Fra-1 regulates its target genes via binding to remote enhancers without exerting major control on chromatin architecture in triple negative breast cancers. Nucleic Acids Res 2021; 49:2488-2508. [PMID: 33533919 PMCID: PMC7968996 DOI: 10.1093/nar/gkab053] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 12/21/2020] [Accepted: 01/25/2021] [Indexed: 12/12/2022] Open
Abstract
The ubiquitous family of dimeric transcription factors AP-1 is made up of Fos and Jun family proteins. It has long been thought to operate principally at gene promoters and how it controls transcription is still ill-understood. The Fos family protein Fra-1 is overexpressed in triple negative breast cancers (TNBCs) where it contributes to tumor aggressiveness. To address its transcriptional actions in TNBCs, we combined transcriptomics, ChIP-seqs, machine learning and NG Capture-C. Additionally, we studied its Fos family kin Fra-2 also expressed in TNBCs, albeit much less. Consistently with their pleiotropic effects, Fra-1 and Fra-2 up- and downregulate individually, together or redundantly many genes associated with a wide range of biological processes. Target gene regulation is principally due to binding of Fra-1 and Fra-2 at regulatory elements located distantly from cognate promoters where Fra-1 modulates the recruitment of the transcriptional co-regulator p300/CBP and where differences in AP-1 variant motif recognition can underlie preferential Fra-1- or Fra-2 bindings. Our work also shows no major role for Fra-1 in chromatin architecture control at target gene loci, but suggests collaboration between Fra-1-bound and -unbound enhancers within chromatin hubs sometimes including promoters for other Fra-1-regulated genes. Our work impacts our view of AP-1.
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Affiliation(s)
- Fabienne Bejjani
- IGMM, Univ Montpellier, CNRS, Montpellier, France
- PRASE, DSST, ER045, Lebanese University, Beirut, Lebanon
| | - Claire Tolza
- IGMM, Univ Montpellier, CNRS, Montpellier, France
| | | | - Damien Downes
- Medical Research Council, Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Oxford University, Oxford, UK
| | - Raphaël Romero
- IMAG, Univ Montpellier, CNRS, Montpellier, France
- LIRMM, Univ Montpellier, CNRS, Montpellier, France
| | | | | | | | - Sophie Lebre
- IMAG, Univ Montpellier, CNRS, Montpellier, France
| | | | - Hughes Parrinello
- Montpellier GenomiX, MGX, BioCampus Montpellier, CNRS, INSERM, Univ. Montpellier, F-34094 Montpellier, France
| | - Marine Rohmer
- Montpellier GenomiX, MGX, BioCampus Montpellier, CNRS, INSERM, Univ. Montpellier, F-34094 Montpellier, France
| | - Tony Kaoma
- Computational Biomedecine, Quantitative Biology Unit, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Laurent Vallar
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Jim R Hughes
- Medical Research Council, Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Oxford University, Oxford, UK
| | - Kazem Zibara
- PRASE, DSST, ER045, Lebanese University, Beirut, Lebanon
- Biology Department, Faculty of Sciences-I, Lebanese University, Beirut, Lebanon
| | - Charles-Henri Lecellier
- IGMM, Univ Montpellier, CNRS, Montpellier, France
- LIRMM, Univ Montpellier, CNRS, Montpellier, France
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39
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Rao S, Yao Y, Bauer DE. Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation. Genome Med 2021; 13:41. [PMID: 33691767 PMCID: PMC7948363 DOI: 10.1186/s13073-021-00857-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 02/12/2021] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.
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Affiliation(s)
- Shuquan Rao
- Division of Hematology/Oncology, Boston Children's Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute; Harvard Stem Cell Institute; Broad Institute; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
| | - Yao Yao
- Division of Hematology/Oncology, Boston Children's Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute; Harvard Stem Cell Institute; Broad Institute; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Daniel E Bauer
- Division of Hematology/Oncology, Boston Children's Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute; Harvard Stem Cell Institute; Broad Institute; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
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40
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Shi C, Rattray M, Barton A, Bowes J, Orozco G. Using functional genomics to advance the understanding of psoriatic arthritis. Rheumatology (Oxford) 2021; 59:3137-3146. [PMID: 32778885 PMCID: PMC7590405 DOI: 10.1093/rheumatology/keaa283] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/17/2020] [Accepted: 04/28/2020] [Indexed: 01/03/2023] Open
Abstract
Psoriatic arthritis (PsA) is a complex disease where susceptibility is determined by genetic and environmental risk factors. Clinically, PsA involves inflammation of the joints and the skin, and, if left untreated, results in irreversible joint damage. There is currently no cure and the few treatments available to alleviate symptoms do not work in all patients. Over the past decade, genome-wide association studies (GWAS) have uncovered a large number of disease-associated loci but translating these findings into functional mechanisms and novel targets for therapeutic use is not straightforward. Most variants have been predicted to affect primarily long-range regulatory regions such as enhancers. There is now compelling evidence to support the use of chromatin conformation analysis methods to discover novel genes that can be affected by disease-associated variants. Here, we will review the studies published in the field that have given us a novel understanding of gene regulation in the context of functional genomics and how this relates to the study of PsA and its underlying disease mechanism.
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Affiliation(s)
- Chenfu Shi
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, Centre for Genetics and Genomics Versus Arthritis
| | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester.,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre
| | - Anne Barton
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, Centre for Genetics and Genomics Versus Arthritis.,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre.,Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - John Bowes
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, Centre for Genetics and Genomics Versus Arthritis.,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre
| | - Gisela Orozco
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, Centre for Genetics and Genomics Versus Arthritis.,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre.,Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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41
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Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits. Cell Syst 2021; 12:5-22. [PMID: 33476553 DOI: 10.1016/j.cels.2020.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/15/2020] [Accepted: 10/07/2020] [Indexed: 02/08/2023]
Abstract
Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.
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42
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Ainsworth HC, Howard TD, Langefeld CD. Intrinsic DNA topology as a prioritization metric in genomic fine-mapping studies. Nucleic Acids Res 2020; 48:11304-11321. [PMID: 33084892 PMCID: PMC7672465 DOI: 10.1093/nar/gkaa877] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 08/23/2020] [Accepted: 09/25/2020] [Indexed: 12/15/2022] Open
Abstract
In genomic fine-mapping studies, some approaches leverage annotation data to prioritize likely functional polymorphisms. However, existing annotation resources can present challenges as many lack information for novel variants and/or may be uninformative for non-coding regions. We propose a novel annotation source, sequence-dependent DNA topology, as a prioritization metric for fine-mapping. DNA topology and function are well-intertwined, and as an intrinsic DNA property, it is readily applicable to any genomic region. Here, we constructed and applied Minor Groove Width (MGW) as a prioritization metric. Using an established MGW-prediction method, we generated a MGW census for 199 038 197 SNPs across the human genome. Summarizing a SNP's change in MGW (ΔMGW) as a Euclidean distance, ΔMGW exhibited a strongly right-skewed distribution, highlighting the infrequency of SNPs that generate dissimilar shape profiles. We hypothesized that phenotypically-associated SNPs can be prioritized by ΔMGW. We tested this hypothesis in 116 regions analyzed by a Massively Parallel Reporter Assay and observed enrichment of large ΔMGW for functional polymorphisms (P = 0.0007). To illustrate application in fine-mapping studies, we applied our MGW-prioritization approach to three non-coding regions associated with systemic lupus erythematosus. Together, this study presents the first usage of sequence-dependent DNA topology as a prioritization metric in genomic association studies.
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Affiliation(s)
- Hannah C Ainsworth
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.,Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Timothy D Howard
- Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.,Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.,Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.,Comprehensive Cancer Center of Wake Forest Baptist Medical Center, Winston-Salem, NC 27157, USA
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43
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Grant SFA, Wells AD, Rich SS. Next steps in the identification of gene targets for type 1 diabetes. Diabetologia 2020; 63:2260-2269. [PMID: 32797243 PMCID: PMC7527360 DOI: 10.1007/s00125-020-05248-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 06/16/2020] [Indexed: 12/17/2022]
Abstract
The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical implementations of the associated variants. From the recognition nearly 50 years ago that genetics (in the form of HLA) distinguishes risk of type 1 diabetes from type 2 diabetes, advances in technology and sample acquisition through collaboration have identified over 60 loci harbouring SNPs associated with type 1 diabetes risk. Coupled with HLA region genes, these variants account for the majority of the genetic risk (~50% of the total risk); however, relatively few variants are located in coding regions of genes exerting a predicted protein change. The vast majority of genetic risk in type 1 diabetes appears to be attributed to regions of the genome involved in gene regulation, but the target effectors of those genetic variants are not readily identifiable. Although past genetic studies clearly implicated immune-relevant cell types involved in risk, the target organ (the beta cell) was left untouched. Through emergent technologies, using combinations of genetics, gene expression, epigenetics, chromosome conformation and gene editing, novel landscapes of how SNPs regulate genes have emerged. Furthermore, both the immune system and the beta cell and their biological pathways have been implicated in a context-specific manner. The use of variants from immune and beta cell studies distinguish type 1 diabetes from type 2 diabetes and, when they are combined in a genetic risk score, open new avenues for prediction and treatment. Graphical abstract.
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Affiliation(s)
- Struan F A Grant
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Departments of Pediatrics and Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Divisions of Human Genetics and Endocrinology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA.
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.
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44
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Corces MR, Shcherbina A, Kundu S, Gloudemans MJ, Frésard L, Granja JM, Louie BH, Eulalio T, Shams S, Bagdatli ST, Mumbach MR, Liu B, Montine KS, Greenleaf WJ, Kundaje A, Montgomery SB, Chang HY, Montine TJ. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer's and Parkinson's diseases. Nat Genet 2020; 52:1158-1168. [PMID: 33106633 PMCID: PMC7606627 DOI: 10.1038/s41588-020-00721-x] [Citation(s) in RCA: 177] [Impact Index Per Article: 44.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 09/18/2020] [Indexed: 02/06/2023]
Abstract
Genome-wide association studies of neurological diseases have identified thousands of variants associated with disease phenotypes. However, most of these variants do not alter coding sequences, making it difficult to assign their function. Here, we present a multi-omic epigenetic atlas of the adult human brain through profiling of single-cell chromatin accessibility landscapes and three-dimensional chromatin interactions of diverse adult brain regions across a cohort of cognitively healthy individuals. We developed a machine-learning classifier to integrate this multi-omic framework and predict dozens of functional SNPs for Alzheimer's and Parkinson's diseases, nominating target genes and cell types for previously orphaned loci from genome-wide association studies. Moreover, we dissected the complex inverted haplotype of the MAPT (encoding tau) Parkinson's disease risk locus, identifying putative ectopic regulatory interactions in neurons that may mediate this disease association. This work expands understanding of inherited variation and provides a roadmap for the epigenomic dissection of causal regulatory variation in disease.
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Affiliation(s)
- M Ryan Corces
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Anna Shcherbina
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Soumya Kundu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Michael J Gloudemans
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Laure Frésard
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeffrey M Granja
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Program in Biophysics, Stanford University, Stanford, CA, USA
| | - Bryan H Louie
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Tiffany Eulalio
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Shadi Shams
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - S Tansu Bagdatli
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Maxwell R Mumbach
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Boxiang Liu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biology, Stanford University, Stanford, CA, USA
- Baidu Research, Sunnyvale, CA, USA
| | - Kathleen S Montine
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - William J Greenleaf
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Applied Physics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
| | - Thomas J Montine
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
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45
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Babenko V, Babenko R, Orlov Y. Analyzing a putative enhancer of optic disc morphology. BMC Genet 2020; 21:73. [PMID: 33092545 PMCID: PMC7583307 DOI: 10.1186/s12863-020-00873-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 06/23/2020] [Indexed: 01/06/2023] Open
Abstract
Background Genome-wide association studies have identified the CDC7-TGFBR3 intergenic region on chromosome 1 to be strongly associated with optic disc area size. The mechanism of its function remained unclear until new data on eQTL markers emerged from the Genotype-Tissue Expression project. The target region was found to contain a strong silencer of the distal (800 kb) Transcription Factor (TF) gene GFI1 (Growth Factor Independent Transcription Repressor 1) specifically in neuroendocrine cells (pituitary gland). GFI1 has also been reported to be involved in the development of sensory neurons and hematopoiesis. Therefore, GFI1, being a developmental gene, is likely to affect optic disc area size by altering the expression of the associated genes via long-range interactions. Results Distribution of haplotypes in the putative enhancer region has been assessed using the data on four continental supergroups generated by the 1000 Genomes Project. The East Asian (EAS) populations were shown to manifest a highly homogenous unimodal haplotype distribution pattern within the region with the major haplotype occurring with the frequency of 0.9. Another European specific haplotype was observed with the frequency of 0.21. The major haplotype appears to be involved in silencing GFI1repressor gene expression, which might be the cause of increased optic disc area characteristic of the EAS populations. The enhancer/eQTL region overlaps AluJo element, which implies that this particular regulatory element is primate-specific and confined to few tissues. Conclusion Population specific distribution of GFI1 enhancer alleles may predispose certain ethnic groups to glaucoma.
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Affiliation(s)
- Vladimir Babenko
- Institute of Cytology and Genetics, Lavrentyeva 10, Novosibirsk, 630090, Russia. .,Novosibirsk State University, Pirogova Str 2, Novosibirsk, 630090, Russia.
| | - Roman Babenko
- Institute of Cytology and Genetics, Lavrentyeva 10, Novosibirsk, 630090, Russia.,Novosibirsk State University, Pirogova Str 2, Novosibirsk, 630090, Russia
| | - Yuri Orlov
- Institute of Cytology and Genetics, Lavrentyeva 10, Novosibirsk, 630090, Russia.,Novosibirsk State University, Pirogova Str 2, Novosibirsk, 630090, Russia.,I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Trubetskaya 8-2, Moscow, 119991, Russia
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Teschendorff AE, Zhu T, Breeze CE, Beck S. EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data. Genome Biol 2020; 21:221. [PMID: 32883324 PMCID: PMC7650528 DOI: 10.1186/s13059-020-02126-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 07/29/2020] [Indexed: 12/19/2022] Open
Abstract
Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT, UK.
| | - Tianyu Zhu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Charles E Breeze
- Altius Institute for Biomedical Sciences, 2211 Elliott Avenue, Seattle, USA
| | - Stephan Beck
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT, UK
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Chen XF, Guo MR, Duan YY, Jiang F, Wu H, Dong SS, Zhou XR, Thynn HN, Liu CC, Zhang L, Guo Y, Yang TL. Multiomics dissection of molecular regulatory mechanisms underlying autoimmune-associated noncoding SNPs. JCI Insight 2020; 5:136477. [PMID: 32879140 PMCID: PMC7526455 DOI: 10.1172/jci.insight.136477] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 07/16/2020] [Indexed: 12/16/2022] Open
Abstract
More than 90% of autoimmune-associated variants are located in noncoding regions, leading to challenges in deciphering the underlying causal roles of functional variants and genes and biological mechanisms. Therefore, to reduce the gap between traditional genetic findings and mechanistic understanding of disease etiologies and clinical drug development, it is important to translate systematically the regulatory mechanisms underlying noncoding variants. Here, we prioritized functional noncoding SNPs with regulatory gene targets associated with 19 autoimmune diseases by incorporating hundreds of immune cell-specific multiomics data. The prioritized SNPs are associated with transcription factor (TF) binding, histone modification, or chromatin accessibility, indicating their allele-specific regulatory roles. Their target genes are significantly enriched in immunologically related pathways and other known immunologically related functions. We found that 90.1% of target genes are regulated by distal SNPs involving several TFs (e.g., the DNA-binding protein CCCTC-binding factor [CTCF]), suggesting the importance of long-range chromatin interaction in autoimmune diseases. Moreover, we predicted potential drug targets for autoimmune diseases, including 2 genes (NFKB1 and SH2B3) with known drug indications on other diseases, highlighting their potential drug repurposing opportunities. Taken together, these findings may provide useful information for future experimental follow-up and drug applications on autoimmune diseases.
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48
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A method for scoring the cell type-specific impacts of noncoding variants in personal genomes. Proc Natl Acad Sci U S A 2020; 117:21364-21372. [PMID: 32817564 PMCID: PMC7474608 DOI: 10.1073/pnas.1922703117] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Here we use the expression and accessibility data from a diverse set of cell types to learn a model for the dependence of the accessibility of a regulatory element on its DNA sequence and TF expression. Using GTEx samples with WGS data, we show that the noncoding variants predicted to affect accessibility are more strongly associated with the expression of nearby genes. To interpret a personal genome, we combine the sequence information with context-specific TF expression to prioritize variants and regulatory elements in any genomic region of interest. This approach should be helpful in the study of risk loci previously identified by GWAS. Results from analysis of height and WGS data from the GTEx project support this hypothesis. A person’s genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. The interpretation of these variants, i.e., the assessment of their potential impact on a person’s phenotype, is currently of great interest in human genetics and medicine. We have developed a prioritization tool called OpenCausal which takes as inputs 1) a personal genome and 2) a reference context-specific TF expression profile and returns a list of noncoding variants prioritized according to their impact on chromatin accessibility for any given genomic region of interest. We applied OpenCausal to 6,430 samples across 18 tissues derived from the GTEx project and found that the variants prioritized by OpenCausal are highly enriched for eQTLs and caQTLs. We further propose a strategy to integrate the predicted open scores with genome-wide association studies (GWAS) data to prioritize putative causal variants and regulatory elements for a given risk locus (i.e., fine-mapping analysis). As an initial example, we applied this method to a GWAS dataset of human height and found that the prioritized putative variants and elements are correlated with the phenotype (i.e., heights of individuals) better than others.
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Lin J, Susztak K. Complexities of Understanding Function from CKD-Associated DNA Variants. Clin J Am Soc Nephrol 2020; 15:1028-1040. [PMID: 32513823 PMCID: PMC7341770 DOI: 10.2215/cjn.15771219] [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: 11/23/2022]
Abstract
Genome-wide association studies (GWASs) have facilitated the unbiased discovery of hundreds of genomic loci associated with CKD and kidney function. The vast majority of disease-associated DNA variants are noncoding. Those that are causal in CKD pathogenesis likely modulate transcription of target genes in a cell type-specific manner. To gain novel biological insights into mechanisms driving the development of CKD, the causal variants (which are usually not the most significant variant reported in a GWAS), their target genes, and causal cell types need to be identified. This functional validation requires a large number of new data sets, complex bioinformatics analyses, and experimental cellular and in vivo studies. Here, we review the basic principles and some of the current approaches being leveraged to assign functional significance to a genotype-phenotype association.
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Affiliation(s)
- Jennie Lin
- Division of Nephrology and Hypertension, Feinberg Cardiovascular and Renal Research Institute, Department of Medicine, Northwestern University, Chicago, Illinois
- Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Genetics, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania
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Abstract
Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.
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Affiliation(s)
- Christoph D. Rau
- Departments of Anesthesiology, Medicine, Physiology
- Current address: Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC 27599
| | - Aldons J. Lusis
- Department of Human Genetics and Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
| | - Yibin Wang
- Departments of Anesthesiology, Medicine, Physiology
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