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Liu Z, Sun YH, Ren Y, Perez JM, Scott D, Tamminga C. Upregulated solute-carrier family genes in the hippocampus of schizophrenia can be rescued by antipsychotic medications. Schizophr Res 2024; 272:39-50. [PMID: 39182310 DOI: 10.1016/j.schres.2024.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 07/24/2024] [Accepted: 08/16/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND AND HYPOTHESIS Our previous studies have found that functional changes in the hippocampal circuit from dentate gyrus (DG) to cornu ammonis 3 and 1 (CA3, CA1) are highly associated with schizophrenia (SZ). However, no studies have explored the genetic expression across the three and two human hippocampal subfields (DG-CA3-CA1 and CA3-CA1) between subjects with SZ and healthy controls (CT). STUDY DESIGN We matched cohorts between CT (n = 13) and SZ (n = 13). Among SZ, 6 subjects were on antipsychotics (AP) while 7 were off AP. We combined RNA-seq data from all three and two hippocampal subfields and performed differentially expressed gene analyses across DG-CA3-CA1 and CA3-CA1 affected by either SZ or AP. STUDY RESULTS We found that differentially expressed genes (DEGs) from effects of SZ and AP across DG-CA3-CA1 and CA3-CA1 were highly associated with gene ontology terms related to hormonal and immune signaling, cellular mitosis and apoptosis, ion and amino acid transports, and protein modification and degradation. Additionally, we found that multiple genes related to solute-carrier family and immune signaling were significantly upregulated across DG-CA3-CA1 and CA3-CA1 in patients with SZ relative to CT, and AP consistently and robustly repressed the expression of these upregulated genes in the DG-CA3-CA1 and CA3-CA1 from subjects with SZ. CONCLUSIONS Together, these data suggest that the upregulated solute-carrier family genes in the hippocampus might have important roles in the pathophysiology of SZ, and that AP may reduce the symptoms of psychosis in SZ via rescuing the solute-carrier gene expression.
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Affiliation(s)
- Zhengshan Liu
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
| | - Yu H Sun
- Department of Biology, University of Rochester, Rochester, NY, United States of America
| | - Yue Ren
- Department of Biology, University of Rochester, Rochester, NY, United States of America
| | - Jessica Marie Perez
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Daniel Scott
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Carol Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
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2
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Matoba N, Le BD, Valone JM, Wolter JM, Mory JT, Liang D, Aygün N, Broadaway KA, Bond ML, Mohlke KL, Zylka MJ, Love MI, Stein JL. Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors. Nat Neurosci 2024:10.1038/s41593-024-01773-6. [PMID: 39349663 DOI: 10.1038/s41593-024-01773-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 08/28/2024] [Indexed: 10/09/2024]
Abstract
Gene regulatory effects have been difficult to detect at many non-coding loci associated with brain-related traits, likely because some genetic variants have distinct functions in specific contexts. To explore context-dependent gene regulation, we measured chromatin accessibility and gene expression after activation of the canonical Wnt pathway in primary human neural progenitors (n = 82 donors). We found that TCF/LEF motifs and brain-structure-associated and neuropsychiatric-disorder-associated variants were enriched within Wnt-responsive regulatory elements. Genetically influenced regulatory elements were enriched in genomic regions under positive selection along the human lineage. Wnt pathway stimulation increased detection of genetically influenced regulatory elements/genes by 66%/53% and enabled identification of 397 regulatory elements primed to regulate gene expression. Stimulation also increased identification of shared genetic effects on molecular and complex brain traits by up to 70%, suggesting that genetic variant function during neurodevelopmental patterning can lead to differences in adult brain and behavioral traits.
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Grants
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- 51145R01MH118349, R01MH120125, R01MH12143383 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01MH118349, R01MH120125, R01MH121433 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
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Affiliation(s)
- Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Brandon D Le
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jordan M Valone
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Justin M Wolter
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, Carrboro, NC, USA
| | - Jessica T Mory
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Marielle L Bond
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mark J Zylka
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, Carrboro, NC, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Carolina Institute for Developmental Disabilities, Carrboro, NC, USA.
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3
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Okada N, Oshima K, Maruko A, Sekine M, Ito N, Wakasugi A, Mori E, Odaguchi H, Kobayashi Y. Intron retention as an excellent marker for diagnosing depression and for discovering new potential pathways for drug intervention. Front Psychiatry 2024; 15:1450708. [PMID: 39364384 PMCID: PMC11446786 DOI: 10.3389/fpsyt.2024.1450708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Accepted: 08/20/2024] [Indexed: 10/05/2024] Open
Abstract
Background Peripheral inflammation is often associated with depressive disorders, and immunological biomarkers of depression remain a focus of investigation. Methods We performed RNA-seq analysis of RNA transcripts of human peripheral blood mononuclear cells from a case-control study including subjects with self-reported depression in the pre-symptomatic state of major depressive disorder and analyzed differentially expressed genes (DEGs) and the frequency of intron retention (IR) using rMATS. Results Among the statistically significant DEGs identified, the 651 upregulated DEGs were particularly enriched in the term "bacterial infection and phagocytosis", whereas the 820 downregulated DEGs were enriched in the terms "antigen presentation" and "T-cell proliferation and maturation". We also analyzed 158 genes for which the IR was increased (IncIR) and 211 genes for which the IR was decreased (DecIR) in the depressed subjects. Although the Gene Ontology terms associated with IncIR and DecIR were very similar to those of the up- and downregulated genes, respectively, IR genes appeared to be particularly enriched in genes with sensor functions, with a preponderance of the term "ciliary assembly and function". The observation that IR genes specifically interact with innate immunity genes suggests that immune-related genes, as well as cilia-related genes, may be excellent markers of depression. Re-analysis of previously published RNA-seq data from patients with MDD showed that common IR genes, particularly our predicted immune- and cilia-related genes, are commonly detected in populations with different levels of depression, providing validity for using IR to detect depression. Conclusion Depression was found to be associated with activation of the innate immune response and relative inactivation of T-cell signaling. The DEGs we identified reflect physiological demands that are controlled at the transcriptional level, whereas the IR results reflect a more direct mechanism for monitoring protein homeostasis. Accordingly, an alteration in IR, namely IncIR or DecIR, is a stress response, and intron-retained transcripts are sensors of the physiological state of the cytoplasm. The results demonstrate the potential of relative IR as a biomarker for the immunological stratification of depressed patients and the utility of IR for the discovery of novel pathways involved in recovery from depression.
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Affiliation(s)
- Norihiro Okada
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | - Kenshiro Oshima
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | - Akiko Maruko
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | - Mariko Sekine
- Kitasato University Kitasato Institute Hospital, Minato-ku, Tokyo, Japan
- Oriental Medicine Research Center, School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | - Naoki Ito
- Oriental Medicine Research Center, School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | - Akino Wakasugi
- Kitasato University Kitasato Institute Hospital, Minato-ku, Tokyo, Japan
- Oriental Medicine Research Center, School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | - Eiko Mori
- Oriental Medicine Research Center, School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | - Hiroshi Odaguchi
- Oriental Medicine Research Center, School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | - Yoshinori Kobayashi
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
- Oriental Medicine Research Center, School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
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4
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Sullivan EL, Bogdan R, Bakhireva L, Levitt P, Jones J, Sheldon M, Croff JM, Thomason M, Lo JO, MacIntyre L, Shrivastava S, Cioffredi LA, Edlow AG, Howell BR, Chaiyachati BH, Lashley-Simms N, Molloy K, Lam C, Stoermann AM, Trinh T, Ambalavanan N, Neiderhiser JM. Biospecimens in the HEALthy Brain and Child Development (HBCD) Study: Rationale and protocol. Dev Cogn Neurosci 2024; 70:101451. [PMID: 39326174 PMCID: PMC11460495 DOI: 10.1016/j.dcn.2024.101451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 07/17/2024] [Accepted: 09/13/2024] [Indexed: 09/28/2024] Open
Abstract
The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The longitudinal collection of biological samples from over 7000 birthing parents and their children within the HBCD study enables research on pre- and postnatal exposures (e.g., substance use, toxicants, nutrition), and biological processes (e.g., genetics, epigenetic signatures, proteins, metabolites) on neurobehavioral developmental outcomes. The following biosamples are collected from the birthing parent: 1) blood (i.e., whole blood, serum, plasma, buffy coat, and dried blood spots) during pregnancy, 2) nail clippings during pregnancy and one month postpartum, 3) urine during pregnancy, and 4) saliva during pregnancy and at in-person postnatal assessments. The following samples are collected from the child at in-person study assessments: 1) saliva, 2) stool, and 3) urine. Additionally, placenta tissue, cord blood, and cord tissue are collected by a subset of HBCD sites. Here, we describe the rationale for the collection of these biospecimens, their current and potential future uses, the collection protocol, and collection success rates during piloting. This information will assist research teams in the planning of future studies utilizing this collection of biological samples.
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Affiliation(s)
- Elinor L Sullivan
- Departments of Psychiatry and Behavioral Neuroscience, Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, USA.
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in Saint Louis, Saint Louis, MO, USA.
| | - Ludmila Bakhireva
- Substance Use Research and Education (SURE) Center, College of Pharmacy, University of New Mexico, Albuquerque, NM, USA.
| | - Pat Levitt
- Department of Pediatrics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Joseph Jones
- United States Drug Testing Laboratories, Des Plaines, IL, USA
| | | | - Julie M Croff
- Department of Rural Health, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA
| | - Moriah Thomason
- Department of Child and Adolescent Psychiatry & Department of Population Health, New York University Langone Health, New York City, NY, USA
| | - Jamie O Lo
- Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, OR, USA
| | - Leigh MacIntyre
- McGill University, Montreal, QC, Canada; Lasso Informatics, Montreal, QC, Canada
| | | | - Leigh-Anne Cioffredi
- Dept of Pediatrics, Larner College of Medicine at the University of Vermont, Burlington, VT, USA; Vermont Children's Hospital, Burlington, VT, USA
| | - Andrea G Edlow
- Department of Obstetrics and Gynecology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Brittany R Howell
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, USA
| | - Barbara H Chaiyachati
- Dept of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA; PolicyLab & Clinical Futures, Children's Hospital of Philadelphia, Philadelphia, USA
| | - Nicole Lashley-Simms
- Department of Psychological & Brain Sciences, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Kelly Molloy
- Departments of Psychiatry and Behavioral Neuroscience, Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, USA
| | - Cris Lam
- University of California, San Diego, San Diego, CA, USA
| | | | - Thanh Trinh
- University of California, San Diego, San Diego, CA, USA
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5
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Aygün N, Vuong C, Krupa O, Mory J, Le BD, Valone JM, Liang D, Shafie B, Zhang P, Salinda A, Wen C, Gandal MJ, Love MI, de la Torre-Ubieta L, Stein JL. Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis. Am J Hum Genet 2024; 111:1877-1898. [PMID: 39168119 PMCID: PMC11393701 DOI: 10.1016/j.ajhg.2024.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/18/2024] [Accepted: 07/23/2024] [Indexed: 08/23/2024] Open
Abstract
The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk postmortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA editing and alternative polyadenylation (APA) within a cell-type-specific population of human neural progenitors and neurons. More RNA editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting that genetically mediated post-transcriptional regulation during brain development leads to differences in brain function.
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Affiliation(s)
- Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Celine Vuong
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica Mory
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Brandon D Le
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jordan M Valone
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Beck Shafie
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Pan Zhang
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Angelo Salinda
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Cindy Wen
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael J Gandal
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Luis de la Torre-Ubieta
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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6
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Sullivan PF, Yao S, Hjerling-Leffler J. Schizophrenia genomics: genetic complexity and functional insights. Nat Rev Neurosci 2024; 25:611-624. [PMID: 39030273 DOI: 10.1038/s41583-024-00837-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/04/2024] [Indexed: 07/21/2024]
Abstract
Determining the causes of schizophrenia has been a notoriously intractable problem, resistant to a multitude of investigative approaches over centuries. In recent decades, genomic studies have delivered hundreds of robust findings that implicate nearly 300 common genetic variants (via genome-wide association studies) and more than 20 rare variants (via whole-exome sequencing and copy number variant studies) as risk factors for schizophrenia. In parallel, functional genomic and neurobiological studies have provided exceptionally detailed information about the cellular composition of the brain and its interconnections in neurotypical individuals and, increasingly, in those with schizophrenia. Taken together, these results suggest unexpected complexity in the mechanisms that drive schizophrenia, pointing to the involvement of ensembles of genes (polygenicity) rather than single-gene causation. In this Review, we describe what we now know about the genetics of schizophrenia and consider the neurobiological implications of this information.
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Affiliation(s)
- Patrick F Sullivan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jens Hjerling-Leffler
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
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7
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Repetto L, Chen J, Yang Z, Zhai R, Timmers PRHJ, Feng X, Li T, Yao Y, Maslov D, Timoshchuk A, Tu F, Twait EL, May-Wilson S, Muckian MD, Prins BP, Png G, Kooperberg C, Johansson Å, Hillary RF, Wheeler E, Pan L, He Y, Klasson S, Ahmad S, Peters JE, Gilly A, Karaleftheri M, Tsafantakis E, Haessler J, Gyllensten U, Harris SE, Wareham NJ, Göteson A, Lagging C, Ikram MA, van Duijn CM, Jern C, Landén M, Langenberg C, Deary IJ, Marioni RE, Enroth S, Reiner AP, Dedoussis G, Zeggini E, Sharapov S, Aulchenko YS, Butterworth AS, Mälarstig A, Wilson JF, Navarro P, Shen X. The genetic landscape of neuro-related proteins in human plasma. Nat Hum Behav 2024:10.1038/s41562-024-01963-z. [PMID: 39210026 DOI: 10.1038/s41562-024-01963-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/22/2024] [Indexed: 09/04/2024]
Abstract
Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets.
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Affiliation(s)
- Linda Repetto
- Biostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Health Data Science Centre, Fondazione Human Technopole, Milan, Italy
| | - Jiantao Chen
- Biostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Zhijian Yang
- Biostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ranran Zhai
- Biostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Paul R H J Timmers
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Xiao Feng
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Ting Li
- Biostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Yue Yao
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Denis Maslov
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
| | - Anna Timoshchuk
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
| | - Fengyu Tu
- Biostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Emma L Twait
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, Netherlands
| | - Sebastian May-Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Marisa D Muckian
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Bram P Prins
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Grace Png
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM), TUM School of Medicine and Health, Munich, Germany
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Lu Pan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yazhou He
- Department of Epidemiology and Medical Statistics, Division of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Sofia Klasson
- Institute of Biomedicine, Department of Laboratory Medicine, the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - James E Peters
- Department of Immunology and Inflammation, Faculty of Medicine, Imperial College London, London, UK
| | - Arthur Gilly
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | | | | | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ulf Gyllensten
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Sarah E Harris
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Andreas Göteson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Cecilia Lagging
- Institute of Biomedicine, Department of Laboratory Medicine, the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | | | | | - Christina Jern
- Institute of Biomedicine, Department of Laboratory Medicine, the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mikael Landén
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Stefan Enroth
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine and Health, Munich, Germany
| | - Sodbo Sharapov
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
- Biostatistics Unit-Population and Medical Genomics Programme, Genomics Research Centre, Fondazione Human Technopole, Milan, Italy
| | - Yurii S Aulchenko
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - Adam S Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Anders Mälarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Emerging Science and Innovation, Pfizer Worldwide Research, Development and Medical, Cambridge, UK
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Pau Navarro
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Xia Shen
- Biostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China.
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK.
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
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8
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Garcia MF, Retallick-Townsley K, Pruitt A, Davidson E, Dai Y, Fitzpatrick SE, Sen A, Cohen S, Livoti O, Khan S, Dossou G, Cheung J, Deans PJM, Wang Z, Huckins L, Hoffman E, Brennand K. Dynamic convergence of autism disorder risk genes across neurodevelopment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.23.609190. [PMID: 39229156 PMCID: PMC11370590 DOI: 10.1101/2024.08.23.609190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Over a hundred risk genes underlie risk for autism spectrum disorder (ASD) but the extent to which they converge on shared downstream targets to increase ASD risk is unknown. To test the hypothesis that cellular context impacts the nature of convergence, here we apply a pooled CRISPR approach to target 29 ASD loss-of-function genes in human induced pluripotent stem cell (hiPSC)-derived neural progenitor cells, glutamatergic neurons, and GABAergic neurons. Two distinct approaches (gene-level and network-level analyses) demonstrate that convergence is greatest in mature glutamatergic neurons. Convergent effects are dynamic, varying in strength, composition, and biological role between cell types, increasing with functional similarity of the ASD genes examined, and driven by cell-type-specific gene co-expression patterns. Stratification of ASD genes yield targeted drug predictions capable of reversing gene-specific convergent signatures in human cells and ASD-related behaviors in zebrafish. Altogether, convergent networks downstream of ASD risk genes represent novel points of individualized therapeutic intervention.
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Affiliation(s)
- Meilin Fernandez Garcia
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Kayla Retallick-Townsley
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - April Pruitt
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06511
| | - Elizabeth Davidson
- Child Study Center, Yale University School of Medicine, New Haven, CT 06511
| | - Yi Dai
- Child Study Center, Yale University School of Medicine, New Haven, CT 06511
| | - Sarah E Fitzpatrick
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06511
| | - Annabel Sen
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Sophie Cohen
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Olivia Livoti
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Suha Khan
- Child Study Center, Yale University School of Medicine, New Haven, CT 06511
| | - Grace Dossou
- Child Study Center, Yale University School of Medicine, New Haven, CT 06511
| | - Jen Cheung
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - P J Michael Deans
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Zuoheng Wang
- Child Study Center, Yale University School of Medicine, New Haven, CT 06511
| | - Laura Huckins
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06511
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Ellen Hoffman
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06511
- Child Study Center, Yale University School of Medicine, New Haven, CT 06511
| | - Kristen Brennand
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06511
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
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9
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Xu L, Li L, Wang Q, Pan B, Zheng L, Lin Z. Effect of pharmacogenomic testing on the clinical treatment of patients with depressive disorder: A randomized clinical trial. J Affect Disord 2024; 359:117-124. [PMID: 38762035 DOI: 10.1016/j.jad.2024.05.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 05/05/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Pharmacotherapy is one of the primary treatment modalities for depression. However, there is considerable variability in the individual response to antidepressant medications. Personalized medicine guided by pharmacogenomic testing may hold promise in addressing this issue. METHODS In this study, 665 depressive patients were randomly enrolled into two groups: the pharmacogenomic testing group (n = 333) and the control group (n = 332). In the testing group, participants underwent pharmacogenomic testing, and clinicians customized the treatment plan with the result, while the control group relied solely on clinicians' experience. The primary outcomes were the proportion of remission and response, assessed with Hamilton Depression Rating Scale (HDRS). The secondary outcomes included changes in HDRS scores over time and frequency of adverse drug reactions by the participants. RESULTS At week 8, the pharmacogenomic testing group showed significantly higher remission rates (24.0 % v.s. 15.1 %; RR = 1.117; P = 0.007) and response rates (39.3 % v.s. 25.7 %; RR = 1.225; P < 0.001) compared to the control group. By week 12, the pharmacogenomic testing group continued to demonstrate significant advantages in remission (31.0 % v.s. 20.0 %; RR = 1.159; P = 0.003) and response (48.7 % v.s. 37.3 %; RR = 1.224; P = 0.006). Additionally, adverse drug reactions were less frequent in the pharmacogenomic testing group. LIMITATIONS This study is not blind to clinicians and it's a single-center study. CONCLUSIONS Pharmacogenomic testing-guided drug therapy can provide greater assistance in the treatment of depression.
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Affiliation(s)
- Lei Xu
- Department of Geriatric Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Liyin Li
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qiutang Wang
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Bing Pan
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Leilei Zheng
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
| | - Zheng Lin
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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10
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Chaney S, Marks S, Wynter R. 'Almost nothing is firmly established': A History of Heredity and Genetics in Mental Health Science. Wellcome Open Res 2024; 9:208. [PMID: 39221444 PMCID: PMC11362721 DOI: 10.12688/wellcomeopenres.20628.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/06/2024] [Indexed: 09/04/2024] Open
Abstract
Background For more than a century, scientists have tried to find the key to causation of mental ill health in heredity and genetics. The difficulty of finding clear and actionable answers in our genes has not stopped them looking. This history offers important context to understanding mental health science today. Methods This article explores the main themes in research on genetics and inheritance in psychiatry from the second half of the nineteenth century to the present day, to address the question: what is the history of genetics as a causative explanation in mental health science? We take a critical historical approach to the literature, interrogating primary and secondary material for the light it brings to the research question, while considering the social and historical context. Results We begin with the statistics gathered in asylums and used to 'prove' the importance of heredity in mental ill health. We then move through early twentieth century Mendelian models of mental inheritance, the eugenics movement, the influence of social psychiatry, new classifications and techniques of the postwar era, the Human Genome Project and Genome Wide Association Studies (GWAS) and epigenetics. Setting these themes in historical context shows that this research was often popular because of wider social, political and cultural issues, which impacted the views of scientists just as they did those of policymakers, journalists and the general public. Conclusions We argue that attempting to unpick this complex history is essential to the modern ethics of mental health and genetics, as well as helping to focus our efforts to better understand causation in mental ill-health.For a succinct timeline of the history of psychiatric genetics, alongside the history of other proposed causes for mental ill-health, visit: https://historyofcauses.co.uk/.
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Affiliation(s)
- Sarah Chaney
- Centre for the History of the Emotions, Queen Mary University of London, London, England, UK
| | - Sarah Marks
- School of Historical Studies, Birkbeck University of London, London, England, UK
| | - Rebecca Wynter
- School of Historical Studies, Universiteit van Amsterdam, Amsterdam, North Holland, The Netherlands
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11
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Ormond C, Ryan NM, Hedman AM, Cannon TD, Sullivan PF, Gill M, Hultman C, Heron EA, Johansson V, Corvin A. Whole genome sequencing study of identical twins discordant for psychosis. Transl Psychiatry 2024; 14:313. [PMID: 39080272 PMCID: PMC11289105 DOI: 10.1038/s41398-024-02982-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 08/02/2024] Open
Abstract
Monozygotic (MZ) twins are often thought to have identical genomes, but recent work has shown that early post-zygotic events can result in a spectrum of DNA variants that are different between MZ twins. Such variants may explain phenotypic discordance and contribute to disease etiology. Here we performed whole genome sequencing in 17 pairs of MZ twins discordant for a psychotic disorder (schizophrenia, schizoaffective disorder or bipolar disorder). We examined various classes of rare variants that are discordant within a twin pair. We identified four genes harboring rare, predicted deleterious missense variants that were private to an affected individual in the cohort. Variants in FOXN1 and FLOT2 would have been categorized as damaging from recent schizophrenia and bipolar exome sequencing studies. Additionally, we identified four rare genic copy number variants (CNVs) private to an affected sample, two of which overlapped genes that have shown evidence for association with schizophrenia or bipolar disorder. One such CNV was a 3q29 duplication previously implicated in autism and developmental delay. We have performed the largest MZ twin study for discordant psychotic phenotypes to date. These findings warrant further investigation using other analytical approaches.
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Affiliation(s)
- Cathal Ormond
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Niamh M Ryan
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Anna M Hedman
- Department of Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tyrone D Cannon
- Departments of Psychology and Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Patrick F Sullivan
- Department of Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Christina Hultman
- Department of Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Elizabeth A Heron
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Viktoria Johansson
- Department of Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Sciences, Psychiatry Unit, Umeå University, Umeå, Sweden
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.
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12
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Hui J, Zhang N, Kang M, Gou Y, Liu C, Zhou R, Liu Y, Wang B, Shi P, Cheng S, Yang X, Pan C, Zhang F. Micronutrient-Associated Single Nucleotide Polymorphism and Mental Health: A Mendelian Randomization Study. Nutrients 2024; 16:2042. [PMID: 38999789 PMCID: PMC11243241 DOI: 10.3390/nu16132042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 06/21/2024] [Accepted: 06/25/2024] [Indexed: 07/14/2024] Open
Abstract
PURPOSE Previous studies have demonstrated the link between micronutrients and mental health. However, it remains uncertain whether this connection is causal. We aim to investigate the potential causal effects of micronutrients on mental health based on linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analysis. METHODS Utilizing publicly available genome-wide association study (GWAS) summary datasets, we performed LDSC and MR analysis to identify candidate micronutrients with potential causal effects on mental health. Single nucleotide polymorphisms (SNPs) significantly linked with candidate micronutrients with a genome-wide significance level (p < 5 × 10-8) were selected as instrumental variables (IVs). To estimate the causal effect of candidate micronutrients on mental health, we employed inverse variance weighted (IVW) regression. Additionally, two sensitivity analyses, MR-Egger and weighted median, were performed to validate our results. RESULTS We found evidence supporting significant causal associations between micronutrients and mental health. LDSC detected several candidate micronutrients, including serum iron (genetic correlation = -0.134, p = 0.032) and vitamin C (genetic correlation = -0.335, p < 0.001) for attention-deficit/hyperactivity disorder (ADHD), iron-binding capacity (genetic correlation = 0.210, p = 0.037) for Alzheimer's disease (AD), and vitamin B12 (genetic correlation = -0.178, p = 0.044) for major depressive disorder (MDD). Further MR analysis suggested a potential causal relationship between vitamin B12 and MDD (b = -0.139, p = 0.009). There was no significant heterogeneity or pleiotropy, indicating the validity of the findings. CONCLUSION In this study, we identified underlying causal relationships between micronutrients and mental health. Notably, more research is necessary to clarify the underlying biological mechanisms by which micronutrients affect mental health.
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Affiliation(s)
- Jingni Hui
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Meijuan Kang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Yifan Gou
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Chen Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Ruixue Zhou
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Ye Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Bingyi Wang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Panxing Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
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13
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Gurguis CI, Kimm TS, Pigott TA. Perspective: the evolution of hormones and person perception-a quantitative genetic framework. Front Psychol 2024; 15:1395974. [PMID: 38952835 PMCID: PMC11215136 DOI: 10.3389/fpsyg.2024.1395974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 06/03/2024] [Indexed: 07/03/2024] Open
Abstract
Evolutionary biology provides a unifying theory for testing hypotheses about the relationship between hormones and person perception. Person perception usually receives attention from the perspective of sexual selection. However, because person perception is one trait in a suite regulated by hormones, univariate approaches are insufficient. In this Perspectives article, quantitative genetics is presented as an important but underutilized framework for testing evolutionary hypotheses within this literature. We note tacit assumptions within the current literature on psychiatric genetics, which imperil the interpretation of findings thus far. As regulators of a diverse manifold of traits, hormones mediate tradeoffs among an array of functions. Hormonal pleiotropy also provides the basis of correlational selection, a process whereby selection on one trait in a hormone-mediated suite generates selection on the others. This architecture provides the basis for conflicts between sexual and natural selection within hormone-mediated suites. Due to its role in person perception, psychiatric disorders, and reproductive physiology, the sex hormone estrogen is highlighted as an exemplar here. The implications of this framework for the evolution of person perception are discussed. Empirical quantification of selection on traits within hormone-mediated suites remains an important gap in this literature with great potential to illuminate the fundamental nature of psychiatric disorders.
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Affiliation(s)
- Christopher I. Gurguis
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School at UTHealth, Houston, TX, United States
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14
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Frei O, Hindley G, Shadrin AA, van der Meer D, Akdeniz BC, Hagen E, Cheng W, O'Connell KS, Bahrami S, Parker N, Smeland OB, Holland D, de Leeuw C, Posthuma D, Andreassen OA, Dale AM. Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets. Nat Genet 2024; 56:1310-1318. [PMID: 38831010 DOI: 10.1038/s41588-024-01771-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/24/2024] [Indexed: 06/05/2024]
Abstract
While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.
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Affiliation(s)
- Oleksandr Frei
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway.
| | - Guy Hindley
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Bayram C Akdeniz
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Espen Hagen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Weiqiu Cheng
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kevin S O'Connell
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nadine Parker
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dominic Holland
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Christiaan de Leeuw
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands
| | - Ole A Andreassen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
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15
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Sawada T, Barbosa AR, Araujo B, McCord AE, D’Ignazio L, Benjamin KJM, Sheehan B, Zabolocki M, Feltrin A, Arora R, Brandtjen AC, Kleinman JE, Hyde TM, Bardy C, Weinberger DR, Paquola ACM, Erwin JA. Recapitulation of Perturbed Striatal Gene Expression Dynamics of Donors' Brains With Ventral Forebrain Organoids Derived From the Same Individuals With Schizophrenia. Am J Psychiatry 2024; 181:493-511. [PMID: 37915216 PMCID: PMC11209846 DOI: 10.1176/appi.ajp.20220723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
OBJECTIVE Schizophrenia is a brain disorder that originates during neurodevelopment and has complex genetic and environmental etiologies. Despite decades of clinical evidence of altered striatal function in affected patients, studies examining its cellular and molecular mechanisms in humans are limited. To explore neurodevelopmental alterations in the striatum associated with schizophrenia, the authors established a method for the differentiation of induced pluripotent stem cells (iPSCs) into ventral forebrain organoids (VFOs). METHODS VFOs were generated from postmortem dural fibroblast-derived iPSCs of four individuals with schizophrenia and four neurotypical control individuals for whom postmortem caudate genotypes and transcriptomic data were profiled in the BrainSeq neurogenomics consortium. Individuals were selected such that the two groups had nonoverlapping schizophrenia polygenic risk scores (PRSs). RESULTS Single-cell RNA sequencing analyses of VFOs revealed differences in developmental trajectory between schizophrenia and control individuals in which inhibitory neuronal cells from the patients exhibited accelerated maturation. Furthermore, upregulated genes in inhibitory neurons in schizophrenia VFOs showed a significant overlap with upregulated genes in postmortem caudate tissue of individuals with schizophrenia compared with control individuals, including the donors of the iPSC cohort. CONCLUSIONS The findings suggest that striatal neurons derived from high-PRS individuals with schizophrenia carry abnormalities that originated during early brain development and that the VFO model can recapitulate disease-relevant cell type-specific neurodevelopmental phenotypes in a dish.
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Affiliation(s)
- Tomoyo Sawada
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Bruno Araujo
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Laura D’Ignazio
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kynon J. M. Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Bonna Sheehan
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Michael Zabolocki
- South Australian Health and Medical Research Institute (SAHMRI), Laboratory for Human Neurophysiology and Genetics, Adelaide, SA, Australia
- Flinders University, Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Adelaide, SA, Australia
| | - Arthur Feltrin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Ria Arora
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Joel E. Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Cedric Bardy
- South Australian Health and Medical Research Institute (SAHMRI), Laboratory for Human Neurophysiology and Genetics, Adelaide, SA, Australia
- Flinders University, Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Adelaide, SA, Australia
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Apuā C. M. Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jennifer A. Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
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16
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Deng YT, Wu BS, Yang L, He XY, Kang JJ, Liu WS, Li ZY, Wu XR, Zhang YR, Chen SD, Ge YJ, Huang YY, Feng JF, Zhu Y, Dong Q, Mao Y, Cheng W, Yu JT. Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults. Nat Hum Behav 2024; 8:1194-1208. [PMID: 38589703 DOI: 10.1038/s41562-024-01861-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 03/11/2024] [Indexed: 04/10/2024]
Abstract
While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene-phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.
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Affiliation(s)
- Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ze-Yu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Xin-Rui Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Ying Zhu
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital Fudan University, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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17
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Bhandari K, Kanodia H, Donato F, Caroni P. Selective vulnerability of the ventral hippocampus-prelimbic cortex axis parvalbumin interneuron network underlies learning deficits of fragile X mice. Cell Rep 2024; 43:114124. [PMID: 38630591 DOI: 10.1016/j.celrep.2024.114124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 03/07/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
High-penetrance mutations affecting mental health can involve genes ubiquitously expressed in the brain. Whether the specific patterns of dysfunctions result from ubiquitous circuit deficits or might reflect selective vulnerabilities of targetable subnetworks has remained unclear. Here, we determine how loss of ubiquitously expressed fragile X mental retardation protein (FMRP), the cause of fragile X syndrome, affects brain networks in Fmr1y/- mice. We find that in wild-type mice, area-specific knockout of FMRP in the adult mimics behavioral consequences of area-specific silencing. By contrast, the functional axis linking the ventral hippocampus (vH) to the prelimbic cortex (PreL) is selectively affected in constitutive Fmr1y/- mice. A chronic alteration in late-born parvalbumin interneuron networks across the vH-PreL axis rescued by VIP signaling specifically accounts for deficits in vH-PreL theta-band network coherence, ensemble assembly, and learning functions of Fmr1y/- mice. Therefore, vH-PreL axis function exhibits a selective vulnerability to loss of FMRP in the vH or PreL, leading to learning and memory dysfunctions in fragile X mice.
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Affiliation(s)
- Komal Bhandari
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
| | - Harsh Kanodia
- Biozentrum, University of Basel, 4058 Basel, Switzerland
| | - Flavio Donato
- Biozentrum, University of Basel, 4058 Basel, Switzerland
| | - Pico Caroni
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland.
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18
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Pérez-Gutiérrez AM, Carmona R, Loucera C, Cervilla JA, Gutiérrez B, Molina E, Lopez-Lopez D, Pérez-Florido J, Zarza-Rebollo JA, López-Isac E, Dopazo J, Martínez-González LJ, Rivera M. Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach. Mol Psychiatry 2024:10.1038/s41380-024-02609-2. [PMID: 38806690 DOI: 10.1038/s41380-024-02609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 05/03/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024]
Abstract
Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.
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Affiliation(s)
- Ana M Pérez-Gutiérrez
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
| | - Rosario Carmona
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
| | - Carlos Loucera
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Jorge A Cervilla
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
- Department of Psychiatry, Faculty of Medicine, University of Granada, Granada, Spain
| | - Blanca Gutiérrez
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
- Department of Psychiatry, Faculty of Medicine, University of Granada, Granada, Spain
| | - Esther Molina
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
- Department of Nursing, Faculty of Health Sciences, University of Granada, Granada, Spain
| | - Daniel Lopez-Lopez
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Javier Pérez-Florido
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
| | - Juan Antonio Zarza-Rebollo
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
| | - Elena López-Isac
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
| | - Joaquín Dopazo
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
| | - Luis Javier Martínez-González
- Genomics Unit, Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research (GENYO), Granada, Spain
| | - Margarita Rivera
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain.
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain.
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain.
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19
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Deng C, Whalen S, Steyert M, Ziffra R, Przytycki PF, Inoue F, Pereira DA, Capauto D, Norton S, Vaccarino FM, Pollen AA, Nowakowski TJ, Ahituv N, Pollard KS. Massively parallel characterization of regulatory elements in the developing human cortex. Science 2024; 384:eadh0559. [PMID: 38781390 DOI: 10.1126/science.adh0559] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/13/2024] [Indexed: 05/25/2024]
Abstract
Nucleotide changes in gene regulatory elements are important determinants of neuronal development and diseases. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 open chromatin regions, including thousands of sequences with cell type-specific accessibility and variants associated with brain gene regulation. In primary cells, we identified 46,802 active enhancer sequences and 164 variants that alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development.
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Affiliation(s)
- Chengyu Deng
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sean Whalen
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Marilyn Steyert
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
- Chan Zuckerberg Biohub, San Francisco, San Francisco, CA 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA
| | - Ryan Ziffra
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA 94143, USA
| | | | - Fumitaka Inoue
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan
| | - Daniela A Pereira
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
- Graduate Program of Genetics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Davide Capauto
- Child Study Center, Yale University, New Haven, CT 06520, USA
| | - Scott Norton
- Child Study Center, Yale University, New Haven, CT 06520, USA
| | - Flora M Vaccarino
- Child Study Center, Yale University, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University, New Haven, CT 06520, USA
| | - Alex A Pollen
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA
- Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Tomasz J Nowakowski
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA
- Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Katherine S Pollard
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, San Francisco, San Francisco, CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
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20
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. Science 2024; 384:eadi5199. [PMID: 38781369 PMCID: PMC11365579 DOI: 10.1126/science.adi5199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Chicago, IL 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - 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
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, 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 Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597 Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA 92697, 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
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, 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
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, USA
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21
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Kim A, Zhang Z, Legros C, Lu Z, de Smith A, Moore JE, Mancuso N, Gazal S. Inferring causal cell types of human diseases and risk variants from candidate regulatory elements. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307556. [PMID: 38798383 PMCID: PMC11118635 DOI: 10.1101/2024.05.17.24307556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.
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Affiliation(s)
- Artem Kim
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zixuan Zhang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Come Legros
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Adam de Smith
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jill E Moore
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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22
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Garofano JS, Borden L, Van Eck K, Ostrander R, Parrish C, Grados M, Chiappini EA, Reynolds EK. Subtypes of Depressed Youth Admitted for Inpatient Psychiatric Care: A Latent Profile Analysis. Res Child Adolesc Psychopathol 2024; 52:713-725. [PMID: 38109023 DOI: 10.1007/s10802-023-01157-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/20/2023] [Indexed: 12/19/2023]
Abstract
Depressed youth frequently present with comorbid symptoms. Comorbidity is related to a poorer prognosis, including treatment resistance, academic problems, risk of suicide, and overall impairment. Studies examining the latent structure of depression support the notion of multiple presentations of depressed youth; however, it is unclear how these presentations are represented among acutely impaired youth. Participants (n = 457) in this naturalistic study were admitted to a psychiatric inpatient unit (Mean age = 14.33 years, SD = 1.94;76% female;46.6% Black/African-American). Selected subscales from the parent-report Behavior Assessment System for Children, Second Edition, were utilized as indicators in a latent profile analysis. Subgroups were validated based on their relationships with meaningful clinical correlates (e.g., family factors, discharge diagnosis) and further described by their associations with demographic variables. A five-class model provided the best balance of fit and parsimony. Subtypes of depressed youth included Predominantly Depressed (39.1%), Oppositional (28.2%), Severely Disruptive (12.3%), Anxious-Oppositional (11.6%), and Anxious-Withdrawn (8.8%). Comorbid symptoms were present in four of the five classes (60.9% of sample). High levels of externalizing symptoms were a prominent clinical feature associated with three classes (52.1% of the sample). Construct validity of the respective classes was demonstrated by differential association with clinical correlates, family characteristics, and demographics. Findings suggest that depressed youth presenting for acute inpatient psychiatric care displayed varied clinical presentations. The identified latent groups aligned with existing research reflecting comorbidity with anxiety, inattention, and externalizing disorders. Findings underscore the need for an increased clinical appreciation of comorbidity and encourage more targeted and effective prevention and treatment strategies.
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Affiliation(s)
- Jeffrey S Garofano
- Division of Child & Adolescent Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Lindsay Borden
- Division of Child & Adolescent Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kathryn Van Eck
- Division of Child & Adolescent Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kennedy Krieger Institute, Baltimore, MD, USA
| | - Rick Ostrander
- Division of Child & Adolescent Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Carisa Parrish
- Division of Child & Adolescent Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Children's Mercy/ University of Missouri, Kansas City, MO, USA
| | - Marco Grados
- Division of Child & Adolescent Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Erika A Chiappini
- Division of Child & Adolescent Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elizabeth K Reynolds
- Division of Child & Adolescent Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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23
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Asgel Z, Kouakou MR, Koller D, Pathak GA, Cabrera-Mendoza B, Polimanti R. Unraveling COVID-19 relationship with anxiety disorders and symptoms using genome-wide data. J Affect Disord 2024; 352:333-341. [PMID: 38382819 PMCID: PMC10939738 DOI: 10.1016/j.jad.2024.02.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 02/08/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND There is still a limited understanding of the dynamics contributing to the comorbidity of COVID-19 and anxiety outcomes. METHODS To dissect the pleiotropic mechanisms contributing to COVID-19/anxiety comorbidity, we used genome-wide data from UK Biobank (up to 420,531 participants), FinnGen Project (up to 329,077 participants), Million Veteran Program (175,163 participants), and COVID-19 Host Genetics Initiative (up to 122,616 cases and 2,475,240 controls). Specifically, we assessed global and local genetic correlation and genetically inferred effects linking COVID-19 outcomes (infection, hospitalization, and severe respiratory symptoms) to anxiety disorders and symptoms. RESULTS We observed a strong genetic correlation of anxiety disorder with COVID-19 positive status (rg = 0.35, p = 2×10-4) and COVID-19 hospitalization (rg = 0.31, p = 7.2×10-4). Among anxiety symptoms, "Tense, sore, or aching muscles during worst period of anxiety" was genetically correlated with COVID-19 positive status (rg = 0.33, p = 0.001), while "Frequent trouble falling or staying asleep during worst period of anxiety" was genetically correlated with COVID-19 hospitalization (rg = 0.24, p = 0.004). Through a latent causal variable analysis, we observed that COVID-19 outcomes have statistically significant genetic causality proportion (gcp) on anxiety symptoms (e.g., COVID-19 positive status→"Recent easy annoyance or irritability" │gcp│ = 0.18, p = 6.72×10-17). Conversely, anxiety disorders appear to have a possible causal effect on COVID-19 (│gcp│ = 0.38, p = 3.17×10-9). Additionally, we also identified multiple loci with evidence of local genetic correlation between anxiety and COVID-19. These appear to be related to genetic effects shared with lung function, brain morphology, alcohol and tobacco use, and hematologic parameters. CONCLUSIONS This study provided insights into the pleiotropic mechanisms linking COVID-19 and anxiety outcomes, suggesting differences between dynamics related to anxiety disorders and those related to anxiety symptoms.
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Affiliation(s)
- Zeynep Asgel
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Manuela R Kouakou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Dora Koller
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Genetics, Microbiology, and Statistics, Faculty of Biology, University of Barcelona, Catalonia, Spain
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | - Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA.
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24
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Veeneman RR, Vermeulen JM, Bialas M, Bhamidipati AK, Abdellaoui A, Munafò MR, Denys D, Bezzina CR, Verweij KJH, Tadros R, Treur JL. Mental illness and cardiovascular health: observational and polygenic score analyses in a population-based cohort study. Psychol Med 2024; 54:931-939. [PMID: 37706306 DOI: 10.1017/s0033291723002635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
BACKGROUND Individuals with serious mental illness have a markedly shorter life expectancy. A major contributor to premature death is cardiovascular disease (CVD). We investigated associations of (genetic liability for) depressive disorder, bipolar disorder and schizophrenia with a range of CVD traits and examined to what degree these were driven by important confounders. METHODS We included participants of the Dutch Lifelines cohort (N = 147 337) with information on self-reported lifetime diagnosis of depressive disorder, bipolar disorder, or schizophrenia and CVD traits. Employing linear mixed-effects models, we examined associations between mental illness diagnoses and CVD, correcting for psychotropic medication, demographic and lifestyle factors. In a subsample (N = 73 965), we repeated these analyses using polygenic scores (PGSs) for the three mental illnesses. RESULTS There was strong evidence that depressive disorder diagnosis is associated with increased arrhythmia and atherosclerosis risk and lower heart rate variability, even after confounder adjustment. Positive associations were also found for the depression PGSs with arrhythmia and atherosclerosis. Bipolar disorder was associated with a higher risk of nearly all CVD traits, though most diminished after adjustment. The bipolar disorder PGSs did not show any associations. While the schizophrenia PGSs was associated with increased arrhythmia risk and lower heart rate variability, schizophrenia diagnosis was not. All mental illness diagnoses were associated with lower blood pressure and a lower risk of hypertension. CONCLUSIONS Our study shows widespread associations of (genetic liability to) mental illness (primarily depressive disorder) with CVD, even after confounder adjustment. Future research should focus on clarifying potential causal pathways between mental illness and CVD.
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Affiliation(s)
- R R Veeneman
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands
| | - J M Vermeulen
- Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands
| | - M Bialas
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands
| | - A K Bhamidipati
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands
| | - A Abdellaoui
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands
| | - M R Munafò
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
| | - D Denys
- Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands
| | - C R Bezzina
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - K J H Verweij
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands
| | - R Tadros
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - J L Treur
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands
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25
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Huckins LM, Brennand K, Bulik CM. Dissecting the biology of feeding and eating disorders. Trends Mol Med 2024; 30:380-391. [PMID: 38431502 DOI: 10.1016/j.molmed.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/28/2024] [Accepted: 01/31/2024] [Indexed: 03/05/2024]
Abstract
Feeding and eating disorders (FEDs) are heterogenous and characterized by varying patterns of dysregulated eating and weight. Genome-wide association studies (GWASs) are clarifying their underlying biology and their genetic relationship to other psychiatric and metabolic/anthropometric traits. Genetic research on anorexia nervosa (AN) has identified eight significant loci and uncovered genetic correlations implicating both psychiatric and metabolic/anthropometric risk factors. Careful explication of these metabolic contributors may be key to developing effective and enduring treatments for devastating, life-altering, and frequently lethal illnesses. We discuss clinical phenomenology, genomics, phenomics, intestinal microbiota, and functional genomics and propose a path that translates variants to genes, genes to pathways, and pathways to metabolic outcomes to advance the science and eventually treatment of FEDs.
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Affiliation(s)
- Laura M Huckins
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Kristen Brennand
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA; Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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26
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Garro-Núñez D, Picado-Martínez MJ, Espinoza-Campos E, Ugalde-Araya D, Macaya G, Raventós H, Chavarría-Soley G. Systematic exploration of a decade of publications on psychiatric genetics in Latin America. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32960. [PMID: 37860990 DOI: 10.1002/ajmg.b.32960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 08/08/2023] [Accepted: 09/29/2023] [Indexed: 10/21/2023]
Abstract
Psychiatric disorders have a great impact in terms of mortality, morbidity, and disability across the lifespan. Considerable effort has been devoted to understanding their complex and heterogeneous genetic architecture, including diverse ancestry populations. Our aim was to review the psychiatric genetics research published with Latin American populations from 2010 to 2019, and classify it according to country of origin, type of analysis, source of funding, and other variables. We found that most publications came from Brazil, Mexico, and Colombia. Also, local funds are generally not large enough for genome-wide studies in Latin America, with the exception of Brazil and Mexico; larger studies are often done in collaboration with international partners, mostly funded by US agencies. In most of the larger studies, the participants are individuals of Latin American ancestry living in the United States, which limits the potential for exploring the complex gene-environment interaction. Family studies, traditionally strong in Latin America, represent about 30% of the total research publications. Scarce local resources for research in Latin America have probably been an important limitation for conducting bigger and more complex studies, contributing to the reduced representation of these populations in global psychiatric genetics studies. Increasing diversity must be a goal to improve generalizability and applicability in clinical settings.
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Affiliation(s)
| | | | | | - Daniela Ugalde-Araya
- Center for Research in Cellular and Molecular Biology, Universidad de Costa Rica, San José, Costa Rica
| | - Gabriel Macaya
- Center for Research in Cellular and Molecular Biology, Universidad de Costa Rica, San José, Costa Rica
| | - Henriette Raventós
- Biology School, Universidad de Costa Rica, San José, Costa Rica
- Center for Research in Cellular and Molecular Biology, Universidad de Costa Rica, San José, Costa Rica
| | - Gabriela Chavarría-Soley
- Biology School, Universidad de Costa Rica, San José, Costa Rica
- Center for Research in Cellular and Molecular Biology, Universidad de Costa Rica, San José, Costa Rica
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27
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585576. [PMID: 38562822 PMCID: PMC10983939 DOI: 10.1101/2024.03.18.585576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA, 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Department of Opthalmology, Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Inc., Chicago, IL, 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA, 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - 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
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Michael Margolis
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Manman Shi
- Tempus Labs, Inc., Chicago, IL, 60654, USA
| | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, 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 Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, 92697, 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
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, 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
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT, 06520, USA
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28
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Ormel J, Vos M, Laceulle OM, Vrijen C, van der Laan CM, Nolte IM, Hartman CA. Distal-to-proximal etiologically relevant variables associated with the general (p) and specific factors of psychopathology. J Child Psychol Psychiatry 2024. [PMID: 38503697 DOI: 10.1111/jcpp.13979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/24/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND The general factor of psychopathology, often denoted as p, captures the common variance among a broad range of psychiatric symptoms. Specific factors are co-modeled based on subsets of closely related symptoms. This paper investigated the extent to which wide-ranging genetic, personal, and environmental etiologically relevant variables are associated with p and specific psychopathology factors. METHODS Using data from four waves (ages 11-19) of TRAILS, we modeled a bifactor model of p and four specific factors [internalizing, externalizing, ADHD, Autism Spectrum Disorder (ASD)]. Next, we examined the associations of 19 etiologically relevant variables with these psychology factors using path models that organized the variables according to the distal-to-proximal risk principle. RESULTS Collectively, the etiologically relevant factors, including temperament traits, accounted for 55% of p's variance, 46% in ADHD, 35% in externalizing, 19% in internalizing, and 7% in ASD. The low 7% is due to insufficient unique variance in ASD indicators that load more strongly on p. Excluding temperament, variables accounted for 29% variance in p, 9% ADHD, 14% EXT, 7% INT, and 4% ASD. Most etiologically relevant factors were generic, predicting p. In addition, we identified effects on specific factors in addition to effects on p (e.g., parental SES, executive functioning); only effects on specific factors (e.g., parental rejection); opposite effects on different factors [e.g., diurnal cortisol (high INT but low EXT, p); developmental delay (high ASD and p but low EXT)]. Frustration, family functioning, parental psychopathology, executive functioning, and fearfulness had strong effects on p. CONCLUSIONS (1) Strong generic effects on p suggest that etiologically relevant factors and psychopathology tend to cluster in persons. (2) While many factors predict p, additional as well as opposite effects on specific factors indicate the relevance of specific psychopathology factors in understanding mental disorder. (3) High frustration, neurodevelopmental problems, and a disadvantaged family environment primarily characterize p.
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Affiliation(s)
- Jonah Ormel
- Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
| | - Melissa Vos
- Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
| | - Odilia M Laceulle
- Department of Developmental Psychology, Utrecht University, Utrecht, The Netherlands
| | - Charlotte Vrijen
- Department of Developmental Psychology, University of Groningen, Groningen, The Netherlands
| | - Camiel M van der Laan
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Catharina A Hartman
- Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
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29
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Chen D, Jia T, Cheng W, Desrivières S, Heinz A, Schumann G, Feng J. Evaluation of behavioral variance/covariance explained by the neuroimaging data through a pattern-based regression. Hum Brain Mapp 2024; 45:e26601. [PMID: 38488475 PMCID: PMC10941514 DOI: 10.1002/hbm.26601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 01/01/2024] [Accepted: 01/08/2024] [Indexed: 03/18/2024] Open
Abstract
Neuroimaging data have been widely used to understand the neural bases of human behaviors. However, most studies were either based on a few predefined regions of interest or only able to reveal limited vital regions, hence not providing an overarching description of the relationship between neuroimaging and behaviors. Here, we proposed a voxel-based pattern regression that not only could investigate the overall brain-associated variance (BAV) for a given behavioral measure but could also evaluate the shared neural bases between different behaviors across multiple neuroimaging data. The proposed method demonstrated consistently high reliability and accuracy through comprehensive simulations. We further implemented this approach on real data of adolescents (IMAGEN project, n = 2089) and adults (HCP project, n = 808) to investigate brain-based variances of multiple behavioral measures, for instance, cognitive behaviors, substance use, and psychiatric disorders. Notably, intelligence-related scores showed similar high BAVs with the gray matter volume across both datasets. Further, our approach allows us to reveal the latent brain-based correlation across multiple behavioral measures, which are challenging to obtain otherwise. For instance, we observed a shared brain architecture underlying depression and externalizing problems in adolescents, while the symptom comorbidity may only emerge later in adults. Overall, our approach will provide an important statistical tool for understanding human behaviors using neuroimaging data.
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Affiliation(s)
- Di Chen
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
| | - Tianye Jia
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
- Institute of Psychiatry, Psychology & NeuroscienceSGDP Centre, King's College LondonLondonUK
| | - Wei Cheng
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
| | - Sylvane Desrivières
- Institute of Psychiatry, Psychology & NeuroscienceSGDP Centre, King's College LondonLondonUK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCMCharité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
| | - Gunter Schumann
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Department of Psychiatry and Psychotherapy CCMCharité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
| | - Jianfeng Feng
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
- Department of Computer ScienceUniversity of WarwickCoventryUK
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30
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Valentim WL, Tylee DS, Polimanti R. A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices. WIREs Mech Dis 2024; 16:e1635. [PMID: 38059513 PMCID: PMC11163995 DOI: 10.1002/wsbm.1635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 10/22/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023]
Abstract
Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.
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Affiliation(s)
- Wander L. Valentim
- Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Daniel S. Tylee
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
- VA CT Healthcare Center, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
- VA CT Healthcare Center, West Haven, CT, USA
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31
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Peyrot WJ, Panagiotaropoulou G, Olde Loohuis LM, Adams MJ, Awasthi S, Ge T, McIntosh AM, Mitchell BL, Mullins N, O'Connell KS, Penninx BWJH, Posthuma D, Ripke S, Ruderfer DM, Uffelmann E, Vilhjalmsson BJ, Zhu Z, Smoller JW, Price AL. Distinguishing different psychiatric disorders using DDx-PRS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.02.24302228. [PMID: 38352307 PMCID: PMC10862992 DOI: 10.1101/2024.02.02.24302228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.
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LaBianca S, Brikell I, Helenius D, Loughnan R, Mefford J, Palmer CE, Walker R, Gådin JR, Krebs M, Appadurai V, Vaez M, Agerbo E, Pedersen MG, Børglum AD, Hougaard DM, Mors O, Nordentoft M, Mortensen PB, Kendler KS, Jernigan TL, Geschwind DH, Ingason A, Dahl AW, Zaitlen N, Dalsgaard S, Werge TM, Schork AJ. Polygenic profiles define aspects of clinical heterogeneity in attention deficit hyperactivity disorder. Nat Genet 2024; 56:234-244. [PMID: 38036780 PMCID: PMC11439085 DOI: 10.1038/s41588-023-01593-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/25/2023] [Indexed: 12/02/2023]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a complex disorder that manifests variability in long-term outcomes and clinical presentations. The genetic contributions to such heterogeneity are not well understood. Here we show several genetic links to clinical heterogeneity in ADHD in a case-only study of 14,084 diagnosed individuals. First, we identify one genome-wide significant locus by comparing cases with ADHD and autism spectrum disorder (ASD) to cases with ADHD but not ASD. Second, we show that cases with ASD and ADHD, substance use disorder and ADHD, or first diagnosed with ADHD in adulthood have unique polygenic score (PGS) profiles that distinguish them from complementary case subgroups and controls. Finally, a PGS for an ASD diagnosis in ADHD cases predicted cognitive performance in an independent developmental cohort. Our approach uncovered evidence of genetic heterogeneity in ADHD, helping us to understand its etiology and providing a model for studies of other disorders.
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Affiliation(s)
- Sonja LaBianca
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Isabell Brikell
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
| | - Dorte Helenius
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Joel Mefford
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Clare E Palmer
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Walker
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jesper R Gådin
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Morten Krebs
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Vivek Appadurai
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Morteza Vaez
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Marianne Giørtz Pedersen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Psychosis Research Unit, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Copenhagen Mental Health Center, Mental Health Services Capital Region of Denmark Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Terry L Jernigan
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Daniel H Geschwind
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrés Ingason
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Andrew W Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Søren Dalsgaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Thomas M Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, USA.
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Perez-Carpena P, Lopez-Escamez JA, Gallego-Martinez Á. A Systematic Review on the Genetic Contribution to Tinnitus. J Assoc Res Otolaryngol 2024; 25:13-33. [PMID: 38334885 PMCID: PMC10907330 DOI: 10.1007/s10162-024-00925-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 12/31/2023] [Indexed: 02/10/2024] Open
Abstract
PURPOSE To assess the available evidence to support a genetic contribution and define the role of common and rare variants in tinnitus. METHODS After a systematic search and quality assessment, 31 records including 383,063 patients were selected (14 epidemiological studies and 17 genetic association studies). General information on the sample size, age, sex, tinnitus prevalence, severe tinnitus distribution, and sensorineural hearing loss was retrieved. Studies that did not include data on hearing assessment were excluded. Relative frequencies were used for qualitative variables to compare different studies and to obtain average values. Genetic variants and genes were listed and clustered according to their potential role in tinnitus development. RESULTS The average prevalence of tinnitus estimated from population-based studies was 26.3% for any tinnitus, and 20% of patients with tinnitus reported it as an annoying symptom. One study has reported population-specific differences in the prevalence of tinnitus, the white ancestry being the population with a higher prevalence. Genome-wide association studies have identified and replicated two common variants in the Chinese population (rs2846071; rs4149577) in the intron of TNFRSF1A, associated with noise-induced tinnitus. Moreover, gene burden analyses in sequencing data from Spanish and Swede patients with severe tinnitus have identified and replicated ANK2, AKAP9, and TSC2 genes. CONCLUSIONS The genetic contribution to tinnitus is starting to be revealed and it shows population-specific effects in European and Asian populations. The common allelic variants associated with tinnitus that showed replication are associated with noise-induced tinnitus. Although severe tinnitus has been associated with rare variants with large effect, their role on hearing or hyperacusis has not been established.
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Affiliation(s)
- Patricia Perez-Carpena
- Otology and Neurotology Group CTS495, Division of Otolaryngology, Department of Surgery, Instituto de Investigación Biosanitaria, Ibs.GRANADA, Universidad de Granada, Granada, Spain.
- Sensorineural Pathology Programme, Centro de Investigación Biomédica en Red en Enfermedades Raras, CIBERER, Madrid, Spain.
- Department of Otolaryngology, Instituto de Investigación Biosanitaria Ibs.GRANADA, Hospital Universitario Virgen de Las Nieves, Granada, Spain.
| | - Jose A Lopez-Escamez
- Otology and Neurotology Group CTS495, Division of Otolaryngology, Department of Surgery, Instituto de Investigación Biosanitaria, Ibs.GRANADA, Universidad de Granada, Granada, Spain.
- Sensorineural Pathology Programme, Centro de Investigación Biomédica en Red en Enfermedades Raras, CIBERER, Madrid, Spain.
- Meniere's Disease Neuroscience Research Program, Faculty of Medicine & Health, School of Medical Sciences, The Kolling Institute, University of Sydney, Sydney, NSW, Australia.
| | - Álvaro Gallego-Martinez
- Otology and Neurotology Group CTS495, Division of Otolaryngology, Department of Surgery, Instituto de Investigación Biosanitaria, Ibs.GRANADA, Universidad de Granada, Granada, Spain
- Sensorineural Pathology Programme, Centro de Investigación Biomédica en Red en Enfermedades Raras, CIBERER, Madrid, Spain
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34
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Yao S, Harder A, Darki F, Chang YW, Li A, Nikouei K, Volpe G, Lundström JN, Zeng J, Wray N, Lu Y, Sullivan PF, Leffler JH. Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.18.24301478. [PMID: 38410450 PMCID: PMC10896415 DOI: 10.1101/2024.01.18.24301478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Understanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets.
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Affiliation(s)
- Shuyang Yao
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fahimeh Darki
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Ang Li
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Kasra Nikouei
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Johan N Lundström
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Monell Chemical Senses Center, Philadelphia, PA, USA
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Naomi Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Jens Hjerling Leffler
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
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35
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Battaglia S, Nazzi C, Thayer JF. Genetic differences associated with dopamine and serotonin release mediate fear-induced bradycardia in the human brain. Transl Psychiatry 2024; 14:24. [PMID: 38225222 PMCID: PMC10789820 DOI: 10.1038/s41398-024-02737-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/17/2024] Open
Abstract
Fear-induced bradycardia, a transient heartbeat deceleration following exposure to threat, is a physiological index observable in humans, especially in fear conditioning experiments. While gaining interest in recent years, it is still currently underemployed in neuroscientific research compared to more popular physiological indices. Besides its use in research, it could also constitute a valuable resource in a clinical psychiatry setting, as many disorders are also characterized by altered heart rate responses. However, differences in fear-induced bradycardia may also be subtended by genetic interindividual differences, thus suggesting precaution when recommending its use in the clinical setting. Here, we discussed the first endeavors that aimed at clarifying the genetic underpinnings of heart rate variations, which suggest that individual genetic differences have a role in defining the characteristics of heart rate responses. Given this, translating heart rate measurements in the clinical setting must be implemented with caution. Future endeavors in this field will aim at identifying these differences even further, thus allowing for more precise clinical interventions.
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Affiliation(s)
- Simone Battaglia
- Department of Psychology, Center for Studies and Research in Cognitive Neuroscience, University of Bologna, Bologna, 40127, Italy.
- Department of Psychology, University of Torino, Torino, 10124, Italy.
| | - Claudio Nazzi
- Department of Psychology, Center for Studies and Research in Cognitive Neuroscience, University of Bologna, Bologna, 40127, Italy
| | - Julian F Thayer
- Department of Psychological Science, 4334 Social and Behavioral Sciences Gateway, University of California, Irvine, CA, 92697, USA
- Department of Psychology, The Ohio State University, Columbus, OH, 43210, USA
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36
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Uffelmann E, Price AL, Posthuma D, Peyrot WJ. Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.12.24301157. [PMID: 38260678 PMCID: PMC10802765 DOI: 10.1101/2024.01.12.24301157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value, based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are on average equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual's genotype data, and a prior disorder probability. The BPC approach transforms the PGS to its underlying liability scale, computes the variances of the PGS in cases and controls, and applies Bayes' Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning dataset with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach.
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Affiliation(s)
- Emil Uffelmann
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
| | | | | | - Alkes L. Price
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex, Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Wouter J. Peyrot
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
- Department of Psychiatry, Amsterdam UMC, The Netherlands
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37
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Abstract
Brain development in humans is achieved through precise spatiotemporal genetic control, the mechanisms of which remain largely elusive. Recently, integration of technological advances in human stem cell-based modelling with genome editing has emerged as a powerful platform to establish causative links between genotypes and phenotypes directly in the human system. Here, we review our current knowledge of complex genetic regulation of each key step of human brain development through the lens of evolutionary specialization and neurodevelopmental disorders and highlight the use of human stem cell-derived 2D cultures and 3D brain organoids to investigate human-enriched features and disease mechanisms. We also discuss opportunities and challenges of integrating new technologies to reveal the genetic architecture of human brain development and disorders.
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Affiliation(s)
- Yi Zhou
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hongjun Song
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA
- The Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guo-Li Ming
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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38
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Niemsiri V, Rosenthal SB, Nievergelt CM, Maihofer AX, Marchetto MC, Santos R, Shekhtman T, Alliey-Rodriguez N, Anand A, Balaraman Y, Berrettini WH, Bertram H, Burdick KE, Calabrese JR, Calkin CV, Conroy C, Coryell WH, DeModena A, Eyler LT, Feeder S, Fisher C, Frazier N, Frye MA, Gao K, Garnham J, Gershon ES, Goes FS, Goto T, Harrington GJ, Jakobsen P, Kamali M, Kelly M, Leckband SG, Lohoff FW, McCarthy MJ, McInnis MG, Craig D, Millett CE, Mondimore F, Morken G, Nurnberger JI, Donovan CO, Øedegaard KJ, Ryan K, Schinagle M, Shilling PD, Slaney C, Stapp EK, Stautland A, Tarwater B, Zandi PP, Alda M, Fisch KM, Gage FH, Kelsoe JR. Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis. Mol Psychiatry 2024; 29:6-19. [PMID: 36991131 PMCID: PMC11078741 DOI: 10.1038/s41380-022-01909-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 11/14/2022] [Accepted: 12/02/2022] [Indexed: 03/31/2023]
Abstract
Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.
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Grants
- R01 MH095741 NIMH NIH HHS
- UL1 TR001442 NCATS NIH HHS
- U19 MH106434 NIMH NIH HHS
- U01 MH092758 NIMH NIH HHS
- T32 MH018399 NIMH NIH HHS
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- Department of Veterans Affairs | Veterans Affairs San Diego Healthcare System (VA San Diego Healthcare System)
- The Halifax group (MA, CVC, JG, CO, and CS) is supported by grants from Canadian Institutes of Health Research (#166098), ERA PerMed project PLOT-BD, Research Nova Scotia, Genome Atlantic, Nova Scotia Health Authority and Dalhousie Medical Research Foundation (Lindsay Family Fund).
- U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS)
- U19MH106434, part of the National Cooperative Reprogrammed Cell Research Groups (NCRCRG) to Study Mental Illness. AHA-Allen Initiative in Brain Health and Cognitive Impairment Award (19PABH134610000). The JPB Foundation, Bob and Mary Jane Engman, Annette C Merle-Smith, R01 MH095741, and Lynn and Edward Streim.
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Affiliation(s)
- Vipavee Niemsiri
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Sara Brin Rosenthal
- Center for Computational Biology and Bioinformatics, University of California, San Diego, La Jolla, CA, USA
| | | | - Adam X Maihofer
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Maria C Marchetto
- Department of Anthropology, University of California, San Diego, La Jolla, CA, USA
- Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Renata Santos
- Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA
- University of Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1261266, Laboratory of Dynamics of Neuronal Structure in Health and Disease, Paris, France
| | - Tatyana Shekhtman
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Ney Alliey-Rodriguez
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
- Department of Psychiatry and Behavioral Neuroscience, Northwestern University, Chicago, IL, USA
| | - Amit Anand
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yokesh Balaraman
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Wade H Berrettini
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Holli Bertram
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Katherine E Burdick
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph R Calabrese
- Mood Disorders Program, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Mood Disorders Program, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Cynthia V Calkin
- Department of Psychiatry and Medical Neuroscience, Dalhousie University, Halifax, NS, Canada
| | - Carla Conroy
- Mood Disorders Program, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Mood Disorders Program, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Anna DeModena
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Scott Feeder
- Department of Psychiatry, The Mayo Clinic, Rochester, MN, USA
| | - Carrie Fisher
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nicole Frazier
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Mark A Frye
- Department of Psychiatry, The Mayo Clinic, Rochester, MN, USA
| | - Keming Gao
- Mood Disorders Program, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Mood Disorders Program, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Julie Garnham
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Toyomi Goto
- Mood Disorders Program, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | | | - Petter Jakobsen
- Norment, Division of Psychiatry, Haukeland University Hospital and Department of Clinical medicine, University of Bergen, Bergen, Norway
| | - Masoud Kamali
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Marisa Kelly
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Susan G Leckband
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Falk W Lohoff
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael J McCarthy
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Melvin G McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - David Craig
- Department of Translational Genomics, University of Southern California, Los Angeles, CA, USA
| | - Caitlin E Millett
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Francis Mondimore
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Gunnar Morken
- Division of Mental Health Care, St Olavs University Hospital, and Department of Mental Health, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - John I Nurnberger
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
- Medical and Molecular Genetics, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Ketil J Øedegaard
- Norment, Division of Psychiatry, Haukeland University Hospital and Department of Clinical medicine, University of Bergen, Bergen, Norway
| | - Kelly Ryan
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Martha Schinagle
- Mood Disorders Program, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Paul D Shilling
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Claire Slaney
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Emma K Stapp
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Andrea Stautland
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Bruce Tarwater
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Peter P Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- National Institute of Mental Health, Klecany, Czech Republic
| | - Kathleen M Fisch
- Center for Computational Biology and Bioinformatics, University of California, San Diego, La Jolla, CA, USA
- Department of Obstetrics, Gynecology & Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Fred H Gage
- Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - John R Kelsoe
- 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.
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Detera-Wadleigh SD, Kassem L, Besancon E, Lopes F, Akula N, Sung H, Blattner M, Sheridan L, Lacbawan LN, Garcia J, Gordovez F, Hosey K, Donner C, Salvini C, Schulze T, Chen DTW, England B, Cross J, Jiang X, Corona W, Russ J, Mallon B, Dutra A, Pak E, Steiner J, Malik N, de Guzman T, Horato N, Mallmann MB, Mendes V, Dűck AL, Nardi AE, McMahon FJ. A resource of induced pluripotent stem cell (iPSC) lines including clinical, genomic, and cellular data from genetically isolated families with mood and psychotic disorders. Transl Psychiatry 2023; 13:397. [PMID: 38104115 PMCID: PMC10725500 DOI: 10.1038/s41398-023-02641-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 10/05/2023] [Accepted: 10/30/2023] [Indexed: 12/19/2023] Open
Abstract
Genome-wide (GWAS) and copy number variant (CNV) association studies have reproducibly identified numerous risk alleles associated with bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ), but biological characterization of these alleles lags gene discovery, owing to the inaccessibility of live human brain cells and inadequate animal models for human psychiatric conditions. Human-derived induced pluripotent stem cells (iPSCs) provide a renewable cellular reagent that can be differentiated into living, disease-relevant cells and 3D brain organoids carrying the full complement of genetic variants present in the donor germline. Experimental studies of iPSC-derived cells allow functional characterization of risk alleles, establishment of causal relationships between genes and neurobiology, and screening for novel therapeutics. Here we report the creation and availability of an iPSC resource comprising clinical, genomic, and cellular data obtained from genetically isolated families with BD and related conditions. Results from the first 324 study participants, 61 of whom have validated pluripotent clones, show enrichment of rare single nucleotide variants and CNVs overlapping many known risk genes and pathogenic CNVs. This growing iPSC resource is available to scientists pursuing functional genomic studies of BD and related conditions.
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Affiliation(s)
- Sevilla D Detera-Wadleigh
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA.
| | - Layla Kassem
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA.
| | - Emily Besancon
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Fabiana Lopes
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Nirmala Akula
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Heejong Sung
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Meghan Blattner
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Laura Sheridan
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Ley Nadine Lacbawan
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Joshua Garcia
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Francis Gordovez
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Katherine Hosey
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Cassandra Donner
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Claudio Salvini
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Thomas Schulze
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
- Institute of Psychiatric Phenomics and Genomics, LMU Munich, 80336, München, Germany
- Department of Psychiatry and Behavioral Sciences, Upstate University Hospital, Syracuse, NY, 13210, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - David T W Chen
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Bryce England
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Joanna Cross
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Xueying Jiang
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Winston Corona
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Jill Russ
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Barbara Mallon
- Center for Scientific Review, Neurotechnology and Vision Branch, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Amalia Dutra
- Cytogenetics and Microscopy Core, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Evgenia Pak
- Cytogenetics and Microscopy Core, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Joe Steiner
- Neurotherapeutics Development Unit, NINDS, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nasir Malik
- Neurotherapeutics Development Unit, NINDS, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Theresa de Guzman
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Natia Horato
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Mariana B Mallmann
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Victoria Mendes
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Amanda L Dűck
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Antonio E Nardi
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Francis J McMahon
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
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40
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Guintivano J, Byrne EM, Kiewa J, Yao S, Bauer AE, Aberg KA, Adams MJ, Campbell A, Campbell ML, Choi KW, Corfield EC, Havdahl A, Hucks D, Koen N, Lu Y, Mægbæk ML, Mullaert J, Peterson RE, Raffield LM, Sallis HM, Sealock JM, Walker A, Watson HJ, Xiong Y, Yang JMK, Anney RJL, Gordon-Smith K, Hubbard L, Jones LA, Mihaescu R, Nyegaard M, Pardiñas AF, Perry A, Saquib N, Shadyab AH, Viktorin A, Andreassen OA, Bigdeli TB, Davis LK, Dennis CL, Di Florio A, Dubertret C, Feng YCA, Frey BN, Grigoriadis S, Gloaguen E, Jones I, Kennedy JL, Krohn H, Kallak TK, Li Y, Martin NG, McIntosh AM, Milgrom J, Trine Munk-Olsen, Oberlander T, Olsen CM, Ramoz N, Reichborn-Kjennerud T, Blackmore ER, Rubinow D, Skalkidou A, Smoller JW, Stein DJ, Stowe ZN, Taylor V, Tebeka S, Tesli M, Van Lieshout RJ, van den Oord EJCG, Vigod SN, Werge T, Westlye LT, Whiteman DC, Zar HJ, Wray N, Meltzer-Brody S, Sullivan P. Meta-Analyses of Genome-Wide Association Studies for Postpartum Depression. Am J Psychiatry 2023; 180:884-895. [PMID: 37849304 PMCID: PMC11163373 DOI: 10.1176/appi.ajp.20230053] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
OBJECTIVE Postpartum depression (PPD) is a common subtype of major depressive disorder (MDD) that is more heritable, yet is understudied in psychiatric genetics. The authors conducted meta-analyses of genome-wide association studies (GWASs) to investigate the genetic architecture of PPD. METHOD Meta-analyses were conducted on 18 cohorts of European ancestry (17,339 PPD cases and 53,426 controls), one cohort of East Asian ancestry (975 cases and 3,780 controls), and one cohort of African ancestry (456 cases and 1,255 controls), totaling 18,770 PPD cases and 58,461 controls. Post-GWAS analyses included 1) single-nucleotide polymorphism (SNP)-based heritability ([Formula: see text]), 2) genetic correlations between PPD and other phenotypes, and 3) enrichment of the PPD GWAS findings in 27 human tissues and 265 cell types from the mouse central and peripheral nervous system. RESULTS No SNP achieved genome-wide significance in the European or the trans-ancestry meta-analyses. The [Formula: see text] of PPD was 0.14 (SE=0.02). Significant genetic correlations were estimated for PPD with MDD, bipolar disorder, anxiety disorders, posttraumatic stress disorder, insomnia, age at menarche, and polycystic ovary syndrome. Cell-type enrichment analyses implicate inhibitory neurons in the thalamus and cholinergic neurons within septal nuclei of the hypothalamus, a pattern that differs from MDD. CONCLUSIONS While more samples are needed to reach genome-wide levels of significance, the results presented confirm PPD as a polygenic and heritable phenotype. There is also evidence that despite a high correlation with MDD, PPD may have unique genetic components. Cell enrichment results suggest GABAergic neurons, which converge on a common mechanism with the only medication approved by the U.S. Food and Drug Administration for PPD (brexanolone).
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Affiliation(s)
- Jerry Guintivano
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Enda M Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Jacqueline Kiewa
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anna E Bauer
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Karolina A Aberg
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Megan L Campbell
- MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics. Institute for Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Global Initiative for Neuropsychiatric Genetics Education in Research, Broad Institute, Cambridge, MA, USA
| | - Karmel W Choi
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Elizabeth C Corfield
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Alexandra Havdahl
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway
| | - Donald Hucks
- Department of Medicine, Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nastassja Koen
- SAMRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Merete L Mægbæk
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, BSS, Aarhus, Denmark
| | - Jimmy Mullaert
- Department of Epidemiology, Biostatistics and Clinical Research, AP-HP.Nord – Université Paris Cité, Paris, France
- UMR 1137 IAME, INSERM, Paris, France
| | - Roseann E Peterson
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hannah M Sallis
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Julia M Sealock
- Analytic & Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Alicia Walker
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Hunna J Watson
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Discipline of Psychology, Curtin University, Perth, Western Australia, Australia
- Division of Paediatrics, The University of Western Australia, Perth, Western Australia, Australia
| | - Ying Xiong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jessica MK Yang
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Richard JL Anney
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | | | - Leon Hubbard
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Lisa A Jones
- Psychological Medicine, University of Worcester, Worcester, UK
| | - Raluca Mihaescu
- Department of Psychiatry, Catharina Hospital, Eindhoven, Netherlands
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Aalborg, Nordjylland, Denmark
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Amy Perry
- Psychological Medicine, University of Worcester, Worcester, UK
| | - Nazmus Saquib
- Department of Clinical Sciences, Sulaiman AlRajhi University, Bukairiyah, Saudi Arabia
| | - Aladdin H Shadyab
- Department of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | - Alexander Viktorin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre of Neurodevelopment Research, University of Oslo, Oslo, Norway
| | - Tim B Bigdeli
- Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
- Research Service, Department of Veterans Affairs New York Harbor Healthcare System, New York, NY, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cindy-Lee Dennis
- Lawrence S Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada
| | - Arianna Di Florio
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Caroline Dubertret
- INSERM U1266, Université de Paris, Paris, France
- Department of Psychiatry, AP-HP, Louis Mourier Hospital, Colombes, France
| | - Yen-Chen A Feng
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Women’s Health Concerns Clinic, St. Joseph’s Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Sophie Grigoriadis
- Department of Psychiatry, Sunnybrook Health Science Centre and Research Institute, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Emilie Gloaguen
- Hôpital Bichat, Department of Epidemiology Biostatistics and Clinical Research, AP-HP, Paris, France
| | - Ian Jones
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - James L Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Molecular Brain Science Dept, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Holly Krohn
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nick G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Jeannette Milgrom
- Parent-Infant Research Institute, Heidelberg Heights, Victoria, Australia
- University of Melbourne, Melbourne, Victoria, Australia
| | - Trine Munk-Olsen
- The National Center for Register-based Research, Aarhus University, Aarhus, Denmark
- Department of Clinical Research, Psychiatric Research Unit, University of Southern Denmark, Odense, Denmark
| | - Tim Oberlander
- BC Children’s Hospital, Vancouver, British Columbia, Canada
- Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Catherine M Olsen
- The Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Nicolas Ramoz
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM UMR1266, Tean-Genetic vulnerability to addictive and psychiatric disorders, Paris, France
- GHU Paris Psychiatrie et Neurosciences, CMME, Hôpital Sainte-Anne, F-75014 Paris, France
| | - Ted Reichborn-Kjennerud
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - David Rubinow
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alkistis Skalkidou
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
| | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Dan J Stein
- SAMRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Zachary N Stowe
- Department of Psychiatry, University of Wisconsin at Madison, Madison, WI, USA
| | - Valerie Taylor
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Sarah Tebeka
- Department of Psychiatry, AP-HP, Louis Mourier Hospital, Colombes, France
- INSERM U1266, Université Paris Cité, Paris, France
| | - Martin Tesli
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Centre for Research and Education in Forensic Psychiatry, Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ryan J Van Lieshout
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Edwin JCG van den Oord
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Simone N Vigod
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, Women’s College Hospital, Toronto, Ontario, Canada
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- LF Center for Geogenetics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Division for Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - David C Whiteman
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Public Health, The University of Queensland, Brisbane, Queensland, Australia
| | - Heather J Zar
- Department of Paediatrics & Child Health, University of Cape Town, Cape Town, Western Cape, South Africa
- SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | | | - Naomi Wray
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Samantha Meltzer-Brody
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Patrick Sullivan
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Adams JAM, Chandra P, Mehta D. The First Large GWAS Meta-Analysis for Postpartum Depression. Am J Psychiatry 2023; 180:862-864. [PMID: 38037399 DOI: 10.1176/appi.ajp.20230794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Affiliation(s)
- Jessica Ann May Adams
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Australia (Adams, Mehta); Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India (Chandra)
| | - Prabha Chandra
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Australia (Adams, Mehta); Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India (Chandra)
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Australia (Adams, Mehta); Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India (Chandra)
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Baranger DAA, Hatoum AS, Polimanti R, Gelernter J, Edenberg HJ, Bogdan R, Agrawal A. Multi-omics cannot replace sample size in genome-wide association studies. GENES, BRAIN, AND BEHAVIOR 2023; 22:e12846. [PMID: 36977197 PMCID: PMC10733567 DOI: 10.1111/gbb.12846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/20/2023] [Accepted: 03/13/2023] [Indexed: 03/30/2023]
Abstract
The integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has been suggested that multi-omics could circumvent or greatly reduce the need to increase GWAS sample sizes for novel variant discovery. We tested whether incorporating multi-omics information in earlier and smaller-sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. We applied 10 different analytic approaches to integrating multi-omics data from 12 sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (alcohol use disorder/problematic alcohol use, major depression/depression, schizophrenia, and intracranial volume/brain volume) could detect genes that were revealed by a later and larger GWAS. Multi-omics data did not reliably identify novel genes in earlier less-powered GWAS (PPV <0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1-8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., intracranial volume and schizophrenia). Although multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), can help to prioritize genes within genome-wide significant loci (PPVs = 0.5-1.0) and translate them into information about disease biology, it does not reliably increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is required.
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Affiliation(s)
- David A. A. Baranger
- Department of Psychological & Brain SciencesWashington University in St. Louis Medical SchoolSaint LouisMissouriUSA
| | - Alexander S. Hatoum
- Department of PsychiatryWashington University School of MedicineSaint LouisMissouriUSA
| | - Renato Polimanti
- Department of Psychiatry, Division of Human GeneticsYale School of MedicineNew HavenConnecticutUSA
- PsychiatryVeterans Affairs Connecticut Healthcare SystemWest HavenConnecticutUSA
| | - Joel Gelernter
- Department of Psychiatry, Division of Human GeneticsYale School of MedicineNew HavenConnecticutUSA
- PsychiatryVeterans Affairs Connecticut Healthcare SystemWest HavenConnecticutUSA
- Department of GeneticsYale School of MedicineNew HavenConnecticutUSA
- Department of NeuroscienceYale School of MedicineNew HavenConnecticutUSA
| | - Howard J. Edenberg
- Department of Biochemistry and Molecular BiologyIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Ryan Bogdan
- Department of Psychological & Brain SciencesWashington University in St. Louis Medical SchoolSaint LouisMissouriUSA
| | - Arpana Agrawal
- Department of PsychiatryWashington University School of MedicineSaint LouisMissouriUSA
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Waszczuk MA, Jonas KG, Bornovalova M, Breen G, Bulik CM, Docherty AR, Eley TC, Hettema JM, Kotov R, Krueger RF, Lencz T, Li JJ, Vassos E, Waldman ID. Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies. Mol Psychiatry 2023; 28:4943-4953. [PMID: 37402851 PMCID: PMC10764644 DOI: 10.1038/s41380-023-02142-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/17/2023] [Accepted: 06/16/2023] [Indexed: 07/06/2023]
Abstract
Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.
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Affiliation(s)
- Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA.
| | - Katherine G Jonas
- Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, NY, USA
| | | | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Cynthia M Bulik
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anna R Docherty
- Huntsman Mental Health Institute, Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - Thalia C Eley
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - John M Hettema
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
- Department of Psychiatry, Texas A&M Health Sciences Center, Bryan, TX, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, NY, USA
| | - Robert F Krueger
- Psychology Department, University of Minnesota, Minneapolis, MN, USA
| | - Todd Lencz
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - James J Li
- Department of Psychology, University of Wisconsin, Madison, WI, USA
- Waisman Center, University of Wisconsin, Madison, WI, USA
| | - Evangelos Vassos
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Irwin D Waldman
- Department of Psychology, Emory University, Atlanta, GA, USA
- Center for Computational and Quantitative Genetics, Emory University, Atlanta, GA, USA
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Peter HL, Giglberger M, Streit F, Frank J, Kreuzpointner L, Rietschel M, Kudielka BM, Wüst S. Association of polygenic scores for depression and neuroticism with perceived stress in daily life during a long-lasting stress period. GENES, BRAIN, AND BEHAVIOR 2023; 22:e12872. [PMID: 37876358 PMCID: PMC10733580 DOI: 10.1111/gbb.12872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 07/31/2023] [Accepted: 10/06/2023] [Indexed: 10/26/2023]
Abstract
Genetic factors contribute significantly to interindividual differences in the susceptibility to stress-related disorders. As stress can also be conceptualized as environmental exposure, controlled gene-environment interaction (GxE) studies with an in-depth phenotyping may help to unravel mechanisms underlying the interplay between genetic factors and stress. In a prospective-longitudinal quasi-experimental study, we investigated whether polygenic scores (PGS) for depression (DEP-PGS) and neuroticism (NEU-PGS), respectively, were associated with responses to chronic stress in daily life. We examined law students (n = 432) over 13 months. Participants in the stress group experienced a long-lasting stress phase, namely the preparation for the first state examination for law students. The control group consisted of law students without particular stress exposure. In the present manuscript, we analyzed perceived stress levels assessed at high frequency and in an ecologically valid manner by ambulatory assessments as well as depression symptoms and two parameters of the cortisol awakening response. The latter was only assessed in a subsample (n = 196). No associations between the DEP-PGS and stress-related variables were found. However, for the NEU-PGS we found a significant GxE effect. Only in individuals experiencing academic stress a higher PGS for neuroticism predicted stronger increases of perceived stress levels until the exam. At baseline, a higher NEU-PGS was associated with higher perceived stress levels in both groups. Despite the small sample size, we provide preliminary evidence that the genetic disposition for neuroticism is associated with stress level increases in daily life during a long-lasting stress period.
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Affiliation(s)
- Hannah L. Peter
- Institute of PsychologyUniversity of RegensburgRegensburgGermany
| | | | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental HealthUniversity of MannheimMannheimGermany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental HealthUniversity of MannheimMannheimGermany
| | | | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental HealthUniversity of MannheimMannheimGermany
| | | | - Stefan Wüst
- Institute of PsychologyUniversity of RegensburgRegensburgGermany
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Li P, Wei J, Zhu Y. CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation. Brief Bioinform 2023; 25:bbad417. [PMID: 37995133 PMCID: PMC10790717 DOI: 10.1093/bib/bbad417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/09/2023] [Accepted: 10/29/2023] [Indexed: 11/25/2023] Open
Abstract
Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.
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Affiliation(s)
- Peilong Li
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Junfeng Wei
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Ying Zhu
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
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Yin Y, Hu S, Tarbox-Berry S, Wang Q. Editorial: Community series in genetic mechanisms of biomarkers in schizophrenia, bipolar disorder and depression, volume II. Front Psychiatry 2023; 14:1267708. [PMID: 38045618 PMCID: PMC10690361 DOI: 10.3389/fpsyt.2023.1267708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/03/2023] [Indexed: 12/05/2023] Open
Affiliation(s)
- Yubing Yin
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, Sichuan, China
| | - Shaohua Hu
- Department of Psychiatry, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Sarah Tarbox-Berry
- Department of Neurology, School of Medicine, Wayne State University, Detroit, MI, United States
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, Sichuan, China
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47
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Michalski C, Wen Z. Leveraging iPSC technology to assess neuro-immune interactions in neurological and psychiatric disorders. Front Psychiatry 2023; 14:1291115. [PMID: 38025464 PMCID: PMC10672983 DOI: 10.3389/fpsyt.2023.1291115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Communication between the immune and the nervous system is essential for human brain development and homeostasis. Disruption of this intricately regulated crosstalk can lead to neurodevelopmental, psychiatric, or neurodegenerative disorders. While animal models have been essential in characterizing the role of neuroimmunity in development and disease, they come with inherent limitations due to species specific differences, particularly with regard to microglia, the major subset of brain resident immune cells. The advent of induced pluripotent stem cell (iPSC) technology now allows the development of clinically relevant models of the central nervous system that adequately reflect human genetic architecture. This article will review recent publications that have leveraged iPSC technology to assess neuro-immune interactions. First, we will discuss the role of environmental stressors such as neurotropic viruses or pro-inflammatory cytokines on neuronal and glial function. Next, we will review how iPSC models can be used to study genetic risk factors in neurological and psychiatric disorders. Lastly, we will evaluate current challenges and future potential for iPSC models in the field of neuroimmunity.
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Affiliation(s)
- Christina Michalski
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Zhexing Wen
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA, United States
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
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48
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Li B, Qu Y, Fan Z, Gong X, Xu H, Wu L, Yan C. Causal relationships between blood lipids and major psychiatric disorders: Univariable and multivariable mendelian randomization analysis. BMC Med Genomics 2023; 16:250. [PMID: 37853421 PMCID: PMC10585856 DOI: 10.1186/s12920-023-01692-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 10/05/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Whether the positive associations of blood lipids with psychiatric disorders are causal is uncertain. We conducted this two-sample Mendelian randomization (MR) analysis to comprehensively investigate associations of blood lipids with psychiatric disorders. METHODS Univariable and multivariable models were established for MR analyses. Inverse variance-weighted (IVW) MR was employed as the main approach; weighted median and MR-Egger were used as sensitivity analysis methods. The possibility of violating MR assumptions was evaluated utilizing several sensitivity analyses, including heterogeneity statistics, horizontal pleiotropy statistics, single SNP analysis, leave-one-out analysis and MR-PRESSO analysis. As instrumental variables, we screened 362 independent single-nucleotide polymorphisms (SNP) related to blood lipids from a recent genome-wide association study involving 76,627 individuals of European ancestry, with a genome-wide significance level of p < 5 × 10- 8. Summary-level information for the six psychiatric disorders was extracted from Psychiatric Genomics Consortium and Alzheimer Disease Genetics Consortium. RESULTS We observed eight significant associations in univariable MR analysis, four of which were corroborated by multivariable MR (MVMR) analysis modified for the other three lipid traits: high-density lipoprotein cholesterol (HDL-C) level with the risk of PTSD (OR = 0.91, 95% CI = 0.85-0.97, p = 0.002) and AD (OR = 0.79, 95% CI = 0.71-0.88, p < 0.001) and triglycerides (TG) level with the risk of MDD (OR = 1.02, 95% CI = 1.003-1.03, p = 0.01) and panic disorder (OR = 0.83, 95% CI = 0.74-0.92, p < 0.001). In addition, four associations were not significant in MVMR analysis after adjustment for three lipid traits: total cholesterol (TC) level with the risk of PTSD, low-density lipoprotein cholesterol (LDL-C) level with the risk of MDD and AD and TG level with the risk of AD. CONCLUSIONS Our results show that blood lipids and psychiatric disorders may be related in a causal manner. This shows that abnormal blood lipid levels may act as reliable biomarker of psychiatric disorders and as suitable targets for their prevention and treatment.
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Affiliation(s)
- Bozhi Li
- Integrative Medicine Research Center, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yue Qu
- Integrative Medicine Research Center, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Zhixin Fan
- Integrative Medicine Research Center, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xiayu Gong
- Integrative Medicine Research Center, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Hanfang Xu
- Integrative Medicine Research Center, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lili Wu
- Integrative Medicine Research Center, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
| | - Can Yan
- Integrative Medicine Research Center, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
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49
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Chen W, Feng J, Jiang S, Guo J, Zhang X, Zhang X, Wang C, Ma Y, Dong Z. Mendelian randomization analyses identify bidirectional causal relationships of obesity with psychiatric disorders. J Affect Disord 2023; 339:807-814. [PMID: 37474010 DOI: 10.1016/j.jad.2023.07.044] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/25/2023] [Accepted: 07/08/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Obesity have been showed to be strongly associated with psychiatric disorders, but the exact causality and the direction of the relationship remain inconclusive. Thus, we aimed to identify the causal associations between obesity and psychiatric disorders using two-sample Mendelian randomization (MR). METHODS Single-nucleotide polymorphisms associated with obesity, including body mass index (BMI), waist-hip ratio (WHR), and waist-hip ratio adjusted for BMI (WHRadjBMI), were extracted from a genome-wide association study of 694,649 European ancestry from the GIANT consortium. Summary level data for 10 psychiatric disorders were obtained from the Psychiatric Genomics Consortium. Inverse-variance weighted (IVW) method was used as the primary analysis, while several sensitivity analyses were applied to evaluate heterogeneity and pleiotropy. RESULTS The main MR results suggested higher BMI or WHR was positively causally associated with an increased risk of attention deficit hyperactivity disorder (ADHD), anorexia nervosa (AN), post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and Alzheimer's disease (ALZ), but negatively causally associated with an increased risk of obsessive-compulsive disorder (OCD) and schizophrenia. For the reverse direction, ADHD and MDD were associated with an increased risk of obesity, but schizophrenia and ALZ were associated with a decreased risk of obesity. CONCLUSION Our findings support evidence of causal relationships between obesity and ADHD, MDD, PTSD, ALZ, SCZ, AN, and OCD, and confirmed the bidirectional causal relationships between obesity and ADHD, MDD, SCZ, and ALZ.
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Affiliation(s)
- Wenhui Chen
- Department of Metabolic and Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China
| | - Jia Feng
- Institute of Biomedicine, Department of Cellular Biology, Jinan University, Guangzhou 510632, China
| | - Shuwen Jiang
- Department of Metabolic and Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China
| | - Jie Guo
- Department of Metabolic and Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China
| | - XiaoLin Zhang
- Department of General Surgery, The Fifth Affiliated Hospital of Jinnan University (Shenhe People's Hospital), Heyuan 517300, China
| | - Xiaoguan Zhang
- Department of General Surgery, Dalang Hospital of Dongguan, Dongguan 523000, China
| | - Cunchuan Wang
- Department of Metabolic and Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China
| | - Yi Ma
- Institute of Biomedicine, Department of Cellular Biology, Jinan University, Guangzhou 510632, China.
| | - Zhiyong Dong
- Department of Metabolic and Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China.
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50
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Zhu K, Bendl J, Rahman S, Vicari JM, Coleman C, Clarence T, Latouche O, Tsankova NM, Li A, Brennand KJ, Lee D, Yuan GC, Fullard JF, Roussos P. Multi-omic profiling of the developing human cerebral cortex at the single-cell level. SCIENCE ADVANCES 2023; 9:eadg3754. [PMID: 37824614 PMCID: PMC10569714 DOI: 10.1126/sciadv.adg3754] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/01/2023] [Indexed: 10/14/2023]
Abstract
The cellular complexity of the human brain is established via dynamic changes in gene expression throughout development that is mediated, in part, by the spatiotemporal activity of cis-regulatory elements (CREs). We simultaneously profiled gene expression and chromatin accessibility in 45,549 cortical nuclei across six broad developmental time points from fetus to adult. We identified cell type-specific domains in which chromatin accessibility is highly correlated with gene expression. Differentiation pseudotime trajectory analysis indicates that chromatin accessibility at CREs precedes transcription and that dynamic changes in chromatin structure play a critical role in neuronal lineage commitment. In addition, we mapped cell type-specific and temporally specific genetic loci implicated in neuropsychiatric traits, including schizophrenia and bipolar disorder. Together, our results describe the complex regulation of cell composition at critical stages in lineage determination and shed light on the impact of spatiotemporal alterations in gene expression on neuropsychiatric disease.
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Affiliation(s)
- Kaiyi Zhu
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Samir Rahman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - James M. Vicari
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Claire Coleman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Tereza Clarence
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ovaun Latouche
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Nadejda M. Tsankova
- Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Aiqun Li
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kristen J. Brennand
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John F. Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
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