{"paper_id":"3f53d13a-b2c9-4a94-b180-12efeb4a53f0","body_text":"Genome-wide Mendelian Randomization Identifies Potential Drug \nTargets for Dorsopathies \nYu Cui1,2,†, Jiarui Guo1,2,†, Yuanxi Lu1,2,†, Mengting Hu1,2, He Zhou1,2, Wancong Zhang2,3,4,*, 1 \nShijie Tang2,3,4,* 2 \n†These authors contributed equally to this work and share first authorship 3 \n 4 \n1Shantou University Medical College, Shantou, Guangdong 515041, China 5 \n2Department of Plastic Surgery and Burns Center, Second Affiliated Hospital, Shantou University 6 \nMedical College, Shantou, Guangdong 515051, China 7 \n3Plastic Surgery Institute of Shantou University Medical College, Shantou, Guangdong 515051, 8 \nChina 9 \n4Shantou Plastic surgery Clinical Research Center, Shantou, Guangdong 515051, China 10 \n* Correspondence:  11 \nWancong Zhang, Shijie Tang 12 \nMartine2007@sina.com , sjtang3@stu.edu.cn   13 \nKeywords: Mendelian randomization, Genetics, Drug target, Dorsopathies, Colocalization 14 \nanalysis, Genome-wide association studies, Expression quantitative trait locus. 15 \nAbstract 16 \nBackground Dorsopathies are a group of musculoskeletal disorders affecting the spinal column and 17 \nrelated structures, contributing significantly to global disability rates and healthcare costs. Despite 18 \ntheir prevalence, the genetic and biological mechanisms underlying dorsopathies are not fully 19 \nunderstood. 20 \nMethod Summary-data-based Mendelian Randomization (SMR) and colocalization analysis were 21 \nemployed, using data from genome-wide association studies (GWAS) and cis-expression quantitative 22 \ntrait loci (cis-eQTLs) databases. Genes with a colocalization posterior probability (PP.H4) above 0.7 23 \nin SMR results were selected for additional analysis. These selected genes underwent MR analysis to 24 \nexamine possible causal connections with dorsopathies, and sensitivity analyses were carried out to 25 \nensure robustness. Additionally, two transcriptome-wide association studies (TWAS) were utilized to 26 \nconfirm and screen for potential drug targets. 27 \nResult We identified four essential genes linked to dorsopathies: NLRC4, CGREF1, KHK, and 28 \nRNF212. Mendelian randomization (MR) analysis revealed a potential causal link between these 29 \ngenes and dorsopathies. Elevated transcription levels of NLRC4, CGREF1, and KHK correlated with 30 \nreduced dorsopathies risk, while increased levels of RNF212 were associated with heightened risk of 31 \ndorsopathies. Regarding methylation sites, an increase in cg04686953 fully mediated the decreased 32 \nrisk of dorsopathies by RNF212. Similarly, the risk effect of cg26638505 and cg18948125 was 33 \nentirely mediated by NLRC4, while CGREF1 predominantly mediated the risk-increasing effect of 34 \ncg06112415 and the decrease effect of cg22740783. 35 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n \n2 \nConclusion Dorsopathies were associated with four pivotal genes: NLRC4, CGREF1, KHK, and 36 \nRNF212. Methylation analysis identified cg04686953 and cg22740783 as protective against 37 \ndorsopathies risk, while cg26638505, cg18948125, and cg06112415 exhibited a risk-increasing 38 \nimpact. 39 \n 40 \n1 Introduction 41 \nDorsopathies encompass a broad spectrum of musculoskeletal issues that impact the spinal column 42 \nand its related structures[1]. These conditions are prevalent among individuals across various age 43 \ngroups and socioeconomic backgrounds, leading affected individuals to seek medical assistance. 44 \nThey significantly contribute to the burden of illness, disability, and distress. Since 1990, there has 45 \nbeen a notable rise in the prevalence of these disorders, rendering them one of the primary causes of 46 \nglobal disability-adjusted life years [2]. However, the pathogenic genes and biological mechanisms 47 \nof the disease remain largely unknown. Research conducted on the general population has indicated a 48 \npotential link between certain dorsopathies and lifestyle choices including smoking [3], a higher body 49 \nmass index[4,5], and lack of physical activity [6]. Additionally, these dorsopathies have been found 50 \nto coincide with various other health complications such as anxiety, depression, diabetes, 51 \ncardiovascular, respiratory, and gastrointestinal ailments[7,8]. Furthermore, specific dorsopathies like 52 \nosteopenia, osteomalacia, and tuberculosis can be influenced by factors such as diet, living 53 \nenvironment, and psychological aspects. These particular disorders often occur alongside systemic 54 \ncomorbidities such as endocrine dysfunction and infection [8]. Relevant limitations in observational 55 \nepidemiological studies include the potential for confounding, reverse causation, and diverse biases, 56 \nwhich hinder comprehensive understanding of disease pathogenesis and identification of treatment 57 \ntargets. 58 \nHowever, the utilization of Mendelian randomization (MR) techniques, which rely on the random 59 \nallocation of genetic variations, allows for the simulation of randomized controlled trials and can 60 \neffectively mitigate the impact of confounding variables. In relation to research on dorsopathies, this 61 \nimplies a more precise evaluation of the causal association between drug targets and disease while 62 \nexcluding any factors that may potentially disrupt the findings [9]. MR utilizes genetic variants as 63 \ninstrumental variables to assess the causal impact of an exposure on outcomes. This approach has 64 \nbeen extensively utilized in other research studies related to diseases and has effectively facilitated 65 \nthe identification of potential therapeutic targets for diverse medical conditions.[10] However, there 66 \nis limited research on employing MR methods to explore potential drug targets for dorsopathies. 67 \nTherefore, to gain a deeper understanding of the pathogenesis of dorsopathies and identify more 68 \neffective treatment approaches, further MR studies are needed to evaluate the drug targets for 69 \ndorsopathies. This will help eliminate factors that could interfere with the results and offer new 70 \nperspectives and methods for the treatment of dorsopathies. In theory, single nucleotide 71 \npolymorphisms (SNPs) are distributed randomly and not impacted by environmental factors, making 72 \nthem an ideal tool for establishing causality. MR is a type of instrumental variable analysis that 73 \nprimarily employs SNPs as genetic instruments to determine the causal impact of an exposure (in this 74 \ninstance, circulatory proteins) on outcomes.[11]. Previous studies have successfully utilized MR to 75 \nidentify biomarkers and treatment targets for various diseases, such as aortic aneurysms [12], 76 \nmultiple sclerosis [13], and breast cancer [14].  77 \nH\nowever, in previous studies, the use of MR To analyze drug targets for dorsopathies is very rare. To 78 \naddress this research gap, our study focuses on utilizing genes as factors of exposure in drug target 79 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n3 \nresearch, investigating the role of genes in dorsopathies development and their potential as viable 80 \ndrug targets. Genes have stable genetic characteristics, and they are unaffected by environmental 81 \nfactors, allowing for a more accurate assessment of the causal relationship between genes and 82 \ndorsopathies [15]. By exploring dorsopathies' potential drug targets through genes as the exposure 83 \nfactor, we aim to offer new perspectives and methods for the treatment and prevention of 84 \ndorsopathies. 85 \nTo investigate potential pathogenic genes related to dorsopathies, we employed a comprehensive 86 \nanalytical approach including summary-data-based Mendelian randomization (SMR) analysis, 87 \ncolocalization analysis, genome-wide association study (GWAS), and transcriptome-wide association 88 \nstudy (TWAS). We utilized a meta-analysis dataset of cis-expression quantitative trait loci (cis-89 \neQTLs) from peripheral blood samples as exposure data, along with results from the extensive 90 \nFinnGen database. Following preliminary analysis, we identified candidate genes and established 91 \ncausal inference using MR methods. Additionally, we conducted mediation analysis of gene-92 \nmediated methylation sites to explore disease related methylation sites. 93 \n2 Method 94 \n2.1 Datasets 95 \nSummary-level data for the GWAS on dorsopathies were obtained from the FinnGen consortium, 96 \ncomprising 117,411 cases and 294,770 controls. This dataset represents the most recent GWAS 97 \nfindings and comprises the largest cohort of dorsopathies cases documented to date. The primary 98 \nobjective of FinnGen is to accumulate and rigorously analyze genomic and national health register 99 \ndata from 500,000 Finnish individuals.[16] In the context of drug development studies, we prioritized 100 \ncis-eQTLs that were in closer proximity to the target gene. These cis-eQTLs used for SMR analyses 101 \nwere sourced from the eQTLGen Consortium [17] and the eQTL meta-analyses conducted on 102 \nperipheral blood samples from a cohort of 31,684 individuals. For the TWAS analyses of eQTL data, 103 \nwe utilized the GTEx v8 European whole blood dataset. Given our specific emphasis on dorsopathies, 104 \nwe meticulously extracted comprehensive eQTL results exclusively from the whole blood samples 105 \nwithin the GTEx dataset [18]. 106 \n 107 \n2.2 SMR analyses 108 \nWe conducted SMR and heterogeneity in dependent instruments (HEIDI) tests analyses on cis 109 \nregions using the SMR software (version 1.03) [19]. The methodologies for SMR analyses are 110 \ndetailed in the original work. In brief, SMR analyses employs a well-established MR approach. This 111 \ntechnique employs a SNP at a prominent cis-eQTL as an instrumental variable (IV). The summary-112 \nlevel eQTL data serve as the exposure variable, and the GWAS data for a specific trait serve as the 113 \noutcome variable. The primary objective is to explore a potential causal or pleiotropic association, 114 \nwherein the same causal variant influences both gene expression and the trait. It is crucial to 115 \nacknowledge that the SMR method lacks the ability to distinguish between a causal association, in 116 \nwhich gene expression causally influences the trait, and a pleiotropic association, in which the same 117 \nSNP affects both gene expression and the trait. This limitation arises because of the single 118 \ninstrumental variable (IV) in the MR method, which cannot differentiate between causality and 119 \npleiotropy. Nevertheless, the HEIDI test can make this distinction by discerning causality and 120 \npleiotropy from linkage. Linkage refers to cases in which two different SNPs in linkage 121 \ndisequilibrium (LD) independently influence gene expression and the trait. Although less biologically 122 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n4 \nintriguing than causality and pleiotropy, the HEIDI test provides clarity in such scenarios. For the 123 \nHEIDI test, a p-value below 0.05 was considered significant, suggesting that the observed association 124 \nwas attributable to linkage. 125 \n 126 \n2.3 GWAS analyses 127 \nMultimarker analyses of genomic annotation (MAGMA) employs a multiple regression model to 128 \nassess the cumulative effect of multiple SNPs within a specific gene region (± 10 kb)[20]. The 129 \nreference panel for calculating LD was derived from Phase 3 of the 1000 Genomes European 130 \npopulation. Significance thresholds for GWAS analyses using both SMR and MAGMA were set at a 131 \nfalse discovery rate (FDR) below 0.05, corrected using the Benjamini-Hochberg method.[21] 132 \n 133 \n2.4 TWAS analyses 134 \nWe conducted validations to integrate dorsopathies GWAS and eQTL data of whole blood from 135 \nGTEx using the FUSION and UTMOST, widely utilized tools in prior TWAS investigations [22,23]. 136 \nFUSION constructs predictive models using various penalized linear models, such as GBLUP, 137 \nLASSO, Elastic Net, for the significant cis-heritability genes estimated from SNPs within 500 kb on 138 \neither side of the gene boundary. Subsequently, it selects the optimal model based on the coefficient 139 \nof determination (R2) calculated through a fivefold cross-validation. For UTMOST, we performed 140 \nrepeated 49 single-tissue association tests for each tissue. TWAS significance for both single-tissue 141 \nanalyses was determined with a Benjamini-Hochberg corrected FDR value below 0.05. 142 \n 143 \n2.5 MR analyses 144 \nIn conducting the two-sample MR analyses [24], we utilized the TwoSampleMR R package. The 145 \nutilization of the two-sample MR framework requires employing two distinct datasets. In this study, 146 \ngenetic instruments, specifically cis-eQTL, served as exposures, while GWAS were utilized to 147 \ndetermine outcome traits. The MR methodology investigates the relationship between gene 148 \nexpression and diseases or traits by employing genetic variants associated with gene expression as 149 \ninstrumental variables (exposure) and GWAS for the outcome measures. Mendelian Randomization 150 \nfacilitates exploration into whether alterations in gene expression causally impact diseases or traits. 151 \nFor instruments represented by a single SNP, we employed the Wald ratio. In cases where 152 \ninstruments consisted of multiple SNPs, we implemented the inverse-variance-weighted MR 153 \napproach. When selecting SNPs, the significance thresholds were defined as P < 5 ×  10^(-8) for 154 \ngenome-wide significance, with a linkage disequilibrium parameter (r^2) set to 0.1, and a genetic 155 \ndistance set to 10 MB. 156 \n 157 \n2.6 Colocalization analyses 158 \nConducted colocalization analyses using the coloc package in the R software environment (version 159 \n4.0.3). Colocalization analyses aims to assess the potential shared causality between SNPs associated 160 \nwith both gene expression and phenotype at a specific locus, thereby indicating the \"colocalization\" 161 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n5 \nof these genetic signals. The analyses calculates posterior probabilities (PPs) for five hypotheses: H0 162 \ndenotes no association with either gene expression or phenotype; H1 signifies an association solely 163 \nwith gene expression; H2 indicates an association exclusively with the phenotype; H3 suggests an 164 \nassociation with both gene expression and phenotype through independent SNPs; and H4 implies an 165 \nassociation with both gene expression and phenotype through shared causal SNPs. A substantial PP 166 \nfor H4 (PP.H4 above 0.70) strongly suggests the presence of shared causal variants influencing both 167 \ngene expression and phenotype [25]. 168 \n 169 \n2.7 Methylation & Mediation analysis 170 \nWe hypothesize that methylation sites exert influence on the pathogenic risk of dorsopathies through 171 \nthree primary pathways: a) they indirectly modulate the pathogenic risk by influencing gene 172 \nexpression; b) the methylation sites themselves have a direct impact on the pathogenic risk of 173 \ndorsopathies; and c) methylation sites may affect the pathogenic risk of dorsopathies by influencing 174 \nother confounders. It is crucial to underscore that the overall impact of methylation on the pathogenic 175 \nrisk of dorsopathies can be conceptualized as the combined action of these three pathways, expressed 176 \nas T=a+b+c, where a denotes the indirect modulation of pathogenic risk by influencing gene 177 \nexpression, b denotes the direct effect of methylation sites on pathogenic risk, and c represents their 178 \ninfluence on pathogenic risk through impacting confounding factors. Significance tests are conducted 179 \nseparately for T, x, y, and a. If all these parameters show significance, it can be inferred that the gene 180 \nplays a role in the intermediate pathway, thereby substantiating the existence of the a) pathway. 181 \n 182 \n3 Result 183 \n3.1 SMR analyses and colocalization for Preliminary Identifying Potential Genes 184 \nIn our initial analyses phase, we utilized the eQTLGen dataset for SMR analyses to pinpoint genes 185 \nsignificantly linked to dorsopathies. Employing the FDR method set our p-value significance 186 \nthreshold. The eQTLGen dataset provided extensive data, yielding 15,633 candidate genes. 187 \nWe identified 269 genes significantly associated with dorsopathies (FDR_P < 0.05). Subsequent 188 \nscrutiny uncovered four potential drug targets (NLRC4, CGREF1, KHK, RNF212) affecting 189 \ndorsopathies through various intersecting methods. For clarity, Manhattan plots illustrated SMR 190 \nresults. Following gene identification, the HEIDI heterogeneity test (p_HEIDI > 0.05) sifted out 191 \ngenes lacking horizontal pleiotropy. SMR locus plots further elucidated gene outcomes. 192 \nWe proceeded with colocalization analyses to merge GWAS and blood eQTL data for genes passing 193 \nthe SMR test. This aimed to determine if these genes colocalized with the dorsopathies trait. The 194 \ncolocalization test results robustly supported colocalization between the trait and all four genes 195 \n(NLRC4: PP.H4 = 0.993, CGREF1: PP.H4 = 0.905, KHK: PP.H4 = 0.773, RNF212: PP.H4 = 0.923) 196 \nmeeting both SMR and HEIDI test criteria. Consequently, we identify these genes as top-priority 197 \ncandidates for subsequent functional studies. 198 \n 199 \n3.2 MR analyses Validates Potential Gene Causal Relationships  200 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n6 \nUsing cis-eQTL data from the eQTLGen Consortium, we conducted two-sample MR analyses on 201 \nEuropean summary statistics of individuals with dorsopathies. The discovery cohort, consisting of 202 \n117,411 cases and 294,770 controls from the FinnGen cohort, underwent inverse variance weighted 203 \n(IVW) MR analyses to combine effect estimates from each genetic instrument. The analysis revealed 204 \nassociations between the genetically predicted expression of 956 genes and dorsopathies risk 205 \nfollowing multiple testing adjustments (FDR correction). Notably, NLRC4 (OR = 0.886, 95%CI = 206 \n0.855-0.917, FDR_P = 1.09e-11), CGREF1 (OR = 0.682, 95%CI = 0.585-0.794, FDR_P = 8.74e-07), 207 \nKHK (OR = 0.936, 95%CI = 0.921-0.951, FDR_P = 3.67e-16), and RNF212 (OR = 1.145, 95%CI = 208 \n1.056-1.241, FDR_P = 1.01e-03) were among the genes identified. To ensure the reliability of our 209 \nfindings, we conducted tests for horizontal pleiotropy, which did not reveal any evidence of its 210 \npresence in the dataset. These additional analyses confirmed the absence of horizontal pleiotropy, 211 \nbolstering the robustness and validity of our MR genetics findings. 212 \n 213 \n3.3 Validation 214 \n3.3.1 TWAS & UTMOST Validates Transcriptome-level Causal Relationships 215 \nIn our quest to enhance causal inference and gain deeper insights into genetic associations with 216 \ndorsopathies, we conducted a TWAS analyses on the four genes identified in previous analyses, 217 \nutilizing Fusion and UTMOST software. This comprehensive approach aimed to elucidate the 218 \ntranscriptional associations of these genes with dorsopathies and bolster the evidence supporting their 219 \npotential causal role. 220 \nThe results revealed significant transcriptional associations for the four genes with dorsopathies, with 221 \nall colocalization probabilities (PP.H4) exceeding 0.7. Specifically, elevated transcription levels of 222 \nNLRC4 (Z score = -5.33068, P = 9.78E-08), CGREF1 (Z score = -4.7644, P = 1.89E-06), and KHK 223 \n(Z score = -4.72939, P = 2.25E-06) were significantly correlated with a decreased risk of 224 \ndorsopathies, whereas an increased transcription level of RNF212 (Z score = 3.751506, P = 225 \n0.000176) was significantly associated with an increased risk of dorsopathies. Notably, these TWAS 226 \nfindings were consistent with those from the SMR analyses, providing robust support for the 227 \ntranscriptional associations of these genes with dorsopathies. 228 \n3.3.2 MAGMA Validates Genome-level Causal Relationships 229 \nWe also conducted a GWAS analyses using MAGMA software on the quartet of genes highlighted in 230 \npreceding studies. This approach aimed to illuminate the associations of these genes with 231 \ndorsopathies at the genome level, thereby reinforcing the evidence underpinning their potential causal 232 \ninvolvement. 233 \nBased on the MAGMA analyses, we identified 855 significant genes that passed the FDR test. The 234 \noutcomes unveiled noteworthy genome-level associations for the aforementioned genes with 235 \ndorsopathies. Specifically, heightened transcription levels of NLRC4 (FDR_P = 0.01851868), 236 \nCGREF1 (P = 0.002068503), and KHK (P = 0.009814817) exhibited significant correlations with 237 \nreduced risk of dorsopathies, whereas increased transcription levels of RNF212 (P = 0.00202954) 238 \nw\nere significantly linked with elevated risk of dorsopathies. Importantly, these GWAS findings 239 \ncorroborated those from the SMR analyses, thereby fortifying the robustness of the transcriptional 240 \nassociations of these genes with dorsopathies. 241 \n 242 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n7 \n3.4 Methylation analysis & Mediation analysis 243 \nWe evaluated the influence of cg23387401 on RNF212 expression (β  = -0.52, P = 2.08e-14) and its 244 \nlink to dorsopathies risk (β  = 0.19, P = 1.20E-07). From these findings, we identified how gene-245 \nmediated methylation impacts dorsopathies risk (β  = -0.10, P = 1.35E-05), with an observed overall 246 \neffect (β  = -0.10, P = 2.68E-06) indicating a 93.79% intermediate effect proportion. This suggests 247 \nthat the rise in cg23387401 is wholly mediated by RNF212, leading to reduced dorsopathies risk. 248 \nLikewise, we assessed cg26638505's impact on NLRC4 expression (β  = -0.81, P = 1.25E-09) and its 249 \nassociation with dorsopathies risk (β  = -0.13, P = 7.87E-09). These calculations revealed the 250 \ninfluence of gene-mediated methylation sites on dorsopathies risk (β  = 0.11, P = 2.87E-05), with an 251 \noverall effect (β  = 0.09, P = 1.43E-03) and an intermediate effect proportion of approximately 252 \n120.00%. This suggests that cg26638505 elevation is entirely mediated by NLRC4, reducing 253 \ndorsopathies risk. 254 \nWe also analyzed cg18948125's effect on NLRC4 expression (β  = -0.93, P = 1.72E-08) and its 255 \nassociation with dorsopathies risk (β  = -0.13, P = 7.87E-09). From this, we derived the impact of 256 \ngene-mediated methylation sites on dorsopathies risk (β  = 0.12, P = 5.50E-05), with an overall 257 \neffect (β  = 0.12, P = 5.59E-04) and an intermediate effect proportion of 104.41%. This suggests that 258 \ncg18948125 elevation primarily contributes to increased dorsopathies risk, mediated by NLRC4. 259 \nSimilarly, cg22740783's influence on CGREF1 expression (β  = 0.32, P = 7.71E-07) and its 260 \nassociation with dorsopathies risk (β  = -0.38, P = 2.12E-05) was calculated. This allowed us to 261 \nidentify the impact of gene-mediated methylation sites on dorsopathies risk (β  = -0.12, P = 1.27E-262 \n03), with an overall effect (β  = -0.13, P = 1.63E-04) and an intermediate effect proportion of 263 \n92.24%. Hence, we propose that the elevation in cg22740783 is fully mediated by CGREF1, leading 264 \nto decreased dorsopathies risk. 265 \nLastly, we evaluated cg06112415's impact on CGREF1 expression (β  = -0.34, P = 1.75E-06) and its 266 \nassociation with dorsopathies risk (β  = -0.38, P = 2.12E-05). Through these assessments, we 267 \nidentified the effect of gene-mediated methylation sites on dorsopathies risk (β  = 0.13, P = 1.49E-268 \n03), with an overall effect (β  = 0.12, P = 5.59E-04) and an intermediate effect proportion of 269 \n110.63%. This suggests that cg06112415 elevation primarily contributes to increased dorsopathies 270 \nrisk, mediated by CGREF1. 271 \n 272 \n4 Discussion 273 \nThis study aimed to offer new perspectives and methods for the treatment and prevention of 274 \ndorsopathies. In our research, the identification of candidate genes and the establishment of causal 275 \ninference were conducted via MR methods, while the mediation analysis was used to explore disease 276 \nrelated gene-mediated methylation sites. As a result, our study revealed four genes as potential 277 \ntherapeutic targets for dorsopathies, including NLRC4, CGREF1, KHK and RNF212. Among these 278 \ngenes, elevated transcription levels of the NLRC4, CGREF1 and KHK were significantly associated 279 \nwith a decreased risk of dorsopathies, whereas a raised transcription level of RNF212 was 280 \nsignificantly related a higher risk of dorsopathies. 281 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n8 \nIn order to find the novel drug targets for dorsopathies, we employed an integrative analysis that 282 \ncombined colocalization with MR to assess causal genes for dorsopathies. Later, we conducted tests 283 \nfor horizontal pleiotropy to ensure the reliability of our MR genetics findings. In addition, TWAS 284 \nanalyses on NLRC4, CGREF1, KHK and RNF212 was conducted through Fusion and UTMOST 285 \nsoftware, which further illuminated the transcriptional associations of these genes with dorsopathies 286 \nand bolstered the evidence supporting their potential causal role. Moreover, we conducted a GWAS 287 \nanalyses whose results were consistent with those from the SMR analyses, which reinforcing the 288 \nevidence underpinning the potential causal association between these four genes with dorsopathies. 289 \nFinally, we evaluated the influence of methylation sites on those genes expression and their links to 290 \ndorsopathies. The results exhibited that the rise in cg23387401 was entirely mediated by RNF212, the 291 \nelevations of cg26638505 and cg18948125 were primarily mediated by NLRC4, and the up-292 \nregulation of cg06112415 was fully mediated by CGREF1, leading to increased dorsopathies risk; 293 \nwhile the rise of cg22740783 was totally mediated by CGREF1, resulting in decreased dorsopathies 294 \nrisk. 295 \nNOD-like receptors (NLRs) family caspase activation and recruitment domain-containing protein 4 296 \n(NLRC4) gene encodes NLRC4, which is mainly expressed in macrophages, neutrophils, dendritic 297 \ncells and glial cells[26]. As a member of the caspase recruitment domain-containing NLR family, 298 \nNLRC4 partners with NLR family of apoptosis inhibitory proteins (NAIP) to assemble 299 \ninflammasome complexes, which termed NAIP/NLRC4 inflammasomes[27]. Inflammasomes 300 \nfunction as intracellular multi-protein platforms that are activated by pathogen-associated molecular 301 \npatterns or damage-associated molecular patterns, triggering innate immune reactions and 302 \ninflammatory caspase-activation to defense pathogens and danger signals and maintain the 303 \nhomeostasis[27–29]. The activation of caspases results in the proteolytic activation of the pro-304 \ninflammatory cytokines interleukin-1β (IL-1β ) and/or interleukin-18 (IL-18)[27]. Meanwhile, 305 \nactivated caspase-1 also cleaves gasdermin D, whose N-terminal fragments become inserted into cell 306 \nmembranes, thereby facilitating the release of more IL-1β  and IL-18[28,29]. In particular, 307 \ninflammasomes were classified into canonical inflammasomes, which activating caspase-1, and non-308 \ncanonical inflammasomes, which primarily activate caspase-4 and/or caspase-5 in human cells[29]. 309 \nSpecifically, the NAIP/NLRC4 inflammasome serves as a as part of the innate immune response and 310 \nsenses a range of intracellular bacteria and bacterial components in the host cell cytosol, such as S. 311 \ntyphimurium, Legionella pneumophila, flagellin and components of the virulence-associated type III 312 \nsecretion apparatus, thereby to mediate host defense against bacterial pathogens[26,27,30]. NAIP 313 \nproteins function as specific cytosolic receptors for a variety of bacterial protein ligands[31], while 314 \nNLRC4 interacts directly with caspase-1 to induce cell death, such as the pyroptosis of macrophage, 315 \nand to cause rapidly initiate inflammation and vascular fluid loss[26]. The localized effects of 316 \nNAIP/NLRC4 inflammasome could defend against bacterial pathogens. However, the aberrant 317 \nactivity of this cluster also leads to multiple clinical manifestations, including macrophage activation 318 \nsyndrome, neonatal enterocolitis and autoimmune disorders[28,31], and promotes the process of 319 \nsome diseases, such as gliomas[29], premature rupture of membranes[26], ulcerative colitis[28], 320 \nrheumatoid arthritis[30] and so on. Sim et al. found that non-canonical pathway molecules of 321 \nNAIP/NLRC4 inflammasomes, including caspase-4, caspase-5, and N-cleaved GSDMD, were 322 \nsignificantly increased with each glioma grade[29]. Additionally, Zhu et al. reported that NLRC4 was 323 \nupregulated in the membranes of patients with premature rupture of fetal membranes and recruited 324 \nmore caspase-1, which promotes the process of rupture of fetal membranes by inducing apoptosis 325 \nand degrading extracellular matrix[26]. The study of An et al. presented that the persistent activation 326 \nof NAIP/NLRC4 inflammasome induces macrophage pyroptosis mediated by caspase1-dependent 327 \ncleavage of GSDMD and releases proinflammatory cytokines including IL-1b and IL-18, facilitating 328 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n9 \nthe occurrence and progression of UC[28]. In addition, research conducted by Delgado-Arévalo et al. 329 \nindicated that NLRC4 as an inflammasome sensor differentially upregulated in CD1c+ cDC from 330 \npatients with rheumatoid arthritis, and this sensor seems to be nonredundantly involved in the 331 \ndetection of intracellular dsDNA[30]. Nevertheless, our research observed the significantly inhibitory 332 \neffect of up-regulated NLRC4 expression on dorsopathies. Thus, further research on the roles and 333 \nmechanisms of NLRC4 and inflammatsome in the progress of dorsopathies is needed.  334 \nCell growth regulator with EF-hand domain 1 (CGREF1) gene encodes a novel secretory protein 335 \nwith 2 Ca2+-binding EF-hand domains which plays an important role in eukaryotic cellular 336 \nsignaling[32]. CGREF1 mRNA has high expressions in HCT116, H1299 and HepG2 cells while 337 \nexpressing at low levels in other cell lines including Raji, Jurkat, BT325, PC12[32]. Mechanistically, 338 \nCGREF1 is regulated by p53[33], and the overexpression of CGREF1 significantly inhibits the 339 \ntranscriptional activity of AP-1, reduces the phosphorylation of ERK (extracellular signal-regulated 340 \nkinases) and p38 MAPK (mitogen-activated protein kinases), and suppresses the proliferation of 341 \nHEK293T and HCT116 cells[32]. Furthermore, CGREF1 can decrease the percent of G2/M and S 342 \nphase and repress cell proliferation while overexpressing[32]. We found that the increased expression 343 \nof CGREF1 is significantly related to the decreased risk of dorsopathies. In the research of Xiang et 344 \nal., the colorectal cancer patients with higher expression of CGREF1 were found to have significantly 345 \nbetter overall survival than patients with lower expression, and the role of CGREF1 in the prognosis 346 \nof Early-onset colorectal cancer was reported[33]. Furthermore, Xie et al. observed that CGREF1 347 \nexpression were high in the tissue of osteosarcoma patients while it were lowly expressed in normal 348 \ntissue, and speculated that CGREF1 can predict drug resistance to osteosarcoma[34]. However, the 349 \nbiological function of CGREF1 is poorly explored, and further study for its mechanisms and roles in 350 \nthese diseases is warranted. 351 \nKetohexokinase (KHK) is an enzyme that phosphorylates fructose to produce fructose-1-phosphate 352 \n(F-1-P) at the first rate-limiting step in fructose metabolism[35,36]. The gene KHK encodes two 353 \nisoforms, KHK-C and KHK-A. Considered as the primary enzyme in fructose metabolism, KHK-C is 354 \nexclusively expressed in a few tissues, especially in the liver, and  drives the aforementioned reaction 355 \nrapidly, resulting in the accumulation of uric acid and transient depletion of intracellular phosphate 356 \nand ATP[35–38]. In the contrary, KHK-A has a much weaker affinity and a higher Michaelis 357 \nconstant for fructose[38]. However, KHK-A can directly phosphorylate phosphoribosyl 358 \npyrophosphate synthetase 1 (PRPS1) by prevention of inhibitory nucleotide binding and facilitation 359 \nof ATP binding[37]. As a result, KHK-A improves nucleic acid synthesis and promotes the G1/S 360 \nphase transition in the cell cycle, accelerating cell proliferation[36,38].  361 \nRecent studies have highlighted the importance of KHK as the key mechanism stimulating the 362 \nvarious adverse metabolic effects of fructose, such as impaired insulin sensitivity, 363 \nhypertriglyceridemia, and oxidative stress[35]. In addition, fructose metabolism seems to provide 364 \nc\nancer cells with the supplementary fuel required for proliferation and metastasis in colon cancer, and 365 \nglioma[36,38]. Moreover, alternated expression of KHK is also related to several diseases. For 366 \ninstance, knocked down the expression of KHK dramatically reduced fructose-induced production of 367 \nreactive oxidative species (ROS) in proximal tubular cells[35], indicating the association between 368 \nKHK and ROS. Furthermore, KHK-A activates and upregulates PRPS1, which could lead to 369 \nenhanced nucleic acid synthesis for tumourigenesis[38]. Kim et al. reported that most cancer cell 370 \nlines predominantly expressed KHK-A rather than KHK-C, and KHK-A overexpression could 371 \naugment cell invasion[36]. Moreover, in their study, upon fructose stimulation, KHK-A acted as a 372 \nnuclear protein kinase and triggered Epithelial-mesenchymal transition in breast cancer, promoting 373 \nbreast cancer metastasis[36]. Besides, loss-of-function variants in KHK can cause essential 374 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n10 \nfructosuria, an autosomal recessive disease characterized by intermittent appearance of fructose in the 375 \nurine[39]. Yang et al. found the the overexpression of KHK-A in cell lines of oesophageal squamous 376 \ncell carcinoma, which may finally promote the proliferation, tumourigenicity and motility of ESCC 377 \ncells[38]. Similarly, KHK-A was considered to play an instrumental role in promoting de novo 378 \nnucleic acid synthesis and hepatocellular carcinoma development[37]. In our study, the raised 379 \nexpression of KHK is found to be positively related to decreased risk of dorsopathies. In oder to 380 \nunderstand the mechanism of changed expression level of KHK in dorsopathies, further research are 381 \nneeded. 382 \nRegulatory factor X2 (RFX2) gene is essential for maintaining normal spermatogenesis and involved 383 \nin spermatogenesis impairment and male infertility in mice[40]. In particular, Ring finger protein 212 384 \n(RNF212) gene is important for crossing over and chiasma formation during meiosis[41]. Mouse 385 \nRnf212 has a central role in designating crossover sites and coupling chromosome synapsis to the 386 \nformation of crossover-specific recombination complexes. In humans, RNF212 has been associated 387 \nwith variation in the genome-wide recombination rate[41]. The protein encoded by RNF212 has 388 \nhomology to two meiotic procrossover factors: Zip3 and ZHP-3[42]. It functions to couple 389 \nchromosome synapsis to the formation of crossover-specific recombination complexes[43]. 390 \nMoreover, with the symbolic RING-finger domains, RNF212 protein is a RING-family E3-ligase for 391 \nSmall ubiquitin-like protein (SUMO), and the latter plays an important role in assembly and 392 \ndisassembly of synaptonemal complex by regulating protein–protein inter action during 393 \nmeiosis[42,43,43]. In addition, RNF212 stabilizes association of a subset of MutSγ  complexes with 394 \nrecombination sites[42,44]. MutSγ  complex is a kind of meiosis-specific recombination factors, 395 \nworking as an attractive target for non-crossover/crossover differentiation[42]. It binds and stabilizes 396 \nDNA strand-exchange intermediates to promote both homolog synapsis and crossingover[44]. In 397 \nmammals, every pair of chromosomes obtains at least one crossover, while the majority of 398 \nrecombination sites yield non-crossovers. Non-crossovers are inferred to arise from the disassembly 399 \nof D-loops and annealing of DNA double-strand breaks ends in a process termed synthesis dependent 400 \nstrand annealing[42]. Designation of crossovers involves the formation of metastable joint molecules 401 \nand selective localization of SUMO-ligase RNF212 to a minority of recombination sites where it 402 \nstabilizes pertinent factors, such as MutSγ [42,44]. Furthermore, this differential RNF212-dependent 403 \nstabilization of key recombination proteins at precrossover sites is thought to be the basic feature of 404 \ncrossover/non-crossover differentiation[42]. It is suggested that RNF212-mediated SUMOylation 405 \nmay stabilizes the association of MutSγ  with nascent crossover CO intermediates in a number of 406 \nways, such as promoting protein-protein interactions, altering ATP binding and hydrolysis (which 407 \nmodulate the binding and dissociation of MutSγ  complexes) or antagonizing ubiquitin-dependent 408 \nprotein turnover[41,42]. 409 \nInsufficient RNF212 accumulating at recombination sites can lead to crossing-over stochastically 410 \nfails[43]. Fujiwara et al. reported that Rnf212 knock out (KO) in mice leads to infertility of male and 411 \nfemale due to the loss of SPCs(spermatocytes) at post-anaphase stage. Moreover, crossing over is 412 \ndiminished by ≥ 90% in Rnf212− /−  mice[43]. In particular, the Rnf212 KO spermatocytes lack 413 \nchiasmata and exhibit depletion of spermatids and mature spermatozoa[41]. A nonsense mutation in 414 \nthe Rnf212 gene was discovered in repro57 mutant mice, and this mutant mice exhibited male 415 \ninfertility, arrest of spermatogenesis in meiosis, and defects in cytological markers of recombination 416 \nand chiasma formation, which is similar to the Rnf212 KO phenotype[41]. In humans, the rate of 417 \ncrossing-over varies significantly between individuals, and higher maternal crossover rates have been 418 \nassociated with greater fecundity[42]. However, in the absence of RNF212, designation of crossover 419 \nsites fails because no MutSγ  complexes are stabilized beyond early pachynema[44]. Yu et al. 420 \ndetected that the frequencies of allele C and the genotype CC at the rs4045481 locus in RNF212 gene 421 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n11 \nwere significantly higher in patients with azoospermia in comparison with controls. Furthermore, 422 \nthey reported that homozygous of allele C (genotype CC) may decrease the activity of pre-mRNA 423 \ndue to the disappearance of the binding motifs of SRSF5, leading to the reduced expression of 424 \nRNF212 and influencing normal spermatogenesis, consequently increasing ther risk of 425 \nazoospermia[40]. Nevertheless, we found that the increased expression of RNF212 exhibited a 426 \npositive correlation with dorsopathies. The influence of raised expression of RNF212 is poor 427 \nclassified, and further researches are required to illustrate the mechanism of increased RNF212 level 428 \nand dorsopathies.  429 \nTo summary, our study revealed the causal associations between the genetically predicted expression 430 \nof four genes (NLRC4, CGREF1, KHK and RNF212) and dorsopathies risk, which offers new 431 \nperspectives and strategies for the improved diagnosis, treatment and prevention of dorsopathies. 432 \nBased on our results, we hypothesized that increased expression level of NLRC4, CGREF1 and KHK 433 \nare associated with decreased risk of dorsopathies, while the increased expression level of RNF212 434 \nhas the opposite effect. 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Muzny, J.G. Reid, Y. Zhu, BGI-578 \nShenzhen, J. Wang, Y. Chang, Q. Feng, X. Fang, X. Guo, M. Jian, H. Jiang, X. Jin, T. Lan, G. 579 \nLi, J. Li, Y. Li, S. Liu, X. Liu, Y. Lu, X. Ma, M. Tang, B. Wang, G. Wang, H. Wu, R. Wu, X. 580 \nXu, Y. Yin, D. Zhang, W. Zhang, J. Zhao, M. Zhao, X. Zheng, Broad Institute of MIT and 581 \nHarvard, E.S. Lander, D.M. Altshuler, S.B. Gabriel, N. Gupta, Coriell Institute for Medical 582 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n15 \nResearch, N. Gharani, L.H. Toji, N.P. Gerry, A.M. Resch, European Molecular Biology 583 \nLaboratory, European Bioinformatics Institute, P. Flicek, J. Barker, L. Clarke, L. Gil, S.E. Hunt, 584 \nG. Kelman, E. Kulesha, R. Leinonen, W.M. McLaren, R. Radhakrishnan, A. Roa, D. Smirnov, 585 \nR.E. Smith, I. Streeter, A. Thormann, I. Toneva, B. Vaughan, X. Zheng-Bradley, Illumina, D.R. 586 \nBentley, R. Grocock, S. Humphray, T. James, Z. Kingsbury, Max Planck Institute for Molecular 587 \nGenetics, H. Lehrach, R. Sudbrak, M.W. Albrecht, V.S. Amstislavskiy, T.A. Borodina, M. 588 \nLienhard, F. Mertes, M. Sultan, B. Timmermann, M.-L. Yaspo, McDonnell Genome Institute at 589 \nWashington University, E.R. Mardis, R.K. Wilson, L. Fulton, R. Fulton, US National Institutes 590 \nof Health, S.T. Sherry, V. Ananiev, Z. Belaia, D. Beloslyudtsev, N. Bouk, C. Chen, D. Church, 591 \nR. Cohen, C. Cook, J. Garner, T. Hefferon, M. Kimelman, C. Liu, J. Lopez, P. Meric, C. 592 \nO’Sullivan, Y. Ostapchuk, L. Phan, S. Ponomarov, V. Schneider, E. Shekhtman, K. Sirotkin, D. 593 \nSlotta, H. Zhang, University of Oxford, G.A. McVean, Wellcome Trust Sanger Institute, R.M. 594 \nDurbin, S. Balasubramaniam, J. Burton, P. Danecek, T.M. Keane, A. Kolb-Kokocinski, S. 595 \nMcCarthy, J. Stalker, M. Quail, Analysis group, Affymetrix, J.P. Schmidt, C.J. Davies, J. 596 \nGollub, T. Webster, B. Wong, Y. Zhan, Albert Einstein College of Medicine, A. Auton, C.L. 597 \nCampbell, Y. Kong, A. Marcketta, Baylor College of Medicine, R.A. Gibbs, F. Yu, L. Antunes, 598 \nM. Bainbridge, D. Muzny, A. Sabo, Z. Huang, BGI-Shenzhen, J. Wang, L.J.M. Coin, L. Fang, 599 \nX. Guo, X. Jin, G. Li, Q. Li, Y. Li, Z. Li, H. Lin, B. Liu, R. Luo, H. Shao, Y. Xie, C. Ye, C. Yu, 600 \nF. Zhang, H. Zheng, H. Zhu, Bilkent University, C. Alkan, E. Dal, F. Kahveci, Boston College, 601 \nG.T. Marth, E.P. Garrison, D. Kural, W.-P. Lee, W. Fung Leong, M. Stromberg, A.N. Ward, J. 602 \nWu, M. Zhang, Broad Institute of MIT and Harvard, M.J. Daly, M.A. DePristo, R.E. Handsaker, 603 \nD.M. Altshuler, E. Banks, G. Bhatia, G. Del Angel, S.B. Gabriel, G. Genovese, N. Gupta, H. Li, 604 \nS. Kashin, E.S. Lander, S.A. McCarroll, J.C. Nemesh, R.E. Poplin, Cold Spring Harbor 605 \nLaboratory, S.C. Yoon, J. Lihm, V. Makarov, Cornell University, A.G. Clark, S. Gottipati, A. 606 \nKeinan, J.L. Rodriguez-Flores, European Molecular Biology Laboratory, J.O. Korbel, T. 607 \nRausch, M.H. Fritz, A.M. Stütz, European Molecular Biology Laboratory, European 608 \nBioinformatics Institute, P. Flicek, K. Beal, L. Clarke, A. Datta, J. Herrero, W.M. McLaren, 609 \nG.R.S. Ritchie, R.E. Smith, D. Zerbino, X. Zheng-Bradley, Harvard University, P.C. Sabeti, I. 610 \nShlyakhter, S.F. Schaffner, J. Vitti, Human Gene Mutation Database, D.N. Cooper, E.V. Ball, 611 \nP.D. Stenson, Illumina, D.R. Bentley, B. Barnes, M. Bauer, R. Keira Cheetham, A. Cox, M. 612 \nEberle, S. Humphray, S. Kahn, L. Murray, J. Peden, R. Shaw, Icahn School of Medicine at 613 \nMount Sinai, E.E. Kenny, Louisiana State University, M.A. Batzer, M.K. Konkel, J.A. Walker, 614 \nMassachusetts General Hospital, D.G. MacArthur, M. Lek, Max Planck Institute for Molecular 615 \nGenetics, R. Sudbrak, V.S. Amstislavskiy, R. Herwig, McDonnell Genome Institute at 616 \nWashington University, E.R. Mardis, L. Ding, D.C. Koboldt, D. Larson, K. Ye, McGill 617 \nUniversity, S. Gravel, National Eye Institute, NIH, A. Swaroop, E. Chew, New York Genome 618 \nCenter, T. Lappalainen, Y. Erlich, M. Gymrek, T. Frederick Willems, Ontario Institute for 619 \nCancer Research, J.T. Simpson, Pennsylvania State University, M.D. Shriver, Rutgers Cancer 620 \nInstitute of New Jersey, J.A. Rosenfeld, Stanford University, C.D. Bustamante, S.B. 621 \nMontgomery, F.M. De La Vega, J.K. Byrnes, A.W. Carroll, M.K. DeGorter, P. Lacroute, B.K. 622 \nMaples, A.R. Martin, A. Moreno-Estrada, S.S. Shringarpure, F. Zakharia, Tel-Aviv University, 623 \nE. Halperin, Y. Baran, The Jackson Laboratory for Genomic Medicine, C. Lee, E. Cerveira, J. 624 \nHwang, A. Malhotra, D. Plewczynski, K. Radew, M. Romanovitch, C. Zhang, Thermo Fisher 625 \nScientific, F.C.L. Hyland, Translational Genomics Research Institute, D.W. Craig, A. 626 \nChristoforides, N. Homer, T. Izatt, A.A. Kurdoglu, S.A. Sinari, K. Squire, US National 627 \nInstitutes of Health, S.T. Sherry, C. Xiao, University of California, San Diego, J. Sebat, D. 628 \nAntaki, M. Gujral, A. Noor, K. Ye, University of California, San Francisco, E.G. Burchard, 629 \nR.D. Hernandez, C.R. Gignoux, University of California, Santa Cruz, D. Haussler, S.J. 630 \nKatzman, W. James Kent, University of Chicago, B. Howie, University College London, A. 631 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n16 \nRuiz-Linares, University of Geneva, E.T. Dermitzakis, University of Maryland School of 632 \nMedicine, S.E. Devine, University of Michigan, G.R. Abecasis, H. Min Kang, J.M. Kidd, T. 633 \nBlackwell, S. Caron, W. Chen, S. Emery, L. Fritsche, C. Fuchsberger, G. Jun, B. Li, R. Lyons, 634 \nC. Scheller, C. Sidore, S. Song, E. Sliwerska, D. Taliun, A. Tan, R. Welch, M. Kate Wing, X. 635 \nZhan, University of Montréal, P. Awadalla, A. Hodgkinson, University of North Carolina at 636 \nChapel Hill, Y. Li, University of North Carolina at Charlotte, X. Shi, A. Quitadamo, University 637 \nof Oxford, G. Lunter, G.A. McVean, J.L. Marchini, S. Myers, C. Churchhouse, O. Delaneau, A. 638 \nGupta-Hinch, W. Kretzschmar, Z. Iqbal, I. Mathieson, A. Menelaou, A. Rimmer, D.K. Xifara, 639 \nUniversity of Puerto Rico, T.K. Oleksyk, University of Texas Health Sciences Center at 640 \nHouston, Y. Fu, X. Liu, M. Xiong, University of Utah, L. Jorde, D. Witherspoon, J. Xing, 641 \nUniversity of Washington, E.E. Eichler, B.L. Browning, S.R. Browning, F. Hormozdiari, P.H. 642 \nSudmant, Weill Cornell Medical College, E. Khurana, Wellcome Trust Sanger Institute, R.M. 643 \nDurbin, M.E. Hurles, C. Tyler-Smith, C.A. Albers, Q. Ayub, S. Balasubramaniam, Y. Chen, V. 644 \nColonna, P. Danecek, L. Jostins, T.M. Keane, S. McCarthy, K. Walter, Y. Xue, Yale University, 645 \nM.B. Gerstein, A. Abyzov, S. Balasubramanian, J. Chen, D. Clarke, Y. Fu, A.O. Harmanci, M. 646 \nJin, D. Lee, J. Liu, X. Jasmine Mu, J. Zhang, Y. Zhang, Structural variation group, BGI-647 \nShenzhen, Y. Li, R. Luo, H. Zhu, Bilkent University, C. Alkan, E. Dal, F. Kahveci, Boston 648 \nCollege, G.T. Marth, E.P. Garrison, D. Kural, W.-P. Lee, A.N. Ward, J. Wu, M. Zhang, Broad 649 \nInstitute of MIT and Harvard, S.A. McCarroll, R.E. Handsaker, D.M. Altshuler, E. Banks, G. 650 \nDel Angel, G. Genovese, C. Hartl, H. Li, S. Kashin, J.C. Nemesh, K. Shakir, Cold Spring 651 \nHarbor Laboratory, S.C. Yoon, J. Lihm, V. Makarov, Cornell University, J. Degenhardt, 652 \nEuropean Molecular Biology Laboratory, J.O. Korbel, M.H. Fritz, S. Meiers, B. Raeder, T. 653 \nRausch, A.M. Stütz, European Molecular Biology Laboratory, European Bioinformatics 654 \nInstitute, P. Flicek, F. Paolo Casale, L. Clarke, R.E. Smith, O. Stegle, X. Zheng-Bradley, 655 \nIllumina, D.R. Bentley, B. Barnes, R. Keira Cheetham, M. Eberle, S. Humphray, S. Kahn, L. 656 \nMurray, R. Shaw, Leiden University Medical Center, E.-W. Lameijer, Louisiana State 657 \nUniversity, M.A. Batzer, M.K. Konkel, J.A. Walker, McDonnell Genome Institute at 658 \nWashington University, L. Ding, I. Hall, K. Ye, Stanford University, P. Lacroute, The Jackson 659 \nLaboratory for Genomic Medicine, C. Lee, E. Cerveira, A. Malhotra, J. Hwang, D. 660 \nPlewczynski, K. Radew, M. Romanovitch, C. Zhang, Translational Genomics Research 661 \nInstitute, D.W. Craig, N. Homer, US National Institutes of Health, D. Church, C. Xiao, 662 \nUniversity of California, San Diego, J. Sebat, D. Antaki, V. Bafna, J. Michaelson, K. Ye, 663 \nUniversity of Maryland School of Medicine, S.E. Devine, E.J. Gardner, University of Michigan, 664 \nG.R. Abecasis, J.M. Kidd, R.E. Mills, G. Dayama, S. Emery, G. Jun, University of North 665 \nCarolina at Charlotte, X. Shi, A. Quitadamo, University of Oxford, G. Lunter, G.A. McVean, 666 \nUniversity of Texas MD Anderson Cancer Center, K. Chen, X. Fan, Z. Chong, T. Chen, 667 \nUniversity of Utah, D. Witherspoon, J. Xing, University of Washington, E.E. Eichler, M.J. 668 \nChaisson, F. Hormozdiari, J. Huddleston, M. Malig, B.J. Nelson, P.H. Sudmant, Vanderbilt 669 \nUniversity School of Medicine, N.F. Parrish, Weill Cornell Medical College, E. Khurana, 670 \nWellcome Trust Sanger Institute, M.E. Hurles, B. Blackburne, S.J. Lindsay, Z. Ning, K. Walter, 671 \nY. Zhang, Yale University, M.B. Gerstein, A. Abyzov, J. Chen, D. Clarke, H. Lam, X. Jasmine 672 \nMu, C. Sisu, J. Zhang, Y. Zhang, Exome group, Baylor College of Medicine, R.A. Gibbs, F. 673 \nYu, M. Bainbridge, D. Challis, U.S. Evani, C. Kovar, J. Lu, D. Muzny, U. Nagaswamy, J.G. 674 \nReid, A. Sabo, J. Yu, BGI-Shenzhen, X. Guo, W. Li, Y. Li, R. Wu, Boston College, G.T. Marth, 675 \nE.P. Garrison, W. Fung Leong, A.N. Ward, Broad Institute of MIT and Harvard, G. Del Angel, 676 \nM.A. DePristo, S.B. Gabriel, N. Gupta, C. Hartl, R.E. Poplin, Cornell University, A.G. Clark, 677 \nJ.L. Rodriguez-Flores, European Molecular Biology Laboratory, European Bioinformatics 678 \nInstitute, P. Flicek, L. Clarke, R.E. Smith, X. Zheng-Bradley, Massachusetts General Hospital, 679 \nD.G. MacArthur, McDonnell Genome Institute at Washington University, E.R. Mardis, R. 680 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n17 \nFulton, D.C. Koboldt, McGill University, S. Gravel, Stanford University, C.D. Bustamante, 681 \nTranslational Genomics Research Institute, D.W. Craig, A. Christoforides, N. Homer, T. Izatt, 682 \nUS National Institutes of Health, S.T. Sherry, C. Xiao, University of Geneva, E.T. Dermitzakis, 683 \nUniversity of Michigan, G.R. Abecasis, H. Min Kang, University of Oxford, G.A. McVean, 684 \nYale University, M.B. Gerstein, S. Balasubramanian, L. Habegger, Functional interpretation 685 \ngroup, Cornell University, H. Yu, European Molecular Biology Laboratory, European 686 \nBioinformatics Institute, P. Flicek, L. Clarke, F. Cunningham, I. Dunham, D. Zerbino, X. 687 \nZheng-Bradley, Harvard University, K. Lage, J. Berg Jespersen, H. Horn, Stanford University, 688 \nS.B. Montgomery, M.K. DeGorter, Weill Cornell Medical College, E. Khurana, Wellcome 689 \nTrust Sanger Institute, C. Tyler-Smith, Y. Chen, V. Colonna, Y. Xue, Yale University, M.B. 690 \nGerstein, S. Balasubramanian, Y. Fu, D. Kim, Chromosome Y group, Albert Einstein College of 691 \nMedicine, A. Auton, A. Marcketta, American Museum of Natural History, R. Desalle, A. 692 \nNarechania, Arizona State University, M.A. Wilson Sayres, Boston College, E.P. Garrison, 693 \nBroad Institute of MIT and Harvard, R.E. Handsaker, S. Kashin, S.A. McCarroll, Cornell 694 \nUniversity, J.L. Rodriguez-Flores, European Molecular Biology Laboratory, European 695 \nBioinformatics Institute, P. Flicek, L. Clarke, X. Zheng-Bradley, New York Genome Center, Y. 696 \nErlich, M. Gymrek, T. Frederick Willems, Stanford University, C.D. Bustamante, F.L. Mendez, 697 \nG. David Poznik, P.A. Underhill, The Jackson Laboratory for Genomic Medicine, C. Lee, E. 698 \nCerveira, A. Malhotra, M. Romanovitch, C. Zhang, University of Michigan, G.R. Abecasis, 699 \nUniversity of Queensland, L. Coin, H. Shao, Virginia Bioinformatics Institute, D. Mittelman, 700 \nWellcome Trust Sanger Institute, C. Tyler-Smith, Q. Ayub, R. Banerjee, M. Cerezo, Y. Chen, 701 \nT.W. Fitzgerald, S. Louzada, A. Massaia, S. McCarthy, G.R. Ritchie, Y. Xue, F. Yang, Data 702 \ncoordination center group, Baylor College of Medicine, R.A. Gibbs, C. Kovar, D. Kalra, W. 703 \nHale, D. Muzny, J.G. Reid, BGI-Shenzhen, J. Wang, X. Dan, X. Guo, G. Li, Y. Li, C. Ye, X. 704 \nZheng, Broad Institute of MIT and Harvard, D.M. Altshuler, European Molecular Biology 705 \nLaboratory, European Bioinformatics Institute, P. Flicek, L. Clarke, X. Zheng-Bradley, 706 \nIllumina, D.R. Bentley, A. Cox, S. Humphray, S. Kahn, Max Planck Institute for Molecular 707 \nGenetics, R. Sudbrak, M.W. Albrecht, M. Lienhard, McDonnell Genome Institute at 708 \nWashington University, D. Larson, Translational Genomics Research Institute, D.W. Craig, T. 709 \nIzatt, A.A. Kurdoglu, US National Institutes of Health, S.T. Sherry, C. Xiao, University of 710 \nCalifornia, Santa Cruz, D. Haussler, University of Michigan, G.R. Abecasis, University of 711 \nOxford, G.A. McVean, Wellcome Trust Sanger Institute, R.M. Durbin, S. Balasubramaniam, 712 \nT.M. Keane, S. McCarthy, J. Stalker, Samples and ELSI group, A. Chakravarti, B.M. Knoppers, 713 \nG.R. Abecasis, K.C. Barnes, C. Beiswanger, E.G. Burchard, C.D. Bustamante, H. Cai, H. Cao, 714 \nR.M. Durbin, N.P. Gerry, N. Gharani, R.A. Gibbs, C.R. Gignoux, S. Gravel, B. Henn, D. Jones, 715 \nL. Jorde, J.S. Kaye, A. Keinan, A. Kent, A. Kerasidou, Y. Li, R. Mathias, G.A. McVean, A. 716 \nMoreno-Estrada, P.N. Ossorio, M. Parker, A.M. Resch, C.N. Rotimi, C.D. Royal, K. Sandoval, 717 \nY. Su, R. Sudbrak, Z. Tian, S. Tishkoff, L.H. Toji, C. Tyler-Smith, M. Via, Y. Wang, H. Yang, 718 \nL. Yang, J. Zhu, Sample collection, British from England and Scotland (GBR), W. Bodmer, 719 \nColombians in Medellín, Colombia (CLM), G. Bedoya, A. Ruiz-Linares, Han Chinese South 720 \n(CHS), Z. Cai, Y. Gao, J. Chu, Finnish in Finland (FIN), L. Peltonen, Iberian Populations in 721 \nSpain (IBS), A. Garcia-Montero, A. Orfao, Puerto Ricans in Puerto Rico (PUR), J. Dutil, J.C. 722 \nMartinez-Cruzado, T.K. Oleksyk, African Caribbean in Barbados (ACB), K.C. Barnes, R.A. 723 \nMathias, A. Hennis, H. Watson, C. McKenzie, Bengali in Bangladesh (BEB), F. Qadri, R. 724 \nLaRocque, P.C. Sabeti, Chinese Dai in Xishuangbanna, China (CDX), J. Zhu, X. Deng, Esan in 725 \nNigeria (ESN), P.C. Sabeti, D. Asogun, O. Folarin, C. Happi, O. Omoniwa, M. Stremlau, R. 726 \nTariyal, Gambian in Western Division – Mandinka (GWD), M. Jallow, F. Sisay Joof, T. Corrah, 727 \nK. Rockett, D. Kwiatkowski, Indian Telugu in the UK (ITU) and Sri Lankan Tamil in the UK 728 \n(STU), J. Kooner, Kinh in Ho Chi Minh City, Vietnam (KHV), T. Tị nh Hiê`n, S.J. Dunstan, N. 729 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n18 \nThuy Hang, Mende in Sierra Leone (MSL), R. Fonnie, R. Garry, L. Kanneh, L. Moses, P.C. 730 \nSabeti, J. Schieffelin, D.S. Grant, Peruvian in Lima, Peru (PEL), C. Gallo, G. Poletti, Punjabi in 731 \nLahore, Pakistan (PJL), D. Saleheen, A. Rasheed, Scientific management, L.D. Brooks, A.L. 732 \nFelsenfeld, J.E. McEwen, Y. Vaydylevich, E.D. Green, A. Duncanson, M. Dunn, J.A. Schloss, 733 \nJ. Wang, H. Yang, Writing group, A. Auton, L.D. Brooks, R.M. Durbin, E.P. Garrison, H. Min 734 \nKang, J.O. Korbel, J.L. Marchini, S. McCarthy, G.A. McVean, G.R. 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(which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n20 \n[41] A. Riera-Escamilla, A. Enguita-Marruedo, D. Moreno-Mendoza, C. Chianese, E. Sleddens-809 \nLinkels, E. Contini, M. Benelli, A. Natali, G.M. Colpi, E. Ruiz-Castañé, M. Maggi, W.M. 810 \nBaarends, C. Krausz, Sequencing of a ‘mouse azoospermia’ gene panel in azoospermic men: 811 \nidentification of RNF212 and STAG3 mutations as novel genetic causes of meiotic arrest, 812 \nHuman Reproduction 34 (2019) 978–988. https://doi.org/10.1093/humrep/dez042. 813 \n[42] A. Reynolds, H. Qiao, Y. Yang, J.K. Chen, N. Jackson, K. Biswas, J.K. Holloway, F. Baudat, B. 814 \nDe Massy, J. Wang, C. Höög, P.E. Cohen, N. Hunter, RNF212 is a dosage-sensitive regulator of 815 \ncrossing-over during mammalian meiosis, Nat Genet 45 (2013) 269–278. 816 \nhttps://doi.org/10.1038/ng.2541. 817 \n[43] Y. Fujiwara, H. Matsumoto, K. Akiyama, A. Srivastava, M. Chikushi, M. Ann Handel, T. 818 \nKunieda, An ENU-induced mutation in the mouse Rnf212 gene is associated with male meiotic 819 \nfailure and infertility, REPRODUCTION 149 (2015) 67–74. https://doi.org/10.1530/REP-14-820 \n0122. 821 \n[44] H. Qiao, H.B.D. Prasada Rao, Y. Yang, J.H. Fong, J.M. Cloutier, D.C. Deacon, K.E. Nagel, 822 \nR.K. Swartz, E. Strong, J.K. Holloway, P.E. Cohen, J. Schimenti, J. Ward, N. Hunter, 823 \nAntagonistic roles of ubiquitin ligase HEI10 and SUMO ligase RNF212 regulate meiotic 824 \nrecombination, Nat Genet 46 (2014) 194–199. https://doi.org/10.1038/ng.2858. 825 \n 826 \n9 Tables 827 \n9.1 Table 1  828 \nGene \nC\nh \nPositio n \nZ_TWA\nS \nP_TW\nAS \nP_TWA S \n(FDR) \nTissue topSN P \nB_SM\nR \nSE_S\nMR \nP_SM\nR \nP_SMR \n(FDR) \nP_HEI\nDI \nN_HEI\nDI \nNLRC\n4 \n2 \n322658\n53 \n-5.33  \n9.8E-\n08 5.1E-05 \nGTExv8.Whole_Bl\nood \nrs38507\n6 \n-0.13  0.02  \n7.9E-\n09 9.5E-06 \n3.6E-\n01 \n20 \nCGRE\nF1 \n2 \n271191\n14 \n-4.76  \n1.9E-\n06 5.0E-04 \nGTExv8.Whole_Bl\nood \nrs23046\n81 \n-0.38  0.09  \n2.1E-\n05 4.6E-03 \n4.4E-\n01 \n20 \nKHK 2 \n270867\n46 \n-4.73  \n2.3E-\n06 5.7E-04 \nGTExv8.Whole_Bl\nood \nrs21190\n26 \n-0.06  0.01  \n2.3E-\n06 1.0E-03 \n2.5E-\n01 \n20 \nRNF2\n12 \n4 \n111356\n1 \n3.75  \n1.8E-\n04 0.01  \nGTExv8.Whole_Bl\nood \nrs49745\n91 \n0.19  0.04  \n1.2E-\n07 9.4E-05 \n5.5E-\n02 \n20 \nTable 1 TWAS / SMR/HEIDI results of the GWAS data on Dorsopathies, blood eQTL 829 \ndata.TWAS/SMR/HEIDI results of the GWAS data on Dorsopathies, blood eQTL data. Ch 830 \nrepresents chromosome;position indicates the gene's position, Z_TWAS, P_TWAS are the Z-score 831 \nand p-value of the TWAS test; Tissue represents the tissue source used in the TWAS analyses. 832 \ntopSNP represents the top SNP in the SMR analyses. P_SMR is the p-value for the SMR test; 833 \nB_SMR is the effect size from the SMR test; SE_SMR is the standard error of B_SMR; P_HEIDI is 834 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n21 \nthe p-value for the HEIDI test; N_HEIDI is the number of SNPs used in the HEIDI test; FDR 835 \nrepresents false discovery rate. 836 \n 837 \n9.2 Table 2 838 \n 839 \nMarker Gene \nMethyl ation \non \nGene \nMethyl ation \non \nDorsop athies \nGene \non \nDorsop athies \nIntermediary analyses  prop \nmediat e\nd \nprop \nmediat ed \nse \nb se p b se p b se p indirec t indirec t_se indirec t_p \ncg0468695\n3 \nRNF21\n2 \n-\n0.5\n2 \n0.0\n7 \n2.08E\n-14 \n-0.1 \n0.0\n2 \n2.68E\n-06 \n0.1\n9 \n0.0\n4 \n1.20E\n-07 \n-0.1 0.02 1.35E-05 93.79 % 0.29 \ncg2663850\n5 \nNLRC4 \n-\n0.8\n1 \n0.1\n3 \n1.25E\n-09 \n0.0\n9 \n0.0\n3 \n1.43E\n-03 \n-\n0.1\n3 \n0.0\n2 \n7.87E\n-09 \n0.11 0.03 2.87E-05 \n120.0 0\n% \n0.47 \ncg1894812\n5 \nNLRC4 \n-\n0.9\n3 \n0.1\n7 \n1.72E\n-08 \n0.1\n2 \n0.0\n3 \n5.59E\n-04 \n-\n0.1\n3 \n0.0\n2 \n7.87E\n-09 \n0.12 0.03 5.50E-05 \n104.4 1\n% \n0.4 \ncg2274078\n3 \nCGRE\nF1 \n0.3\n2 \n0.0\n6 \n7.71E\n-07 \n-\n0.1\n3 \n0.0\n4 \n1.63E\n-04 \n-\n0.3\n8 \n0.0\n9 \n2.12E\n-05 \n-0.12 0.04 1.27E-03 92.24 % 0.38 \ncg0611241\n5 \nCGRE\nF1 \n-\n0.3\n4 \n0.0\n7 \n1.75E\n-06 \n0.1\n2 \n0.0\n3 \n5.59E\n-04 \n-\n0.3\n8 \n0.0\n9 \n2.12E\n-05 \n0.13 0.04 1.49E-03 \n110.6 3\n% \n0.47 \nTable 2 Main Results of Intermediary analyses. b is the effect size, se is the standard error of effect 840 \nsize, and p is the P-value. prop_mediated represents the proportion of indirect effect in the total 841 \neffect. 842 \n 843 \n10 Figures 844 \n10.1 Figure 1 845 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n22 \n 846 \nFig.1  Venn Plot of the main results of multi analysis 847 \n 848 \n10.2 Figure 2 849 \n 850 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n23 \nFig.2 Methylation sites affect disease pathways. 851 \n10.3 Figure 3 852 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n24 \n 853 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n25 \nFig.3 Manhattan plot of the MR/SMR/FUSION analysis results using QTLs and Dorsopathies 854 \nGWAS summary statistics. The red dashed line represents the Bonferroni-corrected significance 855 \nthreshold. 856 \n 857 \n10.4 Figure 4 858 \n 859 \nFig.4  MR results for genes expression significantly associated with dorsopathies after FDR 860 \ncorrection. 861 \n 862 \n10.5 Figure 5 863 \n 864 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n26 \nFig. 5 SMR locus plot illustrating dorsopathies at the gene locus utilizing blood eQTL data. In the 865 \nupper plot, each gray dot represents a SNP identified through GWAS on dorsopathies. A red 866 \ndiamond indicates passage of the SMR test for the probe, while a solid diamond signifies successful 867 \ncompletion of both the SMR and HEIDI tests. In the lower plot, each red cross represents an SNP 868 \nidentified in the eQTL study corresponding to each gene. The x-axis displays the genomic positions 869 \n(Mb, GRCh37) of SNPs, probes, and genes on the chromosome. The y-axis displays the negative 870 \nlogarithm (base 10) of p-values for SNPs identified in the GWAS on dorsopathies, SMR test, and 871 \neQTL study corresponding to each gene. 872 \n 873 \n10.6 Figure 6 874 \n 875 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint \n\n \n27 \nFig. 6 Locus comparison plot displays the results of colocalization analysis for SNPs associated with 876 \ngene expression in blood and datasets related to dorsopathies. Each dot represents a specific SNP, 877 \nwith the color indicating its linkage disequilibrium (LD) value (r2) with the lead GWAS variant, 878 \nmarked by a purple diamond. In the right panel, the x-axis represents genomic positions in 879 \nmegabases (GRCh37) along the chromosome, while the y-axis shows the -log10 p-values for SNPs 880 \nfrom the dorsopathies GWAS (top) and the gene expression eQTL study (bottom). The left panel 881 \ncompares the p-values from the dorsopathies GWAS with those from the gene expression eQTL 882 \nstudy. 883 \n 884 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted May 20, 2024. ; https://doi.org/10.1101/2024.05.01.24306675doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}