1
|
Houzelstein D, Eozenou C, Lagos CF, Elzaiat M, Bignon-Topalovic J, Gonzalez I, Laville V, Schlick L, Wankanit S, Madon P, Kirtane J, Athalye A, Buonocore F, Bigou S, Conway GS, Bohl D, Achermann JC, Bashamboo A, McElreavey K. A conserved NR5A1-responsive enhancer regulates SRY in testis-determination. Nat Commun 2024; 15:2796. [PMID: 38555298 PMCID: PMC10981742 DOI: 10.1038/s41467-024-47162-2] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 03/21/2024] [Indexed: 04/02/2024] Open
Abstract
The Y-linked SRY gene initiates mammalian testis-determination. However, how the expression of SRY is regulated remains elusive. Here, we demonstrate that a conserved steroidogenic factor-1 (SF-1)/NR5A1 binding enhancer is required for appropriate SRY expression to initiate testis-determination in humans. Comparative sequence analysis of SRY 5' regions in mammals identified an evolutionary conserved SF-1/NR5A1-binding motif within a 250 bp region of open chromatin located 5 kilobases upstream of the SRY transcription start site. Genomic analysis of 46,XY individuals with disrupted testis-determination, including a large multigenerational family, identified unique single-base substitutions of highly conserved residues within the SF-1/NR5A1-binding element. In silico modelling and in vitro assays demonstrate the enhancer properties of the NR5A1 motif. Deletion of this hemizygous element by genome-editing, in a novel in vitro cellular model recapitulating human Sertoli cell formation, resulted in a significant reduction in expression of SRY. Therefore, human NR5A1 acts as a regulatory switch between testis and ovary development by upregulating SRY expression, a role that may predate the eutherian radiation. We show that disruption of an enhancer can phenocopy variants in the coding regions of SRY that cause human testis dysgenesis. Since disease causing variants in enhancers are currently rare, the regulation of gene expression in testis-determination offers a paradigm to define enhancer activity in a key developmental process.
Collapse
Affiliation(s)
- Denis Houzelstein
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France.
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France.
| | - Caroline Eozenou
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
- Institut Cochin, Université Paris Cité, INSERM, CNRS, Paris, France
| | - Carlos F Lagos
- Chemical Biology & Drug Discovery Lab, Escuela de Química y Farmacia, Facultad de Medicina y Ciencia, Universidad San Sebastián, Campus Los Leones, Lota 2465 Providencia, 7510157, Santiago, Chile
- Centro Ciencia & Vida, Fundación Ciencia & Vida, Av. del Valle Norte 725, Huechuraba, 8580702, Santiago, Chile
| | - Maëva Elzaiat
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
| | - Joelle Bignon-Topalovic
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
| | - Inma Gonzalez
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
- Institut Pasteur, Université Paris Cité, Epigenomics, Proliferation, and the Identity of Cells Unit, F-75015, Paris, France
| | - Vincent Laville
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
- Institut Pasteur, Université Paris Cité, Stem Cells and Development Unit, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015, Paris, France
| | - Laurène Schlick
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
| | - Somboon Wankanit
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
- Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Prochi Madon
- Department of Assisted Reproduction and Genetics, Jaslok Hospital and Research Centre, Mumbai, India
| | - Jyotsna Kirtane
- Department of Pediatric Surgery, Jaslok Hospital and Research Centre, Mumbai, India
| | - Arundhati Athalye
- Department of Assisted Reproduction and Genetics, Jaslok Hospital and Research Centre, Mumbai, India
| | - Federica Buonocore
- Genetics and Genomic Medicine Research & Teaching Department, UCL GOS Institute of Child Health, University College London, London, United Kingdom
| | - Stéphanie Bigou
- ICV-iPS core facility, Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Gerard S Conway
- Institute for Women's Health, University College London, London, United Kingdom
| | - Delphine Bohl
- ICV-iPS core facility, Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - John C Achermann
- Genetics and Genomic Medicine Research & Teaching Department, UCL GOS Institute of Child Health, University College London, London, United Kingdom
| | - Anu Bashamboo
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
| | - Ken McElreavey
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France.
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France.
| |
Collapse
|
2
|
Girolamo DD, Benavente-Diaz M, Murolo M, Grimaldi A, Lopes PT, Evano B, Kuriki M, Gioftsidi S, Laville V, Tinevez JY, Letort G, Mella S, Tajbakhsh S, Comai G. Extraocular muscle stem cells exhibit distinct cellular properties associated with non-muscle molecular signatures. Development 2024; 151:dev202144. [PMID: 38240380 DOI: 10.1242/dev.202144] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/27/2023] [Indexed: 02/22/2024]
Abstract
Skeletal muscle stem cells (MuSCs) are recognised as functionally heterogeneous. Cranial MuSCs are reported to have greater proliferative and regenerative capacity when compared with those in the limb. A comprehensive understanding of the mechanisms underlying this functional heterogeneity is lacking. Here, we have used clonal analysis, live imaging and single cell transcriptomic analysis to identify crucial features that distinguish extraocular muscle (EOM) from limb muscle stem cell populations. A MyogeninntdTom reporter showed that the increased proliferation capacity of EOM MuSCs correlates with deferred differentiation and lower expression of the myogenic commitment gene Myod. Unexpectedly, EOM MuSCs activated in vitro expressed a large array of extracellular matrix components typical of mesenchymal non-muscle cells. Computational analysis underscored a distinct co-regulatory module, which is absent in limb MuSCs, as driver of these features. The EOM transcription factor network, with Foxc1 as key player, appears to be hardwired to EOM identity as it persists during growth, disease and in vitro after several passages. Our findings shed light on how high-performing MuSCs regulate myogenic commitment by remodelling their local environment and adopting properties not generally associated with myogenic cells.
Collapse
Affiliation(s)
- Daniela Di Girolamo
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
| | - Maria Benavente-Diaz
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
- Sorbonne Universités, Complexité du Vivant, F-75005 Paris, France
| | - Melania Murolo
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
| | - Alexandre Grimaldi
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
- Sorbonne Universités, Complexité du Vivant, F-75005 Paris, France
| | - Priscilla Thomas Lopes
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
| | - Brendan Evano
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
| | - Mao Kuriki
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
| | - Stamatia Gioftsidi
- Université Paris-Est, 77420 Champs-sur- Marne, France
- Freie Universität Berlin, 14195 Berlin, Germany
- Inserm, IMRB U955-E10, 94000 Créteil, France
| | - Vincent Laville
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015 Paris, France
| | - Jean-Yves Tinevez
- Institut Pasteur, Université Paris Cité, Image Analysis Hub, 75015 Paris, France
| | - Gaëlle Letort
- Department of Developmental and Stem Cell Biology, Institut Pasteur, Université de Paris Cité, CNRS UMR 3738, 25 rue du Dr Roux, 75015 Paris, France
| | - Sebastian Mella
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015 Paris, France
| | - Shahragim Tajbakhsh
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
| | - Glenda Comai
- Stem Cells and Development Unit, 25 rue du Dr Roux, Institut Pasteur, 75015 Paris, France
- UMR CNRS 3738, Institut Pasteur, Paris, France
| |
Collapse
|
3
|
Majarian TD, Bentley AR, Laville V, Brown MR, Chasman DI, de Vries PS, Feitosa MF, Franceschini N, Gauderman WJ, Marchek C, Levy D, Morrison AC, Province M, Rao DC, Schwander K, Sung YJ, Rotimi CN, Aschard H, Gu CC, Manning AK. Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption. Front Genet 2022; 13:954713. [PMID: 36544485 PMCID: PMC9760722 DOI: 10.3389/fgene.2022.954713] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium's Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and 'omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.
Collapse
Affiliation(s)
- Timothy D. Majarian
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, US National Institutes of Health, Bethesda, MD, United States
| | - Vincent Laville
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France
| | - Michael R. Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Mary F. Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - W. James Gauderman
- Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States
| | - Casey Marchek
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, United States,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, United States
| | - Daniel Levy
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MA, United States
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Michael Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
| | - Dabeeru C. Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Karen Schwander
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States,Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, US National Institutes of Health, Bethesda, MD, United States
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France,Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - C. Charles Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Alisa K. Manning
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, United States,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, United States,Department of Medicine and Harvard Medical School, Boston, MA, United States,*Correspondence: Alisa K. Manning,
| | | |
Collapse
|
4
|
Rahmouni M, Laville V, Spadoni JL, Jdid R, Eckhart L, Gruber F, Labib T, Coulonges C, Carpentier W, Latreille J, Morizot F, Tschachler E, Ezzedine K, Le Clerc S, Zagury JF. Identification of New Biological Pathways Involved in Skin Aging From the Analysis of French Women Genome-Wide Data. Front Genet 2022; 13:836581. [PMID: 35401686 PMCID: PMC8987498 DOI: 10.3389/fgene.2022.836581] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/01/2022] [Indexed: 11/30/2022] Open
Abstract
Skin aging is an ineluctable process leading to the progressive loss of tissue integrity and is characterized by various outcomes such as wrinkling and sagging. Researchers have identified impacting environmental factors (sun exposure, smoking, etc.) and several molecular mechanisms leading to skin aging. We have previously performed genome-wide association studies (GWAS) in 502 very-well characterized French women, looking for associations with four major outcomes of skin aging, namely, photoaging, solar lentigines, wrinkling, and sagging, and this has led to new insights into the molecular mechanisms of skin aging. Since individual SNP associations in GWAS explain only a small fraction of the genetic impact in complex polygenic phenotypes, we have made the integration of these genotypes into the reference Kegg biological pathways and looked for associations by the gene set enrichment analysis (GSEA) approach. 106 pathways were tested for association with the four outcomes of skin aging. This biological pathway analysis revealed new relevant pathways and genes, some likely specific of skin aging such as the WNT7B and PRKCA genes in the “melanogenesis” pathway and some likely involved in global aging such as the DDB1 gene in the “nucleotide excision repair” pathway, not picked up in the previously published GWAS. Overall, our results suggest that the four outcomes of skin aging possess specific molecular mechanisms such as the “proteasome” and “mTOR signaling pathway” but may also share common molecular mechanisms such as “nucleotide excision repair.”
Collapse
Affiliation(s)
- Myriam Rahmouni
- Équipe Génomique, Bioinformatique et Chimie Moléculaire (EA 7528), Conservatoire National des Arts et Métiers, HESAM Université, Paris, France
| | - Vincent Laville
- Équipe Génomique, Bioinformatique et Chimie Moléculaire (EA 7528), Conservatoire National des Arts et Métiers, HESAM Université, Paris, France
| | - Jean-Louis Spadoni
- Équipe Génomique, Bioinformatique et Chimie Moléculaire (EA 7528), Conservatoire National des Arts et Métiers, HESAM Université, Paris, France
| | - Randa Jdid
- Chanel R&T, Department of Skin Knowledge and Women Beauty, Pantin, France
| | - Leopold Eckhart
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Florian Gruber
- Department of Dermatology, Medical University of Vienna, Vienna, Austria.,Christian Doppler Laboratory for Skin Multimodal Analytical Imaging of Aging and Senescence (SKINMAGINE), Medical University of Vienna, Vienna, Austria
| | - Taoufik Labib
- Équipe Génomique, Bioinformatique et Chimie Moléculaire (EA 7528), Conservatoire National des Arts et Métiers, HESAM Université, Paris, France
| | - Cedric Coulonges
- Équipe Génomique, Bioinformatique et Chimie Moléculaire (EA 7528), Conservatoire National des Arts et Métiers, HESAM Université, Paris, France
| | - Wassila Carpentier
- Plate-Forme Post-Génomique P3S, Hôpital Pitié-Salpêtrière, Paris, France
| | - Julie Latreille
- Chanel R&T, Department of Skin Knowledge and Women Beauty, Pantin, France
| | - Frederique Morizot
- Chanel R&T, Department of Skin Knowledge and Women Beauty, Pantin, France
| | - Erwin Tschachler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Khaled Ezzedine
- Department of Dermatology, Hôpital Henri Mondor and EA 7379 EPIDERM, Créteil, France
| | - Sigrid Le Clerc
- Équipe Génomique, Bioinformatique et Chimie Moléculaire (EA 7528), Conservatoire National des Arts et Métiers, HESAM Université, Paris, France
| | - Jean-François Zagury
- Équipe Génomique, Bioinformatique et Chimie Moléculaire (EA 7528), Conservatoire National des Arts et Métiers, HESAM Université, Paris, France
| |
Collapse
|
5
|
Julienne H, Laville V, McCaw ZR, He Z, Guillemot V, Lasry C, Ziyatdinov A, Nerin C, Vaysse A, Lechat P, Ménager H, Le Goff W, Dube MP, Kraft P, Ionita-Laza I, Vilhjálmsson BJ, Aschard H. Multitrait GWAS to connect disease variants and biological mechanisms. PLoS Genet 2021; 17:e1009713. [PMID: 34460823 PMCID: PMC8437297 DOI: 10.1371/journal.pgen.1009713] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [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: 01/20/2021] [Revised: 09/13/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.
Collapse
Affiliation(s)
- Hanna Julienne
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Vincent Laville
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Zachary R. McCaw
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, United States of America
| | - Vincent Guillemot
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Carla Lasry
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Andrey Ziyatdinov
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Cyril Nerin
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Amaury Vaysse
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Pierre Lechat
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Hervé Ménager
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Wilfried Le Goff
- Sorbonne Université, INSERM, Institute of Cardiometabolism and Nutrition (ICAN), UMR_S 1166, Paris, France
| | - Marie-Pierre Dube
- Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal Heart Institute, Montreal, Canada
- Université de Montréal, Faculty of Medicine, Department of medicine, Université de Montréal, Montreal, Canada
| | - Peter Kraft
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, New York, United States of America
| | - Bjarni J. Vilhjálmsson
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Paris, France
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| |
Collapse
|
6
|
Laville V, Majarian T, de Vries PS, Bentley AR, Feitosa MF, Sung YJ, Rao DC, Manning A, Aschard H. Deriving stratified effects from joint models investigating gene-environment interactions. BMC Bioinformatics 2020; 21:251. [PMID: 32552674 PMCID: PMC7302007 DOI: 10.1186/s12859-020-03569-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [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: 08/02/2019] [Accepted: 05/28/2020] [Indexed: 11/12/2022] Open
Abstract
Background Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models. Results In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the “joint” model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. Conclusions These analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s.
Collapse
Affiliation(s)
- Vincent Laville
- Department of Computational Biology, USR 3756 CNRS, Institut Pasteur, Paris, France.
| | - Timothy Majarian
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mary F Feitosa
- Division of Biostatistics, Department of Genetics, Washington University School of Medecine, St. Louis, MO, 63110, USA
| | - Yun J Sung
- Division of Biostatistics, Department of Genetics, Washington University School of Medecine, St. Louis, MO, 63110, USA
| | - D C Rao
- Division of Biostatistics, Department of Genetics, Washington University School of Medecine, St. Louis, MO, 63110, USA
| | - Alisa Manning
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.,Center for Human Genetics Research, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Hugues Aschard
- Department of Computational Biology, USR 3756 CNRS, Institut Pasteur, Paris, France. .,Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
| | | |
Collapse
|
7
|
Julienne H, Lechat P, Guillemot V, Lasry C, Yao C, Araud R, Laville V, Vilhjalmsson B, Ménager H, Aschard H. JASS: command line and web interface for the joint analysis of GWAS results. NAR Genom Bioinform 2020; 2:lqaa003. [PMID: 32002517 PMCID: PMC6978790 DOI: 10.1093/nargab/lqaa003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 12/03/2019] [Accepted: 01/09/2020] [Indexed: 12/11/2022] Open
Abstract
Genome-wide association study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available. These GWAS have proven their utility for a range of secondary analyses, including in particular the joint analysis of multiple phenotypes to identify new associated genetic variants. However, although several methods have been proposed, there are very few large-scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package incorporates recently developed joint tests such as the omnibus approach and various weighted sum of Z-score tests while solving all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of joint statistics, an optimized data management process and a web interface for exploration purposes. Both benchmark analyses and real data applications demonstrated the robustness and strong potential of JASS for the detection of new associated genetic variants. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.
Collapse
Affiliation(s)
- Hanna Julienne
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Pierre Lechat
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Vincent Guillemot
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Carla Lasry
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Chunzi Yao
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Robinson Araud
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Vincent Laville
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Bjarni Vilhjalmsson
- National Center for Register-Based Research, Aarhus University, DK-8210 Aarhus, Denmark
| | - Hervé Ménager
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Hugues Aschard
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 02115 Boston, MA, USA
| |
Collapse
|
8
|
Noordam R, Bos MM, Wang H, Winkler TW, Bentley AR, Kilpeläinen TO, de Vries PS, Sung YJ, Schwander K, Cade BE, Manning A, Aschard H, Brown MR, Chen H, Franceschini N, Musani SK, Richard M, Vojinovic D, Aslibekyan S, Bartz TM, de Las Fuentes L, Feitosa M, Horimoto AR, Ilkov M, Kho M, Kraja A, Li C, Lim E, Liu Y, Mook-Kanamori DO, Rankinen T, Tajuddin SM, van der Spek A, Wang Z, Marten J, Laville V, Alver M, Evangelou E, Graff ME, He M, Kühnel B, Lyytikäinen LP, Marques-Vidal P, Nolte IM, Palmer ND, Rauramaa R, Shu XO, Snieder H, Weiss S, Wen W, Yanek LR, Adolfo C, Ballantyne C, Bielak L, Biermasz NR, Boerwinkle E, Dimou N, Eiriksdottir G, Gao C, Gharib SA, Gottlieb DJ, Haba-Rubio J, Harris TB, Heikkinen S, Heinzer R, Hixson JE, Homuth G, Ikram MA, Komulainen P, Krieger JE, Lee J, Liu J, Lohman KK, Luik AI, Mägi R, Martin LW, Meitinger T, Metspalu A, Milaneschi Y, Nalls MA, O'Connell J, Peters A, Peyser P, Raitakari OT, Reiner AP, Rensen PCN, Rice TK, Rich SS, Roenneberg T, Rotter JI, Schreiner PJ, Shikany J, Sidney SS, Sims M, Sitlani CM, Sofer T, Strauch K, Swertz MA, Taylor KD, Uitterlinden AG, van Duijn CM, Völzke H, Waldenberger M, Wallance RB, van Dijk KW, Yu C, Zonderman AB, Becker DM, Elliott P, Esko T, Gieger C, Grabe HJ, Lakka TA, Lehtimäki T, North KE, Penninx BWJH, Vollenweider P, Wagenknecht LE, Wu T, Xiang YB, Zheng W, Arnett DK, Bouchard C, Evans MK, Gudnason V, Kardia S, Kelly TN, Kritchevsky SB, Loos RJF, Pereira AC, Province M, Psaty BM, Rotimi C, Zhu X, Amin N, Cupples LA, Fornage M, Fox EF, Guo X, Gauderman WJ, Rice K, Kooperberg C, Munroe PB, Liu CT, Morrison AC, Rao DC, van Heemst D, Redline S. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration. Nat Commun 2019; 10:5121. [PMID: 31719535 PMCID: PMC6851116 DOI: 10.1038/s41467-019-12958-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [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: 03/08/2019] [Accepted: 10/04/2019] [Indexed: 12/12/2022] Open
Abstract
Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles. Sleep duration is associated with an adverse lipid profile. Here, the authors perform genome-wide gene-by-sleep interaction analysis and find 49 previously unreported lipid loci when considering short or long total sleep time.
Collapse
Affiliation(s)
- Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands.
| | - Maxime M Bos
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark.,Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Alisa Manning
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.,Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Center for Precision Health, School of Public Health & School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Solomon K Musani
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Melissa Richard
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle, WA, USA
| | - Lisa de Las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.,Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Mary Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrea R Horimoto
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil
| | | | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Aldi Kraja
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Changwei Li
- Epidemiology and Biostatistics, University of Georgia at Athens College of Public Health, Athens, GA, USA
| | - Elise Lim
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Yongmei Liu
- Public Health Sciences, Epidemiology and Prevention, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands.,Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Salman M Tajuddin
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Ashley van der Spek
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Zhe Wang
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Vincent Laville
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
| | - Maris Alver
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.,Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Maria E Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.,Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Pedro Marques-Vidal
- Medicine, Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Ilja M Nolte
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | | | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Harold Snieder
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Lisa R Yanek
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Correa Adolfo
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Christie Ballantyne
- Section of Cardiovascular Research, Baylor College of Medicine, Houston, TX, USA.,Houston Methodist Debakey Heart and Vascular Center, Houston, TX, USA
| | - Larry Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Nienke R Biermasz
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands.,Einthoven Laboratory for Experimental Vascular Medicine, Leiden, The Netherlands
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Niki Dimou
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | | | - Chuan Gao
- Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, UW Medicine Sleep Center, Medicine, University of Washington, Seattle, WA, USA
| | - Daniel J Gottlieb
- Division of Sleep and Circadian Disorders, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA.,VA Boston Healthcare System, Boston, MA, USA
| | - José Haba-Rubio
- Medicine, Sleep Laboratory, Lausanne University Hospital, Lausanne, Switzerland
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Sami Heikkinen
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland.,Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland
| | - Raphaël Heinzer
- Medicine, Sleep Laboratory, Lausanne University Hospital, Lausanne, Switzerland
| | - James E Hixson
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Pirjo Komulainen
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Jose E Krieger
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil
| | - Jiwon Lee
- Division of Sleep and Circadian Disorders, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Jingmin Liu
- Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, WA, USA
| | - Kurt K Lohman
- Public Health Sciences, Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Lisa W Martin
- Cardiology, School of Medicine and Health Sciences, George Washington University, Washington, D.C., USA
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Yuri Milaneschi
- Cardiology, School of Medicine and Health Sciences, George Washington University, Washington, D.C., USA
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA.,Data Tecnica International, Glen Echo, MD, USA
| | - Jeff O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA.,Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Neuherberg, Germany
| | - Patricia Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.,University of Turku, Turku, Finland
| | - Alex P Reiner
- Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, WA, USA
| | - Patrick C N Rensen
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands.,Einthoven Laboratory for Experimental Vascular Medicine, Leiden, The Netherlands
| | - Treva K Rice
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Till Roenneberg
- Institute of Medical Psychology, Ludwig-Maximilians-Universitat Munchen, Munich, Germany
| | - Jerome I Rotter
- Genomic Outcomes, Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CC, USA
| | - Pamela J Schreiner
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James Shikany
- Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Stephen S Sidney
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Mario Sims
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, WA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.,Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Institute for Medical Informatics Biometry and Epidemiology, Ludwig-Maximilians-Universitat Munchen, Munich, Germany
| | - Morris A Swertz
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Kent D Taylor
- Genomic Outcomes, Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CC, USA
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Neuherberg, Germany
| | - Robert B Wallance
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, USA
| | - Ko Willems van Dijk
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands.,Einthoven Laboratory for Experimental Vascular Medicine, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Caizheng Yu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Alan B Zonderman
- Behavioral Epidemiology Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Diane M Becker
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Paul Elliott
- Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.,MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,National Institute of Health Research Imperial College London Biomedical Research Centre, London, UK.,UK-DRI Dementia Research Institute at Imperial College London, London, UK
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Broad Institute of the Massachusetts Institute of Technology and Harvard University, Boston, MA, USA
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Hans J Grabe
- Department Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Timo A Lakka
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland.,Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland.,Department of Clinical Phsiology and Nuclear Medicine, Kuopia University Hospital, Kuopio, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.,Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Peter Vollenweider
- Medicine, Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Tangchun Wu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yong-Bing Xiang
- SKLORG & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, P. R. China
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Donna K Arnett
- Dean's Office, University of Kentucky College of Public Health, Lexington, KS, USA
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Michele K Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Sharon Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Tanika N Kelly
- Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Stephen B Kritchevsky
- Sticht Center for Healthy Aging and Rehabilitation, Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY, USA.,The Mindich Child Health Development Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexandre C Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil
| | - Mike Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Epidemiology, Medicine and Health Services, University of Washington, Seattle, WA, USA.,Kaiser Permanente Washington, Health Research Institute, Seattle, WA, USA
| | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xiaofeng Zhu
- Department of Population Quantitative and Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,NHLBI Framingham Heart Study, Framingham, MA, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ervin F Fox
- Cardiology, Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Xiuqing Guo
- Genomic Outcomes, Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CC, USA
| | - W James Gauderman
- Biostatistics, Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kenneth Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Charles Kooperberg
- Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, WA, USA
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.,NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, London, UK
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dabeeru C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA. .,Division of Pulmonary Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
9
|
Laville V, Bentley AR, Privé F, Zhu X, Gauderman J, Winkler TW, Province M, Rao DC, Aschard H. VarExp: estimating variance explained by genome-wide GxE summary statistics. Bioinformatics 2019; 34:3412-3414. [PMID: 29726908 DOI: 10.1093/bioinformatics/bty379] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 05/02/2018] [Indexed: 11/14/2022] Open
Abstract
Summary Many genome-wide association studies and genome-wide screening for gene-environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. This is commonly done using individual-level genotype data but it is challenging when results are derived through meta-analyses. Here, we present R package, 'VarExp', that allows for the estimation of the percentage of phenotypic variance explained using summary statistics only. It allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects and both effects jointly. Its implementation integrates all recent methodological developments and does not need external data to be uploaded by users. Availability and implementation The R package is available at https://gitlab.pasteur.fr/statistical-genetics/VarExp.git. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Vincent Laville
- Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, NHGRI, NIH, Bethesda, MD, USA
| | - Florian Privé
- Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France.,Laboratoire TIMC-IMAG, UMR 5525, CNRS, Université Grenoble Alpes, Grenoble, France
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Jim Gauderman
- Division of Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Mike Province
- Division of Statistical Genetics, Washington University in St. Louis, St. Louis, MO, USA
| | - D C Rao
- Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Hugues Aschard
- Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France
| |
Collapse
|
10
|
Laville V, Kang JH, Cousins CC, Iglesias AI, Nagy R, Cooke Bailey JN, Igo RP, Song YE, Chasman DI, Christen WG, Kraft P, Rosner BA, Hu F, Wilson JF, Gharahkhani P, Hewitt AW, Mackey DA, Hysi PG, Hammond CJ, vanDuijn CM, Haines JL, Vitart V, Fingert JH, Hauser MA, Aschard H, Wiggs JL, Khawaja AP, MacGregor S, Pasquale LR. Genetic Correlations Between Diabetes and Glaucoma: An Analysis of Continuous and Dichotomous Phenotypes. Am J Ophthalmol 2019; 206:245-255. [PMID: 31121135 DOI: 10.1016/j.ajo.2019.05.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Revised: 05/03/2019] [Accepted: 05/09/2019] [Indexed: 01/05/2023]
Abstract
PURPOSE A genetic correlation is the proportion of phenotypic variance between traits that is shared on a genetic basis. Here we explore genetic correlations between diabetes- and glaucoma-related traits. DESIGN Cross-sectional study. METHODS We assembled genome-wide association study summary statistics from European-derived participants regarding diabetes-related traits like fasting blood sugar (FBS) and type 2 diabetes (T2D) and glaucoma-related traits (intraocular pressure [IOP], central corneal thickness [CCT], corneal hysteresis [CH], corneal resistance factor [CRF], cup-to-disc ratio [CDR], and primary open-angle glaucoma [POAG]). We included data from the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database, the UK Biobank, and the International Glaucoma Genetics Consortium. We calculated genetic correlation (rg) between traits using linkage disequilibrium score regression. We also calculated genetic correlations between IOP, CCT, and select diabetes-related traits based on individual level phenotype data in 2 Northern European population-based samples using pedigree information and Sequential Oligogenic Linkage Analysis Routines. RESULTS Overall, there was little rg between diabetes- and glaucoma-related traits. Specifically, we found a nonsignificant negative correlation between T2D and POAG (rg = -0.14; P = .16). Using Sequential Oligogenic Linkage Analysis Routines, the genetic correlations between measured IOP, CCT, FBS, fasting insulin, and hemoglobin A1c were null. In contrast, genetic correlations between IOP and POAG (rg ≥ 0.45; P ≤ 3.0 × 10-4) and between CDR and POAG were high (rg = 0.57; P = 2.8 × 10-10). However, genetic correlations between corneal properties (CCT, CRF, and CH) and POAG were low (rg range -0.18 to 0.11) and nonsignificant (P ≥ .07). CONCLUSION These analyses suggest that there is limited genetic correlation between diabetes- and glaucoma-related traits.
Collapse
|
11
|
Aschard H, Laville V, Tchetgen ET, Knights D, Imhann F, Seksik P, Zaitlen N, Silverberg MS, Cosnes J, Weersma RK, Xavier R, Beaugerie L, Skurnik D, Sokol H. Genetic effects on the commensal microbiota in inflammatory bowel disease patients. PLoS Genet 2019; 15:e1008018. [PMID: 30849075 PMCID: PMC6426259 DOI: 10.1371/journal.pgen.1008018] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [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: 09/12/2018] [Revised: 03/20/2019] [Accepted: 02/13/2019] [Indexed: 12/16/2022] Open
Abstract
Several bacteria in the gut microbiota have been shown to be associated with inflammatory bowel disease (IBD), and dozens of IBD genetic variants have been identified in genome-wide association studies. However, the role of the microbiota in the etiology of IBD in terms of host genetic susceptibility remains unclear. Here, we studied the association between four major genetic variants associated with an increased risk of IBD and bacterial taxa in up to 633 IBD cases. We performed systematic screening for associations, identifying and replicating associations between NOD2 variants and two taxa: the Roseburia genus and the Faecalibacterium prausnitzii species. By exploring the overall association patterns between genes and bacteria, we found that IBD risk alleles were significantly enriched for associations concordant with bacteria-IBD associations. To understand the significance of this pattern in terms of the study design and known effects from the literature, we used counterfactual principles to assess the fitness of a few parsimonious gene-bacteria-IBD causal models. Our analyses showed evidence that the disease risk of these genetic variants were likely to be partially mediated by the microbiome. We confirmed these results in extensive simulation studies and sensitivity analyses using the association between NOD2 and F. prausnitzii as a case study.
Collapse
Affiliation(s)
- Hugues Aschard
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- * E-mail: (HA); (DS); (HS)
| | - Vincent Laville
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
| | - Eric Tchetgen Tchetgen
- Department of Statistics, The Wharton School at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Dan Knights
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America
- Biotechnology Institute, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Floris Imhann
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Philippe Seksik
- Department of Gastroenterology, Saint Antoine Hospital, Paris, France
| | - Noah Zaitlen
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Mark S. Silverberg
- Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Jacques Cosnes
- Department of Gastroenterology, Saint Antoine Hospital, Paris, France
- Sorbonne Université, Paris, France
| | - Rinse K. Weersma
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Ramnik Xavier
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Laurent Beaugerie
- Department of Gastroenterology, Saint Antoine Hospital, Paris, France
- Sorbonne Université, Paris, France
| | - David Skurnik
- Division of Infectious Diseases, Harvard Medical School, Boston, Massachusetts, United States of America
- Massachusetts Technology and Analytics, Brookline, Massachusetts, United States of America
- Department of Microbiology, Necker Hospital and University Paris Descartes, Paris, France
- INSERM U1151-Equipe 11, Institut Necker-Enfants Malades, Paris, France
- * E-mail: (HA); (DS); (HS)
| | - Harry Sokol
- Department of Gastroenterology, Saint Antoine Hospital, Paris, France
- Sorbonne Université, Paris, France
- Micalis Institute, AgroParisTech, Jouy-en-Josas, France
- INSERM CRSA UMRS U938, Paris, France
- * E-mail: (HA); (DS); (HS)
| |
Collapse
|
12
|
Kim J, Ziyatdinov A, Laville V, Hu FB, Rimm E, Kraft P, Aschard H. Joint Analysis of Multiple Interaction Parameters in Genetic Association Studies. Genetics 2019; 211:483-494. [PMID: 30578273 PMCID: PMC6366922 DOI: 10.1534/genetics.118.301394] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [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] [Received: 07/21/2018] [Accepted: 12/10/2018] [Indexed: 01/24/2023] Open
Abstract
With growing human genetic and epidemiologic data, there has been increased interest for the study of gene-by-environment (G-E) interaction effects. Still, major questions remain on how to test jointly a large number of interactions between multiple SNPs and multiple exposures. In this study, we first compared the relative performance of four fixed-effect joint analysis approaches using simulated data, considering up to 10 exposures and 300 SNPs: (1) omnibus test, (2) multi-exposure and genetic risk score (GRS) test, (3) multi-SNP and environmental risk score (ERS) test, and (4) GRS-ERS test. Our simulations explored both linear and logistic regression while considering three statistics: the Wald test, the Score test, and the likelihood ratio test (LRT). We further applied the approaches to three large sets of human cohort data (n = 37,664), focusing on type 2 diabetes (T2D), obesity, hypertension, and coronary heart disease with smoking, physical activity, diets, and total energy intake. Overall, GRS-based approaches were the most robust, and had the highest power, especially when the G-E interaction effects were correlated with the marginal genetic and environmental effects. We also observed severe miscalibration of joint statistics in logistic models when the number of events per variable was too low when using either the Wald test or LRT test. Finally, our real data application detected nominally significant interaction effects for three outcomes (T2D, obesity, and hypertension), mainly from the GRS-ERS approach. In conclusion, this study provides guidelines for testing multiple interaction parameters in modern human cohorts including extensive genetic and environmental data.
Collapse
Affiliation(s)
- Jihye Kim
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Andrey Ziyatdinov
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Vincent Laville
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, 75724 Paris, France
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Eric Rimm
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Hugues Aschard
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, 75724 Paris, France
| |
Collapse
|
13
|
Laville V, Le Clerc S, Ezzedine K, Jdid R, Taing L, Labib T, Coulonges C, Ulveling D, Galan P, Guinot C, Fezeu L, Morizot F, Latreille J, Malvy D, Tschachler E, Zagury J. A genome wide association study identifies new genes potentially associated with eyelid sagging. Exp Dermatol 2018; 28:892-898. [DOI: 10.1111/exd.13559] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2018] [Indexed: 01/06/2023]
Affiliation(s)
- Vincent Laville
- Équipe GénomiqueBioinformatique et ApplicationsChaire de BioinformatiqueConservatoire National des Arts et Métiers Paris France
| | - Sigrid Le Clerc
- Équipe GénomiqueBioinformatique et ApplicationsChaire de BioinformatiqueConservatoire National des Arts et Métiers Paris France
| | - Khaled Ezzedine
- Department of DermatologyHenri Mondor Hospital and EA EpiDermE (Epidémiologie en Dermatologie et Evaluation des Thérapeutiques)UPEC‐Université Paris‐Est Créteil France
| | - Randa Jdid
- Department of Skin Knowledge & Women BeautyChanel R & T Pantin France
| | - Lieng Taing
- Équipe GénomiqueBioinformatique et ApplicationsChaire de BioinformatiqueConservatoire National des Arts et Métiers Paris France
| | - Toufik Labib
- Équipe GénomiqueBioinformatique et ApplicationsChaire de BioinformatiqueConservatoire National des Arts et Métiers Paris France
| | - Cédric Coulonges
- Équipe GénomiqueBioinformatique et ApplicationsChaire de BioinformatiqueConservatoire National des Arts et Métiers Paris France
| | - Damien Ulveling
- Équipe GénomiqueBioinformatique et ApplicationsChaire de BioinformatiqueConservatoire National des Arts et Métiers Paris France
| | - Pilar Galan
- Université Paris 13Equipe de Recherche en Epidémiologie Nutritionnelle (EREN)Centre d’Epidemiologie et Biostatistiques Sorbonne Paris Cité (CRESS)Inserm U1153, Inra U1125Cnam, COMUE Sorbonne‐Paris‐Cité Bobigny France
| | - Christiane Guinot
- Computer Science LaboratoryUniversity François Rabelais of Tours Tours France
| | - Leopold Fezeu
- Université Paris 13Equipe de Recherche en Epidémiologie Nutritionnelle (EREN)Centre d’Epidemiologie et Biostatistiques Sorbonne Paris Cité (CRESS)Inserm U1153, Inra U1125Cnam, COMUE Sorbonne‐Paris‐Cité Bobigny France
| | | | - Julie Latreille
- Department of Skin Knowledge & Women BeautyChanel R & T Pantin France
| | - Denis Malvy
- Université Paris 13Equipe de Recherche en Epidémiologie Nutritionnelle (EREN)Centre d’Epidemiologie et Biostatistiques Sorbonne Paris Cité (CRESS)Inserm U1153, Inra U1125Cnam, COMUE Sorbonne‐Paris‐Cité Bobigny France
- Department of Internal Medicine and Tropical DiseasesHôpital Saint‐André Bordeaux France
| | - Erwin Tschachler
- Department of DermatologyUniversity of Vienna Medical School Vienna Austria
| | - Jean‐François Zagury
- Équipe GénomiqueBioinformatique et ApplicationsChaire de BioinformatiqueConservatoire National des Arts et Métiers Paris France
| |
Collapse
|
14
|
Aschard H, Spiegelman D, Laville V, Kraft P, Wang M. A test for gene-environment interaction in the presence of measurement error in the environmental variable. Genet Epidemiol 2018; 42:250-264. [PMID: 29424028 DOI: 10.1002/gepi.22113] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [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: 01/13/2017] [Revised: 11/14/2017] [Accepted: 12/11/2017] [Indexed: 11/10/2022]
Abstract
The identification of gene-environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus transforming the analysis to standard linear regression. Through simulation studies, we show that the proposed approach is valid in the presence of classical exposure measurement error as well as when there is correlation between the exposure and the genetic variant. Simulations also demonstrated that the reverse test has greater power compared to logistic regression. Finally, we confirmed that our approach eliminates bias from exposure measurement error in estimation. Computing times are reduced by as much as fivefold in this new approach. For illustrative purposes, we applied the new approach to an ExGWAS study of interactions with alcohol and body mass index among 1,145 cases with invasive breast cancer and 1,142 controls from the Cancer Genetic Markers of Susceptibility study.
Collapse
Affiliation(s)
- Hugues Aschard
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France.,Department of Epidemiology, Harvard T.H.Chan School of Public Health, Boston, USA
| | - Donna Spiegelman
- Department of Epidemiology, Harvard T.H.Chan School of Public Health, Boston, USA.,Department of Biostatistics, Harvard T.H.Chan School of Public Health, Boston, USA.,Departments of Nutrition and Global Health, Harvard T.H.Chan School of Public Health, Boston, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Masachussetts, USA
| | - Vincent Laville
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
| | - Pete Kraft
- Department of Epidemiology, Harvard T.H.Chan School of Public Health, Boston, USA.,Department of Biostatistics, Harvard T.H.Chan School of Public Health, Boston, USA
| | - Molin Wang
- Department of Epidemiology, Harvard T.H.Chan School of Public Health, Boston, USA.,Department of Biostatistics, Harvard T.H.Chan School of Public Health, Boston, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Masachussetts, USA
| |
Collapse
|
15
|
Lagarde N, Delahaye S, Jérémie A, Ben Nasr N, Guillemain H, Empereur-Mot C, Laville V, Labib T, Réau M, Langenfeld F, Zagury JF, Montes M. Discriminating Agonist from Antagonist Ligands of the Nuclear Receptors Using Different Chemoinformatics Approaches. Mol Inform 2017; 36. [PMID: 28671755 DOI: 10.1002/minf.201700020] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [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: 01/31/2017] [Accepted: 05/30/2017] [Indexed: 11/10/2022]
Abstract
Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn't provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the "pharmacological-profile-to-predict-bound-state" (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes.
Collapse
Affiliation(s)
- Nathalie Lagarde
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Solenne Delahaye
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Aurore Jérémie
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Nesrine Ben Nasr
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Hélène Guillemain
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Charly Empereur-Mot
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Vincent Laville
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Taoufik Labib
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Manon Réau
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Florent Langenfeld
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Jean-François Zagury
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| | - Matthieu Montes
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003, Paris, France
| |
Collapse
|
16
|
Laville V, Le Clerc S, Ezzedine K, Zagury JF. Response to the commentary on ‘A genome-wide association study in Caucasian women suggests the involvement ofHLAgene in the severity of facial solar lentigines’. Pigment Cell Melanoma Res 2016; 30:74-75. [DOI: 10.1111/pcmr.12552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 10/19/2016] [Indexed: 01/06/2023]
Affiliation(s)
- Vincent Laville
- Equipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| | - Sigrid Le Clerc
- Equipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| | - Khaled Ezzedine
- UMR U557; INSERM/U1125 INRA/CNAM; University Paris 13/Centre de recherche en Nutrition Humaine Ile-de-France; Bobigny France
- Department of Dermatology; Hôpital Saint-André; Bordeaux France
| | - Jean-François Zagury
- Equipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| |
Collapse
|
17
|
Ulveling D, Le Clerc S, Cobat A, Labib T, Noirel J, Laville V, Coulonges C, Carpentier W, Nalpas B, Heim MH, Poynard T, Cerny A, Pol S, Bochud PY, Dabis F, Theodorou I, Lévy Y, Salmon D, Abel L, Dominguez S, Zagury JF. A new 3p25 locus is associated with liver fibrosis progression in human immunodeficiency virus/hepatitis C virus-coinfected patients. Hepatology 2016; 64:1462-1472. [PMID: 27339598 DOI: 10.1002/hep.28695] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 05/24/2016] [Accepted: 06/11/2016] [Indexed: 12/11/2022]
Abstract
UNLABELLED There is growing evidence that human genetic variants contribute to liver fibrosis in subjects with hepatitis C virus (HCV) monoinfection, but this aspect has been little investigated in patients coinfected with HCV and human immunodeficiency virus (HIV). We performed the first genome-wide association study of liver fibrosis progression in patients coinfected with HCV and HIV, using the well-characterized French National Agency for Research on AIDS and Viral Hepatitis CO13 HEPAVIH cohort. Liver fibrosis was assessed by elastography (FibroScan), providing a quantitative fibrosis score. After quality control, a genome-wide association study was conducted on 289 Caucasian patients, for a total of 8,426,597 genotyped (Illumina Omni2.5 BeadChip) or reliably imputed single-nucleotide polymorphisms. Single-nucleotide polymorphisms with P values <10-6 were investigated in two independent replication cohorts of European patients infected with HCV alone. Two signals of genome-wide significance (P < 5 × 10-8 ) were obtained. The first, on chromosome 3p25 and corresponding to rs61183828 (P = 3.8 × 10-9 ), was replicated in the two independent cohorts of patients with HCV monoinfection. The cluster of single-nucleotide polymorphisms in linkage disequilibrium with rs61183828 was located close to two genes involved in mechanisms affecting both cell signaling and cell structure (CAV3) or HCV replication (RAD18). The second signal, obtained with rs11790131 (P = 9.3 × 10-9 ) on chromosome region 9p22, was not replicated. CONCLUSION This genome-wide association study identified a new locus associated with liver fibrosis severity in patients with HIV/HCV coinfection, on chromosome 3p25, a finding that was replicated in patients with HCV monoinfection; these results provide new relevant hypotheses for the pathogenesis of liver fibrosis in patients with HIV/HCV coinfection that may help define new targets for drug development or new prognostic tests, to improve patient care. (Hepatology 2016;64:1462-1472).
Collapse
Affiliation(s)
- Damien Ulveling
- Équipe Génomique, Bioinformatique et Applications (EA4627), Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France
| | - Sigrid Le Clerc
- Équipe Génomique, Bioinformatique et Applications (EA4627), Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France
| | - Aurélie Cobat
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, Institut National de la Santé et de la Recherche Médicale (INSERM) U1163, Paris, France.,Paris Descartes University, Imagine Institute, Paris, France
| | - Taoufik Labib
- Équipe Génomique, Bioinformatique et Applications (EA4627), Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France
| | - Josselin Noirel
- Équipe Génomique, Bioinformatique et Applications (EA4627), Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France
| | - Vincent Laville
- Équipe Génomique, Bioinformatique et Applications (EA4627), Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France
| | - Cédric Coulonges
- Équipe Génomique, Bioinformatique et Applications (EA4627), Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France
| | - Wassila Carpentier
- Plateforme Post-Génomique P3S, AP-HP, UPMC Université Paris 6, Faculté de Médecine Pitié Salpétrière, Paris, France
| | - Bertrand Nalpas
- Département d'Hépatologie, Hôpital Cochin (AP-HP), Université Paris Descartes, Paris, France
| | - Markus H Heim
- Department of Gastroenterology, University Hospital, Basel, Switzerland
| | - Thierry Poynard
- Université Pierre et Marie Curie, Service d'Hépato-gastroentérologie, Hôpital Pitié-Salpêtrière (AP-HP), Paris, France
| | | | - Stanislas Pol
- Département d'Hépatologie, Hôpital Cochin (AP-HP), Université Paris Descartes, Paris, France.,INSERM UMS20, Institut Pasteur, Paris, France
| | - Pierre-Yves Bochud
- Infectious Diseases Service, Department of Medicine, University Hospital and University of Lausanne, Lausanne, Switzerland
| | - François Dabis
- Centre de Recherche INSERM U897, Epidemiologie-Biostatistique, Institut de Santé Publique, Epidémiologie et Développement, Université de Bordeaux, Bordeaux, France
| | - Ioannis Theodorou
- Laboratory of Immunity and Infection, Centre d'Immunologie et des Maladies Infectieuses de Paris (CIMI), INSERM U1135, Hôpital Pitié-Salpêtrière (AP-HP), Paris, France.,Plateforme Génomique INSERM-ANRS, Groupe Hospitalier Pitié Salpétrière, AP-HP, UPMC Université Paris 6, Paris, France
| | - Yves Lévy
- INSERM U955, AP-HP, Groupe Henri-Mondor Albert-Chenevier, Immunologie Clinique, Créteil, France
| | - Dominique Salmon
- Department of Infectious Diseases, Cochin Hospital, Paris, France
| | - Laurent Abel
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, Institut National de la Santé et de la Recherche Médicale (INSERM) U1163, Paris, France.,Paris Descartes University, Imagine Institute, Paris, France.,St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY
| | - Stéphanie Dominguez
- INSERM U955, AP-HP, Groupe Henri-Mondor Albert-Chenevier, Immunologie Clinique, Créteil, France.
| | - Jean-François Zagury
- Équipe Génomique, Bioinformatique et Applications (EA4627), Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France.
| | | | | | | |
Collapse
|
18
|
Laville V, Clerc SL, Ezzedine K, Jdid R, Taing L, Labib T, Coulonges C, Ulveling D, Carpentier W, Galan P, Hercberg S, Morizot F, Latreille J, Malvy D, Tschachler E, Zagury JF. A genome-wide association study in Caucasian women suggests the involvement ofHLAgenes in the severity of facial solar lentigines. Pigment Cell Melanoma Res 2016; 29:550-8. [DOI: 10.1111/pcmr.12502] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 06/17/2016] [Indexed: 11/26/2022]
Affiliation(s)
- Vincent Laville
- Équipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| | - Sigrid Le Clerc
- Équipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| | - Khaled Ezzedine
- UMR U557, INSERM/U1125 INRA/CNAM; University Paris 13/Centre de Recherche en Nutrition Humaine Ile-de-France; Bobigny France
- Department of Dermatology; Hôpital Saint-André; Bordeaux France
| | - Randa Jdid
- Department of Skin Knowledge and Women Beauty; Chanel R&T; Pantin France
| | - Lieng Taing
- Équipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| | - Taoufik Labib
- Équipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| | - Cedric Coulonges
- Équipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| | - Damien Ulveling
- Équipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| | | | - Pilar Galan
- UMR U557, INSERM/U1125 INRA/CNAM; University Paris 13/Centre de Recherche en Nutrition Humaine Ile-de-France; Bobigny France
| | - Serge Hercberg
- UMR U557, INSERM/U1125 INRA/CNAM; University Paris 13/Centre de Recherche en Nutrition Humaine Ile-de-France; Bobigny France
- Department of Public Health; Hôpital Avicenne; Bobigny France
| | - Frederique Morizot
- Department of Skin Knowledge and Women Beauty; Chanel R&T; Pantin France
| | - Julie Latreille
- Department of Skin Knowledge and Women Beauty; Chanel R&T; Pantin France
| | - Denis Malvy
- UMR U557, INSERM/U1125 INRA/CNAM; University Paris 13/Centre de Recherche en Nutrition Humaine Ile-de-France; Bobigny France
- Department of Internal Medicine and Tropical Diseases; Hôpital Saint-André; Bordeaux France
| | - Erwin Tschachler
- Department of Dermatology; University of Vienna Medical School; Vienna Austria
| | - Jean-François Zagury
- Équipe Génomique, Bioinformatique et Applications; Chaire de Bioinformatique; Conservatoire National des Arts et Métiers; Paris France
| |
Collapse
|
19
|
Spadoni JL, Rucart P, Le Clerc S, van Manen D, Coulonges C, Ulveling D, Laville V, Labib T, Taing L, Delaneau O, Montes M, Schuitemaker H, Noirel J, Zagury JF. Identification of Genes Whose Expression Profile Is Associated with Non-Progression towards AIDS Using eQTLs. PLoS One 2015; 10:e0136989. [PMID: 26367535 PMCID: PMC4569262 DOI: 10.1371/journal.pone.0136989] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 08/12/2015] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Many genome-wide association studies have been performed on progression towards the acquired immune deficiency syndrome (AIDS) and they mainly identified associations within the HLA loci. In this study, we demonstrate that the integration of biological information, namely gene expression data, can enhance the sensitivity of genetic studies to unravel new genetic associations relevant to AIDS. METHODS We collated the biological information compiled from three databases of expression quantitative trait loci (eQTLs) involved in cells of the immune system. We derived a list of single nucleotide polymorphisms (SNPs) that are functional in that they correlate with differential expression of genes in at least two of the databases. We tested the association of those SNPs with AIDS progression in two cohorts, GRIV and ACS. Tests on permuted phenotypes of the GRIV and ACS cohorts or on randomised sets of equivalent SNPs allowed us to assess the statistical robustness of this method and to estimate the true positive rate. RESULTS Eight genes were identified with high confidence (p = 0.001, rate of true positives 75%). Some of those genes had previously been linked with HIV infection. Notably, ENTPD4 belongs to the same family as CD39, whose expression has already been associated with AIDS progression; while DNAJB12 is part of the HSP90 pathway, which is involved in the control of HIV latency. Our study also drew our attention to lesser-known functions such as mitochondrial ribosomal proteins and a zinc finger protein, ZFP57, which could be central to the effectiveness of HIV infection. Interestingly, for six out of those eight genes, down-regulation is associated with non-progression, which makes them appealing targets to develop drugs against HIV.
Collapse
Affiliation(s)
- Jean-Louis Spadoni
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Pierre Rucart
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Sigrid Le Clerc
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Daniëlle van Manen
- Department of Experimental Immunology, Sanquin Research, Landsteiner Laboratory, and Center for Infectious Diseases and Immunity Amsterdam (CINIMA), Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands
- Crucell Holland B.V., Archimedesweg 4–6, 2333 CN, Leiden, The Netherlands
| | - Cédric Coulonges
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Damien Ulveling
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Vincent Laville
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Taoufik Labib
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Lieng Taing
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Olivier Delaneau
- Département de Génétique et Développement, Faculté de Médecine, Université de Genève, Switzerland
| | - Matthieu Montes
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Hanneke Schuitemaker
- Department of Experimental Immunology, Sanquin Research, Landsteiner Laboratory, and Center for Infectious Diseases and Immunity Amsterdam (CINIMA), Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands
- Crucell Holland B.V., Archimedesweg 4–6, 2333 CN, Leiden, The Netherlands
| | - Josselin Noirel
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
| | - Jean-François Zagury
- Chaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France
- * E-mail:
| |
Collapse
|
20
|
Laville V, Le Clerc S, Ezzedine K, Berlin I, Carpentier W, Jdid R, Galan P, Hercberg S, Guinot C, Morizot F, Latreille J, Tschachler E, Zagury JF. Association génétique entre l’allèle HLA-C*0701 et les lentigines solaires dans une population caucasienne. Ann Dermatol Venereol 2014. [DOI: 10.1016/j.annder.2014.09.320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
21
|
Le Clerc S, Delaneau O, Coulonges C, Spadoni JL, Labib T, Laville V, Ulveling D, Noirel J, Montes M, Schächter F, Caillat-Zucman S, Zagury JF. Evidence after imputation for a role of MICA variants in nonprogression and elite control of HIV type 1 infection. J Infect Dis 2014; 210:1946-50. [PMID: 24939907 DOI: 10.1093/infdis/jiu342] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [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: 11/14/2022] Open
Abstract
Past genome-wide association studies (GWAS) involving individuals with AIDS have mainly identified associations in the HLA region. Using the latest software, we imputed 7 million single-nucleotide polymorphisms (SNPs)/indels of the 1000 Genomes Project from the GWAS-determined genotypes of individuals in the Genomics of Resistance to Immunodeficiency Virus AIDS nonprogression cohort and compared them with those of control cohorts. The strongest signals were in MICA, the gene encoding major histocompatibility class I polypeptide-related sequence A (P = 3.31 × 10(-12)), with a particular exonic deletion (P = 1.59 × 10(-8)) in full linkage disequilibrium with the reference HCP5 rs2395029 SNP. Haplotype analysis also revealed an additive effect between HLA-C, HLA-B, and MICA variants. These data suggest a role for MICA in progression and elite control of human immunodeficiency virus type 1 infection.
Collapse
Affiliation(s)
- Sigrid Le Clerc
- Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers
| | - Olivier Delaneau
- Département de Génétique et Développement, Faculté de Médecine, Université de Genève, Geneva, Switzerland
| | - Cédric Coulonges
- Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers
| | - Jean-Louis Spadoni
- Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers
| | - Taoufik Labib
- Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers
| | - Vincent Laville
- Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers
| | - Damien Ulveling
- Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers
| | - Josselin Noirel
- Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers
| | - Matthieu Montes
- Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers
| | - François Schächter
- Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers
| | | | | |
Collapse
|
22
|
Lagarde N, Ben Nasr N, Jérémie A, Guillemain H, Laville V, Labib T, Zagury JF, Montes M. NRLiSt BDB, the manually curated nuclear receptors ligands and structures benchmarking database. J Med Chem 2014; 57:3117-25. [PMID: 24666037 DOI: 10.1021/jm500132p] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Nuclear receptors (NRs) constitute an important class of drug targets. We created the most exhaustive NR-focused benchmarking database to date, the NRLiSt BDB (NRs ligands and structures benchmarking database). The 9905 compounds and 339 structures of the NRLiSt BDB are ready for structure-based and ligand-based virtual screening. In the present study, we detail the protocol used to generate the NRLiSt BDB and its features. We also give some examples of the errors that we found in ChEMBL that convinced us to manually review all original papers. Since extensive and manually curated experimental data about NR ligands and structures are provided in the NRLiSt BDB, it should become a powerful tool to assess the performance of virtual screening methods on NRs, to assist the understanding of NR's function and modulation, and to support the discovery of new drugs targeting NRs. NRLiSt BDB is freely available online at http://nrlist.drugdesign.fr .
Collapse
Affiliation(s)
- Nathalie Lagarde
- Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers , 292 Rue Saint Martin, 75003 Paris, France
| | | | | | | | | | | | | | | |
Collapse
|
23
|
Boige V, Svrcek M, Michiels S, Pocard M, Laville V, Drusch F, Sabourin J, Dessen P, Ducreux M, Lazar V. Genome-wide expression profiling and tissue array analysis for prediction of recurrences in stage II-III colorectal cancer (CRC). J Clin Oncol 2006. [DOI: 10.1200/jco.2006.24.18_suppl.3572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
3572 Background: Despite substantial progress in molecular pathogenesis of colon cancer (CC), no reliable biomarkers of outcome have yet been identified in patients with resected stage II-III CC. Methods: We analyzed genome-wide mRNA expression profiles in 20 stage II or III left side CC from 10 patient who developed metastasis (M+) and 10 disease free patients followed up for at least 4 years (M-) using high-density oligonucleotide microarrays (Agilent technology). RNA from tumor tissue (T) was hybridized against normal tissue (NT) from the same patient and each experiment was replicated 4 times (with 2 dye-swaps). The goal was to select genes both differentially expressed between T and NT and between M+ and M-. A tissu-array was constructed using 212 stage II and III resected CRC (164 CC, 64 rectal cancers) and their matched NT. For survival analysis, immunohistochemistry (IHC) data was dichotomized at the median value. Results: Analysis of microarray data yielded 27 genes that had a 2-fold difference between the expression in T and NT in at least 5 out of 20 patients and for which the average expression was significantly different between M+ and M- (p<0.01, t-test). Among the 6 most differentially expressed genes between M+ and M- in T, 4 of them were found to be involved in interferon γ pathway and could be evaluated by IHC: CXCL9, CXCL13, PPARγ, THSD. In order to assess macrophage and natural killer (NK) cell infiltration, CD68 and CD57 were also analyzed. Intensity was measured by semi-quantitative scores for the first 4 genes and by the number of infiltrating cells for the others. CXCL9, PPARGγ, CXCL13, CD57 and CD68 were significantly underexpressed in T as compared to NT (p<0.0001, paired t-test). The logrank test stratified by cancer site indicated that high IHC expression of CD57 possessed a significantly better recurrence-free survival (RFS) than those low expression (p=0.004). Multivariate Cox analysis identified tumor site (p=0.001), node stage (p<0.001) and CD57 (p=0.002) as independent predictors of RFS. Conclusions: NK cell infiltration within colorectal cancers is associated with prolonged recurrence-free survival in stage II and III CRC. No significant financial relationships to disclose.
Collapse
Affiliation(s)
- V. Boige
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| | - M. Svrcek
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| | - S. Michiels
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| | - M. Pocard
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| | - V. Laville
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| | - F. Drusch
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| | - J. Sabourin
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| | - P. Dessen
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| | - M. Ducreux
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| | - V. Lazar
- Gustave Roussy Institute, Villejuif, France; CHU St. Antoine, Paris, France
| |
Collapse
|
24
|
Dangles V, Lazar V, Validire P, Richon S, Wertheimer M, Laville V, Janneau JL, Barrois M, Bovin C, Poynard T, Vallancien G, Bellet D. Gene expression profiles of bladder cancers: evidence for a striking effect of in vitro cell models on gene patterns. Br J Cancer 2002; 86:1283-9. [PMID: 11953886 PMCID: PMC2375349 DOI: 10.1038/sj.bjc.6600239] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2001] [Revised: 01/22/2002] [Accepted: 02/19/2002] [Indexed: 11/24/2022] Open
Abstract
In order to assess the effect of in vitro models on the expression of key genes known to be implicated in the development or progression of cancer, we quantified by real-time quantitative PCR the expression of 28 key genes in three bladder cancer tissue specimens and in their derived cell lines, studied either as one-dimensional single cell suspensions, two-dimensional monolayers or three-dimensional spheroids. Global analysis of gene expression profiles showed that in vitro models had a dramatic impact upon gene expression. Remarkably, quantitative differences in gene expression of 2-63-fold were observed in 24 out of 28 genes among the cell models. In addition, we observed that the in vitro model which most closely mimicked in vivo mRNA phenotype varied with both the gene and the patient. These results provide evidence that mRNA expression databases based on cancer cell lines, which are studied to provide a rationale for selection of therapy on the basis of molecular characteristics of a patient's tumour, must be carefully interpreted.
Collapse
Affiliation(s)
- V Dangles
- Laboratoire d'Immunologie des Tumeurs, ESA 8067 CNRS, Faculté des Sciences Pharmaceutiques et Biologiques de Paris, Université Paris V-René Descartes, 4 avenue de l'Observatoire, 75006 Paris, France
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|