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Stone K, Platig J, Quackenbush J, Fagny M. Complex Traits Heritability is Highly Clustered in the eQTL Bipartite Network. bioRxiv 2024:2024.02.27.582063. [PMID: 38464142 PMCID: PMC10925220 DOI: 10.1101/2024.02.27.582063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Single Nucleotide Polymorphisms (SNPs) associated with traits typically explain a small part of the trait genetic heritability-with the remainder thought to be distributed throughout the genome. Such SNPs are likely to alter expression levels of biologically relevant genes. Expression Quantitative Trait Locus (eQTL) networks analysis has helped to functionally characterize such variants. We systematically analyze the distribution of SNP heritability for ten traits across 29 tissue-specific eQTL networks. We find that heritability is clustered in a small number or tissue-specific, functionally relevant SNP-gene modules and that the greatest occurs in local "hubs" that are both the cornerstone of the network's modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. Together, these results define a conceptual framework for understanding complex trait architecture and identifying key mutations carrying most of the heritability.
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Affiliation(s)
- Katherine Stone
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - John Platig
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Maud Fagny
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Genetique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette 91190 France
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Fagny M, Glass K, Kuijjer ML. Editorial: Applications and Methods in Genomic Networks. Front Genet 2022; 13:936015. [PMID: 35754814 PMCID: PMC9214306 DOI: 10.3389/fgene.2022.936015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 05/20/2022] [Indexed: 01/07/2023] Open
Affiliation(s)
- Maud Fagny
- EcoAnthropology Lab, UMR 7206 CNRS/MNHN/Universite Paris Diderot, Muséum National d'Histoire Naturelle, Paris, France,Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE — Le Moulon, Gif-sur-Yvette, France,*Correspondence: Maud Fagny, ; Kimberly Glass, ; Marieke L. Kuijjer,
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States,Harvard Medical School, Boston, MA, United States,Harvard Chan School of Public Health, Boston, MA, United States,*Correspondence: Maud Fagny, ; Kimberly Glass, ; Marieke L. Kuijjer,
| | - Marieke L. Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo, Norway,Department of Pathology, Leiden University Medical Center, Leiden, Netherlands,Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, Netherlands,*Correspondence: Maud Fagny, ; Kimberly Glass, ; Marieke L. Kuijjer,
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Gaynor SM, Fagny M, Lin X, Platig J, Quackenbush J. Connectivity in eQTL networks dictates reproducibility and genomic properties. Cell Rep Methods 2022; 2:100218. [PMID: 35637906 PMCID: PMC9142682 DOI: 10.1016/j.crmeth.2022.100218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 02/08/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023]
Abstract
Expression quantitative trait locus (eQTL) analysis associates SNPs with gene expression; these relationships can be represented as a bipartite network with association strength as "edge weights" between SNPs and genes. However, most eQTL networks use binary edge weights based on thresholded FDR estimates: definitions that influence reproducibility and downstream analyses. We constructed twenty-nine tissue-specific eQTL networks using GTEx data and evaluated a comprehensive set of network specifications based on false discovery rates, test statistics, and p values, focusing on the degree centrality-a metric of an SNP or gene node's potential network influence. We found a thresholded Benjamini-Hochberg q value weighted by the Z-statistic balances metric reproducibility and computational efficiency. Our estimated gene degrees positively correlate with gene degrees in gene regulatory networks, demonstrating that these networks are complementary in understanding regulation. Gene degrees also correlate with genetic diversity, and heritability analyses show that highly connected nodes are enriched for tissue-relevant traits.
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Affiliation(s)
- Sheila M. Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Maud Fagny
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Statistics, Harvard University, Cambridge, MA 02138, USA
| | - John Platig
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
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Lopes-Ramos CM, Chen CY, Kuijjer ML, Paulson JN, Sonawane AR, Fagny M, Platig J, Glass K, Quackenbush J, DeMeo DL. Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues. Cell Rep 2021; 31:107795. [PMID: 32579922 DOI: 10.1016/j.celrep.2020.107795] [Citation(s) in RCA: 148] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Revised: 04/01/2020] [Accepted: 05/29/2020] [Indexed: 11/25/2022] Open
Abstract
Sex differences manifest in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sex differences, we evaluated sex-biased gene regulation by constructing sample-specific gene regulatory networks in 29 human healthy tissues using 8,279 whole-genome expression profiles from the Genotype-Tissue Expression (GTEx) project. We find sex-biased regulatory network structures in each tissue. Even though most transcription factors (TFs) are not differentially expressed between males and females, many have sex-biased regulatory targeting patterns. In each tissue, genes that are differentially targeted by TFs between the sexes are enriched for tissue-related functions and diseases. In brain tissue, for example, genes associated with Parkinson's disease and Alzheimer's disease are targeted by different sets of TFs in each sex. Our systems-based analysis identifies a repertoire of TFs that play important roles in sex-specific architecture of gene regulatory networks, and it underlines sex-specific regulatory processes in both health and disease.
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Affiliation(s)
| | - Cho-Yi Chen
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
| | - Marieke L Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo, Norway
| | - Joseph N Paulson
- Department of Biostatistics, Product Development, Genentech Inc., San Francisco, CA, USA
| | - Abhijeet R Sonawane
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Maud Fagny
- Genetique Quantitative et Evolution-Le Moulon, Universite Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Centre National de la Recherche Scientifique, AgroParisTech, Gif-sur-Yvette, France
| | - John Platig
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kimberly Glass
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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Fagny M, Austerlitz F. Polygenic Adaptation: Integrating Population Genetics and Gene Regulatory Networks. Trends Genet 2021; 37:631-638. [PMID: 33892958 DOI: 10.1016/j.tig.2021.03.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 12/13/2022]
Abstract
The adaptation of populations to local environments often relies on the selection of optimal values for polygenic traits. Here, we first summarize the results obtained from different quantitative genetics and population genetics models, about the genetic architecture of polygenic traits and their response to directional selection. We then highlight the contribution of systems biology to the understanding of the molecular bases of polygenic traits and the evolution of gene regulatory networks involved in these traits. Finally, we discuss the need for a unifying framework merging the fields of population genetics, quantitative genetics and systems biology to better understand the molecular bases of polygenic traits adaptation.
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Affiliation(s)
- Maud Fagny
- UMR7206 Eco-Anthropologie, Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique, Université de Paris, Paris, France.
| | - Frédéric Austerlitz
- UMR7206 Eco-Anthropologie, Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique, Université de Paris, Paris, France
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Kuijjer ML, Fagny M, Marin A, Quackenbush J, Glass K. PUMA: PANDA Using MicroRNA Associations. Bioinformatics 2021; 36:4765-4773. [PMID: 32860050 PMCID: PMC7750953 DOI: 10.1093/bioinformatics/btaa571] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 05/19/2020] [Accepted: 06/10/2020] [Indexed: 12/27/2022] Open
Abstract
Motivation Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. Availability and implementation PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marieke L Kuijjer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo 0318, Norway
| | - Maud Fagny
- UMR7206 Eco-Anthropologie, Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique, Université de Paris, Paris 75016, France
| | - Alessandro Marin
- Centre for Computing in Science Education, Department of Physics, University of Oslo, Oslo 0316, Norway
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.,Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Channing Division of Network Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Harvard Medical School, Boston, MA 02115, USA
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Fagny M, Kuijjer ML, Stam M, Joets J, Turc O, Rozière J, Pateyron S, Venon A, Vitte C. Identification of Key Tissue-Specific, Biological Processes by Integrating Enhancer Information in Maize Gene Regulatory Networks. Front Genet 2021; 11:606285. [PMID: 33505431 PMCID: PMC7834273 DOI: 10.3389/fgene.2020.606285] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/03/2020] [Indexed: 12/27/2022] Open
Abstract
Enhancers are key players in the spatio-temporal coordination of gene expression during numerous crucial processes, including tissue differentiation across development. Characterizing the transcription factors (TFs) and genes they connect, and the molecular functions underpinned is important to better characterize developmental processes. In plants, the recent molecular characterization of enhancers revealed their capacity to activate the expression of several target genes. Nevertheless, identifying these target genes at a genome-wide level is challenging, particularly for large-genome species, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to plant regulatory networks remains poorly understood. Here, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage (V2-IST) and husks (bracts) at flowering. Using systems biology, we integrate genomic, epigenomic, and transcriptomic data to model the regulatory relationships between TFs and their potential target genes, and identify regulatory modules specific to husk and V2-IST. We show that leaves at the V2-IST stage are characterized by the response to hormones and macromolecules biogenesis and assembly, which are regulated by the BBR/BPC and AP2/ERF TF families, respectively. In contrast, husks are characterized by cell wall modification and response to abiotic stresses, which are, respectively, orchestrated by the C2C2/DOF and AP2/EREB families. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped part of the regulatory network in each tissue, and that MITEs have provided potential new TF binding sites involved in husk tissue-specificity.
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Affiliation(s)
- Maud Fagny
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
| | - Marieke Lydia Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, Netherlands
| | - Maike Stam
- Plant Development and (Epi) Genetics, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Johann Joets
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
| | - Olivier Turc
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Julien Rozière
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Orsay, France
- Université de Paris, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2), Orsay, France
| | - Stéphanie Pateyron
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Orsay, France
- Université de Paris, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2), Orsay, France
| | - Anthony Venon
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
| | - Clémentine Vitte
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
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Chaix R, Fagny M, Cosin-Tomás M, Alvarez-López M, Lemee L, Regnault B, Davidson RJ, Lutz A, Kaliman P. Differential DNA methylation in experienced meditators after an intensive day of mindfulness-based practice: Implications for immune-related pathways. Brain Behav Immun 2020; 84:36-44. [PMID: 31733290 PMCID: PMC7010561 DOI: 10.1016/j.bbi.2019.11.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 11/04/2019] [Accepted: 11/06/2019] [Indexed: 12/13/2022] Open
Abstract
The human methylome is dynamically influenced by psychological stress. However, its responsiveness to stress management remains underexplored. Meditation practice has been shown to significantly reduce stress level, among other beneficial neurophysiological outcomes. Here, we evaluated the impact of a day of intensive meditation practice (t2-t1 = 8 h) on the methylome of peripheral blood mononuclear cells in experienced meditators (n = 17). In parallel, we assessed the influence of a day of leisure activities in the same environment on the methylome of matched control subjects with no meditation experience (n = 17). DNA methylation profiles were analyzed using the Illumina 450 K beadchip array. We fitted for each methylation site a linear model for multi-level experiments which adjusts the variation between t1 and t2 for baseline differences. No significant baseline differences in methylation profiles was detected between groups. In the meditation group, we identified 61 differentially methylated sites (DMS) after the intervention. These DMS were enriched in genes mostly associated with immune cell metabolism and ageing and in binding sites for several transcription factors involved in immune response and inflammation, among other functions. In the control group, no significant change in methylation level was observed after the day of leisure activities. These results suggest that a short meditation intervention in trained subjects may rapidly influence the epigenome at sites of potential relevance for immune function and provide a better understanding of the dynamics of the human methylome over short time windows.
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Affiliation(s)
- R Chaix
- Unité d'Eco-anthropologie (EA), Museum National d'Histoire Naturelle, CNRS, Université Paris Diderot, 75016 Paris, France.
| | - M Fagny
- Génétique Quantitative et Évolution, Evolution - Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, France
| | - M Cosin-Tomás
- Department of Human Genetics, McGill University, Research Institute of the McGill University Health Center, Montreal, Quebec, Canada
| | - M Alvarez-López
- Unitat de Farmacologia, Facultat de Farmàcia, Institut de Biomedicina, Universitat de Barcelona (IBUB), Nucli Universitari de Pedralbes, Barcelone, Spain
| | - L Lemee
- Plate-forme de Génotypage des Eucaryotes, Pôle Biomics, Institut Pasteur, Paris, France; Plateforme Biomics, Institut Pasteur, Paris, France
| | - B Regnault
- Plate-forme de Génotypage des Eucaryotes, Pôle Biomics, Institut Pasteur, Paris, France; Biology of Infection Unit, Inserm U1117. Pathogen Discovery Laboratory, Institut Pasteur, Paris, France
| | - R J Davidson
- Center for Healthy Minds, University of Wisconsin-Madison, USA
| | - A Lutz
- Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR5292, Lyon 1 University, Lyon, France
| | - P Kaliman
- Center for Healthy Minds, University of Wisconsin-Madison, USA; Faculty of Health Sciences, Universitat Oberta de Catalunya, Barcelona, Spain.
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Fagny M, Platig J, Kuijjer ML, Lin X, Quackenbush J. Nongenic cancer-risk SNPs affect oncogenes, tumour-suppressor genes, and immune function. Br J Cancer 2019; 122:569-577. [PMID: 31806877 PMCID: PMC7028992 DOI: 10.1038/s41416-019-0614-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 09/23/2019] [Accepted: 10/07/2019] [Indexed: 12/31/2022] Open
Abstract
Background Genome-wide association studies (GWASes) have identified many noncoding germline single-nucleotide polymorphisms (SNPs) that are associated with an increased risk of developing cancer. However, how these SNPs affect cancer risk is still largely unknown. Methods We used a systems biology approach to analyse the regulatory role of cancer-risk SNPs in thirteen tissues. By using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis. We represented both significant cis- and trans-eQTLs as edges in tissue-specific eQTL bipartite networks. Results Each tissue-specific eQTL network is organised into communities that group sets of SNPs and functionally related genes. When mapping cancer-risk SNPs to these networks, we find that in each tissue, these SNPs are significantly overrepresented in communities enriched for immune response processes, as well as tissue-specific functions. Moreover, cancer-risk SNPs are more likely to be ‘cores’ of their communities, influencing the expression of many genes within the same biological processes. Finally, cancer-risk SNPs preferentially target oncogenes and tumour-suppressor genes, suggesting that they may alter the expression of these key cancer genes. Conclusions This approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.
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Affiliation(s)
- Maud Fagny
- Genetique Quantitative et Evolution-Le Moulon, Institut National de la Recherche agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, Paris, France
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Marieke Lydia Kuijjer
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - John Quackenbush
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA. .,Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
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10
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Fogel O, Bugge Tinggaard A, Fagny M, Sigrist N, Roche E, Leclere L, Deleuze JF, Batteux F, Dougados M, Miceli-Richard C, Tost J. Deregulation of microRNA expression in monocytes and CD4 + T lymphocytes from patients with axial spondyloarthritis. Arthritis Res Ther 2019; 21:51. [PMID: 30755244 PMCID: PMC6373047 DOI: 10.1186/s13075-019-1829-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/18/2019] [Indexed: 02/07/2023] Open
Abstract
Background MicroRNAs (MiRs) play an important role in the pathogenesis of chronic inflammatory diseases. This study is the first to investigate miR expression profiles in purified CD4+ T lymphocytes and CD14+ monocytes from patients with axial spondyloarthritis (axSpA) using a high-throughput qPCR approach. Methods A total of 81 axSpA patients fulfilling the 2009 ASAS classification criteria, and 55 controls were recruited from October 2014 to July 2017. CD14+ monocytes and CD4+ T lymphocytes were isolated from peripheral blood mononuclear cells. MiR expression was investigated by qPCR using the Exiqon Human MiRnome panel I analyzing 372 miRNAs. Differentially expressed miRNAs identified in the discovery cohort were validated in the replication cohort. Results We found a major difference in miR expression patterns between T lymphocytes and monocytes regardless of the patient or control status. Comparing disease-specific differentially expressed miRs, 13 miRs were found consistently deregulated in CD14+ cells in both cohorts with miR-361-3p, miR-223-3p, miR-484, and miR-16-5p being the most differentially expressed. In CD4+ T cells, 11 miRs were differentially expressed between patients and controls with miR-16-1-3p, miR-28-5p, miR-199a-5p, and miR-126-3p were the most strongly upregulated miRs among patients. These miRs are involved in disease relevant pathways such as inflammation, intestinal permeability or bone formation. Mir-146a-5p levels correlated inversely with the degree of inflammation in axSpA patients. Conclusions We demonstrate a consistent deregulation of miRs in both monocytes and CD4+ T cells from axSpA patients, which could contribute to the pathophysiology of the disease with potential interest from a therapeutic perspective. Electronic supplementary material The online version of this article (10.1186/s13075-019-1829-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Olivier Fogel
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, 2 rue Gaston Crémieux, Evry, France.,Department of Rheumatology - Hôpital Cochin. Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris, France
| | - Andreas Bugge Tinggaard
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, 2 rue Gaston Crémieux, Evry, France.,Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Maud Fagny
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, 2 rue Gaston Crémieux, Evry, France
| | - Nelly Sigrist
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, 2 rue Gaston Crémieux, Evry, France
| | - Elodie Roche
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, 2 rue Gaston Crémieux, Evry, France
| | - Laurence Leclere
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, 2 rue Gaston Crémieux, Evry, France
| | - Jean-François Deleuze
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, 2 rue Gaston Crémieux, Evry, France
| | | | - Maxime Dougados
- Department of Rheumatology - Hôpital Cochin. Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris, France.,Unité Mixte AP-HP/ Institut Pasteur, Institut Pasteur, Immunoregulation Unit, Paris, France.,INSERM (U1153) : Clinical Epidemiology and Biostatistics, PRES Sorbonne Paris-Cité, Paris, France
| | - Corinne Miceli-Richard
- Department of Rheumatology - Hôpital Cochin. Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris, France.,Unité Mixte AP-HP/ Institut Pasteur, Institut Pasteur, Immunoregulation Unit, Paris, France
| | - Jörg Tost
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, 2 rue Gaston Crémieux, Evry, France.
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11
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Husquin LT, Rotival M, Fagny M, Quach H, Zidane N, McEwen LM, MacIsaac JL, Kobor MS, Aschard H, Patin E, Quintana-Murci L. Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation. Genome Biol 2018; 19:222. [PMID: 30563547 PMCID: PMC6299574 DOI: 10.1186/s13059-018-1601-3] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 12/04/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive. RESULTS We report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation. CONCLUSION Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.
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Affiliation(s)
- Lucas T. Husquin
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France
- Centre National de la Recherche Scientifique (CNRS) UMR2000, 75015 Paris, France
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France
| | - Maxime Rotival
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France
- Centre National de la Recherche Scientifique (CNRS) UMR2000, 75015 Paris, France
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France
| | - Maud Fagny
- Laboratory for Epigenetics & Environment, Centre National de Recherche en Génomique Humaine (CNRGH), CEA-Institut de Biologie François Jacob, 91000 Evry, France
| | - Hélène Quach
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France
- Centre National de la Recherche Scientifique (CNRS) UMR2000, 75015 Paris, France
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France
| | - Nora Zidane
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France
- Centre National de la Recherche Scientifique (CNRS) UMR2000, 75015 Paris, France
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France
| | - Lisa M. McEwen
- Department of Medical Genetics, University of British Columbia, Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, Vancouver, BC Canada
| | - Julia L. MacIsaac
- Department of Medical Genetics, University of British Columbia, Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, Vancouver, BC Canada
| | - Michael S. Kobor
- Department of Medical Genetics, University of British Columbia, Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, Vancouver, BC Canada
| | - Hugues Aschard
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France
| | - Etienne Patin
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France
- Centre National de la Recherche Scientifique (CNRS) UMR2000, 75015 Paris, France
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France
| | - Lluis Quintana-Murci
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France
- Centre National de la Recherche Scientifique (CNRS) UMR2000, 75015 Paris, France
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France
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12
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Barry JD, Fagny M, Paulson JN, Aerts HJWL, Platig J, Quackenbush J. Histopathological Image QTL Discovery of Immune Infiltration Variants. iScience 2018; 5:80-89. [PMID: 30240647 PMCID: PMC6123851 DOI: 10.1016/j.isci.2018.07.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Revised: 05/30/2018] [Accepted: 07/03/2018] [Indexed: 12/20/2022] Open
Abstract
Genotype-to-phenotype association studies typically use macroscopic physiological measurements or molecular readouts as quantitative traits. There are comparatively few suitable quantitative traits available between cell and tissue length scales, a limitation that hinders our ability to identify variants affecting phenotype at many clinically informative levels. Here we show that quantitative image features, automatically extracted from histopathological imaging data, can be used for image quantitative trait loci (iQTLs) mapping and variant discovery. Using thyroid pathology images, clinical metadata, and genomics data from the Genotype-Tissue Expression (GTEx) project, we establish and validate a quantitative imaging biomarker for immune cell infiltration. A total of 100,215 variants were selected for iQTL profiling and tested for genotype-phenotype associations with our quantitative imaging biomarker. Significant associations were found in HDAC9 and TXNDC5. We validated the TXNDC5 association using GTEx cis-expression QTL data and an independent hypothyroidism dataset from the Electronic Medical Records and Genomics network.
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Affiliation(s)
- Joseph D Barry
- Center for Cancer Computational Biology and Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 667 Huntington Avenue, Boston, MA 02115, USA.
| | - Maud Fagny
- Center for Cancer Computational Biology and Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 667 Huntington Avenue, Boston, MA 02115, USA
| | - Joseph N Paulson
- Center for Cancer Computational Biology and Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 667 Huntington Avenue, Boston, MA 02115, USA
| | - Hugo J W L Aerts
- Department of Radiology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - John Platig
- Center for Cancer Computational Biology and Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 667 Huntington Avenue, Boston, MA 02115, USA
| | - John Quackenbush
- Center for Cancer Computational Biology and Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 667 Huntington Avenue, Boston, MA 02115, USA
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13
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Weber A, Schwarz SC, Tost J, Trümbach D, Winter P, Busato F, Tacik P, Windhorst AC, Fagny M, Arzberger T, McLean C, van Swieten JC, Schwarz J, Vogt Weisenhorn D, Wurst W, Adhikary T, Dickson DW, Höglinger GU, Müller U. Epigenome-wide DNA methylation profiling in Progressive Supranuclear Palsy reveals major changes at DLX1. Nat Commun 2018; 9:2929. [PMID: 30050033 PMCID: PMC6062504 DOI: 10.1038/s41467-018-05325-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 06/25/2018] [Indexed: 02/06/2023] Open
Abstract
Genetic, epigenetic, and environmental factors contribute to the multifactorial disorder progressive supranuclear palsy (PSP). Here, we study epigenetic changes by genome-wide analysis of DNA from postmortem tissue of forebrains of patients and controls and detect significant (P < 0.05) methylation differences at 717 CpG sites in PSP vs. controls. Four-hundred fifty-one of these sites are associated with protein-coding genes. While differential methylation only affects a few sites in most genes, DLX1 is hypermethylated at multiple sites. Expression of an antisense transcript of DLX1, DLX1AS, is reduced in PSP brains. The amount of DLX1 protein is increased in gray matter of PSP forebrains. Pathway analysis suggests that DLX1 influences MAPT-encoded Tau protein. In a cell system, overexpression of DLX1 results in downregulation of MAPT while overexpression of DLX1AS causes upregulation of MAPT. Our observations suggest that altered DLX1 methylation and expression contribute to pathogenesis of PSP by influencing MAPT.
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Affiliation(s)
- Axel Weber
- Institute of Human Genetics, Justus-Liebig-Universität, Gießen, 35392, Germany.
| | - Sigrid C Schwarz
- Department of Neurology, Technische Universität München, Munich, 81377, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, 81377, Germany
| | - Jörg Tost
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie Francois Jacob, Evry, 91000, France
| | - Dietrich Trümbach
- Institute of Developmental Genetics, Helmholtz Center München, Munich, 85764, Germany
| | - Pia Winter
- Institute of Human Genetics, Justus-Liebig-Universität, Gießen, 35392, Germany
| | - Florence Busato
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie Francois Jacob, Evry, 91000, France
| | - Pawel Tacik
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, 53127, Germany
| | - Anita C Windhorst
- Institute of Medical Informatics, Justus-Liebig-Universität, Gießen, 35392, Germany
| | - Maud Fagny
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie Francois Jacob, Evry, 91000, France
| | - Thomas Arzberger
- German Center for Neurodegenerative Diseases (DZNE), Munich, 81377, Germany
- Department of Psychiatry, Ludwig-Maximilians-Universität, Munich, 81377, Germany
- Center for Neuropathology and Prion Research, Ludwig-Maximilians-Universität, Munich, 81377, Germany
| | - Catriona McLean
- Alfred Anatomical Pathology and NNF, Victorian Brain Bank, Carlton, VIC, 3053, Australia
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, Rotterdam, 3000, The Netherlands
| | - Johannes Schwarz
- Department of Neurology, Technische Universität München, Munich, 81377, Germany
| | - Daniela Vogt Weisenhorn
- German Center for Neurodegenerative Diseases (DZNE), Munich, 81377, Germany
- Institute of Developmental Genetics, Helmholtz Center München, Munich, 85764, Germany
- Chair of Developmental Genetics, Technische Universität München-Weihenstephan, Neuherberg/Munich, 85764, Germany
| | - Wolfgang Wurst
- German Center for Neurodegenerative Diseases (DZNE), Munich, 81377, Germany
- Institute of Developmental Genetics, Helmholtz Center München, Munich, 85764, Germany
- Chair of Developmental Genetics, Technische Universität München-Weihenstephan, Neuherberg/Munich, 85764, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, 81377, Germany
| | - Till Adhikary
- Institute for Molecular Biology and Tumor Research, Center for Tumor Biology and Immunology, Philipps University, Marburg, 35043, Germany
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Günter U Höglinger
- Department of Neurology, Technische Universität München, Munich, 81377, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, 81377, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, 81377, Germany.
| | - Ulrich Müller
- Institute of Human Genetics, Justus-Liebig-Universität, Gießen, 35392, Germany.
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14
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Chaix R, Alvarez-López MJ, Fagny M, Lemee L, Regnault B, Davidson RJ, Lutz A, Kaliman P. Epigenetic clock analysis in long-term meditators. Psychoneuroendocrinology 2017; 85:210-214. [PMID: 28889075 PMCID: PMC5863232 DOI: 10.1016/j.psyneuen.2017.08.016] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 08/12/2017] [Accepted: 08/12/2017] [Indexed: 10/19/2022]
Abstract
In this paper, we examined whether meditation practice influences the epigenetic clock, a strong and reproducible biomarker of biological aging, which is accelerated by cumulative lifetime stress and with age-related chronic diseases. Using the Illumina 450K array platform, we analyzed the DNA methylome from blood cells of long-term meditators and meditation-naïve controls to estimate their Intrinsic Epigenetic Age Acceleration (IEAA), using Horvath's calculator. IEAA was similar in both groups. However, controls showed a different IEAA trajectory with aging than meditators: older controls (age≥52) had significantly higher IEAAs compared with younger controls (age <52), while meditators were protected from this epigenetic aging effect. Notably, in the meditation group, we found a significant negative correlation between IEAA and the number of years of regular meditation practice. From our results, we hypothesize that the cumulative effects of a regular meditation practice may, in the long-term, help to slow the epigenetic clock and could represent a useful preventive strategy for age-related chronic diseases. Longitudinal randomized controlled trials in larger cohorts are warranted to confirm and further characterize these findings.
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Affiliation(s)
- Raphaëlle Chaix
- Eco-Anthropologie et Ethnobiologie, UMR 7206 CNRS, MNHN, Univ Paris Diderot, Sorbonne Paris Cité, France.
| | - Maria Jesús Alvarez-López
- Unitat de Farmacologia, Facultat de Farmàcia, Institut de Biomedicina, Universitat de Barcelona (IBUB), Nucli Universitari de Pedralbes, Barcelone, 08028, Spain
| | - Maud Fagny
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Laure Lemee
- Plateforme de génotypage des eucaryotes, Biomics, CITECH, Institut Pasteur, 75015 Paris, France
| | - Béatrice Regnault
- Plateforme de génotypage des eucaryotes, Biomics, CITECH, Institut Pasteur, 75015 Paris, France
| | | | - Antoine Lutz
- Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR5292, Lyon 1 University, 69500 Lyon, France
| | - Perla Kaliman
- Center for Mind and Brain, University of California Davis, Davis, CA 95618, USA.
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15
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Sonawane AR, Platig J, Fagny M, Chen CY, Paulson JN, Lopes-Ramos CM, DeMeo DL, Quackenbush J, Glass K, Kuijjer ML. Understanding Tissue-Specific Gene Regulation. Cell Rep 2017; 21:1077-1088. [PMID: 29069589 PMCID: PMC5828531 DOI: 10.1016/j.celrep.2017.10.001] [Citation(s) in RCA: 210] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 08/09/2017] [Accepted: 09/28/2017] [Indexed: 12/20/2022] Open
Abstract
Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.
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Affiliation(s)
- Abhijeet Rajendra Sonawane
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - John Platig
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Maud Fagny
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Cho-Yi Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Joseph Nathaniel Paulson
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Camila Miranda Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Dawn Lisa DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - John Quackenbush
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
| | - Marieke Lydia Kuijjer
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
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16
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Paulson JN, Chen CY, Lopes-Ramos CM, Kuijjer ML, Platig J, Sonawane AR, Fagny M, Glass K, Quackenbush J. Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data. BMC Bioinformatics 2017; 18:437. [PMID: 28974199 PMCID: PMC5627434 DOI: 10.1186/s12859-017-1847-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 09/21/2017] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis. RESULTS We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project. CONCLUSIONS An R package instantiating YARN is available at http://bioconductor.org/packages/yarn .
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Affiliation(s)
- Joseph N. Paulson
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215 USA
- Present address: Genentech, Department of Biostatistics, Product Development, 1 DNA Way, South San Francisco, CA 94080 USA
| | - Cho-Yi Chen
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215 USA
| | - Camila M. Lopes-Ramos
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215 USA
| | - Marieke L. Kuijjer
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215 USA
| | - John Platig
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215 USA
| | - Abhijeet R. Sonawane
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215 USA
| | - Maud Fagny
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215 USA
| | - Kimberly Glass
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215 USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215 USA
| | - John Quackenbush
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215 USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215 USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
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17
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Fagny M, Paulson JN, Kuijjer ML, Sonawane AR, Chen CY, Lopes-Ramos CM, Glass K, Quackenbush J, Platig J. Exploring regulation in tissues with eQTL networks. Proc Natl Acad Sci U S A 2017; 114:E7841-E7850. [PMID: 28851834 PMCID: PMC5604022 DOI: 10.1073/pnas.1707375114] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.
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Affiliation(s)
- Maud Fagny
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Joseph N Paulson
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Marieke L Kuijjer
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Abhijeet R Sonawane
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School Boston, MA 02115
| | - Cho-Yi Chen
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Camila M Lopes-Ramos
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School Boston, MA 02115
| | - John Quackenbush
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115;
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115
| | - John Platig
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115;
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
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Lopes-Ramos CM, Paulson JN, Chen CY, Kuijjer ML, Fagny M, Platig J, Sonawane AR, DeMeo DL, Quackenbush J, Glass K. Regulatory network changes between cell lines and their tissues of origin. BMC Genomics 2017; 18:723. [PMID: 28899340 PMCID: PMC5596945 DOI: 10.1186/s12864-017-4111-x] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 09/01/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. RESULTS We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. CONCLUSIONS Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.
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Affiliation(s)
- Camila M. Lopes-Ramos
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Joseph N. Paulson
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Cho-Yi Chen
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Marieke L. Kuijjer
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Maud Fagny
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - John Platig
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Abhijeet R. Sonawane
- Channing Division of Network Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA USA
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA USA
| | - John Quackenbush
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
| | - Kimberly Glass
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA USA
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19
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Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, Ritz BR, Chen B, Lu AT, Rickabaugh TM, Jamieson BD, Sun D, Li S, Chen W, Quintana-Murci L, Fagny M, Kobor MS, Tsao PS, Reiner AP, Edlefsen KL, Absher D, Assimes TL. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol 2016; 17:171. [PMID: 27511193 PMCID: PMC4980791 DOI: 10.1186/s13059-016-1030-0] [Citation(s) in RCA: 433] [Impact Index Per Article: 54.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 07/18/2016] [Indexed: 01/07/2023] Open
Abstract
Background Epigenetic biomarkers of aging (the “epigenetic clock”) have the potential to address puzzling findings surrounding mortality rates and incidence of cardio-metabolic disease such as: (1) women consistently exhibiting lower mortality than men despite having higher levels of morbidity; (2) racial/ethnic groups having different mortality rates even after adjusting for socioeconomic differences; (3) the black/white mortality cross-over effect in late adulthood; and (4) Hispanics in the United States having a longer life expectancy than Caucasians despite having a higher burden of traditional cardio-metabolic risk factors. Results We analyzed blood, saliva, and brain samples from seven different racial/ethnic groups. We assessed the intrinsic epigenetic age acceleration of blood (independent of blood cell counts) and the extrinsic epigenetic aging rates of blood (dependent on blood cell counts and tracks the age of the immune system). In blood, Hispanics and Tsimane Amerindians have lower intrinsic but higher extrinsic epigenetic aging rates than Caucasians. African-Americans have lower extrinsic epigenetic aging rates than Caucasians and Hispanics but no differences were found for the intrinsic measure. Men have higher epigenetic aging rates than women in blood, saliva, and brain tissue. Conclusions Epigenetic aging rates are significantly associated with sex, race/ethnicity, and to a lesser extent with CHD risk factors, but not with incident CHD outcomes. These results may help elucidate lower than expected mortality rates observed in Hispanics, older African-Americans, and women. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1030-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Steve Horvath
- Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA. .,Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095, USA.
| | - Michael Gurven
- Department of Anthropology, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Morgan E Levine
- Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Benjamin C Trumble
- Department of Anthropology, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Hillard Kaplan
- Department of Anthropology, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Hooman Allayee
- Department of Preventive Medicine and Institute for Genetic Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Beate R Ritz
- Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Brian Chen
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Ake T Lu
- Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Tammy M Rickabaugh
- Department of Medicine, Division of Hematology/Oncology, AIDS Institute, University of California Los Angeles, Los Angeles, CA, USA
| | - Beth D Jamieson
- Department of Medicine, Division of Hematology/Oncology, AIDS Institute, University of California Los Angeles, Los Angeles, CA, USA
| | - Dianjianyi Sun
- Department of Epidemiology, Tulane University, New Orleans, LA, 70112, USA
| | - Shengxu Li
- Department of Epidemiology, Tulane University, New Orleans, LA, 70112, USA
| | - Wei Chen
- Department of Epidemiology, Tulane University, New Orleans, LA, 70112, USA
| | - Lluis Quintana-Murci
- Unit of Human Evolutionary Genetics, Centre National de la Recherche Scientifique, URA3012, URA3012 Institut Pasteur, Paris, 75015, France
| | - Maud Fagny
- Department of Biostatistics, Harvard TH Chan School of Public Health and Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute and Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Philip S Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.,VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Alexander P Reiner
- Department of Epidemiology, Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA, 98109, USA
| | - Kerstin L Edlefsen
- Department of Laboratory Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Themistocles L Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
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20
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Deschamps M, Laval G, Fagny M, Itan Y, Abel L, Casanova JL, Patin E, Quintana-Murci L. Genomic Signatures of Selective Pressures and Introgression from Archaic Hominins at Human Innate Immunity Genes. Am J Hum Genet 2016; 98:5-21. [PMID: 26748513 DOI: 10.1016/j.ajhg.2015.11.014] [Citation(s) in RCA: 180] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 11/06/2015] [Indexed: 01/25/2023] Open
Abstract
Human genes governing innate immunity provide a valuable tool for the study of the selective pressure imposed by microorganisms on host genomes. A comprehensive, genome-wide study of how selective constraints and adaptations have driven the evolution of innate immunity genes is missing. Using full-genome sequence variation from the 1000 Genomes Project, we first show that innate immunity genes have globally evolved under stronger purifying selection than the remainder of protein-coding genes. We identify a gene set under the strongest selective constraints, mutations in which are likely to predispose individuals to life-threatening disease, as illustrated by STAT1 and TRAF3. We then evaluate the occurrence of local adaptation and detect 57 high-scoring signals of positive selection at innate immunity genes, variation in which has been associated with susceptibility to common infectious or autoimmune diseases. Furthermore, we show that most adaptations targeting coding variation have occurred in the last 6,000-13,000 years, the period at which populations shifted from hunting and gathering to farming. Finally, we show that innate immunity genes present higher Neandertal introgression than the remainder of the coding genome. Notably, among the genes presenting the highest Neandertal ancestry, we find the TLR6-TLR1-TLR10 cluster, which also contains functional adaptive variation in Europeans. This study identifies highly constrained genes that fulfill essential, non-redundant functions in host survival and reveals others that are more permissive to change-containing variation acquired from archaic hominins or adaptive variants in specific populations-improving our understanding of the relative biological importance of innate immunity pathways in natural conditions.
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Affiliation(s)
- Matthieu Deschamps
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France; CNRS URA3012, 75015 Paris, France; Université Pierre et Marie Curie, Cellule Pasteur UPMC, 75015 Paris, France
| | - Guillaume Laval
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France; CNRS URA3012, 75015 Paris, France
| | - Maud Fagny
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France; CNRS URA3012, 75015 Paris, France; Université Pierre et Marie Curie, Cellule Pasteur UPMC, 75015 Paris, France
| | - Yuval Itan
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA
| | - Laurent Abel
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U.1163, 75015 Paris, France; Imagine Institute, Paris Descartes University, 75015 Paris, France
| | - Jean-Laurent Casanova
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U.1163, 75015 Paris, France; Imagine Institute, Paris Descartes University, 75015 Paris, France; Howard Hughes Medical Institute, New York, NY 10065, USA; Pediatric Hematology-Immunology Unit, Necker Hospital for Sick Children, 75015 Paris, France
| | - Etienne Patin
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France; CNRS URA3012, 75015 Paris, France
| | - Lluis Quintana-Murci
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France; CNRS URA3012, 75015 Paris, France.
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21
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Fagny M, Patin E, MacIsaac JL, Rotival M, Flutre T, Jones MJ, Siddle KJ, Quach H, Harmant C, McEwen LM, Froment A, Heyer E, Gessain A, Betsem E, Mouguiama-Daouda P, Hombert JM, Perry GH, Barreiro LB, Kobor MS, Quintana-Murci L. The epigenomic landscape of African rainforest hunter-gatherers and farmers. Nat Commun 2015; 6:10047. [PMID: 26616214 PMCID: PMC4674682 DOI: 10.1038/ncomms10047] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 10/28/2015] [Indexed: 12/23/2022] Open
Abstract
The genetic history of African populations is increasingly well documented, yet their patterns of epigenomic variation remain uncharacterized. Moreover, the relative impacts of DNA sequence variation and temporal changes in lifestyle and habitat on the human epigenome remain unknown. Here we generate genome-wide genotype and DNA methylation profiles for 362 rainforest hunter-gatherers and sedentary farmers. We find that the current habitat and historical lifestyle of a population have similarly critical impacts on the methylome, but the biological functions affected strongly differ. Specifically, methylation variation associated with recent changes in habitat mostly concerns immune and cellular functions, whereas that associated with historical lifestyle affects developmental processes. Furthermore, methylation variation—particularly that correlated with historical lifestyle—shows strong associations with nearby genetic variants that, moreover, are enriched in signals of natural selection. Our work provides new insight into the genetic and environmental factors affecting the epigenomic landscape of human populations over time. Genetic and environmental factors affect genome-wide patterns of epigenetic variation. Here, the authors show that while current habitat and historical lifestyle impact the methylome of rainforest hunter-gatherers and sedentary farmers, the biological functions affected and the degree of genetic control differ.
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Affiliation(s)
- Maud Fagny
- Institut Pasteur, Unit of Human Evolutionary Genetics, Paris 75015, France.,Centre National de la Recherche Scientifique, URA3012, Paris 75015, France.,Université Pierre et Marie Curie, Cellule Pasteur UPMC, Paris 75015, France
| | - Etienne Patin
- Institut Pasteur, Unit of Human Evolutionary Genetics, Paris 75015, France.,Centre National de la Recherche Scientifique, URA3012, Paris 75015, France
| | - Julia L MacIsaac
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute and Department of Medical Genetics, University of British Columbia, Vancouver, Canada BC V5Z 4H4
| | - Maxime Rotival
- Institut Pasteur, Unit of Human Evolutionary Genetics, Paris 75015, France.,Centre National de la Recherche Scientifique, URA3012, Paris 75015, France
| | | | - Meaghan J Jones
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute and Department of Medical Genetics, University of British Columbia, Vancouver, Canada BC V5Z 4H4
| | - Katherine J Siddle
- Institut Pasteur, Unit of Human Evolutionary Genetics, Paris 75015, France.,Centre National de la Recherche Scientifique, URA3012, Paris 75015, France
| | - Hélène Quach
- Institut Pasteur, Unit of Human Evolutionary Genetics, Paris 75015, France.,Centre National de la Recherche Scientifique, URA3012, Paris 75015, France
| | - Christine Harmant
- Institut Pasteur, Unit of Human Evolutionary Genetics, Paris 75015, France.,Centre National de la Recherche Scientifique, URA3012, Paris 75015, France
| | - Lisa M McEwen
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute and Department of Medical Genetics, University of British Columbia, Vancouver, Canada BC V5Z 4H4
| | - Alain Froment
- IRD-MNHN, Sorbonne Universités, UMR208, Paris 75005, France
| | - Evelyne Heyer
- CNRS, MNHN, Université Paris Diderot, Sorbonne Paris Cité, Sorbonne Université, UMR7206, Paris 75005, France
| | - Antoine Gessain
- Institut Pasteur, Unité d'Epidémiologie et Physiopathologie des Virus Oncogènes, Paris 75015, France
| | - Edouard Betsem
- Institut Pasteur, Unité d'Epidémiologie et Physiopathologie des Virus Oncogènes, Paris 75015, France.,Faculty of Medicine and Biomedical Sciences, University of Yaoundé I, BP1364 Yaoundé, Cameroon
| | - Patrick Mouguiama-Daouda
- Laboratoire Langue, Culture et Cognition (LCC), Université Omar Bongo, BP 13131 Libreville, Gabon
| | | | - George H Perry
- Departments of Anthropology and Biology, Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Luis B Barreiro
- Université de Montréal, Centre de Recherche CHU Sainte-Justine, Montréal, Canada H3T 1C5
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute and Department of Medical Genetics, University of British Columbia, Vancouver, Canada BC V5Z 4H4
| | - Lluis Quintana-Murci
- Institut Pasteur, Unit of Human Evolutionary Genetics, Paris 75015, France.,Centre National de la Recherche Scientifique, URA3012, Paris 75015, France
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22
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Fagny M, Patin E, Enard D, Barreiro LB, Quintana-Murci L, Laval G. Exploring the occurrence of classic selective sweeps in humans using whole-genome sequencing data sets. Mol Biol Evol 2014; 31:1850-68. [PMID: 24694833 DOI: 10.1093/molbev/msu118] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Genome-wide scans for selection have identified multiple regions of the human genome as being targeted by positive selection. However, only a small proportion has been replicated across studies, and the prevalence of positive selection as a mechanism of adaptive change in humans remains controversial. Here we explore the power of two haplotype-based statistics--the integrated haplotype score (iHS) and the Derived Intraallelic Nucleotide Diversity (DIND) test--in the context of next-generation sequencing data, and evaluate their robustness to demography and other selection modes. We show that these statistics are both powerful for the detection of recent positive selection, regardless of population history, and robust to variation in coverage, with DIND being insensitive to very low coverage. We apply these statistics to whole-genome sequence data sets from the 1000 Genomes Project and Complete Genomics. We found that putative targets of selection were highly significantly enriched in genic and nonsynonymous single nucleotide polymorphisms, and that DIND was more powerful than iHS in the context of small sample sizes, low-quality genotype calling, or poor coverage. As we excluded genomic confounders and alternative selection models, such as background selection, the observed enrichment attests to the action of recent, strong positive selection. Further support to the adaptive significance of these genomic regions came from their enrichment in functional variants detected by genome-wide association studies, informing the relationship between past selection and current benign and disease-related phenotypic variation. Our results indicate that hard sweeps targeting low-frequency standing variation have played a moderate, albeit significant, role in recent human evolution.
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Affiliation(s)
- Maud Fagny
- Institut Pasteur, Human Evolutionary Genetics, Department of Genomes and Genetics, Paris, FranceCentre National de la Recherche Scientifique, URA3012, Paris, FranceUniversité Pierre et Marie Curie, Cellule Pasteur UPMC, Paris, France
| | - Etienne Patin
- Institut Pasteur, Human Evolutionary Genetics, Department of Genomes and Genetics, Paris, FranceCentre National de la Recherche Scientifique, URA3012, Paris, France
| | | | - Luis B Barreiro
- Department of Pediatrics, Sainte-Justine Hospital Research Center, University of Montreal, Montreal, Quebec, Canada
| | - Lluis Quintana-Murci
- Institut Pasteur, Human Evolutionary Genetics, Department of Genomes and Genetics, Paris, FranceCentre National de la Recherche Scientifique, URA3012, Paris, France
| | - Guillaume Laval
- Institut Pasteur, Human Evolutionary Genetics, Department of Genomes and Genetics, Paris, FranceCentre National de la Recherche Scientifique, URA3012, Paris, France
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