genome-wide association study; low back pain; polygenic risk score; shared genetic background; subphenotyping; Humans; Female; Male; Multifactorial Inheritance/genetics; Genome-Wide Association Study; Middle Aged; Phenotype; Polymorphism, Single Nucleotide; Adult; Back Pain/genetics; Chronic Pain/genetics; Genetic Predisposition to Disease; Genetic Background; Back Pain; Chronic Pain; Multifactorial Inheritance; Molecular Biology; Genetics; Genetics (clinical)
Abstract :
[en] Chronic back pain (CBP) is a disabling condition with a lifetime prevalence of 40% and a substantial socioeconomic burden. Because of the high heterogeneity of CBP, subphenotyping may help to improve prediction and support personalized treatment of CBP. To investigate CBP subphenotypes, we decomposed its genetic background into a shared one common to other chronic pain conditions (back, neck, hip, knee, stomach, and head pain) and unshared genetic background specific to CBP. We identified and replicated 18 genes with shared impact across different chronic pain conditions and two genes that were specific for CBP. Among people with CBP, we demonstrated that polygenic risk scores accounting for the shared and unshared genetic backgrounds of CBP may underpin different CBP subphenotypes. These subphenotypes are characterized by varying genetic predisposition to diverse medical conditions and interventions such as diabetes mellitus, myocardial infarction, diagnostic endoscopic procedures, and surgery involving muscles, bones, and joints.
Disciplines :
Genetics & genetic processes
Author, co-author :
Elgaeva, Elizaveta E ; Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10, Ac. Lavrentieva ave., 630090, Novosibirsk, Russia ; Novosibirsk State University, 1, Pirogova str., 630090, Novosibirsk, Russia
Zorkoltseva, Irina V; Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10, Ac. Lavrentieva ave., 630090, Novosibirsk, Russia
Nostaeva, Arina ; Université de Liège - ULiège > Département de gestion vétérinaire des Ressources Animales (DRA) > Génomique animale ; Novosibirsk State University, 1, Pirogova str., 630090, Novosibirsk, Russia
Verzun, Dmitrii A; Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10, Ac. Lavrentieva ave., 630090, Novosibirsk, Russia ; Novosibirsk State University, 1, Pirogova str., 630090, Novosibirsk, Russia
Tiys, Evgeny S; Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10, Ac. Lavrentieva ave., 630090, Novosibirsk, Russia
Timoshchuk, Anna N; MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, 27, building 1, Lomonosovsky ave., 119991, Moscow, Russia ; Moscow Institute of Physics and Technology, 9, Institutsky lane, 141700, Dolgoprudny, Russia
Kirichenko, Anatoliy V; Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10, Ac. Lavrentieva ave., 630090, Novosibirsk, Russia
Svishcheva, Gulnara R; Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10, Ac. Lavrentieva ave., 630090, Novosibirsk, Russia ; Vavilov Institute of General Genetics, RAS, 3, Gubkin str., 119991, Moscow, Russia
Freidin, Maxim B; Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, Westminster Bridge Rd., SE1 7EH, London, UK
Williams, Frances M K; Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, Westminster Bridge Rd., SE1 7EH, London, UK
Suri, Pradeep; Department of Rehabilitation Medicine, University of Washington, 325, Ninth ave., WA 98104, Seattle, USA ; VA Puget Sound Health Care System, 1660, South Columbian Way, WA 98108, Seattle, USA
Aulchenko, Yurii S; Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10, Ac. Lavrentieva ave., 630090, Novosibirsk, Russia ; PolyOmica, 61, Het Vlaggeschip, 5237 PA, 's-Hertogenbosch, The Netherlands
Axenovich, Tatiana I; Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10, Ac. Lavrentieva ave., 630090, Novosibirsk, Russia
Tsepilov, Yakov A; Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10, Ac. Lavrentieva ave., 630090, Novosibirsk, Russia
Manchikanti L, Singh V, Falco FJE. et al. Epidemiology of low back pain in adults. Neuromodulation 2014;17:3-10.
Vos T, Allen C, Arora M. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the global burden of disease study 2015. Lancet 2016;388:1545-1602.
Foster NE, Anema JR, Cherkin D. et al. Prevention and treatment of low back pain: evidence, challenges, and promising directions. Lancet 2018;391:2368-2383.
Reddy K, Sinha P, O'Kane CM. et al. Subphenotypes in critical care: translation into clinical practice. Lancet Respir Med 2020;8: 631-643.
Phillips CJ. Economic burden of chronic pain. Expert Rev Pharmacoecon Outcomes Res 2006;6:591-601.
Loeser JD. Chapter 2 pain as a disease. Handb Clin Neurol 2006;81: 11-20.
Gatchel RJ. The biopsychosocial model of chronic pain. Clin Insights: Chronic Pain 2013;5-17. https://doi.org/10.2217/ EBO.13.469.
Scholz J. Mechanisms of chronic pain. Mol Pain 2014;10:O15.
Crofford LJ. Chronic pain: where the body meets the brain. Trans Am Clin Climatol Assoc 2015;126:167-183.
Battie MC, Videman T, Levalahti E. et al. Heritability of low back pain and the role of disc degeneration 53rd annual meeting of the Orthopaedic Research Society paper No: 0317. Pain 2007;131: 272-280.
Junqueira DRG, Ferreira ML, Refshauge K. et al. Heritability and lifestyle factors in chronic low back pain: results of the Australian twin low back pain study (the AUTBACK study). Eur J Pain (United Kingdom) 2014;18:1410-1418.
Suri P, Palmer MR, Tsepilov YA. et al. Genome-wide metaanalysis of 158, 000 individuals of European ancestry identifies three loci associated with chronic back pain. PLoS Genet 2018;14:e1007601.
Johnston KJA, Adams MJ, Nicholl BI. et al. Genome-wide association study of multisite chronic pain in UK biobank. PLoS Genet 2019;15:e1008164.
Bjornsdottir G, Stefansdottir L, Thorleifsson G. et al. Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology. Nat Commun 2022;13:634.
Freidin MB, Tsepilov YA, Palmer M. et al. Insight into the genetic architecture of back pain and its risk factors from a study of 509, 000 individuals. Pain 2019;160:1361-1373.
Tsepilov YA, Freidin MB, Shadrina AS. et al. Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions. Commun Biol 2020;3:329.
Li S, Brimmers A, Van Boekel RLM. et al. Systematic review and meta-analysis a systematic review of genome-wide association studies for pain, nociception, neuropathy, and pain treatment responses. Pain 2023;164:1891-1911.
Vehof J, Zavos HMS, Lachance G. et al. Shared genetic factors underlie chronic pain syndromes. Pain 2014;155:1562-1568.
Williams FMK, Spector TD, MacGregor AJ. Pain reporting at different body sites is explained by a single underlying genetic factor. Rheumatology 2010;49:1753-1755.
Jensen RK, Jensen TS, Kjaer P. et al. Can pathoanatomical pathways of degeneration in lumbar motion segments be identified by clustering MRI findings. BMC Musculoskelet Disord 2013;14:198.
Ranoux D, Attal N, Morain F. et al.Botulinum toxin type a induces direct analgesic effects in chronic neuropathic pain. Ann Neurol 2008;64:274-283.
Tagliaferri SD, Angelova M, Zhao X. et al. Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews. NPJ Digit Med 2020;3:93.
Tsepilov YA, Elgaeva EE, Nostaeva AV. et al. Development and replication of a genome-wide polygenic risk score for chronic back pain. J Pers Med 2023;13:977.
Svishcheva G, Tiys E, Elgaeva E. et al. A novel framework for analysis of the shared genetic background of correlated traits. Genes (Basel) 2022;13:1694.
Lee S, Wu MC, Lin X. Optimal tests for rare variant effects in sequencing association studies. Biostatistics 2012;13:762-775.
Wang K, Abbott D. A principal components regression approach to multilocus genetic association studies. Genet Epidemiol 2008;32:108-118.
Liu Y, Chen S, Li Z. et al. ACAT: a fast and powerful p value combination method for rare-variant analysis in sequencing studies. Am J Hum Genet 2019;104:410-421.
Shashkova TI, Gorev DD, Pakhomov ED. et al. The GWAS-MAP platform for aggregation of results of genome-wide association studies and the GWAS-MAP|homo database of 70 billion genetic associations of human traits. Vavilovskii Zhurnal Genet Selektsii 2021;24:876-884.
Shashkova TI, Pakhomov ED, Gorev DD. et al. PheLiGe: an interactive database of billions of human genotype-phenotype associations. Nucleic Acids Res 2021;49:D1347-D1350.
McLaren W, Gil L, Hunt SE. et al. The Ensembl variant effect predictor. Genome Biol 2016;17:122.
Rogers MF, Shihab HA, Mort M. et al. FATHMM-XF: accurate prediction of pathogenic point mutations via extended features. Bioinformatics 2018;34:511-513.
Ferlaino M, Rogers MF, Shihab HA. et al. An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome. BMC Bioinformatics 2017;18:442.
Pers TH, Karjalainen JM, Chan Y. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun 2015;6:5890.
Watanabe K, Taskesen E, van Bochoven A. et al. Functional mapping and annotation of genetic associations with FUMA. Nat Commun 1826;8:1826.
Zhu Z, Zhang F, Hu H. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 2016;48:481-487.
Johnston KJA, Ward J, Ray PR. et al. Sex-stratified genome-wide association study of multisite chronic pain in UK biobank. PLoS Genet 2021;17:e1009428.
Zhang F, Rao S, Baranova A. Shared genetic liability between major depressive disorder and osteoarthritis. Bone Joint Res 2022;11:12-22.
Zhang F, Rao S, Cao H. et al. Genetic evidence suggests posttraumatic stress disorder as a subtype of major depressive disorder. J Clin Invest 2022;132:e145942.
Das S, Taylor K, Kozubek J. et al. Genetic risk factors for ME/CFS identified using combinatorial analysis. J Transl Med 2022;20: 598.
Tachmazidou I, Hatzikotoulas K, Southam L. et al. Identification of new therapeutic targets for osteoarthritis through genomewide analyses of UK biobank data. Nat Genet 2019;51:230-236.
Haller G, McCall K, Jenkitkasemwong S. et al. A missense variant in SLC39A8 is associated with severe idiopathic scoliosis. Nat Commun 2018;9:4171.
Park JH, Hogrebe M, Grüneberg M. et al. SLC39A8 deficiency: a disorder of manganese transport and glycosylation. Am J Hum Genet 2015;97:894-903.
Bonaventura E, Barone R, Sturiale L. et al. Clinical, molecular and glycophenotype insights in SLC39A8-CDG. Orphanet J Rare Dis 2021;16:307.
Hidiroglou M, Ivan M, Bryan MK. et al. Assessment of the role of manganese in congenital joint laxity and dwarfism in calves. Ann Rech Vet 1990;21:281-284.
Bortsov AV, Parisien M, Khoury S. et al. Brain-specific genes contribute to chronic but not to acute back pain. Pain Rep 2022;7:e1018.
Barki M, Xue H. GABRB2, a key player in neuropsychiatric disorders and beyond. Gene 2022;809:146021.
Feng C, Zhao J, Ji F. et al. TCF20 dysfunction leads to cortical neurogenesis defects and autistic-like behaviors in mice. EMBO Rep 2020;21:e49239.
Ao X, Parisien M, Zidan M. et al. Rare variant analyses in largescale cohorts identified SLC13A1 associated with chronic pain. Pain 2023;164:1841-1851.
Langford R, Hurrion E, Dawson PA. Genetics and pathophysiology of mammalian sulfate biology. J Genet Genomics 2017;44: 7-20.
Song YQ, Karasugi T, Cheung KMC. et al. Lumbar disc degeneration is linked to a carbohydrate sulfotransferase 3 variant. J Clin Invest 2013;123:4909-4917.
Mountjoy E, Schmidt EM, Carmona M. et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat Genet 2021;53: 1527-1533.
Zorina-Lichtenwalter K, Bango CI, Van Oudenhove L. et al. Genetic risk shared across 24 chronic pain conditions: identification and characterization with genomic structural equation modeling. Pain 2023;164:2239-2252.
Grotzinger AD, Rhemtulla M, de Vlaming R. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav 2019;3: 513-525.
Toikumo S, Vickers-Smith R, Jinwala Z. et al. A multi-ancestry genetic study of pain intensity in 598, 339 veterans. Nat Med 2024;30:1075-1084.
Inquimbert P, Moll M, Latremoliere A. et al. NMDA receptor activation underlies the loss of spinal dorsal horn neurons and the transition to persistent pain after peripheral nerve injury. Cell Rep 2018;23:2678-2689.
Yuan BT, Li MN, Zhu LP. et al. TFAP2A is involved in neuropathic pain by regulating Grin1 expression in glial cells of the dorsal root ganglion. Biochem Pharmacol 2024;227:116427.
Kong L, Zhao Y-P, Tian Q-Y. et al. Extracellular matrix protein 1, a direct targeting molecule of parathyroid hormone-related peptide, negatively regulates chondrogenesis and endochondral ossification via associating with progranulin growth factor. FASEB J 2016;30:2741-2754.
Chebib J, Guillaume F. Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multitrait GWA studies. Genetics 2021;219:iyab159.
Elgart M, Goodman MO, Isasi C. et al. Correlations between complex human phenotypes vary by genetic background, gender, and environment. Cell Rep Med 2022;3:100844.
Elgaeva EE, Tsepilov Y, Freidin MB. et al. ISSLS prize in clinical science 2020. Examining causal effects of body mass index on back pain: a Mendelian randomization study. Eur Spine J 2020;29: 686-691.
Suri P, Elgaeva EE, Williams FMK. et al. Evidence of causal effects of blood pressure on back pain and back pain on type II diabetes provided by a bidirectional Mendelian randomization study. Spine J 2023;23:1161-1171.
Jareebi MA, Lyall DM, Gharawi NF. et al. Causal associations of modifiable risk factors with migraine: evidence from Mendelian randomization analysis. Cureus 2024;16:e53448.
Ornello R, Ripa P, Pistoia F. et al. Migraine and body mass index categories: a systematic review and meta-analysis of observational studies. J Headache Pain 2015;16:27-40.
Elliott MD, Heitner JF, Kim H. et al. Prevalence and prognosis of unrecognized myocardial infarction in asymptomatic patients with diabetes: a two-center study with up to 5 years of followup. Diabetes Care 2019;42:1290-1296.
Chiariello M, Indolfi C. Silent myocardial ischemia in patients with diabetes mellitus. Circulation 1996;93:2089-2091.
Sudlow C, Gallacher J, Allen N. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015;12:e1001779.
Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010;26:2190-2191.
Yang J, Ferreira T, Morris AP. et al.Conditional and joint multipleSNP analysis of GWAS summary statistics identifies additional variants inf luencing complex traits. Nat Genet 2012;44:369-375.
Curtin F, Schulz P. Multiple correlations and Bonferroni's correction. Biol Psychiatry 1998;44:775-777.
Timmers PRHJ, Tiys ES, Sakaue S. et al. Mendelian randomization of genetically independent aging phenotypes identifies LPA and VCAM1 as biological targets for human aging. Nat Aging 2022;2: 19-30.
Lloyd-Jones LR, Zeng J, Sidorenko J. et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat Commun 2019;10:5086.