Extracellular matrix gene variants and susceptibility to sport-related muscle injuries

Extracellular matrix gene variants and susceptibility to sport-related muscle injuries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Extracellular matrix gene variants and susceptibility to sport-related muscle injuries Piotr Kabelis, Agata Rzeszutko-Bełzowska, Agata Leońska-Duniec This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8639032/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The extracellular matrix (ECM) plays a fundamental role in maintaining skeletal muscle structure and facilitating tissue repair following injury. Alterations in its composition can disrupt regeneration, promote fibrosis, and increase susceptibility to muscle damage. The aim of this study was to investigate whether the BGN (rs1042103), DCN (rs13312816 and rs516115), ELN (rs2071307), FBN2 (rs331079), MMP1 (rs1799750), MMP10 (rs486055), and MMP12 (rs2276109) polymorphisms, either individually or in combination, are associated with susceptibility to muscle injury. This study included 142 physically active Caucasian participants with reported noncontact muscle injury and 133 participants without any history of this kind of injury. All the samples were genotyped using real-time polymerase chain reaction (real-time PCR). The results revealed that individuals carrying (i) the GC and CC genotypes of FBN2 rs331079, (ii) the GG genotype of MMP12 rs2276109, (iii) the AG and AA genotypes of BGN rs1042103, (iv) and the AG genotype of DCN rs516115 had an increased risk of muscle injury, whereas the GG genotype appeared to exert a protective effect relative to AA. In contrast, no significant results were obtained for the remaining polymorphisms. Additionally, no significant associations were detected for the DCN and MMP haplotypes; however, an epistatic interaction between DCN (rs13312816) and BGN (rs1042103) was detected. Participants who carried the AG/TT and AA/AT genotype combinations had a higher probability of belonging to the injury group. In conclusion, this report indicates an association between specific FBN2, MMP12, BGN, and DCN genotypes and susceptibility to muscle injury in a physically active Caucasian population. sports genetics genetic susceptibility single nucleotide polymorphism (SNP) haplotype analysis injury risk physical activity Figures Figure 1 Background The extracellular matrix (ECM) plays a fundamental role in maintaining the structural and functional integrity of skeletal muscle and connective tissue. It is primarily composed of collagens, elastin, fibronectin, laminins, proteoglycans, and other glycoproteins, forming a three-dimensional network that provides support for cellular interactions. Through binding to cell surface receptors, ECM components regulate fundamental biological processes, including cell proliferation, adhesion, migration, and differentiation (Ahmad et al., 2020; Ge et al., 2025; Theocharis et al., 2016). Specifically, in skeletal muscle, these molecules and their transmembrane partners support tissue integrity, facilitate contraction and growth, promote regeneration, mediate biochemical signalling, and contribute to overall development. Structural ECM components, such as decorin (DCN) and biglycan (BGN), regulate collagen fibrillogenesis, influence tendon and ligament biomechanics, and modulate signalling pathways, including those involving transforming growth factor-beta (TGF-β) (Robinson et al., 2017). Similarly, elastin (ELN) and fibrillin-2 (FBN2) are essential for elastic fibre assembly and connective tissue resilience (Gardeazabal & Izeta, 2024; Khoury et al., 2015a). Matrix metalloproteinases (MMPs), including MMP1, MMP10, and MMP12, contribute to ECM turnover and tissue remodelling, thereby influencing the balance between matrix degradation and repair, as well as the regulation of inflammation (Cui et al., 2017; Kumar et al., 2022; Vargová et al., 2012). Alterations in ECM structure or function are closely linked to musculoskeletal pathologies, and dysregulation of ECM components contributes to the risk and progression of musculoskeletal injuries (Ahmad et al., 2023; Lamandé & Bateman, 2020). Genetic variation in ECM-related genes, particularly single nucleotide polymorphisms (SNPs), can influence protein expression or activity, thereby altering connective tissue properties, repair efficiency, and overall resilience (Ahmad et al., 2020; Suijkerbuijk et al., 2019). In physically active individuals, who are frequently exposed to repetitive mechanical loading and microtrauma, such genetic differences can modulate susceptibility to muscle injuries and related conditions, including ligament ruptures, sprains, and tendon damage. Following muscle fibre impairment, the ECM undergoes rapid remodelling characterized by the degradation of damaged matrix components and the synthesis of new components, which together provide the structural environment for repair. When this process is dysregulated, excessive or abnormal remodelling may lead to fibrosis, whereas persistent ECM alterations have been linked to muscle atrophy (Ge et al., 2025). Thus, understanding the association between ECM gene variants and injury risk may contribute to identifying genetic predispositions, improving preventive strategies, and developing personalized approaches to training and rehabilitation (Suijkerbuijk et al., 2019). Most studies on the genetic determinants of musculoskeletal injuries have focused on tendon and ligament pathology, particularly Achilles tendinopathy (AT) and anterior cruciate ligament (ACL) rupture (Rahim et al., 2016). In contrast, muscle injuries, which account for approximately 25% of all sport-related injuries (Ekstrand et al., 2011), remain relatively underexplored despite their high incidence and impact on athletes. This imbalance has limited our understanding of the molecular factors contributing specifically to muscle damage and impaired regeneration. Furthermore, the majority of existing research has focused on single candidate polymorphisms, often without considering the cumulative effects of multiple variants. However, the biological processes underlying tissue structure, repair, and adaptation are polygenic and highly interactive (Pompili et al., 2021). Thus, studies that simultaneously examine multiple SNPs and their potential gene–gene interactions are likely to provide the most informative insights into genetic susceptibility to musculoskeletal injuries, including those affecting skeletal muscle. In the present study, eight polymorphisms located in genes encoding key ECM components and remodelling enzymes were analysed: BGN (rs1042103), DCN (rs13312816 and rs516115), ELN (rs2071307), FBN2 (rs331079), MMP1 (rs1799750), MMP10 (rs486055), and MMP12 (rs2276109). An overview of the studied genes, their polymorphisms, and the biological functions of the encoded proteins is provided in Table 1. The aim of this study was to investigate whether these SNPs, either individually or in combination, are associated with susceptibility to muscle injury in a physically active Caucasian cohort. We hypothesized that specific genotypes of the selected genes would be individually associated with increased musculoskeletal injury risk and that interactions among the analysed SNPs would generate genotype combinations predisposing carriers to a greater likelihood of injury. Table 1. Characteristics of the studied genes and polymorphic sites. Gene Chromosome Location Variant Functional location Gene Product Functions of the Protein References BGN Xq28 rs1042103, A/G, p.Thr209Ala Exon 8 Biglycan Collagen fibril assembly and ECM organization; Bone growth and skeletal development; Muscle regeneration; Modulation of TGF-β and BMP signaling; Regulation of inflammation and innate immune response; Contribution to atherosclerosis and aortic valve stenosis; Involvement in cancer-related processes (Kram et al., 2020; Mannion et al., 2014; K. A. Robinson et al., 2017) DCN 12q21.33 rs13312816, A/T Intron 1 Decorin Collagen fibril assembly and ECM organization; Binding to multiple cell surface receptors; Modulation of TGF-β and EGFR signaling; Regulation of inflammation and autophagy; Cancer-related processes; Immune and inflammatory diseases; Regulation of wound healing and fibrosis (Baghy et al., 2020, 2025; Dong et al., 2022; Mannion et al., 2014; K. A. Robinson et al., 2017) rs516115, C/T Intron 3 ELN 7q11.23 rs2071307, A/G, p.Ser422Gly Exon 16 Elastin Elastic fiber formation and ECM organization; Maintenance of tissue elasticity (skin, lungs, arteries); Regulation of vascular development and arterial wall integrity; Contribution to pulmonary and skin function; Involvement in cardiovascular pathology (aneurysms, valve disease, hypertension); Regulation of wound healing and fibrosis (Duque Lasio & Kozel, 2018; Gardeazabal & Izeta, 2024; Khoury et al., 2015b; C. J. Lin et al., 2022; Mithieux & Weiss, 2005) FBN2 5q23.3 rs331079, C/G Intron 7 Fibrillin 2 Microfibril formation and ECM organization; Regulation of elastic fiber assembly; Connective tissue and skeletal development; Limb morphogenesis; Modulation of TGF-β/BMP signaling; Involvement in tendon and ligament integrity; Association with congenital contractural arachnodactyly (Khoury et al., 2015b; Ramirez & Pereira, 1999; Ramirez & Sakai, 2010; Ruigrok et al., 2006) MMP1 11q22.2 rs1799750, C/- Promoter region Matrix metallopeptidase 1 Collagen degradation (types I, II, III); ECM remodeling and turnover; Wound healing and tissue repair; Regulation of inflammation; Promotion of cell migration and angiogenesis; Cancer-related processes; Role in degenerative and cardiovascular diseases (Cui et al., 2017; Kumar et al., 2022; Posthumus et al., 2012; Vargová et al., 2012) MMP10 11q22.2 rs486055, C/T Promoter region Matrix metallopeptidase 10 ECM degradation (proteoglycans, laminin, fibronectin, collagens); Activation of other MMPs; Tissue remodeling and wound healing; Regulation of inflammation; Promotion of cell migration and angiogenesis; Involvement in tumor progression and metastasis; Role in cardiovascular and inflammatory diseases MMP12 11q22.2 rs2276109, C/T Promoter region (AP-1 binding site) Matrix metallopeptidase 12 Elastin degradation and ECM remodeling; Regulation of tissue repair and wound healing; Modulation of inflammation (macrophage-derived); Contribution to lung diseases (emphysema, COPD); Role in atherosclerosis and vascular remodeling; Involvement in tumor progression and angiogenesis Methods Participants The Bioethics Committee at the District Medical Chamber in Gdansk (No. KB-8/22) approved the study. Initially, 812 individuals were assessed for eligibility. However, participants were excluded if they were underage, failed to submit complete documentation, did not provide consent for genetic testing, reported contact-related injuries, or did not meet the physical activity criteria. As a result, a total of 275 unrelated Caucasian individuals were recruited between 2014 and 2020 characterized in Table 2. The selection of participants for the study and control groups was based on the completed International Physical Activity Questionnaire (IPAQ). Most individuals were engaged in endurance disciplines, primarily long-distance running. Additionally, a survey was conducted regarding injuries, the circumstances of their occurrence, and general health conditions. All participants were Polish Caucasians to ensure that there was no ethnicity skew and to avoid potential problems of population stratification. The study group comprised 142 physically active participants (29 females and 113 males) with a self-reported history of non-contact muscle injuries, whose characteristics are presented in Table 2. The most frequent muscle injuries involved the thigh (61.9%), foot (37.3%), calf (25.2%), upper limb girdle (19%), and hip (6.3%). In addition, 28% of the study group reported other injuries, including fractures, dislocations, and sprains. The control group consisted of 133 healthy, unrelated individuals (47 females and 86 males) with no reported history of non-contact muscle injuries. The chosen body mass and composition parameters were assessed with the bioimpedance method using an electronic scale Tanita MC-780 P MA (Arlington Heights, Illinois, United States). Table 2. Characteristics of the study participants Group Number of participants Number of men Number of women Average age Average height Average body weight BMI Main groups Control group 133 86 47 37.2 174 71.7 23.5 Study group 142 113 29 36.1 176 74.2 23.9 Division according to the number of injuries Single 26 19 7 33.6 174 72.5 23.8 Multiple 116 94 22 35.9 176 74.6 23.9 Division by type of injury * Muscle partial rupture or strain 137 109 28 36.0 176 74.1 23.9 Complete muscle rupture 5 4 1 35.4 175 74.4 23.8 Muscle inflammation 2 2 0 32.0 184 78.5 23.1 Other injury 56 45 11 35.6 176 74.6 24.1 * A single participant may be classified into more than one group in the case of multiple injuries; values are presented to three significant figures. Genetic Analyses Genomic DNA was isolated from buccal swabs (Copan FLOQSwabs, Interpath, Murrieta, Australia) collected from each participant, using the High Pure PCR Template Preparation Kit (Roche, Basel, Switzerland) according to the manufacturer’s instructions. Genotyping was performed in duplicate with TaqMan® Pre-Designed SNP Genotyping Assays (Applied Biosystems, Waltham, MA, USA) on a C1000 Touch Thermal Cycler (Bio-Rad, Hercules, CA, USA). The corresponding assay IDs are listed in Table 3. Each reaction mixture consisted of 2.5 μL TaqPath™ ProAmp™ Master Mix (Applied Biosystems), 0.25 μL assay mix (10×), 1 μL distilled water, and 1.25 μL genomic DNA. PCR cycling conditions were: pre-read at 60°C for 30 s; initial denaturation at 95°C for 5 min; 40 cycles of 95°C for 5 s and 60°C for 30 s (primer annealing/extension); and a final extension at 60°C for 30 s. Fluorescence signals were collected and analyzed with CFX Maestro 4.0 Software (Bio-Rad). Table 3. SNP genotyping assays used in the study Gene Polymorphism Assay ID BGN rs1042103 C___8898142_10 DCN rs13312816 C__32613401_10 rs516115 C___2309580_10 ELN rs2071307 C___1253630_1_ FBN2 rs331079 C___1561675_10 MMP1 rs1799750 C__34384693_10 MMP10 rs486055 C____632734_30 MMP12 rs2276109 C__15880589_10 Statistical Analyses All of the analyses were conducted in R (v. 4.4.1). The association and interaction analysis of polymorphisms and haplotypes with the occurrence of muscle injuries, as well as the analysis of variant frequencies and compliance with Hardy‒Weinberg equilibrium (HWE), were conducted using the SNPassoc library (version 2.1-0). Prior to the analysis of polymorphisms, the control group was compared with the study group with respect to additional factors that could interact with genetic variants (age, sex, body weight, height, and body mass index (BMI)), referred to as covariates. Numerical covariates were compared using Student’s t-test, while categorical variables (sex) were compared with the chi-square test. Covariates were included in the primary association models if (1) their values differed significantly between the control and study groups (nominal p < 0.05) or (2) they were significantly associated with any of the polymorphisms. Criterion (1) was met by sex, whereas criterion (2) was met by sex, body height, body weight, and BMI. As body height and weight are components of BMI, sex and BMI were selected as covariates to avoid overadjustment. One of the analysed polymorphisms is located on the X chromosome. For the purposes of the analysis, males were classified as homozygotes, and sex was included as a covariate in all the models (Clayton, 2008). Analyses were conducted only for subgroups containing more than 10 participants. For injury type, comparisons were performed only against the control group, as many participants experienced more than one injury (resulting in partial overlap between injury-type subgroups). Associations of polymorphisms were assessed using generalized linear models, and interactions were tested using the log-likelihood ratio (LTR) test, with sex and BMI included as covariates. Variant pairs showing nominally significant interactions were additionally analysed using generalized linear models separately for each sex (as one of the variants in the pair is located on the X chromosome). Haplotype analysis was performed for variants within the DNC gene and the MMP gene cluster using the haplo.stats package (v1.9.7) and consisted of two stages. First, the most likely haplotypes were inferred using the expectation–maximization (EM) algorithm. In the subsequent step, associations between each haplotype and muscle injuries were assessed using generalized linear models (GLMs). For all analyses, both nominal p values and p values adjusted for multiple testing using the Bonferroni correction were reported (assuming 8 comparisons per analysis, except for interaction analyses, where 28 comparisons were made). We planned the statistical power using the genetic power calculator (GPC), a widely used method for case–control genetic studies (Purcell et al., 2003). With 142 cases and 133 controls (total n = 275), two-sided α = 0.05, and risk-allele frequencies between 0.20 and 0.30, GPC evaluations under additive and dominant models indicated that the study attained ≥80% power to detect moderate associations corresponding to odds ratios of ~1.5–2.0—parameters aligned with our a priori hypotheses and with methodological guidance for common variants. This approach follows the standard practice of targeting ~80% power in biomedical research while recognizing model-dependent efficiency (e.g., trend test-based derivations and comparative evaluations) in case–control designs. Together, these considerations support that our design is sufficiently powered to detect the anticipated effects in this allele frequency range (Bacchetti, 2010; Dorey, 2011). Results An initial analysis was performed to compare the control and study groups with respect to potential covariates. The results revealed that sex was the only factor that differed significantly in frequency between the groups. The full statistical results are presented in Supplementary Table 1. For each allele in both groups, HWE testing was performed (Supplementary Table 2). For variants located on autosomal chromosomes, no significant deviations from HWE were observed in the overall cohort or in the two main subgroups. A substantial deviation was noted only for rs1042103, which is explained by the localization of this polymorphism on the X chromosome. Among female participants, no deviation was detected (p = 0.999). For rs13312816, the HWE test was not performed because of the absence of AA homozygotes in the study sample. Analysis of individual SNPs The genotype frequencies of the analysed polymorphisms are presented in Supplementary Figure 1. For each variant, association analyses were performed under several inheritance models, including dominant, recessive, codominant, overdominant, and additive (log-additive). The results with a nominal p value < 0.05 are presented in Table 4. Associations were assessed using generalized linear models (GLMs), with sex and BMI incorporated as covariates. Table 4. Selected results of associations between the studied variants and muscle injuries. SNP rs331079 Comparison Control group vs. individuals with a single injury Model p-value Adjusted p-value Genotype Odds ratio Lower CI Upper CI dominant 0.028 0.223 G/G 1 G/C-C/C 3.45 1.2 9.95 overdominant 0.021 0.17 G/G-C/C 1 G/C 3.72 1.27 10.84 additive 0.047 0.372 0,1,2 2.84 1.06 7.6 SNP rs2071307 comparison control group vs. individuals with other injuries (fractures, dislocations, sprains) overdominant 0.041 0.329 G/G-A/A 1 A/G 0.51 0.26 0.98 SNP rs2276109 comparison control group vs. individuals with injuries recessive 0.033 0.266 A/A-A/G 1 G/G 7.10 0.82 61.54 SNP rs2276109 comparison control group vs. individuals with a single injury recessive 0.046 0.366 A/A-A/G 1 G/G 11.54 0.99 134.72 SNP rs2276109 comparison control group vs. individuals with muscle strains or tears recessive 0.029 0.238 A/A-A/G 1 G/G 7.37 0.85 63.91 SNP rs516115 comparison control group vs. individuals with two-injury codominant 0.042 0.332 A/A 1 A/G 1.82 1.07 3.12 G/G 0.57 0.14 2.30 overdominant 0.017 0.135 A/A-G/G 1 A/G 1.90 1.12 3.22 SNP rs516115 comparison control group vs. individuals with muscle strains or tears overdominant 0.037 0.295 A/A-G/G 1 A/G 1.72 1.03 2.86 SNP rs516115 comparison control group vs. individuals with other injuries (fractures, dislocations, sprains) dominant 0.022 0.174 A/A 1 A/G-G/G 2.15 1.11 4.15 overdominant 0.018 0.146 A/A-G/G 1 A/G 2.21 1.14 4.28 SNP rs1042103 comparison control group vs. individuals with injuries codominant 0.028 0.227 G/G 1 A/G 3.47 1.27 9.47 A/A 1.56 0.9 2.72 dominant 0.031 0.249 G/G 1 A/G-A/A 1.78 1.05 3.01 overdominant 0.032 0.254 G/G – A/A 1 A/G 2.81 1.07 7.37 SNP rs1042103 comparison control group vs. individuals with a single injury codominant 0.022 0.175 G/G 1 A/G 14.31 1.42 144.1 A/A 2.12 0.78 5.75 overdominant 0.018 0.151 G/G-A/A 1 A/G 9.07 1.01 81.87 SNP rs1042103 comparison control group vs. individuals with muscle strains or tears codominant 0.034 0.27 G/G 1 A/G 3.32 1.21 9.11 A/A 1.6 0.91 2.79 dominant 0.030 0.23 G/G 1 A/G-A/A 1.79 1.05 3.05 overdominant 0.044 0.35 G/G-A/A 1 A/G 2.66 1.01 7.02 SNP rs1042103 comparison control group vs. individuals with other injuries (fractures, dislocations, sprains) dominant 0.030 0.24 G/G 1 A/G-A/A 2.11 1.07 4.17 additive 0.044 0.35 0,1,2 1.43 1.01 2.01 * Comparisons yielding nominal p-values < 0.05 are shown; odds ratios with 95% confidence intervals (CI) were estimated using generalized linear models. Haplotype analysis Among the analysed polymorphisms, two were located within the DCN gene locus (rs516115 and rs13312816). The frequency of each haplotype, along with the odds ratio and p value for the comparison between the control and injury groups, is presented in Supplementary Table 3. Three polymorphisms (rs1799750, rs486055, and rs2276109) were located in close proximity within genes of the MMP family. For these variants, haplotype association analysis with injury occurrence was also performed. The results are shown in Supplementary Table 4. No statistically significant differences were observed in any of the haplotype comparisons. Epistasis analysis For the primary comparison, an interaction analysis between the studied polymorphisms was also performed (Figure 1). The analysis was conducted under a codominant model adjusted for sex and BMI. In this model, the odds ratio is calculated separately for heterozygotes and for homozygotes of the minor allele. For the pair of polymorphisms, rs13312816 and rs1042103, which yielded a p value of ~0.0044, the full results of the interaction model are presented in Table 5. Individuals carrying the rs1042103/rs13312816 AG/TT and AA/AT genotype combinations had 5.44-fold and 4.24-fold greater likelihoods, respectively, of belonging to the injury group. Since rs1042103 is located on the X chromosome, the same analysis was additionally performed separately for each sex, revealing that the epistatic effect was nominally significant in women. Table 5. Interaction analysis of rs13312816 and rs1042103 (codominant model, control vs. injury group). Interaction analysis (p = 0.0044; Bonferroni-corrected p = 0.12) Genotype rs1042103 Genotype rs13312816 Control group (n) Study group (n) Odds ratio Lower 95% CI Upper 95% CI GG TT 68 59 1 AG TT 14 19 5.44 1.88 15.8 AA TT 33 39 1.46 0.80 2.67 GG AT 8 11 1.66 0.61 4.55 AG AT 5 0 0 0 NA AA AT 4 11 4.24 1.18 15.19 Interaction analysis in women (p = 0.0064; Bonferroni-adjusted p = 0.179) Genotype rs1042103 Genotype rs13312816 Control group (n) Study group (n) Odds ratio Lower 95% CI Upper 95% CI GG TT 11 7 1 AG TT 14 19 2.15 0.65 7.11 AA TT 12 1 0,15 0.02 1.42 GG AT 3 0 0 0 NA AG AT 5 0 0 0 NA AA AT 2 2 1.13 0.12 10.46 Interaction analysis in men (p = 0.91; Bonferroni-adjusted p = 1) Genotype rs1042103 Genotype rs13312816 Control group (n) Study group (n) Odds ratio Lower 95% CI Upper 95% CI GG TT 57 52 1 AG TT 0 0 NA NA NA AA TT 21 38 1.91 0.99 3.68 GG AT 5 11 2.34 0.76 7.22 AG AT 0 0 NA NA NA AA AT 2 9 5.02 1.03 24.39 * The analysis was performed using a generalized linear model, with sex and BMI included as covariates in the overall analysis, and BMI alone in the sex-stratified analysis. Discussion Polymorphisms in genes encoding components of the ECM are responsible for a wide variety of inherited musculoskeletal soft tissue disorders. Identifying these SNPs and linking them to specific pathological outcomes has greatly advanced the understanding of ECM component functions. The clinical impact of ECM protein changes depends on their tissue-specific distribution, function, and the specific type of variation. Currently, two main molecular mechanisms are recognized. The first involves polymorphisms that decrease the production of ECM proteins, leading to compromised matrix integrity due to reduced protein levels. The second includes polymorphisms that alter protein structure, which may not only hinder proper secretion but also exert negative effects, disrupting ECM assembly, architecture, or stability (Lamandé & Bateman, 2020). Given the crucial role of the ECM in maintaining tissue resilient force transmission, muscle mass, and the regenerative ability of skeletal muscle, studying genes may be highly relevant for understanding muscle injury susceptibility in physically active individuals (Ge et al., 2025); however, this association remains largely unexplored. Therefore, this research reports a unique analysis of a large set of SNP genotypes in the most promising positional and/or functional candidate genes for muscle injuries of the ECM pathway. The main finding was specific variants in FBN2 (rs331079), MMP12 (rs2276109), DCN (rs516115), and BGN (rs1042103), indicating that certain genotypes may either increase susceptibility or exert a protective effect in a physically active Caucasian population. Specifically, individuals carrying (i) the GC and CC genotypes of FBN2 rs331079, (ii) the GG genotype of MMP12 rs2276109, (iii) the AG and AA genotypes of BGN rs1042103, (iv) and the AG genotype of DCN rs516115 had an increased risk of muscle injury, whereas the GG genotype appeared to exert a protective effect relative to AA. In contrast, no significant results were obtained for the remaining polymorphisms. Additionally, no significant associations were detected for the DCN and MMP haplotypes; however, a novel epistatic interaction between DCN (rs13312816) and BGN (rs1042103) was identified. Participants who carried the AG/TT and AA/AT genotype combinations had a higher probability of belonging to the injury group, with the risk being approximately five times greater. This interaction effect appeared to be driven mainly by female participants. To our knowledge, this is the first study to assess the associations between genotypes, haplotypes, gene–gene interactions, and susceptibility to muscle injury, specifically by examining 8 polymorphisms in ECM-related genes. Therefore, the obtained results cannot be directly compared to those of other studies. Previously, the intronic FBN2 rs331079 polymorphism was associated with an increased risk of both AT and ACL ruptures during physical activity in Caucasian participants from Australia and South Africa. Specifically, individuals carrying the G allele or the GG genotype were approximately twice as likely to develop these tendinopathies. This polymorphism has also been shown to be associated with intracranial aneurysms in a Dutch Caucasian population. However, in this study, the C allele was shown to be a risk factor as opposed to the G allele (Ruigrok et al., 2006), which corroborates our findings indicating that carriers of the GC and CC genotypes have a greater risk of muscle injury. It is important to recognize that muscle function cannot be considered in isolation from tendon properties. The muscle–tendon unit operates as a mechanical continuum, in which tendon stiffness and elasticity directly influence the magnitude and rate of force transmission during physical activity. Previous studies have shown that increased FBN2 expression may increase tendon density and stiffness, potentially reducing tendon compliance during muscle contraction (Cook, 1996). Such alterations could impair the ability of the musculotendinous junction to buffer rapid or excessive loading, thereby increasing the likelihood of muscle strain. Conversely, reduced FBN2 levels may weaken the microfibrillar network within tendons (P. N. Robinson & Godfrey, 2000), compromising structural integrity and limiting their capacity to withstand repetitive stress. Given that FBN2 expression is upregulated during tendon repair (Jelinsky et al., 2011), it is plausible that genetic variation in FBN2 may modulate individual susceptibility to muscle injury through its impact on tendon resilience. This highlights the need to consider ECM-related genes not only in the context of tendon disorders but also as potential contributors to muscle injury risk in physically active populations. Previous studies have suggested that genetic variants within MMP genes are associated with numerous complex disorders, such as a higher risk of ACL rupture (Posthumus et al., 2012). Posthumus et al. (2012) reported that the AG and GG genotypes of the promoter MMP12 rs2276109 variant were significantly underrepresented among participants with noncontact ACL rupture compared with the control group, whereas no other MMP1 , MMP3 ,or MMP10 SNPs were significantly different between groups. Additionally, the two four-variant haplotypes, T-1G-A-A and C-2G-G-G ( MMP10 rs486055, MMP1 rs1799750, MMP3 rs679620, and MMP12 rs2276109, respectively), were significantly different between the controls and the ACL rupture subgroup and between the controls and the noncontact ACL rupture subgroup. The authors suggested that the low-risk haplotype combinations may be associated with the presence of the G alleles for the MMP3 rs679620 and MMP12 rs2276109 variants (Posthumus et al., 2012). In contrast, Lulińska-Kuklik et al. (2019) reported significant underrepresentation of the MMP3 rs679620 G allele in the control group compared with the group of participants reporting noncontact ACL ruptures, suggesting that the G allele is associated with an increased risk for this injury. Further haplotype analysis revealed the G-C ( MMP3 rs679620 and rs591058, respectively) inferred haplotype as a potential risk factor linked to noncontact ACL ruptures (Lulińska-Kuklik et al., 2019). However, in a subsequent study, Lulińska et al. (2020) did not confirm this association of single polymorphisms or haplotypes of the MMP1 , MMP10 , and MMP12 genes with noncontact ACL rupture in a Polish cohort (Lulińska et al., 2020). Our results indicated that the GG genotype of MMP12 rs2276109 may be a risk factor for muscle injury; however, no significant associations were detected for other MMP1 and MMP10 SNPs or for MMP haplotypes. Compared with the G allele, the A allele of the MMP12 rs2276109 polymorphism has been linked to increased affinity for transcription factor activator protein-1 (AP-1) and higher gene expression (Jormsjö et al., 2000). However, whether the over- or underproduction of MMPs is involved in injury risk is not known. A region overlapping the BGN gene has been linked to soft-tissue injury risk, most notably ACL rupture (Ciȩszczyk et al., 2017; Mannion et al., 2014), suggesting its possible relevance to muscle injury due to shared ECM pathways. As BGN is located on the X chromosome and sex is a known intrinsic factor in muscle injury susceptibility (Lin et al., 2018), sex-specific effects of BGN variants should also be considered. Although Mannion et al. (2014) reported no significant associations between the BGN rs1126499 or rs1042103 variants and the risk of ACL injury in either men or women when analysed individually, the C–G haplotype (rs1126499 and rs1042103, respectively) was linked to a reduced risk of ACL injury in female participants (Mannion et al., 2014). Another study further supported the involvement of the BGN rs1042103 polymorphism in genetic susceptibility to ACL rupture. Cięszczyk et al. (2017) reported that the A allele was associated with an increased risk of ACL rupture in Polish males (Ciȩszczyk et al., 2017). Our findings are consistent with these observations, as we also confirmed the role of this SNP in sport-related injury susceptibility; specifically, the AG and AA genotypes of BGN rs1042103 were associated with an increased risk of muscle injury. This polymorphism is a missense variant, resulting in a threonine-to-alanine substitution at codon 209 (p.Thr209Ala). This amino acid change may alter the structure or function of biglycan, potentially by modifying its interaction with collagen fibrils and growth factors. Such alterations could affect extracellular matrix organization and mechanotransduction within the muscle–tendon unit, thereby influencing tissue resilience under mechanical loading (Robinson et al., 2017). Polymorphisms in DCN may be additional factors influencing collagen fibril formation and matrix assembly, thereby contributing to the regulation of musculoskeletal soft tissue homeostasis (Robinson et al., 2017). Previously, the GG genotype of DCN rs516115 was found to be significantly more frequent in female controls than in the ACL injury group, whereas the AA genotype was underrepresented among controls compared with the female noncontact ACL injury subgroup. These findings suggest that the A allele may act as a risk factor for injury development (Mannion et al., 2014). However, Cięszczyk et al. did not replicate this finding, as no significant association was observed between the distribution of the rs516115 genotype and the risk of ACL rupture. Nonetheless, a trend was noted indicating that the AG genotype might be linked to a reduced likelihood of ACL injury (Ciȩszczyk et al., 2017). In the present study, our findings contrasted with those of Cięszczyk et al. (2017), as we observed that the AG genotype was associated with an increased risk of muscle injury, whereas the GG genotype appeared to confer a protective effect compared with AA, which aligns more closely with the results reported by Mannion et al. (2014). A plausible explanation for these divergent findings is that the rs516115 polymorphism may affect decorin expression or mRNA stability, thereby altering collagen fibrillogenesis and matrix organization (Robinson et al., 2017). Thus, even subtle functional changes resulting from genetic variation may have tissue-specific effects. In ligament-dominant structures such as the ACL, reduced decorin levels could increase compliance and lower rupture risk, potentially explaining the protective trend observed for the AG and GG genotypes. Conversely, at the muscle–tendon interface, decreased decorin activity may compromise microstructural stability and impair repair mechanisms, thereby increasing susceptibility to muscle injury. In addition, the novel epistatic analysis conducted in this study revealed a significant interaction between the DCN rs13312816 and BGN rs1042103 polymorphisms. Individuals carrying the AG/TT or AA/AT genotype combinations showed a markedly increased likelihood of muscle injury. This combined effect may reflect the complementary roles of decorin and biglycan in extracellular matrix organization; simultaneous alterations in both proteins could synergistically weaken collagen architecture or impair mechanotransduction, thereby reducing tissue resilience under mechanical loading (Robinson et al., 2017). To our knowledge, this is the first report of a potential epistatic interaction between DCN and BGN variants in relation to sports injury susceptibility, indicating that compared with single-gene effects alone, combined dysregulation of these ECM pathways may have a greater effect on tissue stability. In our study, the analysed polymorphisms within the ELN , MMP1 , and MMP10 genes were not significantly associated with susceptibility to sport-related muscle injury. The genotype distributions did not differ between injured and noninjured individuals, suggesting that these variants are unlikely to play a major role in modulating individual risk within the studied cohort. This observation is consistent with previous findings reported by Lulińska et al. (2020), who also reported no significant associations between MMP1 (rs1799750) and MMP10 (rs486055) variants and susceptibility to noncontact ACL injuries according to various genetic models (p > 0.05) (Lulińska et al., 2020). Similarly, other studies exploring ELN gene polymorphisms have not demonstrated a clear relationship with sport-related musculoskeletal injury risk, further supporting the notion that extracellular matrix gene variants may have a limited contribution to the genetic background of soft-tissue injury susceptibility. Nevertheless, some studies have reported significant associations between these genes and injury risk or severity. Variants of the ELN gene were linked to ligament injury severity, recovery time in elite soccer players (Pruna et al., 2013), and ACL rupture (Lulińska et al., 2023), whereas promoter polymorphisms in MMP1 were associated with tendon pathology and rotator cuff tears (Assunção et al., 2017; Baroneza et al., 2014). Although no consistent associations have been reported for MMP10 polymorphisms, altered MMP10 expression has been observed in injured tendon tissue, suggesting its potential involvement in extracellular matrix remodelling and repair following injury (Minkwitz et al., 2017). Differences in study design, injury type, sample size, and population structure may partly explain these inconsistencies across studies. This study has both strengths and limitations. Although the sample size (n = 275) is adequate in biomedical research for detecting moderate genetic effects and aligns with previous sports injury genetics research (Bacchetti, 2010; Dorey, 2011), larger cohorts would improve the statistical power, especially for subgroup and interaction analyses. The use of self-reported injury data is another limitation, as it may introduce recall bias. However, a major strength of this work is its focus on muscle injuries, which remain underrepresented in genetic studies despite being among the most common causes of time loss in sports. By analysing 8 polymorphisms across 7 ECM-related genes using single- locus , haplotype, and gene–gene interaction approaches, this study offers a more comprehensive view of the genetic architecture underlying muscle injury risk than traditional single-variant analyses do. Conclusions This study provides new evidence that genetic variation in ECM-related genes influences susceptibility to sport-related muscle injury. Variants in FBN2 , MMP12 , BGN , and DCN were associated with either increased risk or protection, indicating that ECM integrity plays a key role in maintaining soft-tissue resilience under mechanical loading. Importantly, we report the first evidence of an interaction between DCN and BGN polymorphisms, suggesting that the combined effects of ECM-related genes may be more informative than single- locus analyses. By shifting attention from ligament and tendon injury, which dominate the current literature, to muscle injury, this study highlights an understudied yet clinically relevant phenotype. Although replication in larger, clinically verified cohorts is needed, these findings support the growing concept that polygenic ECM profiles may help identify athletes at elevated injury risk and provide directions for future personalized preventive strategies. Abbreviations ACL – Anterior Cruciate Ligament AP-1 – Activator Protein-1 AT – Achilles Tendinopathy BMI – Body Mass Index BMP – Bone Morphogenetic Protein DCN – Decorin BGN – Biglycan ECM – Extracellular Matrix EGFR – Epidermal Growth Factor Receptor ELN – Elastin FBN2 – Fibrillin-2 GLM – Generalized Linear Model HWE – Hardy–Weinberg Equilibrium IPAQ – International Physical Activity Questionnaire MMP – Matrix Metalloproteinase MMP1– Matrix Metalloproteinase 1 MMP10– Matrix Metalloproteinase 10 MMP12– Matrix Metalloproteinase 12 PCR- Polymerase Chain Reaction TGF-β- Transforming Growth Factor-beta Declarations Ethics approval Board Statement: The Bioethics Committee at the District Medical Chamber in Gdansk approved the study (No. KB-8/22). The investigation protocols were conducted ethically according to the World Medical Association Declaration of Helsinki and to the Strengthening the Reporting of Genetic Association studies statement (STREGA). Consent to participate Informed consent was obtained from all individual participants included in the study . Consent for publication Not applicable Competing interests The authors declare that they have no competing interests Funding This research was funded by Podkarpackie Innovation Center under the grant program for R&D works of scientific units under the Regional Operational Program of the Podkarpackie Voivodeship for 2014-2020, grant number N3_624 entitled "Prevention of musculoskeletal system injuries among physically active people from the Podkarpackie region based on the assessment of the risk of occurrence of various types of collagen polymorphisms". Authors' contributions P.K., A.R-B., and A.L-D. contributed to study design, data collection, statistical analysis, data interpretation, manuscript preparation, and literature search. All authors reviewed and approved the final manuscript. 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It is primarily composed of collagens, elastin, fibronectin, laminins, proteoglycans, and other glycoproteins, forming a three-dimensional network that provides support for cellular interactions. Through binding to cell surface receptors, ECM components regulate fundamental biological processes, including cell proliferation, adhesion, migration, and differentiation (Ahmad et al., 2020; Ge et al., 2025; Theocharis et al., 2016). Specifically, in skeletal muscle, these molecules and their transmembrane partners support tissue integrity, facilitate contraction and growth, promote regeneration, mediate biochemical signalling, and contribute to overall development.\u003c/p\u003e\n\u003cp\u003eStructural ECM components, such as decorin (DCN) and biglycan (BGN), regulate collagen fibrillogenesis, influence tendon and ligament biomechanics, and modulate signalling pathways, including those involving transforming growth factor-beta (TGF-β) (Robinson et al., 2017). Similarly, elastin (ELN) and fibrillin-2 (FBN2) are essential for elastic fibre assembly and connective tissue resilience (Gardeazabal \u0026amp; Izeta, 2024; Khoury et al., 2015a). Matrix metalloproteinases (MMPs), including MMP1, MMP10, and MMP12, contribute to ECM turnover and tissue remodelling, thereby influencing the balance between matrix degradation and repair, as well as the regulation of inflammation (Cui et al., 2017; Kumar et al., 2022; Vargová et al., 2012). Alterations in ECM structure or function are closely linked to musculoskeletal pathologies, and dysregulation of ECM components contributes to the risk and progression of musculoskeletal injuries (Ahmad et al., 2023; Lamandé \u0026amp; Bateman, 2020).\u003c/p\u003e\n\u003cp\u003eGenetic variation in ECM-related genes, particularly single nucleotide polymorphisms (SNPs), can influence protein expression or activity, thereby altering connective tissue properties, repair efficiency, and overall resilience (Ahmad et al., 2020; Suijkerbuijk et al., 2019). In physically active individuals, who are frequently exposed to repetitive mechanical loading and microtrauma, such genetic differences can modulate susceptibility to muscle injuries and related conditions, including ligament ruptures, sprains, and tendon damage. Following muscle fibre impairment, the ECM undergoes rapid remodelling characterized by the degradation of damaged matrix components and the synthesis of new components, which together provide the structural environment for repair. When this process is dysregulated, excessive or abnormal remodelling may lead to fibrosis, whereas persistent ECM alterations have been linked to muscle atrophy (Ge et al., 2025). Thus, understanding the association between ECM gene variants and injury risk may contribute to identifying genetic predispositions, improving preventive strategies, and developing personalized approaches to training and rehabilitation (Suijkerbuijk et al., 2019).\u003c/p\u003e\n\u003cp\u003eMost studies on the genetic determinants of musculoskeletal injuries have focused on tendon and ligament pathology, particularly Achilles tendinopathy (AT) and anterior cruciate ligament (ACL) rupture (Rahim et al., 2016). In contrast, muscle injuries, which account for approximately 25% of all sport-related injuries (Ekstrand et al., 2011), remain relatively underexplored despite their high incidence and impact on athletes. This imbalance has limited our understanding of the molecular factors contributing specifically to muscle damage and impaired regeneration. Furthermore, the majority of existing research has focused on single candidate polymorphisms, often without considering the cumulative effects of multiple variants. However, the biological processes underlying tissue structure, repair, and adaptation are polygenic and highly interactive (Pompili et al., 2021). Thus, studies that simultaneously examine multiple SNPs and their potential gene–gene interactions are likely to provide the most informative insights into genetic susceptibility to musculoskeletal injuries, including those affecting skeletal muscle.\u003c/p\u003e\n\u003cp\u003eIn the present study, eight polymorphisms located in genes encoding key ECM components and remodelling enzymes were analysed: \u003cem\u003eBGN\u003c/em\u003e (rs1042103), \u003cem\u003eDCN\u003c/em\u003e (rs13312816 and rs516115), \u003cem\u003eELN\u0026nbsp;\u003c/em\u003e(rs2071307), \u003cem\u003eFBN2\u003c/em\u003e (rs331079), \u003cem\u003eMMP1\u003c/em\u003e (rs1799750), \u003cem\u003eMMP10\u003c/em\u003e (rs486055), and \u003cem\u003eMMP12\u003c/em\u003e (rs2276109). An overview of the studied genes, their polymorphisms, and the biological functions of the encoded proteins is provided in Table 1. The aim of this study was to investigate whether these SNPs, either individually or in combination, are associated with susceptibility to muscle injury in a physically active Caucasian cohort. We hypothesized that specific genotypes of the selected genes would be individually associated with increased musculoskeletal injury risk and that interactions among the analysed SNPs would generate genotype combinations predisposing carriers to a greater likelihood of injury.\u003c/p\u003e\n\u003cp\u003eTable 1. Characteristics of the studied genes and polymorphic sites.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGene Product\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFunctions of the Protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eBGN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eXq28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers1042103,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eA/G, p.Thr209Ala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Exon 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBiglycan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCollagen fibril assembly and ECM organization;\u003c/p\u003e\n \u003cp\u003eBone growth and skeletal development;\u003c/p\u003e\n \u003cp\u003eMuscle regeneration;\u003c/p\u003e\n \u003cp\u003eModulation of TGF-β and BMP signaling;\u003c/p\u003e\n \u003cp\u003eRegulation of inflammation and innate immune response;\u003c/p\u003e\n \u003cp\u003eContribution to atherosclerosis and aortic valve stenosis;\u003c/p\u003e\n \u003cp\u003eInvolvement in cancer-related processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Kram et al., 2020; Mannion et al., 2014; K. A. Robinson et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eDCN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e12q21.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers13312816, A/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntron 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eDecorin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCollagen fibril assembly and ECM organization;\u003c/p\u003e\n \u003cp\u003eBinding to multiple cell surface receptors;\u003c/p\u003e\n \u003cp\u003eModulation of TGF-β and EGFR signaling;\u003c/p\u003e\n \u003cp\u003eRegulation of inflammation and autophagy;\u003c/p\u003e\n \u003cp\u003eCancer-related processes;\u003c/p\u003e\n \u003cp\u003eImmune and inflammatory diseases;\u003c/p\u003e\n \u003cp\u003eRegulation of wound healing and fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e(Baghy et al., 2020, 2025; Dong et al., 2022; Mannion et al., 2014; K. A. Robinson et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ers516115,\u003c/p\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntron 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eELN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7q11.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers2071307,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eA/G, p.Ser422Gly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExon 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eElastin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eElastic fiber formation and ECM organization;\u003c/p\u003e\n \u003cp\u003eMaintenance of tissue elasticity (skin, lungs, arteries);\u003c/p\u003e\n \u003cp\u003eRegulation of vascular development and arterial wall integrity;\u003c/p\u003e\n \u003cp\u003eContribution to pulmonary and skin function;\u003c/p\u003e\n \u003cp\u003eInvolvement in cardiovascular pathology (aneurysms, valve disease, hypertension);\u003c/p\u003e\n \u003cp\u003eRegulation of wound healing and fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Duque Lasio \u0026amp; Kozel, 2018; Gardeazabal \u0026amp; Izeta, 2024; Khoury et al., 2015b; C. J. Lin et al., 2022; Mithieux \u0026amp; Weiss, 2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eFBN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5q23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers331079,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntron 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFibrillin 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMicrofibril formation and ECM organization;\u003c/p\u003e\n \u003cp\u003eRegulation of elastic fiber assembly;\u003c/p\u003e\n \u003cp\u003eConnective tissue and skeletal development;\u003c/p\u003e\n \u003cp\u003eLimb morphogenesis;\u003c/p\u003e\n \u003cp\u003eModulation of TGF-β/BMP signaling;\u003c/p\u003e\n \u003cp\u003eInvolvement in tendon and ligament integrity;\u003c/p\u003e\n \u003cp\u003eAssociation with congenital contractural arachnodactyly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Khoury et al., 2015b; Ramirez \u0026amp; Pereira, 1999; Ramirez \u0026amp; Sakai, 2010; Ruigrok et al., 2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMMP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11q22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers1799750,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC/-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePromoter region\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMatrix metallopeptidase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCollagen degradation (types I, II, III);\u003c/p\u003e\n \u003cp\u003eECM remodeling and turnover;\u003c/p\u003e\n \u003cp\u003eWound healing and tissue repair;\u003c/p\u003e\n \u003cp\u003eRegulation of inflammation;\u003c/p\u003e\n \u003cp\u003ePromotion of cell migration and angiogenesis;\u003c/p\u003e\n \u003cp\u003eCancer-related processes;\u003c/p\u003e\n \u003cp\u003eRole in degenerative and cardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e(Cui et al., 2017; Kumar et al., 2022; Posthumus et al., 2012; Vargová et al., 2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMMP10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11q22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers486055,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePromoter region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMatrix metallopeptidase 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eECM degradation (proteoglycans, laminin, fibronectin, collagens);\u003c/p\u003e\n \u003cp\u003eActivation of other MMPs;\u003c/p\u003e\n \u003cp\u003eTissue remodeling and wound healing;\u003c/p\u003e\n \u003cp\u003eRegulation of inflammation;\u003c/p\u003e\n \u003cp\u003ePromotion of cell migration and angiogenesis;\u003c/p\u003e\n \u003cp\u003eInvolvement in tumor progression and metastasis;\u003c/p\u003e\n \u003cp\u003eRole in cardiovascular and inflammatory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMMP12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11q22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers2276109,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePromoter region\u003c/p\u003e\n \u003cp\u003e(AP-1 binding site)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMatrix metallopeptidase 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eElastin degradation and ECM remodeling;\u003c/p\u003e\n \u003cp\u003eRegulation of tissue repair and wound healing;\u003c/p\u003e\n \u003cp\u003eModulation of inflammation (macrophage-derived);\u003c/p\u003e\n \u003cp\u003eContribution to lung diseases (emphysema, COPD);\u003c/p\u003e\n \u003cp\u003eRole in atherosclerosis and vascular remodeling;\u003c/p\u003e\n \u003cp\u003eInvolvement in tumor progression and angiogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Methods","content":"\u003cp\u003eParticipants\u003c/p\u003e\n\u003cp\u003eThe Bioethics Committee at the District Medical Chamber in Gdansk (No. KB-8/22) approved the study. Initially, 812 individuals were assessed for eligibility. However, participants were excluded if they were underage, failed to submit complete documentation, did not provide consent for genetic testing, reported contact-related injuries, or did not meet the physical activity criteria. As a result, a total of 275 unrelated Caucasian individuals were recruited between 2014 and 2020\u0026nbsp;characterized in Table 2. The selection of participants for the study and control groups was based on the completed International Physical Activity Questionnaire (IPAQ). Most individuals were engaged in endurance disciplines, primarily long-distance running. Additionally, a survey was conducted regarding injuries, the circumstances of their occurrence, and general health conditions. All participants were Polish Caucasians to ensure that there was no ethnicity skew and to avoid potential problems of population stratification.\u003c/p\u003e\n\u003cp\u003eThe study group comprised 142 physically active participants (29 females and 113 males) with a self-reported history of non-contact muscle injuries, whose characteristics are presented in Table 2. The most frequent muscle injuries involved the thigh (61.9%), foot (37.3%), calf (25.2%), upper limb girdle (19%), and hip (6.3%). In addition, 28% of the study group reported other injuries, including fractures, dislocations, and sprains. The control group consisted of 133 healthy, unrelated individuals (47 females and 86 males) with no reported history of non-contact muscle injuries.\u003c/p\u003e\n\u003cp\u003eThe chosen body mass and composition parameters were assessed with the bioimpedance method using an electronic scale Tanita MC-780 P MA (Arlington Heights, Illinois, United States).\u003c/p\u003e\n\u003cp\u003eTable 2. Characteristics of the study participants\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of participants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of men\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of women\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAverage age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAverage height\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAverage body weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eMain groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStudy group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eDivision according to the number of injuries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eDivision by type of injury *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMuscle partial rupture or strain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eComplete muscle rupture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMuscle inflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* A single participant may be classified into more than one group in the case of multiple injuries; values are presented to three significant figures.\u003c/p\u003e\n\u003cp\u003eGenetic Analyses\u003c/p\u003e\n\u003cp\u003eGenomic DNA was isolated from buccal swabs (Copan FLOQSwabs, Interpath, Murrieta, Australia) collected from each participant, using the High Pure PCR Template Preparation Kit (Roche, Basel, Switzerland) according to the manufacturer\u0026rsquo;s instructions. Genotyping was performed in duplicate with TaqMan\u0026reg; Pre-Designed SNP Genotyping Assays (Applied Biosystems, Waltham, MA, USA) on a C1000 Touch Thermal Cycler (Bio-Rad, Hercules, CA, USA). The corresponding assay IDs are listed in Table 3. Each reaction mixture consisted of 2.5 \u0026mu;L TaqPath\u0026trade; ProAmp\u0026trade; Master Mix (Applied Biosystems), 0.25 \u0026mu;L assay mix (10\u0026times;), 1 \u0026mu;L distilled water, and 1.25 \u0026mu;L genomic DNA. PCR cycling conditions were: pre-read at 60\u0026deg;C for 30 s; initial denaturation at 95\u0026deg;C for 5 min; 40 cycles of 95\u0026deg;C for 5 s and 60\u0026deg;C for 30 s (primer annealing/extension); and a final extension at 60\u0026deg;C for 30 s. Fluorescence signals were collected and analyzed with CFX Maestro 4.0 Software (Bio-Rad).\u003c/p\u003e\n\u003cp\u003eTable 3. SNP genotyping assays used in the study\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"74%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePolymorphism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAssay ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eBGN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers1042103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC___8898142_10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eDCN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers13312816\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC__32613401_10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ers516115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC___2309580_10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eELN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers2071307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC___1253630_1_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eFBN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers331079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC___1561675_10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMMP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers1799750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC__34384693_10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMMP10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers486055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC____632734_30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMMP12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ers2276109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC__15880589_10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eStatistical Analyses\u003c/p\u003e\n\u003cp\u003eAll of the analyses were conducted in R (v. 4.4.1). The association and interaction analysis of polymorphisms and haplotypes with the occurrence of muscle injuries, as well as the analysis of variant frequencies and compliance with Hardy‒Weinberg equilibrium (HWE), were conducted using the SNPassoc library (version 2.1-0).\u003c/p\u003e\n\u003cp\u003ePrior to the analysis of polymorphisms, the control group was compared with the study group with respect to additional factors that could interact with genetic variants (age, sex, body weight, height, and body mass index (BMI)), referred to as covariates. Numerical covariates were compared using Student\u0026rsquo;s t-test, while categorical variables (sex) were compared with the chi-square test. Covariates were included in the primary association models if (1) their values differed significantly between the control and study groups (nominal p \u0026lt; 0.05) or (2) they were significantly associated with any of the polymorphisms. Criterion (1) was met by sex, whereas criterion (2) was met by sex, body height, body weight, and BMI. As body height and weight are components of BMI, sex and BMI were selected as covariates to avoid overadjustment. One of the analysed polymorphisms is located on the X chromosome. For the purposes of the analysis, males were classified as homozygotes, and sex was included as a covariate in all the models (Clayton, 2008).\u003c/p\u003e\n\u003cp\u003eAnalyses were conducted only for subgroups containing more than 10 participants. For injury type, comparisons were performed only against the control group, as many participants experienced more than one injury (resulting in partial overlap between injury-type subgroups). Associations of polymorphisms were assessed using generalized linear models, and interactions were tested using the log-likelihood ratio (LTR) test, with sex and BMI included as covariates. Variant pairs showing nominally significant interactions were additionally analysed using generalized linear models separately for each sex (as one of the variants in the pair is located on the X chromosome).\u003c/p\u003e\n\u003cp\u003eHaplotype analysis was performed for variants within the DNC gene and the MMP gene cluster using the haplo.stats package (v1.9.7) and consisted of two stages. First, the most likely haplotypes were inferred using the expectation\u0026ndash;maximization (EM) algorithm. In the subsequent step, associations between each haplotype and muscle injuries were assessed using generalized linear models (GLMs).\u003c/p\u003e\n\u003cp\u003eFor all analyses, both nominal p values and p values adjusted for multiple testing using the Bonferroni correction were reported (assuming 8 comparisons per analysis, except for interaction analyses, where 28 comparisons were made).\u003c/p\u003e\n\u003cp\u003eWe planned the statistical power using the genetic power calculator (GPC), a widely used method for case\u0026ndash;control genetic studies (Purcell et al., 2003). With 142 cases and 133 controls (total n = 275), two-sided \u0026alpha; = 0.05, and risk-allele frequencies between 0.20 and 0.30, GPC evaluations under additive and dominant models indicated that the study attained \u0026ge;80% power to detect moderate associations corresponding to odds ratios of ~1.5\u0026ndash;2.0\u0026mdash;parameters aligned with our a priori hypotheses and with methodological guidance for common variants. This approach follows the standard practice of targeting ~80% power in biomedical research while recognizing model-dependent efficiency (e.g., trend test-based derivations and comparative evaluations) in case\u0026ndash;control designs. Together, these considerations support that our design is sufficiently powered to detect the anticipated effects in this allele frequency range (Bacchetti, 2010; Dorey, 2011).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAn initial analysis was performed to compare the control and study groups with respect to potential covariates. The results revealed that sex was the only factor that differed significantly in frequency between the groups. The full statistical results are presented in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003eFor each allele in both groups, HWE testing was performed (Supplementary Table 2). For variants located on autosomal chromosomes, no significant deviations from HWE were observed in the overall cohort or in the two main subgroups. A substantial deviation was noted only for rs1042103, which is explained by the localization of this polymorphism on the X chromosome. Among female participants, no deviation was detected (p = 0.999). For rs13312816, the HWE test was not performed because of the absence of AA homozygotes in the study sample.\u003c/p\u003e\n\u003cp\u003eAnalysis of individual SNPs\u003c/p\u003e\n\u003cp\u003eThe genotype frequencies of the analysed polymorphisms are presented in Supplementary Figure 1. For each variant, association analyses were performed under several inheritance models, including dominant, recessive, codominant, overdominant, and additive (log-additive). The results with a nominal p value \u0026lt; 0.05 are presented in Table 4. Associations were assessed using generalized linear models (GLMs), with sex and BMI incorporated as covariates.\u003c/p\u003e\n\u003cp\u003eTable 4. Selected results of associations between the studied variants and muscle injuries.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers331079\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eComparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003eControl group vs. individuals with a single injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003edominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG/C-C/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eoverdominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G-C/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eadditive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers2071307\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with other injuries (fractures, dislocations, sprains)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eoverdominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G-A/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers2276109\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with injuries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003erecessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A-A/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers2276109\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with a single injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003erecessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A-A/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers2276109\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with muscle strains or tears\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003erecessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A-A/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers516115\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with two-injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003ecodominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eoverdominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers516115\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with muscle strains or tears\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eoverdominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers516115\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with other injuries (fractures, dislocations, sprains)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003edominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eoverdominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1042103\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with injuries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003ecodominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003edominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G-A/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eoverdominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G \u0026ndash; A/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1042103\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with a single injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003ecodominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e144.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eoverdominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G-A/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1042103\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with muscle strains or tears\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003ecodominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003edominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G-A/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eoverdominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G-A/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1042103\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecomparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003econtrol group vs. individuals with other injuries (fractures, dislocations, sprains)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003edominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA/G-A/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eadditive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Comparisons yielding nominal p-values \u0026lt; 0.05 are shown; odds ratios with 95% confidence intervals (CI) were estimated using generalized linear models.\u003c/p\u003e\n\u003cp\u003eHaplotype analysis\u003c/p\u003e\n\u003cp\u003eAmong the analysed polymorphisms, two were located within the \u003cem\u003eDCN\u003c/em\u003e gene \u003cem\u003elocus\u0026nbsp;\u003c/em\u003e(rs516115 and rs13312816). The frequency of each haplotype, along with the odds ratio and p value for the comparison between the control and injury groups, is presented in Supplementary Table 3. Three polymorphisms (rs1799750, rs486055, and rs2276109) were located in close proximity within genes of the \u003cem\u003eMMP\u003c/em\u003e family. For these variants, haplotype association analysis with injury occurrence was also performed. The results are shown in Supplementary Table 4. No statistically significant differences were observed in any of the haplotype comparisons.\u003c/p\u003e\n\u003cp\u003eEpistasis analysis\u003c/p\u003e\n\u003cp\u003eFor the primary comparison, an interaction analysis between the studied polymorphisms was also performed (Figure 1). The analysis was conducted under a codominant model adjusted for sex and BMI. In this model, the odds ratio is calculated separately for heterozygotes and for homozygotes of the minor allele. For the pair of polymorphisms, rs13312816 and rs1042103, which yielded a p value of ~0.0044, the full results of the interaction model are presented in Table 5. Individuals carrying the rs1042103/rs13312816 AG/TT and AA/AT genotype combinations had 5.44-fold and 4.24-fold greater likelihoods, respectively, of belonging to the injury group. Since rs1042103 is located on the X chromosome, the same analysis was additionally performed separately for each sex, revealing that the epistatic effect was nominally significant in women.\u003c/p\u003e\n\u003cp\u003eTable 5. Interaction analysis of rs13312816 and rs1042103 (codominant model, control vs. injury group).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003eInteraction analysis (p = 0.0044; Bonferroni-corrected p = 0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype rs1042103\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype rs13312816\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eControl group (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStudy group (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003eInteraction analysis in women (p = 0.0064; Bonferroni-adjusted p = 0.179)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype rs1042103\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype rs13312816\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eControl group (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStudy group (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003eInteraction analysis in men (p = 0.91; Bonferroni-adjusted p = 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype rs1042103\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype rs13312816\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eControl group (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStudy group (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* The analysis was performed using a generalized linear model, with sex and BMI included as covariates in the overall analysis, and BMI alone in the sex-stratified analysis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePolymorphisms in genes encoding components of the ECM are responsible for a wide variety of inherited musculoskeletal soft tissue disorders. Identifying these SNPs and linking them to specific pathological outcomes has greatly advanced the understanding of ECM component functions. The clinical impact of ECM protein changes depends on their tissue-specific distribution, function, and the specific type of variation. Currently, two main molecular mechanisms are recognized. The first involves polymorphisms that decrease the production of ECM proteins, leading to compromised matrix integrity due to reduced protein levels. The second includes polymorphisms that alter protein structure, which may not only hinder proper secretion but also exert negative effects, disrupting ECM assembly, architecture, or stability (Lamandé \u0026amp; Bateman, 2020). Given the crucial role of\u0026nbsp;the\u0026nbsp;ECM in maintaining tissue\u0026nbsp;resilient\u0026nbsp;force transmission,\u0026nbsp;muscle mass, and\u0026nbsp;the\u0026nbsp;regenerative\u0026nbsp;ability\u0026nbsp;of skeletal muscle,\u0026nbsp;studying genes may be highly relevant for understanding muscle injury susceptibility in physically active individuals (Ge et al., 2025); however, this association remains largely unexplored. Therefore, this research reports\u0026nbsp;a\u0026nbsp;unique analysis of a large set of SNP genotypes in the most promising positional and/or functional candidate genes for muscle injuries of the ECM pathway.\u003c/p\u003e\n\u003cp\u003eThe main finding was specific variants in \u003cem\u003eFBN2\u003c/em\u003e (rs331079), \u003cem\u003eMMP12\u003c/em\u003e (rs2276109), \u003cem\u003eDCN\u003c/em\u003e (rs516115), and \u003cem\u003eBGN\u003c/em\u003e (rs1042103), indicating that certain genotypes may either increase susceptibility or exert a protective effect in a physically active Caucasian population. Specifically, individuals carrying (i) the GC and CC genotypes of \u003cem\u003eFBN2\u003c/em\u003e rs331079, (ii) the GG genotype of \u003cem\u003eMMP12\u003c/em\u003e rs2276109, (iii) the AG and AA genotypes of \u003cem\u003eBGN\u003c/em\u003e rs1042103, (iv) and the AG genotype of \u003cem\u003eDCN\u003c/em\u003e rs516115 had an increased risk of muscle injury, whereas the GG genotype appeared to exert a protective effect relative to AA. In contrast, no significant results were obtained for the remaining polymorphisms. Additionally, no significant associations were detected for the \u003cem\u003eDCN\u003c/em\u003e and \u003cem\u003eMMP\u0026nbsp;\u003c/em\u003ehaplotypes; however, a novel epistatic interaction between \u003cem\u003eDCN\u003c/em\u003e (rs13312816) and \u003cem\u003eBGN\u003c/em\u003e (rs1042103) was identified. Participants who carried the AG/TT and AA/AT genotype combinations had a higher probability of belonging to the injury group, with the risk being approximately five times greater. This interaction effect appeared to be driven mainly by female participants.\u0026nbsp;To our knowledge, this is the first study to assess the\u0026nbsp;associations\u0026nbsp;between genotypes, haplotypes, gene–gene interactions, and susceptibility to muscle injury, specifically\u0026nbsp;by\u0026nbsp;examining 8 polymorphisms in ECM-related genes. Therefore,\u0026nbsp;the\u0026nbsp;obtained results cannot be directly compared to\u0026nbsp;those of\u0026nbsp;other studies.\u003c/p\u003e\n\u003cp\u003ePreviously, the intronic \u003cem\u003eFBN2\u003c/em\u003e rs331079 polymorphism\u0026nbsp;was associated with an increased risk of both AT and ACL ruptures during physical activity in Caucasian participants from Australia and South Africa. Specifically, individuals carrying the G allele or the GG genotype were approximately twice as likely to develop these tendinopathies. This polymorphism has also been shown to be associated with intracranial aneurysms in a Dutch Caucasian population. However, in this study, the C allele was shown to be a risk factor as opposed to the G allele (Ruigrok et al., 2006), which corroborates our findings indicating that carriers of the GC and CC genotypes have a greater risk of muscle injury.\u0026nbsp;It is important to recognize that muscle function cannot be considered in isolation from tendon properties. The muscle–tendon unit operates as a mechanical continuum, in which tendon stiffness and elasticity directly influence the magnitude and rate of force transmission during physical activity. Previous studies have shown that increased FBN2 expression may\u0026nbsp;increase\u0026nbsp;tendon density and stiffness, potentially reducing tendon compliance during muscle contraction (Cook, 1996). Such alterations could impair the ability of the musculotendinous junction to buffer rapid or excessive loading, thereby increasing the likelihood of muscle strain. Conversely, reduced FBN2 levels may weaken the microfibrillar network within tendons (P. N. Robinson \u0026amp; Godfrey, 2000), compromising structural integrity and limiting their capacity to withstand repetitive stress. Given that FBN2 expression is upregulated during tendon repair (Jelinsky et al., 2011), it is plausible that genetic variation in \u003cem\u003eFBN2\u003c/em\u003e may modulate individual susceptibility to muscle injury through its impact on tendon resilience. This highlights the need to consider ECM-related genes not only in the context of tendon disorders but also as potential contributors to muscle injury risk in physically active populations.\u003c/p\u003e\n\u003cp\u003ePrevious studies have suggested that genetic variants within \u003cem\u003eMMP\u003c/em\u003e genes are associated with numerous complex disorders, such as a higher risk of ACL rupture (Posthumus et al., 2012). Posthumus et al. (2012) reported that the AG and GG genotypes of the promoter \u003cem\u003eMMP12\u003c/em\u003e rs2276109 variant were significantly underrepresented among participants with noncontact ACL rupture compared with the control group, whereas no other \u003cem\u003eMMP1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;MMP3\u003c/em\u003e,or\u003cem\u003e\u0026nbsp;MMP10\u003c/em\u003e SNPs were significantly different between groups.\u0026nbsp;Additionally, the two four-variant haplotypes,\u0026nbsp;T-1G-A-A and C-2G-G-G (\u003cem\u003eMMP10\u003c/em\u003e rs486055, \u003cem\u003eMMP1\u003c/em\u003e rs1799750, \u003cem\u003eMMP3\u003c/em\u003e rs679620,\u0026nbsp;and\u0026nbsp;\u003cem\u003eMMP12\u003c/em\u003e rs2276109, respectively),\u0026nbsp;were significantly different between the controls and the ACL rupture subgroup and\u0026nbsp;between\u0026nbsp;the controls and the noncontact ACL rupture subgroup.\u0026nbsp;The authors suggested that the low-risk haplotype combinations may be associated with the presence of the G alleles for the \u003cem\u003eMMP3\u003c/em\u003e rs679620 and \u003cem\u003eMMP12\u0026nbsp;\u003c/em\u003ers2276109 variants (Posthumus et al., 2012). In contrast, Lulińska-Kuklik et al. (2019)\u0026nbsp;reported\u0026nbsp;significant underrepresentation of the \u003cem\u003eMMP3\u003c/em\u003e rs679620 G allele in the control group compared\u0026nbsp;with\u0026nbsp;the group of participants reporting\u0026nbsp;noncontact\u0026nbsp;ACL ruptures, suggesting that the G allele is associated with an increased risk for this injury. Further haplotype analysis\u0026nbsp;revealed\u0026nbsp;the G-C (\u003cem\u003eMMP3\u003c/em\u003e rs679620\u0026nbsp;and rs591058, respectively) inferred haplotype as a potential risk factor linked to noncontact ACL ruptures (Lulińska-Kuklik et al., 2019). However, in a\u0026nbsp;subsequent\u0026nbsp;study,\u0026nbsp;Lulińska et al. (2020) did not confirm this association of single polymorphisms or haplotypes of\u0026nbsp;the\u0026nbsp;\u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eMMP10\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;MMP12\u003c/em\u003e genes with noncontact ACL rupture in a Polish cohort (Lulińska et al., 2020). Our results indicated that the GG genotype of \u003cem\u003eMMP12\u003c/em\u003e rs2276109 may be a risk factor for muscle\u0026nbsp;injury; however, no significant associations were detected for other \u003cem\u003eMMP1\u003c/em\u003e and \u003cem\u003eMMP10\u003c/em\u003e SNPs\u0026nbsp;or for\u0026nbsp;\u003cem\u003eMMP\u003c/em\u003e haplotypes.\u0026nbsp;Compared with the G allele, the\u0026nbsp;A allele of the \u003cem\u003eMMP12\u003c/em\u003e rs2276109 polymorphism has been linked to\u0026nbsp;increased\u0026nbsp;affinity\u0026nbsp;for\u0026nbsp;transcription factor activator protein-1 (AP-1) and higher gene expression (Jormsjö et al., 2000). However,\u0026nbsp;whether the\u0026nbsp;over- or\u0026nbsp;underproduction\u0026nbsp;of MMPs\u0026nbsp;is involved\u0026nbsp;in injury risk\u0026nbsp;is not known.\u003c/p\u003e\n\u003cp\u003eA region overlapping the \u003cem\u003eBGN\u003c/em\u003e gene has been linked to soft-tissue injury risk, most notably ACL rupture (Ciȩszczyk et al., 2017; Mannion et al., 2014), suggesting its possible relevance to muscle injury due to shared ECM pathways. As \u003cem\u003eBGN\u003c/em\u003e is located on the X chromosome and sex is a known intrinsic factor in muscle injury susceptibility (Lin et al., 2018), sex-specific effects of \u003cem\u003eBGN\u003c/em\u003e variants should also be considered. Although Mannion et al. (2014) reported no significant associations between the \u003cem\u003eBGN\u003c/em\u003e rs1126499 or rs1042103 variants and the risk of ACL injury in either men or women when analysed individually, the C–G haplotype (rs1126499 and rs1042103, respectively) was linked to a reduced risk of ACL injury in female participants (Mannion et al., 2014). Another study further supported the involvement of the \u003cem\u003eBGN\u003c/em\u003e rs1042103 polymorphism in genetic susceptibility to ACL rupture. Cięszczyk et al. (2017) reported that the A allele was associated with an increased risk of ACL rupture in Polish males (Ciȩszczyk et al., 2017). Our findings are consistent with these observations, as we also confirmed the role of this SNP in sport-related injury susceptibility; specifically, the AG and AA genotypes of \u003cem\u003eBGN\u003c/em\u003e rs1042103 were associated with an increased risk of muscle injury.\u0026nbsp;This\u0026nbsp;polymorphism is a missense variant, resulting in a threonine-to-alanine substitution at codon 209 (p.Thr209Ala). This amino acid change may alter\u0026nbsp;the\u0026nbsp;structure or function\u0026nbsp;of biglycan, potentially\u0026nbsp;by\u0026nbsp;modifying its interaction with collagen fibrils and growth factors. Such alterations could affect extracellular matrix organization and mechanotransduction within the muscle–tendon unit, thereby influencing tissue resilience under mechanical\u0026nbsp;loading\u0026nbsp;(Robinson et al., 2017).\u003c/p\u003e\n\u003cp\u003ePolymorphisms in \u003cem\u003eDCN\u0026nbsp;\u003c/em\u003emay be additional factors influencing collagen fibril formation and matrix assembly, thereby contributing to the regulation of musculoskeletal soft tissue homeostasis (Robinson et al., 2017). Previously, the GG genotype of \u003cem\u003eDCN\u003c/em\u003e rs516115 was found to be significantly more frequent in female controls than in the ACL injury group, whereas the AA genotype was underrepresented among controls compared with the female noncontact ACL injury subgroup. These findings suggest that the A allele may act as a risk factor for injury development (Mannion et al., 2014). However, Cięszczyk et al. did not replicate this finding, as no significant association was observed between the distribution of the rs516115 genotype and the risk of ACL rupture. Nonetheless, a trend was noted indicating that the AG genotype might be linked to a reduced likelihood of ACL injury (Ciȩszczyk et al., 2017). In the present study, our findings contrasted with those of Cięszczyk et al. (2017), as we observed that the AG genotype was associated with an increased risk of muscle injury, whereas the GG genotype appeared to confer a protective effect compared with AA, which aligns more closely with the results reported by Mannion et al. (2014).\u0026nbsp;A plausible explanation for these divergent findings is that the rs516115 polymorphism may affect decorin expression or mRNA stability, thereby altering collagen fibrillogenesis and matrix organization (Robinson et al., 2017). Thus, even subtle functional changes resulting from genetic variation may\u0026nbsp;have\u0026nbsp;tissue-specific effects. In ligament-dominant structures such as the ACL, reduced decorin levels could increase compliance and lower rupture risk, potentially explaining the protective trend observed for the AG and GG genotypes. Conversely, at the muscle–tendon interface,\u0026nbsp;decreased\u0026nbsp;decorin activity may compromise microstructural stability and impair repair mechanisms, thereby increasing susceptibility to muscle injury.\u003c/p\u003e\n\u003cp\u003eIn addition, the novel epistatic analysis conducted in this study revealed a significant interaction between the \u003cem\u003eDCN\u003c/em\u003e rs13312816 and \u003cem\u003eBGN\u003c/em\u003e rs1042103 polymorphisms. Individuals carrying the AG/TT or AA/AT genotype combinations showed a markedly increased likelihood of muscle injury.\u0026nbsp;This combined effect may reflect the complementary roles of decorin and biglycan in extracellular matrix organization; simultaneous alterations in both proteins could synergistically weaken collagen architecture or impair mechanotransduction, thereby reducing tissue resilience under mechanical loading (Robinson et al., 2017).\u0026nbsp;To our knowledge, this is the first report of a potential epistatic interaction between \u003cem\u003eDCN\u003c/em\u003e and \u003cem\u003eBGN\u003c/em\u003e variants in relation to sports injury susceptibility, indicating that\u0026nbsp;compared with single-gene effects alone,\u0026nbsp;combined dysregulation of these ECM pathways may have a greater\u0026nbsp;effect\u0026nbsp;on tissue stability.\u003c/p\u003e\n\u003cp\u003eIn our study, the analysed polymorphisms within the \u003cem\u003eELN\u003c/em\u003e, \u003cem\u003eMMP1\u003c/em\u003e, and \u003cem\u003eMMP10\u003c/em\u003e genes were not significantly associated with susceptibility to sport-related muscle injury. The genotype distributions did not differ between injured and noninjured individuals, suggesting that these variants are unlikely to play a major role in modulating individual risk within the studied cohort. This observation is consistent with previous findings reported by Lulińska et al. (2020), who also reported no significant associations between \u003cem\u003eMMP1\u003c/em\u003e (rs1799750) and \u003cem\u003eMMP10\u003c/em\u003e (rs486055) variants and susceptibility to noncontact ACL injuries according to various genetic models (p \u0026gt; 0.05) (Lulińska et al., 2020). Similarly, other studies exploring \u003cem\u003eELN\u003c/em\u003e gene polymorphisms have not demonstrated a clear relationship with sport-related musculoskeletal injury risk, further supporting the notion that extracellular matrix gene variants may have a limited contribution to the genetic background of soft-tissue injury susceptibility.\u0026nbsp;Nevertheless, some studies have reported significant associations between these genes and injury risk or severity. Variants of the \u003cem\u003eELN\u003c/em\u003e gene were linked to ligament injury severity, recovery time in elite soccer players (Pruna et al., 2013), and ACL rupture (Lulińska et al., 2023), whereas\u0026nbsp;promoter polymorphisms in \u003cem\u003eMMP1\u003c/em\u003e were associated with tendon pathology and rotator cuff tears (Assunção et al., 2017; Baroneza et al., 2014). Although no consistent associations have been reported for \u003cem\u003eMMP10\u0026nbsp;\u003c/em\u003epolymorphisms, altered MMP10 expression has been observed in injured tendon tissue, suggesting its potential involvement in extracellular matrix remodelling and repair following injury (Minkwitz et al., 2017). Differences in study design, injury type, sample size, and population structure may partly explain these inconsistencies across studies.\u003c/p\u003e\n\u003cp\u003eThis study has both strengths and limitations. Although the sample size (n = 275) is adequate in biomedical research for detecting moderate genetic effects and aligns with previous sports injury genetics research (Bacchetti, 2010; Dorey, 2011), larger cohorts would improve the statistical power, especially for subgroup and interaction analyses. The use of self-reported injury data is another limitation, as it may introduce recall bias. However, a major strength of this work is its focus on muscle injuries, which remain underrepresented in genetic studies despite being among the most common causes of time loss in sports. By analysing 8 polymorphisms across 7 ECM-related genes using single-\u003cem\u003elocus\u003c/em\u003e, haplotype, and gene–gene interaction approaches, this study offers a more comprehensive view of the genetic architecture underlying muscle injury risk than traditional single-variant analyses do.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides new evidence that genetic variation in ECM-related genes influences susceptibility to sport-related muscle injury. Variants in \u003cem\u003eFBN2\u003c/em\u003e, \u003cem\u003eMMP12\u003c/em\u003e, \u003cem\u003eBGN\u003c/em\u003e, and \u003cem\u003eDCN\u003c/em\u003e were associated with either increased risk or protection, indicating that ECM integrity plays a key role in maintaining soft-tissue resilience under mechanical loading. Importantly, we report the first evidence of an interaction between \u003cem\u003eDCN\u003c/em\u003e and \u003cem\u003eBGN\u003c/em\u003e polymorphisms, suggesting that the combined effects of ECM-related genes may be more informative than single-\u003cem\u003elocus\u003c/em\u003e analyses. By shifting attention from ligament and tendon injury, which dominate the current literature, to muscle injury, this study highlights an understudied yet clinically relevant phenotype. Although replication in larger, clinically verified cohorts is needed, these findings support the growing concept that polygenic ECM profiles may help identify athletes at elevated injury risk and provide directions for future personalized preventive strategies.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACL – Anterior Cruciate Ligament\u003c/p\u003e\n\u003cp\u003eAP-1 – Activator Protein-1\u003c/p\u003e\n\u003cp\u003eAT – Achilles Tendinopathy\u003c/p\u003e\n\u003cp\u003eBMI – Body Mass Index\u003c/p\u003e\n\u003cp\u003eBMP – Bone Morphogenetic Protein\u003c/p\u003e\n\u003cp\u003eDCN – Decorin\u003c/p\u003e\n\u003cp\u003eBGN – Biglycan\u003c/p\u003e\n\u003cp\u003eECM – Extracellular Matrix\u003c/p\u003e\n\u003cp\u003eEGFR – Epidermal Growth Factor Receptor\u003c/p\u003e\n\u003cp\u003eELN – Elastin\u003c/p\u003e\n\u003cp\u003eFBN2 – Fibrillin-2\u003c/p\u003e\n\u003cp\u003eGLM – Generalized Linear Model\u003c/p\u003e\n\u003cp\u003eHWE – Hardy–Weinberg Equilibrium\u003c/p\u003e\n\u003cp\u003eIPAQ – International Physical Activity Questionnaire\u003c/p\u003e\n\u003cp\u003eMMP – Matrix Metalloproteinase\u003c/p\u003e\n\u003cp\u003eMMP1– Matrix Metalloproteinase 1\u003c/p\u003e\n\u003cp\u003eMMP10– Matrix Metalloproteinase 10\u003c/p\u003e\n\u003cp\u003eMMP12– Matrix Metalloproteinase 12\u003c/p\u003e\n\u003cp\u003ePCR- Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003eTGF-β- Transforming Growth Factor-beta\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoard Statement: The Bioethics Committee at the District Medical Chamber in Gdansk approved the study (No. KB-8/22). The investigation protocols were conducted ethically according to the World Medical Association Declaration of Helsinki and to the Strengthening the Reporting of Genetic Association studies statement (STREGA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Podkarpackie Innovation Center under the grant program for R\u0026amp;D works of scientific units under the Regional Operational Program of the Podkarpackie Voivodeship for 2014-2020, grant number N3_624 entitled \"Prevention of musculoskeletal system injuries among physically active people from the Podkarpackie region based on the assessment of the risk of occurrence of various types of collagen polymorphisms\".\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP.K., A.R-B., and A.L-D. contributed to study design, data collection, statistical analysis, data interpretation, manuscript preparation, and literature search. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results presented in this manuscript are part of the doctoral dissertation of MSc Piotr Kabelis entitled ‘The relationship between the diversity of selected genes encoding components of the extracellular matrix of connective tissue and the risk of muscle damage in physically active individuals’.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmad, K., Shaikh, S., Ahmad, S. S., Lee, E. J. \u0026amp; Choi, I. (2020). Cross-Talk Between Extracellular Matrix and Skeletal Muscle: Implications for Myopathies. In \u003cem\u003eFrontiers in Pharmacology\u003c/em\u003e (Vol. 11). Frontiers Media S.A. https://doi.org/10.3389/fphar.2020.00142\u003c/li\u003e\n \u003cli\u003eAhmad, K., Shaikh, S., Chun, H. J., Ali, S., Lim, J. H., Ahmad, S. S., Lee, E. 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Exp Suppl. https://doi.org/10.1007/978-3-0348-0364-9_1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"sports genetics, genetic susceptibility, single nucleotide polymorphism (SNP), haplotype analysis, injury risk, physical activity","lastPublishedDoi":"10.21203/rs.3.rs-8639032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8639032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe extracellular matrix (ECM) plays a fundamental role in maintaining skeletal muscle structure and facilitating tissue repair following injury. Alterations in its composition can disrupt regeneration, promote fibrosis, and increase susceptibility to muscle damage. The aim of this study was to investigate whether the BGN (rs1042103), DCN (rs13312816 and rs516115), ELN (rs2071307), FBN2 (rs331079), MMP1 (rs1799750), MMP10 (rs486055), and MMP12 (rs2276109) polymorphisms, either individually or in combination, are associated with susceptibility to muscle injury.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study included 142 physically active Caucasian participants with reported noncontact muscle injury and 133 participants without any history of this kind of injury. All the samples were genotyped using real-time polymerase chain reaction (real-time PCR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results revealed that individuals carrying (i) the GC and CC genotypes of FBN2 rs331079, (ii) the GG genotype of MMP12 rs2276109, (iii) the AG and AA genotypes of BGN rs1042103, (iv) and the AG genotype of DCN rs516115 had an increased risk of muscle injury, whereas the GG genotype appeared to exert a protective effect relative to AA. In contrast, no significant results were obtained for the remaining polymorphisms. Additionally, no significant associations were detected for the DCN and MMP haplotypes; however, an epistatic interaction between DCN (rs13312816) and BGN (rs1042103) was detected. Participants who carried the AG/TT and AA/AT genotype combinations had a higher probability of belonging to the injury group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, this report indicates an association between specific FBN2, MMP12, BGN, and DCN genotypes and susceptibility to muscle injury in a physically active Caucasian population.\u003c/p\u003e","manuscriptTitle":"Extracellular matrix gene variants and susceptibility to sport-related muscle injuries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 13:10:20","doi":"10.21203/rs.3.rs-8639032/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2026-04-12T18:08:27+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-03-25T11:20:29+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-25T11:10:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-18T16:28:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Applied Genetics","date":"2026-01-19T06:38:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"edca8790-8842-49d6-ad7a-3ea6735642e2","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T10:44:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 13:10:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8639032","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8639032","identity":"rs-8639032","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}