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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. THE JOURNAL OF 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] [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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Shared Genetic Regulatory Networks Contribute to Neuropathic and Inflammatory Pain: Multi-Omics Systems Analysis. Biomolecules 2022; 12:biom12101454. [PMID: 36291662 PMCID: PMC9599593 DOI: 10.3390/biom12101454] [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: 09/04/2022] [Revised: 09/27/2022] [Accepted: 10/07/2022] [Indexed: 11/17/2022] Open
Abstract
The mechanisms of chronic pain are complex, and genetic factors play an essential role in the development of chronic pain. Neuropathic pain (NP) and inflammatory pain (IP) are two primary components of chronic pain. Previous studies have uncovered some common biological processes in NP and IP. However, the shared genetic mechanisms remained poorly studied. We utilized multi-omics systematic analyses to investigate the shared genetic mechanisms of NP and IP. First, by integrating several genome-wide association studies (GWASs) with multi-omics data, we revealed the significant overlap of the gene co-expression modules in NP and IP. Further, we uncovered the shared biological pathways, including the previously reported mitochondrial electron transport and ATP metabolism, and stressed the role of genetic factors in chronic pain with neurodegenerative diseases. Second, we identified 24 conservative key drivers (KDs) contributing to NP and IP, containing two well-established pain genes, IL1B and OPRM1, and some novel potential pain genes, such as C5AR1 and SERPINE1. The subnetwork of those KDs highlighted the processes involving the immune system. Finally, gene expression analysis of the KDs in mouse models underlined two of the KDs, SLC6A15 and KCNQ5, with unidirectional regulatory functions in NP and IP. Our study provides strong evidence to support the current understanding of the shared genetic regulatory networks underlying NP and IP and potentially benefit the future common therapeutic avenues for chronic pain.
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Shu P, Ji L, Ping Z, Sun Z, Liu W. Association of insomnia and daytime sleepiness with low back pain: A bidirectional mendelian randomization analysis. Front Genet 2022; 13:938334. [PMID: 36267398 PMCID: PMC9577110 DOI: 10.3389/fgene.2022.938334] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/06/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: Observational research has indicated the presence of a causal relationship between sleep disturbances and low back pain (LBP). However, the link may have been biased by confounding factors. The purpose of this study was to examine the potential causal association of insomnia and daytime sleepiness with LBP by using mendelian randomization (MR). Methods: Genome-wide association study (GWAS) summary statistics of insomnia were obtained from a large-scale GWAS meta-analysis (n = 1,331,010; individuals from UK Biobank and 23andMe) or UK Biobank alone (n = 453,379). The summary statistics of daytime sleepiness were from UK Biobank (n = 452,071) and LBP were provided by the FinnGen Release 6 (210,645 individuals with 16,356 LBP cases and 194,289 controls) or UK Biobank (5,423 cases versus 355,771 controls). Linkage disequilibrium score (LDSC) regression and bidirectional MR analysis was employed to estimate genetic correlation and causal relationship. In the MR analysis, the inverse variance weighted method (IVW) was utilized as the main analysis procedure, while MR-Egger, Weighted median and Robust adjusted profile score (RAPS) were utilized for supplementary analyses. Results: LDSC analysis showed that LBP were significantly genetically correlated with insomnia (rg = 0.57, p = 2.26e-25) and daytime sleepiness (rg = 0.18, p = 0.001). The MR analysis revealed that genetically predicted insomnia was significantly associated with an increased risk of LBP (OR = 1.250, 95% CI: 1.186-1.318; p = 1.69e-16). However, the reverse causality was not confirmed. No evidence was identified supporting causality of daytime sleepiness and LBP. Conclusion: This study demonstrates a putative causal link of insomnia on LBP and a null causal effect of LBP on insomnia. Furthermore, a causal link between daytime sleepiness and LBP were not reported. This finding may stimulate new strategies for patient management in clinical practice, benefiting public health.
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Affiliation(s)
- Peng Shu
- Department of Orthopedic Surgery, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Lixian Ji
- Department of Rheumatology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Zichuan Ping
- Department of Orthopedic Surgery, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Zhibo Sun
- Department of Orthopedics, Renmin Hospital, Wuhan University, Wuhan, China
| | - Wei Liu
- Department of Orthopedic Surgery, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
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