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Kingdom R, Beaumont RN, Wood AR, Weedon MN, Wright CF. Genetic modifiers of rare variants in monogenic developmental disorder loci. Nat Genet 2024; 56:861-868. [PMID: 38637616 PMCID: PMC11096126 DOI: 10.1038/s41588-024-01710-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/06/2024] [Indexed: 04/20/2024]
Abstract
Rare damaging variants in a large number of genes are known to cause monogenic developmental disorders (DDs) and have also been shown to cause milder subclinical phenotypes in population cohorts. Here, we show that carrying multiple (2-5) rare damaging variants across 599 dominant DD genes has an additive adverse effect on numerous cognitive and socioeconomic traits in UK Biobank, which can be partially counterbalanced by a higher educational attainment polygenic score (EA-PGS). Phenotypic deviators from expected EA-PGS could be partly explained by the enrichment or depletion of rare DD variants. Among carriers of rare DD variants, those with a DD-related clinical diagnosis had a substantially lower EA-PGS and more severe phenotype than those without a clinical diagnosis. Our results suggest that the overall burden of both rare and common variants can modify the expressivity of a phenotype, which may then influence whether an individual reaches the threshold for clinical disease.
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Affiliation(s)
- Rebecca Kingdom
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Robin N Beaumont
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Andrew R Wood
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Michael N Weedon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Caroline F Wright
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK.
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2
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Bhattacharyya U, John J, Lencz T, Lam M. Dissecting Schizophrenia Biology Using Pleiotropy with Cognitive Genomics. medRxiv 2024:2024.04.16.24305885. [PMID: 38699340 PMCID: PMC11065000 DOI: 10.1101/2024.04.16.24305885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Given the increasingly large number of loci discovered by psychiatric GWAS, specification of the key biological pathways underlying these loci has become a priority for the field. We have previously leveraged the pleiotropic genetic relationships between schizophrenia and two cognitive phenotypes (educational attainment and cognitive task performance) to differentiate two subsets of illness-relevant SNPs: (1) those with "concordant" alleles, which are associated with reduced cognitive ability/education and increased schizophrenia risk; and (2) those with "discordant" alleles linked to reduced educational and/or cognitive levels but lower schizophrenia susceptibility. In the present study, we extend our prior work, utilizing larger input GWAS datasets and a more powerful statistical approach to pleiotropic meta-analysis, the Pleiotropic Locus Exploration and Interpretation using Optimal test (PLEIO). Our pleiotropic meta-analysis of schizophrenia and the two cognitive phenotypes revealed 768 significant loci (159 novel). Among these, 347 loci harbored concordant SNPs, 270 encompassed discordant SNPs, and 151 "dual" loci contained concordant and discordant SNPs. Competitive gene-set analysis using MAGMA related concordant SNP loci with neurodevelopmental pathways (e.g., neurogenesis), whereas discordant loci were associated with mature neuronal synaptic functions. These distinctions were also observed in BrainSpan analysis of temporal enrichment patterns across developmental periods, with concordant loci containing more prenatally expressed genes than discordant loci. Dual loci were enriched for genes related to mRNA translation initiation, representing a novel finding in the schizophrenia literature.
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Zhang J, Qiu H, Zhao Q, Liao C, Guoli Y, Luo Q, Zhao G, Zhang N, Wang S, Zhang Z, Lei M, Liu F, Peng Y. Genetic overlap between schizophrenia and cognitive performance. Schizophrenia (Heidelb) 2024; 10:31. [PMID: 38443399 PMCID: PMC10914834 DOI: 10.1038/s41537-024-00453-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 02/16/2024] [Indexed: 03/07/2024]
Abstract
Schizophrenia (SCZ), a highly heritable mental disorder, is characterized by cognitive impairment, yet the extent of the shared genetic basis between schizophrenia and cognitive performance (CP) remains poorly understood. Therefore, we aimed to explore the polygenic overlap between SCZ and CP. Specifically, the bivariate causal mixture model (MiXeR) was employed to estimate the extent of genetic overlap between SCZ (n = 130,644) and CP (n = 257,841), and conjunctional false discovery rate (conjFDR) approach was used to identify shared genetic loci. Subsequently, functional annotation and enrichment analysis were carried out on the identified genomic loci. The MiXeR analyses revealed that 9.6 K genetic variants are associated with SCZ and 10.9 K genetic variants for CP, of which 9.5 K variants are shared between these two traits (Dice coefficient = 92.8%). By employing conjFDR, 236 loci were identified jointly associated with SCZ and CP, of which 139 were novel for the two traits. Within these shared loci, 60 exhibited consistent effect directions, while 176 had opposite effect directions. Functional annotation analysis indicated that the shared genetic loci were mainly located in intronic and intergenic regions, and were found to be involved in relevant biological processes such as nervous system development, multicellular organism development, and generation of neurons. Together, our findings provide insights into the shared genetic architecture between SCZ and CP, suggesting common pathways and mechanisms contributing to both traits.
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Affiliation(s)
- Jianfei Zhang
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, China
| | - Hao Qiu
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, China
| | - Qiyu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chongjian Liao
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Yuxuan Guoli
- The Second Hospital of Tianjin Medial University, Tianjin, China
| | - Qi Luo
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Guoshu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Nannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghuan Lei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
| | - Yanmin Peng
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China.
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4
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Kim H, Ahn Y, Yoon J, Jung K, Kim S, Shim I, Park TH, Ko H, Jung SH, Kim J, Park S, Lee DJ, Choi S, Cha S, Kim B, Cho MY, Cho H, Kim DS, Jang Y, Ihm HK, Park WY, Bakhshi H, O Connell KS, Andreassen OA, Kendler KS, Myung W, Won HH. Genome-wide association analyses using machine learning-based phenotyping reveal genetic architecture of occupational creativity and overlap with psychiatric disorders. Psychiatry Res 2024; 333:115753. [PMID: 38335777 DOI: 10.1016/j.psychres.2024.115753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
Creativity is known to be heritable and exhibits familial aggregation with psychiatric disorders; however, the complex nature of their relationship has not been well-established. In the present study, we demonstrate that using an expanded and validated machine learning (ML)-based phenotyping of occupational creativity (OC) can allow us to further understand the trait of creativity, which was previously difficult to define and study. We conducted the largest genome-wide association study (GWAS) on OC with 241,736 participants from the UK Biobank and identified 25 lead variants that have not yet been reported and three candidate causal genes that were previously associated with educational attainment and psychiatric disorders. We found extensive genetic overlap between OC and psychiatric disorders with mixed effect direction through various post-GWAS analyses, including the bivariate causal mixture model. In addition, we discovered a strongly genetic correlation between our original GWAS and the GWAS adjusted for education years (rg = 0.95). Our GWAS analysis via ML-based phenotyping contributes to the understanding of the genetic architecture of creativity, which may inform genetic discovery and genetic prediction in human cognition and psychiatric disorders.
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Affiliation(s)
- Hyejin Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Yeeun Ahn
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Joohyun Yoon
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Kyeongmin Jung
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Soyeon Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Tae Hwan Park
- Department of Plastic Surgery, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwasung, South Korea
| | - Hyunwoong Ko
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, South Korea; Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea; Dental Research Institute, Seoul National University School of Dentistry, Seoul, South Korea
| | - Sang-Hyuk Jung
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Jaeyoung Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Sanghyeon Park
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Dong June Lee
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
| | - Sunho Choi
- Department of Psychiatry, Seoul National University, College of Medicine, Seoul, South Korea
| | - Soojin Cha
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Beomsu Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Min Young Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Hyunbin Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Dan Say Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Yoonjeong Jang
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea; Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Hong Kyu Ihm
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hasan Bakhshi
- Creative Industries Policy and Evidence Centre, Nesta, London, United Kingdom
| | - Kevin S O Connell
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kenneth S Kendler
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea; Department of Psychiatry, Seoul National University, College of Medicine, Seoul, South Korea.
| | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
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5
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Holen B, Kutrolli G, Shadrin AA, Icick R, Hindley G, Rødevand L, O'Connell KS, Frei O, Parker N, Tesfaye M, Deak JD, Jahołkowski P, Dale AM, Djurovic S, Andreassen OA, Smeland OB. Genome-wide analyses reveal shared genetic architecture and novel risk loci between opioid use disorder and general cognitive ability. Drug Alcohol Depend 2024; 256:111058. [PMID: 38244365 DOI: 10.1016/j.drugalcdep.2023.111058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/09/2023] [Accepted: 12/03/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND Opioid use disorder (OUD), a serious health burden worldwide, is associated with lower cognitive function. Recent studies have demonstrated a negative genetic correlation between OUD and general cognitive ability (COG), indicating a shared genetic basis. However, the specific genetic variants involved, and the underlying molecular mechanisms remain poorly understood. Here, we aimed to quantify and identify the genetic basis underlying OUD and COG. METHODS We quantified the extent of genetic overlap between OUD and COG using a bivariate causal mixture model (MiXeR) and identified specific genetic loci applying conditional/conjunctional FDR. Finally, we investigated biological function and expression of implicated genes using available resources. RESULTS We estimated that ~94% of OUD variants (4.8k out of 5.1k variants) also influence COG. We identified three novel OUD risk loci and one locus shared between OUD and COG. Loci identified implicated biological substrates in the basal ganglia. CONCLUSION We provide new insights into the complex genetic risk architecture of OUD and its genetic relationship with COG.
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Affiliation(s)
- Børge Holen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway.
| | - Gleda Kutrolli
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Alexey A Shadrin
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Romain Icick
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway; INSERM UMR-S1144, Université Paris Cité, F-75006, France
| | - Guy Hindley
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Linn Rødevand
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Kevin S O'Connell
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Nadine Parker
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Markos Tesfaye
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway; NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Joseph D Deak
- Yale School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Piotr Jahołkowski
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA 92093, USA; Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Olav B Smeland
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway.
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Cabrera-Mendoza B, Aydin N, Fries GR, Docherty AR, Walss-Bass C, Polimanti R. Estimating the direct effects of the genetic liabilities to bipolar disorder, schizophrenia, and behavioral traits on suicide attempt using a multivariable Mendelian randomization approach. Neuropsychopharmacology 2024:10.1038/s41386-024-01833-2. [PMID: 38396255 DOI: 10.1038/s41386-024-01833-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 01/25/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024]
Abstract
Bipolar disorder (BD) and schizophrenia (SZ) are associated with higher odds of suicide attempt (SA). In this study, we aimed to explore the effect of BD and SZ genetic liabilities on SA, also considering the contribution of behavioral traits, socioeconomic factors, and substance use disorders. Leveraging large-scale genome-wide association data from the Psychiatric Genomics Consortium (PGC) and the UK Biobank (UKB), we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the putative causal effect of BD (41,917 cases, 371,549 controls) and SZ (53,386 cases, 77,258 controls) on SA (26,590 cases, 492,022 controls). Then, we assessed the putative causal effect of BD and SZ on behavioral traits, socioeconomic factors, and substance use disorders. Considering the associations identified, we evaluated the direct causal effect of behavioral traits, socioeconomic factors, and substance use disorders on SA using a multivariable MR approach. The genetic liabilities to BD and SZ were associated with higher odds of SA (BD odds ratio (OR) = 1.24, p = 3.88 × 10-12; SZ OR = 1.09, p = 2.44 × 10-20). However, while the effect of mental distress (OR = 1.17, p = 1.02 × 10-4) and risk-taking (OR = 1.52, p = 0.028) on SA was independent of SZ genetic liability, the BD-SA relationship appeared to account for the effect of these risk factors. Similarly, the association with loneliness on SA was null after accounting for the effect of SZ genetic liability. These findings highlight the complex interplay between genetic risk of psychiatric disorders and behavioral traits in the context of SA, suggesting the need for a comprehensive mental health assessment for high-risk individuals.
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Affiliation(s)
- Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA.
- VA CT Healthcare System, West Haven, CT, 06516, USA.
| | - Necla Aydin
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Gabriel R Fries
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054, Houston, TX, USA
| | - Anna R Docherty
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
- Huntsman Mental Health Institute, Salt Lake City, UT, USA
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Consuelo Walss-Bass
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054, Houston, TX, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare System, West Haven, CT, 06516, USA
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Wahl N, Espeso-Gil S, Chietera P, Nagel A, Laighneach A, Morris DW, Rajarajan P, Akbarian S, Dechant G, Apostolova G. SATB2 organizes the 3D genome architecture of cognition in cortical neurons. Mol Cell 2024; 84:621-639.e9. [PMID: 38244545 PMCID: PMC10923151 DOI: 10.1016/j.molcel.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 10/02/2023] [Accepted: 12/15/2023] [Indexed: 01/22/2024]
Abstract
The DNA-binding protein SATB2 is genetically linked to human intelligence. We studied its influence on the three-dimensional (3D) epigenome by mapping chromatin interactions and accessibility in control versus SATB2-deficient cortical neurons. We find that SATB2 affects the chromatin looping between enhancers and promoters of neuronal-activity-regulated genes, thus influencing their expression. It also alters A/B compartments, topologically associating domains, and frequently interacting regions. Genes linked to SATB2-dependent 3D genome changes are implicated in highly specialized neuronal functions and contribute to cognitive ability and risk for neuropsychiatric and neurodevelopmental disorders. Non-coding DNA regions with a SATB2-dependent structure are enriched for common variants associated with educational attainment, intelligence, and schizophrenia. Our data establish SATB2 as a cell-type-specific 3D genome modulator, which operates both independently and in cooperation with CCCTC-binding factor (CTCF) to set up the chromatin landscape of pyramidal neurons for cognitive processes.
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Affiliation(s)
- Nico Wahl
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria.
| | - Sergio Espeso-Gil
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria; Department of Psychiatry, Friedman Brain Institute, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Paola Chietera
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria
| | - Amelie Nagel
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria
| | - Aodán Laighneach
- Centre for Neuroimaging, Cognition and Genomics (NICOG), School of Biological and Chemical Sciences, University of Galway, Galway, H91 TK33, Ireland
| | - Derek W Morris
- Centre for Neuroimaging, Cognition and Genomics (NICOG), School of Biological and Chemical Sciences, University of Galway, Galway, H91 TK33, Ireland
| | - Prashanth Rajarajan
- Department of Psychiatry, Friedman Brain Institute, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Schahram Akbarian
- Department of Psychiatry, Friedman Brain Institute, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Georg Dechant
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria.
| | - Galina Apostolova
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria.
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8
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Bhattacharyya U, John J, Lam M, Fisher J, Sun B, Baird D, Chen CY, Lencz T. Large-Scale Mendelian Randomization Study Reveals Circulating Blood-based Proteomic Biomarkers for Psychopathology and Cognitive Task Performance. medRxiv 2024:2024.01.18.24301455. [PMID: 38293198 PMCID: PMC10827252 DOI: 10.1101/2024.01.18.24301455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Background Research on peripheral (e.g., blood-based) biomarkers for psychiatric illness has typically been low-throughput in terms of both the number of subjects and the range of assays performed. Moreover, traditional case-control studies examining blood-based biomarkers are subject to potential confounds of treatment and other exposures common to patients with psychiatric illnesses. Our research addresses these challenges by leveraging large-scale, high-throughput proteomics data and Mendelian Randomization (MR) to examine the causal impact of circulating proteins on psychiatric phenotypes and cognitive task performance. Methods We utilized plasma proteomics data from the UK Biobank (3,072 proteins assayed in 34,557 European-ancestry individuals) and deCODE Genetics (4,719 proteins measured across 35,559 Icelandic individuals). Significant proteomic quantitative trait loci (both cis-pQTLs and trans-pQTLs) served as MR instruments, with the most recent GWAS for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance (all excluding overlapping UK Biobank participants) as phenotypic outcomes. Results MR revealed 109 Bonferroni-corrected causal associations (44 novel) involving 88 proteins across the four phenotypes. Several immune-related proteins, including interleukins and complement factors, stood out as pleiotropic across multiple outcome phenotypes. Drug target enrichment analysis identified several novel potential pharmacologic repurposing opportunities, including anti-inflammatory agents for schizophrenia and bipolar disorder and duloxetine for cognitive performance. Conclusions Identification of causal effects for these circulating proteins suggests potential biomarkers for these conditions and offers insights for developing innovative therapeutic strategies. The findings also indicate substantial evidence for the pleiotropic effects of many proteins across different phenotypes, shedding light on the shared etiology among psychiatric conditions and cognitive ability.
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Affiliation(s)
- Upasana Bhattacharyya
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
| | - Jibin John
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
| | - Max Lam
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
| | - Jonah Fisher
- Biogen Inc., Cambridge, MA
- Harvard T.H. Chan School of Public Health, Cambridge, MA
| | | | | | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
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9
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Johnson EC, Austin-Zimmerman I, Thorpe HH, Levey DF, Baranger DA, Colbert SM, Demontis D, Khokhar JY, Davis LK, Edenberg HJ, Forti MD, Sanchez-Roige S, Gelernter J, Agrawal A. Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking. medRxiv 2024:2024.01.17.24301430. [PMID: 38293235 PMCID: PMC10827265 DOI: 10.1101/2024.01.17.24301430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Individuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz), cannabis use disorder (CanUD), and ever-regular tobacco smoking (Smk) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17 - 0.62). Causal inference analyses suggested the presence of horizontal pleiotropy, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for horizontal pleiotropy. We identified 439 pleiotropic loci in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both horizontal pleiotropy and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
| | - Isabelle Austin-Zimmerman
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Hayley Ha Thorpe
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - David Aa Baranger
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO USA
| | - Sarah Mc Colbert
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Ditte Demontis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Department of Biomedicine and Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Jibran Y Khokhar
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Marta Di Forti
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry, UC San Diego School of Medicine, La Jolla, CA, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
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10
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Clarin JD, Reddy N, Alexandropoulos C, Gao WJ. The role of cell adhesion molecule IgSF9b at the inhibitory synapse and psychiatric disease. Neurosci Biobehav Rev 2024; 156:105476. [PMID: 38029609 PMCID: PMC10842117 DOI: 10.1016/j.neubiorev.2023.105476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/15/2023] [Accepted: 11/18/2023] [Indexed: 12/01/2023]
Abstract
Understanding perturbations in synaptic function between health and disease states is crucial to the treatment of neuropsychiatric illness. While genome-wide association studies have identified several genetic loci implicated in synaptic dysfunction in disorders such as autism and schizophrenia, many have not been rigorously characterized. Here, we highlight immunoglobulin superfamily member 9b (IgSF9b), a cell adhesion molecule thought to localize exclusively to inhibitory synapses in the brain. While both pre-clinical and clinical studies suggest its association with psychiatric diseases, our understanding of IgSF9b in synaptic maintenance, neural circuits, and behavioral phenotypes remains rudimentary. Moreover, these functions wield undiscovered influences on neurodevelopment. This review evaluates current literature and publicly available gene expression databases to explore the implications of IgSF9b dysfunction in rodents and humans. Through a focused analysis of one high-risk gene locus, we identify areas requiring further investigation and unearth clues related to broader mechanisms contributing to the synaptic etiology of psychiatric disorders.
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Affiliation(s)
- Jacob D Clarin
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA 19129, United States
| | - Natasha Reddy
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA 19129, United States
| | - Cassandra Alexandropoulos
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA 19129, United States
| | - Wen-Jun Gao
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA 19129, United States.
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11
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Abashkin DA, Karpov DS, Kurishev AO, Marilovtseva EV, Golimbet VE. ASCL1 Is Involved in the Pathogenesis of Schizophrenia by Regulation of Genes Related to Cell Proliferation, Neuronal Signature Formation, and Neuroplasticity. Int J Mol Sci 2023; 24:15746. [PMID: 37958729 PMCID: PMC10648210 DOI: 10.3390/ijms242115746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/25/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Schizophrenia (SZ) is a common psychiatric neurodevelopmental disorder with a complex genetic architecture. Genome-wide association studies indicate the involvement of several transcription factors, including ASCL1, in the pathogenesis of SZ. We aimed to identify ASCL1-dependent cellular and molecular mechanisms associated with SZ. We used Capture-C, CRISPR/Cas9 systems and RNA-seq analysis to confirm the involvement of ASCL1 in SZ-associated pathogenesis, establish a mutant SH-SY5Y line with a functional ASCL1 knockout (ASCL1-del) and elucidate differentially expressed genes that may underlie ASCL1-dependent pathogenic mechanisms. Capture-C confirmed the spatial interaction of the ASCL1 promoter with SZ-associated loci. Transcriptome analysis showed that ASCL1 regulation may be through a negative feedback mechanism. ASCL1 dysfunction affects the expression of genes associated with the pathogenesis of SZ, as well as bipolar and depressive disorders. Genes differentially expressed in ASCL1-del are involved in cell mitosis, neuronal projection, neuropeptide signaling, and the formation of intercellular contacts, including the synapse. After retinoic acid (RA)-induced differentiation, ASCL1 activity is restricted to a small subset of genes involved in neuroplasticity. These data suggest that ASCL1 dysfunction promotes SZ development predominantly before the onset of neuronal differentiation by slowing cell proliferation and impeding the formation of neuronal signatures.
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Affiliation(s)
| | - Dmitry S. Karpov
- Mental Health Research Center, Kashirskoe Sh., 34, Moscow 115522, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov Str. 32, Moscow 119991, Russia
| | | | | | - Vera E. Golimbet
- Mental Health Research Center, Kashirskoe Sh., 34, Moscow 115522, Russia
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12
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González-Peñas J, de Hoyos L, Díaz-Caneja CM, Andreu-Bernabeu Á, Stella C, Gurriarán X, Fañanás L, Bobes J, González-Pinto A, Crespo-Facorro B, Martorell L, Vilella E, Muntané G, Molto MD, Gonzalez-Piqueras JC, Parellada M, Arango C, Costas J. Recent natural selection conferred protection against schizophrenia by non-antagonistic pleiotropy. Sci Rep 2023; 13:15500. [PMID: 37726359 PMCID: PMC10509162 DOI: 10.1038/s41598-023-42578-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 09/12/2023] [Indexed: 09/21/2023] Open
Abstract
Schizophrenia is a debilitating psychiatric disorder associated with a reduced fertility and decreased life expectancy, yet common predisposing variation substantially contributes to the onset of the disorder, which poses an evolutionary paradox. Previous research has suggested balanced selection, a mechanism by which schizophrenia risk alleles could also provide advantages under certain environments, as a reliable explanation. However, recent studies have shown strong evidence against a positive selection of predisposing loci. Furthermore, evolutionary pressures on schizophrenia risk alleles could have changed throughout human history as new environments emerged. Here in this study, we used 1000 Genomes Project data to explore the relationship between schizophrenia predisposing loci and recent natural selection (RNS) signatures after the human diaspora out of Africa around 100,000 years ago on a genome-wide scale. We found evidence for significant enrichment of RNS markers in derived alleles arisen during human evolution conferring protection to schizophrenia. Moreover, both partitioned heritability and gene set enrichment analyses of mapped genes from schizophrenia predisposing loci subject to RNS revealed a lower involvement in brain and neuronal related functions compared to those not subject to RNS. Taken together, our results suggest non-antagonistic pleiotropy as a likely mechanism behind RNS that could explain the persistence of schizophrenia common predisposing variation in human populations due to its association to other non-psychiatric phenotypes.
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Affiliation(s)
- Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain.
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Carol Stella
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Xaquín Gurriarán
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Lourdes Fañanás
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Julio Bobes
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences - Psychiatry, Universidad de Oviedo, ISPA, INEUROPA, Oviedo, Spain
| | - Ana González-Pinto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- BIOARABA Health Research Institute, OSI Araba, University Hospital, University of the Basque Country, Vitoria, Spain
| | - Benedicto Crespo-Facorro
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Psychiatry, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Seville, Spain
| | - Lourdes Martorell
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - Elisabet Vilella
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - Gerard Muntané
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - María Dolores Molto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Genetics, University of Valencia, Campus of Burjassot, Valencia, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
| | - Jose Carlos Gonzalez-Piqueras
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
- Fundación Investigación Hospital Clínico de Valencia, INCLIVA, 46010, Valencia, Spain
| | - Mara Parellada
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier Costas
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
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13
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Gao CX, Dwyer D, Zhu Y, Smith CL, Du L, Filia KM, Bayer J, Menssink JM, Wang T, Bergmeir C, Wood S, Cotton SM. An overview of clustering methods with guidelines for application in mental health research. Psychiatry Res 2023; 327:115265. [PMID: 37348404 DOI: 10.1016/j.psychres.2023.115265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 06/24/2023]
Abstract
Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.
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Affiliation(s)
- Caroline X Gao
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia; Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Dominic Dwyer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Ye Zhu
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Catherine L Smith
- Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Lan Du
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Kate M Filia
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Johanna Bayer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Jana M Menssink
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Teresa Wang
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Christoph Bergmeir
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia; Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Stephen Wood
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Sue M Cotton
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
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14
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Cabrera-Mendoza B, Aydin N, Fries GR, Docherty AR, Walss-Bass C, Polimanti R. Estimating the direct effects of the genetic liabilities to bipolar disorder, schizophrenia, and behavioral traits on suicide attempt using a multivariable Mendelian randomization approach. medRxiv 2023:2023.08.14.23294083. [PMID: 37645805 PMCID: PMC10462224 DOI: 10.1101/2023.08.14.23294083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Bipolar disorder (BD) and schizophrenia (SZ) are associated with higher odds of suicide attempt (SA). In this study, we aimed to explore the effect of BD and SZ genetic liabilities on SA, also considering the contribution of behavioral traits, socioeconomic factors, and substance use disorders. Leveraging large-scale genome-wide association data from the Psychiatric Genomics Consortium (PGC) and the UK Biobank (UKB), we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the putative causal effect of BD (41,917 cases, 371,549 controls) and SZ (53,386 cases, 77,258 controls) on SA (26,590 cases, 492,022 controls). Then, we assessed the putative causal effect of BD and SZ on behavioral traits, socioeconomic factors, and substance use disorders. Considering the associations identified, we evaluated the direct causal effect of behavioral traits, socioeconomic factors, and substance use disorders on SA using a multivariable MR approach. The genetic liabilities to BD and SZ were associated with higher odds of SA (BD odds ratio (OR)=1.24, p=3.88×10-12; SZ OR=1.09, p=2.44×10-20). However, while the effect of mental distress (OR=1.17, p=1.02×10-4) and risk-taking (OR=1.52, p=0.028) on SA was independent of SZ genetic liability, the BD-SA relationship appeared to account for the effect of these risk factors. Similarly, the association with loneliness on SA was null after accounting for the effect of SZ genetic liability. These findings highlight the complex interplay between genetic risk of psychiatric disorders and behavioral traits in the context of SA, suggesting the need for a comprehensive mental health assessment for high-risk individuals.
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Affiliation(s)
- Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale School of Medicine, West Haven, CT 06516, USA
- VA CT Healthcare System, West Haven, CT, 06516, USA
| | - Necla Aydin
- Department of Psychiatry, Yale School of Medicine, West Haven, CT 06516, USA
- Faculty of Medicine, Istanbul University, Turkey
| | - Gabriel R. Fries
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054 Houston, Texas, USA
| | - Anna R. Docherty
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
- Huntsman Mental Health Institute, Salt Lake City, UT, USA
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Consuelo Walss-Bass
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054 Houston, Texas, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, West Haven, CT 06516, USA
- VA CT Healthcare System, West Haven, CT, 06516, USA
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15
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Nurmi EL, Laughlin CP, de Wit H, Palmer AA, MacKillop J, Cannon TD, Bilder RM, Congdon E, Sabb FW, Seaman LC, McElroy JJ, Libowitz MR, Weafer J, Gray J, Dean AC, Hellemann GS, London ED. Polygenic contributions to performance on the Balloon Analogue Risk Task. Mol Psychiatry 2023; 28:3524-3530. [PMID: 37582857 PMCID: PMC10618088 DOI: 10.1038/s41380-023-02123-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 05/03/2023] [Accepted: 06/07/2023] [Indexed: 08/17/2023]
Abstract
Risky decision-making is a common, heritable endophenotype seen across many psychiatric disorders. Its underlying genetic architecture is incompletely explored. We examined behavior in the Balloon Analogue Risk Task (BART), which tests risky decision-making, in two independent samples of European ancestry. One sample (n = 1138) comprised healthy participants and some psychiatric patients (53 schizophrenia, 42 bipolar disorder, 47 ADHD); the other (n = 911) excluded for recent treatment of various psychiatric disorders but not ADHD. Participants provided DNA and performed the BART, indexed by mean adjusted pumps. We constructed a polygenic risk score (PRS) for discovery in each dataset and tested it in the other as replication. Subsequently, a genome-wide MEGA-analysis, combining both samples, tested genetic correlation with risk-taking self-report in the UK Biobank sample and psychiatric phenotypes characterized by risk-taking (ADHD, Bipolar Disorder, Alcohol Use Disorder, prior cannabis use) in the Psychiatric Genomics Consortium. The PRS for BART performance in one dataset predicted task performance in the replication sample (r = 0.13, p = 0.000012, pFDR = 0.000052), as did the reciprocal analysis (r = 0.09, p = 0.0083, pFDR=0.04). Excluding participants with psychiatric diagnoses produced similar results. The MEGA-GWAS identified a single SNP (rs12023073; p = 3.24 × 10-8) near IGSF21, a protein involved in inhibitory brain synapses; replication samples are needed to validate this result. A PRS for self-reported cannabis use (p = 0.00047, pFDR = 0.0053), but not self-reported risk-taking or psychiatric disorder status, predicted behavior on the BART in our MEGA-GWAS sample. The findings reveal polygenic architecture of risky decision-making as measured by the BART and highlight its overlap with cannabis use.
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Affiliation(s)
- E L Nurmi
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, 90024, USA.
| | - C P Laughlin
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, 90024, USA
| | - H de Wit
- Department of Psychiatry, University of Chicago, Chicago, IL, 60637, USA
| | - A A Palmer
- Department of Psychiatry, University of California at San Diego, La Jolla, CA, 92093, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - J MacKillop
- Peter Boris Centre for Addictions Research, McMaster University and St. Joseph's Healthcare Hamilton, Hamilton, ON, L8S4L8, Canada
| | - T D Cannon
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, 06520, USA
| | - R M Bilder
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, 90024, USA
| | - E Congdon
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, 90024, USA
| | - F W Sabb
- Prevention Science Institute, University of Utah, Salt Lake City, UT, 84112, USA
| | - L C Seaman
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, 90024, USA
| | - J J McElroy
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, 90024, USA
| | - M R Libowitz
- Department of Neurobiology, University of Kentucky, Lexington, KY, 40506, USA
| | - J Weafer
- Department of Psychology, University of Kentucky, Lexington, KY, 40506, USA
| | - J Gray
- Department of Psychology, University of Georgia, Athens, GA, 30602, USA
| | - A C Dean
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, 90024, USA
| | - G S Hellemann
- Department of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - E D London
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, 90024, USA
- Department of Molecular and Medical Pharmacology, University of California at Los Angeles, Los Angeles, CA, 90024, USA
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16
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McCutcheon RA, Keefe RSE, McGuire PK. Cognitive impairment in schizophrenia: aetiology, pathophysiology, and treatment. Mol Psychiatry 2023; 28:1902-1918. [PMID: 36690793 PMCID: PMC10575791 DOI: 10.1038/s41380-023-01949-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 01/25/2023]
Abstract
Cognitive deficits are a core feature of schizophrenia, account for much of the impaired functioning associated with the disorder and are not responsive to existing treatments. In this review, we first describe the clinical presentation and natural history of these deficits. We then consider aetiological factors, highlighting how a range of similar genetic and environmental factors are associated with both cognitive function and schizophrenia. We then review the pathophysiological mechanisms thought to underlie cognitive symptoms, including the role of dopamine, cholinergic signalling and the balance between GABAergic interneurons and glutamatergic pyramidal cells. Finally, we review the clinical management of cognitive impairments and candidate novel treatments.
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Affiliation(s)
- Robert A McCutcheon
- Department of Psychiatry, University of Oxford, Oxford, UK.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
- Oxford health NHS Foundation Trust, Oxford health NHS Foundation Trust, Oxford, UK.
| | - Richard S E Keefe
- Departments of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Philip K McGuire
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford health NHS Foundation Trust, Oxford health NHS Foundation Trust, Oxford, UK
- NIHR Oxford Health Biomedical Research Centre, Oxford, UK
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17
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Soo CC, Brandenburg JT, Nebel A, Tollman S, Berkman L, Ramsay M, Choudhury A. Genome-wide association study of population-standardised cognitive performance phenotypes in a rural South African community. Commun Biol 2023; 6:328. [PMID: 36973338 PMCID: PMC10043003 DOI: 10.1038/s42003-023-04636-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/28/2023] [Indexed: 03/29/2023] Open
Abstract
Cognitive function is an indicator for global physical and mental health, and cognitive impairment has been associated with poorer life outcomes and earlier mortality. A standard cognition test, adapted to a rural-dwelling African community, and the Oxford Cognition Screen-Plus were used to capture cognitive performance as five continuous traits (total cognition score, verbal episodic memory, executive function, language, and visuospatial ability) for 2,246 adults in this population of South Africans. A novel common variant, rs73485231, reached genome-wide significance for association with episodic memory using data for ~14 million markers imputed from the H3Africa genotyping array data. Window-based replication of previously implicated variants and regions of interest support the discovery of African-specific associated variants despite the small population size and low allele frequency. This African genome-wide association study identifies suggestive associations with general cognition and domain-specific cognitive pathways and lays the groundwork for further genomic studies on cognition in Africa.
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Affiliation(s)
- Cassandra C Soo
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Jean-Tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Almut Nebel
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Institute of Clinical Molecular Biology, Kiel University, 24105, Kiel, Germany
| | - Stephen Tollman
- MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Lisa Berkman
- MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Harvard Center for Population and Development Studies, Harvard University, Cambridge, MA, USA
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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18
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Oraki Kohshour M, Schulte EC, Heilbronner U, Budde M, Kalman JL, Senner F, Heilbronner M, Reich-Erkelenz D, Schaupp SK, Vogl T, Adorjan K, Anghelescu IG, Arolt V, Baune BT, Dannlowski U, Dietrich D, Fallgatter A, Figge C, Jäger M, Lang FU, Juckel G, Konrad C, Reimer J, Reininghaus EZ, Schmauß M, Spitzer C, von Hagen M, Wiltfang J, Zimmermann J, Andlauer TFM, Nöthen MM, Degenhardt F, Forstner AJ, Rietschel M, Witt SH, Fischer A, Falkai P, Papiol S, Schulze TG. Association between mitochondria-related genes and cognitive performance in the PsyCourse Study. J Affect Disord 2023; 325:1-6. [PMID: 36621676 DOI: 10.1016/j.jad.2023.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/22/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023]
Abstract
BACKGROUND Mitochondria generate energy through oxidative phosphorylation (OXPHOS). The function of key OXPHOS proteins can be altered by variation in mitochondria-related genes, which may increase the risk of mental illness. We investigated the association of mitochondria-related genes and their genetic risk burden with cognitive performance. METHODS We leveraged cross-sectional data from 1320 individuals with a severe psychiatric disorder and 466 neurotypical individuals from the PsyCourse Study. The cognitive tests analyzed were the Trail-Making Test, Verbal Digit Span Test, Digit-Symbol Test, and Multiple Choice Vocabulary Intelligence Test. Association analyses between the cognitive tests, and single-nucleotide polymorphisms (SNPs) mapped to mitochondria-related genes, and their polygenic risk score (PRS) for schizophrenia (SCZ) were performed with PLINK 1.9 and R program. RESULTS We found a significant association (FDR-adjusted p < 0.05) in the Cytochrome C Oxidase Assembly Factor 8 (COA8) gene locus of the OXPHOS pathway with the Verbal Digit Span (forward) test. Mitochondrial PRS was not significantly associated with any of the cognitive tests. LIMITATIONS Moderate statistical power due to relatively small sample size. CONCLUSIONS COA8 encodes a poorly characterized mitochondrial protein involved in apoptosis. Here, this gene was associated with the Verbal Digit Span (forward) test, which evaluates short-term memory. Our results warrant replication and may lead to better understanding of cognitive impairment in mental disorders.
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Vilar-Ribó L, Cabana-Domínguez J, Martorell L, Ramos-Quiroga JA, Sanchez-Roige S, Palmer AA, Vilella E, Ribasés M, Muntané G, Soler Artigas M. Shared genetic architecture between attention-deficit/hyperactivity disorder and lifespan. Neuropsychopharmacology 2023. [PMID: 36906694 DOI: 10.1038/s41386-023-01555-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 02/03/2023] [Accepted: 02/20/2023] [Indexed: 03/13/2023]
Abstract
There is evidence linking ADHD to a reduced life expectancy. The mortality rate in individuals with ADHD is twice that of the general population and it is associated with several factors, such as unhealthy lifestyle behaviors, social adversity, and mental health problems that may in turn increase mortality rates. Since ADHD and lifespan are heritable, we used data from genome-wide association studies (GWAS) of ADHD and parental lifespan, as proxy of individual lifespan, to estimate their genetic correlation, identify genetic loci jointly associated with both phenotypes and assess causality. We confirmed a negative genetic correlation between ADHD and parental lifespan (rg = -0.36, P = 1.41e-16). Nineteen independent loci were jointly associated with both ADHD and parental lifespan, with most of the alleles that increased the risk for ADHD being associated with shorter lifespan. Fifteen loci were novel for ADHD and two were already present in the original GWAS on parental lifespan. Mendelian randomization analyses pointed towards a negative causal effect of ADHD liability on lifespan (P = 1.54e-06; Beta = -0.07), although these results were not confirmed by all sensitivity analyses performed, and further evidence is required. The present study provides the first evidence of a common genetic background between ADHD and lifespan, which may play a role in the reported effect of ADHD on premature mortality risk. These results are consistent with previous epidemiological data describing reduced lifespan in mental disorders and support that ADHD is an important health condition that could negatively affect future life outcomes.
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Van der Auwera S, Garvert L, Ameling S, Völzke H, Nauck M, Völker U, Grabe HJ. The interplay between micro RNAs and genetic liability to Alzheimer's Disease on memory trajectories in the general population. Psychiatry Res 2023; 323:115141. [PMID: 36905902 DOI: 10.1016/j.psychres.2023.115141] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/20/2023] [Accepted: 02/28/2023] [Indexed: 03/13/2023]
Abstract
Deficits in cognitive function and memory are common early symptoms of neurodegenerative disorders, such as Alzheimer's Disease (AD). Several studies have discussed micro RNAs (miRNAs) as potential epigenetic early detection biomarkers. In a longitudinal general population sample (n = 548) from the Study of Health in Pomerania, we analyzed the associations between 167 baseline miRNA levels and changes in verbal memory scores with a mean follow-up time of 7.4 years. We additionally assessed the impact of an individual's genetic liability for AD on verbal memory scores in n = 2,334 subjects and a possible interactions between epigenetic and genetic markers. Results revealed two miRNAs associated with changes in immediate verbal memory over time. In interaction analyses between miRNAs and a polygenic risk score for AD, five miRNAs showed a significant interaction effect on changes in verbal memory. All of these miRNAs have previously been identified in the context of AD, neurodegeneration or cognition. Our study provides candidate miRNAs for a decline in verbal memory as an early symptom of neurodegeneration and AD. Further experimental studies are needed to verify the diagnostic value of these miRNA markers in the prodromal stage of AD.
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Affiliation(s)
- Sandra Van der Auwera
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany; German Center for Neurodegenerative Diseases DZNE, Site Rostock/Greifswald, Greifswald, Germany.
| | - Linda Garvert
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Sabine Ameling
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Henry Völzke
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany; Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Matthias Nauck
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany; Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany; German Center for Neurodegenerative Diseases DZNE, Site Rostock/Greifswald, Greifswald, Germany
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21
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Johnson EC, Kapoor M, Hatoum AS, Zhou H, Polimanti R, Wendt FR, Walters RK, Lai D, Kember RL, Hartz S, Meyers JL, Peterson RE, Ripke S, Bigdeli TB, Fanous AH, Pato CN, Pato MT, Goate AM, Kranzler HR, O'Donovan MC, Walters JTR, Gelernter J, Edenberg HJ, Agrawal A. Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder. Psychol Med 2023; 53:1196-1204. [PMID: 34231451 PMCID: PMC8738774 DOI: 10.1017/s003329172100266x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders. METHODS We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific. RESULTS We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). CONCLUSIONS Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Manav Kapoor
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Hang Zhou
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Frank R Wendt
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Sarah Hartz
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jacquelyn L Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
- Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Roseann E Peterson
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Tim B Bigdeli
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Ayman H Fanous
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Carlos N Pato
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Michele T Pato
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Michael C O'Donovan
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - James T R Walters
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - Joel Gelernter
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
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22
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Kootbodien T, London L, Martin LJ, Defo J, Ramesar R. The shared genetic architecture of suicidal behaviour and psychiatric disorders: A genomic structural equation modelling study. Front Genet 2023; 14:1083969. [PMID: 36959830 PMCID: PMC10028147 DOI: 10.3389/fgene.2023.1083969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 02/09/2023] [Indexed: 03/09/2023] Open
Abstract
Background: Suicidal behaviour (SB) refers to behaviours, ranging from non-fatal suicidal behaviour, such as suicidal ideation and attempt, to completed suicide. Despite recent advancements in genomic technology and statistical methods, it is unclear to what extent the spectrum of suicidal behaviour is explained by shared genetic aetiology. Methods: We identified nine genome-wide association statistics of suicidal behaviour (sample sizes, n, ranging from 62,648 to 125,844), ten psychiatric traits [n up to 386,533] and collectively, nine summary datasets of anthropometric, behavioural and socioeconomic-related traits [n ranging from 58,610 to 941,280]. We calculated the genetic correlation among these traits and modelled this using genomic structural equation modelling, identified shared biological processes and pathways between suicidal behaviour and psychiatric disorders and evaluated potential causal associations using Mendelian randomisation. Results: Among populations of European ancestry, we observed strong positive genetic correlations between suicide ideation, attempt and self-harm (rg range, 0.71-1.09) and moderate to strong genetic correlations between suicidal behaviour traits and a range of psychiatric disorders, most notably, major depression disorder (rg = 0.86, p = 1.62 × 10-36). Multivariate analysis revealed a common factor structure for suicidal behaviour traits, major depression, attention deficit hyperactivity disorder (ADHD) and alcohol use disorder. The derived common factor explained 38.7% of the shared variance across the traits. We identified 2,951 genes and 98 sub-network hub genes associated with the common factor, including pathways associated with developmental biology, signal transduction and RNA degradation. We found suggestive evidence for the protective effects of higher household income level on suicide attempt [OR = 0.55 (0.44-0.70), p = 1.29 × 10-5] and while further investigation is needed, a nominal significant effect of smoking on suicide attempt [OR = 1.24 (1.04-1.44), p = 0.026]. Conclusion: Our findings provide evidence of shared aetiology between suicidal behaviour and psychiatric disorders and indicate potential common molecular mechanisms contributing to the overlapping pathophysiology. These findings provide a better understanding of the complex genetic architecture of suicidal behaviour and have implications for the prevention and treatment of suicidal behaviour.
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Affiliation(s)
- Tahira Kootbodien
- UCT/MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute for Infectious Diseases and Molecular Medicine, University of Cape Town and Affiliated Hospitals, Cape Town, South Africa
- *Correspondence: Tahira Kootbodien,
| | - Leslie London
- School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Lorna J. Martin
- Division of Forensic Medicine and Toxicology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Joel Defo
- UCT/MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute for Infectious Diseases and Molecular Medicine, University of Cape Town and Affiliated Hospitals, Cape Town, South Africa
| | - Raj Ramesar
- UCT/MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute for Infectious Diseases and Molecular Medicine, University of Cape Town and Affiliated Hospitals, Cape Town, South Africa
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23
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Bhatt IS, Wilson N, Dias R, Torkamani A. A genome-wide association study of tinnitus reveals shared genetic links to neuropsychiatric disorders. Sci Rep 2022; 12:22511. [PMID: 36581688 PMCID: PMC9800371 DOI: 10.1038/s41598-022-26413-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/14/2022] [Indexed: 12/30/2022] Open
Abstract
Tinnitus, a phantom perception of sound in the absence of any external sound source, is a prevalent health condition often accompanied by psychiatric comorbidities. Recent genome-wide association studies (GWAS) highlighted a polygenic nature of tinnitus susceptibility. A shared genetic component between tinnitus and psychiatric conditions remains elusive. Here we present a GWAS using the UK Biobank to investigate the genetic processes linked to tinnitus and tinnitus-related distress, followed by gene-set enrichment analyses. The UK Biobank sample comprised 132,438 individuals with tinnitus and genotype data. Among the study sample, 38,525 individuals reported tinnitus, and 26,889 participants mentioned they experienced tinnitus-related distress in daily living. The genome-wide association analyses were conducted on tinnitus and tinnitus-related distress. We conducted enrichment analyses using FUMA to further understand the genetic processes linked to tinnitus and tinnitus-related distress. A genome-wide significant locus (lead SNP: rs71595470) for tinnitus was obtained in the vicinity of GPM6A. Nineteen independent loci reached suggestive association with tinnitus. Fifteen independent loci reached suggestive association with tinnitus-related distress. The enrichment analysis revealed a shared genetic component between tinnitus and psychiatric traits, such as bipolar disorder, feeling worried, cognitive ability, fast beta electroencephalogram, and sensation seeking. Metabolic, cardiovascular, hematological, and pharmacological gene sets revealed a significant association with tinnitus. Anxiety and stress-related gene sets revealed a significant association with tinnitus-related distress. The GWAS signals for tinnitus were enriched in the hippocampus and cortex, and for tinnitus-related distress were enriched in the brain and spinal cord. This study provides novel insights into genetic processes associated with tinnitus and tinnitus-related distress and demonstrates a shared genetic component underlying tinnitus and psychiatric conditions. Further collaborative attempts are necessary to identify genetic components underlying the phenotypic heterogeneity in tinnitus and provide biological insight into the etiology.
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Affiliation(s)
- Ishan Sunilkumar Bhatt
- grid.214572.70000 0004 1936 8294Department of Communication Sciences & Disorders, University of Iowa, 250 Hawkins Dr, Iowa City, IA 52242 USA
| | - Nicholas Wilson
- Department of Integrative Structural and Computational Biology Scripps Science Institute, La Jolla, CA 92037 USA
| | - Raquel Dias
- grid.15276.370000 0004 1936 8091Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32608 USA
| | - Ali Torkamani
- Department of Integrative Structural and Computational Biology Scripps Science Institute, La Jolla, CA 92037 USA
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24
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Lam M, Chen CY, Hill WD, Xia C, Tian R, Levey DF, Gelernter J, Stein MB, Hatoum AS, Huang H, Malhotra AK, Runz H, Ge T, Lencz T. Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology. Nat Commun 2022; 13:6868. [PMID: 36369282 PMCID: PMC9652380 DOI: 10.1038/s41467-022-34418-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Cognitive deficits are known to be related to most forms of psychopathology. Here, we perform local genetic correlation analysis as a means of identifying independent segments of the genome that show biologically interpretable pleiotropic associations between cognitive dimensions and psychopathology. We identify collective segments of the genome, which we call "meta-loci", showing differential pleiotropic patterns for psychopathology relative to either cognitive task performance (CTP) or performance on a non-cognitive factor (NCF) derived from educational attainment. We observe that neurodevelopmental gene sets expressed during the prenatal-early childhood period predominate in CTP-relevant meta-loci, while post-natal gene sets are more involved in NCF-relevant meta-loci. Further, we demonstrate that neurodevelopmental gene sets are dissociable across CTP meta-loci with respect to their spatial distribution across the brain. Additionally, we find that GABA-ergic, cholinergic, and glutamatergic genes drive pleiotropic relationships within dissociable meta-loci.
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Affiliation(s)
- Max Lam
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell, Glen Oaks, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Institute of Mental Health, Singapore, Singapore
| | - Chia-Yen Chen
- Translational Biology, Research and Development, Biogen Inc, Cambridge, MA, USA
| | - W David Hill
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Charley Xia
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ruoyu Tian
- Computational Biology and Human Genetics, Dewpoint Therapeutics, Boston, MA, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Murray B Stein
- VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University in St. Louis Medical School, St. Louis, MO, USA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Anil K Malhotra
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell, Glen Oaks, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Norwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Norwell, Hempstead, NY, USA
| | - Heiko Runz
- Translational Biology, Research and Development, Biogen Inc, Cambridge, MA, USA
| | - Tian Ge
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Todd Lencz
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell, Glen Oaks, NY, USA.
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA.
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Norwell, Hempstead, NY, USA.
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Norwell, Hempstead, NY, USA.
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25
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Batiuk MY, Tyler T, Dragicevic K, Mei S, Rydbirk R, Petukhov V, Deviatiiarov R, Sedmak D, Frank E, Feher V, Habek N, Hu Q, Igolkina A, Roszik L, Pfisterer U, Garcia-Gonzalez D, Petanjek Z, Adorjan I, Kharchenko PV, Khodosevich K. Upper cortical layer-driven network impairment in schizophrenia. Sci Adv 2022; 8:eabn8367. [PMID: 36223459 PMCID: PMC9555788 DOI: 10.1126/sciadv.abn8367] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 08/24/2022] [Indexed: 05/31/2023]
Abstract
Schizophrenia is one of the most widespread and complex mental disorders. To characterize the impact of schizophrenia, we performed single-nucleus RNA sequencing (snRNA-seq) of >220,000 neurons from the dorsolateral prefrontal cortex of patients with schizophrenia and matched controls. In addition, >115,000 neurons were analyzed topographically by immunohistochemistry. Compositional analysis of snRNA-seq data revealed a reduction in abundance of GABAergic neurons and a concomitant increase in principal neurons, most pronounced for upper cortical layer subtypes, which was substantiated by histological analysis. Many neuronal subtypes showed extensive transcriptomic changes, the most marked in upper-layer GABAergic neurons, including down-regulation in energy metabolism and up-regulation in neurotransmission. Transcription factor network analysis demonstrated a developmental origin of transcriptomic changes. Last, Visium spatial transcriptomics further corroborated upper-layer neuron vulnerability in schizophrenia. Overall, our results point toward general network impairment within upper cortical layers as a core substrate associated with schizophrenia symptomatology.
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Affiliation(s)
- Mykhailo Y. Batiuk
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Teadora Tyler
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest H-1085, Hungary
| | - Katarina Dragicevic
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Shenglin Mei
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Rasmus Rydbirk
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Viktor Petukhov
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Ruslan Deviatiiarov
- The National Center for Personalized Medicine of Endocrine Diseases, Moscow 115478, Russia
- Kazan Federal University, Kazan 420043, Russia
| | - Dora Sedmak
- Croatian Institute for Brain Research and Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb 10000, Croatia
| | - Erzsebet Frank
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest H-1085, Hungary
| | - Virginia Feher
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest H-1085, Hungary
| | - Nikola Habek
- Croatian Institute for Brain Research and Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb 10000, Croatia
| | - Qiwen Hu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Anna Igolkina
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- St. Petersburg Polytechnical University, St. Petersburg 195251, Russia
| | - Lilla Roszik
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest H-1085, Hungary
| | - Ulrich Pfisterer
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Diego Garcia-Gonzalez
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Zdravko Petanjek
- Croatian Institute for Brain Research and Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb 10000, Croatia
| | - Istvan Adorjan
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest H-1085, Hungary
| | - Peter V. Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Konstantin Khodosevich
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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26
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Barbu MC, Harris M, Shen X, Aleks S, Green C, Amador C, Walker R, Morris S, Adams M, Sandu A, McNeil C, Waiter G, Evans K, Campbell A, Wardlaw J, Steele D, Murray A, Porteous D, McIntosh A, Whalley H. Epigenome-wide association study of global cortical volumes in generation Scotland: Scottish family health study. Epigenetics 2022; 17:1143-1158. [PMID: 34738878 PMCID: PMC9542280 DOI: 10.1080/15592294.2021.1997404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 10/18/2021] [Accepted: 10/21/2021] [Indexed: 01/22/2023] Open
Abstract
A complex interplay of genetic and environmental risk factors influence global brain structural alterations associated with brain health and disease. Epigenome-wide association studies (EWAS) of global brain imaging phenotypes have the potential to reveal the mechanisms of brain health and disease and can lead to better predictive analytics through the development of risk scores.We perform an EWAS of global brain volumes in Generation Scotland using peripherally measured whole blood DNA methylation (DNAm) from two assessments, (i) at baseline recruitment, ~6 years prior to MRI assessment (N = 672) and (ii) concurrent with MRI assessment (N=565). Four CpGs at baseline were associated with global cerebral white matter, total grey matter, and whole-brain volume (Bonferroni p≤7.41×10-8, βrange = -1.46x10-6 to 9.59 × 10-7). These CpGs were annotated to genes implicated in brain-related traits, including psychiatric disorders, development, and ageing. We did not find significant associations in the meta-analysis of the EWAS of the two sets concurrent with imaging at the corrected level.These findings reveal global brain structural changes associated with DNAm measured ~6 years previously, indicating a potential role of early DNAm modifications in brain structure. Although concurrent DNAm was not associated with global brain structure, the nominally significant findings identified here present a rationale for future investigation of associations between DNA methylation and structural brain phenotypes in larger population-based samples.
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Affiliation(s)
- Miruna Carmen Barbu
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Mat Harris
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Stolicyn Aleks
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Claire Green
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Carmen Amador
- Mrc Human Genetics Unit, Institute of Genetics and Cancer, the University of Edinburgh, UK
| | - Rosie Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Stewart Morris
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Mark Adams
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Anca Sandu
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - Christopher McNeil
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - Gordon Waiter
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - Kathryn Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Archie Campbell
- Mrc Human Genetics Unit, Institute of Genetics and Cancer, the University of Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Douglas Steele
- Imaging Science and Technology, School of Medicine, University of Dundee, DundeeUK
| | - Alison Murray
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - David Porteous
- Mrc Human Genetics Unit, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, UK
| | - Andrew McIntosh
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, UK
| | - Heather Whalley
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
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27
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Viinikainen J, Bryson A, Böckerman P, Kari JT, Lehtimäki T, Raitakari O, Viikari J, Pehkonen J. Does better education mitigate risky health behavior? A mendelian randomization study. Econ Hum Biol 2022; 46:101134. [PMID: 35354116 DOI: 10.1016/j.ehb.2022.101134] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
Education and risky health behaviors are strongly negatively correlated. Education may affect health behaviors by enabling healthier choices through higher disposable income, increasing information about the harmful effects of risky health behaviors, or altering time preferences. Alternatively, the observed negative correlation may stem from reverse causality or unobserved confounders. Based on the data from the Cardiovascular Risk in Young Finns Study linked to register-based information on educational attainment and family background, this paper identifies the causal effect of education on risky health behaviors. To examine causal effects, we used a genetic score as an instrument for years of education. We found that individuals with higher education allocated more attention to healthy habits. In terms of health behaviors, highly educated people were less likely to smoke. Some model specifications also indicated that the highly educated consumed more fruit and vegetables, but the results were imprecise in this regard. No causal effect was found between education and abusive drinking. In brief, inference based on genetic instruments showed that higher education leads to better choices in some but not all dimensions of health behaviors.
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Affiliation(s)
- Jutta Viinikainen
- University of Jyväskylä, Jyväskylä University School of Business and Economics, Jyväskylä, Finland.
| | - Alex Bryson
- University College London, Social Research Institute, London, United Kingdom; National Institute of Economic and Social Research, London, United Kingdom; IZA Institute of Labor Economics, Bonn, Germany
| | - Petri Böckerman
- University of Jyväskylä, Jyväskylä University School of Business and Economics, Jyväskylä, Finland; IZA Institute of Labor Economics, Bonn, Germany; Labour Institute for Economic Research LABORE, Helsinki, Finland
| | - Jaana T Kari
- University of Jyväskylä, Jyväskylä University School of Business and Economics, Jyväskylä, Finland
| | - Terho Lehtimäki
- Tampere University, Department of Clinical Chemistry, Tampere, Finland; Fimlab Laboratories, Tampere, Finland; Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland; Tampere University, Finnish Cardiovascular Research Center, Tampere, Finland
| | - Olli Raitakari
- University of Turku and Turku University Hospital, Centre for Population Health Research, Turku, Finland; University of Turku, Research Centre of Applied and Preventive Cardiovascular Medicine, Turku, Finland; Turku University Hospital, Department of Clinical Physiology and Nuclear Medicine, Turku, Finland
| | - Jorma Viikari
- University of Turku, Department of Medicine, Turku, Finland; Turku University Hospital, Division of Medicine, Turku, Finland
| | - Jaakko Pehkonen
- University of Jyväskylä, Jyväskylä University School of Business and Economics, Jyväskylä, Finland
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28
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Kuo SS, Musket CW, Rupert PE, Almasy L, Gur RC, Prasad KM, Roalf DR, Gur RE, Nimgaonkar VL, Pogue-Geile MF. Age-dependent patterns of schizophrenia genetic risk affect cognition. Schizophr Res 2022; 246:39-48. [PMID: 35709646 DOI: 10.1016/j.schres.2022.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/15/2022] [Accepted: 05/15/2022] [Indexed: 11/15/2022]
Abstract
Cognition shares substantial genetic overlap with schizophrenia, yet it remains unclear whether such genetic effects become significant during developmental periods of elevated risk for schizophrenia, such as the peak age of onset. We introduce an investigative framework integrating epidemiological, developmental, and genetic approaches to determine whether genetic effects shared between schizophrenia and cognition are significant across periods of differing risk for schizophrenia onset, and whether these effects are shared with depression. 771 European-American participants, including 636 (ages 15-84 years) from families with at least two first-degree relatives with schizophrenia and 135 unrelated controls, were divided into three age-risk groups based on ages relative to epidemiological age of onset patterns for schizophrenia: Pre-Peak (before peak age-of-onset: 15 to 22 years), Post-Peak (after peak age-of-onset: 23-42 years), and Plateau (during plateau of age-of-onset: over 42 years). For general cognition and 11 specific cognitive traits, we estimated genetic correlations with schizophrenia and with depression within each age-risk group. Genetic effects shared between deficits in general cognition and schizophrenia were nonsignificant before peak age of onset, yet were high and significant after peak age of onset and during the plateau of onset. These age-dependent genetic effects were largely consistent across specific cognitive traits and not transdiagnostically shared with depression. Schizophrenia genetic effects appear to influence cognitive traits in an age-dependent manner, supporting late developmental and perhaps neurodegenerative models that hypothesize increased expression of schizophrenia risk genes during and after the peak age of risk. Our findings underscore the utility of cognitive traits for tracking schizophrenia genetic effects across the lifespan.
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Affiliation(s)
- Susan S Kuo
- Department of Psychology, University of Pittsburgh, United States of America; Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, United States of America; Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, United States of America
| | - Christie W Musket
- Department of Psychology, University of Pittsburgh, United States of America
| | - Petra E Rupert
- Department of Psychology, University of Pittsburgh, United States of America
| | - Laura Almasy
- Department of Genetics, University of Pennsylvania, United States of America
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, United States of America
| | - Konasale M Prasad
- Department of Psychiatry, University of Pittsburgh, United States of America; Department of Bioengineering, University of Pittsburgh, United States of America; Veteran Affairs Pittsburgh Healthcare System, United States of America
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, United States of America
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, United States of America
| | - Vishwajit L Nimgaonkar
- Department of Psychiatry, University of Pittsburgh, United States of America; Department of Human Genetics, University of Pittsburgh, United States of America
| | - Michael F Pogue-Geile
- Department of Psychology, University of Pittsburgh, United States of America; Department of Psychiatry, University of Pittsburgh, United States of America.
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29
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Ji Y, Wei Q, Chen R, Wang Q, Tao R, Li B. Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery. PLoS Genet 2022; 18:e1009814. [PMID: 35771864 PMCID: PMC9278751 DOI: 10.1371/journal.pgen.1009814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 07/13/2022] [Accepted: 05/26/2022] [Indexed: 12/30/2022] Open
Abstract
A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which can carry as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG's applications to Alzheimer's disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.
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Affiliation(s)
- Ying Ji
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Qiang Wei
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Rui Chen
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Quan Wang
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- * E-mail: (RT); (BL)
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- * E-mail: (RT); (BL)
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30
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Morrill K, Hekman J, Li X, McClure J, Logan B, Goodman L, Gao M, Dong Y, Alonso M, Carmichael E, Snyder-Mackler N, Alonso J, Noh HJ, Johnson J, Koltookian M, Lieu C, Megquier K, Swofford R, Turner-Maier J, White ME, Weng Z, Colubri A, Genereux DP, Lord KA, Karlsson EK. Ancestry-inclusive dog genomics challenges popular breed stereotypes. Science 2022; 376:eabk0639. [PMID: 35482869 DOI: 10.1126/science.abk0639] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Behavioral genetics in dogs has focused on modern breeds, which are isolated subgroups with distinctive physical and, purportedly, behavioral characteristics. We interrogated breed stereotypes by surveying owners of 18,385 purebred and mixed-breed dogs and genotyping 2155 dogs. Most behavioral traits are heritable [heritability (h2) > 25%], and admixture patterns in mixed-breed dogs reveal breed propensities. Breed explains just 9% of behavioral variation in individuals. Genome-wide association analyses identify 11 loci that are significantly associated with behavior, and characteristic breed behaviors exhibit genetic complexity. Behavioral loci are not unusually differentiated in breeds, but breed propensities align, albeit weakly, with ancestral function. We propose that behaviors perceived as characteristic of modern breeds derive from thousands of years of polygenic adaptation that predates breed formation, with modern breeds distinguished primarily by aesthetic traits.
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Affiliation(s)
- Kathleen Morrill
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Morningside Graduate School of Biomedical Sciences, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jessica Hekman
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xue Li
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Morningside Graduate School of Biomedical Sciences, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jesse McClure
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Brittney Logan
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Linda Goodman
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Fauna Bio Inc., Emeryville, CA 94608, USA
| | - Mingshi Gao
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Morningside Graduate School of Biomedical Sciences, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Yinan Dong
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Marjie Alonso
- The International Association of Animal Behavior Consultants, Cranberry Township, PA 16066, USA.,IAABC Foundation, Cranberry Township, PA 16066, USA
| | - Elena Carmichael
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Rice University, Houston, TX 77005, USA
| | - Noah Snyder-Mackler
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85251, USA.,School for Human Evolution and Social Change, Arizona State University, Tempe, AZ 85251, USA.,School of Life Sciences, Arizona State University, Tempe, AZ 85251, USA
| | - Jacob Alonso
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hyun Ji Noh
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jeremy Johnson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Charlie Lieu
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Darwin's Ark Foundation, Seattle, WA 98026, USA
| | - Kate Megquier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ross Swofford
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Michelle E White
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Zhiping Weng
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Andrés Colubri
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Kathryn A Lord
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Elinor K Karlsson
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Morningside Graduate School of Biomedical Sciences, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Darwin's Ark Foundation, Seattle, WA 98026, USA.,Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
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31
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Zhao M, Ma J, Li M, Zhu W, Zhou W, Shen L, Wu H, Zhang N, Wu S, Fu C, Li X, Yang K, Tang T, Shen R, He L, Huai C, Qin S. Different responses to risperidone treatment in Schizophrenia: a multicenter genome-wide association and whole exome sequencing joint study. Transl Psychiatry 2022; 12:173. [PMID: 35484098 PMCID: PMC9050705 DOI: 10.1038/s41398-022-01942-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 12/11/2022] Open
Abstract
Risperidone is routinely used in the clinical management of schizophrenia, but the treatment response is highly variable among different patients. The genetic underpinnings of the treatment response are not well understood. We performed a pharmacogenomic study of the treatment response to risperidone in patients with schizophrenia by using a SNP microarray -based genome-wide association study (GWAS) and whole exome sequencing (WES)-based GWAS. DNA samples were collected from 189 patients for the GWAS and from 222 patients for the WES after quality control in multiple centers of China. Antipsychotic response phenotypes of patients who received eight weeks of risperidone treatment were quantified with percentage change on the Positive and Negative Syndrome Scale (PANSS). The GWAS revealed a significant association between several SNPs and treatment response, such as three GRM7 SNPs (rs141134664, rs57521140, and rs73809055). Gene-based analysis in WES revealed 13 genes that were associated with antipsychotic response, such as GPR12 and MAP2K3. We did not identify shared loci or genes between GWAS and WES, but association signals tended to cluster into the GPCR gene family and GPCR signaling pathway, which may play an important role in the treatment response etiology. This study may provide a research paradigm for pharmacogenomic research, and these data provide a promising illustration of our potential to identify genetic variants underlying antipsychotic responses and may ultimately facilitate precision medicine in schizophrenia.
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Affiliation(s)
- Mingzhe Zhao
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Jingsong Ma
- grid.494629.40000 0004 8008 9315School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang Province China ,grid.494629.40000 0004 8008 9315Institute of Advanced Technology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang Province China
| | - Mo Li
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Wenli Zhu
- The Fourth People’s Hospital of Wuhu, No.1 East Wuxiashan Road, Wuhu, 241003 China
| | - Wei Zhou
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Lu Shen
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Hao Wu
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Na Zhang
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Shaochang Wu
- The Second People’s Hospital of Lishui, No.69 Beihua Road, Lishui, 323020 China
| | - Chunpeng Fu
- The Third People’s Hospital of Shangrao, No.1 Fenghuang East Avenue, Taokan Road, Shangrao, 334000 China
| | - Xianxi Li
- Shanghai Yangpu district mental health center, No.585 Jungong Road, Yangpu District, Shanghai, 900093 China
| | - Ke Yang
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Tiancheng Tang
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Ruoxi Shen
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Lin He
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Cong Huai
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China. .,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Shengying Qin
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China. .,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Astle DE, Holmes J, Kievit R, Gathercole SE. Annual Research Review: The transdiagnostic revolution in neurodevelopmental disorders. J Child Psychol Psychiatry 2022; 63:397-417. [PMID: 34296774 DOI: 10.1111/jcpp.13481] [Citation(s) in RCA: 76] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/04/2021] [Indexed: 12/11/2022]
Abstract
Practitioners frequently use diagnostic criteria to identify children with neurodevelopmental disorders and to guide intervention decisions. These criteria also provide the organising framework for much of the research focussing on these disorders. Study design, recruitment, analysis and theory are largely built on the assumption that diagnostic criteria reflect an underlying reality. However, there is growing concern that this assumption may not be a valid and that an alternative transdiagnostic approach may better serve our understanding of this large heterogeneous population of young people. This review draws on important developments over the past decade that have set the stage for much-needed breakthroughs in understanding neurodevelopmental disorders. We evaluate contemporary approaches to study design and recruitment, review the use of data-driven methods to characterise cognition, behaviour and neurobiology, and consider what alternative transdiagnostic models could mean for children and families. This review concludes that an overreliance on ill-fitting diagnostic criteria is impeding progress towards identifying the barriers that children encounter, understanding underpinning mechanisms and finding the best route to supporting them.
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Affiliation(s)
- Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Joni Holmes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Rogier Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Susan E Gathercole
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Department of Psychiatry, University of Cambridge, Cambridge, UK
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Cao X, Wang X, Zhang S, Sha Q. Gene-based association tests using GWAS summary statistics and incorporating eQTL. Sci Rep 2022; 12:3553. [PMID: 35241742 PMCID: PMC8894384 DOI: 10.1038/s41598-022-07465-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 02/11/2022] [Indexed: 01/29/2023] Open
Abstract
Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Xuexia Wang
- Department of Mathematics, University of North Texas, Denton, TX, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA.
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Charney E. The "Golden Age" of Behavior Genetics? Perspect Psychol Sci 2022; 17:1188-1210. [PMID: 35180032 DOI: 10.1177/17456916211041602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The search for genetic risk factors underlying the presumed heritability of all human behavior has unfolded in two phases. The first phase, characterized by candidate-gene-association (CGA) studies, has fallen out of favor in the behavior-genetics community, so much so that it has been referred to as a "cautionary tale." The second and current iteration is characterized by genome-wide association studies (GWASs), single-nucleotide polymorphism (SNP) heritability estimates, and polygenic risk scores. This research is guided by the resurrection of, or reemphasis on, Fisher's "infinite infinitesimal allele" model of the heritability of complex phenotypes, first proposed over 100 years ago. Despite seemingly significant differences between the two iterations, they are united in viewing the discovery of risk alleles underlying heritability as a matter of finding differences in allele frequencies. Many of the infirmities that beset CGA studies persist in the era of GWASs, accompanied by a host of new difficulties due to the human genome's underlying complexities and the limitations of Fisher's model in the postgenomics era.
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Affiliation(s)
- Evan Charney
- The Samuel DuBois Cook Center on Social Equity, Duke University
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35
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Abstract
OBJECTIVE LINC00461 is a highly conserved intergenic non-protein coding RNA that was implicated in schizophrenia at the genome-wide level. We aim to explore potential mechanisms underlying the involvement of LINC00461 in schizophrenia. METHODS We performed a meta-analysis to investigate the association of LINC00461 rs410216 with schizophrenia, and evaluate the effects of the rs410216 on hippocampal volume and function using the functional magnetic resonance imaging (fMRI) analysis. We utilized the GTEx dataset to profile the expression distribution of LINC00461 across different brain regions, and to investigate the potential impact of the risk SNPs on the expression of LINC00461 and other nearby genes. We compared blood expression levels of LINC00461 between schizophrenia patients and controls. RESULTS Here we show that single-nucleotide polymorphisms (SNPs) located in regulatory elements spanning the LINC00461 region are significantly associated with schizophrenia (index SNP rs410216, Pmeta = 1.43E-05); subjects carrying the risk allele of rs410216 showed decreased hippocampal volume. However, no significant association of the rs410216 variant with hippocampal activation was observed. Moreover, the expression level of LINC00461 mRNA was significantly lower in first-onset schizophrenia patients, and the risk allele also predicts a lower transcriptional level of LINC00461 in the hippocampus. CONCLUSION Together, these convergent lines of evidence implicate inadequate LINC00461 expression in the hippocampus in the development of schizophrenia, providing novel insight into the genetic architecture and biological etiology of schizophrenia.
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Affiliation(s)
- Shuquan Rao
- grid.461843.cState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020 China
| | - Lin Tian
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
| | - Hongbao Cao
- grid.22448.380000 0004 1936 8032School of Systems Biology, George Mason University (GMU), Fairfax, VA USA
| | - Ancha Baranova
- grid.22448.380000 0004 1936 8032School of Systems Biology, George Mason University (GMU), Fairfax, VA USA ,grid.415876.9Research Centre for Medical Genetics, Moscow, 115478 Russia
| | - Fuquan Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China.
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36
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Abstract
Brain asymmetry is a hallmark of the human brain. Recent studies report a certain degree of abnormal asymmetry of brain lateralization between left and right brain hemispheres can be associated with many neuropsychiatric conditions. In this regard, some questions need answers. First, the accelerated brain asymmetry is programmed during the pre-natal period that can be called “accelerated brain decline clock”. Second, can we find the right biomarkers to predict these changes? Moreover, can we establish the dynamics of these changes in order to identify the right time window for proper interventions that can reverse or limit the neurological decline? To find answers to these questions, we performed a systematic online search for the last 10 years in databases using keywords. Conclusion: we need to establish the right in vitro model that meets human conditions as much as possible. New biomarkers are necessary to establish the “good” or the “bad” borders of brain asymmetry at the epigenetic and functional level as early as possible.
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37
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Andreu-Bernabeu Á, Díaz-Caneja CM, Costas J, De Hoyos L, Stella C, Gurriarán X, Alloza C, Fañanás L, Bobes J, González-Pinto A, Crespo-Facorro B, Martorell L, Vilella E, Muntané G, Nacher J, Molto MD, Aguilar EJ, Parellada M, Arango C, González-Peñas J. Polygenic contribution to the relationship of loneliness and social isolation with schizophrenia. Nat Commun 2022; 13:51. [PMID: 35013163 PMCID: PMC8748758 DOI: 10.1038/s41467-021-27598-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 11/26/2021] [Indexed: 12/24/2022] Open
Abstract
Previous research suggests an association of loneliness and social isolation (LNL-ISO) with schizophrenia. Here, we demonstrate a LNL-ISO polygenic score contribution to schizophrenia risk in an independent case-control sample (N = 3,488). We then subset schizophrenia predisposing variation based on its effect on LNL-ISO. We find that genetic variation with concordant effects in both phenotypes shows significant SNP-based heritability enrichment, higher polygenic contribution in females, and positive covariance with mental disorders such as depression, anxiety, attention-deficit hyperactivity disorder, alcohol dependence, and autism. Conversely, genetic variation with discordant effects only contributes to schizophrenia risk in males and is negatively correlated with those disorders. Mendelian randomization analyses demonstrate a plausible bi-directional causal relationship between LNL-ISO and schizophrenia, with a greater effect of LNL-ISO liability on schizophrenia than vice versa. These results illustrate the genetic footprint of LNL-ISO on schizophrenia.
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Affiliation(s)
- Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier Costas
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
| | - Lucía De Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Carol Stella
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Xaquín Gurriarán
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
| | - Clara Alloza
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Lourdes Fañanás
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Julio Bobes
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences-Psychiatry, Universidad de Oviedo, ISPA, INEUROPA, Oviedo, Spain
| | - Ana González-Pinto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- BIOARABA Health Research Institute, OSI Araba, University Hospital, University of the Basque Country, Vitoria, Spain
| | - Benedicto Crespo-Facorro
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitario Virgen del Rocío, Department of Psychiatry, Universidad de Sevilla, Sevilla, Spain
| | - Lourdes Martorell
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain
| | - Elisabet Vilella
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain
| | - Gerard Muntané
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain
| | - Juan Nacher
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Neurobiology Unit, Department of Cell Biology, Interdisciplinary Research Structure for Biotechnology and Biomedicine (BIOTECMED), University of Valencia, Valencia, 46100, Spain
| | - María Dolores Molto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Genetics, University of Valencia, Campus of Burjassot, Valencia, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
| | - Eduardo Jesús Aguilar
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
- Fundación Investigación Hospital Clínico de Valencia, INCLIVA, 46010, Valencia, Spain
| | - Mara Parellada
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain.
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Schilling C, Zillich L, Schredl M, Frank J, Schwarz E, Deuschle M, Meyer-Lindenberg A, Rietschel M, Witt SH, Streit F. Association of polygenic risk for schizophrenia with fast sleep spindle density depends on pro-cognitive variants. Eur Arch Psychiatry Clin Neurosci 2022; 272:1193-203. [PMID: 35723738 DOI: 10.1007/s00406-022-01435-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/15/2022] [Indexed: 11/14/2022]
Abstract
Cognitive impairment is a common feature in schizophrenia and the strongest prognostic factor for long-term outcome. Identifying a trait associated with the genetic background for cognitive outcome in schizophrenia may aid in a deeper understanding of clinical disease subtypes. Fast sleep spindles may represent such a biomarker as they are strongly genetically determined, associated with cognitive functioning and impaired in schizophrenia and unaffected relatives. We measured fast sleep spindle density in 150 healthy adults and investigated its association with a genome-wide polygenic score for schizophrenia (SCZ-PGS). The association between SCZ-PGS and fast spindle density was further characterized by stratifying it to the genetic background of intelligence. SCZ-PGS was positively associated with fast spindle density. This association mainly depended on pro-cognitive genetic variants. Our results strengthen the evidence for a genetic background of spindle abnormalities in schizophrenia. Spindle density might represent an easily accessible marker for a favourable cognitive outcome which should be further investigated in clinical samples.
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Zhu Y, Wang MJ, Crawford KM, Ramírez-Tapia JC, Lussier AA, Davis KA, de Leeuw C, Takesian AE, Hensch TK, Smoller JW, Dunn EC. Sensitive period-regulating genetic pathways and exposure to adversity shape risk for depression. Neuropsychopharmacology 2022; 47:497-506. [PMID: 34689167 PMCID: PMC8674315 DOI: 10.1038/s41386-021-01172-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 07/23/2021] [Accepted: 08/30/2021] [Indexed: 01/03/2023]
Abstract
Animal and human studies have documented the existence of developmental windows (or sensitive periods) when experience can have lasting effects on brain structure or function, behavior, and disease. Although sensitive periods for depression likely arise through a complex interplay of genes and experience, this possibility has not yet been explored in humans. We examined the effect of genetic pathways regulating sensitive periods, alone and in interaction with common childhood adversities, on depression risk. Guided by a translational approach, we: (1) performed association analyses of three gene sets (60 genes) shown in animal studies to regulate sensitive periods using summary data from a genome-wide association study of depression (n = 807,553); (2) evaluated the developmental expression patterns of these genes using data from BrainSpan (n = 31), a transcriptional atlas of postmortem brain samples; and (3) tested gene-by-development interplay (dGxE) by analyzing the combined effect of common variants in sensitive period genes and time-varying exposure to two types of childhood adversity within a population-based birth cohort (n = 6254). The gene set regulating sensitive period opening associated with increased depression risk. Notably, 6 of the 15 genes in this set showed developmentally regulated gene-level expression. We also identified a statistical interaction between caregiver physical or emotional abuse during ages 1-5 years and genetic risk for depression conferred by the opening genes. Genes involved in regulating sensitive periods are differentially expressed across the life course and may be implicated in depression vulnerability. Our findings about gene-by-development interplay motivate further research in large, more diverse samples to further unravel the complexity of depression etiology through a sensitive period lens.
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Affiliation(s)
- Yiwen Zhu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Min-Jung Wang
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | | | - Alexandre A Lussier
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Brain Science, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Kathryn A Davis
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Christiaan de Leeuw
- Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anne E Takesian
- Eaton-Peabody Laboratories, Massachusetts Eye & Ear and Department of Otorhinolaryngology and Head/Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Takao K Hensch
- Center for Brain Science, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordan W Smoller
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Erin C Dunn
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Harvard Center on the Developing Child, Cambridge, MA, USA.
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40
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Abstract
Individual differences in human intelligence, as assessed using cognitive test scores, have a well-replicated, hierarchical phenotypic covariance structure. They are substantially stable across the life course, and are predictive of educational, social, and health outcomes. From this solid phenotypic foundation and importance for life, comes an interest in the environmental, social, and genetic aetiologies of intelligence, and in the foundations of intelligence differences in brain structure and functioning. Here, we summarise and critique the last 10 years or so of molecular genetic (DNA-based) research on intelligence, including the discovery of genetic loci associated with intelligence, DNA-based heritability, and intelligence's genetic correlations with other traits. We summarise new brain imaging-intelligence findings, including whole-brain associations and grey and white matter associations. We summarise regional brain imaging associations with intelligence and interpret these with respect to theoretical accounts. We address research that combines genetics and brain imaging in studying intelligence differences. There are new, though modest, associations in all these areas, and mechanistic accounts are lacking. We attempt to identify growing points that might contribute toward a more integrated 'systems biology' account of some of the between-individual differences in intelligence.
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Affiliation(s)
- Ian J. Deary
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
| | - Simon R. Cox
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
| | - W. David Hill
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
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41
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Djordjevic A, Zivkovic M, Koncar I, Stankovic A, Kuveljic J, Djuric T. Tag Variants of LGALS-3 Containing Haplotype Block in Advanced Carotid Atherosclerosis. J Stroke Cerebrovasc Dis 2021; 31:106212. [PMID: 34814004 DOI: 10.1016/j.jstrokecerebrovasdis.2021.106212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/29/2021] [Accepted: 11/01/2021] [Indexed: 10/19/2022] Open
Abstract
OBJECTIVES Galectin-3 affects a variety of biological processes. It is encoded by LGALS-3, located in unique haplotype block in Caucasians. Most of the studies regarding the gal-3 role in atherosclerosis are focused exclusively on protein/mRNA levels. Genetic analyses of LGALS-3 are scarce. We sought to thoroughly examine the genetic background of gal-3 and to analyze tag variants that cover more than 80% variability of the LGALS-3 containing hap-block in association with carotid plaque presence (CPP). According to Tagger server, rs4040064 G/T, rs11628437 G/A and rs7159490 C/T cover 82% (r2 > 0.8) of the genetic variance of this hap-block. Our aims were to investigate possible association of rs4040064, rs11628437 and rs7159490 haplotypes with CPP in patients with advanced carotid atherosclerosis (CA) and to analyze their possible effect on LGALS-3 mRNA expression in carotid plaques. MATERIALS AND METHODS Study group consisted of 468 patients and 296 controls. Rs4040064, rs11628437, rs7159490 and LGALS-3 mRNA expression were detected by TaqMan® technology. RESULTS We have found that haplotype TAC was associated with the cerebrovascular insult (CVI) occurrence (OR = 1.68, 95% CI = 1.09-2.58, p = 0.02), compared to the referent haplotype. OR was adjusted for hypertension, age and BMI. TAC also showed higher, but not statistically significant, LGALS-3 expression in carotid plaques. CONCLUSIONS Our results suggest that rs4040064, rs11628437 and rs7159490 bear no association with CPP, neither they affect LGALS-3 mRNA in carotid plaques. However, we showed a significant association of haplotype TAC with the CVI occurrence in CA patients from Serbia. Replication and validation of our results are required.
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Affiliation(s)
- Ana Djordjevic
- Department of Radiobiology and Molecular Genetics, "Vinca" Institute of Nuclear Sciences-National Institute of the Republic of Serbia, Mike Petrovica Alasa 12-14, P.O. Box 522, University of Belgrade, Belgrade 11001, Serbia.
| | - Maja Zivkovic
- Department of Radiobiology and Molecular Genetics, "Vinca" Institute of Nuclear Sciences-National Institute of the Republic of Serbia, Mike Petrovica Alasa 12-14, P.O. Box 522, University of Belgrade, Belgrade 11001, Serbia
| | - Igor Koncar
- Clinic for Vascular and Endovascular Surgery, Clinical Center of Serbia, Belgrade, Serbia; Medical Faculty, University of Belgrade, Belgrade, Serbia
| | - Aleksandra Stankovic
- Department of Radiobiology and Molecular Genetics, "Vinca" Institute of Nuclear Sciences-National Institute of the Republic of Serbia, Mike Petrovica Alasa 12-14, P.O. Box 522, University of Belgrade, Belgrade 11001, Serbia
| | - Jovana Kuveljic
- Department of Radiobiology and Molecular Genetics, "Vinca" Institute of Nuclear Sciences-National Institute of the Republic of Serbia, Mike Petrovica Alasa 12-14, P.O. Box 522, University of Belgrade, Belgrade 11001, Serbia
| | - Tamara Djuric
- Department of Radiobiology and Molecular Genetics, "Vinca" Institute of Nuclear Sciences-National Institute of the Republic of Serbia, Mike Petrovica Alasa 12-14, P.O. Box 522, University of Belgrade, Belgrade 11001, Serbia
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Liu L, Feng X, Li H, Cheng Li S, Qian Q, Wang Y. Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5. Brief Bioinform 2021; 22:bbab207. [PMID: 34109382 PMCID: PMC8575025 DOI: 10.1093/bib/bbab207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/30/2021] [Accepted: 05/11/2021] [Indexed: 11/19/2022] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the risk ADHD-associated variants and genes with significant P-values, they may neglect the combined effect of multiple variants with insignificant P-values. Here, we proposed a convolutional neural network (CNN) to classify 1033 individuals diagnosed with ADHD from 950 healthy controls according to their genomic data. The model takes the single nucleotide polymorphism (SNP) loci of P-values $\le{1\times 10^{-3}}$, i.e. 764 loci, as inputs, and achieved an accuracy of 0.9018, AUC of 0.9570, sensitivity of 0.8980 and specificity of 0.9055. By incorporating the saliency analysis for the deep learning network, a total of 96 candidate genes were found, of which 14 genes have been reported in previous ADHD-related studies. Furthermore, joint Gene Ontology enrichment and expression Quantitative Trait Loci analysis identified a potential risk gene for ADHD, EPHA5 with a variant of rs4860671. Overall, our CNN deep learning model exhibited a high accuracy for ADHD classification and demonstrated that the deep learning model could capture variants' combining effect with insignificant P-value, while GWAS fails. To our best knowledge, our model is the first deep learning method for the classification of ADHD with SNPs data.
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Affiliation(s)
- Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & the Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191, Beijing, China
| | - Xikang Feng
- School of Software, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China
| | - Haimei Li
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & the Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191, Beijing, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Qiujin Qian
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & the Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191, Beijing, China
| | - Yufeng Wang
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & the Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191, Beijing, China
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Mahadevan J, Pathak AK, Vemula A, Nadella RK, Viswanath B, Jain S, Purushottam M, Mondal M; Accelerator Program for Discovery in Brain disorders using Stem cells (ADBS) Consortium. Analysis of whole exome sequencing in severe mental illness hints at selection of brain development and immune related genes. Sci Rep 2021; 11:21088. [PMID: 34702870 DOI: 10.1038/s41598-021-00123-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 10/01/2021] [Indexed: 11/15/2022] Open
Abstract
Evolutionary trends may underlie some aspects of the risk for common, non-communicable disorders, including psychiatric disease. We analyzed whole exome sequencing data from 80 unique individuals from India coming from families with two or more individuals with severe mental illness. We used Population Branch Statistics (PBS) to identify variants and genes under positive selection and identified 74 genes as candidates for positive selection. Of these, 20 were previously associated with Schizophrenia, Alzheimer’s disease and cognitive abilities in genome wide association studies. We then checked whether any of these 74 genes were involved in common biological pathways or related to specific cellular or molecular functions. We found that immune related pathways and functions related to innate immunity such as antigen binding were over-represented. We also evaluated for the presence of Neanderthal introgressed segments in these genes and found Neanderthal introgression in a single gene out of the 74 candidate genes. However, the introgression pattern indicates the region is unlikely to be the source for selection. Our findings hint at how selection pressures in individuals from families with a history of severe mental illness may diverge from the general population. Further, it also provides insights into the genetic architecture of severe mental illness, such as schizophrenia and its link to immune factors.
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Barowsky S, Jung JY, Nesbit N, Silberstein M, Fava M, Loggia ML, Smoller JW, Lee PH. Cross-Disorder Genomics Data Analysis Elucidates a Shared Genetic Basis Between Major Depression and Osteoarthritis Pain. Front Genet 2021; 12:687687. [PMID: 34603368 PMCID: PMC8481820 DOI: 10.3389/fgene.2021.687687] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/23/2021] [Indexed: 11/24/2022] Open
Abstract
Osteoarthritis (OA) and major depression (MD) are two debilitating disorders that frequently co-occur and affect millions of the elderly each year. Despite the greater symptom severity, poorer clinical outcomes, and increased mortality of the comorbid conditions, we have a limited understanding of their etiologic relationships. In this study, we conducted the first cross-disorder investigations of OA and MD, using genome-wide association data representing over 247K cases and 475K controls. Along with significant positive genome-wide genetic correlations (r g = 0.299 ± 0.026, p = 9.10 × 10-31), Mendelian randomization (MR) analysis identified a bidirectional causal effect between OA and MD (βOA → MD = 0.09, SE = 0.02, z-score p-value < 1.02 × 10-5; βMD → OA = 0.19, SE = 0.026, p < 2.67 × 10-13), indicating genetic variants affecting OA risk are, in part, shared with those influencing MD risk. Cross-disorder meta-analysis of OA and MD identified 56 genomic risk loci (P meta ≤ 5 × 10-8), which show heightened expression of the associated genes in the brain and pituitary. Gene-set enrichment analysis highlighted "mechanosensory behavior" genes (GO:0007638; P gene_set = 2.45 × 10-8) as potential biological mechanisms that simultaneously increase susceptibility to these mental and physical health conditions. Taken together, these findings show that OA and MD share common genetic risk mechanisms, one of which centers on the neural response to the sensation of mechanical stimulus. Further investigation is warranted to elaborate the etiologic mechanisms of the pleiotropic risk genes, as well as to develop early intervention and integrative clinical care of these serious conditions that disproportionally affect the aging population.
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Affiliation(s)
- Sophie Barowsky
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Jae-Yoon Jung
- Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - Nicholas Nesbit
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Micah Silberstein
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, MA, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Marco L. Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, MA, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Phil H. Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, MA, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
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Fabbri C, Pain O, Hagenaars SP, Lewis CM, Serretti A. Transcriptome-wide association study of treatment-resistant depression and depression subtypes for drug repurposing. Neuropsychopharmacology 2021; 46:1821-9. [PMID: 34158615 DOI: 10.1038/s41386-021-01059-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/19/2021] [Accepted: 06/03/2021] [Indexed: 12/17/2022]
Abstract
Major depressive disorder (MDD) is the single largest contributor to global disability and up to 20-30% of patients do not respond to at least two antidepressants (treatment-resistant depression, TRD). This study leveraged imputed gene expression in TRD to perform a drug repurposing analysis. Among those with MDD, we defined TRD as having at least two antidepressant switches according to primary care records in UK Biobank (UKB). We performed a transcriptome-wide association study (TWAS) of TRD (n = 2165) vs healthy controls (n = 11,188) using FUSION and gene expression levels from 21 tissues. We identified compounds with opposite gene expression signatures (ConnectivityMap data) compared to our TWAS results using the Kolmogorov-Smirnov test, Spearman and Pearson correlation. As symptom patterns are routinely assessed in clinical practice and could be used to provide targeted treatments, we identified MDD subtypes associated with TRD in UKB and analysed them using the same pipeline described for TRD. Anxious MDD (n = 14,954) and MDD with weight gain (n = 4697) were associated with TRD. In the TWAS, two genes were significantly dysregulated (TMEM106B and ATP2A1 for anxious and weight gain MDD, respectively). A muscarinic receptor antagonist was identified as top candidate for repurposing in TRD; inhibition of heat shock protein 90 was the main mechanism of action identified for anxious MDD, while modulators of metabolism such as troglitazone showed promising results for MDD with weight gain. This was the first TWAS of TRD and associated MDD subtypes. Our results shed light on possible pharmacological approaches in individuals with difficult-to-treat depression.
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Lam M, Chen CY, Ge T, Xia Y, Hill DW, Trampush JW, Yu J, Knowles E, Davies G, Stahl EA, Huckins L, Liewald DC, Djurovic S, Melle I, Christoforou A, Reinvang I, DeRosse P, Lundervold AJ, Steen VM, Espeseth T, Räikkönen K, Widen E, Palotie A, Eriksson JG, Giegling I, Konte B, Hartmann AM, Roussos P, Giakoumaki S, Burdick KE, Payton A, Ollier W, Chiba-Falek O, Koltai DC, Need AC, Cirulli ET, Voineskos AN, Stefanis NC, Avramopoulos D, Hatzimanolis A, Smyrnis N, Bilder RM, Freimer NB, Cannon TD, London E, Poldrack RA, Sabb FW, Congdon E, Conley ED, Scult MA, Dickinson D, Straub RE, Donohoe G, Morris D, Corvin A, Gill M, Hariri AR, Weinberger DR, Pendleton N, Bitsios P, Rujescu D, Lahti J, Le Hellard S, Keller MC, Andreassen OA, Deary IJ, Glahn DC, Huang H, Liu C, Malhotra AK, Lencz T. Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics. Neuropsychopharmacology 2021; 46:1788-1801. [PMID: 34035472 PMCID: PMC8357785 DOI: 10.1038/s41386-021-01023-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/22/2021] [Accepted: 04/12/2021] [Indexed: 02/05/2023]
Abstract
Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.
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Affiliation(s)
- Max Lam
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Institute of Mental Health, Singapore, Singapore
| | - Chia-Yen Chen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Biogen, Inc, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Tian Ge
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Yan Xia
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Psychiatry Department, SUNY Upstate Medical University, Syracuse, NY, USA
| | - David W Hill
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, Scotland, UK
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Joey W Trampush
- Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jin Yu
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Emma Knowles
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychic Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Gail Davies
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, Scotland, UK
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Eli A Stahl
- Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura Huckins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David C Liewald
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ingrid Melle
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Andrea Christoforou
- Spaulding Rehabilitation Hospital Boston, Charlestown, MA, USA
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Ivar Reinvang
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Pamela DeRosse
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Vidar M Steen
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Thomas Espeseth
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK
- Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, Finland
| | - Johan G Eriksson
- Department of General Practice, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Folkhälsan Research Center, Helsinki, Finland
| | - Ina Giegling
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, Germany
| | - Bettina Konte
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, Germany
| | - Annette M Hartmann
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, Germany
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2), James J. Peters VA Medical Center, Bronx, NY, USA
| | | | - Katherine E Burdick
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2), James J. Peters VA Medical Center, Bronx, NY, USA
- Department of Psychiatry - Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Antony Payton
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, Manchester, UK
| | - William Ollier
- Centre for Epidemiology, Division of Population Health, Health Services Research & Primary Care, The University of Manchester, Manchester, UK
- School of Healthcare Sciences, Manchester Metropolitan University, Manchester, United Kingdom
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Bryan Alzheimer's Disease Research Center, and Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, USA
| | - Deborah C Koltai
- Psychiatry and Behavioral Sciences, Division of Medical Psychology, and Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - Anna C Need
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - Aristotle N Voineskos
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Nikos C Stefanis
- 2nd Department of Psychiatry, National and Kapodistrian University of Athens Medical School, University General Hospital "ATTIKON", Athens, Greece
- University Mental Health Research Institute, Athens, Greece
- Neurobiology Research Institute, Theodor-Theohari Cozzika Foundation, Athens, Greece
| | - Dimitrios Avramopoulos
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alex Hatzimanolis
- 2nd Department of Psychiatry, National and Kapodistrian University of Athens Medical School, University General Hospital "ATTIKON", Athens, Greece
- University Mental Health Research Institute, Athens, Greece
- Neurobiology Research Institute, Theodor-Theohari Cozzika Foundation, Athens, Greece
| | - Nikolaos Smyrnis
- 2nd Department of Psychiatry, National and Kapodistrian University of Athens Medical School, University General Hospital "ATTIKON", Athens, Greece
- University Mental Health Research Institute, Athens, Greece
| | - Robert M Bilder
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Nelson B Freimer
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Edythe London
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | | | - Fred W Sabb
- Robert and Beverly Lewis Center for Neuroimaging, University of Oregon, Eugene, OR, USA
| | - Eliza Congdon
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | | | - Matthew A Scult
- Weill Cornell Psychiatry at NewYork-Presbyterian, Weill Cornell Medical Center, New York, NY, USA
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Dwight Dickinson
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
| | - Richard E Straub
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Gary Donohoe
- Neuroimaging, Cognition & Genomics (NICOG) Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Derek Morris
- Neuroimaging, Cognition & Genomics (NICOG) Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Neil Pendleton
- Division of Neuroscience and Experimental Psychology/School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, Salford Royal NHS Foundation Trust, University of Manchester, Manchester, UK
| | - Panos Bitsios
- Department of Psychiatry and Behavioral Sciences, Faculty of Medicine, University of Crete, Heraklion, Crete, GR, Greece
| | - Dan Rujescu
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, Germany
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland
| | - Stephanie Le Hellard
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
| | - Ole A Andreassen
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, Scotland, UK
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - David C Glahn
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychic Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Chunyu Liu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Psychiatry Department, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Anil K Malhotra
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Todd Lencz
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA.
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA.
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
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Harvey PD, Bigdeli TB, Fanous AH, Li Y, Rajeevan N, Sayward F, Radhakrishnan K, Huang G, Aslan M. Cooperative Studies Program (CSP) #572: A Study of Serious Mental Illness in Veterans as a Pathway to personalized medicine in Schizophrenia and Bipolar Illness. Pers Med Psychiatry 2021; 27-28:10.1016/j.pmip.2021.100078. [PMID: 34222732 PMCID: PMC8247126 DOI: 10.1016/j.pmip.2021.100078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
BACKGROUND Personalization of psychiatric treatment includes treatment of symptoms, cognition and functional deficits, suicide, and medical co-morbidities. VA Collaborative Study 572 examined a large sample of male and female veterans with schizophrenia (n=3,942) and with bipolar disorder (n=5,414) with phenotyping and genomic analyses. We present the results to date and future directions. METHODS All veterans received a structured diagnostic interview and assessments of suicidal ideation and behavior, PTSD, and health. Veterans with schizophrenia were assessed for negative symptoms and lifetime depression. All were assessed with a cognitive and functional capacity assessment. Data for genome wide association studies were collected. Controls came from the VA Million Veteran Program. RESULTS Suicidal ideation or behavior was present in 66%. Cognitive and functional deficits were consistent with previous studies. 40% of the veterans with schizophrenia had a lifetime major depressive episode and PTSD was present in over 30%. Polygenic risk score (PRS) analyses indicated that cognitive and functional deficits overlapped with PRS for cognition, education, and intelligence in the general population and PRS for suicidal ideation and behavior correlated with previous PRS for depression and suicidal ideation and behavior, as did the PRS for PTSD. DISCUSSION Results to date provide directions for personalization of treatment in SMI, veterans with SMI, and veterans in general. The results of the genomic analyses suggest that cognitive deficits in SMI may be associated with general population features. Upcoming genomic analyses will reexamine the issues above, as well as genomic factors associated with smoking, substance abuse, negative symptoms, and treatment response.
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Affiliation(s)
- Philip D. Harvey
- Bruce W. Carter Miami Veterans Affairs (VA) Medical Center, Miami, FL
- University of Miami School of Medicine, Miami, FL
| | - Tim B. Bigdeli
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY
| | - Ayman H. Fanous
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY
| | - Yuli Li
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Nallakkandi Rajeevan
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Frederick Sayward
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Krishnan Radhakrishnan
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration
- University of Kentucky School of Medicine, Lexington, KY
| | - Grant Huang
- Office of Research and Development, Veterans Health Administration, Washington, DC
| | - Mihaela Aslan
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY
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Abstract
The ability to finely control our movement is key to achieving many of the educational milestones and life-skills we develop throughout our lives. Despite the centrality of coordination to early development, there is a vast gap in our understanding of the underlying biology. Like most complex traits, both genetics and environment influence motor coordination, however, the specific genes, early environmental risk factors and molecular pathways are unknown. Previous studies have shown that about 5% of school-age children experience unexplained difficulties with motor coordination. These children are said to have Developmental Coordination Disorder (DCD). For children with DCD, these motor coordination difficulties significantly impact their everyday life and learning. DCD is associated with poorer academic achievement, reduced quality of life, it can constrain career opportunities and increase the risk of mental health issues in adulthood. Despite the high prevalence of coordination difficulties, many children remain undiagnosed by healthcare professionals. Compounding under-diagnosis in the clinic, research into the etiology of DCD is severely underrepresented in the literature. Here we present the first genome-wide association study to examine the genetic basis of early motor coordination in the context of motor difficulties. Using data from the Avon Longitudinal Study of Parents and Children we generate a derived measure of motor coordination from four components of the Movement Assessment Battery for Children, providing an overall measure of coordination across the full range of ability. We perform the first genome-wide association analysis focused on motor coordination (N = 4542). No single nucleotide polymorphisms (SNPs) met the threshold for genome-wide significance, however, 59 SNPs showed suggestive associations. Three regions contained multiple suggestively associated SNPs, within five preliminary candidate genes: IQSEC1, LRCC1, SYNJ2B2, ADAM20, and ADAM21. Association to the gene IQSEC1 suggests a potential link to axon guidance and dendritic projection processes as a potential underlying mechanism of motor coordination difficulties. This represents an interesting potential mechanism, and whilst further validation is essential, it generates a direct window into the biology of motor coordination difficulties. This research has identified potential biological drivers of DCD, a first step towards understanding this common, yet neglected neurodevelopmental disorder.
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Affiliation(s)
- Hayley S. Mountford
- Department of Biological and Medical Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, United Kingdom
| | - Amanda Hill
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Anna L. Barnett
- Centre for Psychological Research, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, United Kingdom
| | - Dianne F. Newbury
- Department of Biological and Medical Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, United Kingdom
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Zhao B, Shan Y, Yang Y, Yu Z, Li T, Wang X, Luo T, Zhu Z, Sullivan P, Zhao H, Li Y, Zhu H. Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits. Nat Commun 2021; 12:2878. [PMID: 34001886 PMCID: PMC8128893 DOI: 10.1038/s41467-021-23130-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/16/2021] [Indexed: 02/03/2023] Open
Abstract
Structural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10-8. The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10-31) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhaolong Yu
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Patrick Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongyu Zhao
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Tamman AJF, Wendt FR, Pathak GA, Krystal JH, Montalvo-Ortiz JL, Southwick SM, Sippel LM, Gelernter J, Polimanti R, Pietrzak RH. Attachment Style Moderates Polygenic Risk for Posttraumatic Stress in United States Military Veterans: Results From the National Health and Resilience in Veterans Study. Biol Psychiatry 2021; 89:878-887. [PMID: 33276944 DOI: 10.1016/j.biopsych.2020.09.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/15/2020] [Accepted: 09/15/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND A polygenic risk score (PRS) derived from genome-wide association studies of posttraumatic stress disorder (PTSD) may inform risk for this disorder. To date, however, no known study has examined whether social environmental factors such as attachment style may moderate the relation between PRS and PTSD. METHODS We evaluated main and interactive effects of PRS and attachment style on PTSD symptoms in a nationally representative sample of trauma-exposed European-American U.S. military veterans (N = 2030). PRS was derived from a genome-wide association study of PTSD re-experiencing symptoms (N = 146,660) in the Million Veteran Program cohort. Using one-sample Mendelian randomization with data from the UK Biobank (N = 115,099), we evaluated the effects of re-experiencing PRS and attachment style on PTSD symptoms. RESULTS Higher re-experiencing PRS and secure attachment style were independently associated with PTSD symptoms. A significant PRS-by-attachment style interaction was also observed (β = -.11, p = .006), with a positive association between re-experiencing PRS and PTSD symptoms observed only among veterans with an insecure attachment style. One-sample Mendelian randomization analyses suggested that the association between PTSD symptoms and attachment style is bidirectional. PRS enrichment analyses revealed a significant interaction between attachment style and a variant mapping to the IGSF11 gene (rs151177743, p = 2.1 × 10-7), which is implicated in regulating excitatory synaptic transmission and plasticity. CONCLUSIONS Attachment style may moderate polygenic risk for PTSD symptoms, and a novel locus implicated in synaptic transmission and plasticity may serve as a possible biological mediator of this association. These findings may help inform interpersonally oriented treatments for PTSD for individuals with high polygenic risk for this disorder.
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Affiliation(s)
| | - Frank R Wendt
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - John H Krystal
- Clinical Neurosciences Division, U.S. Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, New Haven, Connecticut; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | | | - Steven M Southwick
- Clinical Neurosciences Division, U.S. Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, New Haven, Connecticut; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Lauren M Sippel
- Executive Division, National Center for PTSD, White River Junction, Vermont; Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Joel Gelernter
- Clinical Neurosciences Division, U.S. Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, New Haven, Connecticut; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Robert H Pietrzak
- Clinical Neurosciences Division, U.S. Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, New Haven, Connecticut; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
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