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Simonsson O, Mosing MA, Osika W, Ullén F, Larsson H, Lu Y, Wesseldijk LW. Adolescent Psychedelic Use and Psychotic or Manic Symptoms. JAMA Psychiatry 2024; 81:579-585. [PMID: 38477889 PMCID: PMC10938246 DOI: 10.1001/jamapsychiatry.2024.0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/03/2024] [Indexed: 03/14/2024]
Abstract
Importance While psychedelic-assisted therapy has shown promise in the treatment of certain psychiatric disorders, little is known about the potential risk of psychotic or manic symptoms following naturalistic psychedelic use, especially among adolescents. Objective To investigate associations between naturalistic psychedelic use and self-reported psychotic or manic symptoms in adolescents using a genetically informative design. Design, Setting, and Participants This study included a large sample of adolescent twins (assessed at age 15, 18, and 24 years) born between July 1992 and December 2005 from the Swedish Twin Registry and cross-sectionally evaluated the associations between past psychedelic use and psychotic or manic symptoms at age 15 years. Individuals were included if they answered questions related to past use of psychedelics. Data were analyzed from October 2022 to November 2023. Main Outcomes and Measures Primary outcome measures were self-reported psychotic and manic symptoms at age 15 years. Lifetime use of psychedelics and other drugs was also assessed at the same time point. Results Among the 16 255 participants included in the analyses, 8889 were female and 7366 were male. Among them, 541 participants reported past use of psychedelics, most of whom (535 of 541 [99%]) also reported past use of other drugs (ie, cannabis, stimulants, sedatives, opioids, inhalants, or performance enhancers). When adjusting for substance-specific and substance-aggregated drug use, psychedelic use was associated with reduced psychotic symptoms in both linear regression analyses (β, -0.79; 95% CI, -1.18 to -0.41 and β, -0.39; 95% CI, -0.50 to -0.27, respectively) and co-twin control analyses (β, -0.89; 95% CI, -1.61 to -0.16 and β, -0.24; 95% CI, -0.48 to -0.01, respectively). In relation to manic symptoms, likewise adjusting for substance-specific and substance-aggregated drug use, statistically significant interactions were found between psychedelic use and genetic vulnerability to schizophrenia (β, 0.17; 95% CI, 0.01 to 0.32 and β, 0.17; 95% CI, 0.02 to 0.32, respectively) or bipolar I disorder (β, 0.20; 95% CI, 0.04 to 0.36 and β, 0.17; 95% CI, 0.01 to 0.33, respectively). Conclusions and Relevance The findings in this study suggest that, after adjusting for other drug use, naturalistic use of psychedelic may be associated with lower rates of psychotic symptoms among adolescents. At the same time, the association between psychedelic use and manic symptoms seems to be associated with genetic vulnerability to schizophrenia or bipolar I disorder. These findings should be considered in light of the study's limitations and should therefore be interpreted with caution.
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Affiliation(s)
- Otto Simonsson
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Miriam A. Mosing
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
- Melbourne School of Psychological Sciences, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Walter Osika
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Southern Stockholm Psychiatric District, Region Stockholm, Stockholm, Sweden
| | - Fredrik Ullén
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Laura W. Wesseldijk
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
- Department of Psychiatry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
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D'Antona S, Porro D, Gallivanone F, Bertoli G. Characterization of cell cycle, inflammation, and oxidative stress signaling role in non-communicable diseases: Insights into genetic variants, microRNAs and pathways. Comput Biol Med 2024; 174:108346. [PMID: 38581999 DOI: 10.1016/j.compbiomed.2024.108346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 02/16/2024] [Accepted: 03/17/2024] [Indexed: 04/08/2024]
Abstract
Non-Communicable Diseases (NCDs) significantly impact global health, contributing to over 70% of premature deaths, as reported by the World Health Organization (WHO). These diseases have complex and multifactorial origins, involving genetic, epigenetic, environmental and lifestyle factors. While Genome-Wide Association Study (GWAS) is widely recognized as a valuable tool for identifying variants associated with complex phenotypes; the multifactorial nature of NCDs necessitates a more comprehensive exploration, encompassing not only the genetic but also the epigenetic aspect. For this purpose, we employed a bioinformatics-multiomics approach to examine the genetic and epigenetic characteristics of NCDs (i.e. colorectal cancer, coronary atherosclerosis, squamous cell lung cancer, psoriasis, type 2 diabetes, and multiple sclerosis), aiming to identify novel biomarkers for diagnosis and prognosis. Leveraging GWAS summary statistics, we pinpointed Single Nucleotide Polymorphisms (SNPs) independently associated with each NCD. Subsequently, we identified genes linked to cell cycle, inflammation and oxidative stress mechanisms, revealing shared genes across multiple diseases, suggesting common functional pathways. From an epigenetic perspective, we identified microRNAs (miRNAs) with regulatory functions targeting these genes of interest. Our findings underscore critical genetic pathways implicated in these diseases. In colorectal cancer, the dysregulation of the "Cytokine Signaling in Immune System" pathway, involving LAMA5 and SMAD7, regulated by Hsa-miR-21-5p, Hsa-miR-103a-3p, and Hsa-miR-195-5p, emerged as pivotal. In coronary atherosclerosis, the pathway associated with "binding of TCF/LEF:CTNNB1 to target gene promoters" displayed noteworthy implications, with the MYC factor controlled by Hsa-miR-16-5p as a potential regulatory factor. Squamous cell lung carcinoma analysis revealed significant pathways such as "PTK6 promotes HIF1A stabilization," regulated by Hsa-let-7b-5p. In psoriasis, the "Endosomal/Vacuolar pathway," involving HLA-C and Hsa-miR-148a-3p and Hsa-miR-148b-3p, was identified as crucial. Type 2 Diabetes implicated the "Regulation of TP53 Expression" pathway, controlled by Hsa-miR-106a-5p and Hsa-miR-106b-5p. In conclusion, our study elucidates the genetic framework and molecular mechanisms underlying NCDs, offering crucial insights into potential genetic/epigenetic biomarkers for diagnosis and prognosis. The specificity of pathways and related miRNAs in different pathologies highlights promising candidates for further clinical validation, with the potential to advance personalized treatments and alleviate the global burden of NCDs.
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Affiliation(s)
- Salvatore D'Antona
- Institute of Bioimaging and Molecular Physiology, National Research Council, Via F.lli Cervi 93, 20054, Milan, Italy
| | - Danilo Porro
- Institute of Bioimaging and Molecular Physiology, National Research Council, Via F.lli Cervi 93, 20054, Milan, Italy; National Biodiversity Future Center (NBFC), Palermo, Italy
| | - Francesca Gallivanone
- Institute of Bioimaging and Molecular Physiology, National Research Council, Via F.lli Cervi 93, 20054, Milan, Italy
| | - Gloria Bertoli
- Institute of Bioimaging and Molecular Physiology, National Research Council, Via F.lli Cervi 93, 20054, Milan, Italy; National Biodiversity Future Center (NBFC), Palermo, Italy.
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3
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Schowe AM, Godara M, Czamara D, Adli M, Singer T, Binder EB. Genetic predisposition for negative affect predicts mental health burden during the COVID-19 pandemic. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01795-y. [PMID: 38587666 DOI: 10.1007/s00406-024-01795-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/09/2024] [Indexed: 04/09/2024]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic was accompanied by an increase in mental health challenges including depression, stress, loneliness, and anxiety. Common genetic variants can contribute to the risk for psychiatric disorders and may present a risk factor in times of crises. However, it is unclear to what extent polygenic risk played a role in the mental health response to the COVID-19 pandemic. In this study, we investigate whether polygenic scores (PGSs) for mental health-related traits can distinguish between four resilience-vulnerability trajectories identified during the COVID-19 pandemic and associated lockdowns in 2020/21. We used multinomial regression in a genotyped subsample (n = 1316) of the CovSocial project. The most resilient trajectory characterized by the lowest mental health burden and the highest recovery rates served as the reference group. Compared to this most resilient trajectory, a higher value on the PGS for the well-being spectrum decreased the odds for individuals to be in one of the more vulnerable trajectories (adjusted R-square = 0.3%). Conversely, a higher value on the PGS for neuroticism increased the odds for individuals to be in one of the more vulnerable trajectories (adjusted R-square = 0.2%). Latent change in mental health burden extracted from the resilience-vulnerability trajectories was not associated with any PGS. Although our findings support an influence of PGS on mental health during COVID-19, the small added explained variance suggests limited utility of such genetic markers for the identification of vulnerable individuals in the general population.
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Affiliation(s)
- Alicia M Schowe
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany.
- Graduate School of Systemic Neuroscience, Ludwig Maximilian University, Munich, Germany.
| | - Malvika Godara
- Social Neuroscience Lab, Max Planck Society, 10557, Berlin, Germany.
| | - Darina Czamara
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
| | - Mazda Adli
- Department of Psychiatry and Neurosciences, CCM, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Center for Psychiatry, Psychotherapy and Psychosomatic Medicine, Fliedner Klinik Berlin, Berlin, Germany
| | - Tania Singer
- Social Neuroscience Lab, Max Planck Society, 10557, Berlin, Germany
| | - Elisabeth B Binder
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
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4
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Galimberti M, Levey DF, Deak JD, Zhou H, Stein MB, Gelernter J. Genetic influences and causal pathways shared between cannabis use disorder and other substance use traits. Mol Psychiatry 2024:10.1038/s41380-024-02548-y. [PMID: 38580809 DOI: 10.1038/s41380-024-02548-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/25/2024] [Accepted: 03/28/2024] [Indexed: 04/07/2024]
Abstract
Cannabis use disorder (CanUD) has increased with the legalization of the use of cannabis. Around 20% of individuals using cannabis develop CanUD, and the number of users has grown with increasing ease of access. CanUD and other substance use disorders (SUDs) are associated phenotypically and genetically. We leveraged new CanUD genomics data to undertake genetically-informed analyses with unprecedented power, to investigate the genetic architecture and causal relationships between CanUD and lifetime cannabis use with risk for developing SUDs and substance use traits. Analyses included calculating local and global genetic correlations, genomic structural equation modeling (genomicSEM), and Mendelian Randomization (MR). Results from the genetic correlation and genomicSEM analyses demonstrated that CanUD and cannabis use differ in their relationships with SUDs and substance use traits. We found significant causal effects of CanUD influencing all the analyzed traits: opioid use disorder (OUD) (Inverse variant weighted, IVW β = 0.925 ± 0.082), problematic alcohol use (PAU) (IVW β = 0.443 ± 0.030), drinks per week (DPW) (IVW β = 0.182 ± 0.025), Fagerström Test for Nicotine Dependence (FTND) (IVW β = 0.183 ± 0.052), cigarettes per day (IVW β = 0.150 ± 0.045), current versus former smokers (IVW β = 0.178 ± 0.052), and smoking initiation (IVW β = 0.405 ± 0.042). We also found evidence of bidirectionality showing that OUD, PAU, smoking initiation, smoking cessation, and DPW all increase risk of developing CanUD. For cannabis use, bidirectional relationships were inferred with PAU, smoking initiation, and DPW; cannabis use was also associated with a higher risk of developing OUD (IVW β = 0.785 ± 0.266). GenomicSEM confirmed that CanUD and cannabis use load onto different genetic factors. We conclude that CanUD and cannabis use can increase the risk of developing other SUDs. This has substantial public health implications; the move towards legalization of cannabis use may be expected to increase other kinds of problematic substance use. These harmful outcomes are in addition to the medical harms associated directly with CanUD.
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Affiliation(s)
- Marco Galimberti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joseph D Deak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Murray B Stein
- Department of Psychiatry and School of Public Health, University of California San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, San Diego, CA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
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5
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Wesseldijk LW, Henechowicz TL, Baker DJ, Bignardi G, Karlsson R, Gordon RL, Mosing MA, Ullén F, Fisher SE. Notes from Beethoven's genome. Curr Biol 2024; 34:R233-R234. [PMID: 38531312 DOI: 10.1016/j.cub.2024.01.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/09/2024] [Accepted: 01/09/2024] [Indexed: 03/28/2024]
Abstract
Rapid advances over the last decade in DNA sequencing and statistical genetics enable us to investigate the genomic makeup of individuals throughout history. In a recent notable study, Begg et al.1 used Ludwig van Beethoven's hair strands for genome sequencing and explored genetic predispositions for some of his documented medical issues. Given that it was arguably Beethoven's skills as a musician and composer that made him an iconic figure in Western culture, we here extend the approach and apply it to musicality. We use this as an example to illustrate the broader challenges of individual-level genetic predictions.
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Affiliation(s)
- Laura W Wesseldijk
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany.
| | - Tara L Henechowicz
- Music and Health Sciences Research Collaboratory, Faculty of Music, University of Toronto, Toronto, Canada; Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
| | - David J Baker
- Institute for Logic, Language, and Computation, University of Amsterdam, Amsterdam, The Netherlands
| | - Giacomo Bignardi
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands; Max Planck School of Cognition, Leipzig, Germany
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Reyna L Gordon
- Department of Otolaryngology - Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA; Blair School of Music, Vanderbilt University, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA; Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Miriam A Mosing
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Melbourne School of Psychological Sciences, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, CS Melbourne, Australia
| | - Fredrik Ullén
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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6
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Fox JA, Wyatt Toure M, Heckley A, Fan R, Reader SM, Barrett RDH. Insights into adaptive behavioural plasticity from the guppy model system. Proc Biol Sci 2024; 291:20232625. [PMID: 38471561 DOI: 10.1098/rspb.2023.2625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/29/2024] [Indexed: 03/14/2024] Open
Abstract
Behavioural plasticity allows organisms to respond to environmental challenges on short time scales. But what are the ecological and evolutionary processes that underlie behavioural plasticity? The answer to this question is complex and requires experimental dissection of the physiological, neural and molecular mechanisms contributing to behavioural plasticity as well as an understanding of the ecological and evolutionary contexts under which behavioural plasticity is adaptive. Here, we discuss key insights that research with Trinidadian guppies has provided on the underpinnings of adaptive behavioural plasticity. First, we present evidence that guppies exhibit contextual, developmental and transgenerational behavioural plasticity. Next, we review work on behavioural plasticity in guppies spanning three ecological contexts (predation, parasitism and turbidity) and three underlying mechanisms (endocrinological, neurobiological and genetic). Finally, we provide three outstanding questions that could leverage guppies further as a study system and give suggestions for how this research could be done. Research on behavioural plasticity in guppies has provided, and will continue to provide, a valuable opportunity to improve understanding of the ecological and evolutionary causes and consequences of behavioural plasticity.
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Affiliation(s)
- Janay A Fox
- Department of Biology, McGill University, Montréal, Canada H3A 1B1
| | - M Wyatt Toure
- Department of Biology, McGill University, Montréal, Canada H3A 1B1
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York 10027-6902, NY, USA
| | - Alexis Heckley
- Department of Biology, McGill University, Montréal, Canada H3A 1B1
| | - Raina Fan
- Department of Biology, McGill University, Montréal, Canada H3A 1B1
| | - Simon M Reader
- Department of Biology, McGill University, Montréal, Canada H3A 1B1
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Thomas TR, Tener AJ, Pearlman AM, Imborek KL, Yang JS, Strang JF, Michaelson JJ. Polygenic Scores Clarify the Relationship Between Mental Health and Gender Diversity. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100291. [PMID: 38425476 PMCID: PMC10901838 DOI: 10.1016/j.bpsgos.2024.100291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/21/2023] [Accepted: 12/26/2023] [Indexed: 03/02/2024] Open
Abstract
Background Gender-diverse individuals are at increased risk for mental health problems, but it is unclear whether this is due to shared environmental or genetic factors. Methods In two SPARK samples, we tested for associations of 16 polygenic scores (PGSs) with quantitative measures of gender diversity and mental health. In study 1, 639 independent adults (59% autistic) reported their mental health with the Adult Self-Report and their gender diversity with the Gender Self-Report (GSR). The GSR has 2 dimensions: binary (degree of identification with the gender opposite that implied by sex designated at birth) and nonbinary (degree of identification with a gender that is neither male nor female). In study 2 (N = 5165), we used a categorical measure of gender identity. Results In study 1, neuropsychiatric PGSs were positively associated with Adult Self-Report scores: externalizing was positively associated with the attention-deficit/hyperactivity disorder PGS (β = 0.10 [0.03-0.17]), and internalizing was positively associated with the PGSs for depression (β = 0.07 [0-0.14]) and neuroticism (β = 0.10 [0.03-0.17]). Interestingly, GSR scores were not significantly associated with any neuropsychiatric PGS. However, GSR nonbinary was positively associated with the cognitive performance PGS (β = 0.11 [0.05-0.18]), with the effect size comparable in magnitude to the associations of the neuropsychiatric PGSs with the Adult Self-Report. Additionally, GSR binary was positively associated with the nonheterosexual sexual behavior PGS (β = 0.07 [0-0.14]). In study 2, the cognitive performance PGS effect replicated; transgender and nonbinary individuals had higher PGSs (t316 = 4.16). Conclusions We showed that while gender diversity is phenotypically positively associated with mental health problems, the strongest PGS associations with gender diversity were with the cognitive performance PGS, not the neuropsychiatric PGSs.
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Affiliation(s)
| | - Ashton J. Tener
- Department of Psychiatry, University of Iowa, Iowa City, Iowa
| | | | | | - Ji Seung Yang
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland
| | - John F. Strang
- Gender and Autism Program, Center for Neuroscience, Children’s National Hospital, George Washington University School of Medicine, Washington, District of Columbia
| | - Jacob J. Michaelson
- Department of Psychiatry, University of Iowa, Iowa City, Iowa
- Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa
- Hawkeye Intellectual and Developmental Disabilities Research Center, University of Iowa, Iowa City, Iowa
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8
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Petrican R, Fornito A, Boyland E. Lifestyle Factors Counteract the Neurodevelopmental Impact of Genetic Risk for Accelerated Brain Aging in Adolescence. Biol Psychiatry 2024; 95:453-464. [PMID: 37393046 DOI: 10.1016/j.biopsych.2023.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND The transition from childhood to adolescence is characterized by enhanced neural plasticity and a consequent susceptibility to both beneficial and adverse aspects of one's milieu. METHODS To understand the implications of the interplay between protective and risk-enhancing factors, we analyzed longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study (n = 834; 394 female). We probed the maturational correlates of positive lifestyle variables (friendships, parental warmth, school engagement, physical exercise, healthy nutrition) and genetic vulnerability to neuropsychiatric disorders (major depressive disorder, Alzheimer's disease, anxiety disorders, bipolar disorder, schizophrenia) and sought to further elucidate their implications for psychological well-being. RESULTS Genetic risk factors and lifestyle buffers showed divergent relationships with later attentional and interpersonal problems. These effects were mediated by distinguishable functional neurodevelopmental deviations spanning the limbic, default mode, visual, and control systems. More specifically, greater genetic vulnerability was associated with alterations in the normative maturation of areas rich in dopamine (D2), glutamate, and serotonin receptors and of areas with stronger expression of astrocytic and microglial genes, a molecular signature implicated in the brain disorders discussed here. Greater availability of lifestyle buffers predicted deviations in the normative functional development of higher density GABAergic (gamma-aminobutyric acidergic) receptor regions. The two profiles of neurodevelopmental alterations showed complementary roles in protection against psychopathology, which varied with environmental stress levels. CONCLUSIONS Our results underscore the importance of educational involvement and healthy nutrition in attenuating the neurodevelopmental sequelae of genetic risk factors. They also underscore the importance of characterizing early-life biomarkers associated with adult-onset pathologies.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Liverpool, United Kingdom.
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Emma Boyland
- Institute of Population Health, Department of Psychology, University of Liverpool, Liverpool, United Kingdom
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Walker EF, Aberizk K, Yuan E, Bilgrami Z, Ku BS, Guest RM. Developmental perspectives on the origins of psychotic disorders: The need for a transdiagnostic approach. Dev Psychopathol 2024:1-11. [PMID: 38406831 DOI: 10.1017/s0954579424000397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Research on serious mental disorders, particularly psychosis, has revealed highly variable symptom profiles and developmental trajectories prior to illness-onset. As Dante Cicchetti pointed out decades before the term "transdiagnostic" was widely used, the pathways to psychopathology emerge in a system involving equifinality and multifinality. Like most other psychological disorders, psychosis is associated with multiple domains of risk factors, both genetic and environmental, and there are many transdiagnostic developmental pathways that can lead to psychotic syndromes. In this article, we discuss our current understanding of heterogeneity in the etiology of psychosis and its implications for approaches to conceptualizing etiology and research. We highlight the need for examining risk factors at multiple levels and to increase the emphasis on transdiagnostic developmental trajectories as a key variable associated with etiologic subtypes. This will be increasingly feasible now that large, longitudinal datasets are becoming available and researchers have access to more sophisticated analytic tools, such as machine learning, which can identify more homogenous subtypes with the ultimate goal of enhancing options for treatment and preventive intervention.
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Affiliation(s)
- Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Katrina Aberizk
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Emerald Yuan
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Zarina Bilgrami
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Benson S Ku
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Ryan M Guest
- Department of Psychology, Emory University, Atlanta, GA, USA
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10
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Kloska A, Giełczyk A, Grzybowski T, Płoski R, Kloska SM, Marciniak T, Pałczyński K, Rogalla-Ładniak U, Malyarchuk BA, Derenko MV, Kovačević-Grujičić N, Stevanović M, Drakulić D, Davidović S, Spólnicka M, Zubańska M, Woźniak M. A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe. Int J Mol Sci 2023; 24:15095. [PMID: 37894775 PMCID: PMC10606184 DOI: 10.3390/ijms242015095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/03/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023] Open
Abstract
Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used-Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846-1.000 for all classes.
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Affiliation(s)
- Anna Kloska
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
- Faculty of Medical Sciences, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Agata Giełczyk
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Tomasz Grzybowski
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
| | - Rafał Płoski
- Department of Medical Genetics, Warsaw Medical University, 02106 Warsaw, Poland
| | - Sylwester M. Kloska
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
- Faculty of Medical Sciences, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Tomasz Marciniak
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Urszula Rogalla-Ładniak
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
| | - Boris A. Malyarchuk
- Institute of Biological Problems of the North, Russian Academy of Sciences, 685000 Magadan, Russia
| | - Miroslava V. Derenko
- Institute of Biological Problems of the North, Russian Academy of Sciences, 685000 Magadan, Russia
| | - Nataša Kovačević-Grujičić
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, 11042 Belgrade, Serbia
| | - Milena Stevanović
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, 11042 Belgrade, Serbia
- Faculty of Biology, University of Belgrade, 11000 Belgrade, Serbia
- Serbian Academy of Sciences and Arts, 11000 Belgrade, Serbia
| | - Danijela Drakulić
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, 11042 Belgrade, Serbia
| | - Slobodan Davidović
- Institute for Biological Research “Siniša Stanković”, National Institute of Republic of Serbia, University of Belgrade, 11060 Belgrade, Serbia
| | | | - Magdalena Zubańska
- Faculty of Law and Administration, Department of Criminology and Forensic Sciences, University of Warmia and Mazury, 10726 Olsztyn, Poland
| | - Marcin Woźniak
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
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11
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Lai MC. Mental health challenges faced by autistic people. Nat Hum Behav 2023; 7:1620-1637. [PMID: 37864080 DOI: 10.1038/s41562-023-01718-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/07/2023] [Indexed: 10/22/2023]
Abstract
Mental health challenges impede the well-being of autistic people. This Review outlines contributing neurodevelopmental and physical health conditions, rates and developmental trajectories of mental health challenges experienced by autistic people, as well as unique clinical presentations. A framework is proposed to consider four contributing themes to aid personalized formulation: social-contextual determinants, adverse life experiences, autistic cognitive features, and shared genetic and early environmental predispositions. Current evidence-based and clinical-knowledge-informed intervention guidance and ongoing development of support are highlighted for specific mental health areas. Tailored mental health support for autistic people should be neurodivergence-informed, which is fundamentally humanistic and compatible with the prevailing bio-psycho-social frameworks. The personalized formulation should be holistic, considering physical health and transdiagnostic neurodevelopmental factors, intellectual and communication abilities, and contextual-experiential determinants and their interplay with autistic cognition and biology, alongside resilience. Supporting family well-being is integral. Mutual empathic understanding is fundamental to creating societies in which people across neurotypes are all empowered to thrive.
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Affiliation(s)
- Meng-Chuan Lai
- Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
- Department of Psychology, Faculty of Arts and Science, University of Toronto, Toronto, Ontario, Canada.
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada.
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
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12
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Wesseldijk LW, Ullén F, Mosing MA. Music and Genetics. Neurosci Biobehav Rev 2023; 152:105302. [PMID: 37400010 DOI: 10.1016/j.neubiorev.2023.105302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 07/05/2023]
Abstract
The first part of this review provides a brief historical background of behavior genetic research and how twin and genotype data can be utilized to study genetic influences on individual differences in human behavior. We then review the field of music genetics, from its emergence to large scale twin studies and the recent, first molecular genetic studies of music-related traits. In the second part of the review, we discuss the wider utility of twin and genotype data beyond estimating heritability and gene-finding. We present four examples of music studies that utilized genetically informative samples to analyze causality and gene-environmental interplay for music skills. Overall, research in the field of music genetics has gained much momentum over the last decade and its findings highlight the importance of studying both environmental and genetic factors and particularly their interplay, paving the way for exciting and fruitful times to come.
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Affiliation(s)
- Laura W Wesseldijk
- Department of Neuroscience, Karolinska Institutet, Sweden; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Netherlands; Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany.
| | - Fredrik Ullén
- Department of Neuroscience, Karolinska Institutet, Sweden; Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Miriam A Mosing
- Department of Neuroscience, Karolinska Institutet, Sweden; Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany; Melbourne School of Psychological Sciences, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Australia; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden
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13
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Albiñana C, Zhu Z, Schork AJ, Ingason A, Aschard H, Brikell I, Bulik CM, Petersen LV, Agerbo E, Grove J, Nordentoft M, Hougaard DM, Werge T, Børglum AD, Mortensen PB, McGrath JJ, Neale BM, Privé F, Vilhjálmsson BJ. Multi-PGS enhances polygenic prediction by combining 937 polygenic scores. Nat Commun 2023; 14:4702. [PMID: 37543680 PMCID: PMC10404269 DOI: 10.1038/s41467-023-40330-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/21/2023] [Indexed: 08/07/2023] Open
Abstract
The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.
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Affiliation(s)
- Clara Albiñana
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark.
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark.
| | - Zhihong Zhu
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Andrew J Schork
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, 2100, Denmark
- The Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Andrés Ingason
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, 2100, Denmark
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université de Paris, 25-28 Rue du Dr Roux, 75015, Paris, France
| | - Isabell Brikell
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000, Aarhus C, Denmark
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Cynthia M Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Liselotte V Petersen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Jakob Grove
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000, Aarhus C, Denmark
- Center for Genomics and Personalized Medicine, Aarhus University, 8000, Aarhus C, Denmark
- Bioinformatics Research Centre, Aarhus University, 8000, Aarhus C, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Copenhagen Research Centre on Mental Health (CORE), University of Copenhagen, Copenhagen, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, 2300, Copenhagen S, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, 2100, Denmark
- Lundbeck Foundation Centre for GeoGenetics, GLOBE Institute, University of Copenhagen, 1350, Copenhagen K, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000, Aarhus C, Denmark
- Center for Genomics and Personalized Medicine, Aarhus University, 8000, Aarhus C, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - John J McGrath
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, QLD, 4076, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Florian Privé
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Bjarni J Vilhjálmsson
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark.
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark.
- Bioinformatics Research Centre, Aarhus University, 8000, Aarhus C, Denmark.
- Novo Nordisk Foundation Center for Genomic Mechanisms, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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14
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Schoeler T, Speed D, Porcu E, Pirastu N, Pingault JB, Kutalik Z. Participation bias in the UK Biobank distorts genetic associations and downstream analyses. Nat Hum Behav 2023; 7:1216-1227. [PMID: 37106081 PMCID: PMC10365993 DOI: 10.1038/s41562-023-01579-9] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/07/2023] [Indexed: 04/29/2023]
Abstract
While volunteer-based studies such as the UK Biobank have become the cornerstone of genetic epidemiology, the participating individuals are rarely representative of their target population. To evaluate the impact of selective participation, here we derived UK Biobank participation probabilities on the basis of 14 variables harmonized across the UK Biobank and a representative sample. We then conducted weighted genome-wide association analyses on 19 traits. Comparing the output from weighted genome-wide association analyses (neffective = 94,643 to 102,215) with that from standard genome-wide association analyses (n = 263,464 to 283,749), we found that increasing representativeness led to changes in SNP effect sizes and identified novel SNP associations for 12 traits. While heritability estimates were less impacted by weighting (maximum change in h2, 5%), we found substantial discrepancies for genetic correlations (maximum change in rg, 0.31) and Mendelian randomization estimates (maximum change in βSTD, 0.15) for socio-behavioural traits. We urge the field to increase representativeness in biobank samples, especially when studying genetic correlates of behaviour, lifestyles and social outcomes.
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Affiliation(s)
- Tabea Schoeler
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Department of Clinical, Educational and Health Psychology, University College London, London, UK.
| | - Doug Speed
- Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Eleonora Porcu
- Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nicola Pirastu
- Genomics Research Centre, Human Technopole, Milan, Italy
| | - Jean-Baptiste Pingault
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, Lausanne, Switzerland.
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15
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Genç E, Metzen D, Fraenz C, Schlüter C, Voelkle MC, Arning L, Streit F, Nguyen HP, Güntürkün O, Ocklenburg S, Kumsta R. Structural architecture and brain network efficiency link polygenic scores to intelligence. Hum Brain Mapp 2023; 44:3359-3376. [PMID: 37013679 PMCID: PMC10171514 DOI: 10.1002/hbm.26286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 04/05/2023] Open
Abstract
Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.
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Affiliation(s)
- Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Dorothea Metzen
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Caroline Schlüter
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Manuel C Voelkle
- Psychological Research Methods Department of Psychology, Humboldt University, Berlin, Germany
| | - Larissa Arning
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Fabian Streit
- Department Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Huu Phuc Nguyen
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Onur Güntürkün
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Sebastian Ocklenburg
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Psychology, Medical School Hamburg, Hamburg, Germany
- ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany
| | - Robert Kumsta
- Genetic Psychology, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Behavioural and Cognitive Sciences, Laboratory for Stress and Gene-Environment Interplay, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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16
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Treur JL, Thijssen AB, Smit DJ, Tadros R, Veeneman RR, Denys D, Vermeulen JM, Barc J, Bergstedt J, Pasman JA, Bezzina CR, Verweij KJH. Associations of schizophrenia with arrhythmic disorders and electrocardiogram traits: an in-depth genetic exploration of population samples. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.21.23290286. [PMID: 37292618 PMCID: PMC10246121 DOI: 10.1101/2023.05.21.23290286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Background An important contributor to the decreased life expectancy of individuals with schizophrenia is sudden cardiac death. While arrhythmic disorders play an important role in this, the nature of the relation between schizophrenia and arrhythmia is not fully understood. Methods We leveraged summary-level data of large-scale genome-wide association studies of schizophrenia (53,386 cases 77,258 controls), arrhythmic disorders (atrial fibrillation, 55,114 cases 482,295 controls; Brugada syndrome, 2,820 cases 10,001 controls) and electrocardiogram traits (heart rate (variability), PR interval, QT interval, JT interval, and QRS duration, n=46,952-293,051). First, we examined shared genetic liability by assessing global and local genetic correlations and conducting functional annotation. Next, we explored bidirectional causal relations between schizophrenia and arrhythmic disorders and electrocardiogram traits using Mendelian randomization. Outcomes There was no evidence for global genetic correlations, except between schizophrenia and Brugada (rg=0·14, p=4·0E-04). In contrast, strong positive and negative local genetic correlations between schizophrenia and all cardiac traits were found across the genome. In the strongest associated regions, genes related to immune system and viral response mechanisms were overrepresented. Mendelian randomization indicated a causal, increasing effect of liability to schizophrenia on Brugada syndrome (OR=1·15, p=0·009) and heart rate during activity (beta=0·25, p=0·015). Interpretation While there was little evidence for global genetic correlations, specific genomic regions and biological pathways important for both schizophrenia and arrhythmic disorders and electrocardiogram traits emerged. The putative causal effect of liability to schizophrenia on Brugada warrants increased cardiac monitoring and potentially early medical intervention in patients with schizophrenia. Funding European Research Council Starting Grant.
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Affiliation(s)
- Jorien L Treur
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, the Netherlands
| | - Anaiïs B Thijssen
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, the Netherlands
| | - Dirk Ja Smit
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, the Netherlands
| | - Rafik Tadros
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, 5000 Rue Bélanger, Montréal, QC H1T 1C8, Canada
| | - Rada R Veeneman
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, the Netherlands
| | - Damiaan Denys
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, the Netherlands
| | - Jentien M Vermeulen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, the Netherlands
| | - Julien Barc
- Université de Nantes, CHU Nantes, CNRS, INSERM, l'institut du thorax, 8 Quai Moncousu, 44007 Nantes, France
| | - Jacob Bergstedt
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Joëlle A Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65 Stockholm, Sweden
| | - Connie R Bezzina
- Department of Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, the Netherlands
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17
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Chen CY, Tian R, Ge T, Lam M, Sanchez-Andrade G, Singh T, Urpa L, Liu JZ, Sanderson M, Rowley C, Ironfield H, Fang T, Daly M, Palotie A, Tsai EA, Huang H, Hurles ME, Gerety SS, Lencz T, Runz H. The impact of rare protein coding genetic variation on adult cognitive function. Nat Genet 2023:10.1038/s41588-023-01398-8. [PMID: 37231097 DOI: 10.1038/s41588-023-01398-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 04/13/2023] [Indexed: 05/27/2023]
Abstract
Compelling evidence suggests that human cognitive function is strongly influenced by genetics. Here, we conduct a large-scale exome study to examine whether rare protein-coding variants impact cognitive function in the adult population (n = 485,930). We identify eight genes (ADGRB2, KDM5B, GIGYF1, ANKRD12, SLC8A1, RC3H2, CACNA1A and BCAS3) that are associated with adult cognitive function through rare coding variants with large effects. Rare genetic architecture for cognitive function partially overlaps with that of neurodevelopmental disorders. In the case of KDM5B we show how the genetic dosage of one of these genes may determine the variability of cognitive, behavioral and molecular traits in mice and humans. We further provide evidence that rare and common variants overlap in association signals and contribute additively to cognitive function. Our study introduces the relevance of rare coding variants for cognitive function and unveils high-impact monogenic contributions to how cognitive function is distributed in the normal adult population.
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Affiliation(s)
- Chia-Yen Chen
- Research and Development, Biogen Inc, Cambridge, MA, USA.
| | - Ruoyu Tian
- Research and Development, Biogen Inc, Cambridge, MA, USA
- Dewpoint Therapeutics, Boston, MA, USA
| | - Tian Ge
- 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
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Max Lam
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | | | - Tarjinder Singh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Lea Urpa
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jimmy Z Liu
- Research and Development, Biogen Inc, Cambridge, MA, USA
- GlaxoSmithKline, Philadelphia, PA, USA
| | | | | | | | - Terry Fang
- Research and Development, Biogen Inc, Cambridge, MA, USA
| | - Mark Daly
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aarno Palotie
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ellen A Tsai
- Research and Development, Biogen Inc, Cambridge, MA, USA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | | | - Todd Lencz
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, 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/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Heiko Runz
- Research and Development, Biogen Inc, Cambridge, MA, USA.
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18
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Hartonen T, Jermy B, Sõnajalg H, Vartiainen P, Krebs K, Vabalas A, Leino T, Nohynek H, Sivelä J, Mägi R, Daly M, Ollila HM, Milani L, Perola M, Ripatti S, Ganna A. Nationwide health, socio-economic and genetic predictors of COVID-19 vaccination status in Finland. Nat Hum Behav 2023:10.1038/s41562-023-01591-z. [PMID: 37081098 PMCID: PMC10365990 DOI: 10.1038/s41562-023-01591-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/22/2023] [Indexed: 04/22/2023]
Abstract
Understanding factors associated with COVID-19 vaccination can highlight issues in public health systems. Using machine learning, we considered the effects of 2,890 health, socio-economic and demographic factors in the entire Finnish population aged 30-80 and genome-wide information from 273,765 individuals. The strongest predictors of vaccination status were labour income and medication purchase history. Mental health conditions and having unvaccinated first-degree relatives were associated with reduced vaccination. A prediction model combining all predictors achieved good discrimination (area under the receiver operating characteristic curve, 0.801; 95% confidence interval, 0.799-0.803). The 1% of individuals with the highest predicted risk of not vaccinating had an observed vaccination rate of 18.8%, compared with 90.3% in the study population. We identified eight genetic loci associated with vaccination uptake and derived a polygenic score, which was a weak predictor in an independent subset. Our results suggest that individuals at higher risk of suffering the worst consequences of COVID-19 are also less likely to vaccinate.
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Affiliation(s)
- Tuomo Hartonen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Bradley Jermy
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Hanna Sõnajalg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pekka Vartiainen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kristi Krebs
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andrius Vabalas
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tuija Leino
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Hanna Nohynek
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Jonas Sivelä
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mark Daly
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts General Hospital, Cambridge, MA, USA
- Harvard Medical School, Cambridge, MA, USA
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center of Genomic Medicine, Harvard Medical School, Boston, MA, USA
- Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Markus Perola
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts General Hospital, Cambridge, MA, USA
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Massachusetts General Hospital, Cambridge, MA, USA.
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19
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Gordon RL, Martschenko DO, Nayak S, Niarchou M, Morrison MD, Bell E, Jacoby N, Davis LK. Confronting ethical and social issues related to the genetics of musicality. Ann N Y Acad Sci 2023; 1522:5-14. [PMID: 36851882 PMCID: PMC10613828 DOI: 10.1111/nyas.14972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
New interdisciplinary research into genetic influences on musicality raises a number of ethical and social issues for future avenues of research and public engagement. The historical intersection of music cognition and eugenics heightens the need to vigilantly weigh the potential risks and benefits of these studies and the use of their outcomes. Here, we bring together diverse disciplinary expertise (complex trait genetics, music cognition, musicology, bioethics, developmental psychology, and neuroscience) to interpret and guide the ethical use of findings from recent and future studies. We discuss a framework for incorporating principles of ethically and socially responsible conduct of musicality genetics research into each stage of the research lifecycle: study design, study implementation, potential applications, and communication.
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Affiliation(s)
- Reyna L. Gordon
- Department of Otolaryngology- Head & Neck Surgery, Vanderbilt University Medical Center, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA
| | | | - Srishti Nayak
- Department of Otolaryngology- Head & Neck Surgery, Vanderbilt University Medical Center, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA
| | - Maria Niarchou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, TN, USA
| | - Matthew D. Morrison
- Clive Davis Institute of Recorded Music, Tisch School of the Arts, New York University, New York, NY, USA
| | - Eamonn Bell
- Department of Music/Graduate School of Arts and Sciences, Columbia University, New York, NY, USA
- Department of Computer Science, Durham University, Durham, United Kingdom
| | - Nori Jacoby
- Computational Auditory Perception Research Group, Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, TN, USA
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20
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Petrican R, Fornito A. Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing. Dev Cogn Neurosci 2023; 60:101229. [PMID: 36947895 PMCID: PMC10041470 DOI: 10.1016/j.dcn.2023.101229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/08/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023] Open
Abstract
In adulthood, stress exposure and genetic risk heighten psychological vulnerability by accelerating neurobiological senescence. To investigate whether molecular and brain network maturation processes play a similar role in adolescence, we analysed genetic, as well as longitudinal task neuroimaging (inhibitory control, incentive processing) and early life adversity (i.e., material deprivation, violence) data from the Adolescent Brain and Cognitive Development study (N = 980, age range: 9-13 years). Genetic risk was estimated separately for Major Depressive Disorder (MDD) and Alzheimer's Disease (AD), two pathologies linked to stress exposure and allegedly sharing a causal connection (MDD-to-AD). Adversity and genetic risk for MDD/AD jointly predicted functional network segregation patterns suggestive of accelerated (GABA-linked) visual/attentional, but delayed (dopamine [D2]/glutamate [GLU5R]-linked) somatomotor/association system development. A positive relationship between brain maturation and psychopathology emerged only among the less vulnerable adolescents, thereby implying that normatively maladaptive neurodevelopmental alterations could foster adjustment among the more exposed and genetically more stress susceptible youths. Transcriptomic analyses suggested that sensitivity to stress may underpin the joint neurodevelopmental effect of adversity and genetic risk for MDD/AD, in line with the proposed role of negative emotionality as a precursor to AD, likely to account for the alleged causal impact of MDD on dementia onset.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Bedford Street South, Liverpool L69 7ZA, United Kingdom.
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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21
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Liu S, Smit DJA, Abdellaoui A, van Wingen GA, Verweij KJH. Brain Structure and Function Show Distinct Relations With Genetic Predispositions to Mental Health and Cognition. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:300-310. [PMID: 35961582 DOI: 10.1016/j.bpsc.2022.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/09/2022] [Accepted: 08/01/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND Mental health and cognitive achievement are partly heritable, highly polygenic, and associated with brain variations in structure and function. However, the underlying neural mechanisms remain unclear. METHODS We investigated the association between genetic predispositions to various mental health and cognitive traits and a large set of structural and functional brain measures from the UK Biobank (N = 36,799). We also applied linkage disequilibrium score regression to estimate the genetic correlations between various traits and brain measures based on genome-wide data. To decompose the complex association patterns, we performed a multivariate partial least squares model of the genetic and imaging modalities. RESULTS The univariate analyses showed that certain traits were related to brain structure (significant genetic correlations with total cortical surface area from rg = -0.101 for smoking initiation to rg = 0.230 for cognitive ability), while other traits were related to brain function (significant genetic correlations with functional connectivity from rg = -0.161 for educational attainment to rg = 0.318 for schizophrenia). The multivariate analysis showed that genetic predispositions to attention-deficit/hyperactivity disorder, smoking initiation, and cognitive traits had stronger associations with brain structure than with brain function, whereas genetic predispositions to most other psychiatric disorders had stronger associations with brain function than with brain structure. CONCLUSIONS These results reveal that genetic predispositions to mental health and cognitive traits have distinct brain profiles.
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Affiliation(s)
- Shu Liu
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Dirk J A Smit
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Abdel Abdellaoui
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Guido A van Wingen
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Karin J H Verweij
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
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22
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Gustavson DE, Coleman PL, Wang Y, Nitin R, Petty LE, Bush CT, Mosing MA, Wesseldijk LW, Ullén F, Below JE, Cox NJ, Gordon RL. Exploring the genetics of rhythmic perception and musical engagement in the Vanderbilt Online Musicality Study. Ann N Y Acad Sci 2023; 1521:140-154. [PMID: 36718543 PMCID: PMC10038917 DOI: 10.1111/nyas.14964] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Uncovering the genetic underpinnings of musical ability and engagement is a foundational step for exploring their wide-ranging associations with cognition, health, and neurodevelopment. Prior studies have focused on using twin and family designs, demonstrating moderate heritability of musical phenotypes. The current study used genome-wide complex trait analysis and polygenic score (PGS) approaches utilizing genotype data to examine genetic influences on two musicality traits (rhythmic perception and music engagement) in N = 1792 unrelated adults in the Vanderbilt Online Musicality Study. Meta-analyzed heritability estimates (including a replication sample of Swedish individuals) were 31% for rhythmic perception and 12% for self-reported music engagement. A PGS derived from a recent study on beat synchronization ability predicted both rhythmic perception (β = 0.11) and music engagement (β = 0.19) in our sample, suggesting that genetic influences underlying self-reported beat synchronization ability also influence individuals' rhythmic discrimination aptitude and the degree to which they engage in music. Cross-trait analyses revealed a modest contribution of PGSs from several nonmusical traits (from the cognitive, personality, and circadian chronotype domains) to individual differences in musicality (β = -0.06 to 0.07). This work sheds light on the complex relationship between the genetic architecture of musical rhythm processing, beat synchronization, music engagement, and other nonmusical traits.
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Affiliation(s)
- Daniel E Gustavson
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Peyton L Coleman
- School of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Youjia Wang
- Department of Otolaryngology - Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- College of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Rachana Nitin
- Department of Otolaryngology - Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Catherine T Bush
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Miriam A Mosing
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany
- Melbourne School of Psychological Sciences, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Laura W Wesseldijk
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany
- Melbourne School of Psychological Sciences, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Fredrik Ullén
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Reyna L Gordon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Otolaryngology - Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Psychology, Vanderbilt University, Nashville, Tennessee, USA
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23
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Abdellaoui A, Yengo L, Verweij KJH, Visscher PM. 15 years of GWAS discovery: Realizing the promise. Am J Hum Genet 2023; 110:179-194. [PMID: 36634672 PMCID: PMC9943775 DOI: 10.1016/j.ajhg.2022.12.011] [Citation(s) in RCA: 75] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
It has been 15 years since the advent of the genome-wide association study (GWAS) era. Here, we review how this experimental design has realized its promise by facilitating an impressive range of discoveries with remarkable impact on multiple fields, including population genetics, complex trait genetics, epidemiology, social science, and medicine. We predict that the emergence of large-scale biobanks will continue to expand to more diverse populations and capture more of the allele frequency spectrum through whole-genome sequencing, which will further improve our ability to investigate the causes and consequences of human genetic variation for complex traits and diseases.
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Affiliation(s)
- Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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24
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Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
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Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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25
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Wesseldijk LW, Lu Y, Karlsson R, Ullén F, Mosing MA. A comprehensive investigation into the genetic relationship between music engagement and mental health. Transl Psychiatry 2023; 13:15. [PMID: 36658108 PMCID: PMC9852421 DOI: 10.1038/s41398-023-02308-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 12/05/2022] [Accepted: 01/06/2023] [Indexed: 01/20/2023] Open
Abstract
While music engagement is often regarded as beneficial for mental health, some studies report higher risk for depression and anxiety among musicians. This study investigates whether shared underlying genetic influences (genetic pleiotropy) or gene-environment interaction could be at play in the music-mental health association using measured genotypes. In 5,648 Swedish twins with information on music and sport engagement, creative achievements, self-reported mental health and psychiatric diagnoses based on nationwide patient registries, we derived polygenic scores for major depression, bipolar disorder, schizophrenia, neuroticism, sensitivity to environmental stress, depressive symptoms and general musicality. In line with phenotypic associations, individuals with higher polygenic scores for major depression and bipolar disorder were more likely to play music, practice more music and reach higher levels of general artistic achievements, while a higher genetic propensity for general musicality was marginally associated with a higher risk for a depression diagnosis. Importantly, polygenic scores for major depression and bipolar remained associated with music engagement when excluding individuals who experienced psychiatric symptoms, just as a genetic propensity for general musicality predicted a depression diagnosis regardless of whether and how much individuals played music. In addition, we found no evidence for gene-environment interaction: the phenotypic association between music engagement and mental health outcomes did not differ for individuals with different genetic vulnerability for mental health problems. Altogether, our findings suggest that mental health problems observed in musically active individuals are partly explained by a pre-existing genetic risk for depression and bipolar disorder and likely reflect horizontal pleiotropy (when one gene influences multiple traits), rather than causal influences of mental health on music engagement, or vice versa (referred to as vertical pleiotropy).
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Affiliation(s)
- Laura W. Wesseldijk
- grid.4714.60000 0004 1937 0626Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden ,grid.7177.60000000084992262Department of Psychiatry, Amsterdam UMC, location University of Amsterdam, Amsterdam, Netherlands ,grid.461782.e0000 0004 1795 8610Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany ,grid.1008.90000 0001 2179 088XMelbourne School of Psychological Sciences, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Yi Lu
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Robert Karlsson
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Ullén
- grid.4714.60000 0004 1937 0626Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden ,grid.461782.e0000 0004 1795 8610Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Miriam A. Mosing
- grid.4714.60000 0004 1937 0626Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden ,grid.461782.e0000 0004 1795 8610Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany ,grid.1008.90000 0001 2179 088XMelbourne School of Psychological Sciences, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, Australia ,grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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26
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Petrican R, Paine AL, Escott-Price V, Shelton KH. Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer's disease and/or major depressive disorder. Sci Rep 2023; 13:984. [PMID: 36653486 PMCID: PMC9849214 DOI: 10.1038/s41598-023-28057-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Early life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both major depressive disorder (MDD) and Alzheimer's disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Bedford Street South, Liverpool, L69 7ZA, UK.
| | - Amy L Paine
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, UK
| | - Valentina Escott-Price
- Division of Neuroscience and Mental Health, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Katherine H Shelton
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, UK
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27
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Paquin V, Pries LK, Ten Have M, Bak M, Gunther N, de Graaf R, van Dorsselaer S, Lin BD, van Eijk KR, Kenis G, Richards A, O'Donovan MC, Luykx JJ, Rutten BPF, van Os J, Shah JL, Guloksuz S. Age- and sex-specific associations between risk scores for schizophrenia and self-reported health in the general population. Soc Psychiatry Psychiatr Epidemiol 2023; 58:43-52. [PMID: 35913550 PMCID: PMC9845157 DOI: 10.1007/s00127-022-02346-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 07/19/2022] [Indexed: 01/21/2023]
Abstract
PURPOSE The health correlates of polygenic risk (PRS-SCZ) and exposome (ES-SCZ) scores for schizophrenia may vary depending on age and sex. We aimed to examine age- and sex-specific associations of PRS-SCZ and ES-SCZ with self-reported health in the general population. METHODS Participants were from the population-based Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2). Mental and physical health were measured with the 36-item Short Form Survey 4 times between 2007 and 2018. The PRS-SCZ and ES-SCZ were respectively calculated from common genetic variants and exposures (cannabis use, winter birth, hearing impairment, and five childhood adversity categories). Moderation by age and sex was examined in linear mixed models. RESULTS For PRS-SCZ and ES-SCZ analyses, we included 3099 and 6264 participants, respectively (age range 18-65 years; 55.7-56.1% female). Age and sex did not interact with PRS-SCZ. Age moderated the association between ES-SCZ and mental (interaction: p = 0.02) and physical health (p = 0.0007): at age 18, + 1.00 of ES-SCZ was associated with - 0.10 of mental health and - 0.08 of physical health, whereas at age 65, it was associated with - 0.21 and - 0.23, respectively (all units in standard deviations). Sex moderated the association between ES-SCZ and physical health (p < .0001): + 1.00 of ES-SCZ was associated with - 0.19 of physical health among female and - 0.11 among male individuals. CONCLUSION There were larger associations between higher ES-SCZ and poorer health among female and older individuals. Accounting for these interactions may increase ES-SCZ precision and help uncover populational determinants of environmental influences on health.
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Affiliation(s)
- Vincent Paquin
- Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Lotta-Katrin Pries
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Vijverdalseweg 1, SN.2.068, P.O.Box 616 6200, Maastricht, MD, The Netherlands
| | - Margreet Ten Have
- Department of Epidemiology, Netherlands Institute of Mental Health and Addiction, Utrecht, The Netherlands
| | - Maarten Bak
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Vijverdalseweg 1, SN.2.068, P.O.Box 616 6200, Maastricht, MD, The Netherlands.,FACT, Mondriaan Mental Health, Maastricht, The Netherlands
| | - Nicole Gunther
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Vijverdalseweg 1, SN.2.068, P.O.Box 616 6200, Maastricht, MD, The Netherlands.,School of Psychology, Open University, Heerlen, The Netherlands
| | - Ron de Graaf
- Department of Epidemiology, Netherlands Institute of Mental Health and Addiction, Utrecht, The Netherlands
| | - Saskia van Dorsselaer
- Department of Epidemiology, Netherlands Institute of Mental Health and Addiction, Utrecht, The Netherlands
| | - Bochao D Lin
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Vijverdalseweg 1, SN.2.068, P.O.Box 616 6200, Maastricht, MD, The Netherlands.,Department of Psychiatry, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands.,Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
| | - Kristel R van Eijk
- Department of Neurology, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Gunter Kenis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Vijverdalseweg 1, SN.2.068, P.O.Box 616 6200, Maastricht, MD, The Netherlands
| | - Alexander Richards
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Jurjen J Luykx
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Vijverdalseweg 1, SN.2.068, P.O.Box 616 6200, Maastricht, MD, The Netherlands.,Department of Psychiatry, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands.,Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands.,GGNet Mental Health, Warnsveld, The Netherlands
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Vijverdalseweg 1, SN.2.068, P.O.Box 616 6200, Maastricht, MD, The Netherlands
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Vijverdalseweg 1, SN.2.068, P.O.Box 616 6200, Maastricht, MD, The Netherlands.,Department of Epidemiology, Netherlands Institute of Mental Health and Addiction, Utrecht, The Netherlands.,Department of Psychiatry, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands.,Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands.,Department of Psychosis Studies, Institute of Psychiatry, King's College London, London, UK
| | - Jai L Shah
- Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Sinan Guloksuz
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Vijverdalseweg 1, SN.2.068, P.O.Box 616 6200, Maastricht, MD, The Netherlands. .,Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
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28
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Judd N, Sauce B, Klingberg T. Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics. NPJ SCIENCE OF LEARNING 2022; 7:33. [PMID: 36522329 PMCID: PMC9755250 DOI: 10.1038/s41539-022-00148-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Schooling, socioeconomic status (SES), and genetics all impact intelligence. However, it is unclear to what extent their contributions are unique and if they interact. Here we used a multi-trait polygenic score for cognition (cogPGS) with a quasi-experimental regression discontinuity design to isolate how months of schooling relate to intelligence in 6567 children (aged 9-11). We found large, independent effects of schooling (β ~ 0.15), cogPGS (β ~ 0.10), and SES (β ~ 0.20) on working memory, crystallized (cIQ), and fluid intelligence (fIQ). Notably, two years of schooling had a larger effect on intelligence than the lifetime consequences, since birth, of SES or cogPGS-based inequalities. However, schooling showed no interaction with cogPGS or SES for the three intelligence domains tested. While schooling had strong main effects on intelligence, it did not lessen, nor widen the impact of these preexisting SES or genetic factors.
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Affiliation(s)
- Nicholas Judd
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Bruno Sauce
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Torkel Klingberg
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
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29
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Verweij KJH, Vink JM, Abdellaoui A, Gillespie NA, Derks EM, Treur JL. The genetic aetiology of cannabis use: from twin models to genome-wide association studies and beyond. Transl Psychiatry 2022; 12:489. [PMID: 36411281 PMCID: PMC9678872 DOI: 10.1038/s41398-022-02215-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 11/22/2022] Open
Abstract
Cannabis is among the most widely consumed psychoactive substances worldwide. Individual differences in cannabis use phenotypes can partly be explained by genetic differences. Technical and methodological advances have increased our understanding of the genetic aetiology of cannabis use. This narrative review discusses the genetic literature on cannabis use, covering twin, linkage, and candidate-gene studies, and the more recent genome-wide association studies (GWASs), as well as the interplay between genetic and environmental factors. Not only do we focus on the insights that these methods have provided on the genetic aetiology of cannabis use, but also on how they have helped to clarify the relationship between cannabis use and co-occurring traits, such as the use of other substances and mental health disorders. Twin studies have shown that cannabis use is moderately heritable, with higher heritability estimates for more severe phases of use. Linkage and candidate-gene studies have been largely unsuccessful, while GWASs so far only explain a small portion of the heritability. Dozens of genetic variants predictive of cannabis use have been identified, located in genes such as CADM2, FOXP2, and CHRNA2. Studies that applied multivariate methods (twin models, genetic correlation analysis, polygenic score analysis, genomic structural equation modelling, Mendelian randomisation) indicate that there is considerable genetic overlap between cannabis use and other traits (especially other substances and externalising disorders) and some evidence for causal relationships (most convincingly for schizophrenia). We end our review by discussing implications of these findings and suggestions for future work.
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Affiliation(s)
- Karin J. H. Verweij
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Jacqueline M. Vink
- grid.5590.90000000122931605Behavioural Science Institute, Radboud University Nijmegen, Thomas van Aquinostraat 4, 6525 GD Nijmegen, The Netherlands
| | - Abdel Abdellaoui
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Nathan A. Gillespie
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, 800 East Leigh St, Suite 100, Richmond, VA 23219 USA
| | - Eske M. Derks
- grid.1049.c0000 0001 2294 1395Translational Neurogenomics, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006 Australia
| | - Jorien L. Treur
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
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30
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Jang SK, Evans L, Fialkowski A, Arnett DK, Ashley-Koch AE, Barnes KC, Becker DM, Bis JC, Blangero J, Bleecker ER, Boorgula MP, Bowden DW, Brody JA, Cade BE, Jenkins BWC, Carson AP, Chavan S, Cupples LA, Custer B, Damrauer SM, David SP, de Andrade M, Dinardo CL, Fingerlin TE, Fornage M, Freedman BI, Garrett ME, Gharib SA, Glahn DC, Haessler J, Heckbert SR, Hokanson JE, Hou L, Hwang SJ, Hyman MC, Judy R, Justice AE, Kaplan RC, Kardia SLR, Kelly S, Kim W, Kooperberg C, Levy D, Lloyd-Jones DM, Loos RJF, Manichaikul AW, Gladwin MT, Martin LW, Nouraie M, Melander O, Meyers DA, Montgomery CG, North KE, Oelsner EC, Palmer ND, Payton M, Peljto AL, Peyser PA, Preuss M, Psaty BM, Qiao D, Rader DJ, Rafaels N, Redline S, Reed RM, Reiner AP, Rich SS, Rotter JI, Schwartz DA, Shadyab AH, Silverman EK, Smith NL, Smith JG, Smith AV, Smith JA, Tang W, Taylor KD, Telen MJ, Vasan RS, Gordeuk VR, Wang Z, Wiggins KL, Yanek LR, Yang IV, Young KA, Young KL, Zhang Y, Liu DJ, Keller MC, Vrieze S. Rare genetic variants explain missing heritability in smoking. Nat Hum Behav 2022; 6:1577-1586. [PMID: 35927319 PMCID: PMC9985486 DOI: 10.1038/s41562-022-01408-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/10/2022] [Indexed: 12/11/2022]
Abstract
Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.
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Affiliation(s)
- Seon-Kyeong Jang
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Luke Evans
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Ecology & Evolution, University of Colorado Boulder, Boulder, CO, USA
| | | | - Donna K Arnett
- Dean's Office, University of Kentucky College of Public Health, Lexington, KY, USA
| | | | - Kathleen C Barnes
- Division of Biomedical Informatics & Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Diane M Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | | | - Meher Preethi Boorgula
- Division of Biomedical Informatics & Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E Cade
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Brenda W Campbell Jenkins
- Jackson Heart Study Graduate Training and Education Center, Jackson State University School of Public Health, Jackson, MS, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Sameer Chavan
- Division of Biomedical Informatics & Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Brian Custer
- Vitalant Research Institute, San Francisco, CA, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Surgery, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Sean P David
- Department of Family Medicine, Prtizker School of Medicine, University of Chicago, Chicago, IL, USA
- NorthShore University HealthSystem, Evanston, IL, USA
| | - Mariza de Andrade
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Tasha E Fingerlin
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Center for Genes Environment and Health, National Jewish Health, Denver, CO, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Barry I Freedman
- Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Melanie E Garrett
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Sina A Gharib
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Center for Lung Biology, Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hosptial and Harvard Medical School, Boston, MA, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Susan R Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - John E Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Shih-Jen Hwang
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Matthew C Hyman
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Renae Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health System, Danville, PA, USA
| | - Robert C Kaplan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Shannon Kelly
- Department of Pediatrics, UCSF Benioff Children's Hospital Oakland, Oakland, CA, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | | | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Mark T Gladwin
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Mehdi Nouraie
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | | | - Courtney G Montgomery
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elizabeth C Oelsner
- Division of General Medicine, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Marinelle Payton
- Department of Epidemiology and Biostatistics, Jackson Heart Study Graduate Training and Education Center, Jackson State University School of Public Health, Jackson, MS, USA
| | - Anna L Peljto
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Michael Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Dandi Qiao
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Daniel J Rader
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas Rafaels
- Division of Biomedical Informatics & Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert M Reed
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - David A Schwartz
- Department of Medicine, School of Medicine, University of Colorado Denver, Aurora, CO, USA
- Department of Immunology, School of Medicine, University of Colorado Denver, Aurora, CO, USA
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicholas L Smith
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - J Gustav Smith
- Wallenberg Laboratory/Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University, Gothenburg, Sweden
- Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Albert V Smith
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marilyn J Telen
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Ramachandran S Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Victor R Gordeuk
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Zhe Wang
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kerri L Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ivana V Yang
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kendra A Young
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Kristin L Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yingze Zhang
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Dajiang J Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
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31
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The evolutionary dance between culture, genes, and everything in between. Behav Brain Sci 2022; 45:e153. [PMID: 36098435 DOI: 10.1017/s0140525x2100176x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Uchiyama et al. describe how a more complete measurement of the dynamic nature of culture could help us unmask the true richness of genetic effects on behaviour. I underscore this notion here by reflecting on the role that the dynamic relationship between culture and DNA has played in our evolutionary history and will play in our evolutionary future.
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32
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Culture and causal inference: The impact of cultural differences on the generalisability of findings from Mendelian randomisation studies. Behav Brain Sci 2022; 45:e158. [PMID: 36098429 DOI: 10.1017/s0140525x21001795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Cultural effects can influence the results of causal genetic analyses, such as Mendelian randomisation, but the potential influences of culture on genotype-phenotype associations are not currently well understood. Different genetic variants could be associated with different phenotypes in different populations, or culture could confound or influence the direction of the association between genotypes and phenotypes in different populations.
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33
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Genes influence complex traits through environments that vary between geographic regions. Nat Genet 2022; 54:1265-1266. [PMID: 36068437 DOI: 10.1038/s41588-022-01163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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34
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Gene-environment correlations across geographic regions affect genome-wide association studies. Nat Genet 2022; 54:1345-1354. [PMID: 35995948 PMCID: PMC9470533 DOI: 10.1038/s41588-022-01158-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 07/13/2022] [Indexed: 12/23/2022]
Abstract
Gene-environment correlations affect associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here we showed in up to 43,516 British siblings that educational attainment polygenic scores capture gene-environment correlations, and that migration extends these gene-environment correlations beyond the family to broader geographic regions. We then ran GWASs on 56 complex traits in up to 254,387 British individuals. Controlling for geographic regions significantly decreased the heritability for socioeconomic status (SES)-related traits, most strongly for educational attainment and income. For most traits, controlling for regions significantly reduced genetic correlations with educational attainment and income, most significantly for body mass index/body fat, sedentary behavior and substance use, consistent with gene-environment correlations related to regional socio-economic differences. The effects of controlling for birthplace and current address suggest both passive and active sources of gene-environment correlations. Our results show that the geographic clustering of DNA and SES introduces gene-environment correlations that affect GWAS results.
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35
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Using a polygenic score in a family design to understand genetic influences on musicality. Sci Rep 2022; 12:14658. [PMID: 36038631 PMCID: PMC9424203 DOI: 10.1038/s41598-022-18703-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 08/17/2022] [Indexed: 11/30/2022] Open
Abstract
To further our understanding of the genetics of musicality, we explored associations between a polygenic score for self-reported beat synchronization ability (PGSrhythm) and objectively measured rhythm discrimination, as well as other validated music skills and music-related traits. Using family data, we were able to further explore potential pathways of direct genetic, indirect genetic (through passive gene–environment correlation) and confounding effects (such as population structure and assortative mating). In 5648 Swedish twins, we found PGSrhythm to predict not only rhythm discrimination, but also melody and pitch discrimination (betas between 0.11 and 0.16, p < 0.001), as well as other music-related outcomes (p < 0.05). In contrast, PGSrhythm was not associated with control phenotypes not directly related to music. Associations did not deteriorate within families (N = 243), implying that indirect genetic or confounding effects did not inflate PGSrhythm effects. A correlation (r = 0.05, p < 0.001) between musical enrichment of the family childhood environment and individuals' PGSrhythm, suggests gene–environment correlation. We conclude that the PGSrhythm captures individuals' general genetic musical propensity, affecting musical behavior more likely direct than through indirect or confounding effects.
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36
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Logtenberg E, Overbeek MF, Pasman JA, Abdellaoui A, Luijten M, van Holst RJ, Vink JM, Denys D, Medland SE, Verweij KJH, Treur JL. Investigating the causal nature of the relationship of subcortical brain volume with smoking and alcohol use. Br J Psychiatry 2022; 221:377-385. [PMID: 35049464 DOI: 10.1192/bjp.2021.81] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Structural variation in subcortical brain regions has been linked to substance use, including the most commonly used substances nicotine and alcohol. Pre-existing differences in subcortical brain volume may affect smoking and alcohol use, but there is also evidence that smoking and alcohol use can lead to structural changes. AIMS We assess the causal nature of the complex relationship of subcortical brain volume with smoking and alcohol use, using bi-directional Mendelian randomisation. METHOD Mendelian randomisation uses genetic variants predictive of a certain 'exposure' as instrumental variables to test causal effects on an 'outcome'. Because of random assortment at meiosis, genetic variants should not be associated with confounders, allowing less biased causal inference. We used summary-level data of genome-wide association studies of subcortical brain volumes (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus; n = 50 290) and smoking and alcohol use (smoking initiation, n = 848 460; cigarettes per day, n = 216 590; smoking cessation, n = 378 249; alcoholic drinks per week, n = 630 154; alcohol dependence, n = 46 568). The main analysis, inverse-variance weighted regression, was verified by a wide range of sensitivity methods. RESULTS There was strong evidence that liability to alcohol dependence decreased amygdala and hippocampal volume, and smoking more cigarettes per day decreased hippocampal volume. From subcortical brain volumes to substance use, there was no or weak evidence for causal effects. CONCLUSIONS Our findings suggest that heavy alcohol use and smoking can causally reduce subcortical brain volume. This adds to accumulating evidence that alcohol and smoking affect the brain, and likely mental health, warranting more recognition in public health efforts.
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Affiliation(s)
- Emma Logtenberg
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
| | - Martin F Overbeek
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
| | - Joëlle A Pasman
- Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
| | - Maartje Luijten
- Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Ruth J van Holst
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
| | - Jacqueline M Vink
- Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Damiaan Denys
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
| | - Sarah E Medland
- Psychiatric Genetics Group, QIMR Berghofer Medical Research Institute, Australia
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
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37
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Guo H, Hou L, Shi Y, Jin SC, Zeng X, Li B, Lifton RP, Brueckner M, Zhao H, Lu Q. Quantifying concordant genetic effects of de novo mutations on multiple disorders. eLife 2022; 11:75551. [PMID: 35666111 PMCID: PMC9217133 DOI: 10.7554/elife.75551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
Exome sequencing on tens of thousands of parent-proband trios has identified numerous deleterious de novo mutations (DNMs) and implicated risk genes for many disorders. Recent studies have suggested shared genes and pathways are enriched for DNMs across multiple disorders. However, existing analytic strategies only focus on genes that reach statistical significance for multiple disorders and require large trio samples in each study. As a result, these methods are not able to characterize the full landscape of genetic sharing due to polygenicity and incomplete penetrance. In this work, we introduce EncoreDNM, a novel statistical framework to quantify shared genetic effects between two disorders characterized by concordant enrichment of DNMs in the exome. EncoreDNM makes use of exome-wide, summary-level DNM data, including genes that do not reach statistical significance in single-disorder analysis, to evaluate the overall and annotation-partitioned genetic sharing between two disorders. Applying EncoreDNM to DNM data of nine disorders, we identified abundant pairwise enrichment correlations, especially in genes intolerant to pathogenic mutations and genes highly expressed in fetal tissues. These results suggest that EncoreDNM improves current analytic approaches and may have broad applications in DNM studies.
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Affiliation(s)
- Hanmin Guo
- Center for Statistical Science, Tsinghua UniversityBeijingChina
- Department of Industrial Engineering, Tsinghua UniversityBeijingChina
| | - Lin Hou
- Center for Statistical Science, Tsinghua UniversityBeijingChina
- Department of Industrial Engineering, Tsinghua UniversityBeijingChina
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua UniversityBeijingChina
| | - Yu Shi
- Yale School of Management, Yale UniversityNew HavenUnited States
| | - Sheng Chih Jin
- Department of Genetics, Washington University in St. LouisSt. LouisUnited States
| | - Xue Zeng
- Department of Genetics, Yale UniversityNew HavenUnited States
- Laboratory of Human Genetics and Genomics, Rockefeller UniversityNew YorkUnited States
| | - Boyang Li
- Department of Biostatistics, Yale School of Public HealthNew HavenUnited States
| | - Richard P Lifton
- Department of Genetics, Yale UniversityNew HavenUnited States
- Laboratory of Human Genetics and Genomics, Rockefeller UniversityNew YorkUnited States
| | - Martina Brueckner
- Department of Genetics, Yale UniversityNew HavenUnited States
- Department of Pediatrics, Yale UniversityNew HavenUnited States
| | - Hongyu Zhao
- Department of Genetics, Yale UniversityNew HavenUnited States
- Department of Biostatistics, Yale School of Public HealthNew HavenUnited States
- Program of Computational Biology and Bioinformatics, Yale UniversityNew HavenUnited States
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-MadisonMadisonUnited States
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38
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Petrican R, Fornito A, Jones N. Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged Females. Neuroimage 2022; 255:119209. [PMID: 35429627 DOI: 10.1016/j.neuroimage.2022.119209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 04/05/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Adverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one's environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer's Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold.
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Affiliation(s)
- Raluca Petrican
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom.
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Natalie Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
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39
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Niarchou M, Gustavson DE, Sathirapongsasuti JF, Anglada-Tort M, Eising E, Bell E, McArthur E, Straub P, McAuley JD, Capra JA, Ullén F, Creanza N, Mosing MA, Hinds DA, Davis LK, Jacoby N, Gordon RL. Genome-wide association study of musical beat synchronization demonstrates high polygenicity. Nat Hum Behav 2022; 6:1292-1309. [PMID: 35710621 PMCID: PMC9489530 DOI: 10.1038/s41562-022-01359-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 04/21/2022] [Indexed: 02/02/2023]
Abstract
Moving in synchrony to the beat is a fundamental component of musicality. Here we conducted a genome-wide association study to identify common genetic variants associated with beat synchronization in 606,825 individuals. Beat synchronization exhibited a highly polygenic architecture, with 69 loci reaching genome-wide significance (P < 5 × 10-8) and single-nucleotide-polymorphism-based heritability (on the liability scale) of 13%-16%. Heritability was enriched for genes expressed in brain tissues and for fetal and adult brain-specific gene regulatory elements, underscoring the role of central-nervous-system-expressed genes linked to the genetic basis of the trait. We performed validations of the self-report phenotype (through separate experiments) and of the genome-wide association study (polygenic scores for beat synchronization were associated with patients algorithmically classified as musicians in medical records of a separate biobank). Genetic correlations with breathing function, motor function, processing speed and chronotype suggest shared genetic architecture with beat synchronization and provide avenues for new phenotypic and genetic explorations.
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Affiliation(s)
- Maria Niarchou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA. .,Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Daniel E. Gustavson
- grid.412807.80000 0004 1936 9916Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA ,grid.412807.80000 0004 1936 9916Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN USA
| | | | - Manuel Anglada-Tort
- grid.461782.e0000 0004 1795 8610Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Else Eising
- grid.419550.c0000 0004 0501 3839Department of Language and Genetics, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Eamonn Bell
- grid.21729.3f0000000419368729Department of Music, Columbia University, New York, NY USA ,grid.8250.f0000 0000 8700 0572Department of Computer Science, Durham University, Durham, UK
| | - Evonne McArthur
- grid.412807.80000 0004 1936 9916Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | - Peter Straub
- grid.412807.80000 0004 1936 9916Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | | | - J. Devin McAuley
- grid.17088.360000 0001 2150 1785Department of Psychology, Michigan State University, East Lansing, MI USA
| | - John A. Capra
- grid.266102.10000 0001 2297 6811Bakar Computational Health Sciences Institute, University of California, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Department of Epidemiology & Biostatistics, University of California, San Francisco, CA USA
| | - Fredrik Ullén
- grid.465198.7Department of Neuroscience, Karolinska Institutet, Solna, Sweden ,grid.461782.e0000 0004 1795 8610Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Nicole Creanza
- grid.152326.10000 0001 2264 7217Department of Biological Sciences, Vanderbilt University, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN USA
| | - Miriam A. Mosing
- grid.465198.7Department of Neuroscience, Karolinska Institutet, Solna, Sweden ,grid.461782.e0000 0004 1795 8610Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany ,grid.1008.90000 0001 2179 088XMelbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria Australia
| | - David A. Hinds
- grid.420283.f0000 0004 0626 085823andMe, Inc, Sunnyvale, CA USA
| | - Lea K. Davis
- grid.412807.80000 0004 1936 9916Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA ,grid.412807.80000 0004 1936 9916Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN USA ,grid.412807.80000 0004 1936 9916Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN USA ,grid.412807.80000 0004 1936 9916Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN USA
| | - Nori Jacoby
- grid.461782.e0000 0004 1795 8610Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Reyna L. Gordon
- grid.412807.80000 0004 1936 9916Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA ,grid.412807.80000 0004 1936 9916Department of Otolaryngology—Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Department of Psychology, Vanderbilt University, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN USA
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Abstract
During the past decade, polygenic scores have become a fast-growing area of research in the behavioural sciences. The ability to directly assess people's genetic propensities has transformed research by making it possible to add genetic predictors of traits to any study. The value of polygenic scores in the behavioural sciences rests on using inherited DNA differences to predict, from birth, common disorders and complex traits in unrelated individuals in the population. This predictive power of polygenic scores does not require knowing anything about the processes that lie between genes and behaviour. It also does not mandate disentangling the extent to which the prediction is due to assortative mating, genotype-environment correlation, or even population stratification. Although bottom-up explanation from genes to brain to behaviour will remain the long-term goal of the behavioural sciences, prediction is also a worthy achievement because it has immediate practical utility for identifying individuals at risk and is the necessary first step towards explanation. A high priority for research must be to increase the predictive power of polygenic scores to be able to use them as an early warning system to prevent problems.
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Treur JL, Munafò MR, Logtenberg E, Wiers RW, Verweij KJH. Using Mendelian randomization analysis to better understand the relationship between mental health and substance use: a systematic review. Psychol Med 2021; 51:1593-1624. [PMID: 34030749 PMCID: PMC8327626 DOI: 10.1017/s003329172100180x] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/17/2021] [Accepted: 04/21/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Poor mental health has consistently been associated with substance use (smoking, alcohol drinking, cannabis use, and consumption of caffeinated drinks). To properly inform public health policy it is crucial to understand the mechanisms underlying these associations, and most importantly, whether or not they are causal. METHODS In this pre-registered systematic review, we assessed the evidence for causal relationships between mental health and substance use from Mendelian randomization (MR) studies, following PRISMA. We rated the quality of included studies using a scoring system that incorporates important indices of quality, such as the quality of phenotype measurement, instrument strength, and use of sensitivity methods. RESULTS Sixty-three studies were included for qualitative synthesis. The final quality rating was '-' for 16 studies, '- +' for 37 studies, and '+'for 10 studies. There was robust evidence that higher educational attainment decreases smoking and that there is a bi-directional, increasing relationship between smoking and (symptoms of) mental disorders. Another robust finding was that higher educational attainment increases alcohol use frequency, but decreases binge-drinking and alcohol use problems, and that mental disorders causally lead to more alcohol drinking without evidence for the reverse. CONCLUSIONS The current MR literature increases our understanding of the relationship between mental health and substance use. Bi-directional causal relationships are indicated, especially for smoking, providing further incentive to strengthen public health efforts to decrease substance use. Future MR studies should make use of large(r) samples in combination with detailed phenotypes, a wide range of sensitivity methods, and triangulate with other research methods.
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Affiliation(s)
- Jorien L. Treur
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Addiction Development and Psychopathology (ADAPT) Lab, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - Marcus R. Munafò
- School of Psychological Science, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, the University of Bristol, Bristol, UK
| | - Emma Logtenberg
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Reinout W. Wiers
- Addiction Development and Psychopathology (ADAPT) Lab, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
- Center for Urban Mental Health, University of Amsterdam, Amsterdam, the Netherlands
| | - Karin J. H. Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
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Odintsova VV, Rebattu V, Hagenbeek FA, Pool R, Beck JJ, Ehli EA, van Beijsterveldt CEM, Ligthart L, Willemsen G, de Geus EJC, Hottenga JJ, Boomsma DI, van Dongen J. Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles. Front Psychiatry 2021; 12:688464. [PMID: 34393852 PMCID: PMC8357987 DOI: 10.3389/fpsyt.2021.688464] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/15/2021] [Indexed: 11/13/2022] Open
Abstract
We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6%) of which 54.4% was captured by the MS. The two predictors captured 16% of former vs. never smoking initiation variance (MS:15.5%, PGS: 0.5%), 17.7% of prenatal maternal smoking variance (MS:16.9%, PGS: 0.8%), 11.9% of BMI variance (MS: 6.4%, PGS 5.5%), and 1.9% of birth weight variance (MS: 0.4%, PGS: 1.5%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6%), where the MSs contributed 1.5%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.
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Affiliation(s)
- Veronika V Odintsova
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Valerie Rebattu
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - René Pool
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jeffrey J Beck
- Avera Institute for Human Genetics, Sioux Falls, SD, United States
| | - Erik A Ehli
- Avera Institute for Human Genetics, Sioux Falls, SD, United States
| | - Catharina E M van Beijsterveldt
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Lannie Ligthart
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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