101
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Mallard TT, Savage JE, Johnson EC, Huang Y, Edwards AC, Hottenga JJ, Grotzinger AD, Gustavson DE, Jennings MV, Anokhin A, Dick DM, Edenberg HJ, Kramer JR, Lai D, Meyers JL, Pandey AK, Paige Harden K, Nivard MG, de Geus EJC, Boomsma DI, Agrawal A, Davis LK, Clarke TK, Palmer AA, Sanchez-Roige S. Item-Level Genome-Wide Association Study of the Alcohol Use Disorders Identification Test in Three Population-Based Cohorts. Am J Psychiatry 2022; 179:58-70. [PMID: 33985350 PMCID: PMC9272895 DOI: 10.1176/appi.ajp.2020.20091390] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
OBJECTIVE Genome-wide association studies (GWASs) of the Alcohol Use Disorders Identification Test (AUDIT), a 10-item screen for alcohol use disorder (AUD), have elucidated novel loci for alcohol consumption and misuse. However, these studies also revealed that GWASs can be influenced by numerous biases (e.g., measurement error, selection bias), which may have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders and/or medical conditions. The authors used genomic structural equation modeling to elucidate the genetics of alcohol consumption and problematic consequences of alcohol use as measured by AUDIT. METHODS To explore these unexpected differences in genetic correlations, the authors conducted the first item-level and the largest GWAS of AUDIT items (N=160,824) and applied a multivariate framework to mitigate previous biases. RESULTS The authors identified novel patterns of similarity (and dissimilarity) among the AUDIT items and found evidence of a correlated two-factor structure at the genetic level ("consumption" and "problems," rg=0.80). Moreover, by applying empirically derived weights to each of the AUDIT items, the authors constructed an aggregate measure of alcohol consumption that was strongly associated with alcohol dependence (rg=0.67), moderately associated with several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by conducting polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, the authors identified novel genetic associations between alcohol consumption, alcohol misuse, and health. CONCLUSIONS This work further emphasizes the value of AUDIT for both clinical and genetic studies of AUD and the importance of using multivariate methods to study genetic associations that are more closely related to AUD.
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Affiliation(s)
- Travis T Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, 78712
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Netherlands, 1081HV
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO 63110
| | - Yuye Huang
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093
| | - Alexis C Edwards
- Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA 23298
| | - Jouke J Hottenga
- Dept of Biological Psychology & EMGO+ Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, NL
| | | | - Daniel E Gustavson
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093
| | - Andrey Anokhin
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO 63110
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Richmond, VA 23220
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202
| | - John R Kramer
- Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA 52242
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 4622
| | - Jacquelyn L Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY 11203
| | - Ashwini K Pandey
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY 11203
| | | | - Michel G Nivard
- Dept of Biological Psychology & EMGO+ Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Eco JC de Geus
- Dept of Biological Psychology & EMGO+ Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Dorret I Boomsma
- Dept of Biological Psychology & EMGO+ Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO 63110
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Toni-Kim Clarke
- Division of Psychiatry, University of Edinburgh, Scotland, UK, EH8 9YL
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232
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102
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Pratt BM, Won H. Advances in profiling chromatin architecture shed light on the regulatory dynamics underlying brain disorders. Semin Cell Dev Biol 2022; 121:153-160. [PMID: 34483043 PMCID: PMC8761161 DOI: 10.1016/j.semcdb.2021.08.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 08/18/2021] [Accepted: 08/23/2021] [Indexed: 01/03/2023]
Abstract
Understanding the exquisitely complex nature of the three-dimensional organization of the genome and how it affects gene regulation remains a central question in biology. Recent advances in sequencing- and imaging-based approaches in decoding the three-dimensional chromatin landscape have enabled a systematic characterization of gene regulatory architecture. In this review, we outline how chromatin architecture provides a reference atlas to predict the functional consequences of non-coding variants associated with human traits and disease. High-throughput perturbation assays such as massively parallel reporter assays (MPRA) and CRISPR-based genome engineering in combination with a reference atlas opened an avenue for going beyond observational studies to experimentally validating the regulatory principles of the genome. We conclude by providing a suggested path forward by calling attention to barriers that can be addressed for a more complete understanding of the regulatory landscape of the human brain.
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Affiliation(s)
- Brandon M Pratt
- Department of Pharmacology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599, USA.
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103
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Räsänen N, Tiihonen J, Koskuvi M, Lehtonen Š, Koistinaho J. The iPSC perspective on schizophrenia. Trends Neurosci 2021; 45:8-26. [PMID: 34876311 DOI: 10.1016/j.tins.2021.11.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/29/2021] [Accepted: 11/10/2021] [Indexed: 12/17/2022]
Abstract
Over a decade of schizophrenia research using human induced pluripotent stem cell (iPSC)-derived neural models has provided substantial data describing neurobiological characteristics of the disorder in vitro. Simultaneously, translation of the results into general mechanistic concepts underlying schizophrenia pathophysiology has been trailing behind. Given that modeling brain function using cell cultures is challenging, the gap between the in vitro models and schizophrenia as a clinical disorder has remained wide. In this review, we highlight reproducible findings and emerging trends in recent schizophrenia-related iPSC studies. We illuminate the relevance of the results in the context of human brain development, with a focus on processes coinciding with critical developmental periods for schizophrenia.
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Affiliation(s)
- Noora Räsänen
- Neuroscience Center, University of Helsinki, Helsinki, Finland
| | - Jari Tiihonen
- Neuroscience Center, University of Helsinki, Helsinki, Finland; Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden; Center for Psychiatric Research, Stockholm City Council, Stockholm, Sweden; Department of Forensic Psychiatry, University of Eastern Finland, Niuvanniemi Hospital, Kuopio, Finland
| | - Marja Koskuvi
- Neuroscience Center, University of Helsinki, Helsinki, Finland; A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Šárka Lehtonen
- Neuroscience Center, University of Helsinki, Helsinki, Finland; A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jari Koistinaho
- Neuroscience Center, University of Helsinki, Helsinki, Finland; A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
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104
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Schilder BM, Navarro E, Raj T. Multi-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms. Neurobiol Dis 2021; 163:105580. [PMID: 34871738 PMCID: PMC10101343 DOI: 10.1016/j.nbd.2021.105580] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/17/2021] [Accepted: 12/02/2021] [Indexed: 02/07/2023] Open
Abstract
Genome-Wide Association Studies (GWAS) have elucidated the genetic components of Parkinson's Disease (PD). However, because the vast majority of GWAS association signals fall within non-coding regions, translating these results into an interpretable, mechanistic understanding of the disease etiology remains a major challenge in the field. In this review, we provide an overview of the approaches to prioritize putative causal variants and genes as well as summarise the primary findings of previous studies. We then discuss recent efforts to integrate multi-omics data to identify likely pathogenic cell types and biological pathways implicated in PD pathogenesis. We have compiled full summary statistics of cell-type, tissue, and phentoype enrichment analyses from multiple studies of PD GWAS and provided them in a standardized format as a resource for the research community (https://github.com/RajLabMSSM/PD_omics_review). Finally, we discuss the experimental, computational, and conceptual advances that will be necessary to fully elucidate the effects of functional variants and genes on cellular dysregulation and disease risk.
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Affiliation(s)
- Brian M Schilder
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom; UK Dementia Research Institute at Imperial College London, London, United Kingdom.
| | - Elisa Navarro
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Sección Departamental de Bioquímica y Biología Molecular, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Towfique Raj
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
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105
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Giambartolomei C, Seo JH, Schwarz T, Freund MK, Johnson RD, Spisak S, Baca SC, Gusev A, Mancuso N, Pasaniuc B, Freedman ML. H3K27ac HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility. Am J Hum Genet 2021; 108:2284-2300. [PMID: 34822763 PMCID: PMC8715276 DOI: 10.1016/j.ajhg.2021.11.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/02/2021] [Indexed: 12/26/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified more than 200 prostate cancer (PrCa) risk regions, which provide potential insights into causal mechanisms. Multiple lines of evidence show that a significant proportion of PrCa risk can be explained by germline causal variants that dysregulate nearby target genes in prostate-relevant tissues, thus altering disease risk. The traditional approach to explore this hypothesis has been correlating GWAS variants with steady-state transcript levels, referred to as expression quantitative trait loci (eQTLs). In this work, we assess the utility of chromosome conformation capture (3C) coupled with immunoprecipitation (HiChIP) to identify target genes for PrCa GWAS risk loci. We find that interactome data confirm previously reported PrCa target genes identified through GWAS/eQTL overlap (e.g., MLPH). Interestingly, HiChIP identifies links between PrCa GWAS variants and genes well-known to play a role in prostate cancer biology (e.g., AR) that are not detected by eQTL-based methods. HiChIP predicted enhancer elements at the AR and NKX3-1 prostate cancer risk loci, and both were experimentally confirmed to regulate expression of the corresponding genes through CRISPR interference (CRISPRi) perturbation in LNCaP cells. Our results demonstrate that looping data harbor additional information beyond eQTLs and expand the number of PrCa GWAS loci that can be linked to candidate susceptibility genes.
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Affiliation(s)
- Claudia Giambartolomei
- Central RNA Lab, Istituto Italiano di Tecnologia, Genova 16163, Italy; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ji-Heui Seo
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA; The Center for Cancer Genome Discovery, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Tommer Schwarz
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Malika Kumar Freund
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ruth Dolly Johnson
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Sandor Spisak
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Sylvan C Baca
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Alexander Gusev
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Johnson Comprehensive Cancer Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Matthew L Freedman
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA; The Center for Cancer Genome Discovery, Dana Farber Cancer Institute, Boston, MA 02215, USA.
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106
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Pagni S, Mills JD, Frankish A, Mudge JM, Sisodiya SM. Non-coding regulatory elements: Potential roles in disease and the case of epilepsy. Neuropathol Appl Neurobiol 2021; 48:e12775. [PMID: 34820881 DOI: 10.1111/nan.12775] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/04/2021] [Accepted: 11/16/2021] [Indexed: 12/27/2022]
Abstract
Non-coding DNA (ncDNA) refers to the portion of the genome that does not code for proteins and accounts for the greatest physical proportion of the human genome. ncDNA includes sequences that are transcribed into RNA molecules, such as ribosomal RNAs (rRNAs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and un-transcribed sequences that have regulatory functions, including gene promoters and enhancers. Variation in non-coding regions of the genome have an established role in human disease, with growing evidence from many areas, including several cancers, Parkinson's disease and autism. Here, we review the features and functions of the regulatory elements that are present in the non-coding genome and the role that these regions have in human disease. We then review the existing research in epilepsy and emphasise the potential value of further exploring non-coding regulatory elements in epilepsy. In addition, we outline the most widely used techniques for recognising regulatory elements throughout the genome, current methodologies for investigating variation and the main challenges associated with research in the field of non-coding DNA.
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Affiliation(s)
- Susanna Pagni
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,Chalfont Centre for Epilepsy, Chalfont St Peter, UK
| | - James D Mills
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,Chalfont Centre for Epilepsy, Chalfont St Peter, UK.,Amsterdam UMC, Department of (Neuro)Pathology, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, Netherlands
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,Chalfont Centre for Epilepsy, Chalfont St Peter, UK
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107
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Ma S, Dalgleish J, Lee J, Wang C, Liu L, Gill R, Buxbaum JD, Chung WK, Aschard H, Silverman EK, Cho MH, He Z, Ionita-Laza I. Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes. Proc Natl Acad Sci U S A 2021; 118:e2105191118. [PMID: 34799441 PMCID: PMC8617518 DOI: 10.1073/pnas.2105191118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2021] [Indexed: 02/03/2023] Open
Abstract
Gene-based tests are valuable techniques for identifying genetic factors in complex traits. Here, we propose a gene-based testing framework that incorporates data on long-range chromatin interactions, several recent technical advances for region-based tests, and leverages the knockoff framework for synthetic genotype generation for improved gene discovery. Through simulations and applications to genome-wide association studies (GWAS) and whole-genome sequencing data for multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests in the literature, identifies genes that replicate in larger studies, and can provide a more narrow focus on the possible causal genes at a locus by reducing the confounding effect of linkage disequilibrium. Furthermore, our results show that incorporating genetic variation in distal regulatory elements tends to improve power over conventional tests. Results for UK Biobank and BioBank Japan traits are also available in a publicly accessible database that allows researchers to query gene-based results in an easy fashion.
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Affiliation(s)
- Shiyang Ma
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - James Dalgleish
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Justin Lee
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305
| | - Chen Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Linxi Liu
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260
| | - Richard Gill
- Department of Human Genetics, Genentech, South San Francisco, CA 94080
- Department of Epidemiology, Columbia University, New York, NY 10032
| | - Joseph D Buxbaum
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Wendy K Chung
- Department of Pediatrics, Columbia University, New York, NY 10032
- Department of Medicine, Columbia University, New York, NY 10032
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, 75015 Paris, France
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Zihuai He
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305
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108
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Air pollution interacts with genetic risk to influence cortical networks implicated in depression. Proc Natl Acad Sci U S A 2021; 118:2109310118. [PMID: 34750260 DOI: 10.1073/pnas.2109310118] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2021] [Indexed: 01/10/2023] Open
Abstract
Air pollution is a reversible cause of significant global mortality and morbidity. Epidemiological evidence suggests associations between air pollution exposure and impaired cognition and increased risk for major depressive disorders. However, the neural bases of these associations have been unclear. Here, in healthy human subjects exposed to relatively high air pollution and controlling for socioeconomic, genomic, and other confounders, we examine across multiple levels of brain network function the extent to which particulate matter (PM2.5) exposure influences putative genetic risk mechanisms associated with depression. Increased ambient PM2.5 exposure was associated with poorer reasoning and problem solving and higher-trait anxiety/depression. Working memory and stress-related information transfer (effective connectivity) across cortical and subcortical brain networks were influenced by PM2.5 exposure to differing extents depending on the polygenic risk for depression in gene-by-environment interactions. Effective connectivity patterns from individuals with higher polygenic risk for depression and higher exposures with PM2.5, but not from those with lower genetic risk or lower exposures, correlated spatially with the coexpression of depression-associated genes across corresponding brain regions in the Allen Brain Atlas. These converging data suggest that PM2.5 exposure affects brain network functions implicated in the genetic mechanisms of depression.
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109
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Kondratyev NV, Alfimova MV, Golov AK, Golimbet VE. Bench Research Informed by GWAS Results. Cells 2021; 10:3184. [PMID: 34831407 PMCID: PMC8623533 DOI: 10.3390/cells10113184] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/11/2021] [Accepted: 11/11/2021] [Indexed: 12/15/2022] Open
Abstract
Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.
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Affiliation(s)
| | | | - Arkadiy K. Golov
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
- Institute of Gene Biology, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vera E. Golimbet
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
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110
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Baranova A, Cao H, Zhang F. Shared genetic liability and causal effects between major depressive disorder and insomnia. Hum Mol Genet 2021; 31:1336-1345. [PMID: 34761251 DOI: 10.1093/hmg/ddab328] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 11/13/2022] Open
Abstract
Deciphering the genetic relationships between major depressive disorder (MDD) and insomnia may facilitate understanding biological mechanisms as well as inform more effective treatment regimens for these conditions. Here we attempted to investigate mechanisms underlying relationships between MDD and insomnia in the context of shared genetic variations. Shared genetic variation was evaluated by polygenic analysis. In two-sample bidirectional Mendelian randomization analysis, causal relationships between MDD and insomnia were investigated; the list of shared genomic loci was identified using cross-trait meta-analysis. Putatively causal genes for the two diseases were prioritized by fine-mapping of transcriptome-wide associations. Polygenic analysis identified 15.1 thousand variants as causally influencing MDD, and 10.8 thousand variants as influencing insomnia. Among these variants, 8.5 thousand were shared between the two diseases. Mendelian randomization analysis suggests that genetic liability to MDD and to insomnia have mutual causal effects (MDD on insomnia with OR = 1.25 and insomnia on MDD with OR = 2.23). Cross-trait meta-analyses identified 89 genomic loci as being shared between MDD and insomnia, with some of them being prioritized as causal in subsequent fine-mapping of transcriptome-wide association signals. Analysis highlights possible role of endogenous production of nitric oxide in the brain, and the gonadotropic secretion in the pituitary as possibly physiological connectors of MDD and insomnia. Here we show a substantial shared genetic liability and mutual causal links between MDD and insomnia. Presented findings provide novel insight into phenotypic relationship between these two interconnected conditions.
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Affiliation(s)
- Ancha Baranova
- School of Systems Biology, George Mason University, Fairfax, 22030, USA.,Research Centre for Medical Genetics, Moscow, 115478, Russia
| | - Hongbao Cao
- School of Systems Biology, George Mason University, Fairfax, 22030, USA
| | - Fuquan Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
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111
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Plooster M, Rossi G, Farrell MS, McAfee JC, Bell JL, Ye M, Diering GH, Won H, Gupton SL, Brennwald P. Schizophrenia-Linked Protein tSNARE1 Regulates Endosomal Trafficking in Cortical Neurons. J Neurosci 2021; 41:9466-9481. [PMID: 34642214 PMCID: PMC8580139 DOI: 10.1523/jneurosci.0556-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 12/13/2022] Open
Abstract
TSNARE1, which encodes the protein tSNARE1, is a high-confidence gene candidate for schizophrenia risk, but nothing is known about its cellular or physiological function. We identified the major gene products of TSNARE1 and their cytoplasmic localization and function in endosomal trafficking in cortical neurons. We validated three primary isoforms of TSNARE1 expressed in human brain, all of which encode a syntaxin-like Qa SNARE domain. RNA-sequencing data from adult and fetal human brain suggested that the majority of tSNARE1 lacks a transmembrane domain that is thought to be necessary for membrane fusion. Biochemical data demonstrate that tSNARE1 can compete with Stx12 for incorporation into an endosomal SNARE complex, supporting its possible role as an inhibitory SNARE. Live-cell imaging in cortical neurons from mice of both sexes demonstrated that brain tSNARE1 isoforms localized to the endosomal network. The most abundant brain isoform, tSNARE1c, localized most frequently to Rab7+ late endosomes, and endogenous tSNARE1 displayed a similar localization in human neural progenitor cells and neuroblastoma cells. In mature rat neurons from both sexes, tSNARE1 localized to the dendritic shaft and dendritic spines, supporting a role for tSNARE1 at the postsynapse. Expression of either tSNARE1b or tSNARE1c, which differ only in their inclusion or exclusion of an Myb-like domain, delayed the trafficking of the dendritic endosomal cargo Nsg1 into late endosomal and lysosomal compartments. These data suggest that tSNARE1 regulates endosomal trafficking in cortical neurons, likely by negatively regulating early endosomal to late endosomal trafficking.SIGNIFICANCE STATEMENT Schizophrenia is a severe and polygenic neuropsychiatric disorder. Understanding the functions of high-confidence candidate genes is critical toward understanding how their dysfunction contributes to schizophrenia pathogenesis. TSNARE1 is one of the high-confidence candidate genes for schizophrenia risk, yet nothing was known about its cellular or physiological function. Here we describe the major isoforms of TSNARE1 and their cytoplasmic localization and function in the endosomal network in cortical neurons. Our results are consistent with the hypothesis that the majority of brain tSNARE1 acts as a negative regulator to endolysosomal trafficking.
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Affiliation(s)
- Melissa Plooster
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Guendalina Rossi
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Martilias S Farrell
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Jessica C McAfee
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Jessica L Bell
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Michael Ye
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Graham H Diering
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, North Carolina 27599
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, North Carolina 27599
- Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Hyejung Won
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Stephanie L Gupton
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, North Carolina 27599
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, North Carolina 27599
- Lineberger Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Patrick Brennwald
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, North Carolina 27599
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112
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Yurko R, Roeder K, Devlin B, G'Sell M. An approach to gene-based testing accounting for dependence of tests among nearby genes. Brief Bioinform 2021; 22:6359004. [PMID: 34459489 DOI: 10.1093/bib/bbab329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/20/2021] [Accepted: 07/29/2021] [Indexed: 11/14/2022] Open
Abstract
In genome-wide association studies (GWAS), it has become commonplace to test millions of single-nucleotide polymorphisms (SNPs) for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive $P$-value thresholding, guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.
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Affiliation(s)
- Ronald Yurko
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Kathryn Roeder
- Department of Computational Biology, Carnegie Mellon University, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA
| | - Max G'Sell
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
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113
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Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease. Biochem Soc Trans 2021; 49:2091-2100. [PMID: 34581766 PMCID: PMC8589426 DOI: 10.1042/bst20210128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/25/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022]
Abstract
Parkinson's disease (PD) is the second most prevalent late-onset neurodegenerative disorder worldwide after Alzheimer's disease for which available drugs only deliver temporary symptomatic relief. Loss of dopaminergic neurons (DaNs) in the substantia nigra and intracellular alpha-synuclein inclusions are the main hallmarks of the disease but the events that cause this degeneration remain uncertain. Despite cell types other than DaNs such as astrocytes, microglia and oligodendrocytes have been recently associated with the pathogenesis of PD, we still lack an in-depth characterisation of PD-affected brain regions at cell-type resolution that could help our understanding of the disease mechanisms. Nevertheless, publicly available large-scale brain-specific genomic, transcriptomic and epigenomic datasets can be further exploited to extract different layers of cell type-specific biological information for the reconstruction of cell type-specific transcriptional regulatory networks. By intersecting disease risk variants within the networks, it may be possible to study the functional role of these risk variants and their combined effects at cell type- and pathway levels, that, in turn, can facilitate the identification of key regulators involved in disease progression, which are often potential therapeutic targets.
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114
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Karlsson Linnér R, Mallard TT, Barr PB, Sanchez-Roige S, Madole JW, Driver MN, Poore HE, de Vlaming R, Grotzinger AD, Tielbeek JJ, Johnson EC, Liu M, Rosenthal SB, Ideker T, Zhou H, Kember RL, Pasman JA, Verweij KJH, Liu DJ, Vrieze S, Kranzler HR, Gelernter J, Harris KM, Tucker-Drob EM, Waldman ID, Palmer AA, Harden KP, Koellinger PD, Dick DM. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat Neurosci 2021; 24:1367-1376. [PMID: 34446935 PMCID: PMC8484054 DOI: 10.1038/s41593-021-00908-3] [Citation(s) in RCA: 149] [Impact Index Per Article: 49.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 07/13/2021] [Indexed: 02/07/2023]
Abstract
Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates.
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Affiliation(s)
| | - Travis T Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Peter B Barr
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - James W Madole
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Morgan N Driver
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Holly E Poore
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Ronald de Vlaming
- Department of Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | - Jorim J Tielbeek
- Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Sara Brin Rosenthal
- Center for Computational Biology and Bioinformatics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Department of Psychiatry, VA CT Healthcare System, West Haven, CT, USA
| | - Rachel L Kember
- Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Mental Illness Research Education and Clinical Center, Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Joëlle A Pasman
- Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Dajiang J Liu
- Department of Public Health Sciences, Penn State University, Hershey, PA, USA
- Institute of Personalized Medicine, Penn State University, Hershey, PA, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Henry R Kranzler
- Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Mental Illness Research Education and Clinical Center, Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Department of Psychiatry, VA CT Healthcare System, West Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, West Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, West Haven, CT, USA
| | - Kathleen Mullan Harris
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Irwin D Waldman
- Department of Psychology, Emory University, Atlanta, GA, USA
- Center for Computational and Quantitative Genetics, Emory University, Atlanta, GA, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Philipp D Koellinger
- Department of Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA.
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA.
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA.
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115
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Feleke R, Reynolds RH, Smith AM, Tilley B, Taliun SAG, Hardy J, Matthews PM, Gentleman S, Owen DR, Johnson MR, Srivastava PK, Ryten M. Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases. Acta Neuropathol 2021; 142:449-474. [PMID: 34309761 PMCID: PMC8357687 DOI: 10.1007/s00401-021-02343-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 02/07/2023]
Abstract
Parkinson's disease (PD), Parkinson's disease with dementia (PDD) and dementia with Lewy bodies (DLB) are three clinically, genetically and neuropathologically overlapping neurodegenerative diseases collectively known as the Lewy body diseases (LBDs). A variety of molecular mechanisms have been implicated in PD pathogenesis, but the mechanisms underlying PDD and DLB remain largely unknown, a knowledge gap that presents an impediment to the discovery of disease-modifying therapies. Transcriptomic profiling can contribute to addressing this gap, but remains limited in the LBDs. Here, we applied paired bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from 28 individuals, including healthy controls, PD, PDD and DLB cases (n = 7 per group), to transcriptomically profile the LBDs. Using this approach, we (i) found transcriptional alterations in multiple cell types across the LBDs; (ii) discovered evidence for widespread dysregulation of RNA splicing, particularly in PDD and DLB; (iii) identified potential splicing factors, with links to other dementia-related neurodegenerative diseases, coordinating this dysregulation; and (iv) identified transcriptomic commonalities and distinctions between the LBDs that inform understanding of the relationships between these three clinical disorders. Together, these findings have important implications for the design of RNA-targeted therapies for these diseases and highlight a potential molecular "window" of therapeutic opportunity between the initial onset of PD and subsequent development of Lewy body dementia.
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Affiliation(s)
- Rahel Feleke
- Department of Brain Sciences, Imperial College London, London, UK
| | - Regina H Reynolds
- Department of Neurodegenerative Disease, University College London, London, UK
- Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK
| | - Amy M Smith
- Dementia Research Institute at Imperial College London, London, UK
| | - Bension Tilley
- Department of Brain Sciences, Imperial College London, London, UK
| | - Sarah A Gagliano Taliun
- Department of Medicine, Université de Montréal, Montréal, QC, Canada
- Montréal Heart Institute, Montréal, QC, Canada
- Department of Neurosciences, Université de Montréal, Montréal, QC, Canada
| | - John Hardy
- Department of Neurodegenerative Disease, University College London, London, UK
- UK Dementia Research Institute at University College London, London, UK
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London, London, UK
- Dementia Research Institute at Imperial College London, London, UK
| | - Steve Gentleman
- Department of Brain Sciences, Imperial College London, London, UK
- Dementia Research Institute at Imperial College London, London, UK
| | - David R Owen
- Department of Brain Sciences, Imperial College London, London, UK
| | | | - Prashant K Srivastava
- Dementia Research Institute at Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Mina Ryten
- Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK.
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK.
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116
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Fadason T, Farrow S, Gokuladhas S, Golovina E, Nyaga D, O'Sullivan JM, Schierding W. Assigning function to SNPs: Considerations when interpreting genetic variation. Semin Cell Dev Biol 2021; 121:135-142. [PMID: 34446357 DOI: 10.1016/j.semcdb.2021.08.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 08/12/2021] [Indexed: 12/26/2022]
Abstract
Assigning function to single nucleotide polymorphisms (SNPs) to understand the mechanisms that link genetic and phenotypic variation and disease is an area of intensive research that is necessary to contribute to the continuing development of precision medicine. However, despite the apparent simplicity that is captured in the name SNP - 'single nucleotide' changes are not easy to functionally characterize. This complexity arises from multiple features of the genome including the fact that function is development and environment specific. As such, we are often fooled by our terminology and underlying assumptions that there is a single function for a SNP. Here we discuss some of what is known about SNPs, their functions and how we can go about characterizing them.
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Affiliation(s)
- Tayaza Fadason
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Sophie Farrow
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | | | - Evgeniia Golovina
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Denis Nyaga
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand; Garvan Institute of Medical Research, Sydney, New South Wales, Australia; MRC Lifecourse Epidemiology Unit, University of Southampton, United Kingdom.
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
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117
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Mould AW, Hall NA, Milosevic I, Tunbridge EM. Targeting synaptic plasticity in schizophrenia: insights from genomic studies. Trends Mol Med 2021; 27:1022-1032. [PMID: 34419330 DOI: 10.1016/j.molmed.2021.07.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/19/2021] [Accepted: 07/26/2021] [Indexed: 12/12/2022]
Abstract
Patients with schizophrenia experience cognitive dysfunction and negative symptoms that do not respond to current drug treatments. Historical evidence is consistent with the hypothesis that these deficits are due, at least in part, to altered cortical synaptic plasticity (the ability of synapses to strengthen or weaken their activity), making this an attractive pathway for therapeutic intervention. However, while synaptic transmission and plasticity is well understood in model systems, it has been challenging to identify specific therapeutic targets for schizophrenia. New information is emerging from genomic findings, which converge on synaptic plasticity and provide a new window on the neurobiology of schizophrenia. Translating this information into therapeutic advances will require a multidisciplinary and collaborative approach.
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Affiliation(s)
- Arne W Mould
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Nicola A Hall
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Ira Milosevic
- Wellcome Centre for Human Genetics, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Elizabeth M Tunbridge
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK.
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118
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Mulvey B, Dougherty JD. Transcriptional-regulatory convergence across functional MDD risk variants identified by massively parallel reporter assays. Transl Psychiatry 2021; 11:403. [PMID: 34294677 PMCID: PMC8298436 DOI: 10.1038/s41398-021-01493-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/02/2021] [Accepted: 06/16/2021] [Indexed: 02/07/2023] Open
Abstract
Family and population studies indicate clear heritability of major depressive disorder (MDD), though its underlying biology remains unclear. The majority of single-nucleotide polymorphism (SNP) linkage blocks associated with MDD by genome-wide association studies (GWASes) are believed to alter transcriptional regulators (e.g., enhancers, promoters) based on enrichment of marks correlated with these functions. A key to understanding MDD pathophysiology will be elucidation of which SNPs are functional and how such functional variants biologically converge to elicit the disease. Furthermore, retinoids can elicit MDD in patients and promote depressive-like behaviors in rodent models, acting via a regulatory system of retinoid receptor transcription factors (TFs). We therefore sought to simultaneously identify functional genetic variants and assess retinoid pathway regulation of MDD risk loci. Using Massively Parallel Reporter Assays (MPRAs), we functionally screened over 1000 SNPs prioritized from 39 neuropsychiatric trait/disease GWAS loci, selecting SNPs based on overlap with predicted regulatory features-including expression quantitative trait loci (eQTL) and histone marks-from human brains and cell cultures. We identified >100 SNPs with allelic effects on expression in a retinoid-responsive model system. Functional SNPs were enriched for binding sequences of retinoic acid-receptive transcription factors (TFs), with additional allelic differences unmasked by treatment with all-trans retinoic acid (ATRA). Finally, motifs overrepresented across functional SNPs corresponded to TFs highly specific to serotonergic neurons, suggesting an in vivo site of action. Our application of MPRAs to screen MDD-associated SNPs suggests a shared transcriptional-regulatory program across loci, a component of which is unmasked by retinoids.
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Affiliation(s)
- Bernard Mulvey
- Departments of Genetics and Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Joseph D Dougherty
- Departments of Genetics and Psychiatry, Washington University in St. Louis, St. Louis, MO, USA.
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119
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Abstract
The influence of human genetic variants on the vaginal bacterial traits (VBTs) of pregnant women is still unknown. Using a genome-wide association approach based on the 16S rRNA bacteriome analysis, a total of 72 host genetic variant (single nucleotide polymorphisms [SNPs], indels, or copy number variations [CNVs])-VBT associations were found that reached the genome-wide significance level (P < 5 × 10-8) with an acceptable genomic inflation factor λ of <1.1. The majority of these SNPs that reached the genome-wide significance level had a relatively low minor allele frequency (MAF), and only seven of them had MAFs greater than 0.05. rs303212, located at the IFIT1 gene on chromosome 10, was the most eye-catching variant, which had a genome-wide association with the relative abundance (RAB) of Actinobacteria and Bifidobacteriaceae and also had a suggestive association with the RAB of a few common vaginal bacteria including Actinobacteriota, Firmicutes, Lactobacillus, and Gardnerella vaginalis and the beta diversity weighted UniFrac (P < 1 × 10-5). The findings of the study suggest that the vaginal bacteriome may be influenced by a number of genetic variants across the human genome and that interferon signaling may have an important influence on vaginal bacterial communities during pregnancy. IMPORTANCE Knowledge about the influence of host genetics on the vaginal bacteriome in pregnancy is still limited. Although a number of environmental and behavioral factors may exert influences on the structure of vaginal bacterial communities, the vaginal bacteriome often undergoes a relatively fixed transition to a more stable and less diverse state as the menstrual cycle stops, which raises questions on the effects of human genetics. We utilized a genome-wide approach to identify the associations between genetic variants and multiple VBTs and performed enrichment analyses. The human genetics during pregnancy may be involved in multiple pathways. The results may disclose innate functional factors involved in shaping the vaginal bacteriome during pregnancy and provide insight into the establishment of specific strategies for prevention and clinical treatment of pregnancy complications.
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120
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Chen J, Dong G, Song L, Zhao X, Cao J, Luo X, Feng J, Zhao XM. Integration of Multimodal Data for Deciphering Brain Disorders. Annu Rev Biomed Data Sci 2021; 4:43-56. [PMID: 34465176 DOI: 10.1146/annurev-biodatasci-092820-020354] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.
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Affiliation(s)
- Jingqi Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; , .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Ministry of Education, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
| | - Guiying Dong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Liting Song
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Jixin Cao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Xiaohui Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; , .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Ministry of Education, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; , .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Ministry of Education, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
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121
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Limited Association between Schizophrenia Genetic Risk Factors and Transcriptomic Features. Genes (Basel) 2021; 12:genes12071062. [PMID: 34356078 PMCID: PMC8304922 DOI: 10.3390/genes12071062] [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] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/29/2021] [Accepted: 07/08/2021] [Indexed: 11/16/2022] Open
Abstract
Schizophrenia is a polygenic disorder with many genomic regions contributing to schizophrenia risk. The majority of genetic variants associated with schizophrenia lie in the non-coding genome and are thought to contribute to transcriptional regulation. Extensive transcriptomic dysregulation has been detected from postmortem brain samples of schizophrenia-affected individuals. However, the relationship between schizophrenia genetic risk factors and transcriptomic features has yet to be explored. Herein, we examined whether varying gene expression features, including differentially expressed genes (DEGs), co-expression networks, and central hubness of genes, contribute to the heritability of schizophrenia. We leveraged quantitative trait loci and chromatin interaction profiles to identify schizophrenia risk variants assigned to the genes that represent different transcriptomic features. We then performed stratified linkage disequilibrium score regression analysis on these variants to estimate schizophrenia heritability enrichment for different gene expression features. Notably, DEGs and co-expression networks showed nominal heritability enrichment. This nominal association can be partly explained by cellular heterogeneity, as DEGs were associated with the genetic risk of schizophrenia in a cell type-specific manner. Moreover, DEGs were enriched for target genes of schizophrenia-associated transcription factors, suggesting that the transcriptomic signatures of schizophrenia are the result of transcriptional regulatory cascades elicited by genetic risk factors.
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122
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D'Souza L, Channakkar AS, Muralidharan B. Chromatin remodelling complexes in cerebral cortex development and neurodevelopmental disorders. Neurochem Int 2021; 147:105055. [PMID: 33964373 PMCID: PMC7611358 DOI: 10.1016/j.neuint.2021.105055] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 04/11/2021] [Accepted: 04/24/2021] [Indexed: 12/19/2022]
Abstract
The diverse number of neurons in the cerebral cortex are generated during development by neural stem cells lining the ventricle, and they continue maturing postnatally. Dynamic chromatin regulation in these neural stem cells is a fundamental determinant of the emerging property of the functional neural network, and the chromatin remodellers are critical determinants of this process. Chromatin remodellers participate in several steps of this process from proliferation, differentiation, migration leading to complex network formation which forms the basis of higher-order functions of cognition and behaviour. Here we review the role of these ATP-dependent chromatin remodellers in cortical development in health and disease and highlight several key mouse mutants of the subunits of the complexes which have revealed how the remodelling mechanisms control the cortical stem cell chromatin landscape for expression of stage-specific transcripts. Consistent with their role in cortical development, several putative risk variants in the subunits of the remodelling complexes have been identified as the underlying causes of several neurodevelopmental disorders. A basic understanding of the detailed molecular mechanism of their action is key to understating how mutations in the same networks lead to disease pathologies and perhaps pave the way for therapeutic development for these complex multifactorial disorders.
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Affiliation(s)
- Leora D'Souza
- Brain Development and Disease Mechanisms, Institute for Stem Cell Science and Regenerative Medicine (inStem), Bangalore Life Science Cluster, Bangalore, India
| | - Asha S Channakkar
- Brain Development and Disease Mechanisms, Institute for Stem Cell Science and Regenerative Medicine (inStem), Bangalore Life Science Cluster, Bangalore, India
| | - Bhavana Muralidharan
- Brain Development and Disease Mechanisms, Institute for Stem Cell Science and Regenerative Medicine (inStem), Bangalore Life Science Cluster, Bangalore, India.
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Hu B, Won H, Mah W, Park RB, Kassim B, Spiess K, Kozlenkov A, Crowley CA, Pochareddy S, Li Y, Dracheva S, Sestan N, Akbarian S, Geschwind DH. Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders. Nat Commun 2021; 12:3968. [PMID: 34172755 PMCID: PMC8233376 DOI: 10.1038/s41467-021-24243-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 05/26/2021] [Indexed: 02/06/2023] Open
Abstract
Cellular heterogeneity in the human brain obscures the identification of robust cellular regulatory networks, which is necessary to understand the function of non-coding elements and the impact of non-coding genetic variation. Here we integrate genome-wide chromosome conformation data from purified neurons and glia with transcriptomic and enhancer profiles, to characterize the gene regulatory landscape of two major cell classes in the human brain. We then leverage cell-type-specific regulatory landscapes to gain insight into the cellular etiology of several brain disorders. We find that Alzheimer's disease (AD)-associated epigenetic dysregulation is linked to neurons and oligodendrocytes, whereas genetic risk factors for AD highlighted microglia, suggesting that different cell types may contribute to disease risk, via different mechanisms. Moreover, integration of glutamatergic and GABAergic regulatory maps with genetic risk factors for schizophrenia (SCZ) and bipolar disorder (BD) identifies shared (parvalbumin-expressing interneurons) and distinct cellular etiologies (upper layer neurons for BD, and deeper layer projection neurons for SCZ). Collectively, these findings shed new light on cell-type-specific gene regulatory networks in brain disorders.
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Affiliation(s)
- Benxia Hu
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Hyejung Won
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
| | - Won Mah
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Royce B Park
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bibi Kassim
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Keeley Spiess
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
| | - Alexey Kozlenkov
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- James J. Peters VA Medical Center, Bronx, NY, USA
| | - Cheynna A Crowley
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Sirisha Pochareddy
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Stella Dracheva
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- James J. Peters VA Medical Center, Bronx, NY, USA
| | - Nenad Sestan
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Department of Comparative Medicine, Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, CT, USA
- Program in Cellular Neuroscience, Neurodegeneration, and Repair and Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Schahram Akbarian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel H Geschwind
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, CA, USA.
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, CA, 90095, USA.
- Department of Human Genetics, David Geffen School of Medicine University of California, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, CA, USA.
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124
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Yao Y, Guo W, Zhang S, Yu H, Yan H, Zhang H, Sanders AR, Yue W, Duan J. Cell type-specific and cross-population polygenic risk score analyses of MIR137 gene pathway in schizophrenia. iScience 2021; 24:102785. [PMID: 34308291 PMCID: PMC8283158 DOI: 10.1016/j.isci.2021.102785] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 05/18/2021] [Accepted: 06/23/2021] [Indexed: 12/03/2022] Open
Abstract
Cell type-specific pathway-based polygenic risk scores (PRSs) may better inform disease biology and improve the precision of PRS-based clinical prediction. For microRNA-137 (MIR137), a leading neuropsychiatric risk gene and a post-transcriptional master regulator, we conducted a cell type-specific gene set PRS analysis in both European and Han Chinese schizophrenia (SZ) samples. We found that the PRS of neuronal MIR137-target genes better explains SZ risk than PRS derived from MIR137-target genes in iPSC or from the reported gene sets showing MIR137-altered expression. Compared with the PRS derived from the whole genome or the target genes of TCF4, the PRS of neuronal MIR137-target genes explained a disproportionally larger (relative to SNP number) SZ risk in the European sample, but with a more modest advantage in the Han Chinese sample. Our study demonstrated a cell type-specific polygenic contribution of MIR137-target genes to SZ risk, highlighting the value of cell type-specific pathway-based PRS analysis for uncovering disease-relevant biological features. PRS of neural MIR137 target genes better explains schizophrenia (SZ) risk variance SZ risk and SNP heritability explained by MIR137 target genes is cell type-specific MIR137 target genes explain a disproportionally larger SZ risk than genomic control PRS of MIR137 target genes better explains SZ risk in Europeans than in Han Chinese
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Affiliation(s)
- Yin Yao
- Department of Computational Biology, Life Science Institutes and School of Life Science and Human Phenomics Institute, Fudan University, Shanghai 200438, China
| | - Wei Guo
- Genetic Epidemiology Research Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Siwei Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Hao Yu
- Peking University Sixth Hospital (Institute of Mental Health), Beijing 100191, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China.,Department of Psychiatry, Jining Medical University, Jining, Shandong 272067, China.,Shandong Key Laboratory of Behavioral Medicine, Jining Medical University, Jining, Shandong 272067, China
| | - Hao Yan
- Peking University Sixth Hospital (Institute of Mental Health), Beijing 100191, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Hanwen Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Alan R Sanders
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA.,Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL 60637, USA
| | - Weihua Yue
- Peking University Sixth Hospital (Institute of Mental Health), Beijing 100191, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China.,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100191, China
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA.,Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL 60637, USA
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125
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Liang Y, Liu HJ, Yan J, Tian F. Natural Variation in Crops: Realized Understanding, Continuing Promise. ANNUAL REVIEW OF PLANT BIOLOGY 2021; 72:357-385. [PMID: 33481630 DOI: 10.1146/annurev-arplant-080720-090632] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Crops feed the world's population and shape human civilization. The improvement of crop productivity has been ongoing for almost 10,000 years and has evolved from an experience-based to a knowledge-driven practice over the past three decades. Natural alleles and their reshuffling are long-standing genetic changes that affect how crops respond to various environmental conditions and agricultural practices. Decoding the genetic basis of natural variation is central to understanding crop evolution and, in turn, improving crop breeding. Here, we review current advances in the approaches used to map the causal alleles of natural variation, provide refined insights into the genetics and evolution of natural variation, and outline how this knowledge promises to drive the development of sustainable agriculture under the dome of emerging technologies.
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Affiliation(s)
- Yameng Liang
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China; ,
| | - Hai-Jun Liu
- Gregor Mendel Institute, Austrian Academy of Sciences, Vienna BioCenter, 1030 Vienna, Austria;
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China;
| | - Feng Tian
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China; ,
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126
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Novel loci and potential mechanisms of major depressive disorder, bipolar disorder, and schizophrenia. SCIENCE CHINA-LIFE SCIENCES 2021; 65:167-183. [PMID: 34159505 DOI: 10.1007/s11427-020-1934-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/08/2021] [Indexed: 12/21/2022]
Abstract
Different psychiatric disorders share genetic relationships and pleiotropic loci to certain extent. We integrated and analyzed datasets related to major depressive disorder (MDD), bipolar disorder (BIP), and schizophrenia (SCZ) from the Psychiatric Genomics Consortium using multitrait analysis of genome-wide association analysis (MTAG). MTAG significantly increased the effective sample size from 99,773 to 119,754 for MDD, from 909,061 to 1,450,972 for BIP, and from 856,677 to 940,613 for SCZ. We discovered 7, 32, and 43 novel lead single nucleotide polymorphisms (SNPs) and 1, 6, and 3 novel causal SNPs for MDD, BIP, and SCZ, respectively, after fine-mapping. We identified rs8039305 in the FURIN gene as a novel pleiotropic locus across the three disorders. We performed marker analysis of genomic annotation (MAGMA) and Hi-C-coupled MAGMA (H-MAGMA) based gene-set analysis and identified 101 genes associated with the three disorders, which were enriched in the regulation of postsynaptic membranes, postsynaptic membrane dopaminergic synapses, and Notch signaling pathway. Next, we performed Mendelian randomization analysis using different tools and detected a causal effect of BIP on SCZ. Overall, we demonstrated the usage of combined genome-wide association studies summary statistics for exploring potential novel mechanisms of the three psychiatric disorders, providing an alternative approach to integrate publicly available summary data.
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127
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Shulman ED, Elkon R. Genetic mapping of developmental trajectories for complex traits and diseases. Comput Struct Biotechnol J 2021; 19:3458-3469. [PMID: 34194671 PMCID: PMC8220172 DOI: 10.1016/j.csbj.2021.05.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/30/2021] [Accepted: 05/30/2021] [Indexed: 11/04/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified numerous common genetic variants associated with complex human traits and diseases. However, the translation of GWAS discoveries into biological and clinical insights is highly challenging. In this study, we present a novel bioinformatics approach for enhancing the functional interpretation of GWAS signals, based on their integration with single-cell (sc)RNA-seq datasets that examine developmental processes. Our approach performs three tasks: (1) Identification of links between cell differentiation trajectories and traits; (2) Elucidation of biological processes and molecular pathways that underlie such trajectory-trait links; and (3) Prioritization of target genes that carry the links between trajectories, pathways and traits. We applied our method to a set of 11 traits of various pathologies, and 12 scRNA-seq datasets of diverse developmental processes, and it readily detected well-established biological connections, including those between the maturation of cortical inhibitory interneurons and schizophrenia, hepatocytes and cholesterol levels, and pancreatic beta-islet cells and type-2 diabetes. For each of these associations, our method pinpointed top candidate genes that are strongly associated with both the kinetics of the differentiation trajectory and the disease's genetic risk. By the identification of trajectory-disease links, molecular pathways that underlie them and prioritizing candidate risk genes, our method improves the understanding of the etiology of complex diseases, and thus holds promise for enhancing rational drug development that is aimed at targeting specific biological processes that mediate the genetic predisposition to diseases.
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Affiliation(s)
- Eldad David Shulman
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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128
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Jia P, Manuel AM, Fernandes BS, Dai Y, Zhao Z. Distinct effect of prenatal and postnatal brain expression across 20 brain disorders and anthropometric social traits: a systematic study of spatiotemporal modularity. Brief Bioinform 2021; 22:6291943. [PMID: 34086851 DOI: 10.1093/bib/bbab214] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/30/2021] [Accepted: 05/15/2021] [Indexed: 02/06/2023] Open
Abstract
Different spatiotemporal abnormalities have been implicated in different neuropsychiatric disorders and anthropometric social traits, yet an investigation in the temporal network modularity with brain tissue transcriptomics has been lacking. We developed a supervised network approach to investigate the genome-wide association study (GWAS) results in the spatial and temporal contexts and demonstrated it in 20 brain disorders and anthropometric social traits. BrainSpan transcriptome profiles were used to discover significant modules enriched with trait susceptibility genes in a developmental stage-stratified manner. We investigated whether, and in which developmental stages, GWAS-implicated genes are coordinately expressed in brain transcriptome. We identified significant network modules for each disorder and trait at different developmental stages, providing a systematic view of network modularity at specific developmental stages for a myriad of brain disorders and traits. Specifically, we observed a strong pattern of the fetal origin for most psychiatric disorders and traits [such as schizophrenia (SCZ), bipolar disorder, obsessive-compulsive disorder and neuroticism], whereas increased co-expression activities of genes were more strongly associated with neurological diseases [such as Alzheimer's disease (AD) and amyotrophic lateral sclerosis] and anthropometric traits (such as college completion, education and subjective well-being) in postnatal brains. Further analyses revealed enriched cell types and functional features that were supported and corroborated prior knowledge in specific brain disorders, such as clathrin-mediated endocytosis in AD, myelin sheath in multiple sclerosis and regulation of synaptic plasticity in both college completion and education. Our study provides a landscape view of the spatiotemporal features in a myriad of brain-related disorders and traits.
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Affiliation(s)
- Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX 77030, USA
| | - Astrid M Manuel
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX 77030, USA
| | - Brisa S Fernandes
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX 77030, USA
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129
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Jin T, Rehani P, Ying M, Huang J, Liu S, Roussos P, Wang D. scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks. Genome Med 2021; 13:95. [PMID: 34044854 PMCID: PMC8161957 DOI: 10.1186/s13073-021-00908-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 05/13/2021] [Indexed: 02/06/2023] Open
Abstract
Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer's disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .
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Affiliation(s)
- Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA
| | - Peter Rehani
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA
- Department of Integrative Biology, University of Wisconsin - Madison, Madison, WI, 53706, USA
- Present address: Morgridge Institute for Research, Madison, WI, 53715, USA
| | - Mufang Ying
- Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA
- Present address: Department of Statistics, Rutgers University, Piscataway, NJ, 08854, USA
| | - Jiawei Huang
- Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA
| | - Panagiotis Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53706, USA.
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA.
- Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, 53706, USA.
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130
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Xie Y, Zhang X, Liu F, Qin W, Fu J, Xue K, Yu C. Brain mRNA Expression Associated with Cortical Volume Alterations in Autism Spectrum Disorder. Cell Rep 2021; 32:108137. [PMID: 32937121 DOI: 10.1016/j.celrep.2020.108137] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 05/23/2020] [Accepted: 08/21/2020] [Indexed: 12/16/2022] Open
Abstract
Numerous studies report abnormal cerebral cortex volume (CCV) in autism spectrum disorder (ASD); however, genes related to CCV abnormalities in ASD remain largely unknown. Here, we identify genes associated with CCV alterations in ASD by performing spatial correlations between the gene expression of 6 donated brains and neuroimaging data from 1,404 ASD patients and 1,499 controls. Based on spatial correlations between gene expression and CCV differences from two independent meta-analyses and between gene expression and individual CCV distributions of 404 patients and 496 controls, we identify 417 genes associated with both CCV differences and individual CCV distributions. These genes are enriched for genetic association signals and genes downregulated in the ASD post-mortem brain. The expression patterns of these genes are correlated with brain activation patterns of language-related neural processes frequently impaired in ASD. These findings highlight a model whereby genetic risk impacts gene expression (downregulated), which leads to CCV alterations in ASD.
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Affiliation(s)
- Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Xue Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, P.R. China.
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131
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Yurko R, Roeder K, Devlin B, G'Sell M. H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives. Ann Hum Genet 2021; 85:97-100. [PMID: 33372276 DOI: 10.1111/ahg.12412] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 12/04/2020] [Accepted: 12/07/2020] [Indexed: 11/26/2022]
Affiliation(s)
- Ronald Yurko
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Kathryn Roeder
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Max G'Sell
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
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132
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Wendt FR, Pathak GA, Levey DF, Nuñez YZ, Overstreet C, Tyrrell C, Adhikari K, De Angelis F, Tylee DS, Goswami A, Krystal JH, Abdallah CG, Stein MB, Kranzler HR, Gelernter J, Polimanti R. Sex-stratified gene-by-environment genome-wide interaction study of trauma, posttraumatic-stress, and suicidality. Neurobiol Stress 2021; 14:100309. [PMID: 33665242 PMCID: PMC7905234 DOI: 10.1016/j.ynstr.2021.100309] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 01/20/2021] [Accepted: 02/10/2021] [Indexed: 12/27/2022] Open
Abstract
Epidemiologic studies recognize that trauma and posttraumatic stress are associated with heightened suicidal behavior severity, yet examination of these associations from a genetic perspective is limited. We performed a multivariate gene-by-environment genome-wide interaction study (GEWIS) of suicidality in 123,633 individuals using a covariance matrix based on 26 environments related to traumatic experiences, posttraumatic stress, social support, and socioeconomic status. We discovered five suicidality risk loci, including the male-associated rs2367967 (CWC22), which replicated in an independent cohort. All GEWIS-significant loci exhibited interaction effects where at least 5% of the sample had environmental profiles conferring opposite SNP effects from the majority. We identified PTSD as a primary driving environment for GxE at suicidality risk loci. The male suicidality GEWIS was enriched for three middle-temporal-gyrus inhibitory neuron transcriptomic profiles: SCUBE- and PVALB-expressing cells (β = 0.028, p = 3.74 × 10-4), OPRM1-expressing cells (β = 0.030, p = 0.001), and SPAG17-expressing cells (β = 0.029, p = 9.80 × 10-4). Combined with gene-based analyses (CNTN5 p association = 2.38 × 10-9, p interaction = 1.51 × 10-3; PSMD14 p association = 2.04 × 10-7, p interaction = 7.76 × 10-6; HEPACAM p association = 2.43 × 10-6, p interaction = 3.82 × 10-7) including information about brain chromatin interaction profiles (UBE2E3 in male neuron p = 1.07 × 10-5), our GEWIS points to extracellular matrix biology and synaptic plasticity as biological interactors with the effects of potentially modifiable lifetime traumatic experiences on genetic risk for suicidality. Characterization of molecular basis for the effects of traumatic experience and posttraumatic stress on risk of suicidal behaviors may help to identify novel targets for which more effective treatments can be developed for use in high-risk populations.
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Affiliation(s)
- Frank R. Wendt
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - Gita A. Pathak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - Daniel F. Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - Yaira Z. Nuñez
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - Cassie Overstreet
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
- National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, West Haven CT, 06520, USA
| | - Chelsea Tyrrell
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - Keyrun Adhikari
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - Flavio De Angelis
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - Daniel S. Tylee
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - Aranyak Goswami
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - John H. Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Chadi G. Abdallah
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
| | - Murray B. Stein
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
- Departments of Psychiatry and Family Medicine & Public Health, University of California San Diego, La Jolla, CA, USA
| | - Henry R. Kranzler
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, 19104, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, 06510, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
- VA CT Healthcare System, West Haven, CT, 06520, USA
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Palmer RHC, Johnson EC, Won H, Polimanti R, Kapoor M, Chitre A, Bogue MA, Benca‐Bachman CE, Parker CC, Verma A, Reynolds T, Ernst J, Bray M, Kwon SB, Lai D, Quach BC, Gaddis NC, Saba L, Chen H, Hawrylycz M, Zhang S, Zhou Y, Mahaffey S, Fischer C, Sanchez‐Roige S, Bandrowski A, Lu Q, Shen L, Philip V, Gelernter J, Bierut LJ, Hancock DB, Edenberg HJ, Johnson EO, Nestler EJ, Barr PB, Prins P, Smith DJ, Akbarian S, Thorgeirsson T, Walton D, Baker E, Jacobson D, Palmer AA, Miles M, Chesler EJ, Emerson J, Agrawal A, Martone M, Williams RW. Integration of evidence across human and model organism studies: A meeting report. GENES, BRAIN, AND BEHAVIOR 2021; 20:e12738. [PMID: 33893716 PMCID: PMC8365690 DOI: 10.1111/gbb.12738] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/11/2021] [Accepted: 04/21/2021] [Indexed: 12/13/2022]
Abstract
The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.
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Affiliation(s)
- Rohan H. C. Palmer
- Behavioral Genetics of Addiction Laboratory, Department of PsychologyEmory UniversityAtlantaGeorgiaUSA
| | - Emma C. Johnson
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Hyejung Won
- Department of Genetics and Neuroscience CenterUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Renato Polimanti
- Department of PsychiatryYale University School of MedicineWest HavenConnecticutUSA
| | - Manav Kapoor
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Apurva Chitre
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | | | - Chelsie E. Benca‐Bachman
- Behavioral Genetics of Addiction Laboratory, Department of PsychologyEmory UniversityAtlantaGeorgiaUSA
| | - Clarissa C. Parker
- Department of Psychology and Program in NeuroscienceMiddlebury CollegeMiddleburyVermontUSA
| | - Anurag Verma
- Biomedical and Translational Informatics LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Jason Ernst
- Department of Biological ChemistryUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael Bray
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Soo Bin Kwon
- Department of Biological ChemistryUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Dongbing Lai
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Bryan C. Quach
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Nathan C. Gaddis
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Laura Saba
- Department of Pharmaceutical SciencesUniversity of Colorado, Anschutz Medical CampusAuroraColoradoUSA
| | - Hao Chen
- Department of Pharmacology, Addiction Science, and ToxicologyUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | | | - Shan Zhang
- Department of Statistics and ProbabilityMichigan State UniversityEast LansingMichiganUSA
| | - Yuan Zhou
- Department of Department of BiostatisticsUniversity of FloridaGainesvilleFloridaUSA
| | - Spencer Mahaffey
- Department of Pharmaceutical Sciences, School of PharmacyUniversity of Colorado DenverAuroraColoradoUSA
| | - Christian Fischer
- Department of Genetics, Genomics and InformaticsUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | - Sandra Sanchez‐Roige
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Anita Bandrowski
- Department of NeuroscienceUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Qing Lu
- Department of Department of BiostatisticsUniversity of FloridaGainesvilleFloridaUSA
| | - Li Shen
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | - Joel Gelernter
- Department of PsychiatryYale University School of MedicineWest HavenConnecticutUSA
| | - Laura J. Bierut
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Dana B. Hancock
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Howard J. Edenberg
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Biochemistry and Molecular BiologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Eric O. Johnson
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Eric J. Nestler
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Peter B. Barr
- Department of PsychologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Pjotr Prins
- Department of Genetics, Genomics and InformaticsUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | - Desmond J. Smith
- Department of Molecular and Medical PharmacologyDavid Geffen School of Medicine, UCLALos AngelesCaliforniaUSA
| | - Schahram Akbarian
- Friedman Brain Institute and Departments of Psychiatry and NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | | | - Erich Baker
- Department of Computer ScienceBaylor UniversityWacoTexasUSA
| | - Daniel Jacobson
- Computational and Predictive Biology, BiosciencesOak Ridge National LaboratoryOak RidgeTennesseeUSA
- Department of PsychologyUniversity of Tennessee KnoxvilleKnoxvilleTennesseeUSA
| | - Abraham A. Palmer
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Institute for Genomic Medicine, University of California San DiegoLa JollaCaliforniaUSA
| | - Michael Miles
- Department of Pharmacology and ToxicologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | | | | | - Arpana Agrawal
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Maryann Martone
- Department of NeuroscienceUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Robert W. Williams
- Department of Genetics, Genomics and InformaticsUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
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Pathak GA, Wendt FR, Levey DF, Mecca AP, van Dyck CH, Gelernter J, Polimanti R. Pleiotropic effects of telomere length loci with brain morphology and brain tissue expression. Hum Mol Genet 2021; 30:1360-1370. [PMID: 33831179 PMCID: PMC8255129 DOI: 10.1093/hmg/ddab102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 02/09/2021] [Accepted: 03/29/2021] [Indexed: 12/21/2022] Open
Abstract
Several studies have reported association between leukocyte telomere length (LTL) and neuropsychiatric disorders. Although telomere length is affected by environmental factors, genetic variants in certain loci are strongly associated with LTL. Thus, we aimed to identify the genomic relationship between genetic variants of LTL with brain-based regulatory changes and brain volume. We tested genetic colocalization of seven and nine LTL loci in two ancestry groups, European (EUR) and East-Asian (EAS), respectively, with brain morphology measures for 101 T1-magnetic resonance imaging-based region of interests (n = 21 821). The posterior probability (>90%) was observed for 'fourth ventricle', 'gray matter' and 'cerebellar vermal lobules I-IV' volumes. We then tested causal relationship using LTL loci for gene and methylation expression. We found causal pleiotropy for gene (EAS = four genes; EUR = five genes) and methylation expression (EUR = 17 probes; EAS = 4 probes) of brain tissues (P ≤ 2.47 × 10-6). Integrating chromatin profiles with LTL-single nucleotide polymorphisms identified 45 genes (EUR) and 79 genes (EAS) (P ≤ 9.78×10-7). We found additional 38 LTL-genes using chromatin-based gene mapping for EUR ancestry population. Gene variants in three LTL-genes-GPR37, OBFC1 and RTEL1/RTEL1-TNFRSF6B-show convergent evidence of pleiotropy with brain morphology, gene and methylation expression and chromatin association. Mapping gene functions to drug-gene interactions, we identified process 'transmission across chemical synapses' (P < 2.78 × 10-4). This study provides evidence that genetic variants of LTL have pleiotropic roles with brain-based effects that could explain the phenotypic association of LTL with several neuropsychiatric traits.
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Affiliation(s)
- Gita A Pathak
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT 06551, USA,Veteran Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Frank R Wendt
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT 06551, USA,Veteran Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT 06551, USA,Veteran Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Adam P Mecca
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT 06551, USA,Alzheimer’s Disease Research Unit, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Christopher H van Dyck
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT 06551, USA,Alzheimer’s Disease Research Unit, Yale University School of Medicine, New Haven, CT 06511, USA,Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA,Department of Neurology, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT 06551, USA,Veteran Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Renato Polimanti
- To whom correspondence should be addressed at: VA CT 116A2, 950 Campbell Avenue, West Haven, CT 06516, USA. Tel: +1 2039375711 ext. 5745; Fax: +1 2039373897;
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135
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Harabula I, Pombo A. The dynamics of chromatin architecture in brain development and function. Curr Opin Genet Dev 2021; 67:84-93. [DOI: 10.1016/j.gde.2020.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 12/07/2020] [Accepted: 12/13/2020] [Indexed: 12/22/2022]
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136
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Zhu A, Matoba N, Wilson EP, Tapia AL, Li Y, Ibrahim JG, Stein JL, Love MI. MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity. PLoS Genet 2021; 17:e1009455. [PMID: 33872308 PMCID: PMC8084342 DOI: 10.1371/journal.pgen.1009455] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/29/2021] [Accepted: 02/26/2021] [Indexed: 11/18/2022] Open
Abstract
Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus's estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
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Affiliation(s)
- Anqi Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Emma P. Wilson
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Amanda L. Tapia
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Joseph G. Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michael I. Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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137
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Ramanan VK, Lesnick TG, Przybelski SA, Heckman MG, Knopman DS, Graff-Radford J, Lowe VJ, Machulda MM, Mielke MM, Jack CR, Petersen RC, Ross OA, Vemuri P. Coping with brain amyloid: genetic heterogeneity and cognitive resilience to Alzheimer's pathophysiology. Acta Neuropathol Commun 2021; 9:48. [PMID: 33757599 PMCID: PMC7986461 DOI: 10.1186/s40478-021-01154-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 03/08/2021] [Indexed: 12/13/2022] Open
Abstract
Although abnormal accumulation of amyloid in the brain is an early biomarker of Alzheimer's disease (AD), wide variation in cognitive trajectories during life can be seen in the setting of brain amyloidosis, ranging from maintenance of normal function to progression to dementia. It is widely presumed that cognitive resilience (i.e., coping) to amyloidosis may be influenced by environmental, lifestyle, and inherited factors, but relatively little in specifics is known about this architecture. Here, we leveraged multimodal longitudinal data from a large, population-based sample of older adults to discover genetic factors associated with differential cognitive resilience to brain amyloidosis determined by positron emission tomography (PET). Among amyloid-PET positive older adults, the AD risk allele APOE ɛ4 was associated with worse longitudinal memory trajectories as expected, and was thus covaried in the main analyses. Through a genome-wide association study (GWAS), we uncovered a novel association with cognitive resilience on chromosome 8 at the MTMR7/CNOT7/ZDHHC2/VPS37A locus (p = 4.66 × 10-8, β = 0.23), and demonstrated replication in an independent cohort. Post-hoc analyses confirmed this association as specific to the setting of elevated amyloid burden and not explained by differences in tau deposition or cerebrovascular disease. Complementary gene-based analyses and publically available functional data suggested that the causative variant at this locus may tag CNOT7 (CCR4-NOT Transcription Complex Subunit 7), a gene linked to synaptic plasticity and hippocampal-dependent learning and memory. Pathways related to cell adhesion and immune system activation displayed enrichment of association in the GWAS. Our findings, resulting from a unique study design, support the hypothesis that genetic heterogeneity is one of the factors that explains differential cognitive resilience to brain amyloidosis. Further characterization of the underlying biological mechanisms influencing cognitive resilience may facilitate improved prognostic counseling, therapeutic application, and trial enrollment in AD.
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Affiliation(s)
- Vijay K Ramanan
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Timothy G Lesnick
- Department of Health Sciences Research, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Scott A Przybelski
- Department of Health Sciences Research, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Michael G Heckman
- Division of Biomedical Statistics and Informatics, Mayo Clinic-Florida, Jacksonville, FL, 32224, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - Jonathan Graff-Radford
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Michelle M Mielke
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
- Department of Health Sciences Research, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
- Department of Health Sciences Research, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic-Florida, Jacksonville, FL, 32224, USA
- Department of Clinical Genomics, Mayo Clinic-Florida, Jacksonville, FL, 32224, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA.
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138
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Silberstein M, Nesbit N, Cai J, Lee PH. Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities. J Genet Genomics 2021; 48:173-183. [PMID: 33896739 PMCID: PMC8286309 DOI: 10.1016/j.jgg.2021.01.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/24/2021] [Accepted: 01/25/2021] [Indexed: 12/23/2022]
Abstract
Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments.
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Affiliation(s)
- Micah Silberstein
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nicholas Nesbit
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jacquelyn Cai
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Phil H Lee
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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139
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Bhattacharya A, Li Y, Love MI. MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies. PLoS Genet 2021; 17:e1009398. [PMID: 33684137 PMCID: PMC7971899 DOI: 10.1371/journal.pgen.1009398] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 03/18/2021] [Accepted: 02/04/2021] [Indexed: 02/06/2023] Open
Abstract
Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1-2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, University of California-Los Angeles, Los Angeles, California, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michael I. Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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140
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Gerring ZF, Mina-Vargas A, Gamazon ER, Derks EM. E-MAGMA: an eQTL-informed method to identify risk genes using genome-wide association study summary statistics. Bioinformatics 2021; 37:2245-2249. [PMID: 33624746 PMCID: PMC8388029 DOI: 10.1093/bioinformatics/btab115] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 02/02/2021] [Accepted: 02/18/2021] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with human traits and diseases, but the exact causal genes are largely unknown. Common genetic risk variants are enriched in non-protein-coding regions of the genome and often affect gene expression (expression quantitative trait loci, eQTL) in a tissue-specific manner. To address this challenge, we developed a methodological framework, E-MAGMA, which converts genome-wide association summary statistics into gene-level statistics by assigning risk variants to their putative genes based on tissue-specific eQTL information. RESULTS We compared E-MAGMA to three eQTL informed gene-based approaches using simulated phenotype data. Phenotypes were simulated based on eQTL reference data using GCTA for all genes with at least one eQTL at chromosome 1. We performed 10 simulations per gene. The eQTL-h2 (i.e., the proportion of variation explained by the eQTLs) was set at 1%, 2%, and 5%. We found E-MAGMA outperforms other gene-based approaches across a range of simulated parameters (e.g. the number of identified causal genes). When applied to genome-wide association summary statistics for five neuropsychiatric disorders, E-MAGMA identified more putative candidate causal genes compared to other eQTL-based approaches. By integrating tissue-specific eQTL information, these results show E-MAGMA will help to identify novel candidate causal genes from genome-wide association summary statistics and thereby improve the understanding of the biological basis of complex disorders. AVAILABILITY A tutorial and input files are made available in a github repository: https://github.com/eskederks/eMAGMA-tutorial. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zachary F Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Angela Mina-Vargas
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Clare Hall, University of Cambridge, Cambridge, United Kingdom
| | - Eske M Derks
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
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141
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Palmer RHC, Benca-Bachman CE, Huggett SB, Bubier JA, McGeary JE, Ramgiri N, Srijeyanthan J, Yang J, Visscher PM, Yang J, Knopik VS, Chesler EJ. Multi-omic and multi-species meta-analyses of nicotine consumption. Transl Psychiatry 2021; 11:98. [PMID: 33542196 PMCID: PMC7862377 DOI: 10.1038/s41398-021-01231-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 12/15/2020] [Accepted: 12/17/2020] [Indexed: 12/23/2022] Open
Abstract
Cross-species translational approaches to human genomic analyses are lacking. The present study uses an integrative framework to investigate how genes associated with nicotine use in model organisms contribute to the genetic architecture of human tobacco consumption. First, we created a model organism geneset by collecting results from five animal models of nicotine exposure (RNA expression changes in brain) and then tested the relevance of these genes and flanking genetic variation using genetic data from human cigarettes per day (UK BioBank N = 123,844; all European Ancestry). We tested three hypotheses: (1) DNA variation in, or around, the 'model organism geneset' will contribute to the heritability to human tobacco consumption, (2) that the model organism genes will be enriched for genes associated with human tobacco consumption, and (3) that a polygenic score based off our model organism geneset will predict tobacco consumption in the AddHealth sample (N = 1667; all European Ancestry). Our results suggested that: (1) model organism genes accounted for ~5-36% of the observed SNP-heritability in human tobacco consumption (enrichment: 1.60-31.45), (2) model organism genes, but not negative control genes, were enriched for the gene-based associations (MAGMA, H-MAGMA, SMultiXcan) for human cigarettes per day, and (3) polygenic scores based on our model organism geneset predicted cigarettes per day in an independent sample. Altogether, these findings highlight the advantages of using multiple species evidence to isolate genetic factors to better understand the etiological complexity of tobacco and other nicotine consumption.
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Affiliation(s)
- Rohan H. C. Palmer
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Chelsie E. Benca-Bachman
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Spencer B. Huggett
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Jason A. Bubier
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory, Bar Harbor, ME USA
| | - John E. McGeary
- grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Brown University, Providence, RI USA ,grid.413904.b0000 0004 0420 4094Providence Veterans Affairs Medical Center, Providence, RI USA
| | - Nikhil Ramgiri
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Jenani Srijeyanthan
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Jingjing Yang
- grid.189967.80000 0001 0941 6502Department of Human Genetics, Emory University School of Medicine, Atlanta, GA USA
| | - Peter M. Visscher
- grid.1003.20000 0000 9320 7537Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD Australia
| | - Jian Yang
- grid.1003.20000 0000 9320 7537Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD Australia
| | - Valerie S. Knopik
- grid.169077.e0000 0004 1937 2197Department of Human Development and Family Studies, Purdue University, West Lafayette, IN USA
| | - Elissa J. Chesler
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory, Bar Harbor, ME USA
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142
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Dobrindt K, Zhang H, Das D, Abdollahi S, Prorok T, Ghosh S, Weintraub S, Genovese G, Powell SK, Lund A, Akbarian S, Eggan K, McCarroll S, Duan J, Avramopoulos D, Brennand KJ. Publicly Available hiPSC Lines with Extreme Polygenic Risk Scores for Modeling Schizophrenia. Complex Psychiatry 2021; 6:68-82. [PMID: 34883504 PMCID: PMC7923934 DOI: 10.1159/000512716] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/27/2020] [Indexed: 07/23/2023] Open
Abstract
Schizophrenia (SZ) is a common and debilitating psychiatric disorder with limited effective treatment options. Although highly heritable, risk for this polygenic disorder depends on the complex interplay of hundreds of common and rare variants. Translating the growing list of genetic loci significantly associated with disease into medically actionable information remains an important challenge. Thus, establishing platforms with which to validate the impact of risk variants in cell-type-specific and donor-dependent contexts is critical. Towards this, we selected and characterized a collection of 12 human induced pluripotent stem cell (hiPSC) lines derived from control donors with extremely low and high SZ polygenic risk scores (PRS). These hiPSC lines are publicly available at the California Institute for Regenerative Medicine (CIRM). The suitability of these extreme PRS hiPSCs for CRISPR-based isogenic comparisons of neurons and glia was evaluated across 3 independent laboratories, identifying 9 out of 12 meeting our criteria. We report a standardized resource of publicly available hiPSCs on which we hope to perform genome engineering and generate diverse kinds of functional data, with comparisons across studies facilitated by the use of a common set of genetic backgrounds.
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Affiliation(s)
- Kristina Dobrindt
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Hanwen Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Debamitra Das
- Department of Genetic Medicine and Psychiatry, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sara Abdollahi
- Department of Genetic Medicine and Psychiatry, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tim Prorok
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Sulagna Ghosh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
| | - Sarah Weintraub
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
| | - Samuel K. Powell
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anina Lund
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Schahram Akbarian
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kevin Eggan
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Steven McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois, USA
| | - Dimitrios Avramopoulos
- Department of Genetic Medicine and Psychiatry, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kristen J. Brennand
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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143
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Dai Y, Hu R, Manuel AM, Liu A, Jia P, Zhao Z. CSEA-DB: an omnibus for human complex trait and cell type associations. Nucleic Acids Res 2021; 49:D862-D870. [PMID: 33211888 PMCID: PMC7778923 DOI: 10.1093/nar/gkaa1064] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 10/18/2020] [Accepted: 10/21/2020] [Indexed: 12/20/2022] Open
Abstract
During the past decade, genome-wide association studies (GWAS) have identified many genetic variants with susceptibility to several thousands of complex diseases or traits. The genetic regulation of gene expression is highly tissue-specific and cell type-specific. Recently, single-cell technology has paved the way to dissect cellular heterogeneity in human tissues. Here, we present a reference database for GWAS trait-associated cell type-specificity, named Cell type-Specific Enrichment Analysis DataBase (CSEA-DB, available at https://bioinfo.uth.edu/CSEADB/). Specifically, we curated total of 5120 GWAS summary statistics data for a wide range of human traits and diseases followed by rigorous quality control. We further collected >900 000 cells from the leading consortia such as Human Cell Landscape, Human Cell Atlas, and extensive literature mining, including 752 tissue cell types from 71 adult and fetal tissues across 11 human organ systems. The tissues and cell types were annotated with Uberon and Cell Ontology. By applying our deTS algorithm, we conducted 10 250 480 times of trait-cell type associations, reporting a total of 598 (11.68%) GWAS traits with at least one significantly associated cell type. In summary, CSEA-DB could serve as a repository of association map for human complex traits and their underlying cell types, manually curated GWAS, and single-cell transcriptome resources.
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Affiliation(s)
- Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ruifeng Hu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Astrid Marilyn Manuel
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Andi Liu
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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144
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Pei G, Hu R, Dai Y, Manuel AM, Zhao Z, Jia P. Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations. Nucleic Acids Res 2021; 49:53-66. [PMID: 33300042 PMCID: PMC7797043 DOI: 10.1093/nar/gkaa1137] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/22/2020] [Accepted: 12/08/2020] [Indexed: 02/06/2023] Open
Abstract
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.
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Affiliation(s)
- Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ruifeng Hu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Astrid Marilyn Manuel
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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145
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Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry. Mol Psychiatry 2021; 26:6209-6217. [PMID: 34728798 PMCID: PMC8562028 DOI: 10.1038/s41380-021-01335-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/21/2021] [Accepted: 09/29/2021] [Indexed: 12/31/2022]
Abstract
The growing prevalence of opioid use disorder (OUD) constitutes an urgent health crisis. Ample evidence indicates that risk for OUD is heritable. As a surrogate (or proxy) for OUD, we explored the genetic basis of using prescription opioids 'not as prescribed'. We hypothesized that misuse of opiates might be a heritable risk factor for OUD. To test this hypothesis, we performed a genome-wide association study (GWAS) of problematic opioid use (POU) in 23andMe research participants of European ancestry (N = 132,113; 21% cases). We identified two genome-wide significant loci (rs3791033, an intronic variant of KDM4A; rs640561, an intergenic variant near LRRIQ3). POU showed positive genetic correlations with the two largest available GWAS of OUD and opioid dependence (rg = 0.64, 0.80, respectively). We also identified numerous additional genetic correlations with POU, including alcohol dependence (rg = 0.74), smoking initiation (rg = 0.63), pain relief medication intake (rg = 0.49), major depressive disorder (rg = 0.44), chronic pain (rg = 0.42), insomnia (rg = 0.39), and loneliness (rg = 0.28). Although POU was positively genetically correlated with risk-taking (rg = 0.38), conditioning POU on risk-taking did not substantially alter the magnitude or direction of these genetic correlations, suggesting that POU does not simply reflect a genetic tendency towards risky behavior. Lastly, we performed phenome- and lab-wide association analyses, which uncovered additional phenotypes that were associated with POU, including respiratory failure, insomnia, ischemic heart disease, and metabolic and blood-related biomarkers. We conclude that opioid misuse can be measured in population-based cohorts and provides a cost-effective complementary strategy for understanding the genetic basis of OUD.
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146
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Mulvey B, Lagunas T, Dougherty JD. Massively Parallel Reporter Assays: Defining Functional Psychiatric Genetic Variants Across Biological Contexts. Biol Psychiatry 2021; 89:76-89. [PMID: 32843144 PMCID: PMC7938388 DOI: 10.1016/j.biopsych.2020.06.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 06/09/2020] [Accepted: 06/10/2020] [Indexed: 12/18/2022]
Abstract
Neuropsychiatric phenotypes have long been known to be influenced by heritable risk factors, directly confirmed by the past decade of genetic studies that have revealed specific genetic variants enriched in disease cohorts. However, the initial hope that a small set of genes would be responsible for a given disorder proved false. The more complex reality is that a given disorder may be influenced by myriad small-effect noncoding variants and/or by rare but severe coding variants, many de novo. Noncoding genomic sequences-for which molecular functions cannot usually be inferred-harbor a large portion of these variants, creating a substantial barrier to understanding higher-order molecular and biological systems of disease. Fortunately, novel genetic technologies-scalable oligonucleotide synthesis, RNA sequencing, and CRISPR (clustered regularly interspaced short palindromic repeats)-have opened novel avenues to experimentally identify biologically significant variants en masse. Massively parallel reporter assays (MPRAs) are an especially versatile technique resulting from such innovations. MPRAs are powerful molecular genetics tools that can be used to screen thousands of untranscribed or untranslated sequences and their variants for functional effects in a single experiment. This approach, though underutilized in psychiatric genetics, has several useful features for the field. We review methods for assaying putatively functional genetic variants and regions, emphasizing MPRAs and the opportunities they hold for dissection of psychiatric polygenicity. We discuss literature applying functional assays in neurogenetics, highlighting strengths, caveats, and design considerations-especially regarding disease-relevant variables (cell type, neurodevelopment, and sex), and we ultimately propose applications of MPRA to both computational and experimental neurogenetics of polygenic disease risk.
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Affiliation(s)
- Bernard Mulvey
- Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Tomás Lagunas
- Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Joseph D Dougherty
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
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147
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Powell SK, O'Shea C, Brennand KJ, Akbarian S. Parsing the Functional Impact of Noncoding Genetic Variants in the Brain Epigenome. Biol Psychiatry 2021; 89:65-75. [PMID: 33131715 PMCID: PMC7718420 DOI: 10.1016/j.biopsych.2020.06.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 05/29/2020] [Accepted: 06/01/2020] [Indexed: 12/31/2022]
Abstract
The heritability of common psychiatric disorders has motivated global efforts to identify risk-associated genetic variants and elucidate molecular pathways connecting DNA sequence to disease-associated brain dysfunction. The overrepresentation of risk variants among gene regulatory loci instead of protein-coding loci, however, poses a unique challenge in discerning which among the many thousands of variants identified contribute functionally to disease etiology. Defined broadly, psychiatric epigenomics seeks to understand the effects of disease-associated genetic variation on functional readouts of chromatin in an effort to prioritize variants in terms of their impact on gene expression in the brain. Here, we provide an overview of epigenomic mapping in the human brain and highlight findings of particular relevance to psychiatric genetics. Computational methods, including convolutional neuronal networks, and other machine learning approaches hold great promise for elucidating the functional impact of both common and rare genetic variants, thereby refining the epigenomic architecture of psychiatric disorders and enabling integrative analyses of regulatory noncoding variants in the context of large population-level genome and phenome databases.
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Affiliation(s)
- Samuel K Powell
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Callan O'Shea
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kristen J Brennand
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Schahram Akbarian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
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148
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Hu B, Won H. Chromatin architecture provides a roadmap to improve our understanding of psychiatric disorders. Neuropsychopharmacology 2021; 46:234-235. [PMID: 32859998 PMCID: PMC7688951 DOI: 10.1038/s41386-020-00822-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Benxia Hu
- grid.410711.20000 0001 1034 1720UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599 USA ,grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Hyejung Won
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, 27599, USA. .,Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.
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149
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Dai Y, O'Brien TD, Pei G, Zhao Z, Jia P. Characterization of genome-wide association study data reveals spatiotemporal heterogeneity of mental disorders. BMC Med Genomics 2020; 13:192. [PMID: 33371872 PMCID: PMC7771094 DOI: 10.1186/s12920-020-00832-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 11/23/2020] [Indexed: 12/15/2022] Open
Abstract
Background Psychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BIP), major depressive disorder (MDD), attention deficit-hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) are often related to brain development. Both shared and unique biological and neurodevelopmental processes have been reported to be involved in these disorders. Methods In this work, we developed an integrative analysis framework to seek for the sensitive spatiotemporal point during brain development underlying each disorder. Specifically, we first identified spatiotemporal gene co-expression modules for four brain regions three developmental stages (prenatal, birth to 11 years old, and older than 13 years), totaling 12 spatiotemporal sites. By integrating GWAS summary statistics and the spatiotemporal co-expression modules, we characterized the risk genes and their co-expression partners for five disorders. Results We found that SCZ and BIP, ASD and ADHD tend to cluster with each other and keep a distance from other psychiatric disorders. At the gene level, we identified several genes that were shared among the most significant modules, such as CTNNB1 and LNX1, and a hub gene, ATF2, in multiple modules. Moreover, we pinpointed two spatiotemporal points in the prenatal stage with active expression activities and highlighted one postnatal point for BIP. Further functional analysis of the disorder-related module highlighted the apoptotic signaling pathway for ASD and the immune-related and cell-cell adhesion function for SCZ, respectively. Conclusion Our study demonstrated the dynamic changes of disorder-related genes at the network level, shedding light on the spatiotemporal regulation during brain development.
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Affiliation(s)
- Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA
| | - Timothy D O'Brien
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA
| | - Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA. .,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA. .,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA.
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Townsley KG, Brennand KJ, Huckins LM. Massively parallel techniques for cataloguing the regulome of the human brain. Nat Neurosci 2020; 23:1509-1521. [PMID: 33199899 PMCID: PMC8018778 DOI: 10.1038/s41593-020-00740-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/13/2020] [Indexed: 12/14/2022]
Abstract
Complex brain disorders are highly heritable and arise from a complex polygenic risk architecture. Many disease-associated loci are found in non-coding regions that house regulatory elements. These elements influence the transcription of target genes-many of which demonstrate cell-type-specific expression patterns-and thereby affect phenotypically relevant molecular pathways. Thus, cell-type-specificity must be considered when prioritizing candidate risk loci, variants and target genes. This Review discusses the use of high-throughput assays in human induced pluripotent stem cell-based neurodevelopmental models to probe genetic risk in a cell-type- and patient-specific manner. The application of massively parallel reporter assays in human induced pluripotent stem cells can characterize the human regulome and test the transcriptional responses of putative regulatory elements. Parallel CRISPR-based screens can further functionally dissect this genetic regulatory architecture. The integration of these emerging technologies could decode genetic risk into medically actionable information, thereby improving genetic diagnosis and identifying novel points of therapeutic intervention.
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Affiliation(s)
- Kayla G Townsley
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, 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
| | - Kristen J Brennand
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY, USA.
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