Genome-wide association study on pharmacological outcomes of musculoskeletal pain in UK Biobank

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However, the treatment outcome is usually unsatisfying due to inter-individual differences. To investigate the genetic component of treatment outcome differences, we performed a genome-wide association study (GWAS) in ~ 23 000 participants with musculoskeletal pain from the UK Biobank. NSAID vs. opioid users was compared as a reflection of the treatment outcome of NSAIDs. We identified one genome-wide significant hit in chromosome 4 (rs549224715, P = 3.88×10 − 8 ). Suggestive significant (P < 1×10 − 6) loci were mapped to 28 target genes, including eight genes linked to neuropathic pain processes or musculoskeletal development. Pathway and network analyses identified immunity-related processes and a (putative) central role of EGFR . However, this study lacked power and should be viewed as a first step to elucidate the genetic background of musculoskeletal pain treatment. Musculoskeletal pain Analgesic ladder Genome-wide association study UK Biobank Non-steroidal anti-inflammatory drugs (NSAIDs) Figures Figure 1 1. Introduction Chronic musculoskeletal pain is one of the most frequent causes of suffering and disability 1 . The nature of musculoskeletal pain can be nociceptive or neuropathic, for which the corresponding pain management differs. The treatment of nociceptive musculoskeletal pain follows the WHO three-step analgesic ladder 2 : the first treatment step is non-opioid analgesics, such as non-steroidal anti-inflammatory drugs (NSAIDs); the second step is weak opioids for mild to moderate pain, such as tramadol; the third step is strong opioids for moderate to severe pain, such as morphine. Unfortunately, effective pain management is challenged by inter-individual differences, with unsatisfied pain treatment rates ranging from 34 to 79% 3 . The underlying factors of ineffective pain treatment are multifactorial, including demographic characteristics (age, sex, socioeconomic status) 4 , 5 , lifestyle (smoking and alcohol intake) 6 , comorbidities (psychological status) 7 , and genetic factors. The genetic background of pain treatment outcomes has been investigated using candidate gene approaches. Some drug-metabolizing genes are associated with treatment outcomes for specific drugs, e.g., CYP2D6 and codeine 8 . In addition, genes implicated in pain (sensitivity) may contribute to pain treatment outcomes because greater pain sensitivity is associated with increased opioid use 9 and poorer chronic pain treatment outcomes 10 . However, none of these findings predict pain treatment outcomes sufficiently to optimize pain treatment in a clinical setting. Furthermore, these studies are limited by small gene panel and sample size and report contradictory results 11 . The most investigated genetic variant is the 118A to G basepair change in the OPRM1 gene. Genetic variants in OPRM1 are thought to influence the opioid response by altering the µ-opioid receptor binding affinity of exogenous opioid ligands, such as morphine 12 . The G allele was associated with higher opioid dosing 13 , 14 but shown to be protective against pain in other studies 15 , 16 . Therefore, definitive conclusions on these genetic associations cannot be drawn yet, and a non-hypothesis driven approach in a large population is needed. Except for several recent, successful large-scale GWASs of chronic pain phenotypes 17 – 19 , the number of GWASs focusing on pain treatment outcomes is still limited. Moreover, the most frequently used phenotype in GWASs investigating pain treatment is the response to certain drugs for acute pain (e.g., analgesic requirement or pain relief score after surgery 20 , 21 ), but long-term pain treatment outcomes are less investigated. This study sought to identify genetic variants associated with long-term pain treatment outcomes in people with musculoskeletal pain from the UK Biobank. A GWAS was performed including subjects treated according to the WHO analgesic ladders, and comparisons were made between NSAID and opioid users as a reflection of pain treatment outcomes. 2. Method We conducted a GWAS comparing NSAID users and opioid users using data from the UK Biobank, and post-GWAS analyses were performed for suggestively significant (P < 1×10 − 6 ) signals. 2.1 Study population The UK Biobank is a general population cohort with over 0.5 million participants aged 40–69 recruited across the United Kingdom (UK) 22 . Recently released primary care (general practitioners, 'GPs') data provides longitudinal structured diagnosis and prescription data, which were used for phenotype definition. UK Biobank obtained informed consent from all participants. 2.2 Phenotype definition To define the pain treatment outcome, we first extracted all musculoskeletal pain patients with pain prescriptions (NSAIDs and opioids) from the GP data (see Supplementary data 1 and 2 for diagnosis and prescriptions codes included in this study). Only participants with a musculoskeletal pain diagnosis record and a pain prescription record occurring on the same date were included for analysis to ensure that the prescriptions are indeed for musculoskeletal pain treatment. Pain treatment outcomes were defined as a dichotomous score (case/control): NSAID users were defined as controls and opioid users as cases. Opioid users were analyzed as a whole because the strong opioid user group is small (n = 365) and assuming mechanistic similarities between weak and strong opioids. Participants who did not meet the following two quality control (QC) steps were removed. First, participants with only one treatment event were removed to safeguard the inclusion of only participants with relatively long-term treatment. Second, a chronological check was applied for the first prescription of each ladder to ensure that the treatment ladder was correctly followed, i.e., opioids followed initial NSAID use. As the GP data is longitudinal, by using this definition, we could distinguish between patients who stay at NSAID treatment and who go to the next level of the analgesic ladder. The following text will refer to this phenotype as pain treatment outcomes. 2.3 Genome-wide association study A GWAS was conducted using binary phenotypes, i.e., NSAID users (controls) versus opioid users (cases). For markers on the autosomal chromosomes and PAR region of the X chromosome, GWAS on pain treatment outcomes was conducted using a linear function in GCTA 23 , adjusting for age, sex, BMI, depression history, smoking status, drinking frequency, assessment center, genotyping array, and the first ten principal components (PCs). Markers on the non-PAR region of the X chromosome were analyzed by a sex-stratified analysis in the XWAS 24 . A p-value less than 5 × 10 − 8 was considered genome-wide significant, and P-values between 1 × 10 − 6 and 5 × 10 − 8 were defined as suggestively significant. Details on genotyping and quality control methods, covariates definition, and heritability/power calculation can be found in supplementary materials. To examine the nature of pain between groups, all NMP diagnosis codes were grouped into one of the following categories: inflammatory, mechanical, and mechanism not specified. The percentage of subjects in each diagnosis category was compared by a χ² test. Besides the binary case/control analysis, three additional analyses were performed: a GWAS using an ordinal outcome (NSAID, weak opioid, and strong opioid users), a subtype GWAS focusing on inflammatory pain, and a GWAS with a less stringent phenotype definition for validation purposes. Details on these analyses can be found in the supplementary materials. 2.4 Functional annotation 2.4.1 Bayesian fine-mapping of lead loci Lead SNPs were analyzed using Bayesian fine-mapping in PAINTOR 25 to identify the most likely causal SNPs in each locus. PAINTOR calculates the posterior probability (PP) of causality for SNPs in each genomic region by leveraging the strength of association (Z score) and the LD pattern. The 1000 Genomes (Phase 3) were used for LD matrix calculation. The calculated PP for each SNP was sorted from high to low, and variants together reaching a PP of 0.95 were used to define 95% credible sets. 2.4.2 Functional annotation of SNPs present in the 95% credible sets SNPs in the 95% credible sets were annotated for regulatory functions in HaploReg v4.1 26 . The analyzed regulatory functions were (1) the presence of exonic, nonsynonymous variants in high LD (r 2 ≥ 0.8), (2) overlap with epigenetic histone marks of active enhancers (H3K4me1 and H3K27ac) and active promoters (H3K4me3 and H3K9ac), and (3) the sensitivity to DNase. As histone marker overlap is tissue-specific, relevant cell lines were selected from the complete data set (see Table S1). Besides regulatory functions, potential pleiotropy effects (previously reported associations with other phenotypes) of the variants were investigated in Haploreg. For SNPs not available in Haploreg, proxy SNPs were obtained by LD proxy ( https://ldlink.nci.nih.gov/ ). For loci containing more than ten likely causal variants, only the lead SNP and SNPs with the maximum posterior probability (PPmax) of the SNPs in one locus were annotated. 2.5 Gene mapping To map suggestively significant (P-value 0.6) with them to genes, three strategies were adopted in FUMA: positional mapping, expression quantitative trait loci (eQTL) mapping, and chromatin interaction mapping. For the positional mapping, SNPs were mapped to known protein-coding genes based on physical distance (within a 10 kb window). For eQTL mapping, SNPs were mapped to genes up to 1 Mb away based on known cis-eQTLs. As gene expression is tissue-specific, we selected the following tissues for mapping: brain, muscle, kidney, liver, nerve, skin, and fibroblast. Significant eQTLs were defined as eQTLs with a false discovery rate (FDR) < 0.05. Finally, chromatin interactions were assessed. Chromatin interaction can occur in two genomic regions that are spatially close when DNA folds together, even if the genomic regions are at a long-range physical distance. Genes in regions of chromatin interaction containing candidate SNPs were assessed in the same tissues as the eQTL mapping. An FDR < 1 × 10 − 6 was defined as a significant interaction. 2.6 Post-GWAS analysis We conducted the following post-GWAS analysis: pathway enrichment analysis and genetic correlation analysis (for details see supplementary materials). 3. Results 3.1 GWAS After quality control, we identified 12 726 NSAID users (control) and 11 089 opioid users (cases) in the UK Biobank dataset. Table 1 summarizes the demographics of the cases and controls, and all tested covariates were found to be significantly different (P < 0.0001). There were 9 435 994 SNPs available for GWAS analysis after quality control. The genomic control value (lambda) was 1.008. One intergenic locus located at chr4 reached genome-wide significance, in which the most significant SNP was rs549224715 (P = 3.92 × 10 − 8 ) ( Fig. 1 , Table 2) . Seven loci surpassed the suggestive P-value threshold (P < 1 × 10 − 6 ), and no other independent SNPs (SNPs remaining significant after conditioning on lead SNPs in the locus) were identified in each locus. The SNP heritability was 0.16 (P-value = 0.16). A GWAS was conducted using ordinal phenotypes (NSAID, weak opioid, and strong opioid users), and the results were consistent with the GWAS using binary outcomes (Figure S1). We conducted a subtype GWAS in patients with inflammatory pain to investigate if a more homogeneous phenotype would yield additional signals and a secondary GWAS with less strict criteria for diagnosis definition to validate our results (see supplementary materials). However, we did not find any genome-wide significant loci or overlapped suggestively significant loci with the GWAS using binary outcomes as described in detail above (Figure S2, Figure S3, Table S7, Table S8). 3.2 Functional annotation 3.2.1 Statistical fine-mapping of loci and functional annotation of SNPs As GWAS signals can caused by SNPs in linkage disequilibrium with the likely causal SNPs, we calculated the posterior probability for variants in each genomic locus and created 95% credible sets (see methods). In five out of eight loci, the lead SNPs in the locus had the maximum PP (PP max ) (Figure S4). Since all variants in the 95% credible sets were in non-coding regions, the regulatory effects of these variants were investigated by examining overlap with epigenomic markers of active enhancers or promoters in Haploreg. The results suggested that most genetic variants were potentially involved in transcriptional regulatory modulation (Figure S4). We assessed whether the SNPs in the 95% credible sets were previously reported to be associated with other traits. However, no pleiotropic effects were identified. 3.2.2 Gene mapping After mapping GWAS candidate SNPs (SNPs that are in LD (r2 > 0.6) of any independent significant SNPs) to genes, a total of 28 unique mapped genes were identified ( Table 3 ). Five genes were mapped by genomic location, nine genes were identified by cis-eQTL mapping, 18 genes were annotated as SNPs in regions where 3D chromatin interactions occurred, and four genes were identified by at least two mapping strategies. 3.3 Pathway enrichment Pathway enrichment analysis in IPA prioritized 15 significant pathways with an FDR < 0.05, in which the top prioritized pathways were mainly implicated in the immunological response. (Table S3). The network analysis yielded a total of 25 prioritized networks. The top network contained 33 genes with the EGFR protein in the center. EGFR remained in the center after merging the five networks with the lowest P-value (Table S4, Figure S5). 3.4 Genetic correlation with other traits The genetic correlation analysis did not yield significant correlations (Bonferroni corrected P-value < 8.39 × 10 − 5 ). The top correlated trait was overall health rating (rg = 0.5316, P = 0.0087), followed by years of schooling 27 (rg = -0.5431, P = 0.0102) (Table S5). However, among the nominally significant correlations (P < 0.05), we found an overrepresentation of pain and socioeconomic status traits compared to the other traits (43.48% vs. 8.55%, P = 3.35 × 10 − 12 , Table S6). 4. Discussion To our knowledge, this is the first GWAS reporting on long-term pain treatment outcomes. We identified one genome-wide significant hit and seven loci with suggestive significance. Although pain or pain treatment is characterized by sex differences, i.e., females are more vulnerable to pain and opioid use 5 , no significant signals were found on the X chromosome. The functional link between the genome-wide significant SNP (rs549224715) on chr4p11 and pain treatment outcome remains unclear. The nearest gene, CWH43 , is associated with Seckel Syndrome, characterized by growth delays before birth. Another gene, TXK , was mapped by eQTL to this SNP and played a role in regulating the adaptive immune response 28 . Therefore, this association is worth further validation and investigation. This study is the first to report the narrow-sense heritability of the NSAID treatment outcome. Our study indicates that NSAID treatment outcome has a moderate heritability, although the P-value is insignificant. The insignificant P-value could be due to the lack of power of GWAS results. Since there are no comparable previous results, we examined whether the heritability is in line with the heritability of response to opioid analgesics or chronic pain. The heritability in our study is in agreement with the heritability of opioid response (60% in cold pressor induced pain and 12% in heat pressor) in a twin study 29 and chronic pain (0.08 to 0.31) 7 . One possible reason for the low heritability is that the narrow-sense heritability only captures the additive genetic components of common variants without the contributions of non-additive effects, rare variants, and structural variants. Another reason could be that pain treatment outcome is a highly complex phenotype with other contributing factors such as employment status and psychological factors 30 . Most variants in the 95% credible sets showed potential transcription regulatory functions, which aligns with research indicating that epigenetic changes are involved in chronic pain 31 and pain treatment 32 . Some preliminary published results indicate that epigenetic restructuring can happen in response to opioid analgesic use. For instance, hypermethylation in both the promoter region of a candidate gene ( OPRM1) and global DNA methylation was observed after opioid use 33 , 34 . In total, we pinpointed 28 genes that linked to the identified SNPs based on physical, eQTL, and chromatin interaction mapping. Four identified genes are prioritized as these are involved in neuropathic pain. NPTX2 was identified by both eQTL mapping and gene-based analysis with the lowest P-value (2.71 × 10 − 5 ) (Table S2). This gene encodes a member of the neuronal pentraxins family, which are involved in excitatory synapse formation. NPTX2 is thought to play a role in anxiety 35 and is downregulated in the brain in induced chronic neuropathic pain 36 and induced endometriosis 37 mouse models. The other three genes are involved in: spinal sensitization and neuropathic pain states after peripheral nerve injury 38 , 39 ( THBS4 ); synaptic plasticity associated with chronic inflammatory pain 40 and neuropathic pain processing after nerve injury 41 ( HOMER1 ); nerve injury-induced membrane receptor trafficking in dorsal root ganglions in neuropathic pain conditions 42 ( IPCEF1 ). In addition, we identified four genes linked to muscular or skeletal dystrophy: CMYA5 , SGCB , TMEM130 , and FN1 . These genes are of interest as musculoskeletal dystrophy is characterized by pain. In addition, no candidate genes were implicated in the metabolism and working mechanisms of NSAIDs, which could indicate that participants are more likely to use opioids because of pain or disease progression. However, our results do not exclude the role of those genes in pain treatment outcomes. One reason is that the subcategories in NSAIDs, such as non-selective NSAIDs and selective COX-2 inhibitors, were analyzed as a whole, which may dilute their effect on the pain treatment outcomes difference. However, stratified analyses per drug were impossible as the groups would become too small to obtain sufficient power. No variants involved in opioid processing were identified in line with our expectation because our phenotype is a proxy for NSAIDs treatment outcome. The other possible reason is that rare variants in drug-metabolizing genes can contribute significantly to treatment response differences 43 . Our study had 80% power to identify SNPs with a risk allele frequency of 5% and genotypic relative risk of 1.135, but we lack the power to detect variants with lower frequencies or smaller effect sizes. Therefore, it would be interesting to investigate the effect of rare variants in a larger sample with a Next-Generation Sequencing-based method. Although no correlations with pain treatment outcomes remained significant after Bonferroni correction, the enrichment of top correlations with nominal significance was perhaps expected. The overrepresentation of pain phenotypes indicates that opioid users tend to have more chronic and severe pain conditions 44 . The correlations between pain treatment outcomes and education/occupation also matched reports that people carrying out strenuous occupations (jobs involving heavy manual or physical work) are more likely to report pain 18 , 45 . Our study indicates that people with strenuous occupations are more likely to require analgesics at a higher step on the analgesic ladder. The pathway enrichment and network analysis should be interpreted carefully as the input consisted of nominally significant genes from the GWAS analysis. Top prioritized pathways were mainly implicated in immunity-related processes. One of the identified pathways was retinoic acid-mediated apoptosis signaling. Studies on the link between this pathway and pain are inconsistent. Retinoic acid (RA) administration can reduce chemotherapy-induced neuropathy 46 or inhibit prostaglandin synthesis in astrocytes 47 , an important mediator of inflammation and pain signaling. In contrast, topical application of RA can induce retinoid-elicited irritation 48 . The network analysis emphasizes the role of EGFR (a member of the ErbB family of receptors) in pain treatment outcomes. Some links between EGFR and pain can be found in the literature. For instance, EGFR inhibition can block inflammatory chronic pain progression in preclinical studies 49 and relieve neuropathic pain in clinical settings 50 , suggesting that the role of EGFR in pain treatment outcomes is worth further investigating. Our subtype GWAS focusing on inflammatory pain did not identify genes linked to inflammation, nor did it strengthen the associations found in our primary GWAS. This could be explained by the loss of power due to the decreased sample size, and it could also indicate that inflammatory factors may not be a predictor of the severity of NMP 51 . The secondary analysis aimed to validate the GWAS findings in a more heterogeneous group. However, we could not replicate our findings. Unfortunately, replicating the results in other independent cohorts is difficult due to the limited number of publicly available large-scale data similar to UK Biobank and the lack of cohorts measuring long-term pain treatment outcomes. However, it is still worth exploring the genetic background of pain treatment outcomes in a large cohort with a specific pain treatment outcomes definition, such as the ongoing Pain Predict Genetics cohort in our center (NCT02383342). By utilizing a derived phenotype in the UKB, a large sample size with long-term pain treatment outcomes to NSAIDs was available for analysis. However, it is difficult to validate this derived phenotype and assess pain chronicity because of a lack of appropriate diagnosis codes for pain in the current International Classification of Diseases (ICD). Despite the limitations in phenotype definition, the group characteristics are similar to previous publications, with a roughly even share of NSAID users and opioid users in the population 52 , and the reported risk factors for using opioids are also in line with previous literature 44 , 52 . In conclusion, we identified one locus achieving genome-wide significance for a derived pain treatment outcome phenotype. Some identified genes could be linked to neuropathic pain and musculoskeletal development. However, this study should be viewed as an initial stepping stone for future research. Declarations Acknowledge The authors thank Ward De Witte for assistance with data analysis. This research has been conducted using the UK Biobank Resource under Application Number 52524. The authors are grateful to the UK Biobank participants for making such research possible. Funding information S.L. was supported by China Scholarship Council (CSC) Grant number 201908130179. This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. This work is part of the research programme Computing Time National Computing Facilities Processing Round pilots 2018 with project number 17666, which is (partly) financed by the Dutch Research Council (NWO). Data availability Summary statistics of the primary analysis are available at DANS archive (https://doi.org/10.17026/dans-xns-un6c). Gene mapping results are available at FUMA (https://fuma.ctglab.nl/browse/378). Author contributions Song Li analyzed the data and prepared the manuscript. Geert Poelmans contributed to the pathway and network analysis and revised the manuscript. Regina L.M. van Boekel contributed to the phenotype definition and revised the manuscript. Marieke J.H. 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Pain severity and analgesics use in the community-dwelling older population: a drug utilization study from Germany. Eur J Clin Pharmacol 2020; 76 (12) : 1695-1707. Meng W, Adams MJ, Palmer CNA, Shi J, Auton A, Ryan KA , et al . Genome-wide association study of knee pain identifies associations with GDF5 and COL27A1 in UK Biobank. Commun Biol 2019; 2: 321. Arrieta Ó, Hernández-Pedro N, Fernández-González-Aragón MC, Saavedra-Pérez D, Campos-Parra AD, Ríos-Trejo M , et al . Retinoic acid reduces chemotherapy-induced neuropathy in an animal model and patients with lung cancer. Neurology 2011; 77 (10) : 987-995. Kampmann E, Johann S, van Neerven S, Beyer C, Mey J. Anti-inflammatory effect of retinoic acid on prostaglandin synthesis in cultured cortical astrocytes. J Neurochem 2008; 106 (1) : 320-332. Leyden JJ, Grossman R, Nighland M. Cumulative irritation potential of topical retinoid formulations. J Drugs Dermatol 2008; 7 (8 Suppl) : s14-18. Martin LJ, Smith SB, Khoutorsky A, Magnussen CA, Samoshkin A, Sorge RE , et al . Epiregulin and EGFR interactions are involved in pain processing. J Clin Invest 2017; 127 (9) : 3353-3366. Kersten C, Cameron MG, Bailey AG, Fallon MT, Laird BJ, Paterson V , et al . Relief of Neuropathic Pain Through Epidermal Growth Factor Receptor Inhibition: A Randomized Proof-of-Concept Trial. Pain Med 2019; 20 (12) : 2495-2505. Generaal E, Vogelzangs N, Macfarlane GJ, Geenen R, Smit JH, Dekker J , et al . Basal inflammation and innate immune response in chronic multisite musculoskeletal pain. Pain 2014; 155 (8) : 1605-1612. Miller GF, Guy GP, Zhang K, Mikosz CA, Xu L. Prevalence of Nonopioid and Opioid Prescriptions Among Commercially Insured Patients with Chronic Pain. Pain Med 2019; 20 (10) : 1948-1954. Tables Table 1 to 3 are available in the Supplementary Files section. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Table1.xlsx Table 1 Table2.xlsx Table 2 Table3.xlsx Table 3 supplementarymaterials.pdf Cite Share Download PDF Status: Published Journal Publication published 16 Aug, 2023 Read the published version in The Pharmacogenomics Journal → Version 1 posted Editorial decision: revise 05 Dec, 2022 Review # 2 received at journal 22 Nov, 2022 Reviewer # 2 agreed at journal 28 Oct, 2022 Review # 1 received at journal 21 Sep, 2022 Reviewer # 1 agreed at journal 21 Sep, 2022 Reviewers invited by journal 19 Aug, 2022 Submission checks completed at journal 25 Jul, 2022 Unknown event 20 Jul, 2022 Editor assigned by journal 19 Jul, 2022 First submitted to journal 19 Jul, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1874694","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":123802594,"identity":"182def8c-35cd-4e96-ab9b-d1fd2babbade","order_by":0,"name":"Marieke Coenen","email":"data:image/png;base64,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","orcid":"","institution":"Radboud university medical center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Marieke","middleName":"","lastName":"Coenen","suffix":""},{"id":123802595,"identity":"12f6ccae-779c-4703-a705-5f3988914aa5","order_by":1,"name":"Song Li","email":"","orcid":"https://orcid.org/0000-0002-1429-3161","institution":"Radboud University Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Li","suffix":""},{"id":123802596,"identity":"9c51aeab-aa11-4cb6-981f-77c21a541a64","order_by":2,"name":"Geert Poelmans","email":"","orcid":"","institution":"Radboud University Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Geert","middleName":"","lastName":"Poelmans","suffix":""},{"id":123802597,"identity":"aad75c87-b5d4-4451-b361-9fdb83c0350b","order_by":3,"name":"Regina van Boekel","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Regina","middleName":"van","lastName":"Boekel","suffix":""}],"badges":[],"createdAt":"2022-07-19 15:20:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1874694/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1874694/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41397-023-00314-x","type":"published","date":"2023-08-16T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25565125,"identity":"53b72c5f-edc5-4a7c-b695-94d07b2e3b84","added_by":"auto","created_at":"2022-08-23 17:46:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129520,"visible":true,"origin":"","legend":"\u003cp\u003eQ-Q plot and Manhattan plot of primary analysis for pain treatment outcome. (a) Q-Q plot of the GWAS results. The red line indicates the distribution under the null hypothesis, and the shaded area indicates the 95% confidence band. (b) Manhattan plot of the GWAS results. The red line corresponds to the genome-wide significance threshold of 5 × 10\u003csup\u003e-8\u003c/sup\u003e, whereas the blue indicates the suggestive threshold of 1 × 10\u003csup\u003e-6\u003c/sup\u003e. Lead variants are highlighted as orange diamonds. Variants in one locus (within 400 Kb) are highlighted in orange.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1874694/v1/fc40acc6ca2829c42bdcb852.png"},{"id":41682594,"identity":"ebcde5bb-24b8-475c-99fe-d87f559248f1","added_by":"auto","created_at":"2023-08-17 07:11:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":530251,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1874694/v1/1578d2b7-0631-4a8a-a7d6-a72b3cda0909.pdf"},{"id":25565124,"identity":"738df85a-c351-4884-8d95-40a2f3ee04f5","added_by":"auto","created_at":"2022-08-23 17:46:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13133,"visible":true,"origin":"","legend":"Table 1","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1874694/v1/90a8596f8fd65441c506036c.xlsx"},{"id":25565122,"identity":"e445bbb7-c4a2-4d80-91d6-04cb4e1a3248","added_by":"auto","created_at":"2022-08-23 17:46:24","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12873,"visible":true,"origin":"","legend":"Table 2","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1874694/v1/56ef8e815c553dd29c8c5a07.xlsx"},{"id":25566113,"identity":"df91505e-cd91-429f-8f83-f1931d78a641","added_by":"auto","created_at":"2022-08-23 17:51:24","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13985,"visible":true,"origin":"","legend":"Table 3","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1874694/v1/2d3f7e00b0d60786bf6061a6.xlsx"},{"id":25565126,"identity":"11216753-7789-4593-8461-7186b447e540","added_by":"auto","created_at":"2022-08-23 17:46:25","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4886419,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1874694/v1/3b430c090a7128b4f9c9dab6.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Genome-wide association study on pharmacological outcomes of musculoskeletal pain in UK Biobank","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic musculoskeletal pain is one of the most frequent causes of suffering and disability \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The nature of musculoskeletal pain can be nociceptive or neuropathic, for which the corresponding pain management differs. The treatment of nociceptive musculoskeletal pain follows the WHO three-step analgesic ladder \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e: the first treatment step is non-opioid analgesics, such as non-steroidal anti-inflammatory drugs (NSAIDs); the second step is weak opioids for mild to moderate pain, such as tramadol; the third step is strong opioids for moderate to severe pain, such as morphine.\u003c/p\u003e \u003cp\u003eUnfortunately, effective pain management is challenged by inter-individual differences, with unsatisfied pain treatment rates ranging from 34 to 79% \u003csup\u003e3\u003c/sup\u003e. The underlying factors of ineffective pain treatment are multifactorial, including demographic characteristics (age, sex, socioeconomic status) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, lifestyle (smoking and alcohol intake) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, comorbidities (psychological status) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and genetic factors. The genetic background of pain treatment outcomes has been investigated using candidate gene approaches. Some drug-metabolizing genes are associated with treatment outcomes for specific drugs, e.g., \u003cem\u003eCYP2D6\u003c/em\u003e and codeine \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In addition, genes implicated in pain (sensitivity) may contribute to pain treatment outcomes because greater pain sensitivity is associated with increased opioid use \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and poorer chronic pain treatment outcomes \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, none of these findings predict pain treatment outcomes sufficiently to optimize pain treatment in a clinical setting. Furthermore, these studies are limited by small gene panel and sample size and report contradictory results \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The most investigated genetic variant is the 118A to G basepair change in the \u003cem\u003eOPRM1\u003c/em\u003e gene. Genetic variants in \u003cem\u003eOPRM1\u003c/em\u003e are thought to influence the opioid response by altering the \u0026micro;-opioid receptor binding affinity of exogenous opioid ligands, such as morphine \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The G allele was associated with higher opioid dosing \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e but shown to be protective against pain in other studies \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Therefore, definitive conclusions on these genetic associations cannot be drawn yet, and a non-hypothesis driven approach in a large population is needed. Except for several recent, successful large-scale GWASs of chronic pain phenotypes \u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, the number of GWASs focusing on pain treatment outcomes is still limited. Moreover, the most frequently used phenotype in GWASs investigating pain treatment is the response to certain drugs for acute pain (e.g., analgesic requirement or pain relief score after surgery \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e), but long-term pain treatment outcomes are less investigated.\u003c/p\u003e \u003cp\u003eThis study sought to identify genetic variants associated with long-term pain treatment outcomes in people with musculoskeletal pain from the UK Biobank. A GWAS was performed including subjects treated according to the WHO analgesic ladders, and comparisons were made between NSAID and opioid users as a reflection of pain treatment outcomes.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003eWe conducted a GWAS comparing NSAID users and opioid users using data from the UK Biobank, and post-GWAS analyses were performed for suggestively significant (P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) signals.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe UK Biobank is a general population cohort with over 0.5\u0026nbsp;million participants aged 40\u0026ndash;69 recruited across the United Kingdom (UK) \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Recently released primary care (general practitioners, 'GPs') data provides longitudinal structured diagnosis and prescription data, which were used for phenotype definition. UK Biobank obtained informed consent from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Phenotype definition\u003c/h2\u003e \u003cp\u003eTo define the pain treatment outcome, we first extracted all musculoskeletal pain patients with pain prescriptions (NSAIDs and opioids) from the GP data (see Supplementary data 1 and 2 for diagnosis and prescriptions codes included in this study). Only participants with a musculoskeletal pain diagnosis record and a pain prescription record occurring on the same date were included for analysis to ensure that the prescriptions are indeed for musculoskeletal pain treatment.\u003c/p\u003e \u003cp\u003ePain treatment outcomes were defined as a dichotomous score (case/control): NSAID users were defined as controls and opioid users as cases. Opioid users were analyzed as a whole because the strong opioid user group is small (n\u0026thinsp;=\u0026thinsp;365) and assuming mechanistic similarities between weak and strong opioids. Participants who did not meet the following two quality control (QC) steps were removed. First, participants with only one treatment event were removed to safeguard the inclusion of only participants with relatively long-term treatment. Second, a chronological check was applied for the first prescription of each ladder to ensure that the treatment ladder was correctly followed, i.e., opioids followed initial NSAID use. As the GP data is longitudinal, by using this definition, we could distinguish between patients who stay at NSAID treatment and who go to the next level of the analgesic ladder. The following text will refer to this phenotype as pain treatment outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Genome-wide association study\u003c/h2\u003e \u003cp\u003eA GWAS was conducted using binary phenotypes, i.e., NSAID users (controls) versus opioid users (cases). For markers on the autosomal chromosomes and PAR region of the X chromosome, GWAS on pain treatment outcomes was conducted using a linear function in GCTA \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, adjusting for age, sex, BMI, depression history, smoking status, drinking frequency, assessment center, genotyping array, and the first ten principal components (PCs). Markers on the non-PAR region of the X chromosome were analyzed by a sex-stratified analysis in the XWAS \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. A p-value less than 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e was considered genome-wide significant, and P-values between 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e and 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e were defined as suggestively significant. Details on genotyping and quality control methods, covariates definition, and heritability/power calculation can be found in supplementary materials.\u003c/p\u003e \u003cp\u003eTo examine the nature of pain between groups, all NMP diagnosis codes were grouped into one of the following categories: inflammatory, mechanical, and mechanism not specified. The percentage of subjects in each diagnosis category was compared by a χ\u0026sup2; test.\u003c/p\u003e \u003cp\u003eBesides the binary case/control analysis, three additional analyses were performed: a GWAS using an ordinal outcome (NSAID, weak opioid, and strong opioid users), a subtype GWAS focusing on inflammatory pain, and a GWAS with a less stringent phenotype definition for validation purposes. Details on these analyses can be found in the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional annotation\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Bayesian fine-mapping of lead loci\u003c/h2\u003e \u003cp\u003eLead SNPs were analyzed using Bayesian fine-mapping in PAINTOR \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e to identify the most likely causal SNPs in each locus. PAINTOR calculates the posterior probability (PP) of causality for SNPs in each genomic region by leveraging the strength of association (Z score) and the LD pattern. The 1000 Genomes (Phase 3) were used for LD matrix calculation. The calculated PP for each SNP was sorted from high to low, and variants together reaching a PP of 0.95 were used to define 95% credible sets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Functional annotation of SNPs present in the 95% credible sets\u003c/h2\u003e \u003cp\u003eSNPs in the 95% credible sets were annotated for regulatory functions in HaploReg v4.1 \u003csup\u003e26\u003c/sup\u003e. The analyzed regulatory functions were (1) the presence of exonic, nonsynonymous variants in high LD (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.8), (2) overlap with epigenetic histone marks of active enhancers (H3K4me1 and H3K27ac) and active promoters (H3K4me3 and H3K9ac), and (3) the sensitivity to DNase. As histone marker overlap is tissue-specific, relevant cell lines were selected from the complete data set (see Table S1). Besides regulatory functions, potential pleiotropy effects (previously reported associations with other phenotypes) of the variants were investigated in Haploreg. For SNPs not available in Haploreg, proxy SNPs were obtained by LD proxy (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ldlink.nci.nih.gov/\u003c/span\u003e\u003cspan address=\"https://ldlink.nci.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For loci containing more than ten likely causal variants, only the lead SNP and SNPs with the maximum posterior probability (PPmax) of the SNPs in one locus were annotated.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Gene mapping\u003c/h2\u003e \u003cp\u003eTo map suggestively significant (P-value\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) GWAS SNPs and SNPs in LD (LD\u0026thinsp;\u0026gt;\u0026thinsp;0.6) with them to genes, three strategies were adopted in FUMA: positional mapping, expression quantitative trait loci (eQTL) mapping, and chromatin interaction mapping. For the positional mapping, SNPs were mapped to known protein-coding genes based on physical distance (within a 10 kb window). For eQTL mapping, SNPs were mapped to genes up to 1 Mb away based on known cis-eQTLs. As gene expression is tissue-specific, we selected the following tissues for mapping: brain, muscle, kidney, liver, nerve, skin, and fibroblast. Significant eQTLs were defined as eQTLs with a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Finally, chromatin interactions were assessed. Chromatin interaction can occur in two genomic regions that are spatially close when DNA folds together, even if the genomic regions are at a long-range physical distance. Genes in regions of chromatin interaction containing candidate SNPs were assessed in the same tissues as the eQTL mapping. An FDR\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e was defined as a significant interaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Post-GWAS analysis\u003c/h2\u003e \u003cp\u003eWe conducted the following post-GWAS analysis: pathway enrichment analysis and genetic correlation analysis (for details see supplementary materials).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 GWAS\u003c/h2\u003e \u003cp\u003eAfter quality control, we identified 12 726 NSAID users (control) and 11 089 opioid users (cases) in the UK Biobank dataset. \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e summarizes the demographics of the cases and controls, and all tested covariates were found to be significantly different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eThere were 9 435 994 SNPs available for GWAS analysis after quality control. The genomic control value (lambda) was 1.008. One intergenic locus located at chr4 reached genome-wide significance, in which the most significant SNP was rs549224715 (P\u0026thinsp;=\u0026thinsp;3.92 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eTable\u0026nbsp;2)\u003c/b\u003e. Seven loci surpassed the suggestive P-value threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), and no other independent SNPs (SNPs remaining significant after conditioning on lead SNPs in the locus) were identified in each locus. The SNP heritability was 0.16 (P-value\u0026thinsp;=\u0026thinsp;0.16). A GWAS was conducted using ordinal phenotypes (NSAID, weak opioid, and strong opioid users), and the results were consistent with the GWAS using binary outcomes (Figure S1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted a subtype GWAS in patients with inflammatory pain to investigate if a more homogeneous phenotype would yield additional signals and a secondary GWAS with less strict criteria for diagnosis definition to validate our results (see supplementary materials). However, we did not find any genome-wide significant loci or overlapped suggestively significant loci with the GWAS using binary outcomes as described in detail above (Figure S2, Figure S3, Table S7, Table S8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Functional annotation\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Statistical fine-mapping of loci and functional annotation of SNPs\u003c/h2\u003e \u003cp\u003eAs GWAS signals can caused by SNPs in linkage disequilibrium with the likely causal SNPs, we calculated the posterior probability for variants in each genomic locus and created 95% credible sets (see methods). In five out of eight loci, the lead SNPs in the locus had the maximum PP (PP\u003csub\u003emax\u003c/sub\u003e) (Figure S4).\u003c/p\u003e \u003cp\u003eSince all variants in the 95% credible sets were in non-coding regions, the regulatory effects of these variants were investigated by examining overlap with epigenomic markers of active enhancers or promoters in Haploreg. The results suggested that most genetic variants were potentially involved in transcriptional regulatory modulation (Figure S4).\u003c/p\u003e \u003cp\u003eWe assessed whether the SNPs in the 95% credible sets were previously reported to be associated with other traits. However, no pleiotropic effects were identified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Gene mapping\u003c/h2\u003e \u003cp\u003eAfter mapping GWAS candidate SNPs (SNPs that are in LD (r2\u0026thinsp;\u0026gt;\u0026thinsp;0.6) of any independent significant SNPs) to genes, a total of 28 unique mapped genes were identified (\u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e). Five genes were mapped by genomic location, nine genes were identified by cis-eQTL mapping, 18 genes were annotated as SNPs in regions where 3D chromatin interactions occurred, and four genes were identified by at least two mapping strategies.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Pathway enrichment\u003c/h2\u003e \u003cp\u003ePathway enrichment analysis in IPA prioritized 15 significant pathways with an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, in which the top prioritized pathways were mainly implicated in the immunological response. (Table S3).\u003c/p\u003e \u003cp\u003eThe network analysis yielded a total of 25 prioritized networks. The top network contained 33 genes with the EGFR protein in the center. EGFR remained in the center after merging the five networks with the lowest P-value (Table S4, Figure S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Genetic correlation with other traits\u003c/h2\u003e \u003cp\u003eThe genetic correlation analysis did not yield significant correlations (Bonferroni corrected P-value\u0026thinsp;\u0026lt;\u0026thinsp;8.39 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). The top correlated trait was overall health rating (rg\u0026thinsp;=\u0026thinsp;0.5316, P\u0026thinsp;=\u0026thinsp;0.0087), followed by years of schooling \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e (rg = -0.5431, P\u0026thinsp;=\u0026thinsp;0.0102) (Table S5). However, among the nominally significant correlations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we found an overrepresentation of pain and socioeconomic status traits compared to the other traits (43.48% vs. 8.55%, P\u0026thinsp;=\u0026thinsp;3.35 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e, Table S6).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is the first GWAS reporting on long-term pain treatment outcomes. We identified one genome-wide significant hit and seven loci with suggestive significance. Although pain or pain treatment is characterized by sex differences, i.e., females are more vulnerable to pain and opioid use \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, no significant signals were found on the X chromosome. The functional link between the genome-wide significant SNP (rs549224715) on chr4p11 and pain treatment outcome remains unclear. The nearest gene, \u003cem\u003eCWH43\u003c/em\u003e, is associated with Seckel Syndrome, characterized by growth delays before birth. Another gene, \u003cem\u003eTXK\u003c/em\u003e, was mapped by eQTL to this SNP and played a role in regulating the adaptive immune response \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Therefore, this association is worth further validation and investigation.\u003c/p\u003e \u003cp\u003eThis study is the first to report the narrow-sense heritability of the NSAID treatment outcome. Our study indicates that NSAID treatment outcome has a moderate heritability, although the P-value is insignificant. The insignificant P-value could be due to the lack of power of GWAS results. Since there are no comparable previous results, we examined whether the heritability is in line with the heritability of response to opioid analgesics or chronic pain. The heritability in our study is in agreement with the heritability of opioid response (60% in cold pressor induced pain and 12% in heat pressor) in a twin study \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and chronic pain (0.08 to 0.31) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. One possible reason for the low heritability is that the narrow-sense heritability only captures the additive genetic components of common variants without the contributions of non-additive effects, rare variants, and structural variants. Another reason could be that pain treatment outcome is a highly complex phenotype with other contributing factors such as employment status and psychological factors \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMost variants in the 95% credible sets showed potential transcription regulatory functions, which aligns with research indicating that epigenetic changes are involved in chronic pain \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and pain treatment \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Some preliminary published results indicate that epigenetic restructuring can happen in response to opioid analgesic use. For instance, hypermethylation in both the promoter region of a candidate gene (\u003cem\u003eOPRM1)\u003c/em\u003e and global DNA methylation was observed after opioid use \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn total, we pinpointed 28 genes that linked to the identified SNPs based on physical, eQTL, and chromatin interaction mapping. Four identified genes are prioritized as these are involved in neuropathic pain. \u003cem\u003eNPTX2\u003c/em\u003e was identified by both eQTL mapping and gene-based analysis with the lowest P-value (2.71 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) (Table S2). This gene encodes a member of the neuronal pentraxins family, which are involved in excitatory synapse formation. NPTX2 is thought to play a role in anxiety \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and is downregulated in the brain in induced chronic neuropathic pain \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and induced endometriosis \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e mouse models. The other three genes are involved in: spinal sensitization and neuropathic pain states after peripheral nerve injury \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e (\u003cem\u003eTHBS4\u003c/em\u003e); synaptic plasticity associated with chronic inflammatory pain \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and neuropathic pain processing after nerve injury \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e (\u003cem\u003eHOMER1\u003c/em\u003e); nerve injury-induced membrane receptor trafficking in dorsal root ganglions in neuropathic pain conditions \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e (\u003cem\u003eIPCEF1\u003c/em\u003e). In addition, we identified four genes linked to muscular or skeletal dystrophy: \u003cem\u003eCMYA5\u003c/em\u003e, \u003cem\u003eSGCB\u003c/em\u003e, \u003cem\u003eTMEM130\u003c/em\u003e, and \u003cem\u003eFN1\u003c/em\u003e. These genes are of interest as musculoskeletal dystrophy is characterized by pain.\u003c/p\u003e \u003cp\u003eIn addition, no candidate genes were implicated in the metabolism and working mechanisms of NSAIDs, which could indicate that participants are more likely to use opioids because of pain or disease progression. However, our results do not exclude the role of those genes in pain treatment outcomes. One reason is that the subcategories in NSAIDs, such as non-selective NSAIDs and selective COX-2 inhibitors, were analyzed as a whole, which may dilute their effect on the pain treatment outcomes difference. However, stratified analyses per drug were impossible as the groups would become too small to obtain sufficient power. No variants involved in opioid processing were identified in line with our expectation because our phenotype is a proxy for NSAIDs treatment outcome. The other possible reason is that rare variants in drug-metabolizing genes can contribute significantly to treatment response differences \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our study had 80% power to identify SNPs with a risk allele frequency of 5% and genotypic relative risk of 1.135, but we lack the power to detect variants with lower frequencies or smaller effect sizes. Therefore, it would be interesting to investigate the effect of rare variants in a larger sample with a Next-Generation Sequencing-based method.\u003c/p\u003e \u003cp\u003eAlthough no correlations with pain treatment outcomes remained significant after Bonferroni correction, the enrichment of top correlations with nominal significance was perhaps expected. The overrepresentation of pain phenotypes indicates that opioid users tend to have more chronic and severe pain conditions \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The correlations between pain treatment outcomes and education/occupation also matched reports that people carrying out strenuous occupations (jobs involving heavy manual or physical work) are more likely to report pain \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Our study indicates that people with strenuous occupations are more likely to require analgesics at a higher step on the analgesic ladder.\u003c/p\u003e \u003cp\u003eThe pathway enrichment and network analysis should be interpreted carefully as the input consisted of nominally significant genes from the GWAS analysis. Top prioritized pathways were mainly implicated in immunity-related processes. One of the identified pathways was retinoic acid-mediated apoptosis signaling. Studies on the link between this pathway and pain are inconsistent. Retinoic acid (RA) administration can reduce chemotherapy-induced neuropathy \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e or inhibit prostaglandin synthesis in astrocytes \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, an important mediator of inflammation and pain signaling. In contrast, topical application of RA can induce retinoid-elicited irritation \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The network analysis emphasizes the role of EGFR (a member of the ErbB family of receptors) in pain treatment outcomes. Some links between EGFR and pain can be found in the literature. For instance, EGFR inhibition can block inflammatory chronic pain progression in preclinical studies \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and relieve neuropathic pain in clinical settings \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, suggesting that the role of EGFR in pain treatment outcomes is worth further investigating.\u003c/p\u003e \u003cp\u003eOur subtype GWAS focusing on inflammatory pain did not identify genes linked to inflammation, nor did it strengthen the associations found in our primary GWAS. This could be explained by the loss of power due to the decreased sample size, and it could also indicate that inflammatory factors may not be a predictor of the severity of NMP \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The secondary analysis aimed to validate the GWAS findings in a more heterogeneous group. However, we could not replicate our findings. Unfortunately, replicating the results in other independent cohorts is difficult due to the limited number of publicly available large-scale data similar to UK Biobank and the lack of cohorts measuring long-term pain treatment outcomes. However, it is still worth exploring the genetic background of pain treatment outcomes in a large cohort with a specific pain treatment outcomes definition, such as the ongoing Pain Predict Genetics cohort in our center (NCT02383342).\u003c/p\u003e \u003cp\u003eBy utilizing a derived phenotype in the UKB, a large sample size with long-term pain treatment outcomes to NSAIDs was available for analysis. However, it is difficult to validate this derived phenotype and assess pain chronicity because of a lack of appropriate diagnosis codes for pain in the current International Classification of Diseases (ICD). Despite the limitations in phenotype definition, the group characteristics are similar to previous publications, with a roughly even share of NSAID users and opioid users in the population \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, and the reported risk factors for using opioids are also in line with previous literature \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, we identified one locus achieving genome-wide significance for a derived pain treatment outcome phenotype. Some identified genes could be linked to neuropathic pain and musculoskeletal development. However, this study should be viewed as an initial stepping stone for future research.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch1\u003eAcknowledge\u003c/h1\u003e\n\u003cp\u003eThe authors thank Ward De Witte for assistance with data analysis. This research has been conducted using the UK Biobank Resource under Application Number 52524. The authors are grateful to the UK Biobank participants for making such research possible. Funding information\u003c/p\u003e\n\u003cp\u003eS.L. was supported by China Scholarship Council (CSC) Grant number 201908130179. This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. This work is part of the research programme Computing Time National Computing Facilities Processing Round pilots 2018 with project number 17666, which is (partly) financed by the Dutch Research Council (NWO).\u003c/p\u003e\n\u003ch1\u003eData availability\u003c/h1\u003e\n\u003cp\u003eSummary statistics of the primary analysis are available at DANS archive (https://doi.org/10.17026/dans-xns-un6c).\u003c/p\u003e\n\u003cp\u003eGene mapping results are available at FUMA (https://fuma.ctglab.nl/browse/378).\u003c/p\u003e\n\u003ch1\u003eAuthor contributions\u003c/h1\u003e\n\u003cp\u003eSong Li\u003csup\u003e\u0026nbsp;\u003c/sup\u003eanalyzed the data and prepared the manuscript. Geert Poelmans\u003csup\u003e\u0026nbsp;\u003c/sup\u003econtributed to the pathway and network analysis and revised the manuscript. Regina L.M. van Boekel\u003csup\u003e\u0026nbsp;\u003c/sup\u003econtributed to the phenotype definition and revised the manuscript. Marieke J.H. Coenen\u003csup\u003e\u0026nbsp;\u003c/sup\u003econceptualized the study, supervised the overall project and revised the manusrcript. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003ch1\u003eConflicts of interest disclosures\u003c/h1\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEhrlich GE. Back pain. \u003cem\u003eJ Rheumatol Suppl\u003c/em\u003e 2003; \u003cstrong\u003e67:\u003c/strong\u003e 26-31.\u003c/li\u003e\n\u003cli\u003eEl-Tallawy SN, Nalamasu R, Salem GI, LeQuang JAK, Pergolizzi JV, Christo PJ. 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However, the treatment outcome is usually unsatisfying due to inter-individual differences. To investigate the genetic component of treatment outcome differences, we performed a genome-wide association study (GWAS) in ~\u0026thinsp;23 000 participants with musculoskeletal pain from the UK Biobank. NSAID vs. opioid users was compared as a reflection of the treatment outcome of NSAIDs. We identified one genome-wide significant hit in chromosome 4 (rs549224715, P\u0026thinsp;=\u0026thinsp;3.88\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). Suggestive significant (P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;6) loci were mapped to 28 target genes, including eight genes linked to neuropathic pain processes or musculoskeletal development. Pathway and network analyses identified immunity-related processes and a (putative) central role of \u003cem\u003eEGFR\u003c/em\u003e. However, this study lacked power and should be viewed as a first step to elucidate the genetic background of musculoskeletal pain treatment.\u003c/p\u003e","manuscriptTitle":"Genome-wide association study on pharmacological outcomes of musculoskeletal pain in UK Biobank","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-23 17:46:22","doi":"10.21203/rs.3.rs-1874694/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2022-12-05T20:29:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2022-11-22T19:52:29+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2022-10-28T16:11:40+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2022-09-21T20:16:51+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2022-09-21T11:23:40+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2022-08-19T07:20:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-07-25T14:48:16+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2022-07-20T10:11:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-07-19T15:15:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Pharmacogenomics Journal","date":"2022-07-19T15:15:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-pharmacogenomics-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tpj","sideBox":"Learn more about [The Pharmacogenomics Journal](http://www.nature.com/tpj/)","snPcode":"41397","submissionUrl":"https://mts-tpj.nature.com/cgi-bin/main.plex","title":"The Pharmacogenomics Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d2c6cca8-5fc4-40f6-a452-05c0ebb5a287","owner":[],"postedDate":"August 23rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-08-17T07:11:16+00:00","versionOfRecord":{"articleIdentity":"rs-1874694","link":"https://doi.org/10.1038/s41397-023-00314-x","journal":{"identity":"the-pharmacogenomics-journal","isVorOnly":false,"title":"The Pharmacogenomics Journal"},"publishedOn":"2023-08-16 04:00:00","publishedOnDateReadable":"August 16th, 2023"},"versionCreatedAt":"2022-08-23 17:46:22","video":"","vorDoi":"10.1038/s41397-023-00314-x","vorDoiUrl":"https://doi.org/10.1038/s41397-023-00314-x","workflowStages":[]},"version":"v1","identity":"rs-1874694","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1874694","identity":"rs-1874694","version":["v1"]},"buildId":"PuwtSGGF21Z2LVbsuOoB7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}