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Holen B, Kutrolli G, Shadrin AA, Icick R, Hindley G, Rødevand L, O'Connell KS, Frei O, Parker N, Tesfaye M, Deak JD, Jahołkowski P, Dale AM, Djurovic S, Andreassen OA, Smeland OB. Genome-wide analyses reveal shared genetic architecture and novel risk loci between opioid use disorder and general cognitive ability. Drug Alcohol Depend 2024; 256:111058. [PMID: 38244365 DOI: 10.1016/j.drugalcdep.2023.111058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/09/2023] [Accepted: 12/03/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND Opioid use disorder (OUD), a serious health burden worldwide, is associated with lower cognitive function. Recent studies have demonstrated a negative genetic correlation between OUD and general cognitive ability (COG), indicating a shared genetic basis. However, the specific genetic variants involved, and the underlying molecular mechanisms remain poorly understood. Here, we aimed to quantify and identify the genetic basis underlying OUD and COG. METHODS We quantified the extent of genetic overlap between OUD and COG using a bivariate causal mixture model (MiXeR) and identified specific genetic loci applying conditional/conjunctional FDR. Finally, we investigated biological function and expression of implicated genes using available resources. RESULTS We estimated that ~94% of OUD variants (4.8k out of 5.1k variants) also influence COG. We identified three novel OUD risk loci and one locus shared between OUD and COG. Loci identified implicated biological substrates in the basal ganglia. CONCLUSION We provide new insights into the complex genetic risk architecture of OUD and its genetic relationship with COG.
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Affiliation(s)
- Børge Holen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway.
| | - Gleda Kutrolli
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Alexey A Shadrin
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Romain Icick
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway; INSERM UMR-S1144, Université Paris Cité, F-75006, France
| | - Guy Hindley
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Linn Rødevand
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Kevin S O'Connell
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Nadine Parker
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Markos Tesfaye
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway; NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Joseph D Deak
- Yale School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Piotr Jahołkowski
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA 92093, USA; Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Olav B Smeland
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway.
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Shadrin AA, Hindley G, Hagen E, Parker N, Tesfaye M, Jaholkowski P, Rahman Z, Kutrolli G, Fominykh V, Djurovic S, Smeland OB, O’Connell KS, van der Meer D, Frei O, Andreassen OA, Dale AM. Dissecting the genetic overlap between three complex phenotypes with trivariate MiXeR. medRxiv 2024:2024.02.23.24303236. [PMID: 38464132 PMCID: PMC10925360 DOI: 10.1101/2024.02.23.24303236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Comorbidities are an increasing global health challenge. Accumulating evidence suggests overlapping genetic architectures underlying comorbid complex human traits and disorders. The bivariate causal mixture model (MiXeR) can quantify the polygenic overlap between complex phenotypes beyond global genetic correlation. Still, the pattern of genetic overlap between three distinct phenotypes, which is important to better characterize multimorbidities, has previously not been possible to quantify. Here, we present and validate the trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three phenotypes using summary statistics from genome-wide association studies (GWAS). Our simulations show that the trivariate MiXeR can reliably reconstruct different patterns of genetic overlap. We further demonstrate how the tool can be used to estimate the proportions of genetic overlap between three phenotypes using real GWAS data, providing examples of complex patterns of genetic overlap between diverse human traits and diseases that could not be deduced from bivariate analyses. This contributes to a better understanding of the etiology of complex phenotypes and the nature of their relationship, which may aid in dissecting comorbidity patterns and their biological underpinnings.
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Affiliation(s)
- Alexey A. Shadrin
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Guy Hindley
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Espen Hagen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nadine Parker
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Markos Tesfaye
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Piotr Jaholkowski
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Zillur Rahman
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gleda Kutrolli
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Vera Fominykh
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Olav B. Smeland
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kevin S. O’Connell
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Oleksandr Frei
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, 0316 Oslo, Norway
| | - Ole A. Andreassen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, United States of America
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA 92093, United States of America
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, United States of America
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Icick R, Shadrin A, Holen B, Karadag N, Parker N, O'Connell K, Frei O, Bahrami S, Høegh M, Lagerberg T, Cheng W, Seibert T, Djurovic S, Dale A, Zhou H, Edenberg H, Gelernter J, Smeland O, Hindley G, Andreassen O. Identification of Novel Loci and Cross-Disorder Pleiotropy Through Multi-Ancestry Genome-Wide Analysis of Alcohol Use Disorder in Over One Million Individuals. Res Sq 2023:rs.3.rs-3755915. [PMID: 38196616 PMCID: PMC10775504 DOI: 10.21203/rs.3.rs-3755915/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Alcohol use disorder (AUD) is highly heritable and burdensome worldwide. Genome-wide association studies (GWASs) can provide new evidence regarding the aetiology of AUD. We report a multi-ancestry GWASs across diverse ancestries focusing on a narrow AUD phenotype, using novel statistical tools in a total sample of 1,041,450 individuals [102,079 cases; European, 75,583; African, 20,689 (mostly African-American); Hispanic American, 3,449; East Asian, 2,254; South Asian, 104; descent]. Cross-ancestry functional analyses were performed with European and African samples. Thirty-seven genome-wide significant loci were identified, of which seven were novel for AUD and six for other alcohol phenotypes. Loci were mapped to genes enriched for brain regions relevant for AUD (striatum, hypothalamus, and prefrontal cortex) and potential drug targets (GABAergic, dopaminergic and serotonergic neurons). African-specific analysis yielded a unique pattern of immune-related gene sets. Polygenic overlap and positive genetic correlations showed extensive shared genetic architecture between AUD and both mental and general medical phenotypes, suggesting they are not only complications of alcohol use but also share genetic liability with AUD. Leveraging a cross-ancestry approach allowed identification of novel genetic loci for AUD and underscores the value of multi-ancestry genetic studies. These findings advance our understanding of AUD risk and clinically-relevant comorbidities.
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Affiliation(s)
| | | | - Børge Holen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo
| | - Naz Karadag
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo
| | - Nadine Parker
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo
| | - Kevin O'Connell
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo
| | | | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo
| | - Margrethe Høegh
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo
| | - Trine Lagerberg
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo
| | - Tyler Seibert
- Department of Radiation Medicine and Applied Sciences, Department of Radiology, Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo; NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen
| | - Anders Dale
- Department of Neurosciences, University of California San Diego
| | | | | | | | - Olav Smeland
- NORMENT Centre for Mental Disorders Research, University of Oslo and Oslo University Hospital
| | - Guy Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo
| | - Ole Andreassen
- Oslo University Hospital & Institute of Clinical Medicine, University of Oslo
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Kosted R, Kirsch DE, Le V, Fromme K, Lippard ETC. Subjective response to alcohol: Interactive effects of early life stress, parental risk for mood and substance use disorders, and drinking context. Pharmacol Biochem Behav 2023; 229:173591. [PMID: 37353164 PMCID: PMC10902860 DOI: 10.1016/j.pbb.2023.173591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/12/2023] [Accepted: 06/19/2023] [Indexed: 06/25/2023]
Abstract
Early life stress, specifically childhood maltreatment, and parental risk for mood and substance use disorders (SUDs) are associated with increased risk for alcohol use disorder (AUD). There is limited data on how these factors interact to contribute to alcohol-related outcomes. Prior work has suggested early life stress may increase sensitivity to psychostimulants and that subjective response to alcohol is heritable. It is unclear if early life stress alters sensitivity to alcohol and interacts with parental risk for mood/SUDs, which in turn may act as a risk factor for AUD. The current study uses within-subjects placebo-controlled alcohol administration methods to investigate the effects of childhood maltreatment on subjective response to alcohol in young adults with and without parental risk of mood/SUDs. Additionally, we explored interactions with drinking context (i.e., drinking in a bar vs. non-bar context). Within individuals with parental risk for mood/SUDs, there was a positive relation between total Childhood Trauma Questionnaire (CTQ) score and how drunk individuals reported feeling across both alcohol and placebo conditions (parental risk group-by-CTQ interaction p = .01; main effect of CTQ within individuals with parental risk for mood/SUDs p = .005). When exploring interactions with drinking context (bar vs. non-bar context), we observed a significant drinking context-by-parental risk-by-CTQ interaction (p = .03), with CTQ score positively associated with greater positive valence/positive arousal feelings in the parental risk group if they consumed their beverages in the bar context (p = .004) but not if they consumed their beverages in the non-bar context. Results suggest childhood maltreatment may contribute to variation in subjective response to the positive effects of alcohol-possibly mediated by alcohol cues and/or expectancies-in young adults with parental risk for mood/SUDs.
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Affiliation(s)
- Raquel Kosted
- Department of Psychiatry and Behavioral Sciences, University of Texas, Austin, TX, USA
| | - Dylan E Kirsch
- Department of Psychiatry and Behavioral Sciences, University of Texas, Austin, TX, USA; Waggoner Center for Alcohol and Addiction Research, University of Texas, Austin, TX, USA; Institute for Neuroscience, University of Texas, Austin, TX, USA
| | - Vanessa Le
- Department of Psychiatry and Behavioral Sciences, University of Texas, Austin, TX, USA
| | - Kim Fromme
- Department of Psychology, University of Texas, Austin, TX, USA; Waggoner Center for Alcohol and Addiction Research, University of Texas, Austin, TX, USA
| | - Elizabeth T C Lippard
- Department of Psychiatry and Behavioral Sciences, University of Texas, Austin, TX, USA; Department of Psychology, University of Texas, Austin, TX, USA; Waggoner Center for Alcohol and Addiction Research, University of Texas, Austin, TX, USA; Institute for Neuroscience, University of Texas, Austin, TX, USA; Institute of Early Life Adversity Research, University of Texas, Austin, TX, USA.
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5
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Shang MY, Wu Y, Zhang CY, Qi HX, Zhang Q, Huo JH, Wang L, Wang C, Li M. Bidirectional genetic overlap between bipolar disorder and intelligence. BMC Med 2022; 20:464. [PMID: 36447210 PMCID: PMC9710050 DOI: 10.1186/s12916-022-02668-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/17/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Bipolar disorder (BD) is a highly heritable psychiatric illness exhibiting substantial correlation with intelligence. METHODS To investigate the shared genetic signatures between BD and intelligence, we utilized the summary statistics from genome-wide association studies (GWAS) to conduct the bivariate causal mixture model (MiXeR) and conjunctional false discovery rate (conjFDR) analyses. Subsequent expression quantitative trait loci (eQTL) mapping in human brain and enrichment analyses were also performed. RESULTS Analysis with MiXeR suggested that approximately 10.3K variants could influence intelligence, among which 7.6K variants were correlated with the risk of BD (Dice: 0.80), and 47% of these variants predicted BD risk and intelligence in consistent allelic directions. The conjFDR analysis identified 37 distinct genomic loci that were jointly associated with BD and intelligence with a conjFDR < 0.01, and 16 loci (43%) had the same directions of allelic effects in both phenotypes. Brain eQTL analyses found that genes affected by the "concordant loci" were distinct from those modulated by the "discordant loci". Enrichment analyses suggested that genes related to the "concordant loci" were significantly enriched in pathways/phenotypes related with synapses and sleep quality, whereas genes associated with the "discordant loci" were enriched in pathways related to cell adhesion, calcium ion binding, and abnormal emotional phenotypes. CONCLUSIONS We confirmed the polygenic overlap with mixed directions of allelic effects between BD and intelligence and identified multiple genomic loci and risk genes. This study provides hints for the mesoscopic phenotypes of BD and relevant biological mechanisms, promoting the knowledge of the genetic and phenotypic heterogeneity of BD. The essential value of leveraging intelligence in BD investigations is also highlighted.
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Affiliation(s)
- Meng-Yuan Shang
- Zhejiang Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang, China.,School of Basic Medical Science, School of Medicine, Ningbo University, Ningbo, Zhejiang, China
| | - Yong Wu
- Research Center for Mental Health and Neuroscience, Wuhan Mental Health Center, Wuhan, Hubei, China
| | - Chu-Yi Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Hao-Xiang Qi
- Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Qing Zhang
- Zhejiang Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang, China.,School of Basic Medical Science, School of Medicine, Ningbo University, Ningbo, Zhejiang, China
| | - Jin-Hua Huo
- Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Lu Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Chuang Wang
- Zhejiang Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang, China. .,School of Basic Medical Science, School of Medicine, Ningbo University, Ningbo, Zhejiang, China.
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.
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