Single-cell and genome-wide Mendelian randomization identifies causative genes for gout

Single-cell and genome-wide Mendelian randomization identifies causative genes for gout | 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 Single-cell and genome-wide Mendelian randomization identifies causative genes for gout yubiao yang, Ping Hu, Qinnan Zhang, Boyuan Ma, Jinyu Chen, Bitao Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3687354/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Gout is a prevalent manifestation of metabolic osteoarthritis induced by elevated blood uric acid levels. The purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and elucidate its pathogenesis. Methods The study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and gout. Results We identified 17 association signals for gout at unique genetic loci, including four genes related by protein-protein interaction network (PPI) analysis: TRIM46, THBS3, MTX1, and KRTCAP2. Additionally, we discerned 22 methylation sites in relation to gout. The study also found that genes such as TRIM46, MAP3K11, KRTCAP2, and TM7SF2 could potentially elevate the risk of gout. Through a Mendelian randomization (MR) analysis, we identified three proteins causally associated with gout: ADH1B, BMP1, and HIST1H3A. Conclusion According to our findings, gout is linked with the expression and function of particular genes and proteins. These genes and proteins have the potential to function as novel diagnostic and therapeutic targets for gout. These discoveries shed new light on the pathological mechanisms of gout and clear the way for future research on this condition. Mendelian randomization Gout Summary-data-based Mendelian Randomization GWAS scRNA-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 KEYPOINT · This study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and explored the gene expression regulation mechanisms of gout. · This study used two-sample Mendelian randomization method to understand the causal relationship between proteins and gout. · This study identified 17 genetic loci associated with gout, including four genes related by protein-protein interaction network (PPI) analysis: TRIM46, THBS3, MTX1 and KRTCAP2. · This study also identified 22 methylation sites related to gout, and genes that may increase the risk of gout, such as TRIM46, MAP3K11, KRTCAP2 and TM7SF2. · This study determined three proteins causally associated with gout by Mendelian randomization (MR) analysis: ADH1B, BMP1 and HIST1H3A. Introduction Gout is a prevalent type of metabolic osteoarthritis induced by elevated blood uric acid levels. Uric acid results from the breakdown of purines, which occurs in numerous substances and cells[ 1 – 3 ]. Hyperuricemia occurs when uric acid production increases due to diet and lifestyle, or when uric acid excretion decreases due to kidney dysfunction[ 4 ]. At a certain level of uric acid saturation in the blood, needle-like urate crystals form and deposit in the joints, cartilages, tendons, etc., triggering an immune response and inflammation[ 5 ]. A high level of is prevalent in mainland China at a rate of 13.3%, affecting approximately 177 million people; gout is prevalent at a rate of 1.1%, affecting approximately 14.66 million people[ 6 ]. In China, the prevalence rate is marginally lower than in Europe and the United States, but it has been rising over the past decade. There are also regional variations; the prevalence is generally higher in economically developed, coastal, and urban regions than in economically less developed, inland, and rural regions. This could be due to living conditions, dietary practices, environmental factors, etc[ 7 ]. Some investigations indicate that gout has a genetic component. 10–25% of gout patients' close relatives have hyperuricemia; if one parent has gout, 40–50% of the child's children will have gout; if parents have gout, up to 75% of the child's children will have gout[ 8 , 9 ]. Genome-wide association studies (GWAS) is the main approach for identifying genetic causes of disease, but the majority of GWAS loci are in noncoding regions, making functional annotation and mechanistic explanations challenging[ 10 ]. In recent years, single-cell transcriptomics (scRNA-seq) has become an essential instrument for researching diseases in order to obtain insight into their pathogenesis. Unlike traditional aggregate methodologies, scRNA-seq technology can provide gene expression information from individual cells, thereby overcoming the problem of cellular heterogeneity and providing a more precise and comprehensive perspective for studying diseases[ 11 ]. Owing to single-cell transcriptomics, eQTL analysis has emerged as a significant instrument for delving deeper into the mechanisms behind gout. The combination of single-cell transcriptomics and eQTL analysis can shed light on the cell-specificity of gene expression regulation by correlating data from single-cell transcriptomics with genetic variations in individuals and pinpointing locations that regulate gene expression. The present research aims to combine gout GWAS data, scRNA-seq data, and eQTL and mQTL data to investigate the genetic regulation mechanism of gout disease and further our understanding of its pathogenesis. To investigate the dynamic regulatory network in the pathogenesis of gout disease, we will conduct a comprehensive analysis of the expression regulation of gout-related genes in individual cells. And we will comprehend the causal connection between proteins and gout by pQTL and a Mendelian randomization investigation with two samples. Through this study, we expect to elucidate the pathological mechanisms of gout disease and provide new hints and research strategies for gout(Fig. 1 ). Materials and Method Gout GWAS data sources We sourced gout GWAS data from the Finnish database ( https://www.finngen.fi/en ), encompassing 4607 instances and 335038 control subjects of European descent. For an in-depth look at the collection of samples, methods of analysis, and findings, kindly refer to the original publication. Quantitative Trait Locus Data Sources Using data from the eQTLGen consortium, we retrieved cis-eQTLs located within a 1000 kb range of genetic variants exhibiting a robust correlation with gene expression in relation to blood tissue eQTL data. The eQTLGen consortium includes information on 10,317 SNPs associated with the characterization of 31,684 individuals. eQTLGen does not include variants associated with X and Y chromosome and mitochondrial DNA gene expression levels, however[ 12 ]. For mQTL data pertaining to blood tissue, we collected peripheral blood samples from two European cohorts: the BSGS (n = 614) and the LBC (n = 1366). Using the Illumina HumanMethylation450 microarray, the methylation status of the samples was evaluated. To compile the mQTL summary data, we performed a meta-analysis of BSGS and LBC data. Only DNA methylation probes containing at least one cis-mQTL (P < 5×10 − 8 ) and restricted to SNPs within 2 Mb of each probe were included[ 13 – 16 ]. For pQTL data related to blood tissue, we employed the MR cis-pQTL tool to choose SNPs demonstrating a strong correlation with protein expression from five proteomic databases. We included solely SNPs with a p-value of at least 5×10 − 8 for their association with protein expression. In addition, we integrated the plasma pQTL data of Ferkingstad et al., who conducted measurements of 4907 plasma proteins in a group of 35,559 participants from Iceland[ 17 , 18 ]. Sources of single-cell sequencing data for gouty blood peripheral mononuclear cells We employed the GSE211783 dataset from the GEO database, containing single-cell RNA-sequencing data of peripheral blood from three patients with gout during acute flare and three during remission. Yu H and his team carried out scRNA-seq on PBMC from these patients using 10x Genomics technology, and their results were validated via flow cytometry and LC-MS/MS[ 19 ]. Mendelian randomization analysis based on summary data We utilized the SMR software application to implement the SMR & HEIDI method, which combines data from GWAS and eQTL studies to assess for multifaceted correlations among levels of gene expression and complex characteristics of interest[ 20 ]. For LD calculations, we utilized 1000 Genomes European Reference Data[ 21 ]. To gain a deeper understanding of the relationship between eQTLs and mQTLs in terms of disease risk, we conducted a three-step SMR analysis. First, we employed SNPs as instrumental variables, significantly correlated expression of genes as exposure variables, and GWAS as outcome variables. In the second stage, DNAm was used as the exposure variable and GWAS was used as the outcome variable. In the third stage, DNAm was added as the exposure variable, and gene expression was added as the outcome variable. Only significant signals from stages one and two were included in step three[ 22 ]. The Benjamini-Hochberg (BH) method was used to compute the FDR of the p-value of the SMR, and FDR SMR 0.05 and heterogeneity HEIDI > 0.01 were used as inclusion criteria for the outcome. Two-sample Mendelian randomization analysis We conducted a two-sample randomization Mendelian analysis utilising "TwoSampleMR" with plasma proteins as the exposure and gout as the outcome. We used the Bonferroni correction to account for multiple testing and a p-value threshold of (P < 1.126 × 10 − 5 , 0.05/4441) to evaluate the results for telomere length-related proteins. Bayesian Colocalization Analysis Conventionally, the five postulates of colocalization analysis (Supplementary Methods) are defined as follows: Both the exposure and outcome phenotypes are not associated with the SNP. H1: While the primary phenotype (exposure) correlates with the SNP, the secondary phenotype (outcome) does not. H2: The secondary phenotype (result) correlates with the SNP, whereas the primary phenotype (exposure) does not. Both phenotypes are associated with the SNP, but these associations are distinct. H4: Both phenotypes correlate with the SNP, and the causal SNP is shared by both associations. Bayesian colocalization analysis utilizing the 'coloc' package with default parameters ( https://github.com/chr1swallace/coloc ) is employed to determine the probability that two characteristics share the same causative variation. As pointed out previously, Bayesian colocalization presents posterior probabilities for the five possibilities regarding whether two characteristics share a single variant. In the present investigation, we calculated the likelihood of the posterior for Hypotheses 3 (PPH3), i.e., both the protein and gout have a relationship with the SNP, but these relationships are independent, and 4 (PPH4), i.e., both the protein and MS have a relationship with the SNP, and these relationships share the same causal SNP. We designated a gene as having colocalization evidence if its gene-based PPH4 was greater than 80%. Integrating Multi-Omics Data Using Summary-data-based Mendelian Randomization (SMR) Analysis We utilized the SMR tool ( http://cnsgenomics.com/software/smr/ ) to discover consistent Summary-data-based Mendelian Randomization (SMR) associations across multiple omics. This instrument combines GWAS summary statistics with eQTL or mQTL summary statistics to determine whether particular mutations influence the risk of disease by influencing gene expression or methylation of DNA. The summary GWAS data for gout were entered as'mygwas. ma'. The methylation quantitative trait loci (mQTL) and expression quantitative trait loci (eQTL) data were respectively entered as'mymqtl' and'myeqtl'. The SMR instrument processed the input data using the following command: smr --bfile mydata --gwas-summary mygwas.ma --beqtl-summary mymqtl --beqtl-summary myeqtl --out myplot --plot --probe ENSG00000163462 --probe-wind 500 --gene-list glist-hg19 The created diagrams provide an overview of the GWAS results displaying the p-values of each SNP, the eQTL results illustrating the effect of each SNP on the expression of genes, and the mQTL results illustrating the effect of each SNP on DNA methylation. This helps to comprehend how variations in gene expression or DNA methylation may affect disease risk. We intended to identify and validate putative genetic regulatory mechanisms by integrating these results and employing additional statistical analyses (such as the HEIDI test). Such knowledge is essential for comprehending the pathophysiology of diseases and identifying new therapeutic targets. Results Genome-wide cis-eQTL and SMR analysis of gout outcomes We performed SMR analysis on 15,324 SNPs in the blood that represent pertinent gene expression and gout outcomes in blood. To compensate for the genome-wide kind I errors, we performed FDR correction (P 0.01) incorporated in the SMR programme to determine if the associations were caused by sharing causes of variation as opposed to pleiotropy. We discovered 17 gout relationship signatures at distinct genetic loci(Fig. 2 , Additional file 1: Table S1 ). Four genes, TRIM46, THBS3, MTX1, and KRTCAP2, were found to be interconnected via protein-protein interaction network (PPI) analysis(Additional file 2: Figure S1 ). [ 23 ]A review article, for instance, described the complicated connection between uric acid, gout, and brain disease, and mentioned the THBS3 gene as a gene associated with uric acid metabolism and gout, with variants affecting uric acid excretion and deposition. [ 24 ]This is consistent with our finding that a reduction in the standard deviation of THBS3 expression was associated with an 18% risk reduction (beta=-0.18, FDR = 4.12×10 − 5 ). Bayesian co-localization analysis The SMR analysis has identified 17 genes as gout-causing genes. The posterior probabilities of Hypothesis 4 for THBS3, GBAP1, MTX1, THBS3-AS1, FUT8, UNC13D, LYPD2, and GNGT2 are greater than 0.80, per the Bayesian colocalization analysis (Additional file 3: Figure S2 ). This suggests that there is a high probability that these genes are associated with gout and that they are likely caused by shared variants. For TM7SF2, CDC42EP2, and TIGD3, however, the posterior probabilities of Hypothesis 3 exceed 0.80. This suggests that these genes are associated with gout, but are most likely controlled by distinct variants.(Table 1 ) Table 1 Results of eQTL-GWAS co-localization exposure nsnps PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf THBS3 4641 0 4.60E-06 0 0.011 0.989 GBAP1 4381 0 1.09E-05 0 0.028 0.972 MTX1 4343 2.56E-64 3.66E-05 6.69E-61 0.095 0.905 MAP3K11 4510 0 2.18E-04 0 0.414 0.586 THBS3-AS1 4520 1.56E-18 3.54E-06 4.09E-15 0.008 0.992 PCNX3 4456 4.59E-63 2.21E-04 8.72E-60 0.419 0.581 TRIM46 4623 5.78E-19 1.19E-05 1.51E-15 0.03 0.97 TM7SF2 4651 6.02E-105 4.65E-04 1.14E-101 0.881 0.118 FUT8 7007 0 0.012 0 0.149 0.839 UNC13D 6469 7.85E-130 0.022 2.41E-129 0.066 0.913 KAT5 4419 8.46E-11 1.42E-04 1.61E-07 0.269 0.73 LYPD2 8220 0 0.025 0 0.04 0.935 CDC42EP2 4557 2.49E-60 5.09E-04 4.71E-57 0.966 0.033 KRTCAP2 4623 2.97E-14 2.91E-04 7.77E-11 0.76 0.24 SIGLEC11 6722 0 1.74E-29 0 1 0 GNGT2 5757 0 0.044 0 0.094 0.861 TIGD3 4570 6.50E-35 5.09E-04 1.23E-31 0.966 0.033 The results of the Bayesian colocalization analysis can assist us in evaluating the veracity of these hypotheses, thereby providing crucial hints for future investigation. In particular, the posterior probability represents an estimate of the posterior probability distribution, given the data and prior assumptions. It reflects the likelihood of various hypotheses being supported by the data. Therefore, a greater posterior probability suggests that the hypothesis is more credible given the data. Genome-wide cis-mQTL SMR analysis and gene endings To further elucidate the pathogenesis of gout, an SMR analysis, FDR correction, and HEIDI test were conducted between blood mQTL and gout. We identified 22 methylation sites associated with gout, in which multiple methylation sites on SLC2A9 and SIPA1 were regulated, thereby influencing gout disease (Fig. 3 , Additional file 4: Table S2 ). For instance, elevated DNAm at cg25361844 increased disease risk (beta = 0.37), whereas decreased DNAm at cg17480646 increased disease risk (beta = 0.13). Intriguingly, a study of European and Polynesian populations discovered that a prevalent variant in the ABCG2 (rs2231142) was positively associated with hyperuricemia and gout, meaning that populations carrying alleles with this variant had increased uric acid levels and a higher likelihood of gout. [ 25 ]However, in the present research, the most significant SNP in this locus, ABCG2, rs10011796, was associated negatively with gout risk. This is an intriguing discovery that warrants further study. In addition, since it is already known that methylation of genes affects gene expression, we mapped gene methyl to expression via sharing variation in genetics and performed an SMR study of the causative connection among methyl and translation of relevant genes. Analysis of blood eQTL and mQTL with SMR of gout disease In accordance to the three-step SMR study described in the methodology part, we filtered out key disease-related signals. We discovered that TRIM46 had a positive correlation with gout (beta = 1.34); therefore, upregulation of DNAm on the cg15699386 locus would result in an increase in TRIM46 expression (beta = 0.24), which would increase the risk of gout development (beta = 0.43). High DNAm expression at two other loci on the TRIM46 gene (cg05778494, cg00577578) exhibits a negative correlation with this gene's expression (beta=-0.20, beta=-0.08) (Fig. 4 , Additional file 5: Table S3 ). Five DNAm sites within the open reading frame (ORF) are substantially linked to TRIM46 and Gout, two of which are in the promoter area region and three in the enhancer region. Using SMR on our omics data, we demonstrate that TRIM46 is a key gene for Gout and may uncover its plausible molecular pathogenesis mechanisms (Fig. 5 , Additional file 6: Figure S3 ). Single-cell sequencing analysis Eight alleles have been identified that increase the risk of gout, a disease that results from an excess of uric acid in the blood. These genes are predominantly expressed in the monocytes/macrophages, plasma cells, mast cells, and myeloid dendritic cells (MDCs) of the immune system. The genes MAP3K11, KRTCAP2, and PCNX3 influence the function of macrophages, plasma cells, and MDCs, respectively, to increase gout risk. We also discovered a significant correlation between mast cell TM7SF2 expression and gout risk (beta = 1.34) (Fig. 6 , Additional file 7: Figure S4 ). Screening the proteome for gout cure-related proteins We performed a Mendelian randomization analysis to find proteins that are causally linked to gout to find potential therapeutic targets. ADH1B (OR = 0.15, 95% CI = 0.07–0.35), BMP1 (OR = 7.04, 95% CI = 3.35–14.78), and HIST1H3A (OR = 205.85,95%CI = 78.35–540.8) were found to be significantly associated with gout at the Bonferroni-corrected threshold (P 1.719 10 − 6) (Fig. 6 , Additional file 8: Table S4 ). Five SNPs that affect the expression or function of these proteins were correlated with them. However, no protein-protein interaction (PPI) was identified between these three proteins, suggesting that they may act independently in the pathogenesis of gout. Discussion Using GWAS data, scRNA-seq, eQTL, mQTL data, and pQTL with a two-sample Mendelian randomization study, the purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and improve our understanding of its pathogenesis. The results demonstrated that mast cells play a crucial role in gout, as they contain the maximum number of disease-associated risk alleles. Prior research has demonstrated a correlation between an increase in mast cells in the synovium of gouty joints and tissue injury[ 26 ]. TRIM46 was identified as the most prevalent susceptibility gene with a promotion factor for gout among these mast cells (beta = 1.34). Notably, a variant site on the TRIM46 gene may influence the activity or expression level of the TRIM46 protein, thus modifying its function in the regulation of microtubule organisation and neuronal polarity. Therefore, this may impair the kidneys' or intestines' ability to excrete uric acid, resulting in a rise in uric acid levels and a higher likelihood of gout[ 27 , 28 ]. In addition, the variant locus on the TRIM46 gene may interact with a history of smoking, thereby increasing the risk of developing gout. By disrupting the equilibrium of uric acid metabolism and inflammatory response, smoking can influence the formation and deposition of urate crystals. Moreover, distinct alleles of the TRIM46 gene may modify the effects of smoking on uric acid levels or inflammatory response, thereby increasing the risk of gout[ 29 ]. In addition, THBS3 and MTX1, both of which are highly expressed in mast cells, share a promoter region. The interrelationship between these two genes and their combined effect on the development of gout will be investigated in the future. In addition, we found that the gene KRTCAP2 may function via plasma cells, pDC, and mDC cells to confer gouty disease risk. KRTCAP2 has been linked to autoimmune plasmatic dysplasia, Alzheimer's disease, and numerous tumor types[ 30 – 32 ]. Additionally, the KRTCAP2 gene may influence the production and clearance of uric acid by modifying the expression as well as the function of the enzyme xanthine oxidoreductase (XOR), thereby impacting uric acid production and clearance[ 33 ]. Moreover, our findings indicate that MAP2K11 has been linked with an elevated risk of gout. Prior study has revealed this MAP3K11 protein is extensively expressed in a variety of tissues, including the nervous system, kidney, liver, pancreas, and lungs[ 34 ]. This indicates that the MAP3K11 protein may participate in a variety of physiological processes and signaling pathways. MAP2K11 was identified as a critical component of the p38 signaling path, which has a close connection with uric acid excretion, synoviocyte apoptosis, and autophagy[ 35 , 36 ]. Inhibition of MAP2K11 expression or activity protects renal function and uric acid excretion by preventing hyperuricemia-induced apoptosis and autophagy in renal tubular epithelial cells[ 37 ]. Inhibiting the expression or activity of MAP2K11 in a mouse model study decreased the severity of gouty arthritis by inhibiting the apoptosis and autophagy of synoviocytes and chondrocytes[ 38 ]. Involved in the beginning and development of arthritis with gout, MAP2K11 is highlighted as a potential therapeutic target by these findings. In addition, a double-sample MR study on protein connections and gout disease revealed associations between three proteins and gout. The strongest correlation was observed with the BMP-1 protein, a metalloproteinase that cleaves numerous matrix proteins, such as collagen, bone morphogenetic proteins, and transforming growth factor (TGF-β)[ 39 ]. BMPs as well as TGF- are essential cytokines that control the differentiation and function of numerous immune cells, such as NK cells, T cells, and macrophages[ 40 ]. These cells played an important role in the immune system's reaction to gout as well as can exert proinflammatory or anti-inflammatory effects[ 41 ]. BMPs and TGF- also affect uric acid metabolism and excretion, and this, in turn, affects blood levels of uric acid and MSU crystal formation[ 42 ]. BMPs, for instance, can inhibit the expression of the uric acid synthesizing enzyme xanthine oxidase (XO) in the liver, thereby reducing uric acid production, whereas TGF- promotes tubular reabsorption of uric acid, resulting in elevated serum uric acid levels[ 43 , 44 ]. Consequently, the BMP-1 protein emerges as a promising protein target for further research. Despite these noteworthy results, the study has some limitations. As only samples of European provenance were included, the use of existing gout GWAS data and single-cell transcriptomics data may introduce some bias. Consequently, the generalizability of the results to patients with gout of other racial or geographical groups requires further investigation. The second objective of the study was to investigate the dynamic regulatory network in the pathogenesis of gout. However, the pathogenesis of gout is a complex and constantly changing process. Data from a single time point may not adequately reflect the dynamics of gout progression, necessitating additional research into the disease's long-term progression. Conclusion Our findings suggest that gout is associated with particular genes which are expressed in specific kinds of cells and plays crucial roles within the pathogenesis of the disease. Additionally, we discovered that DNA methylation may modulate the expression and function of these genes, thereby influencing the gout risk. In addition, we identified three proteins that are causally linked to gout and could act as novel targets for the disease's treatment and detection. These results shed new light on the pathological mechanisms of gout and open up new research avenues. To clarify the relationship between genes, proteins, and gout and their potential therapeutic strategies, additional research is required. Abbreviations MR: Mendelian randomization SMR: Summary-data-based Mendelian Randomization GWAS: Genome-wide association study eQTL: Expression Quantitative Trait Loci mQTL: Methylation Quantitative Trait Loci pQTL: Protein Quantitative Trait Loci SNP: Single nucleotide polymorphism scRNA-seq: Single-cell RNA sequencing FDR: False Discovery Rate CI: Confidence interval OR: Odds ratio Se: standard error IVW: Inverse variance weighting LD: Linkage disequilibrium BH: Benjamini-Hochberg ORF: Open Reading Frame Declarations • Ethics approval and consent to participate Not applicable. This study did not involve any human or animal experiments. • Consent for publication All authors have agreed to the publication of this manuscript. • Competing interests The authors declare that they have no competing interests. • Funding The research was supported by National Natural Science Foundation of China (No. 81371957). • Authors' contributions Ping Hu and Yubiao Yang conceived the study and were primarily responsible for data analysis and manipulation of some software. They also jointly completed the manuscript. Qinnan Zhang, Boyuan Ma, Jinyu Chen, and Derong Liu were instrumental in performing data and image collation. Derong Liu and Jun Ma contributed their expertise to the interpretation of data. As corresponding authors, Jian Hao and Xianhu Zhou took on the responsibility of reviewing and making necessary corrections to the manuscript. All authors contributed to the article and approved the submission of the final manuscript. • Acknowledgements We would like to extend our sincere gratitude to the National Natural Science Foundation of China (No. 81371957) for their generous financial support, which was instrumental in the successful completion of this research. We also thank Created with BioRender.com for providing the tools to create the figures. • Availability of data and material The datasets used in our study are publicly accessible. Detailed sources can be found in the Methods section of this paper. References Kaneko K, Aoyagi Y, Fukuuchi T, Inazawa K, Yamaoka N. Total Purine and Purine Base Content of Common Foodstuffs for Facilitating Nutritional Therapy for Gout and Hyperuricemia. Biological and Pharmaceutical Bulletin. 2014;37:709–21. de Oliveira EP, Burini RC. High plasma uric acid concentration: causes and consequences. Diabetology & Metabolic Syndrome. 2012;4:12. Mei Y, Dong B, Geng Z, Xu L. Excess Uric Acid Induces Gouty Nephropathy Through Crystal Formation: A Review of Recent Insights. Frontiers in Endocrinology [Internet]. 2022 [cited 2023 Jul 16];13. Available from: https://www.frontiersin.org/articles/10.3389/fendo.2022.911968 Uric acid and cardiovascular disease. Clinica Chimica Acta. 2018;484:150–63. Urate crystal deposition, prevention and various diagnosis techniques of GOUT arthritis disease: a comprehensive review | SpringerLink [Internet]. [cited 2023 Jul 16]. Available from: https://springer.m7h.net/article/10.1007/s13755-018-0058-9 Prevalence of Hyperuricemia and Gout in Mainland China from 2000 to 2014: A Systematic Review and Meta-Analysis [Internet]. [cited 2023 Jul 16]. Available from: https://www.hindawi.com/journals/bmri/2015/762820/ Yang J, Liu Z, Zhang C, Zhao Y, Sun S, Wang S, et al. The prevalence of hyperuricemia and its correlates in an inland Chinese adult population, urban and rural of Jinan. Rheumatol Int. 2013;33:1511–7. Gout. Novel therapies for treatment of gout and hyperuricemia | Arthritis Research & Therapy | Full Text [Internet]. [cited 2023 Jul 16]. Available from: https://arthritis-research.biomedcentral.com/articles/10.1186/ar2738 Feig DI, Kang D-H, Johnson RJ. Uric Acid and Cardiovascular Risk. N Engl J Med. 2008;359:1811–21. Toyoda Y, Nakatochi M, Nakayama A, Kawamura Y, Nakaoka H, Wakai K, et al. SNP-based heritability estimates of gout and its subtypes determined by genome-wide association studies of clinically defined gout. Rheumatology (Oxford). 2023;62:e144–6. Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing - PubMed [Internet]. [cited 2023 Jul 21]. Available from: https://pubmed.ncbi.nlm.nih.gov/32707839/ Qi T, Wu Y, Fang H, Zhang F, Liu S, Zeng J, et al. Genetic control of RNA splicing and its distinct role in complex trait variation. Nat Genet. 2022;54:1355–63. Ng B, White CC, Klein H-U, Sieberts SK, McCabe C, Patrick E, et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat Neurosci. 2017;20:1418–26. Hannon E, Spiers H, Viana J, Pidsley R, Burrage J, Murphy TM, et al. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci. 2016;19:48–54. Jaffe AE, Gao Y, Deep-Soboslay A, Tao R, Hyde TM, Weinberger DR, et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci. 2016;19:40–7. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood - PubMed [Internet]. [cited 2023 Jul 17]. Available from: https://pubmed.ncbi.nlm.nih.gov/29891976/ Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet. 2020;52:1122–31. Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53:1712–21. Mao X, An Q, Xi H, Yang X-J, Zhang X, Yuan S, et al. Single-Cell RNA Sequencing of hESC-Derived 3D Retinal Organoids Reveals Novel Genes Regulating RPC Commitment in Early Human Retinogenesis. Stem Cell Reports. 2019;13:747–60. Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526:68–74. Xu S, Li X, Zhang S, Qi C, Zhang Z, Ma R, et al. Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn’s disease: a multi-omics Mendelian randomization study. BMC Med. 2023;21:179. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49:D605–12. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51:D638–46. Latourte A, Dumurgier J, Paquet C, Richette P. Hyperuricemia, Gout, and the Brain-an Update. Curr Rheumatol Rep. 2021;23:82. Wrigley R, Phipps-Green AJ, Topless RK, Major TJ, Cadzow M, Riches P, et al. Pleiotropic effect of the ABCG2 gene in gout: involvement in serum urate levels and progression from hyperuricemia to gout. Arthritis Research & Therapy. 2020;22:45. Chen R, Yin C, Fang J, Liu B. The NLRP3 inflammasome: an emerging therapeutic target for chronic pain. Journal of Neuroinflammation. 2021;18:84. Merriman TR. An update on the genetic architecture of hyperuricemia and gout. Arthritis Research & Therapy. 2015;17:98. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations | Nature Genetics [Internet]. [cited 2023 Jul 17]. Available from: https://nature.m7h.net/articles/ng.2500 Rasheed H. Relationship of Gout and Dyslipidemia [Internet] [Thesis]. University of Otago; 2015 [cited 2023 Jul 17]. Available from: https://ourarchive.otago.ac.nz/handle/10523/5809 Cancers | Free Full-Text | Aberrant MUC1-TRIM46-KRTCAP2 Chimeric RNAs in High-Grade Serous Ovarian Carcinoma [Internet]. [cited 2023 Jul 17]. Available from: https://www.mdpi.com/2072-6694/7/4/878 KRTCAP2 as an immunological and prognostic biomarker of hepatocellular carcinoma. Colloids and Surfaces B: Biointerfaces. 2023;222:113124. O’Hanlon TP, Rider LG, Gan L, Fannin R, Paules RS, Umbach DM, et al. Gene expression profiles from discordant monozygotic twins suggest that molecular pathways are shared among multiple systemic autoimmune diseases. Arthritis Res Ther. 2011;13:1–13. Lee S, Yang H-K, Lee H-J, Park DJ, Kong S-H, Park SK. Systematic review of gastric cancer-associated genetic variants, gene-based meta-analysis, and gene-level functional analysis to identify candidate genes for drug development. Frontiers in Genetics [Internet]. 2022 [cited 2023 Jul 17];13. Available from: https://www.frontiersin.org/articles/10.3389/fgene.2022.928783 Xu L, Geman D, Winslow RL. Large-scale integration of cancer microarray data identifies a robust common cancer signature. BMC Bioinformatics. 2007;8:1–13. Burton JC, Antoniades W, Okalova J, Roos MM, Grimsey NJ. Atypical p38 Signaling, Activation, and Implications for Disease. International Journal of Molecular Sciences. 2021;22:4183. Xu Y, Sun Q, Yuan F, Dong H, Zhang H, Geng R, et al. RND2 attenuates apoptosis and autophagy in glioblastoma cells by targeting the p38 MAPK signalling pathway. Journal of Experimental & Clinical Cancer Research. 2020;39:174. Improve dosimetric outcome in stage III non-small-cell lung cancer treatment using spot-scanning proton arc (SPArc) therapy - PubMed [Internet]. [cited 2023 Jul 17]. Available from: https://pubmed.ncbi.nlm.nih.gov/29486782/ Fan Y, Yang J, Xie S, He J, Huang S, Chen J, et al. Systematic analysis of inflammation and pain pathways in a mouse model of gout. Mol Pain. 2022;18:17448069221097760. Hartigan N, Garrigue-Antar L, Kadler KE. Bone Morphogenetic Protein-1 (BMP-1). Journal of Biological Chemistry. 2003;278:18045–9. Frontiers | Dynamics of Transforming Growth Factor (TGF)-β Superfamily Cytokine Induction During HIV-1 Infection Are Distinct From Other Innate Cytokines [Internet]. [cited 2023 Jul 17]. Available from: https://www.frontiersin.org/articles/10.3389/fimmu.2020.596841/full Wu M, Tian Y, Wang Q, Guo C. Gout: a disease involved with complicated immunoinflammatory responses: a narrative review. Clin Rheumatol. 2020;39:2849–59. Frontiers | The Role of the Intestine in the Development of Hyperuricemia [Internet]. [cited 2023 Jul 17]. Available from: https://www.frontiersin.org/articles/10.3389/fimmu.2022.845684/full Sun H, Wu Y, Bian H, Yang H, Wang H, Meng X, et al. Function of Uric Acid Transporters and Their Inhibitors in Hyperuricaemia. Frontiers in Pharmacology [Internet]. 2021 [cited 2023 Jul 17];12. Available from: https://www.frontiersin.org/articles/10.3389/fphar.2021.667753 López-Hernández FJ, López-Novoa JM. Role of TGF-β in chronic kidney disease: an integration of tubular, glomerular and vascular effects. Cell Tissue Res. 2012;347:141–54. Additional Declarations No competing interests reported. Supplementary Files FigureS1.png Figure S1. ppi network diagram FigureS2.tif Figure S2. Bayesian co-localization analysis of 17 potential causative genes and gout. The diamond-shaped purple dots represent SNPs with the smallest sum of P values in the corresponding genes pqtl and gout GWAS. FigureS3.tif Figure S3. Multifaceted Connections Between DNA Methylation and Gene Expression. We integrated GWAS summary statistics with eQTL or mQTL summary statistics through the omics online platform to investigate whether specific genetic variants influence disease risk by impacting gene expression or DNA methylation. FigureS4.tif Figure S4. Single-cell sequencing analysis. A: Violin Plots of Quality Control Metrics for Single-Cell RNA-Seq Data. nFeature_RNA: number of unique RNA molecular species detected. nCount_RNA: number of total RNA molecules in the cell. percent.mt: percentage of mitochondrial genes as a percentage of total RNA. percent.HB: may indicate the percentage of hemoglobin genes in total RNA. B: Comparative Scatter Plots of RNA Counts vs. Mitochondrial Gene Percentage and Unique RNA Features. Left: Scatter Plot of Total RNA Count vs. Mitochondrial Gene Percentage in Single-Cell RNA-Seq Data. Right: Scatter Plot of Total RNA Count vs. Unique RNA Features in Single-Cell RNA-Seq Data. C: Principal Component Analysis (PCA) of Single-Cell Gene Expression Data. D: Top 10 Highly Variable Genes in Single-Cell RNA-Seq Data. E: Elbow Plot for Determining Optimal Number of Principal Components. F: UMAP of Single-Cell Gene Expression Data Grouped by disease Type (MS vs PTC) G: Preliminary UMAP Plot of Single-Cell Gene Expression Data H: UMAP Visualization of Cell Clusters in Single-Cell RNA-Seq Data I: Bar Plots Showing Variation in Cell Type Proportions Across Different Groups. TableS1.xlsx Table S1. Genome-wide cis-eQTL and SMR analysis of gout outcomes TableS2.xlsx Table S2. Genome-wide cis-mQTL SMR analysis and gene endings TableS3.xlsx Table S3. Analysis of blood eQTL and mQTL with SMR of gout disease TableS4.xlsx Table S4. Mendelian randomization results between protein quantitative trait loci and gout risk. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Apr, 2024 Reviews received at journal 01 Apr, 2024 Reviewers agreed at journal 12 Jan, 2024 Reviewers agreed at journal 07 Dec, 2023 Reviewers invited by journal 07 Dec, 2023 Editor assigned by journal 04 Dec, 2023 Submission checks completed at journal 03 Dec, 2023 First submitted to journal 30 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3687354","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":255937532,"identity":"0f8a4096-574d-4f81-a1cb-ad5dff06aaa3","order_by":0,"name":"yubiao yang","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"yubiao","middleName":"","lastName":"yang","suffix":""},{"id":255937535,"identity":"6ce25f2f-fb46-4263-9eca-7ce90fc43ae4","order_by":1,"name":"Ping Hu","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Hu","suffix":""},{"id":255937536,"identity":"0e36980e-4e53-46fa-b9b5-30e9feac178c","order_by":2,"name":"Qinnan Zhang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qinnan","middleName":"","lastName":"Zhang","suffix":""},{"id":255937538,"identity":"9621f9f9-2b47-4654-9076-90965f475c20","order_by":3,"name":"Boyuan Ma","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Boyuan","middleName":"","lastName":"Ma","suffix":""},{"id":255937541,"identity":"295e4921-70cb-4ee8-a759-396fc7e421e2","order_by":4,"name":"Jinyu Chen","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinyu","middleName":"","lastName":"Chen","suffix":""},{"id":255937542,"identity":"500d4e81-73e8-4ff9-aaf9-616744e29452","order_by":5,"name":"Bitao Wang","email":"","orcid":"","institution":"Ningbo University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bitao","middleName":"","lastName":"Wang","suffix":""},{"id":255937547,"identity":"e07676e6-2e4c-4260-b7ea-dc71eb0a8282","order_by":6,"name":"Jun ma","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"ma","suffix":""},{"id":255937549,"identity":"4f4c9b0c-0be8-4f42-b6c9-634d4ba6470f","order_by":7,"name":"Derong Liu","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Derong","middleName":"","lastName":"Liu","suffix":""},{"id":255937552,"identity":"13ff0dab-c3eb-4815-b269-94771b1705f8","order_by":8,"name":"Jian Hao","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Hao","suffix":""},{"id":255937554,"identity":"31a420ab-7685-487c-af46-10d9adb19a4f","order_by":9,"name":"Xianhu Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCRBhAELMBxgYGxigXOK0sCWQogWsjMeAOC3ys5uPSfMU2OWZS+R8fvlzx7bEBvbmbRIMNXdwamGccyxNcoZBcrHljNxtFpJnbic28Bwrk2A49gynFmaJHDOJDwbMiRtu5G4zMGwDagGJMDYcxqmFDaQgwaAeqCXnmUEiSIv8G/xaeCC2HAZpYX5wEGwLD34tEhJpyZYzDI4nbjjzzIyxse22cRtPWrFFwjHcWuRnJB+8zfOnOnHD8eTHH3+23ZbtZz+88caHGtxaUP0FJkFEAlEagKH3gUiFo2AUjIJRMMIAAEV6V4Pp0RhTAAAAAElFTkSuQmCC","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xianhu","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2023-11-30 14:14:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3687354/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3687354/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47714398,"identity":"dc2fc0e3-ea11-4518-b80c-f5afc86cd9a0","added_by":"auto","created_at":"2023-12-06 13:39:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2170389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the analyses performed.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/2e55dda75ede33dc67745bb7.png"},{"id":47714400,"identity":"a3755637-8381-4b0f-8dee-4eaa547f7a8c","added_by":"auto","created_at":"2023-12-06 13:39:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe outcomes of Mendelian randomization, showcasing the relationship between expression quantitative trait loci and the risk of gout, are presented. b_SMR is a marker for the magnitude of effect (β) of the gene variant on the expression of genes. A positive relationship is indicated when β is greater than zero, whereas β less than zero implies a negative relationship. OR, representing odd ratios, is determined from the projection of the causal estimate (βcoefficient). The confidence interval, represented by 95%CI, is calculated utilizing β and standard error (SE).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/68fd2ac47a43b9a0ddf1c64f.png"},{"id":47714401,"identity":"ffa03ab4-27b3-4494-a9b5-322be2b33e94","added_by":"auto","created_at":"2023-12-06 13:39:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe findings of Mendelian randomization, establishing a connection between methylation quantitative trait loci and the danger of gout, are illustrated. b_SMR symbolizes the impact magnitude (β) of the variant location on DNA methylation. When β is greater than zero, it signifies a positive association, and conversely, β less than zero signifies a negative association. OR, signifying odd ratios, is derived from the forecasted causal estimate (β coefficients). The term 95%CI represents confidence boundaries, ascertained using β and standard error (SE).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/db23ebd4aad16183c5891d97.png"},{"id":47714399,"identity":"421fe4be-f912-4ead-9157-abe39c6ab7bf","added_by":"auto","created_at":"2023-12-06 13:39:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":194393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe outcomes from Mendelian randomization, illustrating the correlation between methylation quantitative trait loci and expression quantitative trait loci, are reported. The influence magnitude (β) of a DNA methylation variant location on gene expression is indicated by SMR. A positive association is suggested when β is greater than zero, while a negative association is implied when β is less than zero. OR, the acronym for odd ratios, is computed based on the projected causal estimate (β coefficients). The term 95%CI signifies confidence ranges, which are derived using β and standard error (SE).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/e8c6691b2fe2e414cb8fddf6.png"},{"id":47716162,"identity":"4805fe72-bcea-4204-961b-a24e275db97b","added_by":"auto","created_at":"2023-12-06 13:47:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":852597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultifaceted Connections Between DNA Methylation and Gene Expression\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/1e4f9e980bf37b82ca6034e0.png"},{"id":47714406,"identity":"e070e66e-1104-42d2-8dd0-648b036a8417","added_by":"auto","created_at":"2023-12-06 13:39:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":154622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell sequencing analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/19c3d366cd1fc749a64d4e04.png"},{"id":47718712,"identity":"b1e0248c-8ebc-4ea0-a6fa-c84f7889a1bc","added_by":"auto","created_at":"2023-12-06 14:03:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2545338,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/eaf97114-7e47-4baa-ab06-1db373b1be0f.pdf"},{"id":47717468,"identity":"14eb20c3-16d6-48d2-bcab-7f81d0f7c312","added_by":"auto","created_at":"2023-12-06 13:55:31","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":969222,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. ppi network diagram\u003c/p\u003e","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/755f04edea34caa3f7c62452.png"},{"id":47714411,"identity":"0770f3a7-1cd0-4761-8d22-349d35604db4","added_by":"auto","created_at":"2023-12-06 13:39:31","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6476476,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2. Bayesian co-localization analysis of 17 potential causative genes and gout. The diamond-shaped purple dots represent SNPs with the smallest sum of P values in the corresponding genes pqtl and gout GWAS.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/1800eb87597e59acfd569fa0.tif"},{"id":47714412,"identity":"62d117c4-4864-436b-b844-bf6cbda4fdc0","added_by":"auto","created_at":"2023-12-06 13:39:32","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":24063756,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3. Multifaceted Connections Between DNA Methylation and Gene Expression. We integrated GWAS summary statistics with eQTL or mQTL summary statistics through the omics online platform to investigate whether specific genetic variants influence disease risk by impacting gene expression or DNA methylation.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/158d537dc2d82a9e975dc498.tif"},{"id":47714410,"identity":"b15886cd-1e9d-4a5b-9022-1b65da3e8302","added_by":"auto","created_at":"2023-12-06 13:39:31","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6147472,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S4. Single-cell sequencing analysis.\u003c/p\u003e\n\u003cp\u003eA: Violin Plots of Quality Control Metrics for Single-Cell RNA-Seq Data. nFeature_RNA: number of unique RNA molecular species detected. nCount_RNA: number of total RNA molecules in the cell. percent.mt: percentage of mitochondrial genes as a percentage of total RNA. percent.HB: may indicate the percentage of hemoglobin genes in total RNA.\u003c/p\u003e\n\u003cp\u003eB: Comparative Scatter Plots of RNA Counts vs. Mitochondrial Gene Percentage and Unique RNA Features. Left: Scatter Plot of Total RNA Count vs. Mitochondrial Gene Percentage in Single-Cell RNA-Seq Data. Right: Scatter Plot of Total RNA Count vs. Unique RNA Features in Single-Cell RNA-Seq Data.\u003c/p\u003e\n\u003cp\u003eC: Principal Component Analysis (PCA) of Single-Cell Gene Expression Data.\u003c/p\u003e\n\u003cp\u003eD: Top 10 Highly Variable Genes in Single-Cell RNA-Seq Data.\u003c/p\u003e\n\u003cp\u003eE: Elbow Plot for Determining Optimal Number of Principal Components.\u003c/p\u003e\n\u003cp\u003eF: UMAP of Single-Cell Gene Expression Data Grouped by disease Type (MS vs PTC)\u003c/p\u003e\n\u003cp\u003eG: Preliminary UMAP Plot of Single-Cell Gene Expression Data\u003c/p\u003e\n\u003cp\u003eH: UMAP Visualization of Cell Clusters in Single-Cell RNA-Seq Data\u003c/p\u003e\n\u003cp\u003eI: Bar Plots Showing Variation in Cell Type Proportions Across Different Groups.\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/592f1fec14909f92683e5ecc.tif"},{"id":47714405,"identity":"253c0d0b-b2d8-4042-a9b4-9efaf1ae59e1","added_by":"auto","created_at":"2023-12-06 13:39:31","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12452,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1. Genome-wide cis-eQTL and SMR analysis of gout outcomes\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/7d2714daae761c8ed03ea690.xlsx"},{"id":47714407,"identity":"95b625a5-1f67-40b4-b78b-6f2dff537292","added_by":"auto","created_at":"2023-12-06 13:39:31","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":13660,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2. Genome-wide cis-mQTL SMR analysis and gene endings\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/940eb27b880c296da7f7d79a.xlsx"},{"id":47714409,"identity":"945d4f57-c0fc-4118-a935-af7bdb6fcd52","added_by":"auto","created_at":"2023-12-06 13:39:31","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":13956,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3. Analysis of blood eQTL and mQTL with SMR of gout disease\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/fcb212b7aae95018893c53f2.xlsx"},{"id":47716164,"identity":"b37eb275-5f3c-4378-b6f8-486d80dbdee5","added_by":"auto","created_at":"2023-12-06 13:47:31","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":9195,"visible":true,"origin":"","legend":"\u003cp\u003eTable S4. Mendelian randomization results between protein quantitative trait loci and gout risk.\u003c/p\u003e","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3687354/v1/cd01febcc7db0b8f365bc02c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell and genome-wide Mendelian randomization identifies causative genes for gout","fulltext":[{"header":"KEYPOINT","content":"\u003cp\u003e\u0026middot; This study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and explored the gene expression regulation mechanisms of gout.\u003c/p\u003e\n\u003cp\u003e\u0026middot; This study used two-sample Mendelian randomization method to understand the causal relationship between proteins and gout.\u003c/p\u003e\n\u003cp\u003e\u0026middot; This study identified 17 genetic loci associated with gout, including four genes related by protein-protein interaction network (PPI) analysis: TRIM46, THBS3, MTX1 and KRTCAP2.\u003c/p\u003e\n\u003cp\u003e\u0026middot; This study also identified 22 methylation sites related to gout, and genes that may increase the risk of gout, such as TRIM46, MAP3K11, KRTCAP2 and TM7SF2.\u003c/p\u003e\n\u003cp\u003e\u0026middot; This study determined three proteins causally associated with gout by Mendelian randomization (MR) analysis: ADH1B, BMP1 and HIST1H3A.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eGout is a prevalent type of metabolic osteoarthritis induced by elevated blood uric acid levels. Uric acid results from the breakdown of purines, which occurs in numerous substances and cells[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hyperuricemia occurs when uric acid production increases due to diet and lifestyle, or when uric acid excretion decreases due to kidney dysfunction[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. At a certain level of uric acid saturation in the blood, needle-like urate crystals form and deposit in the joints, cartilages, tendons, etc., triggering an immune response and inflammation[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA high level of is prevalent in mainland China at a rate of 13.3%, affecting approximately 177\u0026nbsp;million people; gout is prevalent at a rate of 1.1%, affecting approximately 14.66\u0026nbsp;million people[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In China, the prevalence rate is marginally lower than in Europe and the United States, but it has been rising over the past decade. There are also regional variations; the prevalence is generally higher in economically developed, coastal, and urban regions than in economically less developed, inland, and rural regions. This could be due to living conditions, dietary practices, environmental factors, etc[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome investigations indicate that gout has a genetic component. 10\u0026ndash;25% of gout patients' close relatives have hyperuricemia; if one parent has gout, 40\u0026ndash;50% of the child's children will have gout; if parents have gout, up to 75% of the child's children will have gout[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Genome-wide association studies (GWAS) is the main approach for identifying genetic causes of disease, but the majority of GWAS loci are in noncoding regions, making functional annotation and mechanistic explanations challenging[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In recent years, single-cell transcriptomics (scRNA-seq) has become an essential instrument for researching diseases in order to obtain insight into their pathogenesis. Unlike traditional aggregate methodologies, scRNA-seq technology can provide gene expression information from individual cells, thereby overcoming the problem of cellular heterogeneity and providing a more precise and comprehensive perspective for studying diseases[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOwing to single-cell transcriptomics, eQTL analysis has emerged as a significant instrument for delving deeper into the mechanisms behind gout. The combination of single-cell transcriptomics and eQTL analysis can shed light on the cell-specificity of gene expression regulation by correlating data from single-cell transcriptomics with genetic variations in individuals and pinpointing locations that regulate gene expression.\u003c/p\u003e \u003cp\u003eThe present research aims to combine gout GWAS data, scRNA-seq data, and eQTL and mQTL data to investigate the genetic regulation mechanism of gout disease and further our understanding of its pathogenesis. To investigate the dynamic regulatory network in the pathogenesis of gout disease, we will conduct a comprehensive analysis of the expression regulation of gout-related genes in individual cells. And we will comprehend the causal connection between proteins and gout by pQTL and a Mendelian randomization investigation with two samples. Through this study, we expect to elucidate the pathological mechanisms of gout disease and provide new hints and research strategies for gout(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGout GWAS data sources\u003c/h2\u003e \u003cp\u003eWe sourced gout GWAS data from the Finnish database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), encompassing 4607 instances and 335038 control subjects of European descent. For an in-depth look at the collection of samples, methods of analysis, and findings, kindly refer to the original publication.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Trait Locus Data Sources\u003c/h2\u003e \u003cp\u003eUsing data from the eQTLGen consortium, we retrieved cis-eQTLs located within a 1000 kb range of genetic variants exhibiting a robust correlation with gene expression in relation to blood tissue eQTL data. The eQTLGen consortium includes information on 10,317 SNPs associated with the characterization of 31,684 individuals. eQTLGen does not include variants associated with X and Y chromosome and mitochondrial DNA gene expression levels, however[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor mQTL data pertaining to blood tissue, we collected peripheral blood samples from two European cohorts: the BSGS (n\u0026thinsp;=\u0026thinsp;614) and the LBC (n\u0026thinsp;=\u0026thinsp;1366). Using the Illumina HumanMethylation450 microarray, the methylation status of the samples was evaluated. To compile the mQTL summary data, we performed a meta-analysis of BSGS and LBC data. Only DNA methylation probes containing at least one cis-mQTL (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) and restricted to SNPs within 2 Mb of each probe were included[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor pQTL data related to blood tissue, we employed the MR cis-pQTL tool to choose SNPs demonstrating a strong correlation with protein expression from five proteomic databases. We included solely SNPs with a p-value of at least 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e for their association with protein expression. In addition, we integrated the plasma pQTL data of Ferkingstad et al., who conducted measurements of 4907 plasma proteins in a group of 35,559 participants from Iceland[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSources of single-cell sequencing data for gouty blood peripheral mononuclear cells\u003c/h2\u003e \u003cp\u003eWe employed the GSE211783 dataset from the GEO database, containing single-cell RNA-sequencing data of peripheral blood from three patients with gout during acute flare and three during remission. Yu H and his team carried out scRNA-seq on PBMC from these patients using 10x Genomics technology, and their results were validated via flow cytometry and LC-MS/MS[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization analysis based on summary data\u003c/h2\u003e \u003cp\u003eWe utilized the SMR software application to implement the SMR \u0026amp; HEIDI method, which combines data from GWAS and eQTL studies to assess for multifaceted correlations among levels of gene expression and complex characteristics of interest[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For LD calculations, we utilized 1000 Genomes European Reference Data[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To gain a deeper understanding of the relationship between eQTLs and mQTLs in terms of disease risk, we conducted a three-step SMR analysis. First, we employed SNPs as instrumental variables, significantly correlated expression of genes as exposure variables, and GWAS as outcome variables. In the second stage, DNAm was used as the exposure variable and GWAS was used as the outcome variable. In the third stage, DNAm was added as the exposure variable, and gene expression was added as the outcome variable. Only significant signals from stages one and two were included in step three[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The Benjamini-Hochberg (BH) method was used to compute the FDR of the p-value of the SMR, and FDR SMR 0.05 and heterogeneity HEIDI\u0026thinsp;\u0026gt;\u0026thinsp;0.01 were used as inclusion criteria for the outcome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTwo-sample Mendelian randomization analysis\u003c/h2\u003e \u003cp\u003eWe conducted a two-sample randomization Mendelian analysis utilising \"TwoSampleMR\" with plasma proteins as the exposure and gout as the outcome. We used the Bonferroni correction to account for multiple testing and a p-value threshold of (P\u0026thinsp;\u0026lt;\u0026thinsp;1.126 \u0026times; 10 \u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, 0.05/4441) to evaluate the results for telomere length-related proteins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBayesian Colocalization Analysis\u003c/h2\u003e \u003cp\u003eConventionally, the five postulates of colocalization analysis (Supplementary Methods) are defined as follows: Both the exposure and outcome phenotypes are not associated with the SNP. H1: While the primary phenotype (exposure) correlates with the SNP, the secondary phenotype (outcome) does not. H2: The secondary phenotype (result) correlates with the SNP, whereas the primary phenotype (exposure) does not. Both phenotypes are associated with the SNP, but these associations are distinct. H4: Both phenotypes correlate with the SNP, and the causal SNP is shared by both associations.\u003c/p\u003e \u003cp\u003eBayesian colocalization analysis utilizing the 'coloc' package with default parameters (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/chr1swallace/coloc\u003c/span\u003e\u003cspan address=\"https://github.com/chr1swallace/coloc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is employed to determine the probability that two characteristics share the same causative variation. As pointed out previously, Bayesian colocalization presents posterior probabilities for the five possibilities regarding whether two characteristics share a single variant. In the present investigation, we calculated the likelihood of the posterior for Hypotheses 3 (PPH3), i.e., both the protein and gout have a relationship with the SNP, but these relationships are independent, and 4 (PPH4), i.e., both the protein and MS have a relationship with the SNP, and these relationships share the same causal SNP. We designated a gene as having colocalization evidence if its gene-based PPH4 was greater than 80%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIntegrating Multi-Omics Data Using Summary-data-based Mendelian Randomization (SMR) Analysis\u003c/h2\u003e \u003cp\u003eWe utilized the SMR tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cnsgenomics.com/software/smr/\u003c/span\u003e\u003cspan address=\"http://cnsgenomics.com/software/smr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to discover consistent Summary-data-based Mendelian Randomization (SMR) associations across multiple omics. This instrument combines GWAS summary statistics with eQTL or mQTL summary statistics to determine whether particular mutations influence the risk of disease by influencing gene expression or methylation of DNA.\u003c/p\u003e \u003cp\u003eThe summary GWAS data for gout were entered as'mygwas. ma'. The methylation quantitative trait loci (mQTL) and expression quantitative trait loci (eQTL) data were respectively entered as'mymqtl' and'myeqtl'. The SMR instrument processed the input data using the following command:\u003c/p\u003e \u003cp\u003esmr --bfile mydata --gwas-summary mygwas.ma --beqtl-summary mymqtl --beqtl-summary myeqtl --out myplot --plot --probe ENSG00000163462 --probe-wind 500 --gene-list glist-hg19\u003c/p\u003e \u003cp\u003eThe created diagrams provide an overview of the GWAS results displaying the p-values of each SNP, the eQTL results illustrating the effect of each SNP on the expression of genes, and the mQTL results illustrating the effect of each SNP on DNA methylation. This helps to comprehend how variations in gene expression or DNA methylation may affect disease risk.\u003c/p\u003e \u003cp\u003eWe intended to identify and validate putative genetic regulatory mechanisms by integrating these results and employing additional statistical analyses (such as the HEIDI test). Such knowledge is essential for comprehending the pathophysiology of diseases and identifying new therapeutic targets.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide cis-eQTL and SMR analysis of gout outcomes\u003c/h2\u003e \u003cp\u003eWe performed SMR analysis on 15,324 SNPs in the blood that represent pertinent gene expression and gout outcomes in blood. To compensate for the genome-wide kind I errors, we performed FDR correction (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which revealed strong evidence of association, followed by HEIDI testing (P\u0026thinsp;\u0026gt;\u0026thinsp;0.01) incorporated in the SMR programme to determine if the associations were caused by sharing causes of variation as opposed to pleiotropy. We discovered 17 gout relationship signatures at distinct genetic loci(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Four genes, TRIM46, THBS3, MTX1, and KRTCAP2, were found to be interconnected via protein-protein interaction network (PPI) analysis(Additional file 2: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]A review article, for instance, described the complicated connection between uric acid, gout, and brain disease, and mentioned the THBS3 gene as a gene associated with uric acid metabolism and gout, with variants affecting uric acid excretion and deposition. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]This is consistent with our finding that a reduction in the standard deviation of THBS3 expression was associated with an 18% risk reduction (beta=-0.18, FDR\u0026thinsp;=\u0026thinsp;4.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBayesian co-localization analysis\u003c/h2\u003e \u003cp\u003eThe SMR analysis has identified 17 genes as gout-causing genes. The posterior probabilities of Hypothesis 4 for THBS3, GBAP1, MTX1, THBS3-AS1, FUT8, UNC13D, LYPD2, and GNGT2 are greater than 0.80, per the Bayesian colocalization analysis (Additional file 3: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This suggests that there is a high probability that these genes are associated with gout and that they are likely caused by shared variants. For TM7SF2, CDC42EP2, and TIGD3, however, the posterior probabilities of Hypothesis 3 exceed 0.80. This suggests that these genes are associated with gout, but are most likely controlled by distinct variants.(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of eQTL-GWAS co-localization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ensnps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePP.H0.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePP.H1.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP.H2.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP.H3.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePP.H4.abf\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHBS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56E-64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.66E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.69E-61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP3K11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHBS3-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.54E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.09E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCNX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.59E-63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.72E-60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRIM46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.78E-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTM7SF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.02E-105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.65E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14E-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFUT8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUNC13D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.85E-130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.41E-129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKAT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.46E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYPD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC42EP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49E-60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.09E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.71E-57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRTCAP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.91E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.77E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIGLEC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74E-29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNGT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIGD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.50E-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.09E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23E-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the Bayesian colocalization analysis can assist us in evaluating the veracity of these hypotheses, thereby providing crucial hints for future investigation. In particular, the posterior probability represents an estimate of the posterior probability distribution, given the data and prior assumptions. It reflects the likelihood of various hypotheses being supported by the data. Therefore, a greater posterior probability suggests that the hypothesis is more credible given the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide cis-mQTL SMR analysis and gene endings\u003c/h2\u003e \u003cp\u003eTo further elucidate the pathogenesis of gout, an SMR analysis, FDR correction, and HEIDI test were conducted between blood mQTL and gout. We identified 22 methylation sites associated with gout, in which multiple methylation sites on SLC2A9 and SIPA1 were regulated, thereby influencing gout disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Additional file 4: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). For instance, elevated DNAm at cg25361844 increased disease risk (beta\u0026thinsp;=\u0026thinsp;0.37), whereas decreased DNAm at cg17480646 increased disease risk (beta\u0026thinsp;=\u0026thinsp;0.13). Intriguingly, a study of European and Polynesian populations discovered that a prevalent variant in the ABCG2 (rs2231142) was positively associated with hyperuricemia and gout, meaning that populations carrying alleles with this variant had increased uric acid levels and a higher likelihood of gout. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]However, in the present research, the most significant SNP in this locus, ABCG2, rs10011796, was associated negatively with gout risk. This is an intriguing discovery that warrants further study. In addition, since it is already known that methylation of genes affects gene expression, we mapped gene methyl to expression via sharing variation in genetics and performed an SMR study of the causative connection among methyl and translation of relevant genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of blood eQTL and mQTL with SMR of gout disease\u003c/h2\u003e \u003cp\u003eIn accordance to the three-step SMR study described in the methodology part, we filtered out key disease-related signals. We discovered that TRIM46 had a positive correlation with gout (beta\u0026thinsp;=\u0026thinsp;1.34); therefore, upregulation of DNAm on the cg15699386 locus would result in an increase in TRIM46 expression (beta\u0026thinsp;=\u0026thinsp;0.24), which would increase the risk of gout development (beta\u0026thinsp;=\u0026thinsp;0.43). High DNAm expression at two other loci on the TRIM46 gene (cg05778494, cg00577578) exhibits a negative correlation with this gene's expression (beta=-0.20, beta=-0.08) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Additional file 5: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFive DNAm sites within the open reading frame (ORF) are substantially linked to TRIM46 and Gout, two of which are in the promoter area region and three in the enhancer region.\u003c/p\u003e \u003cp\u003eUsing SMR on our omics data, we demonstrate that TRIM46 is a key gene for Gout and may uncover its plausible molecular pathogenesis mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Additional file 6: Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell sequencing analysis\u003c/h2\u003e \u003cp\u003eEight alleles have been identified that increase the risk of gout, a disease that results from an excess of uric acid in the blood. These genes are predominantly expressed in the monocytes/macrophages, plasma cells, mast cells, and myeloid dendritic cells (MDCs) of the immune system. The genes MAP3K11, KRTCAP2, and PCNX3 influence the function of macrophages, plasma cells, and MDCs, respectively, to increase gout risk. We also discovered a significant correlation between mast cell TM7SF2 expression and gout risk (beta\u0026thinsp;=\u0026thinsp;1.34) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Additional file 7: Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eScreening the proteome for gout cure-related proteins\u003c/h2\u003e \u003cp\u003eWe performed a Mendelian randomization analysis to find proteins that are causally linked to gout to find potential therapeutic targets. ADH1B (OR\u0026thinsp;=\u0026thinsp;0.15, 95% CI\u0026thinsp;=\u0026thinsp;0.07\u0026ndash;0.35), BMP1 (OR\u0026thinsp;=\u0026thinsp;7.04, 95% CI\u0026thinsp;=\u0026thinsp;3.35\u0026ndash;14.78), and HIST1H3A (OR\u0026thinsp;=\u0026thinsp;205.85,95%CI\u0026thinsp;=\u0026thinsp;78.35\u0026ndash;540.8) were found to be significantly associated with gout at the Bonferroni-corrected threshold (P 1.719 10\u0026thinsp;\u0026minus;\u0026thinsp;6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Additional file 8: Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Five SNPs that affect the expression or function of these proteins were correlated with them. However, no protein-protein interaction (PPI) was identified between these three proteins, suggesting that they may act independently in the pathogenesis of gout.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing GWAS data, scRNA-seq, eQTL, mQTL data, and pQTL with a two-sample Mendelian randomization study, the purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and improve our understanding of its pathogenesis.\u003c/p\u003e \u003cp\u003eThe results demonstrated that mast cells play a crucial role in gout, as they contain the maximum number of disease-associated risk alleles. Prior research has demonstrated a correlation between an increase in mast cells in the synovium of gouty joints and tissue injury[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. TRIM46 was identified as the most prevalent susceptibility gene with a promotion factor for gout among these mast cells (beta\u0026thinsp;=\u0026thinsp;1.34). Notably, a variant site on the TRIM46 gene may influence the activity or expression level of the TRIM46 protein, thus modifying its function in the regulation of microtubule organisation and neuronal polarity. Therefore, this may impair the kidneys' or intestines' ability to excrete uric acid, resulting in a rise in uric acid levels and a higher likelihood of gout[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, the variant locus on the TRIM46 gene may interact with a history of smoking, thereby increasing the risk of developing gout. By disrupting the equilibrium of uric acid metabolism and inflammatory response, smoking can influence the formation and deposition of urate crystals. Moreover, distinct alleles of the TRIM46 gene may modify the effects of smoking on uric acid levels or inflammatory response, thereby increasing the risk of gout[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, THBS3 and MTX1, both of which are highly expressed in mast cells, share a promoter region. The interrelationship between these two genes and their combined effect on the development of gout will be investigated in the future.\u003c/p\u003e \u003cp\u003eIn addition, we found that the gene KRTCAP2 may function via plasma cells, pDC, and mDC cells to confer gouty disease risk. KRTCAP2 has been linked to autoimmune plasmatic dysplasia, Alzheimer's disease, and numerous tumor types[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, the KRTCAP2 gene may influence the production and clearance of uric acid by modifying the expression as well as the function of the enzyme xanthine oxidoreductase (XOR), thereby impacting uric acid production and clearance[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, our findings indicate that MAP2K11 has been linked with an elevated risk of gout. Prior study has revealed this MAP3K11 protein is extensively expressed in a variety of tissues, including the nervous system, kidney, liver, pancreas, and lungs[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This indicates that the MAP3K11 protein may participate in a variety of physiological processes and signaling pathways. MAP2K11 was identified as a critical component of the p38 signaling path, which has a close connection with uric acid excretion, synoviocyte apoptosis, and autophagy[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Inhibition of MAP2K11 expression or activity protects renal function and uric acid excretion by preventing hyperuricemia-induced apoptosis and autophagy in renal tubular epithelial cells[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Inhibiting the expression or activity of MAP2K11 in a mouse model study decreased the severity of gouty arthritis by inhibiting the apoptosis and autophagy of synoviocytes and chondrocytes[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Involved in the beginning and development of arthritis with gout, MAP2K11 is highlighted as a potential therapeutic target by these findings.\u003c/p\u003e \u003cp\u003eIn addition, a double-sample MR study on protein connections and gout disease revealed associations between three proteins and gout. The strongest correlation was observed with the BMP-1 protein, a metalloproteinase that cleaves numerous matrix proteins, such as collagen, bone morphogenetic proteins, and transforming growth factor (TGF-β)[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. BMPs as well as TGF- are essential cytokines that control the differentiation and function of numerous immune cells, such as NK cells, T cells, and macrophages[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These cells played an important role in the immune system's reaction to gout as well as can exert proinflammatory or anti-inflammatory effects[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. BMPs and TGF- also affect uric acid metabolism and excretion, and this, in turn, affects blood levels of uric acid and MSU crystal formation[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. BMPs, for instance, can inhibit the expression of the uric acid synthesizing enzyme xanthine oxidase (XO) in the liver, thereby reducing uric acid production, whereas TGF- promotes tubular reabsorption of uric acid, resulting in elevated serum uric acid levels[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Consequently, the BMP-1 protein emerges as a promising protein target for further research.\u003c/p\u003e \u003cp\u003eDespite these noteworthy results, the study has some limitations. As only samples of European provenance were included, the use of existing gout GWAS data and single-cell transcriptomics data may introduce some bias. Consequently, the generalizability of the results to patients with gout of other racial or geographical groups requires further investigation. The second objective of the study was to investigate the dynamic regulatory network in the pathogenesis of gout. However, the pathogenesis of gout is a complex and constantly changing process. Data from a single time point may not adequately reflect the dynamics of gout progression, necessitating additional research into the disease's long-term progression.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings suggest that gout is associated with particular genes which are expressed in specific kinds of cells and plays crucial roles within the pathogenesis of the disease. Additionally, we discovered that DNA methylation may modulate the expression and function of these genes, thereby influencing the gout risk. In addition, we identified three proteins that are causally linked to gout and could act as novel targets for the disease's treatment and detection. These results shed new light on the pathological mechanisms of gout and open up new research avenues. To clarify the relationship between genes, proteins, and gout and their potential therapeutic strategies, additional research is required.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMR: Mendelian randomization\u003c/p\u003e\n\u003cp\u003eSMR: Summary-data-based Mendelian Randomization\u003c/p\u003e\n\u003cp\u003eGWAS: Genome-wide association study\u003c/p\u003e\n\u003cp\u003eeQTL: Expression Quantitative Trait Loci\u003c/p\u003e\n\u003cp\u003emQTL: Methylation Quantitative Trait Loci\u003c/p\u003e\n\u003cp\u003epQTL: Protein Quantitative Trait Loci\u003c/p\u003e\n\u003cp\u003eSNP: Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003escRNA-seq: Single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003eFDR: False Discovery Rate\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u003c/p\u003e\n\u003cp\u003eOR: Odds ratio\u003c/p\u003e\n\u003cp\u003eSe: standard error\u003c/p\u003e\n\u003cp\u003eIVW: Inverse variance weighting\u003c/p\u003e\n\u003cp\u003eLD: Linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eBH: Benjamini-Hochberg\u003c/p\u003e\n\u003cp\u003eORF: Open Reading Frame\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026bull; \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study did not involve any human or animal experiments.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have agreed to the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was supported by National Natural Science Foundation of China (No. 81371957).\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePing Hu and Yubiao Yang conceived the study and were primarily responsible for data analysis and manipulation of some software. They also jointly completed the manuscript. Qinnan Zhang, Boyuan Ma, Jinyu Chen, and Derong Liu were instrumental in performing data and image collation. Derong Liu and Jun Ma contributed their expertise to the interpretation of data. As corresponding authors, Jian Hao and Xianhu Zhou took on the responsibility of reviewing and making necessary corrections to the manuscript. All authors contributed to the article and approved the submission of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to extend our sincere gratitude to the National Natural Science Foundation of China (No. 81371957) for their generous financial support, which was instrumental in the successful completion of this research. We also thank Created with BioRender.com for providing the tools to create the figures.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in our study are publicly accessible. Detailed sources can be found in the Methods section of this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKaneko K, Aoyagi Y, Fukuuchi T, Inazawa K, Yamaoka N. Total Purine and Purine Base Content of Common Foodstuffs for Facilitating Nutritional Therapy for Gout and Hyperuricemia. Biological and Pharmaceutical Bulletin. 2014;37:709\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003ede Oliveira EP, Burini RC. High plasma uric acid concentration: causes and consequences. Diabetology \u0026amp; Metabolic Syndrome. 2012;4:12. \u003c/li\u003e\n\u003cli\u003eMei Y, Dong B, Geng Z, Xu L. Excess Uric Acid Induces Gouty Nephropathy Through Crystal Formation: A Review of Recent Insights. Frontiers in Endocrinology [Internet]. 2022 [cited 2023 Jul 16];13. Available from: https://www.frontiersin.org/articles/10.3389/fendo.2022.911968\u003c/li\u003e\n\u003cli\u003eUric acid and cardiovascular disease. Clinica Chimica Acta. 2018;484:150\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eUrate crystal deposition, prevention and various diagnosis techniques of GOUT arthritis disease: a comprehensive review | SpringerLink [Internet]. [cited 2023 Jul 16]. Available from: https://springer.m7h.net/article/10.1007/s13755-018-0058-9\u003c/li\u003e\n\u003cli\u003ePrevalence of Hyperuricemia and Gout in Mainland China from 2000 to 2014: A Systematic Review and Meta-Analysis [Internet]. [cited 2023 Jul 16]. Available from: https://www.hindawi.com/journals/bmri/2015/762820/\u003c/li\u003e\n\u003cli\u003eYang J, Liu Z, Zhang C, Zhao Y, Sun S, Wang S, et al. The prevalence of hyperuricemia and its correlates in an inland Chinese adult population, urban and rural of Jinan. Rheumatol Int. 2013;33:1511\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eGout. Novel therapies for treatment of gout and hyperuricemia | Arthritis Research \u0026amp; Therapy | Full Text [Internet]. [cited 2023 Jul 16]. Available from: https://arthritis-research.biomedcentral.com/articles/10.1186/ar2738\u003c/li\u003e\n\u003cli\u003eFeig DI, Kang D-H, Johnson RJ. Uric Acid and Cardiovascular Risk. N Engl J Med. 2008;359:1811\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eToyoda Y, Nakatochi M, Nakayama A, Kawamura Y, Nakaoka H, Wakai K, et al. SNP-based heritability estimates of gout and its subtypes determined by genome-wide association studies of clinically defined gout. Rheumatology (Oxford). 2023;62:e144\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eBiological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing - PubMed [Internet]. [cited 2023 Jul 21]. Available from: https://pubmed.ncbi.nlm.nih.gov/32707839/\u003c/li\u003e\n\u003cli\u003eQi T, Wu Y, Fang H, Zhang F, Liu S, Zeng J, et al. Genetic control of RNA splicing and its distinct role in complex trait variation. Nat Genet. 2022;54:1355\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eNg B, White CC, Klein H-U, Sieberts SK, McCabe C, Patrick E, et al. An xQTL map integrates the genetic architecture of the human brain\u0026rsquo;s transcriptome and epigenome. Nat Neurosci. 2017;20:1418\u0026ndash;26. \u003c/li\u003e\n\u003cli\u003eHannon E, Spiers H, Viana J, Pidsley R, Burrage J, Murphy TM, et al. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci. 2016;19:48\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eJaffe AE, Gao Y, Deep-Soboslay A, Tao R, Hyde TM, Weinberger DR, et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci. 2016;19:40\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eIdentifying gene targets for brain-related traits using transcriptomic and methylomic data from blood - PubMed [Internet]. [cited 2023 Jul 17]. Available from: https://pubmed.ncbi.nlm.nih.gov/29891976/\u003c/li\u003e\n\u003cli\u003eZheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet. 2020;52:1122\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eFerkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53:1712\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eMao X, An Q, Xi H, Yang X-J, Zhang X, Yuan S, et al. Single-Cell RNA Sequencing of hESC-Derived 3D Retinal Organoids Reveals Novel Genes Regulating RPC Commitment in Early Human Retinogenesis. Stem Cell Reports. 2019;13:747\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eGenomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526:68\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eXu S, Li X, Zhang S, Qi C, Zhang Z, Ma R, et al. Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn\u0026rsquo;s disease: a multi-omics Mendelian randomization study. BMC Med. 2023;21:179. \u003c/li\u003e\n\u003cli\u003eSzklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49:D605\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eSzklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51:D638\u0026ndash;46. \u003c/li\u003e\n\u003cli\u003eLatourte A, Dumurgier J, Paquet C, Richette P. Hyperuricemia, Gout, and the Brain-an Update. Curr Rheumatol Rep. 2021;23:82. \u003c/li\u003e\n\u003cli\u003eWrigley R, Phipps-Green AJ, Topless RK, Major TJ, Cadzow M, Riches P, et al. Pleiotropic effect of the ABCG2 gene in gout: involvement in serum urate levels and progression from hyperuricemia to gout. Arthritis Research \u0026amp; Therapy. 2020;22:45. \u003c/li\u003e\n\u003cli\u003eChen R, Yin C, Fang J, Liu B. The NLRP3 inflammasome: an emerging therapeutic target for chronic pain. Journal of Neuroinflammation. 2021;18:84. \u003c/li\u003e\n\u003cli\u003eMerriman TR. An update on the genetic architecture of hyperuricemia and gout. Arthritis Research \u0026amp; Therapy. 2015;17:98. \u003c/li\u003e\n\u003cli\u003eGenome-wide association analyses identify 18 new loci associated with serum urate concentrations | Nature Genetics [Internet]. [cited 2023 Jul 17]. Available from: https://nature.m7h.net/articles/ng.2500\u003c/li\u003e\n\u003cli\u003eRasheed H. Relationship of Gout and Dyslipidemia [Internet] [Thesis]. University of Otago; 2015 [cited 2023 Jul 17]. Available from: https://ourarchive.otago.ac.nz/handle/10523/5809\u003c/li\u003e\n\u003cli\u003eCancers | Free Full-Text | Aberrant MUC1-TRIM46-KRTCAP2 Chimeric RNAs in High-Grade Serous Ovarian Carcinoma [Internet]. [cited 2023 Jul 17]. Available from: https://www.mdpi.com/2072-6694/7/4/878\u003c/li\u003e\n\u003cli\u003eKRTCAP2 as an immunological and prognostic biomarker of hepatocellular carcinoma. Colloids and Surfaces B: Biointerfaces. 2023;222:113124. \u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Hanlon TP, Rider LG, Gan L, Fannin R, Paules RS, Umbach DM, et al. Gene expression profiles from discordant monozygotic twins suggest that molecular pathways are shared among multiple systemic autoimmune diseases. Arthritis Res Ther. 2011;13:1\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003eLee S, Yang H-K, Lee H-J, Park DJ, Kong S-H, Park SK. Systematic review of gastric cancer-associated genetic variants, gene-based meta-analysis, and gene-level functional analysis to identify candidate genes for drug development. Frontiers in Genetics [Internet]. 2022 [cited 2023 Jul 17];13. Available from: https://www.frontiersin.org/articles/10.3389/fgene.2022.928783\u003c/li\u003e\n\u003cli\u003eXu L, Geman D, Winslow RL. Large-scale integration of cancer microarray data identifies a robust common cancer signature. BMC Bioinformatics. 2007;8:1\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003eBurton JC, Antoniades W, Okalova J, Roos MM, Grimsey NJ. Atypical p38 Signaling, Activation, and Implications for Disease. International Journal of Molecular Sciences. 2021;22:4183. \u003c/li\u003e\n\u003cli\u003eXu Y, Sun Q, Yuan F, Dong H, Zhang H, Geng R, et al. RND2 attenuates apoptosis and autophagy in glioblastoma cells by targeting the p38 MAPK signalling pathway. Journal of Experimental \u0026amp; Clinical Cancer Research. 2020;39:174. \u003c/li\u003e\n\u003cli\u003eImprove dosimetric outcome in stage III non-small-cell lung cancer treatment using spot-scanning proton arc (SPArc) therapy - PubMed [Internet]. [cited 2023 Jul 17]. Available from: https://pubmed.ncbi.nlm.nih.gov/29486782/\u003c/li\u003e\n\u003cli\u003eFan Y, Yang J, Xie S, He J, Huang S, Chen J, et al. Systematic analysis of inflammation and pain pathways in a mouse model of gout. Mol Pain. 2022;18:17448069221097760. \u003c/li\u003e\n\u003cli\u003eHartigan N, Garrigue-Antar L, Kadler KE. Bone Morphogenetic Protein-1 (BMP-1). Journal of Biological Chemistry. 2003;278:18045\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eFrontiers | Dynamics of Transforming Growth Factor (TGF)-\u0026beta; Superfamily Cytokine Induction During HIV-1 Infection Are Distinct From Other Innate Cytokines [Internet]. [cited 2023 Jul 17]. Available from: https://www.frontiersin.org/articles/10.3389/fimmu.2020.596841/full\u003c/li\u003e\n\u003cli\u003eWu M, Tian Y, Wang Q, Guo C. Gout: a disease involved with complicated immunoinflammatory responses: a narrative review. Clin Rheumatol. 2020;39:2849\u0026ndash;59. \u003c/li\u003e\n\u003cli\u003eFrontiers | The Role of the Intestine in the Development of Hyperuricemia [Internet]. [cited 2023 Jul 17]. Available from: https://www.frontiersin.org/articles/10.3389/fimmu.2022.845684/full\u003c/li\u003e\n\u003cli\u003eSun H, Wu Y, Bian H, Yang H, Wang H, Meng X, et al. Function of Uric Acid Transporters and Their Inhibitors in Hyperuricaemia. Frontiers in Pharmacology [Internet]. 2021 [cited 2023 Jul 17];12. Available from: https://www.frontiersin.org/articles/10.3389/fphar.2021.667753\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Hern\u0026aacute;ndez FJ, L\u0026oacute;pez-Novoa JM. Role of TGF-\u0026beta; in chronic kidney disease: an integration of tubular, glomerular and vascular effects. Cell Tissue Res. 2012;347:141\u0026ndash;54. \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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, Gout, Summary-data-based Mendelian Randomization, GWAS, scRNA-seq","lastPublishedDoi":"10.21203/rs.3.rs-3687354/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3687354/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGout is a prevalent manifestation of metabolic osteoarthritis induced by elevated blood uric acid levels. The purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and elucidate its pathogenesis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and gout.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 17 association signals for gout at unique genetic loci, including four genes related by protein-protein interaction network (PPI) analysis: TRIM46, THBS3, MTX1, and KRTCAP2. Additionally, we discerned 22 methylation sites in relation to gout. The study also found that genes such as TRIM46, MAP3K11, KRTCAP2, and TM7SF2 could potentially elevate the risk of gout. Through a Mendelian randomization (MR) analysis, we identified three proteins causally associated with gout: ADH1B, BMP1, and HIST1H3A.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAccording to our findings, gout is linked with the expression and function of particular genes and proteins. These genes and proteins have the potential to function as novel diagnostic and therapeutic targets for gout. These discoveries shed new light on the pathological mechanisms of gout and clear the way for future research on this condition.\u003c/p\u003e","manuscriptTitle":"Single-cell and genome-wide Mendelian randomization identifies causative genes for gout","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-06 13:39:26","doi":"10.21203/rs.3.rs-3687354/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-02T13:31:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-01T13:12:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"adf51871-f466-4c31-9d0d-01f8cd283ecc","date":"2024-01-12T07:51:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2cb431a4-f8bd-47a3-a84e-fba262b1c9fc","date":"2023-12-08T00:41:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-07T19:15:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-04T09:09:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-04T00:14:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Arthritis Research \u0026 Therapy","date":"2023-11-30T14:02:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a84090b-850c-4403-99ad-26f6a4cde25b","owner":[],"postedDate":"December 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-26T22:08:23+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-06 13:39:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3687354","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3687354","identity":"rs-3687354","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}