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Farrell SF, Sterling M, Klyne DM, Mustafa S, Campos AI, Kho PF, Lundberg M, Rentería ME, Ngo TT, Cuéllar-Partida G. Genetic impact of blood C-reactive protein levels on chronic spinal & widespread pain. Eur Spine J 2023:10.1007/s00586-023-07711-7. [PMID: 37069442 DOI: 10.1007/s00586-023-07711-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 02/27/2023] [Accepted: 04/06/2023] [Indexed: 04/19/2023]
Abstract
PURPOSE Causal mechanisms underlying systemic inflammation in spinal & widespread pain remain an intractable experimental challenge. Here we examined whether: (i) associations between blood C-reactive protein (CRP) and chronic back, neck/shoulder & widespread pain can be explained by shared underlying genetic variants; and (ii) higher CRP levels causally contribute to these conditions. METHODS Using genome-wide association studies (GWAS) of chronic back, neck/shoulder & widespread pain (N = 6063-79,089 cases; N = 239,125 controls) and GWAS summary statistics for blood CRP (Pan-UK Biobank N = 400,094 & PAGE consortium N = 28,520), we employed cross-trait bivariate linkage disequilibrium score regression to determine genetic correlations (rG) between these chronic pain phenotypes and CRP levels (FDR < 5%). Latent causal variable (LCV) and generalised summary data-based Mendelian randomisation (GSMR) analyses examined putative causal associations between chronic pain & CRP (FDR < 5%). RESULTS Higher CRP levels were genetically correlated with chronic back, neck/shoulder & widespread pain (rG range 0.26-0.36; P ≤ 8.07E-9; 3/6 trait pairs). Although genetic causal proportions (GCP) did not explain this finding (GCP range - 0.32-0.08; P ≥ 0.02), GSMR demonstrated putative causal effects of higher CRP levels contributing to each pain type (beta range 0.027-0.166; P ≤ 9.82E-03; 3 trait pairs) as well as neck/shoulder pain effects on CRP levels (beta [S.E.] 0.030 [0.021]; P = 6.97E-04). CONCLUSION This genetic evidence for higher CRP levels in chronic spinal (back, neck/shoulder) & widespread pain warrants further large-scale multimodal & prospective longitudinal studies to accelerate the identification of novel translational targets and more effective therapeutic strategies.
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Affiliation(s)
- Scott F Farrell
- RECOVER Injury Research Centre, The University of Queensland, Level 7 STARS Hospital, 296 Herston Rd, Herston, QLD, 4029, Australia.
- NHMRC Centre of Research Excellence: Better Health Outcomes for Compensable Injury, The University of Queensland, Herston, QLD, Australia.
- Tess Cramond Pain & Research Centre, Royal Brisbane & Women's Hospital, Herston, QLD, Australia.
| | - Michele Sterling
- RECOVER Injury Research Centre, The University of Queensland, Level 7 STARS Hospital, 296 Herston Rd, Herston, QLD, 4029, Australia
- NHMRC Centre of Research Excellence: Better Health Outcomes for Compensable Injury, The University of Queensland, Herston, QLD, Australia
| | - David M Klyne
- NHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury & Health; School of Health & Rehabilitation Sciences, The University of Queensland, St Lucia, QLD, Australia
| | - Sanam Mustafa
- Davies Livestock Research Centre, The University of Adelaide, Roseworthy, SA, Australia
| | - Adrián I Campos
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
- Genetic Epidemiology Laboratory, Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Pik-Fang Kho
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Molecular Cancer Epidemiology Laboratory, Population Health Program, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Mischa Lundberg
- Institute of Biological Psychiatry, Boserupvej 2, 4000, Roskilde, Denmark
- Transformational Bioinformatics, CSIRO Health & Biosecurity, North Ryde, NSW, Australia
- UQ Diamantina Institute, The University of Queensland & Translational Research Institute, Woolloongabba, QLD, Australia
| | - Miguel E Rentería
- Genetic Epidemiology Laboratory, Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Trung Thanh Ngo
- RECOVER Injury Research Centre, The University of Queensland, Level 7 STARS Hospital, 296 Herston Rd, Herston, QLD, 4029, Australia
| | - Gabriel Cuéllar-Partida
- UQ Diamantina Institute, The University of Queensland & Translational Research Institute, Woolloongabba, QLD, Australia
- Gilead Sciences, Foster City, CA, USA
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Farrell SF, Kho PF, Lundberg M, Campos AI, Rentería ME, de Zoete RMJ, Sterling M, Ngo TT, Cuéllar-Partida G. A Shared Genetic Signature for Common Chronic Pain Conditions and its Impact on Biopsychosocial Traits. J Pain 2023; 24:369-386. [PMID: 36252619 DOI: 10.1016/j.jpain.2022.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/28/2022] [Accepted: 10/06/2022] [Indexed: 11/11/2022]
Abstract
The multiple comorbidities & dimensions of chronic pain present a formidable challenge in disentangling its aetiology. Here, we performed genome-wide association studies of 8 chronic pain types using UK Biobank data (N =4,037-79,089 cases; N = 239,125 controls), followed by bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine (respectively) their genetic correlations and genetic causal proportion (GCP) parameters with 1,492 other complex traits. We report evidence of a shared genetic signature across chronic pain types as their genetic correlations and GCP directions were broadly consistent across an array of biopsychosocial traits. Across 5,942 significant genetic correlations, 570 trait pairs could be explained by a causal association (|GCP| >0.6; 5% false discovery rate), including 82 traits affected by pain while 410 contributed to an increased risk of chronic pain (cf. 78 with a decreased risk) such as certain somatic pathologies (eg, musculoskeletal), psychiatric traits (eg, depression), socioeconomic factors (eg, occupation) and medical comorbidities (eg, cardiovascular disease). This data-driven phenome-wide association analysis has demonstrated a novel and efficient strategy for identifying genetically supported risk & protective traits to enhance the design of interventional trials targeting underlying causal factors and accelerate the development of more effective treatments with broader clinical utility. PERSPECTIVE: Through large-scale phenome-wide association analyses of >1,400 biopsychosocial traits, this article provides evidence for a shared genetic signature across 8 common chronic pain types. It lays the foundation for further translational studies focused on identifying causal genetic variants and pathophysiological pathways to develop novel diagnostic & therapeutic technologies and strategies.
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Affiliation(s)
- Scott F Farrell
- RECOVER Injury Research Centre, The University of Queensland, Herston, Queensland, Australia; NHMRC Centre of Research Excellence: Better Health Outcomes for Compensable Injury, The University of Queensland, Herston, Queensland, Australia; Tess Cramond Pain & Research Centre, Royal Brisbane & Women's Hospital, Herston, Queensland, Australia.
| | - Pik-Fang Kho
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California; Molecular Cancer Epidemiology Laboratory, Population Health Program, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia; School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Mischa Lundberg
- UQ Diamantina Institute, The University of Queensland & Translational Research Institute, Woolloongabba, Queensland, Australia; Transformational Bioinformatics, CSIRO Health & Biosecurity, North Ryde, New South Wales, Australia
| | - Adrián I Campos
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia; Genetic Epidemiology Laboratory, Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Miguel E Rentería
- Genetic Epidemiology Laboratory, Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Rutger M J de Zoete
- School of Allied Health Science and Practice, The University of Adelaide, Adelaide, South Australia, Australia
| | - Michele Sterling
- RECOVER Injury Research Centre, The University of Queensland, Herston, Queensland, Australia; NHMRC Centre of Research Excellence: Better Health Outcomes for Compensable Injury, The University of Queensland, Herston, Queensland, Australia
| | - Trung Thanh Ngo
- RECOVER Injury Research Centre, The University of Queensland, Herston, Queensland, Australia
| | - Gabriel Cuéllar-Partida
- UQ Diamantina Institute, The University of Queensland & Translational Research Institute, Woolloongabba, Queensland, Australia
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3
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Rabinowitz JA, Campos AI, Ong JS, García-Marín LM, Alcauter S, Mitchell BL, Grasby KL, Cuéllar-Partida G, Gillespie NA, Huhn AS, Martin NG, Thompson PM, Medland SE, Maher BS, Rentería ME. Shared Genetic Etiology between Cortical Brain Morphology and Tobacco, Alcohol, and Cannabis Use. Cereb Cortex 2022; 32:796-807. [PMID: 34379727 PMCID: PMC8841600 DOI: 10.1093/cercor/bhab243] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified genetic variants associated with brain morphology and substance use behaviors (SUB). However, the genetic overlap between brain structure and SUB has not been well characterized. We leveraged GWAS summary data of 71 brain imaging measures and alcohol, tobacco, and cannabis use to investigate their genetic overlap using linkage disequilibrium score regression. We used genomic structural equation modeling to model a "common SUB genetic factor" and investigated its genetic overlap with brain structure. Furthermore, we estimated SUB polygenic risk scores (PRS) and examined whether they predicted brain imaging traits using the Adolescent Behavior and Cognitive Development (ABCD) study. We identified 8 significant negative genetic correlations, including between (1) alcoholic drinks per week and average cortical thickness, and (2) intracranial volume with age of smoking initiation. We observed 5 positive genetic correlations, including those between (1) insula surface area and lifetime cannabis use, and (2) the common SUB genetic factor and pericalcarine surface area. SUB PRS were associated with brain structure variation in ABCD. Our findings highlight a shared genetic etiology between cortical brain morphology and SUB and suggest that genetic variants associated with SUB may be causally related to brain structure differences.
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Affiliation(s)
- Jill A Rabinowitz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Adrian I Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Jue-Sheng Ong
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Luis M García-Marín
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| | - Brittany L Mitchell
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Science, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Queensland 4059, Australia
| | - Katrina L Grasby
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Gabriel Cuéllar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Andrew S Huhn
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Baltimore, MD 21205, USA
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, USA
| | - Sarah E Medland
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4072, Australia
- School of Biomedical Science, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Queensland 4059, Australia
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Farrell SF, Campos AI, Kho PF, de Zoete RMJ, Sterling M, Rentería ME, Ngo TT, Cuéllar-Partida G. Genetic basis to structural grey matter associations with chronic pain. Brain 2021; 144:3611-3622. [PMID: 34907416 DOI: 10.1093/brain/awab334] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/06/2021] [Accepted: 08/12/2021] [Indexed: 01/26/2023] Open
Abstract
Structural neuroimaging studies of individuals with chronic pain conditions have often observed decreased regional grey matter at a phenotypic level. However, it is not known if this association can be attributed to genetic factors. Here we employed a novel integrative data-driven and hypothesis-testing approach to determine whether there is a genetic basis to grey matter morphology differences in chronic pain. Using publicly available genome-wide association study summary statistics for regional chronic pain conditions (n = 196 963) and structural neuroimaging measures (n = 19 629-34 000), we applied bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine the genetic correlations (rG) and genetic causal proportion (GCP) between these complex traits, respectively. Five a priori brain regions (i.e. prefrontal cortex, cingulate cortex, insula, thalamus and superior temporal gyrus) were selected based on systematic reviews of grey matter morphology studies in chronic pain. Across this evidence-based selection of five brain regions, 10 significant negative genetic correlations (out of 369) were found (false discovery rate < 5%), suggesting a shared genetic basis to both reduced regional grey matter morphology and the presence of chronic pain. Specifically, negative genetic correlations were observed between reduced insula grey matter morphology and chronic pain in the abdomen (mean insula cortical thickness), hips (left insula volume) and neck/shoulders (left and right insula volume). Similarly, a shared genetic basis was found for reduced posterior cingulate cortex volume in chronic pain of the hip (left and right posterior cingulate), neck/shoulder (left posterior cingulate) and chronic pain at any site (left posterior cingulate); and for reduced pars triangularis volume in chronic neck/shoulder (left pars triangularis) and widespread pain (right pars triangularis). Across these negative genetic correlations, a significant genetic causal proportion was only found between mean insula thickness and chronic abdominal pain [rG (standard error, SE) = -0.25 (0.08), P = 1.06 × 10-3; GCP (SE) = -0.69 (0.20), P = 4.96 × 10-4]. This finding suggests that the genes underlying reduced cortical thickness of the insula causally contribute to an increased risk of chronic abdominal pain. Altogether, these results provide independent corroborating evidence for observational reports of decreased grey matter of particular brain regions in chronic pain. Further, we show for the first time that this association is mediated (in part) by genetic factors. These novel findings warrant further investigation into the neurogenetic pathways that underlie the development and prolongation of chronic pain conditions.
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Affiliation(s)
- Scott F Farrell
- RECOVER Injury Research Centre, The University of Queensland, Herston, QLD, Australia.,NHMRC Centre for Research Excellence in Road Traffic Injury Recovery, The University of Queensland, Herston, QLD, Australia.,Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
| | - Adrián I Campos
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, St Lucia, QLD, Australia.,Genetic Epidemiology Laboratory, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Pik-Fang Kho
- Molecular Cancer Epidemiology Laboratory, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.,School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Rutger M J de Zoete
- School of Allied Health Science and Practice, The University of Adelaide, Adelaide, SA, Australia
| | - Michele Sterling
- RECOVER Injury Research Centre, The University of Queensland, Herston, QLD, Australia.,NHMRC Centre for Research Excellence in Road Traffic Injury Recovery, The University of Queensland, Herston, QLD, Australia
| | - Miguel E Rentería
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, St Lucia, QLD, Australia.,Genetic Epidemiology Laboratory, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Trung Thanh Ngo
- Diamantina Institute, The University of Queensland and Translational Research Institute, Woolloongabba, QLD, Australia
| | - Gabriel Cuéllar-Partida
- Diamantina Institute, The University of Queensland and Translational Research Institute, Woolloongabba, QLD, Australia
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5
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García-Marín LM, Campos AI, Cuéllar-Partida G, Medland SE, Kollins SH, Rentería ME. Large-scale genetic investigation reveals genetic liability to multiple complex traits influencing a higher risk of ADHD. Sci Rep 2021; 11:22628. [PMID: 34799595 PMCID: PMC8604995 DOI: 10.1038/s41598-021-01517-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022] Open
Abstract
Attention Deficit-Hyperactivity Disorder (ADHD) is a complex psychiatric and neurodevelopmental disorder that develops during childhood and spans into adulthood. ADHD’s aetiology is complex, and evidence about its cause and risk factors is limited. We leveraged genetic data from genome-wide association studies (GWAS) and performed latent causal variable analyses using a hypothesis-free approach to infer causal associations between 1387 complex traits and ADHD. We identified 37 inferred potential causal associations with ADHD risk. Our results reveal that genetic variants associated with iron deficiency anemia (ICD10), obesity, type 2 diabetes, synovitis and tenosynovitis (ICD10), polyarthritis (ICD10), neck or shoulder pain, and substance use in adults display partial genetic causality on ADHD risk in children. Genetic variants associated with ADHD have a partial genetic causality increasing the risk for chronic obstructive pulmonary disease and carpal tunnel syndrome. Protective factors for ADHD risk included genetic variants associated with the likelihood of participating in socially supportive and interactive activities. Our results show that genetic liability to multiple complex traits influences a higher risk for ADHD, highlighting the potential role of cardiometabolic phenotypes and physical pain in ADHD’s aetiology. These findings have the potential to inform future clinical studies and development of interventions.
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Affiliation(s)
- Luis M García-Marín
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. .,School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
| | - Adrián I Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Gabriel Cuéllar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia.,23andMe, Inc, Sunnyvale, CA, USA
| | - Sarah E Medland
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Scott H Kollins
- Department of Psychiatry & Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA.,Holmusk Technologies, Inc., New York, NY, USA
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. .,School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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García-Marín LM, Campos AI, Martin NG, Cuéllar-Partida G, Rentería ME. Phenome-wide analysis highlights putative causal relationships between self-reported migraine and other complex traits. J Headache Pain 2021; 22:66. [PMID: 34238214 PMCID: PMC8268337 DOI: 10.1186/s10194-021-01284-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 06/18/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Migraine is a complex neurological disorder that is considered the most common disabling brain disorder affecting 14 % of people worldwide. The present study sought to infer potential causal relationships between self-reported migraine and other complex traits, using genetic data and a hypothesis-free approach. METHODS We leveraged available summary statistics from genome-wide association studies (GWAS) of 1,504 phenotypes and self-reported migraine and inferred pair-wise causal relationships using the latent causal variable (LCV) method. RESULTS We identify 18 potential causal relationships between self-reported migraine and other complex traits. Hypertension and blood clot formations were causally associated with an increased migraine risk, possibly through vasoconstriction and platelet clumping. We observed that sources of abdominal pain and discomfort might influence a higher risk for migraine. Moreover, occupational and environmental factors such as working with paints, thinner or glues, and being exposed to diesel exhaust were causally associated with higher migraine risk. Psychiatric-related phenotypes, including stressful life events, increased migraine risk. In contrast, ever feeling unenthusiastic / disinterested for a whole week, a phenotype related to the psychological well-being of individuals, was a potential outcome of migraine. CONCLUSIONS Overall, our results suggest a potential vascular component to migraine, highlighting the role of vasoconstriction and platelet clumping. Stressful life events and occupational variables potentially influence a higher migraine risk. Additionally, a migraine could impact the psychological well-being of individuals. Our findings provide novel testable hypotheses for future studies that may inform the design of new interventions to prevent or reduce migraine risk and recurrence.
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Affiliation(s)
- Luis M García-Marín
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Adrián I Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Gabriel Cuéllar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
- Present address: 23andMe, Inc, Sunnyvale, California, USA
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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García-Marín LM, Campos AI, Kho PF, Martin NG, Cuéllar-Partida G, Rentería ME. Phenome-wide screening of GWAS data reveals the complex causal architecture of obesity. Hum Genet 2021; 140:1253-1265. [PMID: 34057592 DOI: 10.1007/s00439-021-02298-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 05/26/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE In the present study, we sought to identify causal relationships between obesity and other complex traits and conditions using a data-driven hypothesis-free approach that uses genetic data to infer causal associations. METHODS We leveraged available summary-based genetic data from genome-wide association studies on 1498 phenotypes and applied the latent causal variable method (LCV) between obesity and all traits. RESULTS We identified 110 traits causally associated with obesity. Of those, 109 were causal outcomes of obesity, while only leg pain in calves was a causal determinant of obesity. Causal outcomes of obesity included 26 phenotypes associated with cardiovascular diseases, 22 anthropometric measurements, nine with the musculoskeletal system, nine with behavioural or lifestyle factors including loneliness or isolation, six with respiratory diseases, five with body bioelectric impedances, four with psychiatric phenotypes, four related to the nervous system, four with disabilities or long-standing illness, three with the gastrointestinal system, three with use of analgesics, two with metabolic diseases, one with inflammatory response and one with the neurodevelopmental disorder ADHD, among others. In particular, some causal outcomes of obesity included hypertension, stroke, ever having a period of extreme irritability, low forced vital capacity and forced expiratory volume, diseases of the musculoskeletal system, diabetes, carpal tunnel syndrome, loneliness or isolation, high leukocyte count, and ADHD. CONCLUSIONS Our results indicate that obesity causally affects a wide range of traits and comorbid diseases, thus providing an overview of the metabolic, physiological, and neuropsychiatric impact of obesity on human health.
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Affiliation(s)
- Luis M García-Marín
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Adrián I Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Pik-Fang Kho
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Gabriel Cuéllar-Partida
- University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia.
- 23andMe, Inc, Sunnyvale, CA, USA.
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.
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8
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García-Marín LM, Campos AI, Martin NG, Cuéllar-Partida G, Rentería ME. Inference of causal relationships between sleep-related traits and 1,527 phenotypes using genetic data. Sleep 2021; 44:5893494. [PMID: 32805044 DOI: 10.1093/sleep/zsaa154] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/30/2020] [Indexed: 12/20/2022] Open
Abstract
STUDY OBJECTIVE Sleep is essential for both physical and mental health, and there is a growing interest in understanding how different factors shape individual variation in sleep duration, quality and patterns, or confer risk for sleep disorders. The present study aimed to identify novel inferred causal relationships between sleep-related traits and other phenotypes, using a genetics-driven hypothesis-free approach not requiring longitudinal data. METHODS We used summary-level statistics from genome-wide association studies and the latent causal variable (LCV) method to screen the phenome and infer causal relationships between seven sleep-related traits (insomnia, daytime dozing, easiness of getting up in the morning, snoring, sleep duration, napping, and morningness) and 1,527 other phenotypes. RESULTS We identify 84 inferred causal relationships. Among other findings, connective tissue disorders increase insomnia risk and reduce sleep duration; depression-related traits increase insomnia and daytime dozing; insomnia, napping, and snoring are affected by obesity and cardiometabolic traits and diseases; and working with asbestos, thinner, or glues may increase insomnia risk, possibly through an increased risk of respiratory disease or socio-economic related factors. CONCLUSION Overall, our results indicate that changes in sleep variables are predominantly the consequence, rather than the cause, of other underlying phenotypes and diseases. These insights could inform the design of future epidemiological and interventional studies in sleep medicine and research.
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Affiliation(s)
- Luis M García-Marín
- Genetic Epidemiology Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Adrián I Campos
- Genetic Epidemiology Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Genetic Epidemiology Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Gabriel Cuéllar-Partida
- Genetic Epidemiology Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Miguel E Rentería
- Genetic Epidemiology Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.,Global Brain Health Institute, University of California, San Francisco, CA, USA
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Roughan WH, Campos AI, García-Marín LM, Cuéllar-Partida G, Lupton MK, Hickie IB, Medland SE, Wray NR, Byrne EM, Ngo TT, Martin NG, Rentería ME. Comorbid Chronic Pain and Depression: Shared Risk Factors and Differential Antidepressant Effectiveness. Front Psychiatry 2021; 12:643609. [PMID: 33912086 PMCID: PMC8072020 DOI: 10.3389/fpsyt.2021.643609] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/12/2021] [Indexed: 02/06/2023] Open
Abstract
The bidirectional relationship between depression and chronic pain is well-recognized, but their clinical management remains challenging. Here we characterize the shared risk factors and outcomes for their comorbidity in the Australian Genetics of Depression cohort study (N = 13,839). Participants completed online questionnaires about chronic pain, psychiatric symptoms, comorbidities, treatment response and general health. Logistic regression models were used to examine the relationship between chronic pain and clinical and demographic factors. Cumulative linked logistic regressions assessed the effect of chronic pain on treatment response for 10 different antidepressants. Chronic pain was associated with an increased risk of depression (OR = 1.86 [1.37-2.54]), recent suicide attempt (OR = 1.88 [1.14-3.09]), higher use of tobacco (OR = 1.05 [1.02-1.09]) and misuse of painkillers (e.g., opioids; OR = 1.31 [1.06-1.62]). Participants with comorbid chronic pain and depression reported fewer functional benefits from antidepressant use and lower benefits from sertraline (OR = 0.75 [0.68-0.83]), escitalopram (OR = 0.75 [0.67-0.85]) and venlafaxine (OR = 0.78 [0.68-0.88]) when compared to participants without chronic pain. Furthermore, participants taking sertraline (OR = 0.45 [0.30-0.67]), escitalopram (OR = 0.45 [0.27-0.74]) and citalopram (OR = 0.32 [0.15-0.67]) specifically for chronic pain (among other indications) reported lower benefits compared to other participants taking these same medications but not for chronic pain. These findings reveal novel insights into the complex relationship between chronic pain and depression. Treatment response analyses indicate differential effectiveness between particular antidepressants and poorer functional outcomes for these comorbid conditions. Further examination is warranted in targeted interventional clinical trials, which also include neuroimaging genetics and pharmacogenomics protocols. This work will advance the delineation of disease risk indicators and novel aetiological pathways for therapeutic intervention in comorbid pain and depression as well as other psychiatric comorbidities.
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Affiliation(s)
- William H. Roughan
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Adrián I. Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Luis M. García-Marín
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Gabriel Cuéllar-Partida
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- UQ Diamantina Institute, The University of Queensland and Translational Research Institute, Brisbane, QLD, Australia
| | - Michelle K. Lupton
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ian B. Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - Sarah E. Medland
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Enda M. Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Trung Thanh Ngo
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- UQ Diamantina Institute, The University of Queensland and Translational Research Institute, Brisbane, QLD, Australia
| | - Nicholas G. Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Miguel E. Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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Mitchell BL, Cuéllar-Partida G, Grasby KL, Campos AI, Strike LT, Hwang LD, Okbay A, Thompson PM, Medland SE, Martin NG, Wright MJ, Rentería ME. Educational attainment polygenic scores are associated with cortical total surface area and regions important for language and memory. Neuroimage 2020; 212:116691. [PMID: 32126298 DOI: 10.1016/j.neuroimage.2020.116691] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 02/06/2020] [Accepted: 02/26/2020] [Indexed: 02/01/2023] Open
Abstract
It is well established that higher cognitive ability is associated with larger brain size. However, individual variation in intelligence exists despite brain size and recent studies have shown that a simple unifactorial view of the neurobiology underpinning cognitive ability is probably unrealistic. Educational attainment (EA) is often used as a proxy for cognitive ability since it is easily measured, resulting in large sample sizes and, consequently, sufficient statistical power to detect small associations. This study investigates the association between three global (total surface area (TSA), intra-cranial volume (ICV) and average cortical thickness) and 34 regional cortical measures with educational attainment using a polygenic scoring (PGS) approach. Analyses were conducted on two independent target samples of young twin adults with neuroimaging data, from Australia (N = 1097) and the USA (N = 723), and found that higher EA-PGS were significantly associated with larger global brain size measures, ICV and TSA (R2 = 0.006 and 0.016 respectively, p < 0.001) but not average thickness. At the regional level, we identified seven cortical regions-in the frontal and temporal lobes-that showed variation in surface area and average cortical thickness over-and-above the global effect. These regions have been robustly implicated in language, memory, visual recognition and cognitive processing. Additionally, we demonstrate that these identified brain regions partly mediate the association between EA-PGS and cognitive test performance. Altogether, these findings advance our understanding of the neurobiology that underpins educational attainment and cognitive ability, providing focus points for future research.
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Affiliation(s)
- Brittany L Mitchell
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Gabriel Cuéllar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Katrina L Grasby
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Adrian I Campos
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Lachlan T Strike
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah E Medland
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia; Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Miguel E Rentería
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
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11
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Campos AI, García-Marín LM, Byrne EM, Martin NG, Cuéllar-Partida G, Rentería ME. Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank. Nat Commun 2020; 11:817. [PMID: 32060260 PMCID: PMC7021827 DOI: 10.1038/s41467-020-14625-1] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/22/2020] [Indexed: 12/15/2022] Open
Abstract
Although snoring is common in the general population, its aetiology has been largely understudied. Here we report a genetic study on snoring (n ~ 408,000; snorers ~ 152,000) using data from the UK Biobank. We identify 42 genome-wide significant loci, with an SNP-based heritability estimate of ~10% on the liability scale. Genetic correlations with body mass index, alcohol intake, smoking, schizophrenia, anorexia nervosa and neuroticism are observed. Gene-based associations identify 173 genes, including DLEU7, MSRB3 and POC5, highlighting genes expressed in the brain, cerebellum, lungs, blood and oesophagus. We use polygenic scores (PGS) to predict recent snoring and probable obstructive sleep apnoea (OSA) in an independent Australian sample (n ~ 8000). Mendelian randomization analyses suggest a potential causal relationship between high BMI and snoring. Altogether, our results uncover insights into the aetiology of snoring as a complex sleep-related trait and its role in health and disease beyond it being a cardinal symptom of OSA. Snoring is common in the population and tends to be more prevalent in older and/or male individuals. Here, the authors perform GWAS for habitual snoring, identify 41 genomic loci and explore potential causal relationships with anthropometric and cardiometabolic disease traits.
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Affiliation(s)
- Adrián I Campos
- Genetic Epidemiology Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Luis M García-Marín
- Genetic Epidemiology Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Zapopan, Jalisco, México
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Nicholas G Martin
- Genetic Epidemiology Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Gabriel Cuéllar-Partida
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia. .,University of Queensland Diamantina Institute, Brisbane, QLD, Australia.
| | - Miguel E Rentería
- Genetic Epidemiology Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. .,Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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