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Jones AG, Connelly GG, Dalapati T, Wang L, Schott BH, San Roman AK, Ko DC. Biological sex affects functional variation across the human genome. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.03.24313025. [PMID: 39281750 PMCID: PMC11398442 DOI: 10.1101/2024.09.03.24313025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
Humans display sexual dimorphism across many traits, but little is known about underlying genetic mechanisms and impacts on disease. We utilized single-cell RNA-seq of 480 lymphoblastoid cell lines to reveal that the vast majority (79%) of sex-biased genes are targets of transcription factors that display sex-biased expression. Further, we developed a two-step regression method that identified sex-biased expression quantitative trait loci (sb-eQTL) across the genome. In contrast to previous work, these sb-eQTL are abundant (n=10,754; FDR 5%) and reproducible (replication up to π1=0.56). These sb-eQTL are enriched in over 600 GWAS phenotypes, including 120 sb-eQTL associated with the female-biased autoimmune disease multiple sclerosis. Our results demonstrate widespread genetic impacts on sexual dimorphism and identify possible mechanisms and clinical targets for sex differences in diverse diseases.
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Affiliation(s)
- Angela G Jones
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University; Durham, NC, USA
- Duke University Program in Genetics and Genomics, Duke University; Durham, NC, USA
| | - Guinevere G Connelly
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University; Durham, NC, USA
- Duke University Program in Genetics and Genomics, Duke University; Durham, NC, USA
| | - Trisha Dalapati
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University; Durham, NC, USA
| | - Liuyang Wang
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University; Durham, NC, USA
| | - Benjamin H Schott
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University; Durham, NC, USA
- Duke University Program in Genetics and Genomics, Duke University; Durham, NC, USA
| | - Adrianna K San Roman
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University; Durham, NC, USA
- Duke University Program in Genetics and Genomics, Duke University; Durham, NC, USA
| | - Dennis C Ko
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University; Durham, NC, USA
- Duke University Program in Genetics and Genomics, Duke University; Durham, NC, USA
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University; Durham, NC, USA
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2
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Retallick-Townsley KG, Lee S, Cartwright S, Cohen S, Sen A, Jia M, Young H, Dobbyn L, Deans M, Fernandez-Garcia M, Huckins LM, Brennand KJ. Dynamic stress- and inflammatory-based regulation of psychiatric risk loci in human neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602755. [PMID: 39026810 PMCID: PMC11257632 DOI: 10.1101/2024.07.09.602755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The prenatal environment can alter neurodevelopmental and clinical trajectories, markedly increasing risk for psychiatric disorders in childhood and adolescence. To understand if and how fetal exposures to stress and inflammation exacerbate manifestation of genetic risk for complex brain disorders, we report a large-scale context-dependent massively parallel reporter assay (MPRA) in human neurons designed to catalogue genotype x environment (GxE) interactions. Across 240 genome-wide association study (GWAS) loci linked to ten brain traits/disorders, the impact of hydrocortisone, interleukin 6, and interferon alpha on transcriptional activity is empirically evaluated in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons. Of ~3,500 candidate regulatory risk elements (CREs), 11% of variants are active at baseline, whereas cue-specific CRE regulatory activity range from a high of 23% (hydrocortisone) to a low of 6% (IL-6). Cue-specific regulatory activity is driven, at least in part, by differences in transcription factor binding activity, the gene targets of which show unique enrichments for brain disorders as well as co-morbid metabolic and immune syndromes. The dynamic nature of genetic regulation informs the influence of environmental factors, reveals a mechanism underlying pleiotropy and variable penetrance, and identifies specific risk variants that confer greater disorder susceptibility after exposure to stress or inflammation. Understanding neurodevelopmental GxE interactions will inform mental health trajectories and uncover novel targets for therapeutic intervention.
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Affiliation(s)
- Kayla G. Retallick-Townsley
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Seoyeon Lee
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Sam Cartwright
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Sophie Cohen
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Annabel Sen
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Meng Jia
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Hannah Young
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lee Dobbyn
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Deans
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Meilin Fernandez-Garcia
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Laura M. Huckins
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
| | - Kristen J. Brennand
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
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3
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Xu Y, Li Y, Richard SA, Sun Y, Zhu C. Genetic pathways in cerebral palsy: a review of the implications for precision diagnosis and understanding disease mechanisms. Neural Regen Res 2024; 19:1499-1508. [PMID: 38051892 PMCID: PMC10883492 DOI: 10.4103/1673-5374.385855] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/02/2023] [Indexed: 12/07/2023] Open
Abstract
ABSTRACT Cerebral palsy is a diagnostic term utilized to describe a group of permanent disorders affecting movement and posture. Patients with cerebral palsy are often only capable of limited activity, resulting from non-progressive disturbances in the fetal or neonatal brain. These disturbances severely impact the child's daily life and impose a substantial economic burden on the family. Although cerebral palsy encompasses various brain injuries leading to similar clinical outcomes, the understanding of its etiological pathways remains incomplete owing to its complexity and heterogeneity. This review aims to summarize the current knowledge on the genetic factors influencing cerebral palsy development. It is now widely acknowledged that genetic mutations and alterations play a pivotal role in cerebral palsy development, which can be further influenced by environmental factors. Despite continuous research endeavors, the underlying factors contributing to cerebral palsy remain are still elusive. However, significant progress has been made in genetic research that has markedly enhanced our comprehension of the genetic factors underlying cerebral palsy development. Moreover, these genetic factors have been categorized based on the identified gene mutations in patients through clinical genotyping, including thrombosis, angiogenesis, mitochondrial and oxidative phosphorylation function, neuronal migration, and cellular autophagy. Furthermore, exploring targeted genotypes holds potential for precision treatment. In conclusion, advancements in genetic research have substantially improved our understanding of the genetic causes underlying cerebral palsy. These breakthroughs have the potential to pave the way for new treatments and therapies, consequently shaping the future of cerebral palsy research and its clinical management. The investigation of cerebral palsy genetics holds the potential to significantly advance treatments and management strategies. By elucidating the underlying cellular mechanisms, we can develop targeted interventions to optimize outcomes. A continued collaboration between researchers and clinicians is imperative to comprehensively unravel the intricate genetic etiology of cerebral palsy.
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Affiliation(s)
- Yiran Xu
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
- National Health Council (NHC) Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Zhengzhou, Henan Province, China
| | - Yifei Li
- Department of Human Anatomy, School of Basic Medicine and Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Seidu A Richard
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Yanyan Sun
- Department of Human Anatomy, School of Basic Medicine and Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Changlian Zhu
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
- Center for Brain Repair and Rehabilitation, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
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4
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Benjamin KJM, Arora R, Feltrin AS, Pertea G, Giles HH, Stolz JM, D'Ignazio L, Collado-Torres L, Shin JH, Ulrich WS, Hyde TM, Kleinman JE, Weinberger DR, Paquola ACM, Erwin JA. Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus. Nat Commun 2024; 15:3980. [PMID: 38730231 PMCID: PMC11087501 DOI: 10.1038/s41467-024-48048-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 04/15/2024] [Indexed: 05/12/2024] Open
Abstract
Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n = 399, dorsolateral prefrontal cortex: n = 377, and hippocampus: n = 394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.
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Affiliation(s)
- Kynon J M Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Ria Arora
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Biology, Johns Hopkins University Krieger School of Arts & Sciences, Baltimore, MD, USA
| | | | - Geo Pertea
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Hunter H Giles
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Laura D'Ignazio
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Apuã C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Jennifer A Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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5
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Kasela S, Aguet F, Kim-Hellmuth S, Brown BC, Nachun DC, Tracy RP, Durda P, Liu Y, Taylor KD, Johnson WC, Van Den Berg D, Gabriel S, Gupta N, Smith JD, Blackwell TW, Rotter JI, Ardlie KG, Manichaikul A, Rich SS, Barr RG, Lappalainen T. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects. Am J Hum Genet 2024; 111:133-149. [PMID: 38181730 PMCID: PMC10806864 DOI: 10.1016/j.ajhg.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 01/07/2024] Open
Abstract
Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.
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Affiliation(s)
- Silva Kasela
- New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.
| | | | - Sarah Kim-Hellmuth
- New York Genome Center, New York, NY, USA; Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany; Computational Health Center, Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Brielin C Brown
- New York Genome Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA
| | - Daniel C Nachun
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Russell P Tracy
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Peter Durda
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Yongmei Liu
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David Van Den Berg
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua D Smith
- Northwest Genomics Center, University of Washington, Seattle, WA, USA
| | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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6
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Seah C, Signer R, Deans M, Bader H, Rusielewicz T, Hicks EM, Young H, Cote A, Townsley K, Xu C, Hunter CJ, McCarthy B, Goldberg J, Dobariya S, Holtzherimer PE, Young KA, Noggle SA, Krystal JH, Paull D, Girgenti MJ, Yehuda R, Brennand KJ, Huckins LM. Common genetic variation impacts stress response in the brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.27.573459. [PMID: 38234801 PMCID: PMC10793429 DOI: 10.1101/2023.12.27.573459] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
To explain why individuals exposed to identical stressors experience divergent clinical outcomes, we determine how molecular encoding of stress modifies genetic risk for brain disorders. Analysis of post-mortem brain (n=304) revealed 8557 stress-interactive expression quantitative trait loci (eQTLs) that dysregulate expression of 915 eGenes in response to stress, and lie in stress-related transcription factor binding sites. Response to stress is robust across experimental paradigms: up to 50% of stress-interactive eGenes validate in glucocorticoid treated hiPSC-derived neurons (n=39 donors). Stress-interactive eGenes show brain region- and cell type-specificity, and, in post-mortem brain, implicate glial and endothelial mechanisms. Stress dysregulates long-term expression of disorder risk genes in a genotype-dependent manner; stress-interactive transcriptomic imputation uncovered 139 novel genes conferring brain disorder risk only in the context of traumatic stress. Molecular stress-encoding explains individualized responses to traumatic stress; incorporating trauma into genomic studies of brain disorders is likely to improve diagnosis, prognosis, and drug discovery.
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7
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Kang JB, Shen AZ, Gurajala S, Nathan A, Rumker L, Aguiar VRC, Valencia C, Lagattuta KA, Zhang F, Jonsson AH, Yazar S, Alquicira-Hernandez J, Khalili H, Ananthakrishnan AN, Jagadeesh K, Dey K, Daly MJ, Xavier RJ, Donlin LT, Anolik JH, Powell JE, Rao DA, Brenner MB, Gutierrez-Arcelus M, Luo Y, Sakaue S, Raychaudhuri S. Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution. Nat Genet 2023; 55:2255-2268. [PMID: 38036787 PMCID: PMC10787945 DOI: 10.1038/s41588-023-01586-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 10/19/2023] [Indexed: 12/02/2023]
Abstract
The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues. To mitigate technical confounding, we developed scHLApers, a pipeline to accurately quantify single-cell HLA expression using personalized reference genomes. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B and T cells. For example, a T cell HLA-DQA1 eQTL ( rs3104371 ) is strongest in cytotoxic cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Amber Z Shen
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Saisriram Gurajala
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Vitor R C Aguiar
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Cristian Valencia
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology and the Center for Health Artificial Intelligence, University of Colorado School of Medicine, Aurora, CO, USA
| | - Anna Helena Jonsson
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Seyhan Yazar
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | | | - Hamed Khalili
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ashwin N Ananthakrishnan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Kushal Dey
- Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Physiology, Biophysics and Systems Biology Program, Weill Cornell Medicine, New York, NY, USA
| | - Mark J Daly
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ramnik J Xavier
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Jennifer H Anolik
- Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Joseph E Powell
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Deepak A Rao
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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8
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Kasela S, Aguet F, Kim-Hellmuth S, Brown BC, Nachun DC, Tracy RP, Durda P, Liu Y, Taylor KD, Craig Johnson W, Berg DVD, Gabriel S, Gupta N, Smith JD, Blackwell TW, Rotter JI, Ardlie KG, Manichaikul A, Rich SS, Graham Barr R, Lappalainen T. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.26.546528. [PMID: 37425716 PMCID: PMC10326995 DOI: 10.1101/2023.06.26.546528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.
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Affiliation(s)
- Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Sarah Kim-Hellmuth
- New York Genome Center, New York, NY, USA
- Department of Pediatrics, Dr. von Hauner Children’s Hospital, University Hospital LMU Munich, Munich, Germany
- Computational Health Center, Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Brielin C. Brown
- New York Genome Center, New York, NY, USA
- Data Science Institute, Columbia University, New York, NY, USA
| | | | - Russell P. Tracy
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Peter Durda
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Yongmei Liu
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kent D. Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David Van Den Berg
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua D. Smith
- Northwest Genomic Center, University of Washington, Seattle, WA, USA
| | - Thomas W. Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Ani Manichaikul
- Center for Public health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S. Rich
- Center for Public health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R. Graham Barr
- Epidemiology and Medicine, Columbia University Medical Center, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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9
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Thorlacius GE, Björk A, Wahren-Herlenius M. Genetics and epigenetics of primary Sjögren syndrome: implications for future therapies. Nat Rev Rheumatol 2023; 19:288-306. [PMID: 36914790 PMCID: PMC10010657 DOI: 10.1038/s41584-023-00932-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2023] [Indexed: 03/14/2023]
Abstract
In primary Sjögren syndrome (pSS), chronic inflammation of exocrine glands results in tissue destruction and sicca symptoms, primarily of the mouth and eyes. Fatigue, arthralgia and myalgia are also common symptoms, whereas extraglandular manifestations that involve the respiratory, nervous and vascular systems occur in a subset of patients. The disease predominantly affects women, with an estimated female to male ratio of 14 to 1. The aetiology of pSS, however, remains incompletely understood, and effective treatment is lacking. Large-scale genetic and epigenetic investigations have revealed associations between pSS and genes in both innate and adaptive immune pathways. The genetic variants mediate context-dependent effects, and both sex and environmental factors can influence the outcome. As such, genetic and epigenetic studies can provide insight into the dysregulated molecular mechanisms, which in turn might reveal new therapeutic possibilities. This Review discusses the genetic and epigenetic features that have been robustly connected with pSS, putting them into the context of cellular function, carrier sex and environmental challenges. In all, the observations point to several novel opportunities for early detection, treatment development and the pathway towards personalized medicine.
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Affiliation(s)
- Gudny Ella Thorlacius
- Division of Rheumatology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Albin Björk
- Division of Rheumatology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
- Center for Rheumatology, Academic Specialist Center, Stockholm, Sweden
| | - Marie Wahren-Herlenius
- Division of Rheumatology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
- Broegelmann Research Laboratory, Department of Clinical Science, University of Bergen, Bergen, Norway.
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10
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Seah C, Huckins LM, Brennand KJ. Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders. Biol Psychiatry 2023; 93:642-650. [PMID: 36658083 DOI: 10.1016/j.biopsych.2022.09.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/27/2022] [Accepted: 09/27/2022] [Indexed: 01/21/2023]
Abstract
Genome-wide association studies reveal the complex polygenic architecture underlying psychiatric disorder risk, but there is an unmet need to validate causal variants, resolve their target genes(s), and explore their functional impacts on disorder-related mechanisms. Disorder-associated loci regulate transcription of target genes in a cell type- and context-specific manner, which can be measured through expression quantitative trait loci. In this review, we discuss methods and insights from context-specific modeling of genetically and environmentally regulated expression. Human induced pluripotent stem cell-derived cell type and organoid models have uncovered context-specific psychiatric disorder associations by investigating tissue-, cell type-, sex-, age-, and stressor-specific genetic regulation of expression. Techniques such as massively parallel reporter assays and pooled CRISPR (clustered regularly interspaced short palindromic repeats) screens make it possible to functionally fine-map genome-wide association study loci and validate their target genes at scale. Integration of disorder-associated contexts with these patient-specific human induced pluripotent stem cell models makes it possible to uncover gene by environment interactions that mediate disorder risk, which will ultimately improve our ability to diagnose and treat psychiatric disorders.
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Affiliation(s)
- Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York; Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York; Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
| | - Kristen J Brennand
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York; Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
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11
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Gholami M, Zoughi M, Hasanzad M, Larijani B, Amoli MM. Haplotypic variants of COVID-19 related genes are associated with blood pressure and metabolites levels. J Med Virol 2023; 95:e28355. [PMID: 36443248 PMCID: PMC9877746 DOI: 10.1002/jmv.28355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 07/27/2022] [Accepted: 11/22/2022] [Indexed: 12/03/2022]
Abstract
The genetic association of coronavirus disease 2019 (COVID-19) with its complications has not been fully understood. This study aimed to identify variants and haplotypes of candidate genes implicated in COVID-19 related traits by combining the literature review and pathway analysis. To explore such genes, the protein-protein interactions and relevant pathways of COVID-19-associated genes were assessed. A number of variants on candidate genes were identified from Genome-wide association studies (GWASs) which were associated with COVID-19 related traits (p ˂ 10-6 ). Haplotypic blocks were assessed using haplotypic structures among the 1000 Genomes Project (r2 ≥ 0.8, D' ≥ 0.8). Further functional analyses were performed on the selected variants. The results demonstrated that a group of variants in ACE and AGT genes were significantly correlated with COVID-19 related traits. Three haplotypes were identified to be involved in the blood metabolites levels and the development of blood pressure. Functional analyses revealed that most GWAS index variants were expression quantitative trait loci and had transcription factor binding sites, exonic splicing enhancers or silencer activities. Furthermore, the proxy haplotype variants, rs4316, rs4353, rs4359, and three variants, namely rs2493133, rs2478543, and rs5051, were associated with blood metabolite and systolic blood pressure, respectively. These variants exerted more regulatory effects compared with other GWAS variants. The present study indicates that the genetic variants and candidate haplotypes of COVID-19 related genes are associated with blood pressure and blood metabolites. However, further observational studies are warranted to confirm these results.
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Affiliation(s)
- Morteza Gholami
- Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular‐Cellular Sciences InstituteTehran University of Medical SciencesTehranIran,Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences InstituteTehran University of Medical SciencesTehranIran
| | - Marziyeh Zoughi
- Metabolomics and genomics research center endocrinology and metabolism molecular‐cellular sciences instituteTehran University of medical sciencesTehranIran
| | - Mandana Hasanzad
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences InstituteTehran University of Medical SciencesTehranIran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences InstituteTehran University of Medical SciencesTehranIran
| | - Mahsa M. Amoli
- Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular‐Cellular Sciences InstituteTehran University of Medical SciencesTehranIran
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12
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Porcu E, Claringbould A, Weihs A, Lepik K, Richardson TG, Völker U, Santoni FA, Teumer A, Franke L, Reymond A, Kutalik Z. Limited evidence for blood eQTLs in human sexual dimorphism. Genome Med 2022; 14:89. [PMID: 35953856 PMCID: PMC9373355 DOI: 10.1186/s13073-022-01088-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 07/14/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS-associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits. METHODS To explore this scenario, we combined sex-specific whole blood RNA-seq eQTL data from 3447 European individuals included in BIOS Consortium and GWAS data from UK Biobank. Next, to test the presence of sex-biased causal effect of gene expression on complex traits, we performed sex-specific transcriptome-wide Mendelian randomization (TWMR) analyses on the two most sexually dimorphic traits, waist-to-hip ratio (WHR) and testosterone levels. Finally, we performed power analysis to calculate the GWAS sample size needed to observe sex-specific trait associations driven by sex-biased eQTLs. RESULTS Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-biased cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-biased eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-biased trait-associations. In addition, we observed that sex-specific causal effects of gene expression on complex traits are not driven by sex-specific eQTLs. Power analyses using real eQTL- and causal-effect sizes showed that millions of samples would be necessary to observe sex-biased trait associations that are fully driven by sex-biased cis-eQTLs. Compensatory effects may further hamper their detection. CONCLUSIONS Our results suggest that sex-specific eQTLs in whole blood do not translate to detectable sex-specific trait associations of complex diseases, and vice versa that the observed sex-specific trait associations cannot be explained by sex-specific eQTLs.
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Affiliation(s)
- Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Center for Primary Care and Public Health, Lausanne, Switzerland.
| | - Annique Claringbould
- University Medical Centre Groningen, Groningen, the Netherlands.,Structural and Computational Biology Unit, European Molecular Biology Laboratories (EMBL), Heidelberg, Germany
| | - Antoine Weihs
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Kaido Lepik
- Institute of Computer Science, University of Tartu, Tartu, Estonia.,Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, OX3 7DQ, UK
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Federico A Santoni
- Endocrine, Diabetes, and Metabolism Service, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Alexander Teumer
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Lude Franke
- University Medical Centre Groningen, Groningen, the Netherlands
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Center for Primary Care and Public Health, Lausanne, Switzerland. .,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
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13
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Sikder S, Arunkumar G, Melters DP, Dalal Y. Breaking the aging epigenetic barrier. Front Cell Dev Biol 2022; 10:943519. [PMID: 35966762 PMCID: PMC9366916 DOI: 10.3389/fcell.2022.943519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Aging is an inexorable event occurring universally for all organisms characterized by the progressive loss of cell function. However, less is known about the key events occurring inside the nucleus in the process of aging. The advent of chromosome capture techniques and extensive modern sequencing technologies have illuminated a rather dynamic structure of chromatin inside the nucleus. As cells advance along their life cycle, chromatin condensation states alter which leads to a different epigenetic landscape, correlated with modified gene expression. The exact factors mediating these changes in the chromatin structure and function remain elusive in the context of aging cells. The accumulation of DNA damage, reactive oxygen species and loss of genomic integrity as cells cease to divide can contribute to a tumor stimulating environment. In this review, we focus on genomic and epigenomic changes occurring in an aged cell which can contribute to age-related tumor formation.
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Affiliation(s)
| | | | | | - Yamini Dalal
- Chromatin Structure and Epigenetic Mechanisms, Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, NCI, NIH, Bethesda, MD, United States
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14
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Sex differences in the genetic regulation of the blood transcriptome response to glucocorticoid receptor activation. Transl Psychiatry 2021; 11:632. [PMID: 34903727 PMCID: PMC8669026 DOI: 10.1038/s41398-021-01756-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 11/25/2021] [Indexed: 12/13/2022] Open
Abstract
Substantial sex differences have been reported in the physiological response to stress at multiple levels, including the release of the stress hormone, cortisol. Here, we explore the genomic variants in 93 females and 196 males regulating the initial transcriptional response to cortisol via glucocorticoid receptor (GR) activation. Gene expression levels in peripheral blood were obtained before and after GR-stimulation with the selective GR agonist dexamethasone to identify differential expression following GR-activation. Sex stratified analyses revealed that while the transcripts responsive to GR-stimulation were mostly overlapping between males and females, the quantitative trait loci (eQTLs) regulation differential transcription to GR-stimulation was distinct. Sex-stratified eQTL SNPs (eSNPs) were located in different functional genomic elements and sex-stratified transcripts were enriched within postmortem brain transcriptional profiles associated with Major Depressive Disorder (MDD) specifically in males and females in the cingulate cortex. Female eSNPs were enriched among SNPs linked to MDD in genome-wide association studies. Finally, transcriptional sensitive genetic profile scores derived from sex-stratified eSNPS regulating differential transcription to GR-stimulation were predictive of depression status and depressive symptoms in a sex-concordant manner in a child and adolescent cohort (n = 584). These results suggest the potential of eQTLs regulating differential transcription to GR-stimulation as biomarkers of sex-specific biological risk for stress-related psychiatric disorders.
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15
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Bernabeu E, Canela-Xandri O, Rawlik K, Talenti A, Prendergast J, Tenesa A. Sex differences in genetic architecture in the UK Biobank. Nat Genet 2021; 53:1283-1289. [PMID: 34493869 DOI: 10.1038/s41588-021-00912-0] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 07/12/2021] [Indexed: 01/05/2023]
Abstract
Males and females present differences in complex traits and in the risk of a wide array of diseases. Genotype by sex (GxS) interactions are thought to account for some of these differences. However, the extent and basis of GxS are poorly understood. In the present study, we provide insights into both the scope and the mechanism of GxS across the genome of about 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits. We also found that, in some cases, sex-agnostic analyses may be missing trait-associated loci and looked into possible improvements in the prediction of high-level phenotypes. Finally, we studied the potential functional role of the differences observed through sex-biased gene expression and gene-level analyses. Our results suggest the need to consider sex-aware analyses for future studies to shed light onto possible sex-specific molecular mechanisms.
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Affiliation(s)
- Elena Bernabeu
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Midlothian, UK
| | - Oriol Canela-Xandri
- Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Midlothian, UK
| | - Andrea Talenti
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Midlothian, UK
| | - James Prendergast
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Midlothian, UK
| | - Albert Tenesa
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Midlothian, UK.
- Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK.
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16
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Laskar RS, Li P, Ecsedi S, Abedi-Ardekani B, Durand G, Robinot N, Hubert JN, Janout V, Zaridze D, Mukeria A, Mates D, Holcatova I, Foretova L, Swiatkowska B, Dzamic Z, Milosavljevic S, Olaso R, Boland A, Deleuze JF, Muller DC, McKay JD, Brennan P, Le Calvez-Kelm F, Scelo G, Chanudet E. Sexual dimorphism in cancer: insights from transcriptional signatures in kidney tissue and renal cell carcinoma. Hum Mol Genet 2021; 30:343-355. [PMID: 33527138 PMCID: PMC8098110 DOI: 10.1093/hmg/ddab031] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 12/14/2022] Open
Abstract
Sexual dimorphism in cancer incidence and outcome is widespread. Understanding the underlying mechanisms is fundamental to improve cancer prevention and clinical management. Sex disparities are particularly striking in kidney cancer: across diverse populations, men consistently show unexplained 2-fold increased incidence and worse prognosis. We have characterized genome-wide expression and regulatory networks of 609 renal tumors and 256 non-tumor renal tissues. Normal kidney displayed sex-specific transcriptional signatures, including higher expression of X-linked tumor suppressor genes in women. Sex-dependent genotype-phenotype associations unraveled women-specific immune regulation. Sex differences were markedly expanded in tumors, with male-biased expression of key genes implicated in metabolism, non-malignant diseases with male predominance and carcinogenesis, including markers of tumor infiltrating leukocytes. Analysis of sex-dependent RCC progression and survival uncovered prognostic markers involved in immune response and oxygen homeostasis. In summary, human kidney tissues display remarkable sexual dimorphism at the molecular level. Sex-specific transcriptional signatures further shape renal cancer, with relevance for clinical management.
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Affiliation(s)
- Ruhina S Laskar
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
| | - Peng Li
- Laboratory of Population Health, Max Planck Institute for Demographic Research, 18057 Rostock, Germany
| | - Szilvia Ecsedi
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
| | - Behnoush Abedi-Ardekani
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
| | - Geoffroy Durand
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
| | - Nivonirina Robinot
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
| | - Jean-Noël Hubert
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
| | - Vladimir Janout
- Science and Research Center, Faculty of Health Sciences, Palacky University, 77900 Olomouc, Czech Republic
| | - David Zaridze
- Department of Epidemiology and Prevention, Russian N.N. Blokhin Cancer Research Centre, 115478 Moscow, Russian Federation
| | - Anush Mukeria
- Department of Epidemiology and Prevention, Russian N.N. Blokhin Cancer Research Centre, 115478 Moscow, Russian Federation
| | - Dana Mates
- Department of Environmental Health, National Institute of Public Health, 050463 Bucharest, Romania
| | - Ivana Holcatova
- Department of Public Health and Preventive Medicine, Charles University, Second Faculty of Medicine, 15006 Prague, Czech Republic
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, 60200 Brno, Czech Republic
| | - Beata Swiatkowska
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, 91-348 Lodz, Poland
| | - Zoran Dzamic
- Clinic of Urology, Clinical Center of Serbia (KCS), University of Belgrade - Faculty of Medicine, 11000 Belgrade, Serbia
| | - Sasa Milosavljevic
- International Organisation for Cancer Prevention and Research, 11070 Belgrade, Serbia
| | - Robert Olaso
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, 91057, Evry, France
| | - Anne Boland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, 91057, Evry, France
| | - Jean-François Deleuze
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, 91057, Evry, France
| | - David C Muller
- Faculty of Medicine, School of Public Health, Imperial College London, W21NY London, UK
| | - James D McKay
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
| | - Paul Brennan
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
| | - Florence Le Calvez-Kelm
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
| | - Ghislaine Scelo
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
- Unit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, 8-10124 Turin, Italy
| | - Estelle Chanudet
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France
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17
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Johnston KJA, Ward J, Ray PR, Adams MJ, McIntosh AM, Smith BH, Strawbridge RJ, Price TJ, Smith DJ, Nicholl BI, Bailey MES. Sex-stratified genome-wide association study of multisite chronic pain in UK Biobank. PLoS Genet 2021; 17:e1009428. [PMID: 33830993 PMCID: PMC8031124 DOI: 10.1371/journal.pgen.1009428] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 02/16/2021] [Indexed: 12/30/2022] Open
Abstract
Chronic pain is highly prevalent worldwide and imparts a significant socioeconomic and public health burden. Factors influencing susceptibility to, and mechanisms of, chronic pain development, are not fully understood, but sex is thought to play a significant role, and chronic pain is more prevalent in women than in men. To investigate sex differences in chronic pain, we carried out a sex-stratified genome-wide association study of Multisite Chronic Pain (MCP), a derived chronic pain phenotype, in UK Biobank on 178,556 men and 209,093 women, as well as investigating sex-specific genetic correlations with a range of psychiatric, autoimmune and anthropometric phenotypes and the relationship between sex-specific polygenic risk scores for MCP and chronic widespread pain. We also assessed whether MCP-associated genes showed expression pattern enrichment across tissues. A total of 123 SNPs at five independent loci were significantly associated with MCP in men. In women, a total of 286 genome-wide significant SNPs at ten independent loci were discovered. Meta-analysis of sex-stratified GWAS outputs revealed a further 87 independent associated SNPs. Gene-level analyses revealed sex-specific MCP associations, with 31 genes significantly associated in females, 37 genes associated in males, and a single gene, DCC, associated in both sexes. We found evidence for sex-specific pleiotropy and risk for MCP was found to be associated with chronic widespread pain in a sex-differential manner. Male and female MCP were highly genetically correlated, but at an rg of significantly less than 1 (0.92). All 37 male MCP-associated genes and all but one of 31 female MCP-associated genes were found to be expressed in the dorsal root ganglion, and there was a degree of enrichment for expression in sex-specific tissues. Overall, the findings indicate that sex differences in chronic pain exist at the SNP, gene and transcript abundance level, and highlight possible sex-specific pleiotropy for MCP. Results support the proposition of a strong central nervous-system component to chronic pain in both sexes, additionally highlighting a potential role for the DRG and nociception.
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Affiliation(s)
- Keira J. A. Johnston
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, United Kingdom
- Division of Psychiatry, University of Edinburgh, Edinburgh, Scotland, United Kingdom
- School of Life Sciences, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Joey Ward
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Pradipta R. Ray
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas, United States of America
| | - Mark J. Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Andrew M. McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Blair H. Smith
- Division of Population Health Sciences, University of Dundee, Ninewells Hospital and Medical School, Dundee, Scotland, United Kingdom
| | - Rona J. Strawbridge
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, United Kingdom
- Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Theodore J. Price
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas, United States of America
| | - Daniel J. Smith
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Barbara I. Nicholl
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Mark E. S. Bailey
- School of Life Sciences, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, Scotland, United Kingdom
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18
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Wang K, Basu M, Malin J, Hannenhalli S. A transcription-centric model of SNP-age interaction. PLoS Genet 2021; 17:e1009427. [PMID: 33770080 PMCID: PMC7997000 DOI: 10.1371/journal.pgen.1009427] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 02/16/2021] [Indexed: 12/23/2022] Open
Abstract
Complex age-associated phenotypes are caused, in part, by an interaction between an individual's genotype and age. The mechanisms governing such interactions are however not entirely understood. Here, we provide a novel transcriptional mechanism-based framework-SNiPage, to investigate such interactions, whereby a transcription factor (TF) whose expression changes with age (age-associated TF), binds to a polymorphic regulatory element in an allele-dependent fashion, rendering the target gene's expression dependent on both, the age and the genotype. Applying SNiPage to GTEx, we detected ~637 significant TF-SNP-Gene triplets on average across 25 tissues, where the TF binds to a regulatory SNP in the gene's promoter or putative enhancer and potentially regulates its expression in an age- and allele-dependent fashion. The detected SNPs are enriched for epigenomic marks indicative of regulatory activity, exhibit allele-specific chromatin accessibility, and spatial proximity to their putative gene targets. Furthermore, the TF-SNP interaction-dependent target genes have established links to aging and to age-associated diseases. In six hypertension-implicated tissues, detected interactions significantly inform hypertension state of an individual. Lastly, the age-interacting SNPs exhibit a greater proximity to the reported phenotype/diseases-associated SNPs than eSNPs identified in an interaction-independent fashion. Overall, we present a novel mechanism-based model, and a novel framework SNiPage, to identify functionally relevant SNP-age interactions in transcriptional control and illustrate their potential utility in understanding complex age-associated phenotypes.
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Affiliation(s)
- Kun Wang
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Mahashweta Basu
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
- Institute for Genome Sciences, University of Maryland, Baltimore, Maryland, United States of America
| | - Justin Malin
- Laboratory of Genome Integrity, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
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19
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Stein MM, Conery M, Magnaye KM, Clay SM, Billstrand C, Nicolae R, Naughton K, Ober C, Thompson EE. Sex-specific differences in peripheral blood leukocyte transcriptional response to LPS are enriched for HLA region and X chromosome genes. Sci Rep 2021; 11:1107. [PMID: 33441806 PMCID: PMC7806814 DOI: 10.1038/s41598-020-80145-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 12/08/2020] [Indexed: 02/08/2023] Open
Abstract
Sex-specific differences in prevalence are well documented for many common, complex diseases, especially for immune-mediated diseases, yet the precise mechanisms through which factors associated with biological sex exert their effects throughout life are not well understood. We interrogated sex-specific transcriptional responses of peripheral blood leukocytes (PBLs) to innate immune stimulation by lipopolysaccharide (LPS) in 46 male and 66 female members of the Hutterite community, who practice a communal lifestyle. We identified 1217 autosomal and 54 X-linked genes with sex-specific responses to LPS, as well as 71 autosomal and one X-linked sex-specific expression quantitative trait loci (eQTLs). Despite a similar proportion of the 15 HLA genes responding to LPS compared to all expressed autosomal genes, there was a significant over-representation of genes with sex by treatment interactions among HLA genes. We also observed an enrichment of sex-specific differentially expressed genes in response to LPS for X-linked genes compared to autosomal genes, suggesting that HLA and X-linked genes may disproportionately contribute to sex disparities in risk for immune-mediated diseases.
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Affiliation(s)
- Michelle M Stein
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Mitch Conery
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Kevin M Magnaye
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Selene M Clay
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | | | - Raluca Nicolae
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Katherine Naughton
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Emma E Thompson
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
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20
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Lopes-Ramos CM, Quackenbush J, DeMeo DL. Genome-Wide Sex and Gender Differences in Cancer. Front Oncol 2020; 10:597788. [PMID: 33330090 PMCID: PMC7719817 DOI: 10.3389/fonc.2020.597788] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 10/19/2020] [Indexed: 12/12/2022] Open
Abstract
Despite their known importance in clinical medicine, differences based on sex and gender are among the least studied factors affecting cancer susceptibility, progression, survival, and therapeutic response. In particular, the molecular mechanisms driving sex differences are poorly understood and so most approaches to precision medicine use mutational or other genomic data to assign therapy without considering how the sex of the individual might influence therapeutic efficacy. The mandate by the National Institutes of Health that research studies include sex as a biological variable has begun to expand our understanding on its importance. Sex differences in cancer may arise due to a combination of environmental, genetic, and epigenetic factors, as well as differences in gene regulation, and expression. Extensive sex differences occur genome-wide, and ultimately influence cancer biology and outcomes. In this review, we summarize the current state of knowledge about sex-specific genetic and genome-wide influences in cancer, describe how differences in response to environmental exposures and genetic and epigenetic alterations alter the trajectory of the disease, and provide insights into the importance of integrative analyses in understanding the interplay of sex and genomics in cancer. In particular, we will explore some of the emerging analytical approaches, such as the use of network methods, that are providing a deeper understanding of the drivers of differences based on sex and gender. Better understanding these complex factors and their interactions will improve cancer prevention, treatment, and outcomes for all individuals.
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Affiliation(s)
- Camila M Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.,Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, United States
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21
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Anderson WD, Soh JY, Innis SE, Dimanche A, Ma L, Langefeld CD, Comeau ME, Das SK, Schadt EE, Björkegren JLM, Civelek M. Sex differences in human adipose tissue gene expression and genetic regulation involve adipogenesis. Genome Res 2020; 30:1379-1392. [PMID: 32967914 PMCID: PMC7605264 DOI: 10.1101/gr.264614.120] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 08/27/2020] [Indexed: 02/06/2023]
Abstract
Sex differences in adipose tissue distribution and function are associated with sex differences in cardiometabolic disease. While many studies have revealed sex differences in adipocyte cell signaling and physiology, there is a relative dearth of information regarding sex differences in transcript abundance and regulation. We investigated sex differences in subcutaneous adipose tissue transcriptional regulation using omic-scale data from ∼3000 geographically and ethnically diverse human samples. We identified 162 genes with robust sex differences in expression. Differentially expressed genes were implicated in oxidative phosphorylation and adipogenesis. We further determined that sex differences in gene expression levels could be related to sex differences in the genetics of gene expression regulation. Our analyses revealed sex-specific genetic associations, and this finding was replicated in a study of 98 inbred mouse strains. The genes under genetic regulation in human and mouse were enriched for oxidative phosphorylation and adipogenesis. Enrichment analysis showed that the associated genetic loci resided within binding motifs for adipogenic transcription factors (e.g., PPARG and EGR1). We demonstrated that sex differences in gene expression could be influenced by sex differences in genetic regulation for six genes (e.g., FADS1 and MAP1B). These genes exhibited dynamic expression patterns during adipogenesis and robust expression in mature human adipocytes. Our results support a role for adipogenesis-related genes in subcutaneous adipose tissue sex differences in the genetic and environmental regulation of gene expression.
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Affiliation(s)
- Warren D Anderson
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA
| | - Joon Yuhl Soh
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA
| | - Sarah E Innis
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Alexis Dimanche
- Physics Department, Southwestern University, Georgetown, Texas 78626, USA
| | - Lijiang Ma
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27101, USA
| | - Mary E Comeau
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27101, USA
| | - Swapan K Das
- Department of Internal Medicine, Section of Endocrinology and Metabolism, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27101, USA
| | - Eric E Schadt
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Johan L M Björkegren
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Mete Civelek
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22904, USA
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22
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Association of a genetic risk score with BMI along the life-cycle: Evidence from several US cohorts. PLoS One 2020; 15:e0239067. [PMID: 32941506 PMCID: PMC7497990 DOI: 10.1371/journal.pone.0239067] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 08/29/2020] [Indexed: 12/27/2022] Open
Abstract
We use data from the National Longitudinal Study of Adolescent to Adult Health and from the Health and Retirement Study to explore how the effect of individuals’ genetic predisposition to higher BMI —measured by BMI polygenic scores— changes over the life-cycle for several cohorts. We find that the effect of BMI polygenic scores on BMI increases significantly as teenagers transition into adulthood (using the Add Health cohort, born 1974-83). However, this is not the case for individuals aged 55+ who were born in earlier HRS cohorts (1931-53), whose life-cycle pattern of genetic influence on BMI is remarkably stable as they move into old-age.
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23
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Gleason KJ, Yang F, Pierce BL, He X, Chen LS. Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits. Genome Biol 2020; 21:236. [PMID: 32912334 PMCID: PMC7488447 DOI: 10.1186/s13059-020-02125-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 07/29/2020] [Indexed: 01/10/2023] Open
Abstract
To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method, Primo, for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines association patterns of SNPs to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.
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Affiliation(s)
- Kevin J. Gleason
- Department of Public Health Sciences, University of Chicago, 5841 South Maryland Ave MC2000, Chicago, 60637 IL USA
| | - Fan Yang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, Aurora, 80045 CO USA
| | - Brandon L. Pierce
- Department of Public Health Sciences, University of Chicago, 5841 South Maryland Ave MC2000, Chicago, 60637 IL USA
- Department of Human Genetics, University of Chicago, 920 E 58th St, Chicago, 60637 IL USA
| | - Xin He
- Department of Human Genetics, University of Chicago, 920 E 58th St, Chicago, 60637 IL USA
| | - Lin S. Chen
- Department of Public Health Sciences, University of Chicago, 5841 South Maryland Ave MC2000, Chicago, 60637 IL USA
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24
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Oliva M, Muñoz-Aguirre M, Kim-Hellmuth S, Wucher V, Gewirtz ADH, Cotter DJ, Parsana P, Kasela S, Balliu B, Viñuela A, Castel SE, Mohammadi P, Aguet F, Zou Y, Khramtsova EA, Skol AD, Garrido-Martín D, Reverter F, Brown A, Evans P, Gamazon ER, Payne A, Bonazzola R, Barbeira AN, Hamel AR, Martinez-Perez A, Soria JM, Pierce BL, Stephens M, Eskin E, Dermitzakis ET, Segrè AV, Im HK, Engelhardt BE, Ardlie KG, Montgomery SB, Battle AJ, Lappalainen T, Guigó R, Stranger BE. The impact of sex on gene expression across human tissues. Science 2020; 369:eaba3066. [PMID: 32913072 PMCID: PMC8136152 DOI: 10.1126/science.aba3066] [Citation(s) in RCA: 307] [Impact Index Per Article: 76.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 08/03/2020] [Indexed: 12/12/2022]
Abstract
Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.
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Affiliation(s)
- Meritxell Oliva
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Manuel Muñoz-Aguirre
- Centre for Genomic Regulation, Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
- Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Barcelona, Catalonia, Spain
| | - Sarah Kim-Hellmuth
- Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Valentin Wucher
- Centre for Genomic Regulation, Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Ariel D H Gewirtz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Daniel J Cotter
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Princy Parsana
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Brunilda Balliu
- Department of Computational Medicine, University of California, Los Angeles, CA, USA
| | - Ana Viñuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Stephane E Castel
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, Scripps Research Translational Institute, La Jolla, CA, USA
| | | | - Yuxin Zou
- Department of Statistics, University of Chicago, Chicago, IL, USA
| | - Ekaterina A Khramtsova
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
- Computational Sciences, Janssen Pharmaceuticals, Spring House, PA, USA
| | - Andrew D Skol
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Center for Translational Data Science, University of Chicago, Chicago, IL, USA
- Department of Pathology and Laboratory Medicine, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Diego Garrido-Martín
- Centre for Genomic Regulation, Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Ferran Reverter
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | | | - Patrick Evans
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Clare Hall, University of Cambridge, Cambridge, UK
| | - Anthony Payne
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Alvaro N Barbeira
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Andrew R Hamel
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Angel Martinez-Perez
- Genomics of Complex Diseases Group, Research Institute Hospital de la Sant Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
| | - José Manuel Soria
- Genomics of Complex Diseases Group, Research Institute Hospital de la Sant Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
| | - Brandon L Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Eleazar Eskin
- Departments of Computational Medicine, Computer Science, and Human Genetics, University of California, Los Angeles, CA, USA
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Ayellet V Segrè
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Barbara E Engelhardt
- Department of Computer Science, Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
- Genomics plc, Oxford, UK
| | | | - Stephen B Montgomery
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Alexis J Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Roderic Guigó
- Centre for Genomic Regulation, Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Barbara E Stranger
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Center for Translational Data Science, University of Chicago, Chicago, IL, USA
- Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Chicago, IL, USA
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25
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Delaney DM, Hoekstra LA, Janzen FJ. Becoming creatures of habit: Among- and within-individual variation in nesting behaviour shift with age. J Evol Biol 2020; 33:1614-1624. [PMID: 32897610 DOI: 10.1111/jeb.13701] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 08/24/2020] [Accepted: 08/31/2020] [Indexed: 11/29/2022]
Abstract
The quantification of repeatability has enabled behavioural and evolutionary ecologists to assess the heritable potential of traits. For behavioural traits that vary across life, age-related variation should be accounted for to prevent biasing the microevolutionary estimate of interest. Moreover, to gain a mechanistic understanding of ontogenetic variation in behaviour, among- and within-individual variance should be quantified across life. We leveraged a 30-year study of painted turtles (Chrysemys picta) to assess how age contributes to variation in the repeatability of nesting behaviours. We found that four components of nesting behaviour were repeatable and that accounting for age increased the repeatability estimate for maternal choice of canopy cover over nests. We detected canalization (diminished within-individual variance with age) of canopy cover choice in a reduced data set despite no shift in repeatability. Additionally, random regression analysis revealed that females became more divergent from each other in their choice of canopy cover with age. Thus, properly modelling age-related variance should more precisely estimate heritable potential, and assessing among- and within-individual variance components in addition to repeatability will offer a more mechanistic understanding of behavioural variation across age.
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Affiliation(s)
- David M Delaney
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA.,Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Luke A Hoekstra
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA.,Department of Integrative Biology, Oklahoma State University, Stillwater, OK, USA
| | - Fredric J Janzen
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA.,Kellogg Biological Station, Michigan State University, Hickory Corners, MI, USA
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26
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Kassam I, Wu Y, Yang J, Visscher PM, McRae AF. Tissue-specific sex differences in human gene expression. Hum Mol Genet 2020; 28:2976-2986. [PMID: 31044242 DOI: 10.1093/hmg/ddz090] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 04/12/2019] [Accepted: 04/24/2019] [Indexed: 02/07/2023] Open
Abstract
Despite extensive sex differences in human complex traits and disease, the male and female genomes differ only in the sex chromosomes. This implies that most sex-differentiated traits are the result of differences in the expression of genes that are common to both sexes. While sex differences in gene expression have been observed in a range of different tissues, the biological mechanisms for tissue-specific sex differences (TSSDs) in gene expression are not well understood. A total of 30 640 autosomal and 1021 X-linked transcripts were tested for heterogeneity in sex difference effect sizes in n = 617 individuals across 40 tissue types in Genotype-Tissue Expression (GTEx). This identified 65 autosomal and 66 X-linked TSSD transcripts (corresponding to unique genes) at a stringent significance threshold. Results for X-linked TSSD transcripts showed mainly concordant direction of sex differences across tissues and replicate previous findings. Autosomal TSSD transcripts had mainly discordant direction of sex differences across tissues. The top cis-expression quantitative trait loci (eQTLs) across tissues for autosomal TSSD transcripts are located a similar distance away from the nearest androgen and estrogen binding motifs and the nearest enhancer, as compared to cis-eQTLs for transcripts with stable sex differences in gene expression across tissue types. Enhancer regions that overlap top cis-eQTLs for TSSD transcripts, however, were found to be more dispersed across tissues. These observations suggest that androgen and estrogen regulatory elements in a cis region may play a common role in sex differences in gene expression, but TSSD in gene expression may additionally be due to causal variants located in tissue-specific enhancer regions.
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Affiliation(s)
| | - Yang Wu
- Institute for Molecular Bioscience
| | - Jian Yang
- Institute for Molecular Bioscience.,Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience.,Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience.,Queensland Brain Institute, The University of Queensland, Brisbane, Australia
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27
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Ramírez Sepúlveda JI, Bolin K, Mofors J, Leonard D, Svenungsson E, Jönsen A, Bengtsson C, Nordmark G, Rantapää Dahlqvist S, Bengtsson AA, Rönnblom L, Sjöwall C, Gunnarsson I, Wahren-Herlenius M. Sex differences in clinical presentation of systemic lupus erythematosus. Biol Sex Differ 2019; 10:60. [PMID: 31843005 PMCID: PMC6915972 DOI: 10.1186/s13293-019-0274-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 11/25/2019] [Indexed: 12/19/2022] Open
Abstract
Objective Systemic lupus erythematosus (SLE) predominantly affects women, but previous studies suggest that men with SLE present a more severe disease phenotype. In this study, we investigated a large and well-characterized patient group with the aim of identifying sex differences in disease manifestations, with a special focus on renal involvement. Methods We studied a Swedish multi-center SLE cohort including 1226 patients (1060 women and 166 men) with a mean follow-up time of 15.8 ± 13.4 years. Demographic data, disease manifestations including ACR criteria, serology and renal histopathology were investigated. Renal outcome and mortality were analyzed in subcohorts. Results Female SLE patients presented more often with malar rash (p < 0.0001), photosensitivity (p < 0.0001), oral ulcers (p = 0.01), and arthritis (p = 0.007). Male patients on the other hand presented more often with serositis (p = 0.0003), renal disorder (p < 0.0001), and immunologic disorder (p = 0.04) by the ACR definitions. With regard to renal involvement, women were diagnosed with nephritis at an earlier age (p = 0.006), while men with SLE had an overall higher risk for progression into end-stage renal disease (ESRD) with a hazard ratio (HR) of 5.1 (95% CI, 2.1–12.5). The mortality rate among men with SLE and nephritis compared with women was HR 1.7 (95% CI, 0.8–3.8). Conclusion SLE shows significant sex-specific features, whereby men are affected by a more severe disease with regard to both renal and extra-renal manifestations. Additionally, men are at a higher risk of developing ESRD which may require an increased awareness and monitoring in clinical practice.
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Affiliation(s)
| | - Karin Bolin
- Department of Medical Sciences, Rheumatology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Johannes Mofors
- Rheumatology Unit, Department of Medicine, Karolinska Institutet, SE-171 76, Stockholm, Sweden
| | - Dag Leonard
- Department of Medical Sciences, Rheumatology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Elisabet Svenungsson
- Rheumatology Unit, Department of Medicine, Karolinska Institutet, SE-171 76, Stockholm, Sweden.,Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Andreas Jönsen
- Department of Clinical Sciences, Rheumatology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Christine Bengtsson
- Department of Public Health and Clinical Medicine/Rheumatology, Umeå University, Umeå, Sweden
| | | | - Gunnel Nordmark
- Department of Medical Sciences, Rheumatology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Anders A Bengtsson
- Department of Public Health and Clinical Medicine/Rheumatology, Umeå University, Umeå, Sweden
| | - Lars Rönnblom
- Department of Medical Sciences, Rheumatology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Christopher Sjöwall
- Department of Clinical and Experimental Medicine, Rheumatology/Division of Neuro and Inflammation Sciences, Linköping University, Linköping, Sweden
| | - Iva Gunnarsson
- Rheumatology Unit, Department of Medicine, Karolinska Institutet, SE-171 76, Stockholm, Sweden.,Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Marie Wahren-Herlenius
- Rheumatology Unit, Department of Medicine, Karolinska Institutet, SE-171 76, Stockholm, Sweden. .,Rheumatology, Karolinska University Hospital, Stockholm, Sweden.
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28
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Lardenoije R, Roubroeks JAY, Pishva E, Leber M, Wagner H, Iatrou A, Smith AR, Smith RG, Eijssen LMT, Kleineidam L, Kawalia A, Hoffmann P, Luck T, Riedel-Heller S, Jessen F, Maier W, Wagner M, Hurlemann R, Kenis G, Ali M, del Sol A, Mastroeni D, Delvaux E, Coleman PD, Mill J, Rutten BPF, Lunnon K, Ramirez A, van den Hove DLA. Alzheimer's disease-associated (hydroxy)methylomic changes in the brain and blood. Clin Epigenetics 2019; 11:164. [PMID: 31775875 PMCID: PMC6880587 DOI: 10.1186/s13148-019-0755-5] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 09/26/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Late-onset Alzheimer's disease (AD) is a complex multifactorial affliction, the pathogenesis of which is thought to involve gene-environment interactions that might be captured in the epigenome. The present study investigated epigenome-wide patterns of DNA methylation (5-methylcytosine, 5mC) and hydroxymethylation (5-hydroxymethylcytosine, 5hmC), as well as the abundance of unmodified cytosine (UC), in relation to AD. RESULTS We identified epigenetic differences in AD patients (n = 45) as compared to age-matched controls (n = 35) in the middle temporal gyrus, pertaining to genomic regions close to or overlapping with genes such as OXT (- 3.76% 5mC, pŠidák = 1.07E-06), CHRNB1 (+ 1.46% 5hmC, pŠidák = 4.01E-04), RHBDF2 (- 3.45% UC, pŠidák = 4.85E-06), and C3 (- 1.20% UC, pŠidák = 1.57E-03). In parallel, in an independent cohort, we compared the blood methylome of converters to AD dementia (n = 54) and non-converters (n = 42), at a preclinical stage. DNA methylation in the same region of the OXT promoter as found in the brain was found to be associated with subsequent conversion to AD dementia in the blood of elderly, non-demented individuals (+ 3.43% 5mC, pŠidák = 7.14E-04). CONCLUSIONS The implication of genome-wide significant differential methylation of OXT, encoding oxytocin, in two independent cohorts indicates it is a promising target for future studies on early biomarkers and novel therapeutic strategies in AD.
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Affiliation(s)
- Roy Lardenoije
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Janou A. Y. Roubroeks
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Ehsan Pishva
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Markus Leber
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, 50937 Cologne, Germany
| | - Holger Wagner
- Department of Neurodegeneration and Gerontopsychiatry, University of Bonn, 53127 Bonn, Germany
| | - Artemis Iatrou
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
| | - Adam R. Smith
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Rebecca G. Smith
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Lars M. T. Eijssen
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
- Department of Bioinformatics—BiGCaT, Maastricht University, Maastricht, The Netherlands
| | - Luca Kleineidam
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, 50937 Cologne, Germany
- Department of Neurodegeneration and Gerontopsychiatry, University of Bonn, 53127 Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Amit Kawalia
- Department of Neurodegeneration and Gerontopsychiatry, University of Bonn, 53127 Bonn, Germany
| | - Per Hoffmann
- Institute of Human Genetics, University of Bonn, 53127 Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, 53127 Bonn, Germany
- Division of Medical Genetics, University Hospital and Department of Biomedicine, University of Basel, CH-4058 Basel, Switzerland
| | - Tobias Luck
- Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, 04103 Leipzig, Germany
| | - Steffi Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, 04103 Leipzig, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, 50937 Cologne, Germany
| | - Wolfgang Maier
- Department of Neurodegeneration and Gerontopsychiatry, University of Bonn, 53127 Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Michael Wagner
- Department of Neurodegeneration and Gerontopsychiatry, University of Bonn, 53127 Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - René Hurlemann
- Department of Psychiatry and Division of Medical Psychology, University of Bonn, 53105 Bonn, Germany
| | - Gunter Kenis
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
| | - Muhammad Ali
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow, Russian Federation
- CIC bioGUNE, Bizkaia Technology Park, 801 Building, 48160 Derio, Spain
- IKERBASQUE, Basque Foundation for Science, Dolgoprudny Bilbao, Spain
| | - Diego Mastroeni
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
- L.J. Roberts Center for Alzheimer’s Research Banner Sun Health Research Institute, Sun City, AZ USA
- Biodesign Institute, Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA
| | - Elaine Delvaux
- L.J. Roberts Center for Alzheimer’s Research Banner Sun Health Research Institute, Sun City, AZ USA
- Biodesign Institute, Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA
| | - Paul D. Coleman
- L.J. Roberts Center for Alzheimer’s Research Banner Sun Health Research Institute, Sun City, AZ USA
- Biodesign Institute, Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
- Institute of Psychiatry, King’s College London, London, UK
| | - Bart P. F. Rutten
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
| | - Katie Lunnon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Alfredo Ramirez
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, 50937 Cologne, Germany
- Department of Neurodegeneration and Gerontopsychiatry, University of Bonn, 53127 Bonn, Germany
| | - Daniël L. A. van den Hove
- School for Mental Health and Neuroscience (MHeNS), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands
- Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany
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29
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Balliu B, Durrant M, Goede OD, Abell N, Li X, Liu B, Gloudemans MJ, Cook NL, Smith KS, Knowles DA, Pala M, Cucca F, Schlessinger D, Jaiswal S, Sabatti C, Lind L, Ingelsson E, Montgomery SB. Genetic regulation of gene expression and splicing during a 10-year period of human aging. Genome Biol 2019; 20:230. [PMID: 31684996 PMCID: PMC6827221 DOI: 10.1186/s13059-019-1840-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 09/27/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Molecular and cellular changes are intrinsic to aging and age-related diseases. Prior cross-sectional studies have investigated the combined effects of age and genetics on gene expression and alternative splicing; however, there has been no long-term, longitudinal characterization of these molecular changes, especially in older age. RESULTS We perform RNA sequencing in whole blood from the same individuals at ages 70 and 80 to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging at a population and individual level. We observe that individuals are more similar to their own expression profiles later in life than profiles of other individuals their own age. We identify 1291 and 294 genes differentially expressed and alternatively spliced with age, as well as 529 genes with outlying individual trajectories. Further, we observe a strong correlation of genetic effects on expression and splicing between the two ages, with a small subset of tested genes showing a reduction in genetic associations with expression and splicing in older age. CONCLUSIONS These findings demonstrate that, although the transcriptome and its genetic regulation is mostly stable late in life, a small subset of genes is dynamic and is characterized by a reduction in genetic regulation, most likely due to increasing environmental variance with age.
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Affiliation(s)
- Brunilda Balliu
- Department of Pathology, Stanford University School of Medicine, Stanford, USA.
| | - Matthew Durrant
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Olivia de Goede
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Nathan Abell
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Xin Li
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | - Boxiang Liu
- Department of Biology, Stanford University School of Medicine, Stanford, USA
| | | | - Naomi L Cook
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Kevin S Smith
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | | | - Mauro Pala
- Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy
| | - Francesco Cucca
- Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy
| | | | - Siddhartha Jaiswal
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | - Chiara Sabatti
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, USA.
- Stanford Diabetes Research Center, Stanford University, Stanford, USA.
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, USA.
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30
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Vandiedonck C. Genetic association of molecular traits: A help to identify causative variants in complex diseases. Clin Genet 2019; 93:520-532. [PMID: 29194587 DOI: 10.1111/cge.13187] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 11/24/2017] [Accepted: 11/27/2017] [Indexed: 12/14/2022]
Abstract
In the past 15 years, major progresses have been made in the understanding of the genetic basis of regulation of gene expression. These new insights have revolutionized our approach to resolve the genetic variation underlying complex diseases. Gene transcript levels were the first expression phenotypes that were studied. They are heritable and therefore amenable to genome-wide association studies. The genetic variants that modulate them are called expression quantitative trait loci. Their study has been extended to other molecular quantitative trait loci (molQTLs) that regulate gene expression at the various levels, from chromatin state to cellular responses. Altogether, these studies have generated a wealth of basic information on the genome-wide patterns of gene expression and their inter-individual variation. Most importantly, molQTLs have become an invaluable asset in the genetic study of complex diseases. Although the identification of the disease-causing variants on the basis of their overlap with molQTLs requires caution, molQTLs can help to prioritize the relevant candidate gene(s) in the disease-associated regions and bring a functional interpretation of the associated variants, therefore, bridging the gap between genotypes and clinical phenotypes.
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Affiliation(s)
- C Vandiedonck
- Univ Paris Diderot, Sorbonne Paris Cité, Paris, France
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31
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Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain. Am J Hum Genet 2019; 105:562-572. [PMID: 31447098 DOI: 10.1016/j.ajhg.2019.07.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/22/2019] [Indexed: 12/20/2022] Open
Abstract
Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a large number of environmental factors at high granularity might not always be feasible. Instead, here we propose extracting cellular embedding of environmental factors from gene expression data by using latent variable (LV) analysis and taking these LVs as environmental proxies in detecting gene-by-environment (GxE) interaction effects on gene expression, i.e., GxE expression quantitative trait loci (eQTLs). Applying this approach to two largest brain eQTL datasets (n = 1,100), we show that LVs and GxE eQTLs in one dataset replicate well in the other dataset. Combining the two samples via meta-analysis, 895 GxE eQTLs are identified. On average, GxE effect explains an additional ∼4% variation in expression of each gene that displays a GxE effect. Ten of these 52 genes are associated with cell-type-specific eQTLs, and the remaining genes are multi-functional. Furthermore, after substituting LVs with expression of transcription factors (TF), we found 91 TF-specific eQTLs, which demonstrates an important use of our brain GxE eQTLs.
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32
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Khramtsova EA, Davis LK, Stranger BE. The role of sex in the genomics of human complex traits. Nat Rev Genet 2019; 20:173-190. [PMID: 30581192 DOI: 10.1038/s41576-018-0083-1] [Citation(s) in RCA: 168] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Nearly all human complex traits and disease phenotypes exhibit some degree of sex differences, including differences in prevalence, age of onset, severity or disease progression. Until recently, the underlying genetic mechanisms of such sex differences have been largely unexplored. Advances in genomic technologies and analytical approaches are now enabling a deeper investigation into the effect of sex on human health traits. In this Review, we discuss recent insights into the genetic models and mechanisms that lead to sex differences in complex traits. This knowledge is critical for developing deeper insight into the fundamental biology of sex differences and disease processes, thus facilitating precision medicine.
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Affiliation(s)
- Ekaterina A Khramtsova
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.,Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Lea K Davis
- Division of Medical Genetics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. .,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Barbara E Stranger
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA. .,Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA. .,Center for Data Intensive Science, University of Chicago, Chicago, IL, USA.
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33
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Shen JJ, Wang YF, Yang W. Sex-Interacting mRNA- and miRNA-eQTLs and Their Implications in Gene Expression Regulation and Disease. Front Genet 2019; 10:313. [PMID: 31024623 PMCID: PMC6465513 DOI: 10.3389/fgene.2019.00313] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 03/21/2019] [Indexed: 12/26/2022] Open
Abstract
Despite sex being an important epidemiological and physiological factor, not much is known about how sex works to interact with genotypes to result in different phenotypes. Both messenger RNA (mRNA) and microRNA (miRNA) may be differentially expressed between the sexes in different physiological conditions, and both may be differentially regulated between males and females. Using whole transcriptome data on lymphoblastoid cell lines from 338 samples of European origin, we tried to uncover genes differentially expressed between the two sexes and sex-interacting expression quantitative trait loci (ss-eQTLs). Two miRNAs were found to be differentially expressed between the two sexes, both of which were found to be functionally implicated in breast cancer. Using two stage linear regression analysis, 21 mRNA ss-eQTL and 3 miRNA ss-eQTLs were discovered. We replicated two of the mRNA ss-eQTLs (p < 0.1) using a separate dataset of gene expression data derived from monocytes. Three mRNA ss-eQTLs are in high linkage disequilibrium with variants also found to be associated with sexually dimorphic traits. Taken together, we believe the ss-eQTLs presented will assist researchers in uncovering the basis of sex-biased gene expression regulation, and ultimately help us understand the genetic basis of differences in phenotypes between sexes.
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Affiliation(s)
- Jiangshan J Shen
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong.,Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Jining, China.,Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, China
| | - Yong-Fei Wang
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Wanling Yang
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
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34
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Norheim F, Hasin-Brumshtein Y, Vergnes L, Chella Krishnan K, Pan C, Seldin MM, Hui ST, Mehrabian M, Zhou Z, Gupta S, Parks BW, Walch A, Reue K, Hofmann SM, Arnold AP, Lusis AJ. Gene-by-Sex Interactions in Mitochondrial Functions and Cardio-Metabolic Traits. Cell Metab 2019; 29:932-949.e4. [PMID: 30639359 PMCID: PMC6447452 DOI: 10.1016/j.cmet.2018.12.013] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 08/29/2018] [Accepted: 12/17/2018] [Indexed: 12/20/2022]
Abstract
We studied sex differences in over 50 cardio-metabolic traits in a panel of 100 diverse inbred strains of mice. The results clearly showed that the effects of sex on both clinical phenotypes and gene expression depend on the genetic background. In support of this, genetic loci associated with the traits frequently showed sex specificity. For example, Lyplal1, a gene implicated in human obesity, was shown to underlie a sex-specific locus for diet-induced obesity. Global gene expression analyses of tissues across the panel implicated adipose tissue "beiging" and mitochondrial functions in the sex differences. Isolated mitochondria showed gene-by-sex interactions in oxidative functions, such that some strains (C57BL/6J) showed similar function between sexes, whereas others (DBA/2J and A/J) showed increased function in females. Reduced adipose mitochondrial function in males as compared to females was associated with increased susceptibility to obesity and insulin resistance. Gonadectomy studies indicated that gonadal hormones acting in a tissue-specific manner were responsible in part for the sex differences.
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Affiliation(s)
- Frode Norheim
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA; Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Yehudit Hasin-Brumshtein
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Laurent Vergnes
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Karthickeyan Chella Krishnan
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Calvin Pan
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Marcus M Seldin
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Simon T Hui
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Margarete Mehrabian
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zhiqiang Zhou
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sonul Gupta
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Brian W Parks
- Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Karen Reue
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Susanna M Hofmann
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, München 80336, Germany; Medizinische Klinik und Poliklinik IV, Klinikum der Ludwig Maximilian Universität (LMU), Munich, Germany
| | - Arthur P Arnold
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Aldons J Lusis
- Department of Medicine/Division of Cardiology and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA; Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
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35
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Guo S, Zhou Y, Zeng P, Xu G, Wang G, Cui Q. Identification and analysis of the human sex-biased genes. Brief Bioinform 2019; 19:188-198. [PMID: 28028006 DOI: 10.1093/bib/bbw125] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Indexed: 01/28/2023] Open
Abstract
Tremendous differences between human sexes are universally observed. Therefore, identifying and analyzing the sex-biased genes are becoming basically important for uncovering the mystery of sex differences and personalized medicine. Here, we presented a computational method to identify sex-biased genes from public gene expression databases. We obtained 1407 female-biased genes (FGs) and 1096 male-biased genes (MGs) across 14 different tissues. Bioinformatics analysis revealed that compared with MGs, FGs have higher evolutionary rate, higher single-nucleotide polymorphism density, less homologous gene numbers and smaller phyletic age. FGs have lower expression level, higher tissue specificity and later expressed stage in body development. Moreover, FGs are highly involved in immune-related functions, whereas MGs are more enriched in metabolic process. In addition, cellular network analysis revealed that MGs have higher degree, more cellular activating signaling and tend to be located in cellular inner space, whereas FGs have lower degree, more cellular repressing signaling and tend to be located in cellular outer space. Finally, the identified sex-biased genes and the discovered biological insights together can be a valuable resource helpful for investigating sex-biased physiology and medicine, for example sex-biased disease diagnosis and therapy, which represents one important aspect of personalized and precision medicine.
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Affiliation(s)
- Sisi Guo
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China
| | - Yuan Zhou
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China
| | - Pan Zeng
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China
| | - Guoheng Xu
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China
| | - Guoqing Wang
- Department of Pathogenobiology, College of Basic Medicine, Jilin University, Changchun, Jilin, China
| | - Qinghua Cui
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China
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Viñuela A, Brown AA, Buil A, Tsai PC, Davies MN, Bell JT, Dermitzakis ET, Spector TD, Small KS. Age-dependent changes in mean and variance of gene expression across tissues in a twin cohort. Hum Mol Genet 2018; 27:732-741. [PMID: 29228364 PMCID: PMC5886097 DOI: 10.1093/hmg/ddx424] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 11/10/2017] [Accepted: 11/29/2017] [Indexed: 12/13/2022] Open
Abstract
Changes in the mean and variance of gene expression with age have consequences for healthy aging and disease development. Age-dependent changes in phenotypic variance have been associated with a decline in regulatory functions leading to increase in disease risk. Here, we investigate age-related mean and variance changes in gene expression measured by RNA-seq of fat, skin, whole blood and derived lymphoblastoid cell lines (LCLs) expression from 855 adult female twins. We see evidence of up to 60% of age effects on transcription levels shared across tissues, and 47% of those on splicing. Using gene expression variance and discordance between genetically identical MZ twin pairs, we identify 137 genes with age-related changes in variance and 42 genes with age-related discordance between co-twins; implying the latter are driven by environmental effects. We identify four eQTLs whose effect on expression is age-dependent (FDR 5%). Combined, these results show a complicated mix of environmental and genetically driven changes in expression with age. Using the twin structure in our data, we show that additive genetic effects explain considerably more of the variance in gene expression than aging, but less that other environmental factors, potentially explaining why reliable expression-derived biomarkers for healthy-aging have proved elusive compared with those derived from methylation.
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Affiliation(s)
- Ana Viñuela
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Andrew A Brown
- Wellcome Trust Sanger Institute, Hinxton CB10 1SA, Cambridge, UK
- Division of Mental Health and Addiction, NORMENT, KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo 0450, Norway
| | - Alfonso Buil
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Matthew N Davies
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
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37
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Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges. Proc Natl Acad Sci U S A 2017; 115:E488-E497. [PMID: 29282317 PMCID: PMC5776984 DOI: 10.1073/pnas.1714765115] [Citation(s) in RCA: 142] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Identifying the drivers of the interindividual diversity of the human immune system is crucial to understand their consequences on immune-mediated diseases. By examining the transcriptional responses of 1,000 individuals to various microbial challenges, we show that age and sex influence the expression of many immune-related genes, but their effects are overall moderate, whereas genetic factors affect a smaller gene set but with a stronger effect. We identify numerous genetic variants that affect transcriptional variation on infection, many of which are associated with autoimmune or inflammatory disorders. These results enable additional exploration of the role of regulatory variants in the pathogenesis of immune-related diseases and improve our understanding of the respective effects of age, sex, and genetics on immune response variation. The contribution of host genetic and nongenetic factors to immunological differences in humans remains largely undefined. Here, we generated bacterial-, fungal-, and viral-induced immune transcriptional profiles in an age- and sex-balanced cohort of 1,000 healthy individuals and searched for the determinants of immune response variation. We found that age and sex affected the transcriptional response of most immune-related genes, with age effects being more stimulus-specific relative to sex effects, which were largely shared across conditions. Although specific cell populations mediated the effects of age and sex on gene expression, including CD8+ T cells for age and CD4+ T cells and monocytes for sex, we detected a direct effect of these intrinsic factors for the majority of immune genes. The mapping of expression quantitative trait loci (eQTLs) revealed that genetic factors had a stronger effect on immune gene regulation than age and sex, yet they affected a smaller number of genes. Importantly, we identified numerous genetic variants that manifested their regulatory effects exclusively on immune stimulation, including a Candida albicans-specific master regulator at the CR1 locus. These response eQTLs were enriched in disease-associated variants, particularly for autoimmune and inflammatory disorders, indicating that differences in disease risk may result from regulatory variants exerting their effects only in the presence of immune stress. Together, this study quantifies the respective effects of age, sex, genetics, and cellular heterogeneity on the interindividual variability of immune responses and constitutes a valuable resource for further exploration in the context of different infection risks or disease outcomes.
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38
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Lindén M, Ramírez Sepúlveda JI, James T, Thorlacius GE, Brauner S, Gómez-Cabrero D, Olsson T, Kockum I, Wahren-Herlenius M. Sex influences eQTL effects of SLE and Sjögren's syndrome-associated genetic polymorphisms. Biol Sex Differ 2017; 8:34. [PMID: 29070082 PMCID: PMC5657123 DOI: 10.1186/s13293-017-0153-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 10/09/2017] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) are autoimmune disorders characterized by autoantibodies, dysregulated B cells, and notably high female-to-male incidence ratios. Genome-wide association studies have identified several susceptibility SNPs for both diseases. Many SNPs in the genome are expression quantitative trait loci (eQTLs), with context-dependent effects. Assuming that sex is a biological context, we investigated whether SLE/pSS SNPs act as eQTLs in B cells and used a disease-targeted approach to understand if they display sex-specific effects. METHODS We used genome-wide genotype and gene expression data from primary B cells from 125 males and 162 females. The MatrixEQTL R package was used to identify eQTLs within a genomic window of 2 Mb centered on each of 22 established SLE and/or pSS susceptibility SNPs. To find sex-specific eQTLs, we used a linear model with a SNP * sex interaction term. RESULTS We found ten SNPs affecting the expression of 16 different genes (FDR < 0.05). rs7574865-INPP1, rs7574865-MYO1B, rs4938573-CD3D, rs11755393-SNRPC, and rs4963128-PHRF1 were novel observations for the immune compartment and B cells. By analyzing the SNP * sex interaction terms, we identified six genes with differentially regulated expression in females compared to males, depending on the genotype of SLE/pSS-associated SNPs: SLC39A8 (BANK1 locus), CD74 (TNIP1 locus), PXK, CTSB (BLK/FAM167A locus), ARCN1 (CXCR5 locus), and DHX9 (NCF2 locus). CONCLUSIONS We identified several unknown sex-specific eQTL effects of SLE/pSS-associated genetic polymorphisms and provide novel insight into how gene-sex interactions may contribute to the sex bias in systemic autoimmune diseases.
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Affiliation(s)
- Magdalena Lindén
- Unit of Experimental Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Jorge I Ramírez Sepúlveda
- Unit of Experimental Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Tojo James
- Unit of Neuroimmunology, Department of Clinical Neuroscience, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Gudny Ella Thorlacius
- Unit of Experimental Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Susanna Brauner
- Unit of Neuroimmunology, Department of Clinical Neuroscience, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - David Gómez-Cabrero
- Unit of Computational Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.,Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.,Science for Life Laboratory, 17121, Solna, Sweden.,Mucosal and Salivary Biology Division, King's College London Dental Institute, London, SE1 9RT, UK
| | - Tomas Olsson
- Unit of Neuroimmunology, Department of Clinical Neuroscience, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Ingrid Kockum
- Unit of Neuroimmunology, Department of Clinical Neuroscience, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Marie Wahren-Herlenius
- Unit of Experimental Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.
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Ritchie MD, Davis JR, Aschard H, Battle A, Conti D, Du M, Eskin E, Fallin MD, Hsu L, Kraft P, Moore JH, Pierce BL, Bien SA, Thomas DC, Wei P, Montgomery SB. Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 2017; 186:771-777. [PMID: 28978191 PMCID: PMC5860556 DOI: 10.1093/aje/kwx229] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 04/07/2017] [Accepted: 04/10/2017] [Indexed: 12/12/2022] Open
Abstract
A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.
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Affiliation(s)
- Marylyn D. Ritchie
- Correspondence to Dr. Stephen B. Montgomery, Departments of Genetics and Pathology, Stanford University School of Medicine, Stanford, CA 94305 (e-mail: ); or Dr. Marylyn D. Ritchie, Geisinger Health System, 205 Hood Center for Health Research, Center Street, Danville, PA 17821(e-mail: )
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Stephen B. Montgomery
- Correspondence to Dr. Stephen B. Montgomery, Departments of Genetics and Pathology, Stanford University School of Medicine, Stanford, CA 94305 (e-mail: ); or Dr. Marylyn D. Ritchie, Geisinger Health System, 205 Hood Center for Health Research, Center Street, Danville, PA 17821(e-mail: )
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40
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Time-dependent genetic effects on gene expression implicate aging processes. Genome Res 2017; 27:545-552. [PMID: 28302734 PMCID: PMC5378173 DOI: 10.1101/gr.207688.116] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Accepted: 01/23/2017] [Indexed: 01/04/2023]
Abstract
Gene expression is dependent on genetic and environmental factors. In the last decade, a large body of research has significantly improved our understanding of the genetic architecture of gene expression. However, it remains unclear whether genetic effects on gene expression remain stable over time. Here, we show, using longitudinal whole-blood gene expression data from a twin cohort, that the genetic architecture of a subset of genes is unstable over time. In addition, we identified 2213 genes differentially expressed across time points that we linked with aging within and across studies. Interestingly, we discovered that most differentially expressed genes were affected by a subset of 77 putative causal genes. Finally, we observed that putative causal genes and down-regulated genes were affected by a loss of genetic control between time points. Taken together, our data suggest that instability in the genetic architecture of a subset of genes could lead to widespread effects on the transcriptome with an aging signature.
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41
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The transcriptional architecture of phenotypic dimorphism. Nat Ecol Evol 2017; 1:6. [PMID: 28812569 DOI: 10.1038/s41559-016-0006] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 09/06/2016] [Indexed: 12/11/2022]
Abstract
The profound differences in gene expression between the sexes are increasingly used to study the molecular basis of sexual dimorphism, sexual selection and sexual conflict. Studies of transcriptional architecture, based on comparisons of gene expression, have also been implemented for a wide variety of other intra-specific polymorphisms. These efforts are based on key assumptions regarding the relationship between transcriptional architecture, phenotypic variation and the target of selection. Some of these assumptions are better supported by available evidence than others. In all cases, the evidence is largely circumstantial, leaving considerable gaps in our understanding of the relationship between transcriptional and phenotypic dimorphism.
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42
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Genetic Mechanisms Leading to Sex Differences Across Common Diseases and Anthropometric Traits. Genetics 2016; 205:979-992. [PMID: 27974502 DOI: 10.1534/genetics.116.193623] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 12/08/2016] [Indexed: 01/10/2023] Open
Abstract
Common diseases often show sex differences in prevalence, onset, symptomology, treatment, or prognosis. Although studies have been performed to evaluate sex differences at specific SNP associations, this work aims to comprehensively survey a number of complex heritable diseases and anthropometric traits. Potential genetically encoded sex differences we investigated include differential genetic liability thresholds or distributions, gene-sex interaction at autosomal loci, major contribution of the X-chromosome, or gene-environment interactions reflected in genes responsive to androgens or estrogens. Finally, we tested the overlap between sex-differential association with anthropometric traits and disease risk. We utilized complementary approaches of assessing GWAS association enrichment and SNP-based heritability estimation to explore explicit sex differences, as well as enrichment in sex-implicated functional categories. We do not find consistent increased genetic load in the lower-prevalence sex, or a disproportionate role for the X-chromosome in disease risk, despite sex-heterogeneity on the X for several traits. We find that all anthropometric traits show less than complete correlation between the genetic contribution to males and females, and find a convincing example of autosome-wide genome-sex interaction in multiple sclerosis (P = 1 × 10-9). We also find some evidence for hormone-responsive gene enrichment, and striking evidence of the contribution of sex-differential anthropometric associations to common disease risk, implying that general mechanisms of sexual dimorphism determining secondary sex characteristics have shared effects on disease risk.
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43
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Zhernakova DV, Deelen P, Vermaat M, van Iterson M, van Galen M, Arindrarto W, van 't Hof P, Mei H, van Dijk F, Westra HJ, Bonder MJ, van Rooij J, Verkerk M, Jhamai PM, Moed M, Kielbasa SM, Bot J, Nooren I, Pool R, van Dongen J, Hottenga JJ, Stehouwer CDA, van der Kallen CJH, Schalkwijk CG, Zhernakova A, Li Y, Tigchelaar EF, de Klein N, Beekman M, Deelen J, van Heemst D, van den Berg LH, Hofman A, Uitterlinden AG, van Greevenbroek MMJ, Veldink JH, Boomsma DI, van Duijn CM, Wijmenga C, Slagboom PE, Swertz MA, Isaacs A, van Meurs JBJ, Jansen R, Heijmans BT, 't Hoen PAC, Franke L. Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet 2016; 49:139-145. [PMID: 27918533 DOI: 10.1038/ng.3737] [Citation(s) in RCA: 269] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 11/02/2016] [Indexed: 02/07/2023]
Abstract
Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.
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Affiliation(s)
- Daria V Zhernakova
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - Patrick Deelen
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands.,University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Martijn Vermaat
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Maarten van Iterson
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Michiel van Galen
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Wibowo Arindrarto
- Sequence Analysis Support Core, Leiden University Medical Center, Leiden, the Netherlands
| | - Peter van 't Hof
- Sequence Analysis Support Core, Leiden University Medical Center, Leiden, the Netherlands
| | - Hailiang Mei
- Sequence Analysis Support Core, Leiden University Medical Center, Leiden, the Netherlands
| | - Freerk van Dijk
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands.,University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Harm-Jan Westra
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Marc Jan Bonder
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - Jeroen van Rooij
- Department of Internal Medicine, ErasmusMC, Rotterdam, the Netherlands
| | - Marijn Verkerk
- Department of Internal Medicine, ErasmusMC, Rotterdam, the Netherlands
| | - P Mila Jhamai
- Department of Internal Medicine, ErasmusMC, Rotterdam, the Netherlands
| | - Matthijs Moed
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Szymon M Kielbasa
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Jan Bot
- SURFsara, Amsterdam, the Netherlands
| | | | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Jouke J Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Coen D A Stehouwer
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Carla J H van der Kallen
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Casper G Schalkwijk
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Alexandra Zhernakova
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - Yang Li
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - Ettje F Tigchelaar
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - Niek de Klein
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - Marian Beekman
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Joris Deelen
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Diana van Heemst
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Leonard H van den Berg
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Albert Hofman
- Department of Epidemiology, ErasmusMC, Rotterdam, the Netherlands
| | | | - Marleen M J van Greevenbroek
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, ErasmusMC, Rotterdam, the Netherlands
| | - Cisca Wijmenga
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Morris A Swertz
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands.,University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Aaron Isaacs
- School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands.,Genetic Epidemiology Unit, Department of Epidemiology, ErasmusMC, Rotterdam, the Netherlands.,Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | | | - Rick Jansen
- Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Lude Franke
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
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Singh SK, Lupo PJ, Scheurer ME, Saxena A, Kennedy AE, Ibrahimou B, Barbieri MA, Mills KI, McCauley JL, Okcu MF, Dorak MT. A childhood acute lymphoblastic leukemia genome-wide association study identifies novel sex-specific risk variants. Medicine (Baltimore) 2016; 95:e5300. [PMID: 27861356 PMCID: PMC5120913 DOI: 10.1097/md.0000000000005300] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Childhood acute lymphoblastic leukemia (ALL) occurs more frequently in males. Reasons behind sex differences in childhood ALL risk are unknown. In the present genome-wide association study (GWAS), we explored the genetic basis of sex differences by comparing genotype frequencies between male and female cases in a case-only study to assess effect-modification by sex.The case-only design included 236 incident cases of childhood ALL consecutively recruited at the Texas Children's Cancer Center in Houston, Texas from 2007 to 2012. All cases were non-Hispanic whites, aged 1 to 10 years, and diagnosed with confirmed B-cell precursor ALL. Genotyping was performed using the Illumina HumanCoreExome BeadChip on the Illumina Infinium platform. Besides the top 100 statistically most significant results, results were also analyzed by the top 100 highest effect size with a nominal statistical significance (P <0.05).The statistically most significant sex-specific association (P = 4 × 10) was with the single nucleotide polymorphism (SNP) rs4813720 (RASSF2), an expression quantitative trait locus (eQTL) for RASSF2 in peripheral blood. rs4813720 is also a strong methylation QTL (meQTL) for a CpG site (cg22485289) within RASSF2 in pregnancy, at birth, childhood, and adolescence. cg22485289 is one of the hypomethylated CpG sites in ALL compared with pre-B cells. Two missense SNPs, rs12722042 and 12722039, in the HLA-DQA1 gene yielded the highest effect sizes (odds ratio [OR] ∼ 14; P <0.01) for sex-specific results. The HLA-DQA1 SNPs belong to DQA1*01 and confirmed the previously reported male-specific association with DQA1*01. This finding supports the proposed infection-related etiology in childhood ALL risk for males. Further analyses revealed that most SNPs (either direct effect or through linkage disequilibrium) were within active enhancers or active promoter regions and had regulatory effects on gene expression levels.Cumulative data suggested that RASSF2 rs4813720, which correlates with increased RASSF2 expression, may counteract the suppressor effect of estrogen-regulated miR-17-92 on RASSF2 resulting in protection in males. Given the amount of sex hormone-related mechanisms suggested by our findings, future studies should examine prenatal or early postnatal programming by sex hormones when hormone levels show a large variation.
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Affiliation(s)
- Sandeep K. Singh
- Department of Environmental and Occupational Health, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL
- Department of Biological Sciences, Florida International University, Miami, FL
| | - Philip J. Lupo
- Department of Pediatrics, Section of Hematology-Oncology, Texas Children's Cancer Center
| | - Michael E. Scheurer
- Department of Pediatrics, Section of Hematology-Oncology, Baylor College of Medicine, Houston, TX
| | - Anshul Saxena
- Department of Health Promotion and Disease Prevention, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL
| | - Amy E. Kennedy
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Boubakari Ibrahimou
- Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL
| | | | - Ken I. Mills
- Centre for Cancer Research and Cell Biology (CCRCB), Queen's University Belfast, Belfast, UK
| | - Jacob L. McCauley
- Dr. John T. Macdonald Foundation, Department of Human Genetics, John P. Hussman Institute for Human Genomics, Biorepository Facility, Center for Genome Technology University of Miami, Miller School of Medicine
| | - Mehmet Fatih Okcu
- Department of Pediatrics, Section of Hematology-Oncology, Baylor College of Medicine, Houston, TX
| | - Mehmet Tevfik Dorak
- Department of Epidemiology, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL
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Glastonbury C, Viñuela A, Buil A, Halldorsson G, Thorleifsson G, Helgason H, Thorsteinsdottir U, Stefansson K, Dermitzakis E, Spector T, Small K. Adiposity-Dependent Regulatory Effects on Multi-tissue Transcriptomes. Am J Hum Genet 2016; 99:567-579. [PMID: 27588447 PMCID: PMC5011064 DOI: 10.1016/j.ajhg.2016.07.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 07/01/2016] [Indexed: 10/25/2022] Open
Abstract
Obesity is a global epidemic that is causally associated with a range of diseases, including type 2 diabetes and cardiovascular disease, at the population-level. However, there is marked heterogeneity in obesity-related outcomes among individuals. This might reflect genotype-dependent responses to adiposity. Given that adiposity, measured by BMI, is associated with widespread changes in gene expression and regulatory variants mediate the majority of known complex trait loci, we sought to identify gene-by-BMI (G × BMI) interactions on the regulation of gene expression in a multi-tissue RNA-sequencing (RNA-seq) dataset from the TwinsUK cohort (n = 856). At a false discovery rate of 5%, we identified 16 cis G × BMI interactions (top cis interaction: CHURC1, rs7143432, p = 2.0 × 10(-12)) and one variant regulating 53 genes in trans (top trans interaction: ZNF423, rs3851570, p = 8.2 × 10(-13)), all in adipose tissue. The interactions were adipose-specific and enriched for variants overlapping adipocyte enhancers, and regulated genes were enriched for metabolic and inflammatory processes. We replicated a subset of the interactions in an independent adipose RNA-seq dataset (deCODE genetics, n = 754). We also confirmed the interactions with an alternate measure of obesity, dual-energy X-ray absorptiometry (DXA)-derived visceral-fat-volume measurements, in a subset of TwinsUK individuals (n = 682). The identified G × BMI regulatory effects demonstrate the dynamic nature of gene regulation and reveal a functional mechanism underlying the heterogeneous response to obesity. Additionally, we have provided a web browser allowing interactive exploration of the dataset, including of association between expression, BMI, and G × BMI regulatory effects in four tissues.
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Dean R, Mank JE. Tissue Specificity and Sex-Specific Regulatory Variation Permit the Evolution of Sex-Biased Gene Expression. Am Nat 2016; 188:E74-84. [PMID: 27501094 DOI: 10.1086/687526] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Genetic correlations between males and females are often thought to constrain the evolution of sexual dimorphism. However, sexually dimorphic traits and the underlying sexually dimorphic gene expression patterns are often rapidly evolving. We explore this apparent paradox by measuring the genetic correlation in gene expression between males and females (Cmf) across broad evolutionary timescales, using two RNA-sequencing data sets spanning multiple populations and multiple species. We find that unbiased genes have higher Cmf than sex-biased genes, consistent with intersexual genetic correlations constraining the evolution of sexual dimorphism. However, we found that highly sex-biased genes (both male and female biased) also had higher tissue specificity, and unbiased genes had greater expression breadth, suggesting that pleiotropy may constrain the breakdown of intersexual genetic correlations. Finally, we show that genes with high Cmf showed some degree of sex-specific changes in gene expression in males and females. Together, our results suggest that genetic correlations between males and females may be less important in constraining the evolution of sex-biased gene expression than pleiotropy. Sex-specific regulatory variation and tissue specificity may resolve the paradox of widespread sex bias within a largely shared genome.
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47
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Sex bias in paediatric autoimmune disease – Not just about sex hormones? J Autoimmun 2016; 69:12-23. [DOI: 10.1016/j.jaut.2016.02.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 02/25/2016] [Accepted: 02/29/2016] [Indexed: 02/06/2023]
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48
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Kukurba KR, Parsana P, Balliu B, Smith KS, Zappala Z, Knowles DA, Favé MJ, Davis JR, Li X, Zhu X, Potash JB, Weissman MM, Shi J, Kundaje A, Levinson DF, Awadalla P, Mostafavi S, Battle A, Montgomery SB. Impact of the X Chromosome and sex on regulatory variation. Genome Res 2016; 26:768-77. [PMID: 27197214 PMCID: PMC4889977 DOI: 10.1101/gr.197897.115] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Accepted: 04/18/2016] [Indexed: 02/07/2023]
Abstract
The X Chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. Improving our understanding of these differences offers to elucidate the molecular mechanisms underlying sex-specific traits and diseases. However, to date, most studies have either ignored the X Chromosome or had insufficient power to test for the sex-specific impact of genetic variation. By analyzing whole blood transcriptomes of 922 individuals, we have conducted the first large-scale, genome-wide analysis of the impact of both sex and genetic variation on patterns of gene expression, including comparison between the X Chromosome and autosomes. We identified a depletion of expression quantitative trait loci (eQTL) on the X Chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X Chromosome. To resolve the molecular mechanisms underlying such effects, we generated chromatin accessibility data through ATAC-sequencing to connect sex-specific chromatin accessibility to sex-specific patterns of expression and regulatory variation. As sex-specific regulatory variants discovered in our study can inform sex differences in heritable disease prevalence, we integrated our data with genome-wide association study data for multiple immune traits identifying several traits with significant sex biases in genetic susceptibilities. Together, our study provides genome-wide insight into how genetic variation, the X Chromosome, and sex shape human gene regulation and disease.
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Affiliation(s)
- Kimberly R Kukurba
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Princy Parsana
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Brunilda Balliu
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Kevin S Smith
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Zachary Zappala
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - David A Knowles
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Marie-Julie Favé
- Sainte-Justine University Hospital Research Centre, Department of Pediatrics, University of Montreal, Montreal, Québec H3T 1J4, Canada
| | - Joe R Davis
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Xin Li
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Xiaowei Zhu
- Department of Psychiatry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - James B Potash
- Department of Psychiatry, University of Iowa Hospitals & Clinics, Iowa City, Iowa 52242, USA
| | - Myrna M Weissman
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, New York 10032, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Computer Science, Stanford University, Stanford, California 94305, USA
| | - Douglas F Levinson
- Department of Psychiatry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Philip Awadalla
- Sainte-Justine University Hospital Research Centre, Department of Pediatrics, University of Montreal, Montreal, Québec H3T 1J4, Canada
| | - Sara Mostafavi
- Department of Statistics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Computer Science, Stanford University, Stanford, California 94305, USA;
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49
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Lee D, Lee C. Age- and gender-dependent heterogeneous proportion of variation explained by SNPs in quantitative traits reflecting human health. AGE (DORDRECHT, NETHERLANDS) 2015; 37:19. [PMID: 25701395 PMCID: PMC4336297 DOI: 10.1007/s11357-015-9756-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Accepted: 02/10/2015] [Indexed: 06/04/2023]
Abstract
Age-related effects are often included as covariates in the analytical model for genome-wide association analysis of quantitative traits reflecting human health. Nevertheless, previous studies have hardly examined the effects of age on the proportion of variation explained by single nucleotide polymorphisms (PVSNP) in these traits. In this study, the PVSNP estimates of body mass index (BMI), waist-to-hip ratio, pulse pressure, high-density lipoprotein cholesterol level, triglyceride level (TG), low-density lipoprotein cholesterol level, and glucose level were obtained from Korean consortium metadata partitioned by gender or by age. Restricted maximum likelihood estimates of the PVSNP were obtained in a mixed model framework. Previous studies using pedigree data suggested possible differential heritability of certain traits with regard to gender, which we observed in our current study (BMI and TG; P < 0.05). However, the PVSNP analysis based on age revealed that, with respect to every trait tested, individuals aged 40 to 49 exhibited significantly lower PVSNP estimates than individuals aged 50 to 59 or 60 to 69 (P < 0.05). The consistent heterogeneous PVSNP with respect to age may be due to degenerated genetic functions in individuals between the ages of 50 and 69. Our results suggest the genetic mechanism of age- and gender-dependent PVSNP of quantitative traits related to human health should be further examined.
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Affiliation(s)
- Dain Lee
- Department of Bioinformatics and Life Science, Soongsil University, 511 Sangdo-dong, Dongjak-gu, Seoul, 156-743 South Korea
| | - Chaeyoung Lee
- Department of Bioinformatics and Life Science, Soongsil University, 511 Sangdo-dong, Dongjak-gu, Seoul, 156-743 South Korea
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50
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Abstract
The diversity of regulatory genetic variants and their mechanisms of action reflect the complexity and context-specificity of gene regulation. Regulatory variants are important in human disease and defining such variants and establishing mechanism is crucial to the interpretation of disease-association studies. This review describes approaches for identifying and functionally characterizing regulatory variants, illustrated using examples from common diseases. Insights from recent advances in resolving the functional epigenomic regulatory landscape in which variants act are highlighted, showing how this has enabled functional annotation of variants and the generation of hypotheses about mechanism of action. The utility of quantitative trait mapping at the transcript, protein and metabolite level to define association of specific genes with particular variants and further inform disease associations are reviewed. Establishing mechanism of action is an essential step in resolving functional regulatory variants, and this review describes how this is being facilitated by new methods for analyzing allele-specific expression, mapping chromatin interactions and advances in genome editing. Finally, integrative approaches are discussed together with examples highlighting how defining the mechanism of action of regulatory variants and identifying specific modulated genes can maximize the translational utility of genome-wide association studies to understand the pathogenesis of diseases and discover new drug targets or opportunities to repurpose existing drugs to treat them.
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Affiliation(s)
- Julian Charles Knight
- Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN UK
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