Multi-omics and molecular testing: A new insight into the genetic mechanisms of sarcopenia and arthritis

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Our study endeavors to explore the unclear genetic structure between them. Using advanced statistical genetic approaches and genome-wide association study (GWAS) summary statistics, we explored the shared genetic basis among multiple manifestations of sarcopenia and four distinct arthritic conditions: osteoarthritis, rheumatoid arthritis, psoriatic arthritis, and gouty arthritis. A local analysis method for variant annotation was applied to approximately 2,495 genomic regions of equal size and partial independence, determining binary local genetic correlations among these regions. Cross-phenotype association GWAS studies have revealed many genetic variations associated with complex traits. Transcriptome-wide association studies were conducted using weights from various human tissues to identify risk loci. We functionally annotated genomic multi-markers and fine-mapping colocalization by conducting the whole-genome unified testing of molecular characteristics. Significant correlations between sarcopenia and four types of arthritis were detected through comprehensive and local genetic correlation analyses. At the genomic level, we identified 19 unique bivariate regions, including chr3q27.3, chr5q35.3, and chr12q13.2-q13.3, involving multiple human cell lines such as KBM7, GM12878, and IMR90. Gene enrichment analyses revealed that the selected loci primarily signaled through elementary pathways, including central nervous system neuron axonogenesis, glutamatergic synapse, and beta-catenin binding. Specifically, GDF5 and DNAJC27 were prioritized as the most probable candidate genes via precision transcriptomics. Our study has identified pleiotropic genomic regions linking sarcopenia and arthritis, providing effective strategies for the treatment of diseases. Sarcopenia Arthritis Genetic architecture Phenotypic loci Figures Figure 1 Figure 2 Figure 3 Key Points 1. Local genetic analysis has confirmed the significance of the binary regions. 2. We conducted a whole - genome test on the molecules, and marked the risk genomes. 3. We prioritize the fine analysis of probabilities to emphasize the causal genes in every region. Introduction Aging is a complex and multifactorial physiological process. It is at the forefront of life science research and biomedicine, closely correlated with various diseases such as cardiovascular disease, diabetes, neurodegenerative diseases, and cancer. In recent years, population aging has intensified, leading to a significant increase in chronic diseases as a major health threat. Studies have identified that the gradual decline in both muscle and bone strength is a primary characteristic of the aging process. Consequently, muscle and joint diseases, serving as typical examples of these conditions, are garnering greater global attention from geriatricians [ 1 , 2 ]. Sarcopenia is a common progressive muscle disease that mainly causes a remarkable decrease in muscle strength and mass [ 3 ]. It often occurs with aging, limiting patient mobility and increasing the risk of health problems and adverse events like fractures and falls. With the acceleration of population aging, the number of people with sarcopenia is continuously growing. The European Working Group on Sarcopenia in Older Adults revised the definition of sarcopenia (EWGSOP2) in 2018, emphasizing the critical role of deceased muscle strength as a potentially important indicator of the condition. Assessing muscle strength proves to be a faster and more resource-efficient method than measuring muscle mass, and a stronger relationship exists between muscle weakness and adverse health outcomes [ 3 ]. Arthritis is a group of disorders that share common joint inflammation and degeneration characteristics. Among these, osteoarthritis is the most prevalent, accounting for approximately 65% of cases [ 4 ]. This condition encompasses several types, including degenerative osteoarthritis (OA), rheumatoid arthritis (RA), psoriatic arthritis (PSA), and metabolic gouty arthritis (GA). Additionally, other forms of arthritis can be triggered by infections or trauma. The risks posed by arthritis are remarkable, leading to loss of joint function, increased risk of cardiovascular diseases, and a substantial reduction in patients' quality of life. Furthermore, the financial burden of arthritis is considerable for both the patient's family and society [ 5 , 6 ]. The onset and progression of arthritis would be facilitated by altered biomechanics in bone and muscle interactions surrounding the joints, potentially contributing to muscle atrophy or weakness. The skeletal muscle is indispensable in maintaining dynamic joint stability [ 7 ]. Moreover, sarcopenia can cause a reduction of muscle mass and strength, such as the atrophy of the quadriceps, ultimately diminishing the stability of weight-bearing joints like the knee and hip, which may contribute to the development and worsening of OA [ 8 ]. A longitudinal research reported that lower limb muscle strength and mass were associated with knee OA (KOA). Patients with sarcopenia were more prone to experience symptomatic KOA compared to those without sarcopenia [ 8 ]. Sarcopenia is a significant comorbidity in patients with RA [ 7 ]. The decreased skeletal muscle mass and density exert a notable adverse effect on the physical condition and disability rates of patients with RA. Muscle atrophy accelerates the progression of arthritis through three main pathways: abnormal mechanical stress, inflammatory mediator release, and signaling pathway dysregulation. Joint inflammation, in turn, would trigger a vicious cycle of muscle wasting, such as joint degeneration, reduced activity, and further muscle atrophy [ 9 ]. Conversely, arthritis-induced pain and activity limitations can exacerbate this cycle. Therefore, comprehension of the association between sarcopenia and arthritis is imperative for developing more effective treatments for comorbidities. In our research, we utilized datasets from large-scale cross-trait genome-wide association studies (GWAS), and we employed advanced statistical genetics algorithms to systematically delve into the shared genetic foundation of sarcopenia and arthritis. Present genetic correlation techniques comprehensively assess the genetic interrelations between phenotype pairs, encompassing the overall genome-wide impact on these characteristics. Genetic correlation is a widespread metric to evaluate the overlap in genetics. It can be theoretically examined across the entire genome, thus representing the average of shared genetic effects among all causal loci [ 10 ]. It is also anticipated that a genetic overlap will derive from the intersection of clinical and pathological features. Our intensive research aims to provide a more comprehensive and insightful new perspective on the genetic foundation of aging. Methods Data resource The precise GWAS datasets pertaining to arthritis and sarcopenia-related traits were sourced from the IEU OpenGWAS database project ( https://gwas.mrcieu.ac.uk/ ). According to the definition provided by EWGSOP, the data used to cover a wide range of statistics related to sarcopenia, such as usual walking pace (UWP), appendicular lean mass (ALM), hand grip strength (left: HGSL, right: HGSR), leg fat percentage (left: LFPL, right: LFPR), arm fat percentage (left: AFPL, right: AFPR). The arthritis information comprises four types (OA, RA, PSA, and GA) (Table 1 ). In order to maintain a uniform population distribution in GWAS studies, the focus was primarily on Europeans, and GRCh37 was utilized for data aggregation. Additionally, the conversion of GRCh38 data to a format compatible with GRCh37 must be accomplished. Single nucleotide polymorphisms (SNPs) that represent genetic variations at a genome-wide significance level ( P < 5 x 10 − 8 ) were filtered as instrumental variables (IVs) [ 11 ]. Figure 1 illustrates the design of the experiment. Table 1 Overview of traits included in our study. Phenotype ID Sample SNP-based heritability Total,N SNPs Consortium Global h 2 SNP Mean χ² OA ebi-a-GCST005814 50,508 15,845,511 NA 0.108 1.04 PSA ieu-b−5116 26,351 10,894,596 M Soomro 0.152 1.11 RA ukb-b−11874 463,010 9,851,867 MRC-IEU 0.112 1.05 GA ukb-b−12765 463,010 9,851,867 MRC-IEU 0.105 1.04 UWP ukb-b−4711 459,915 9,851,867 MRC-IEU 0.118 1.69 ALM ebi-a-GCST90000026 205,513 18,164,071 NA 0.352 1.08 HGSL ukb-a−374 335,821 10,894,596 Neale Lab 0.201 1.73 HGSR ukb-a−379 335,821 10,894,596 Neale Lab 0.201 1.73 LFPL ukb-b−18377 454,826 9,851,867 MRC-IEU 0.194 2.93 LFPR ukb-b−20531 454,826 9,851,867 MRC-IEU 0.194 2.93 AFPL ukb-b−20188 454,724 9,851,867 MRC-IEU 0.208 3.07 AFPR ukb-b−12854 454,789 9,851,867 MRC-IEU 0.204 3.04 Abbreviations: SNPs, single nucleotide polymorphisms; OA, osteoarthritis; PSA, psoriatic arthritis; RA, rheumatoid arthritis; GA, gouty arthritis; UWP, usual walking pace; ALM, appendicular lean mass; HGSL, hand grip strength (left); HGSR, hand grip strength (right); LFPL, leg fat percentage (left); LFPR, leg fat percentage (right); AFPL, arm fat percentage (left); AFPR, arm fat percentage (right). Genetic Correlation Analysis We conducted multiple genetic correlation analyses to explore the shared genetic foundation between traits. We employed linkage disequilibrium score regression (LDSC) along with high-definition likelihood (HDL) to evaluate the genome-wide genetic correlations [ 10 , 12 ]. LDSC, based on S-LDSC (stratified) and LD models, performed essential functionalities that encompassed (1) evaluating the heritability of observed-scale SNP for each trait, (2) determining global genetic correlation (rg), and (3) assessing the extent of sample overlap. Estimates of rg ranged from − 1 to 1. Significance thresholds were established using the false discovery rate (FDR) corrected P -value of 0.05. Compared to LDSC, HDL decreased genetic variance by approximately 60%, equivalent to a 2.5 times increase in sample size and allowing for the detection of more significant genetic correlations [ 12 ]. Therefore, we used both methods to evaluate genetic correlations between traits. To precisely measure the local genetic connections between traits within the genome, we employed two methods: LAVA (Local Variation Association Analysis) and SUPERGNOVA (SUPER GeNetic cOVariance Analyzer) [ 13 , 14 ]. LAVA was applied to identify independent regions in the genome where two traits exhibit a strong correlation. LAVA provides 2495 independent blocks, with a set threshold of P = 0.05/2495 derived from the 1000 Genome Europe reference. These blocks were utilized to estimate the typical bivariate genetic connections between traits and localized heritability. By using this method, it was possible to reduce the LD (Linkage Disequilibrium) between each block to a minimum. In addition, the FDR method was employed to interpret the results of multiple comparisons of the genetic correlation tests described above. SUPERGNOVA was employed to estimate local pairwise correlations and facilitated joint genome-wide analyses of multiple traits. This device carefully separates the whole genome into roughly 2,353 segments, effectively filtering out SNPs with missing values or rare variations where the minor allele frequency was less than 5%. Cross Phenotype Association Numerous genetic variations associated with complex traits have been revealed in GWAS studies. Some studies demonstrated that numerous detected genetic loci could concurrently be related to multiple characteristics, a phenomenon referred to as cross phenotype association (CPASSOC) [ 15 ]. Considering trait-specific heterogeneity effects, CPASSOC used a weighted meta-analysis approach to assign weights to the sample size of GWAS summary data, thereby promoting the calculation of statistical heterogeneity (SHet). Significant shared signals related to those where the locus has achieved a genome-wide considerable level in the joint analysis ( P meta < 5 x 10 − 8 ), and each trait from GWAS should achieve suggestive significance ( P single trait < 1 x 10 − 5 ). Multi-trait colocalization analysis Colocalization analysis was performed to explore potential causative variations in gene expression associated with sarcopenia and arthritis [ 16 ]. A Bayesian model was utilized to support various hypotheses of distinct exclusivity, ranging from H0 to H4, which represented different associations: no association, expression-only, diseases-only, independent, and shared causal variance. For each shared locus, we extracted summary statistics for variants within a 1.0 megabase (Mb) region around the index SNP and computed the posterior probabilities of the H4 hypothesis (PPH4) and the H3 hypothesis (PPH3). The colocalization result was generally considered plausible when the sum of the posterior probabilities of PPH3 and PPH4 exceeded 0.8. Functional annotation with tissue-specific expression and gene set enrichment analysis MAGMA (Multiple Annotation gene-set Analysis) applied a multiple regression model for gene analysis [ 17 ]. It integrated the signals related to SNPs within a specific gene region (± 5 kb) to acquire p-values and identify genes within or intersecting with pluripotent motifs. MAGMA facilitated a comprehensive exploration of genotype tissue expression version 8 (GTEx.V8) tissue enrichment, encompassing 54 diverse tissue types, allowing for the precise detection of specific tissues associated with shared genes [ 18 ]. To identify candidate genes with potential statistical associations across different traits, we employed the summary data-based Mendelian randomization (SMR) approach in our cross-trait meta-analyses [ 19 ]. SMR integrated GWAS data with expression quantitative trait loci (eQTL) data, connecting DNA methylation levels to detect associations between phenotypes and gene expression. This facilitated an understanding of the associations between genetic factors and their potential impacts on specific traits. In analyzing SMR for two traits, genes with shared functions between sarcopenia and arthritis were detected through the Benjamini-Hochberg FDR test and the instrument-dependent heterogeneity outlier test. To ensure there were no significant heterogeneous relationships, recommended FDR 0.01, and N > 10 SNPs. With the assistance of Gene Ontology (GO) annotation, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the Gene Set Enrichment Analysis (GSEA) technique, we performed a comprehensive identification of gene regulatory pathways to gain a deeper comprehension of the pleiotropic genes explored by MAGMA [ 20 – 22 ]. GO analysis methodically organized genes into a structured hierarchy, dividing them into three main aspects: biological processes (BP), molecular functions (MF), and cellular components (CC). Performing GSEA analysis on the complete list of sequenced genes revealed significant biological functions and pathways. This method could lessen the risk of overlooking potentially relevant features through rigorous screening criteria. GSEA was accurately employed by utilizing a combination of criteria containing a normalized enrichment score (NES) absolute value greater than 1 (|NES| >1), PNES ≤ 0.05, and FDR < 0.25. Correlation Analysis of Bivariate Loci FUSION (Functional Summarisation Imputation) was extensively utilized for transcriptomic analysis across diverse phenotypes. FUSION integrated expression weights from GTEx.V8 datasets of GWAS data with individual features using transcriptomics to identify genes that transcended the boundaries of significant bivariate loci [ 23 ]. The method built an accurate prediction model for significant cis-genes identified by SNPs within 500 kb of either side of the gene boundary. The Benjamini-Hochberg correction (FDR < 0.05) indicated the significant transcriptomics results. FOCUS (Fine Mapping of Causal Gene Sets), based on a Bayesian method, directly predicted expression correlations and offered posterior probabilities (PIP) of causality in relevant tissue types to prioritize genes [ 24 ]. Genes with a PIP value of 0.9 or higher were considered potential causal candidates. Results Genome-wide genetic correlations and overlap Initially, we evaluated the shared heritability between sarcopenia and arthritis through cross-trait LDSC and HDL analyses to identify target traits. This heritability was widely prevalent worldwide. It's worth mentioning that these 32 trait pairs exhibit not just genetic correlation but also remarkable genetic overlap. Among all traits analyzed, LDSC analysis highlighted the most notable positive correlation between RA and HGSR (rg = 0.449, P LDSC = 4.30 x 10 − 3 , SE = 0.072), while HDL demonstrated that OA exhibited the most significant positive correlation with ALM (rg = 0.394, P LDSC = 3.00 x 10 − 4 , SE = 0.015). Table 2 demonstrates remarkable genetic correlations among the traits. After the Bonferroni correction, LAVA analysis identified 73 specific genetic regions associated with arthritis and sarcopenia (Figs. 2A-B). By eliminating the complex linkage imbalance structure within the MHC region, 19 bivariate regions were successfully detected. In the region of chr5q23.3-q31.1, PSA and HGSR, as well as PSA and HGSL, have exhibited the strongest genetic link including gene CDC42SE2, HINT1, and LYRM7 (HGSR: P = 6.62 x 10 − 6 , HGSL: P = 2.15 x 10 − 5 ). However, this area remained relatively under-researched, with various human cell populations such as KBM7, GM12878, and IMR90. Utilizing protein-coding on chromosome 12q13.2-q13.3, PSA and ALM have detected several unexplored genes, including NEUROD4, TESPA1, OR6C74, and OR6C1. In addition, five bivariate overlap regions were dug out (chr3q27.3, chr5q15, chr5q23.3-q31.1, chr12p12.1, and chr19p13.2). These overlap regions collectively encompassed 30 loci. Eight important genetic areas were found between RA and sarcopenia, with unduplicated regions included chr5q35.3, chr5q35.1, chr14q32.12-q32.13, and chr19p13.3. SUPERGNOVA analysis demonstrated that the most notable association existed between RA and sarcopenia. Specifically, a significant negative correlation with ALM was observed on chr5q11.2 ( P = 2.73 x 10 − 7 ), while a remarkable positive correlation was found with HGSR at chr16q24.2 ( P = 1.51 x 10 − 5 ). The results from diverse genetic techniques were consistent, further reinforcing the robustness of genetic correlations between traits. Table 2 Genetic connections and loci associated with sarcopenia and arthritis Trait pair LDSC HDL CPSSSOC rg-LDSC P-LDSC SE rg-HDL P-HDL SE SNP P-SHet Locus OA-UWP −0.438 5.77E−24 0.045 −0.176 2.01E−36 0.002 rs13107325 5.83E−24 BANK1 OA-ALM 0.149 2.60E−02 0.043 0.394 3.00E−04 0.015 rs6060369 1.32E−20 GDF5 OA-HGSL −0.249 2.52E−18 0.042 −0.107 2.03E−02 0.003 rs2248393 1.52E−30 GDF5 OA-HGSR −0.203 3.32E−13 0.043 −0.109 1.65E−02 0.004 rs2248393 2.45E−28 GDF5 OA-LFPL 0.446 1.05E−28 0.040 0.214 1.02E−39 0.006 rs62048402 1.31E−152 RPGRIP1L OA-LFPR 0.442 2.17E−28 0.040 0.214 1.65E−42 0.006 rs1421085 6.37E−150 RPGRIP1L OA-AFPL 0.421 2.19E−27 0.039 0.217 7.02E−04 0.007 rs56094641 1.31E−216 RPGRIP1L OA-AFPR 0.423 3.64E−27 0.039 0.217 7.25E−04 0.006 rs56094641 2.03E−180 RPGRIP1L PSA-UWP −0.114 9.01E−03 0.044 0.072 5.68E−03 0.003 rs41272611 9.95E−178 ABCF1 PSA-ALM −0.123 4.80E−02 0.036 0.374 4.00E−02 0.015 rs6060369 5.15E−24 GDF5 PSA-HGSL 0.061 2.03E−03 0.045 −0.102 9.37E−03 0.003 rs143384 4.23E−29 GDF5 PSA-HGSR 0.147 3.10E−02 0.046 0.104 1.27E−02 0.003 rs41272611 2.46E−161 ABCF1 PSA-LFPL 0.154 4.01E−04 0.043 0.204 1.05E−05 0.006 rs41272611 1.12E−174 ABCF1 PSA-LFPR 0.148 5.00E−04 0.043 0.204 1.90E−05 0.006 rs1421085 2.55E−142 RPGRIP1L PSA-AFPL 0.151 2.02E−04 0.041 0.189 3.99E−06 0.027 rs56094641 1.21E−215 RPGRIP1L PSA-AFPR 0.149 4.21E−04 0.039 0.187 8.79E−06 0.026 rs56094641 1.58E−198 RPGRIP1L RA-UWP −0.409 2.36E−08 0.073 −0.072 5.81E−11 0.002 rs3104415 5.26E−68 HLA-DQA1 RA-ALM −0.124 4.10E−02 0.059 −0.372 8.04E−03 0.015 rs12959273 3.56E−48 NFATC1 RA-HGSL 0.165 2.30E−02 0.073 0.101 1.40E−03 0.003 rs3104415 7.62E−79 HLA-DQA1 RA-HGSR 0.449 4.30E−03 0.072 0.102 5.59E−04 0.003 rs112852122 2.43E−29 PREX1 RA-LFPL 0.249 9.32E−05 0.064 0.201 1.44E−08 0.005 rs62048402 9.76E−147 RPGRIP1L RA-LFPR 0.244 1.03E−04 0.063 0.201 9.90E−09 0.005 rs1421085 3.85E−150 RPGRIP1L RA-AFPL 0.224 7.00E−04 0.066 0.169 1.84E−07 0.004 rs56094641 1.16E−203 RPGRIP1L RA-AFPR 0.216 1.20E−03 0.067 0.167 2.36E−07 0.004 rs56094641 2.68E−196 RPGRIP1L GA-UWP −0.168 1.10E−02 0.104 −0.072 1.9E−03 0.002 rs2725245 5.44E−18 ABCG2 GA-ALM 0.206 3.90E−02 0.100 0.362 9.02E−04 0.015 rs1260326 3.56E−15 IFT172 GA-HGSL −0.117 4.80E−02 0.105 −0.111 6.00E−04 0.003 rs143384 4.23E−29 GDF5 GA-HGSR −0.123 2.30E−02 0.102 −0.102 5.00E−04 0.003 rs143384 2.42E−38 GDF5 GA-LFPL 0.091 3.50E−02 0.096 0.200 2.70E−03 0.005 rs62048402 3.38E−147 RPGRIP1L GA-LFPR 0.088 3.50E−02 0.094 0.201 2.80E−03 0.005 rs1421085 2.55E−142 RPGRIP1L GA-AFPL 0.212 1.69E−02 0.098 0.098 1.05E−02 0.012 rs56094641 8.83E−216 RPGRIP1L GA-AFPR 0.208 2.01E−02 0.097 0.092 2.23E−02 0.012 rs56094641 2.03E−180 RPGRIP1L Abbreviations: LDSC, linkage disequilibrium score regression; HDL, high-definition likelihood; CPASSOC, cross phenotype association; rg, genetic correlation; SE, standard error; SNP, single nucleotide polymorphism; SHet, statistical heterogeneity between traits; OA, osteoarthritis; PSA, psoriatic arthritis; RA, rheumatoid arthritis; GA, gouty arthritis; UWP, usual walking pace; ALM, appendicular lean mass; HGSL, hand grip strength (left); HGSR hand grip strength (right); LFPL, leg fat percentage (left); LFPR, leg fat percentage (right); AFPL, arm fat percentage. Genomic locus characterisation and candidate gene analysis Based on strong evidence of the genetic association among traits, we utilized CPASSOC to comprehensively analyze polymorphism levels that covered a total of 13,671,468 SNPs (Fig. 2C). Among the identified top SNPs, those mapped at RPGRIP1L (a novel gene with limited research focus), specifically rs62048402, rs1421085, and rs56094641, were determined to have the highest frequency. Additionally, the gene GDF5 has been found to facilitate cartilage repair, BANK1 and HLA-DQA1 were strongly related to the immune system, and ABCG2 may be associated with hyperuricemia [25–27]. Furthermore, several loci were discovered that have been relatively less reported, namely ABCF1, IFT172, PREX1, and NFATC1. By conducting colocalization in cis-eQTL and trans-eQTL analyses, we have verified the shared causal involvement of loci identified by CPASSOC. A specific region located at chr18q13 had the highest posterior probability (PPH4 = 0.99, candidate SNP: rs60389750, mapped at NFATC1), which existed a shared localization among RA, PSA, and LFPL. Previous studies have strongly confirmed the critical role played by NFATC1 in RA-associated bone destruction. Meanwhile, NFATC1 was involved in muscle differentiation and regeneration processes and could enhance the oxidative metabolism of muscle, which was closely correlated with the regulation of exercise adaptations [28]. Additionally, RA was colocalized with HGSR at chr20q13.13 (PPH4 = 0.84, candidate SNP: rs3734254, mapped at PPARD), HGSL at chr19p13.3 (PPH4 = 0.97, candidate SNP: rs12977739, mapped at PTPRS), and ALM at chr8p21.3 (PPH4 = 0.81, candidate SNP: rs117567323, mapped at EGR3). Similarly, GA was colocalized with ALM at chr2p23.3 (PPH4 = 0.94, candidate SNP: rs1260326, mapped at IFT172), and OA was colocalized with LFPR at chr6p21.31 (PPH4 = 0.84, candidate SNP: rs3734254, mapped at PPARD) (Supplementary Tables 1–6). Analysis of the shared biological mechanisms among multi-potential patterns MAGMA analysis has unveiled that eight traits exhibit remarkable enrichment within the brain region. Specifically, ALM ( P = 5.66 x 10 − 4 ), LFPL ( P = 5.14 x 10 − 4 ), and LFPR ( P = 9.98 x 10 − 4 ) were predominantly clustered in the putamen area. Likewise, HGSL ( P = 3.94 x 10 − 3) and HGSR ( P = 6.53 x 10 − 4 ) primarily thrived in the caudate region. AFPL ( P = 1.09 x 10 − 3 ) and AFPR ( P = 7.03 x 10 − 4 ) converged harmoniously in the frontal cortex (BA9), while RA ( P = 1.79 x 10 − 2 ) took center stage in the nucleus accumbens region. Additionally, OA ( P = 8.91 x 10 − 3 ) and UWP ( P = 6.41 x 10 − 4 ) exhibited remarkable accumulation in muscle skeletal tissue. PSA ( P = 5.51 x 10 − 3 ) showed specific enrichment within lymphocytes, while GA ( P = 1.37 x 10 − 3 ) manifested significance in the uterus (Fig. 3A). Using MAGMA, we further analyzed the CPASSOC results at the gene level, identifying 63,095 loci that passed the test (Supplementary Tables 7–18). SMR confirmed 271 pleiotropic probes by integrating candidate genes with specific MAGMA tissue enrichment analysis (Fig. 3B, Supplementary Tables 19–26). The most prominent probe is HGSR on chromosome 16 in the skeletal muscle tissue, located at ENSG00000260442 (tagging RP11-22P6.3, P SMR = 2.04 x 10 − 22 , P HEIDI = 0.023). Additionally, in the brain cerebellum tissue, AFPR and AFPL have shared a common top probe ENSG00000164989, mapped at gene CCDC171 (AFPR: P SMR = 1.58 x 10 − 15 , P HEIDI = 0.111; AFPL: P SMR = 2.32 x 10 − 16 , P HEIDI = 0.108). Likewise, UWP and LFPR also possessed a common primary probe ENSG00000004534, localized to the gene RBM6 (UWP: P SMR = 3.54 x 10 − 9 , P HEIDI = 0.643; LFPR: P SMR = 7.87 x 10 − 19 , P HEIDI = 0.012). To delve deeper into the biological mechanisms of the pleiotropic genes, GSEA was utilized to probe subtle variations in gene expression levels with heightened precision using the MAGMA-enriched loci. In the GSEA framework, we analyzed the signal transduction pathways of 3 potential pathogenic genes, employing co-expression analysis to explore their respective functions and successfully meeting the criteria, which were KRAS_SIGNALING_DN, P53_PATHWAY, and PI3K_AKT_MTOR_SIGNALING (Fig. 3C, Supplementary Table 27). GO and KEGG signal pathway analysis suggested that these traits were associated with the central nervous system, neuronal differentiation, and chondrogenesis. The main pathways correlated with arthritis in sarcopenia have been determined using BP (GO:0021955, P = 6.22 x 10 − 8 , Padjust = 3.83 x 10 − 4 ), CC (GO:0098978, P = 1.46 x 10 − 6 , Padjust = 1.06 x 10 − 3 ), and MF (GO:0098978, P = 2.84 x 10 − 5 , Padjust = 3.17 x 10 − 2 ) as key factors: central nervous system neuron axonogenesis, glutamatergic synapse, and beta-catenin binding (Fig. 3D, Supplementary Tables 28–30). Precise transcriptomic analysis In total, 4761 transcriptomic loci related to sarcopenia and arthritis were recognized. Among these loci, 1052 genes were selected and determined to be significant ( P < 1.05 x 10 − 5 ) (Supplementary Tables 31–42). The most significant transcriptional locus was GDF5 ( P = 3.24 x 10 − 130 ), which was present in the brain putamen in ALM, as verified by multiple cross-phenotypic associations. Similarly, GDF5 also existed as the most significant locus for HGSL and HGSR in the prostate, consistent with our previous exploration at the genomic level. LFPL and LFPR have a transcriptomic differential site in the basal ganglia that involves the DNAJC27 loci (LFPL: P = 2.50 x 10 − 47 , LFPR: P = 2.76 x 10 − 46 ). This region is rarely reported and has not been explored in depth. FOCUS could incorporate predicted expression correlations for more enhanced transcriptomic analysis. Additionally, FOCUS has confirmed the critical correlation between GDF5 (PIP > 0.9) and diverse forms of sarcopenia and arthritis. One of the most significant genes in the brain for UWP is RBM6, which could activate locus related to inflammation and oxidative stress through the mTOR signaling pathway, thereby regulating protein synthesis and catabolism processes and ultimately affecting muscle degeneration [29]. Discussion This is the first study to investigate the extensive correlation and genetic overlap between sarcopenia and arthritis through a large-scale GWAS, adopting a multi-trait analysis with multi-omics frameworks. The multi-annotated gene set analyses and the gene-level overlapping studies indicated that genes were significantly enriched in the muscle-skeletal tissue and brain region. Furthermore, pathways such as central nervous system neuron axonogenesis and positive regulation of neuron differentiation are involved in developing sarcopenia and arthritis, suggesting that the loci in brain regions hold a pivotal role in regulating these disorders. Firstly, two genetic correlation analysis was performed to prioritize six sarcopenia-related traits. There were 19 binary regions between sarcopenia and arthritis determined after removing the complex linkage imbalances in the MHC region. Besides, in detecting local ancestry variance annotation, we found 10 binary shared regions between PSA and sarcopenia, which accounted for the most significant proportion. Meanwhile, the local genetic regions of sarcopenia and arthritis were mainly concentrated on chromosome 5 (mainly at chr5q15-q35.3), and the genetic variations in this region significantly impacted the risk of developing both. RA with LFPR and LFPL have the most significant binary genetic regions at chr5q35.3. Sarcopenia is a common comorbidity of RA. Compared to the general population, individuals with RA exhibit a more substantial decrease in muscle strength and quality as they age [30]. A meta-analysis has proved that DAS28 and HAQ can accurately predict the incidence of sarcopenia in RA patients [31]. A significant association between the development of OA and sarcopenia was demonstrated, especially in smokers. Decreased muscle mass in the lower extremities of sarcopenia patients could cause a reduction in dynamic joint stability, resulting in increased shear forces on the cartilage and accelerating its degeneration. Furthermore, patients with OA exhibit elevated levels of leptin secreted by visceral fat, which surpasses the blood-brain barrier and curtails the release of growth hormone. This brings about compromised muscle synthesis, particularly evident in obese OA patients [4, 32]. GA and sarcopenia have not been extensively researched. Our study revealed that OA colocalizes with ALM at IFT172, a locus that has not yet been reported. One previous study indicated high uric acid levels can negatively impact muscle health. Specifically, hyperuricemia impairs muscle satellite cell differentiation by inhibiting the PI3K/Akt/mTOR pathway and diminishes myosin heavy chain synthesis. Consequently, these factors cause muscle protein degradation and muscle health deterioration. These discoveries are crucial for comprehending the connection between arthritis and sarcopenia [33]. Secondly, integrated analyses of multi-traits and cross-populations in GWAS, along with transcriptomic approaches and precise mapping of causal gene sets, collectively pointed to the significance of GDF5, HLA-DQA1, BANK1, and ABCG2, which was significantly enriched in the basal ganglia of the nucleus accumbens and caudate nucleus, highlighting the critical role of the muscle-joint axis in the progress of disorders. GDF5 is explicitly expressed in the superficial layers of joint cartilage, especially during developmental stages. It regulates joint formation and plays a vital role in the development of OA, which can potentially be a rejuvenating treatment for age-related neuromuscular failure [34]. The expression of GDF5 is regulated by DNA methylation. Increased GDF5 has the potential to inhibit satellite cell differentiation and impair skeletal muscle regeneration [35]. Moreover, HLA-DQA1 and GDF5 have been identified as crucial in regulating the cell cycle, safeguarding against cancer, modulating transcription, and contributing to developing and maintaining the musculoskeletal system. Consequently, they were regarded as essential indicators of aging [36]. BLK has been recognized as a crucial risk factor for the progression of RA in both Asian and European populations. In contrast, investigating BANK1 single nucleotide variants in RA patients has been limited. A genotypic analysis and a large cross-ethnic meta-analysis have demonstrated that polymorphisms of both BLK and BANK1 interact with RA [37]. ABCG2 can mark numerous cell types within skeletal muscle and makes a difference in the positive regulation of muscle regeneration [38]. Furthermore, we have discovered several loci with limited research, including RPGRIP1L, ABCF1, DNAJC27, and RBM6. These genes hold promise as potential drug targets for treating sarcopenia and arthritis and are worth investigating and studying in the future. Thirdly, gene set enrichment analysis demonstrates the existence of the PI3K_AKT_MTOR and IL6_JAK_STAT3 signaling pathways, which are involved in regulating aging and arthritis. Polygonatum sibiricum polysaccharide (PSP) effectively mitigates skeletal muscle aging and mitochondrial dysfunction through the PI3K/Akt/mTOR signaling pathway [29, 39]. In skeletal muscle cells or animal models subjected to aging or oxidative stress, PSP can elevate the phosphorylation levels of PI3K, Akt, and mTOR. Hence, PSP can mitigate the damage to skeletal muscle induced by chronic inflammation. The JAK-STAT pathway promotes skeletal muscle aging through various mechanisms that amplify inflammation, prevent regeneration, result in mitochondrial damage, and disrupt metabolism. In addition, JAK-STAT and mTOR signals work collaboratively, contributing to an imbalance between protein synthesis and catabolism, further exacerbating muscle atrophy [40]. Our research recognizes certain limitations. Firstly, it is essential to explore GWAS data from populations of various ethnic backgrounds, containing those of European origin, to validate the reproducibility of the results of other diverse ancestry. Secondly, GWAS data identifies common disease-related variants, making discovering information about rare or structural variants challenging. Lastly, the generalizability of the research may be restricted by the absence of external validation. Hence, future cross-sectional and longitudinal studies should investigate the underlying biological mechanisms. Conclusion In conclusion, our study offers valuable insights into the shared genetic relationship between sarcopenia and arthritis. Comprehensive analyses of multiple annotated gene sets have discovered a complex interplay between tissues and genes, encompassing a multitude of biological pathways. The findings have shed light on the loci in brain and musculoskeletal regions, revealing the complex mechanisms behind the aging process and arthritis. Abbreviations EWGSOP2, European Working Group on Sarcopenia in Older Adults revised the definition of sarcopenia OA, Osteoarthritis RA, Rheumatoid Arthritis PSA, Psoriatic Arthritis GA, Gouty Arthritis KOA, knee Osteoarthritis GWAS, Genome-wide Association Studies UWP, Usual Walking Pace ALM, Appendicular Lean Mass HGSL, Hand Grip Strength (Left) HGSR, Hand Grip Strength (Right) LFPL, Leg Fat Percentage (Left) LFPR, Leg Fat Percentage (Right) AFPL, Arm Fat Percentage (Left) AFPR, Arm Fat Percentage (Right) SNPs, Single Nucleotide Polymorphisms LSDC, Linkage Disequilibrium Score Regression LD, Linkage Disequilibrium FDR, False Discovery Rate LAVA, Local Analysis of Covariant Association SUPERGNOVA, SUPER GeNetic cOVariance Analyzer IVs, Instrumental Variables CPASSOC, Cross-phenotype Association Shet, Statistical Heterogeneity Mb, Megabase PPH4, Posterior Probabilities of the H4 PPH3, Posterior Probabilities of the H3 MAGMA, Multiple Annotation gene-set Analysis SMR, Summary data-based Mendelian randomization GO, Gene Ontology KEGG, Kyoto Encyclopedia of Genes and Genomes HEIDI, Instrument-dependent Heterogeneity eQTL, expression Quantitative Trait Loci BP, Biological Processes MF, Molecular Functions CC, Cellular Components GSEA, Gene Set Enrichment Analysis NES, Normalized Enrichment Score FUSION, Functional Summarisation Imputation GTEx.V8, Genotype-Tissue Expression Version 8 FOCUS, Fine-mapping of Causal Gene Sets PIP, Posteriori Probability of Causality PSP, Polygonatum sibiricum polysaccharide Declarations Acknowledgements We are grateful to all the authors of GWAS, GTEx.V8, and eQTL, as well as the participants who contributed to the aggregated statistics. Authors’ contributions Zhonghai Wang designed the study, interpreted the results and wrote original draft. Meiling Yu conducted preliminary statistical analysis and data validation. Han Wang revised the manuscript for important intellectual Content. Every author made contributions to the paper and gave their approval to the submitted version. Funding None. Data availability Publicly available data sets were analyzed in this study. The summary-level statistics of GWAS dataset for metabolic traits and psoriasis can be accessed through IEU open GWAS project (https://gwas.mrcieu.ac.uk/datasets/). The locus file used for LAVA analyses was accessed at https://github.com/lava-nc. The 1000 Genomes Project Phase 3 LD reference panel data for MAGMA was obtained from https://ctg.cncr.nl/software/magma. The eQTL data can be downloaded at https://cnsgenomics.com/data/SMR/#eQTLsummarydata. FUSION can be acquired from http://gusevlab.org/projects/fusion/#installation. The data of FOCUS can be downloaded at https://github.com/bogdanlab/focus. Gene set analyses were conducted with the help of GSEA software (https://software.broadinstitute.org/gsea/index.jsp) Conflicts of Interest The authors declare no conflicts of interest. thics approval and consent to participate The data we used were obtained from published studies approved by the corresponding ethics committee. Consent for publication Not applicable. References Dennison EM, Sayer AA, Cooper C. Epidemiology of sarcopenia and insight into possible therapeutic targets. Nat Rev Rheumatol 2017;13:340-347. doi: 10.1038/nrrheum.2017.60 Voisin S, Jacques M, Landen S, et al. Meta-analysis of genome-wide DNA methylation and integrative omics of age in human skeletal muscle. J Cachexia Sarcopenia Muscle 2021;12:1064-1078. doi: 10.1002/jcsm.12741 Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019;48:601. doi: 10.1093/ageing/afz046 Misra D, Fielding RA, Felson DT, et al. Risk of Knee Osteoarthritis With Obesity, Sarcopenic Obesity, and Sarcopenia. Arthritis Rheumatol 2019;71:232-237. doi: 10.1002/art.40692 Rausch Osthoff AK, Niedermann K, Braun J, et al. 2018 EULAR recommendations for physical activity in people with inflammatory arthritis and osteoarthritis. Ann Rheum Dis 2018;77:1251-1260. doi: 10.1136/annrheumdis-2018-213585 England BR, Thiele GM, Anderson DR, Mikuls TR. Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. BMJ 2018;361:k1036. doi: 10.1136/bmj.k1036 Bennett JL, Pratt AG, Dodds R, Sayer AA, Isaacs JD. Rheumatoid sarcopenia: loss of skeletal muscle strength and mass in rheumatoid arthritis. Nat Rev Rheumatol 2023;19:239-251. doi: 10.1038/s41584-023-00921-9 Litwic A, Edwards MH, Dennison EM, Cooper C. Epidemiology and burden of osteoarthritis. Br Med Bull 2013;105:185-99. doi: 10.1093/bmb/lds038 Wilkinson DJ, Piasecki M, Atherton PJ. The age-related loss of skeletal muscle mass and function: Measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans. Ageing Res Rev 2018;47:123-132. doi: 10.1016/j.arr.2018.07.005 Bulik-Sullivan BK, Loh PR, Finucane HK, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 2015;47:291-5. doi: 10.1038/ng.3211 Lyon MS, Andrews SJ, Elsworth B, Gaunt TR, Hemani G, Marcora E. The variant call format provides efficient and robust storage of GWAS summary statistics. Genome Biol 2021;22:32. doi: 10.1186/s13059-020-02248-0 Ning Z, Pawitan Y, Shen X. High-definition likelihood inference of genetic correlations across human complex traits. Nat Genet 2020;52:859-864. doi: 10.1038/s41588-020-0653-y Werme J, van der Sluis S, Posthuma D, de Leeuw CA. An integrated framework for local genetic correlation analysis. Nat Genet 2022;54:274-282. doi: 10.1038/s41588-022-01017-y Zhang Y, Lu Q, Ye Y, et al. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biol 2021;22:262. doi: 10.1186/s13059-021-02478-w Zhu X, Feng T, Tayo BO, et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am J Hum Genet 2015;96:21-36. doi: 10.1016/j.ajhg.2014.11.011 Soskic B, Cano-Gamez E, Smyth DJ, et al. Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation. Nat Genet 2022;54:817-826. doi: 10.1038/s41588-022-01066-3. Sey NYA, Pratt BM, Won H. Annotating genetic variants to target genes using H-MAGMA. Nat Protoc 2023;18:22-35. doi: 10.1038/s41596-022-00745-z Oliva M, Muñoz-Aguirre M, Kim-Hellmuth S, et al. The impact of sex on gene expression across human tissues. Science 2020;369:eaba3066. doi: 10.1126/science.aba3066 Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 2016;48:481-7. doi: 10.1038/ng.3538 Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res 2015;43:D1049-56. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30. doi: 10.1093/nar/28.1.27 Kuleshov MV, Jones MR, Rouillard AD, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 2016;44:W90-7. doi: 10.1093/nar/gkw377 Liu L, Yan R, Guo P, et al. Conditional transcriptome-wide association study for fine-mapping candidate causal genes. Nat Genet 2024;56:348-356. doi: 10.1038/s41588-023-01645-y Mancuso N, Freund MK, Johnson R, et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat Genet 2019;51:675-682. doi: 10.1038/s41588-019-0367-1 Parrish WR, Byers BA, Su D, et al. Intra-articular therapy with recombinant human GDF5 arrests disease progression and stimulates cartilage repair in the rat medial meniscus transection (MMT) model of osteoarthritis. Osteoarthritis Cartilage 2017;25:554-560. doi: 10.1016/j.joca.2016.11.002 Georg I, Díaz-Barreiro A, Morell M, Pey AL, Alarcón-Riquelme ME. BANK1 interacts with TRAF6 and MyD88 in innate immune signaling in B cells. Cell Mol Immunol 2020;17:954-965. doi: 10.1038/s41423-019-0254-9 Hoque KM, Dixon EE, Lewis RM, et al. The ABCG2 Q141K hyperuricemia and gout associated variant illuminates the physiology of human urate excretion. Nat Commun 2020;11:2767. doi: 10.1038/s41467-020-16525-w Bae S, Kim K, Kang K, et al. RANKL-responsive epigenetic mechanism reprograms macrophages into bone-resorbing osteoclasts. Cell Mol Immunol 2023;20:94-109. doi: 10.1038/s41423-022-00959-x. Li Y, Liu Z, Yan H, et al. Polygonatum sibiricum polysaccharide ameliorates skeletal muscle aging and mitochondrial dysfunction via PI3K/Akt/mTOR signaling pathway. Phytomedicine 2025;136:156316. doi: 10.1016/j.phymed.2024.156316 Cano-García L, Manrique-Arija S, Domínguez-Quesada C, et al. Sarcopenia and Nutrition in Elderly Rheumatoid Arthritis Patients: A Cross-Sectional Study to Determine Prevalence and Risk Factors. Nutrients 2023;15:2440. doi: 10.3390/nu15112440 Li TH, Chang YS, Liu CW, et al. The prevalence and risk factors of sarcopenia in rheumatoid arthritis patients: A systematic review and meta-regression analysis. Semin Arthritis Rheum 2021;51:236-245. doi: 10.1016/j.semarthrit.2020.10.002 Liao CD, Chen HC, Huang MH, Liou TH, Lin CL, Huang SW. Comparative Efficacy of Intra-Articular Injection, Physical Therapy, and Combined Treatments on Pain, Function, and Sarcopenia Indices in Knee Osteoarthritis: A Network Meta-Analysis of Randomized Controlled Trials. Int J Mol Sci 2023;24:6078. doi: 10.3390/ijms24076078 Hsu WH, Wang SY, Chao YM, Chang KV, Han DS, Lin YL. Novel metabolic and lipidomic biomarkers of sarcopenia. J Cachexia Sarcopenia Muscle 2024;15:2175-2186. doi: 10.1002/jcsm.13567 Nielsen RL, Monfeuga T, Kitchen RR, et al. Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning. Nat Commun 2024 ;15:2817. doi: 10.1038/s41467-024-46663-4 Hatazawa Y, Ono Y, Hirose Y, et al Reduced Dnmt3a increases Gdf5 expression with suppressed satellite cell differentiation and impaired skeletal muscle regeneration. FASEB J 2018;32:1452-1467. doi: 10.1096/fj.201700573R Jones G, Trajanoska K, Santanasto AJ, et al. Genome-wide meta-analysis of muscle weakness identifies 15 susceptibility loci in older men and women. Nat Commun 2021;12:654. doi: 10.1038/s41467-021-20918-w Génin E, Coustet B, Allanore Y, et al. Epistatic interaction between BANK1 and BLK in rheumatoid arthritis: results from a large trans-ethnic meta-analysis. PLoS One 2013;8:e61044. doi: 10.1371/journal.pone.0061044 Doyle MJ, Zhou S, Tanaka KK, et al. Abcg2 labels multiple cell types in skeletal muscle and participates in muscle regeneration. J Cell Biol 2011;195:147-63. doi: 10.1083/jcb.201103159 Tang G, Du Y, Guan H, et al. Butyrate ameliorates skeletal muscle atrophy in diabetic nephropathy by enhancing gut barrier function and FFA2-mediated PI3K/Akt/mTOR signals. Br J Pharmacol 2022;179:159-178. doi: 10.1111/bph.15693 Cui J, Shibata Y, Zhu T, Zhou J, Zhang J. Osteocytes in bone aging: Advances, challenges, and future perspectives. Ageing Res Rev 2022;77:101608. doi: 10.1016/j.arr.2022.101608 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Clinical and Experimental Medicine → Version 1 posted Editorial decision: Revision requested 18 Oct, 2025 Reviews received at journal 15 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 12 Sep, 2025 First submitted to journal 11 Sep, 2025 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. 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17:27:30","extension":"html","order_by":51,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166523,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7589916/v1/122fb84de69e6b13611ae489.html"},{"id":93258155,"identity":"1072ba02-eb12-402b-959e-b8fe96431223","added_by":"auto","created_at":"2025-10-10 17:19:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1182418,"visible":true,"origin":"","legend":"\u003cp\u003eAn outline of our study design.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7589916/v1/cf9a7a4b8ff78dd213a92d69.png"},{"id":93258156,"identity":"f9a3cafd-210d-408e-a149-0ae490de56f3","added_by":"auto","created_at":"2025-10-10 17:19:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1531250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic correlation between sarcopenia and arthritis\u003c/strong\u003e. (A) Chord diagrams illustrate the relationships between features, with line thickness indicating the number of significant themes. (B) Divided by the dark blue square diagonal line, the top half of the figure shows the results of genetic correlations based on the regression of LAVA scores against LDSC, while the bottom half of the figure displays the number of genetic loci that crossed significantly between traits. Asterisk marks indicate the presence of significant correlations, while white shading highlights the presence of substantial MHC regions between traits. (C) The circular heatmap exhibits the distribution of the loci of this study on chromosomes using cross-phenotypic correlation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7589916/v1/46f43f065f618c7f42de713e.png"},{"id":93258157,"identity":"9d251acc-c957-4763-aaa3-200684f6a60d","added_by":"auto","created_at":"2025-10-10 17:19:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2765974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the shared biological mechanisms among multi-potential patterns.\u003c/strong\u003e (A) Enrichment analysis of 54 human tissues by multi-annotated gene sets. (B) Summary data-based analysis shows shared susceptible probes between eight sarcopenia-related traits and arthritis. The black label with asterisks is the top probe. (C) GSEA analysis reveals candidate genes with down-regulated pathways and regions of concentrated chromosomes. (D) The main essential pathway types are based on GO and KEGG enrichment analysis. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7589916/v1/b181b05ddadcd4f02bf9b0ec.png"},{"id":100614755,"identity":"c4068b81-083c-4050-92a0-f9824f2e8664","added_by":"auto","created_at":"2026-01-19 17:24:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7224035,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7589916/v1/dbe81d5b-a64a-4684-8036-5f0bb108c4e4.pdf"},{"id":93258879,"identity":"498f89bc-5339-430b-83a2-c2fcef5ae13c","added_by":"auto","created_at":"2025-10-10 17:27:29","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11475707,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7589916/v1/de99d2051e3d6c6af61911b2.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics and molecular testing: A new insight into the genetic mechanisms of sarcopenia and arthritis","fulltext":[{"header":"Key Points","content":"\u003cp\u003e1. Local genetic analysis has confirmed the significance of the binary regions.\u003c/p\u003e\u003cp\u003e2. We conducted a whole - genome test on the molecules, and marked the risk genomes.\u003c/p\u003e\u003cp\u003e3. We prioritize the fine analysis of probabilities to emphasize the causal genes in every region.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAging is a complex and multifactorial physiological process. It is at the forefront of life science research and biomedicine, closely correlated with various diseases such as cardiovascular disease, diabetes, neurodegenerative diseases, and cancer. In recent years, population aging has intensified, leading to a significant increase in chronic diseases as a major health threat. Studies have identified that the gradual decline in both muscle and bone strength is a primary characteristic of the aging process. Consequently, muscle and joint diseases, serving as typical examples of these conditions, are garnering greater global attention from geriatricians [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSarcopenia is a common progressive muscle disease that mainly causes a remarkable decrease in muscle strength and mass [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It often occurs with aging, limiting patient mobility and increasing the risk of health problems and adverse events like fractures and falls. With the acceleration of population aging, the number of people with sarcopenia is continuously growing. The European Working Group on Sarcopenia in Older Adults revised the definition of sarcopenia (EWGSOP2) in 2018, emphasizing the critical role of deceased muscle strength as a potentially important indicator of the condition. Assessing muscle strength proves to be a faster and more resource-efficient method than measuring muscle mass, and a stronger relationship exists between muscle weakness and adverse health outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Arthritis is a group of disorders that share common joint inflammation and degeneration characteristics. Among these, osteoarthritis is the most prevalent, accounting for approximately 65% of cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This condition encompasses several types, including degenerative osteoarthritis (OA), rheumatoid arthritis (RA), psoriatic arthritis (PSA), and metabolic gouty arthritis (GA). Additionally, other forms of arthritis can be triggered by infections or trauma. The risks posed by arthritis are remarkable, leading to loss of joint function, increased risk of cardiovascular diseases, and a substantial reduction in patients' quality of life. Furthermore, the financial burden of arthritis is considerable for both the patient's family and society [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe onset and progression of arthritis would be facilitated by altered biomechanics in bone and muscle interactions surrounding the joints, potentially contributing to muscle atrophy or weakness. The skeletal muscle is indispensable in maintaining dynamic joint stability [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, sarcopenia can cause a reduction of muscle mass and strength, such as the atrophy of the quadriceps, ultimately diminishing the stability of weight-bearing joints like the knee and hip, which may contribute to the development and worsening of OA [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A longitudinal research reported that lower limb muscle strength and mass were associated with knee OA (KOA). Patients with sarcopenia were more prone to experience symptomatic KOA compared to those without sarcopenia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Sarcopenia is a significant comorbidity in patients with RA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The decreased skeletal muscle mass and density exert a notable adverse effect on the physical condition and disability rates of patients with RA. Muscle atrophy accelerates the progression of arthritis through three main pathways: abnormal mechanical stress, inflammatory mediator release, and signaling pathway dysregulation. Joint inflammation, in turn, would trigger a vicious cycle of muscle wasting, such as joint degeneration, reduced activity, and further muscle atrophy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Conversely, arthritis-induced pain and activity limitations can exacerbate this cycle. Therefore, comprehension of the association between sarcopenia and arthritis is imperative for developing more effective treatments for comorbidities.\u003c/p\u003e\u003cp\u003eIn our research, we utilized datasets from large-scale cross-trait genome-wide association studies (GWAS), and we employed advanced statistical genetics algorithms to systematically delve into the shared genetic foundation of sarcopenia and arthritis. Present genetic correlation techniques comprehensively assess the genetic interrelations between phenotype pairs, encompassing the overall genome-wide impact on these characteristics. Genetic correlation is a widespread metric to evaluate the overlap in genetics. It can be theoretically examined across the entire genome, thus representing the average of shared genetic effects among all causal loci [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is also anticipated that a genetic overlap will derive from the intersection of clinical and pathological features. Our intensive research aims to provide a more comprehensive and insightful new perspective on the genetic foundation of aging.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData resource\u003c/h2\u003e\u003cp\u003eThe precise GWAS datasets pertaining to arthritis and sarcopenia-related traits were sourced from the IEU OpenGWAS database project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). According to the definition provided by EWGSOP, the data used to cover a wide range of statistics related to sarcopenia, such as usual walking pace (UWP), appendicular lean mass (ALM), hand grip strength (left: HGSL, right: HGSR), leg fat percentage (left: LFPL, right: LFPR), arm fat percentage (left: AFPL, right: AFPR). The arthritis information comprises four types (OA, RA, PSA, and GA) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In order to maintain a uniform population distribution in GWAS studies, the focus was primarily on Europeans, and GRCh37 was utilized for data aggregation. Additionally, the conversion of GRCh38 data to a format compatible with GRCh37 must be accomplished. Single nucleotide polymorphisms (SNPs) that represent genetic variations at a genome-wide significance level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) were filtered as instrumental variables (IVs) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the design of the experiment.\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\u003eOverview of traits included in our study.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePhenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSNP-based heritability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal,N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSNPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eConsortium\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGlobal h\u003csup\u003e2\u003c/sup\u003e SNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMean\u003cb\u003eχ\u0026sup2;\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eebi-a-GCST005814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50,508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15,845,511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eieu-b\u0026minus;5116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26,351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,894,596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM Soomro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-b\u0026minus;11874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e463,010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9,851,867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMRC-IEU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-b\u0026minus;12765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e463,010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9,851,867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMRC-IEU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUWP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-b\u0026minus;4711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e459,915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9,851,867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMRC-IEU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eebi-a-GCST90000026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e205,513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18,164,071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHGSL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-a\u0026minus;374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e335,821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,894,596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNeale Lab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHGSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-a\u0026minus;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e335,821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,894,596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNeale Lab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLFPL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-b\u0026minus;18377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e454,826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9,851,867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMRC-IEU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLFPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-b\u0026minus;20531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e454,826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9,851,867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMRC-IEU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFPL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-b\u0026minus;20188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e454,724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9,851,867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMRC-IEU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-b\u0026minus;12854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e454,789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9,851,867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMRC-IEU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: SNPs, single nucleotide polymorphisms; OA, osteoarthritis; PSA, psoriatic arthritis; RA, rheumatoid arthritis; GA, gouty arthritis; UWP, usual walking pace; ALM, appendicular lean mass; HGSL, hand grip strength (left); HGSR, hand grip strength (right); LFPL, leg fat percentage (left); LFPR, leg fat percentage (right); AFPL, arm fat percentage (left); AFPR, arm fat percentage (right).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGenetic Correlation Analysis\u003c/h3\u003e\n\u003cp\u003eWe conducted multiple genetic correlation analyses to explore the shared genetic foundation between traits. We employed linkage disequilibrium score regression (LDSC) along with high-definition likelihood (HDL) to evaluate the genome-wide genetic correlations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. LDSC, based on S-LDSC (stratified) and LD models, performed essential functionalities that encompassed (1) evaluating the heritability of observed-scale SNP for each trait, (2) determining global genetic correlation (rg), and (3) assessing the extent of sample overlap. Estimates of rg ranged from \u0026minus;\u0026thinsp;1 to 1. Significance thresholds were established using the false discovery rate (FDR) corrected \u003cem\u003eP\u003c/em\u003e-value of 0.05. Compared to LDSC, HDL decreased genetic variance by approximately 60%, equivalent to a 2.5 times increase in sample size and allowing for the detection of more significant genetic correlations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, we used both methods to evaluate genetic correlations between traits. To precisely measure the local genetic connections between traits within the genome, we employed two methods: LAVA (Local Variation Association Analysis) and SUPERGNOVA (SUPER GeNetic cOVariance Analyzer) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLAVA was applied to identify independent regions in the genome where two traits exhibit a strong correlation. LAVA provides 2495 independent blocks, with a set threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05/2495 derived from the 1000 Genome Europe reference. These blocks were utilized to estimate the typical bivariate genetic connections between traits and localized heritability. By using this method, it was possible to reduce the LD (Linkage Disequilibrium) between each block to a minimum. In addition, the FDR method was employed to interpret the results of multiple comparisons of the genetic correlation tests described above.\u003c/p\u003e\u003cp\u003eSUPERGNOVA was employed to estimate local pairwise correlations and facilitated joint genome-wide analyses of multiple traits. This device carefully separates the whole genome into roughly 2,353 segments, effectively filtering out SNPs with missing values or rare variations where the minor allele frequency was less than 5%.\u003c/p\u003e\n\u003ch3\u003eCross Phenotype Association\u003c/h3\u003e\n\u003cp\u003eNumerous genetic variations associated with complex traits have been revealed in GWAS studies. Some studies demonstrated that numerous detected genetic loci could concurrently be related to multiple characteristics, a phenomenon referred to as cross phenotype association (CPASSOC) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Considering trait-specific heterogeneity effects, CPASSOC used a weighted meta-analysis approach to assign weights to the sample size of GWAS summary data, thereby promoting the calculation of statistical heterogeneity (SHet). Significant shared signals related to those where the locus has achieved a genome-wide considerable level in the joint analysis (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003emeta\u003c/em\u003e\u003c/sub\u003e \u0026lt; 5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), and each trait from GWAS should achieve suggestive significance (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003esingle trait\u003c/em\u003e\u003c/sub\u003e \u0026lt; 1 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e).\u003c/p\u003e\n\u003ch3\u003eMulti-trait colocalization analysis\u003c/h3\u003e\n\u003cp\u003eColocalization analysis was performed to explore potential causative variations in gene expression associated with sarcopenia and arthritis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A Bayesian model was utilized to support various hypotheses of distinct exclusivity, ranging from H0 to H4, which represented different associations: no association, expression-only, diseases-only, independent, and shared causal variance. For each shared locus, we extracted summary statistics for variants within a 1.0 megabase (Mb) region around the index SNP and computed the posterior probabilities of the H4 hypothesis (PPH4) and the H3 hypothesis (PPH3). The colocalization result was generally considered plausible when the sum of the posterior probabilities of PPH3 and PPH4 exceeded 0.8.\u003c/p\u003e\n\u003ch3\u003eFunctional annotation with tissue-specific expression and gene set enrichment analysis\u003c/h3\u003e\n\u003cp\u003eMAGMA (Multiple Annotation gene-set Analysis) applied a multiple regression model for gene analysis [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. It integrated the signals related to SNPs within a specific gene region (\u0026plusmn;\u0026thinsp;5 kb) to acquire p-values and identify genes within or intersecting with pluripotent motifs. MAGMA facilitated a comprehensive exploration of genotype tissue expression version 8 (GTEx.V8) tissue enrichment, encompassing 54 diverse tissue types, allowing for the precise detection of specific tissues associated with shared genes [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTo identify candidate genes with potential statistical associations across different traits, we employed the summary data-based Mendelian randomization (SMR) approach in our cross-trait meta-analyses [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. SMR integrated GWAS data with expression quantitative trait loci (eQTL) data, connecting DNA methylation levels to detect associations between phenotypes and gene expression. This facilitated an understanding of the associations between genetic factors and their potential impacts on specific traits. In analyzing SMR for two traits, genes with shared functions between sarcopenia and arthritis were detected through the Benjamini-Hochberg FDR test and the instrument-dependent heterogeneity outlier test. To ensure there were no significant heterogeneous relationships, recommended FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, a HEIDI (Instrument-dependent Heterogeneity) test \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.01, and N\u0026thinsp;\u0026gt;\u0026thinsp;10 SNPs.\u003c/p\u003e\n\u003cp\u003eWith the assistance of Gene Ontology (GO) annotation, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the Gene Set Enrichment Analysis (GSEA) technique, we performed a comprehensive identification of gene regulatory pathways to gain a deeper comprehension of the pleiotropic genes explored by MAGMA [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. GO analysis methodically organized genes into a structured hierarchy, dividing them into three main aspects: biological processes (BP), molecular functions (MF), and cellular components (CC). Performing GSEA analysis on the complete list of sequenced genes revealed significant biological functions and pathways. This method could lessen the risk of overlooking potentially relevant features through rigorous screening criteria. GSEA was accurately employed by utilizing a combination of criteria containing a normalized enrichment score (NES) absolute value greater than 1 (|NES| \u0026gt;1), PNES\u0026thinsp;\u0026le;\u0026thinsp;0.05, and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation Analysis of Bivariate Loci\u003c/h2\u003e\n \u003cp\u003eFUSION (Functional Summarisation Imputation) was extensively utilized for transcriptomic analysis across diverse phenotypes. FUSION integrated expression weights from GTEx.V8 datasets of GWAS data with individual features using transcriptomics to identify genes that transcended the boundaries of significant bivariate loci [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The method built an accurate prediction model for significant cis-genes identified by SNPs within 500 kb of either side of the gene boundary. The Benjamini-Hochberg correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicated the significant transcriptomics results. FOCUS (Fine Mapping of Causal Gene Sets), based on a Bayesian method, directly predicted expression correlations and offered posterior probabilities (PIP) of causality in relevant tissue types to prioritize genes [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Genes with a PIP value of 0.9 or higher were considered potential causal candidates.\u003c/p\u003e\u003cbr\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eGenome-wide genetic correlations and overlap\u003c/h2\u003eInitially, we evaluated the shared heritability between sarcopenia and arthritis through cross-trait LDSC and HDL analyses to identify target traits. This heritability was widely prevalent worldwide. It's worth mentioning that these 32 trait pairs exhibit not just genetic correlation but also remarkable genetic overlap. Among all traits analyzed, LDSC analysis highlighted the most notable positive correlation between RA and HGSR (rg = 0.449, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eLDSC\u003c/em\u003e\u003c/sub\u003e = 4.30 x 10\u003csup\u003e− 3\u003c/sup\u003e, SE = 0.072), while HDL demonstrated that OA exhibited the most significant positive correlation with ALM (rg = 0.394, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eLDSC\u003c/em\u003e\u003c/sub\u003e = 3.00 x 10\u003csup\u003e− 4\u003c/sup\u003e, SE = 0.015). Table 2 demonstrates remarkable genetic correlations among the traits.\u003cp\u003eAfter the Bonferroni correction, LAVA analysis identified 73 specific genetic regions associated with arthritis and sarcopenia (Figs. 2A-B). By eliminating the complex linkage imbalance structure within the MHC region, 19 bivariate regions were successfully detected. In the region of chr5q23.3-q31.1, PSA and HGSR, as well as PSA and HGSL, have exhibited the strongest genetic link including gene CDC42SE2, HINT1, and LYRM7 (HGSR: \u003cem\u003eP\u003c/em\u003e = 6.62 x 10\u003csup\u003e− 6\u003c/sup\u003e, HGSL: \u003cem\u003eP\u003c/em\u003e = 2.15 x 10\u003csup\u003e− 5\u003c/sup\u003e). However, this area remained relatively under-researched, with various human cell populations such as KBM7, GM12878, and IMR90. Utilizing protein-coding on chromosome 12q13.2-q13.3, PSA and ALM have detected several unexplored genes, including NEUROD4, TESPA1, OR6C74, and OR6C1. In addition, five bivariate overlap regions were dug out (chr3q27.3, chr5q15, chr5q23.3-q31.1, chr12p12.1, and chr19p13.2). These overlap regions collectively encompassed 30 loci. Eight important genetic areas were found between RA and sarcopenia, with unduplicated regions included chr5q35.3, chr5q35.1, chr14q32.12-q32.13, and chr19p13.3.\u003c/p\u003e\n \u003cp\u003eSUPERGNOVA analysis demonstrated that the most notable association existed between RA and sarcopenia. Specifically, a significant negative correlation with ALM was observed on chr5q11.2 (\u003cem\u003eP\u003c/em\u003e = 2.73 x 10\u003csup\u003e− 7\u003c/sup\u003e), while a remarkable positive correlation was found with HGSR at chr16q24.2 (\u003cem\u003eP\u003c/em\u003e = 1.51 x 10\u003csup\u003e− 5\u003c/sup\u003e). The results from diverse genetic techniques were consistent, further reinforcing the robustness of genetic correlations between traits.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGenetic connections and loci associated with sarcopenia and arthritis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eTrait pair\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003eLDSC\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003eHDL\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003eCPSSSOC\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003erg-LDSC\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eP-LDSC\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSE\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003erg-HDL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eP-HDL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSE\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSNP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eP-SHet\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eLocus\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOA-UWP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.438\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.77E−24\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.045\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e−0.176\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.01E−36\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers13107325\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.83E−24\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eBANK1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOA-ALM\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.149\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.60E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.043\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.394\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.00E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.015\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers6060369\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.32E−20\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGDF5\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOA-HGSL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.249\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.52E−18\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.042\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e−0.107\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.03E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers2248393\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.52E−30\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGDF5\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOA-HGSR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.203\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.32E−13\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.043\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e−0.109\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.65E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.004\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers2248393\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.45E−28\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGDF5\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOA-LFPL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.446\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.05E−28\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.040\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.214\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.02E−39\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.006\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers62048402\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.31E−152\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOA-LFPR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.442\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.17E−28\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.040\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.214\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.65E−42\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.006\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers1421085\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.37E−150\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOA-AFPL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.421\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.19E−27\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.039\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.217\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.02E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.007\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers56094641\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.31E−216\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOA-AFPR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.423\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.64E−27\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.039\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.217\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.25E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.006\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers56094641\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.03E−180\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePSA-UWP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.114\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.01E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.044\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.072\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.68E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers41272611\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.95E−178\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eABCF1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePSA-ALM\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.123\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.80E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.036\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.374\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.00E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.015\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers6060369\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.15E−24\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGDF5\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePSA-HGSL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.061\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.03E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.045\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e−0.102\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.37E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers143384\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.23E−29\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGDF5\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePSA-HGSR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.147\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.10E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.046\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.104\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.27E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers41272611\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.46E−161\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eABCF1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePSA-LFPL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.154\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.01E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.043\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.204\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.05E−05\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.006\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers41272611\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.12E−174\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eABCF1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n 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align=\"left\"\u003e0.041\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.189\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.99E−06\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.027\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers56094641\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.21E−215\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePSA-AFPR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.149\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.21E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.039\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.187\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e8.79E−06\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.026\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers56094641\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.58E−198\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRA-UWP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.409\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.36E−08\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.073\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e−0.072\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.81E−11\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers3104415\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.26E−68\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHLA-DQA1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRA-ALM\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.124\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.10E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.059\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e−0.372\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e8.04E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.015\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers12959273\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.56E−48\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNFATC1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRA-HGSL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.165\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.30E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.073\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.101\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.40E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers3104415\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.62E−79\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHLA-DQA1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRA-HGSR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.449\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.30E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.072\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.102\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.59E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers112852122\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.43E−29\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ePREX1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRA-LFPL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.249\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.32E−05\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.064\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.201\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.44E−08\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers62048402\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.76E−147\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRA-LFPR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.244\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.03E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.063\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.201\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.90E−09\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers1421085\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.85E−150\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRA-AFPL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.224\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.00E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.066\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.169\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.84E−07\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.004\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers56094641\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.16E−203\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRA-AFPR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.216\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.20E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.067\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.167\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.36E−07\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.004\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers56094641\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.68E−196\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGA-UWP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.168\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.10E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.104\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e−0.072\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.9E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers2725245\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.44E−18\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eABCG2\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGA-ALM\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.206\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.90E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.100\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.362\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.02E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.015\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers1260326\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.56E−15\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eIFT172\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGA-HGSL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.117\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.80E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.105\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e−0.111\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.00E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers143384\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.23E−29\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGDF5\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGA-HGSR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e−0.123\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.30E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.102\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e−0.102\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.00E−04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers143384\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.42E−38\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGDF5\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGA-LFPL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.091\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.50E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.096\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.200\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.70E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers62048402\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.38E−147\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGA-LFPR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.088\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.50E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.094\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.201\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.80E−03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers1421085\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.55E−142\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGA-AFPL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.212\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.69E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.098\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.098\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.05E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.012\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers56094641\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e8.83E−216\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGA-AFPR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.208\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.01E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.097\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.092\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.23E−02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.012\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ers56094641\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.03E−180\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRPGRIP1L\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eAbbreviations: LDSC, linkage disequilibrium score regression; HDL, high-definition likelihood; CPASSOC, cross phenotype association; rg, genetic correlation; SE, standard error; SNP, single nucleotide polymorphism; SHet, statistical heterogeneity between traits; OA, osteoarthritis; PSA, psoriatic arthritis; RA, rheumatoid arthritis; GA, gouty arthritis; UWP, usual walking pace; ALM, appendicular lean mass; HGSL, hand grip strength (left); HGSR hand grip strength (right); LFPL, leg fat percentage (left); LFPR, leg fat percentage (right); AFPL, arm fat percentage.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003eGenomic locus characterisation and candidate gene analysis\u003cbr\u003e\n \u003cp\u003eBased on strong evidence of the genetic association among traits, we utilized CPASSOC to comprehensively analyze polymorphism levels that covered a total of 13,671,468 SNPs (Fig. 2C). Among the identified top SNPs, those mapped at RPGRIP1L (a novel gene with limited research focus), specifically rs62048402, rs1421085, and rs56094641, were determined to have the highest frequency. Additionally, the gene GDF5 has been found to facilitate cartilage repair, BANK1 and HLA-DQA1 were strongly related to the immune system, and ABCG2 may be associated with hyperuricemia [25–27]. Furthermore, several loci were discovered that have been relatively less reported, namely ABCF1, IFT172, PREX1, and NFATC1.\u003c/p\u003e\n \u003cp\u003eBy conducting colocalization in cis-eQTL and trans-eQTL analyses, we have verified the shared causal involvement of loci identified by CPASSOC. A specific region located at chr18q13 had the highest posterior probability (PPH4 = 0.99, candidate SNP: rs60389750, mapped at NFATC1), which existed a shared localization among RA, PSA, and LFPL. Previous studies have strongly confirmed the critical role played by NFATC1 in RA-associated bone destruction. Meanwhile, NFATC1 was involved in muscle differentiation and regeneration processes and could enhance the oxidative metabolism of muscle, which was closely correlated with the regulation of exercise adaptations [28]. Additionally, RA was colocalized with HGSR at chr20q13.13 (PPH4 = 0.84, candidate SNP: rs3734254, mapped at PPARD), HGSL at chr19p13.3 (PPH4 = 0.97, candidate SNP: rs12977739, mapped at PTPRS), and ALM at chr8p21.3 (PPH4 = 0.81, candidate SNP: rs117567323, mapped at EGR3). Similarly, GA was colocalized with ALM at chr2p23.3 (PPH4 = 0.94, candidate SNP: rs1260326, mapped at IFT172), and OA was colocalized with LFPR at chr6p21.31 (PPH4 = 0.84, candidate SNP: rs3734254, mapped at PPARD) (Supplementary Tables 1–6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eAnalysis of the shared biological mechanisms among multi-potential patterns\u003c/h2\u003eMAGMA analysis has unveiled that eight traits exhibit remarkable enrichment within the brain region. Specifically, ALM (\u003cem\u003eP\u003c/em\u003e = 5.66 x 10\u003csup\u003e− 4\u003c/sup\u003e), LFPL (\u003cem\u003eP\u003c/em\u003e = 5.14 x 10\u003csup\u003e− 4\u003c/sup\u003e), and LFPR (\u003cem\u003eP\u003c/em\u003e = 9.98 x 10\u003csup\u003e− 4\u003c/sup\u003e) were predominantly clustered in the putamen area. Likewise, HGSL (\u003cem\u003eP\u003c/em\u003e = 3.94 x 10 − 3) and HGSR (\u003cem\u003eP\u003c/em\u003e = 6.53 x 10\u003csup\u003e− 4\u003c/sup\u003e) primarily thrived in the caudate region. AFPL (\u003cem\u003eP\u003c/em\u003e = 1.09 x 10\u003csup\u003e− 3\u003c/sup\u003e) and AFPR (\u003cem\u003eP\u003c/em\u003e = 7.03 x 10\u003csup\u003e− 4\u003c/sup\u003e) converged harmoniously in the frontal cortex (BA9), while RA (\u003cem\u003eP\u003c/em\u003e = 1.79 x 10\u003csup\u003e− 2\u003c/sup\u003e) took center stage in the nucleus accumbens region. Additionally, OA (\u003cem\u003eP\u003c/em\u003e = 8.91 x 10\u003csup\u003e− 3\u003c/sup\u003e) and UWP (\u003cem\u003eP\u003c/em\u003e = 6.41 x 10\u003csup\u003e− 4\u003c/sup\u003e) exhibited remarkable accumulation in muscle skeletal tissue. PSA (\u003cem\u003eP\u003c/em\u003e = 5.51 x 10\u003csup\u003e− 3\u003c/sup\u003e) showed specific enrichment within lymphocytes, while GA (\u003cem\u003eP\u003c/em\u003e = 1.37 x 10\u003csup\u003e− 3\u003c/sup\u003e) manifested significance in the uterus (Fig. 3A). Using MAGMA, we further analyzed the CPASSOC results at the gene level, identifying 63,095 loci that passed the test (Supplementary Tables\u0026nbsp;7–18).\u003cp\u003eSMR confirmed 271 pleiotropic probes by integrating candidate genes with specific MAGMA tissue enrichment analysis (Fig. 3B, Supplementary Tables 19–26). The most prominent probe is HGSR on chromosome 16 in the skeletal muscle tissue, located at ENSG00000260442 (tagging RP11-22P6.3, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eSMR\u003c/em\u003e\u003c/sub\u003e = 2.04 x 10\u003csup\u003e− 22\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eHEIDI\u003c/em\u003e\u003c/sub\u003e = 0.023). Additionally, in the brain cerebellum tissue, AFPR and AFPL have shared a common top probe ENSG00000164989, mapped at gene CCDC171 (AFPR: \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eSMR\u003c/em\u003e\u003c/sub\u003e = 1.58 x 10\u003csup\u003e− 15\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eHEIDI\u003c/em\u003e\u003c/sub\u003e = 0.111; AFPL: \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eSMR\u003c/em\u003e\u003c/sub\u003e = 2.32 x 10\u003csup\u003e− 16\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eHEIDI\u003c/em\u003e\u003c/sub\u003e = 0.108). Likewise, UWP and LFPR also possessed a common primary probe ENSG00000004534, localized to the gene RBM6 (UWP: \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eSMR\u003c/em\u003e\u003c/sub\u003e = 3.54 x 10\u003csup\u003e− 9\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eHEIDI\u003c/em\u003e\u003c/sub\u003e = 0.643; LFPR: \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eSMR\u003c/em\u003e\u003c/sub\u003e = 7.87 x 10\u003csup\u003e− 19\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eHEIDI\u003c/em\u003e\u003c/sub\u003e = 0.012).\u003c/p\u003e\n \u003cp\u003eTo delve deeper into the biological mechanisms of the pleiotropic genes, GSEA was utilized to probe subtle variations in gene expression levels with heightened precision using the MAGMA-enriched loci. In the GSEA framework, we analyzed the signal transduction pathways of 3 potential pathogenic genes, employing co-expression analysis to explore their respective functions and successfully meeting the criteria, which were KRAS_SIGNALING_DN, P53_PATHWAY, and PI3K_AKT_MTOR_SIGNALING (Fig. 3C, Supplementary Table 27). GO and KEGG signal pathway analysis suggested that these traits were associated with the central nervous system, neuronal differentiation, and chondrogenesis. The main pathways correlated with arthritis in sarcopenia have been determined using BP (GO:0021955, \u003cem\u003eP\u003c/em\u003e = 6.22 x 10\u003csup\u003e− 8\u003c/sup\u003e, \u003cem\u003ePadjust\u003c/em\u003e = 3.83 x 10\u003csup\u003e− 4\u003c/sup\u003e), CC (GO:0098978, \u003cem\u003eP\u003c/em\u003e = 1.46 x 10\u003csup\u003e− 6\u003c/sup\u003e, \u003cem\u003ePadjust\u003c/em\u003e = 1.06 x 10\u003csup\u003e− 3\u003c/sup\u003e), and MF (GO:0098978, \u003cem\u003eP\u003c/em\u003e = 2.84 x 10\u003csup\u003e− 5\u003c/sup\u003e, \u003cem\u003ePadjust\u003c/em\u003e = 3.17 x 10\u003csup\u003e− 2\u003c/sup\u003e) as key factors: central nervous system neuron axonogenesis, glutamatergic synapse, and beta-catenin binding (Fig. 3D, Supplementary Tables 28–30).\u003c/p\u003e\n \u003ch2\u003ePrecise transcriptomic analysis\u003c/h2\u003e\n \u003cp\u003eIn total, 4761 transcriptomic loci related to sarcopenia and arthritis were recognized. Among these loci, 1052 genes were selected and determined to be significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 1.05 x 10\u003csup\u003e− 5\u003c/sup\u003e) (Supplementary Tables\u0026nbsp;31–42). The most significant transcriptional locus was GDF5 (\u003cem\u003eP\u003c/em\u003e = 3.24 x 10\u003csup\u003e− 130\u003c/sup\u003e), which was present in the brain putamen in ALM, as verified by multiple cross-phenotypic associations. Similarly, GDF5 also existed as the most significant locus for HGSL and HGSR in the prostate, consistent with our previous exploration at the genomic level. LFPL and LFPR have a transcriptomic differential site in the basal ganglia that involves the DNAJC27 loci (LFPL: \u003cem\u003eP\u003c/em\u003e = 2.50 x 10\u003csup\u003e− 47\u003c/sup\u003e, LFPR: \u003cem\u003eP\u003c/em\u003e = 2.76 x 10\u003csup\u003e− 46\u003c/sup\u003e). This region is rarely reported and has not been explored in depth. FOCUS could incorporate predicted expression correlations for more enhanced transcriptomic analysis. Additionally, FOCUS has confirmed the critical correlation between GDF5 (PIP \u0026gt; 0.9) and diverse forms of sarcopenia and arthritis. One of the most significant genes in the brain for UWP is RBM6, which could activate locus related to inflammation and oxidative stress through the mTOR signaling pathway, thereby regulating protein synthesis and catabolism processes and ultimately affecting muscle degeneration [29].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to investigate the extensive correlation and genetic overlap between sarcopenia and arthritis through a large-scale GWAS, adopting a multi-trait analysis with multi-omics frameworks. The multi-annotated gene set analyses and the gene-level overlapping studies indicated that genes were significantly enriched in the muscle-skeletal tissue and brain region. Furthermore, pathways such as central nervous system neuron axonogenesis and positive regulation of neuron differentiation are involved in developing sarcopenia and arthritis, suggesting that the loci in brain regions hold a pivotal role in regulating these disorders.\u003c/p\u003e\n\u003cp\u003eFirstly, two genetic correlation analysis was performed to prioritize six sarcopenia-related traits. There were 19 binary regions between sarcopenia and arthritis determined after removing the complex linkage imbalances in the MHC region. Besides, in detecting local ancestry variance annotation, we found 10 binary shared regions between PSA and sarcopenia, which accounted for the most significant proportion. Meanwhile, the local genetic regions of sarcopenia and arthritis were mainly concentrated on chromosome 5 (mainly at chr5q15-q35.3), and the genetic variations in this region significantly impacted the risk of developing both. RA with LFPR and LFPL have the most significant binary genetic regions at chr5q35.3. Sarcopenia is a common comorbidity of RA. Compared to the general population, individuals with RA exhibit a more substantial decrease in muscle strength and quality as they age [30]. A meta-analysis has proved that DAS28 and HAQ can accurately predict the incidence of sarcopenia in RA patients [31]. A significant association between the development of OA and sarcopenia was demonstrated, especially in smokers. Decreased muscle mass in the lower extremities of sarcopenia patients could cause a reduction in dynamic joint stability, resulting in increased shear forces on the cartilage and accelerating its degeneration. Furthermore, patients with OA exhibit elevated levels of leptin secreted by visceral fat, which surpasses the blood-brain barrier and curtails the release of growth hormone. This brings about compromised muscle synthesis, particularly evident in obese OA patients [4, 32]. GA and sarcopenia have not been extensively researched. Our study revealed that OA colocalizes with ALM at IFT172, a locus that has not yet been reported. One previous study indicated high uric acid levels can negatively impact muscle health. Specifically, hyperuricemia impairs muscle satellite cell differentiation by inhibiting the PI3K/Akt/mTOR pathway and diminishes myosin heavy chain synthesis. Consequently, these factors cause muscle protein degradation and muscle health deterioration. These discoveries are crucial for comprehending the connection between arthritis and sarcopenia [33].\u003c/p\u003e\n\u003cp\u003eSecondly, integrated analyses of multi-traits and cross-populations in GWAS, along with transcriptomic approaches and precise mapping of causal gene sets, collectively pointed to the significance of GDF5, HLA-DQA1, BANK1, and ABCG2, which was significantly enriched in the basal ganglia of the nucleus accumbens and caudate nucleus, highlighting the critical role of the muscle-joint axis in the progress of disorders. GDF5 is explicitly expressed in the superficial layers of joint cartilage, especially during developmental stages. It regulates joint formation and plays a vital role in the development of OA, which can potentially be a rejuvenating treatment for age-related neuromuscular failure [34]. The expression of GDF5 is regulated by DNA methylation. Increased GDF5 has the potential to inhibit satellite cell differentiation and impair skeletal muscle regeneration [35]. Moreover, HLA-DQA1 and GDF5 have been identified as crucial in regulating the cell cycle, safeguarding against cancer, modulating transcription, and contributing to developing and maintaining the musculoskeletal system. Consequently, they were regarded as essential indicators of aging [36]. BLK has been recognized as a crucial risk factor for the progression of RA in both Asian and European populations. In contrast, investigating BANK1 single nucleotide variants in RA patients has been limited. A genotypic analysis and a large cross-ethnic meta-analysis have demonstrated that polymorphisms of both BLK and BANK1 interact with RA [37]. ABCG2 can mark numerous cell types within skeletal muscle and makes a difference in the positive regulation of muscle regeneration [38]. Furthermore, we have discovered several loci with limited research, including RPGRIP1L, ABCF1, DNAJC27, and RBM6. These genes hold promise as potential drug targets for treating sarcopenia and arthritis and are worth investigating and studying in the future.\u003c/p\u003e\n\u003cp\u003eThirdly, gene set enrichment analysis demonstrates the existence of the PI3K_AKT_MTOR and IL6_JAK_STAT3 signaling pathways, which are involved in regulating aging and arthritis. Polygonatum sibiricum polysaccharide (PSP) effectively mitigates skeletal muscle aging and mitochondrial dysfunction through the PI3K/Akt/mTOR signaling pathway [29, 39]. In skeletal muscle cells or animal models subjected to aging or oxidative stress, PSP can elevate the phosphorylation levels of PI3K, Akt, and mTOR. Hence, PSP can mitigate the damage to skeletal muscle induced by chronic inflammation. The JAK-STAT pathway promotes skeletal muscle aging through various mechanisms that amplify inflammation, prevent regeneration, result in mitochondrial damage, and disrupt metabolism. In addition, JAK-STAT and mTOR signals work collaboratively, contributing to an imbalance between protein synthesis and catabolism, further exacerbating muscle atrophy [40].\u003c/p\u003e\n\u003cp\u003eOur research recognizes certain limitations. Firstly, it is essential to explore GWAS data from populations of various ethnic backgrounds, containing those of European origin, to validate the reproducibility of the results of other diverse ancestry. Secondly, GWAS data identifies common disease-related variants, making discovering information about rare or structural variants challenging. Lastly, the generalizability of the research may be restricted by the absence of external validation. Hence, future cross-sectional and longitudinal studies should investigate the underlying biological mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study offers valuable insights into the shared genetic relationship between sarcopenia and arthritis. Comprehensive analyses of multiple annotated gene sets have discovered a complex interplay between tissues and genes, encompassing a multitude of biological pathways. The findings have shed light on the loci in brain and musculoskeletal regions, revealing the complex mechanisms behind the aging process and arthritis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEWGSOP2, European Working Group on Sarcopenia in Older Adults revised the definition of sarcopenia\u003c/p\u003e\n\u003cp\u003eOA, Osteoarthritis\u003c/p\u003e\n\u003cp\u003eRA, Rheumatoid Arthritis\u003c/p\u003e\n\u003cp\u003ePSA, Psoriatic Arthritis\u003c/p\u003e\n\u003cp\u003eGA, Gouty Arthritis\u003c/p\u003e\n\u003cp\u003eKOA, knee Osteoarthritis\u003c/p\u003e\n\u003cp\u003eGWAS, Genome-wide Association Studies\u003c/p\u003e\n\u003cp\u003eUWP, Usual Walking Pace\u003c/p\u003e\n\u003cp\u003eALM, Appendicular Lean Mass\u003c/p\u003e\n\u003cp\u003eHGSL, Hand Grip Strength (Left)\u003c/p\u003e\n\u003cp\u003eHGSR, Hand Grip Strength (Right)\u003c/p\u003e\n\u003cp\u003eLFPL, Leg Fat Percentage (Left)\u003c/p\u003e\n\u003cp\u003eLFPR, Leg Fat Percentage (Right)\u003c/p\u003e\n\u003cp\u003eAFPL, Arm Fat Percentage (Left)\u003c/p\u003e\n\u003cp\u003eAFPR, Arm Fat Percentage (Right)\u003c/p\u003e\n\u003cp\u003eSNPs, Single Nucleotide Polymorphisms\u003c/p\u003e\n\u003cp\u003eLSDC, Linkage Disequilibrium Score Regression\u003c/p\u003e\n\u003cp\u003eLD, Linkage Disequilibrium\u003c/p\u003e\n\u003cp\u003eFDR, False Discovery Rate\u003c/p\u003e\n\u003cp\u003eLAVA, Local Analysis of Covariant Association\u003c/p\u003e\n\u003cp\u003eSUPERGNOVA, SUPER GeNetic cOVariance Analyzer\u003c/p\u003e\n\u003cp\u003eIVs, Instrumental Variables\u003c/p\u003e\n\u003cp\u003eCPASSOC, Cross-phenotype Association\u003c/p\u003e\n\u003cp\u003eShet, Statistical Heterogeneity\u003c/p\u003e\n\u003cp\u003eMb, Megabase\u003c/p\u003e\n\u003cp\u003ePPH4, Posterior Probabilities of the H4\u003c/p\u003e\n\u003cp\u003ePPH3, Posterior Probabilities of the H3\u003c/p\u003e\n\u003cp\u003eMAGMA, Multiple Annotation gene-set Analysis\u003c/p\u003e\n\u003cp\u003eSMR, Summary data-based Mendelian randomization\u003c/p\u003e\n\u003cp\u003eGO, Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG, Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eHEIDI, Instrument-dependent Heterogeneity\u003c/p\u003e\n\u003cp\u003eeQTL, expression Quantitative Trait Loci\u003c/p\u003e\n\u003cp\u003eBP, Biological Processes\u003c/p\u003e\n\u003cp\u003eMF, Molecular Functions\u003c/p\u003e\n\u003cp\u003eCC, Cellular Components\u003c/p\u003e\n\u003cp\u003eGSEA, Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eNES, Normalized Enrichment Score\u003c/p\u003e\n\u003cp\u003eFUSION, Functional Summarisation Imputation\u003c/p\u003e\n\u003cp\u003eGTEx.V8, Genotype-Tissue Expression Version 8\u003c/p\u003e\n\u003cp\u003eFOCUS, Fine-mapping of Causal Gene Sets\u003c/p\u003e\n\u003cp\u003ePIP, Posteriori Probability of Causality\u003c/p\u003e\n\u003cp\u003ePSP, Polygonatum sibiricum polysaccharide\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the authors of GWAS, GTEx.V8, and eQTL, as well as the participants who contributed to the aggregated statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhonghai Wang designed the study, interpreted the results and wrote original draft. Meiling Yu conducted preliminary statistical analysis and data validation. Han Wang revised the manuscript for important intellectual Content. Every author made contributions to the paper and gave their approval to the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available data sets were analyzed in this study. The summary-level statistics of GWAS dataset for metabolic traits and psoriasis can be accessed through IEU open GWAS project (https://gwas.mrcieu.ac.uk/datasets/). The locus file used for LAVA analyses was accessed at https://github.com/lava-nc. The 1000 Genomes Project Phase 3 LD reference panel data for MAGMA was obtained from https://ctg.cncr.nl/software/magma. The eQTL data can be downloaded at https://cnsgenomics.com/data/SMR/#eQTLsummarydata. FUSION can be acquired from http://gusevlab.org/projects/fusion/#installation. The data of FOCUS can be downloaded at https://github.com/bogdanlab/focus. Gene set analyses were conducted with the help of GSEA software (https://software.broadinstitute.org/gsea/index.jsp)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data we used were obtained from published studies approved by the corresponding ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDennison EM, Sayer AA, Cooper C. Epidemiology of sarcopenia and insight into possible therapeutic targets. \u003cem\u003eNat Rev Rheumatol\u003c/em\u003e 2017;13:340-347. doi: 10.1038/nrrheum.2017.60\u003c/li\u003e\n\u003cli\u003eVoisin S, Jacques M, Landen S, et al. Meta-analysis of genome-wide DNA methylation and integrative omics of age in human skeletal muscle.\u003cem\u003e J Cachexia Sarcopenia Muscle\u003c/em\u003e 2021;12:1064-1078. doi: 10.1002/jcsm.12741\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. \u003cem\u003eAge Ageing\u003c/em\u003e 2019;48:601. doi: 10.1093/ageing/afz046\u003c/li\u003e\n\u003cli\u003eMisra D, Fielding RA, Felson DT, et al. Risk of Knee Osteoarthritis With Obesity, Sarcopenic Obesity, and Sarcopenia. \u003cem\u003eArthritis Rheumatol\u003c/em\u003e 2019;71:232-237. doi: 10.1002/art.40692\u003c/li\u003e\n\u003cli\u003eRausch Osthoff AK, Niedermann K, Braun J, et al. 2018 EULAR recommendations for physical activity in people with inflammatory arthritis and osteoarthritis. \u003cem\u003eAnn Rheum Dis\u003c/em\u003e 2018;77:1251-1260. doi: 10.1136/annrheumdis-2018-213585\u003c/li\u003e\n\u003cli\u003eEngland BR, Thiele GM, Anderson DR, Mikuls TR. Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. \u003cem\u003eBMJ\u003c/em\u003e 2018;361:k1036. doi: 10.1136/bmj.k1036\u003c/li\u003e\n\u003cli\u003eBennett JL, Pratt AG, Dodds R, Sayer AA, Isaacs JD. Rheumatoid sarcopenia: loss of skeletal muscle strength and mass in rheumatoid arthritis. \u003cem\u003eNat Rev Rheumatol\u003c/em\u003e 2023;19:239-251. doi: 10.1038/s41584-023-00921-9\u003c/li\u003e\n\u003cli\u003eLitwic A, Edwards MH, Dennison EM, Cooper C. Epidemiology and burden of osteoarthritis. \u003cem\u003eBr Med Bull\u003c/em\u003e 2013;105:185-99. doi: 10.1093/bmb/lds038\u003c/li\u003e\n\u003cli\u003eWilkinson DJ, Piasecki M, Atherton PJ. The age-related loss of skeletal muscle mass and function: Measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans. \u003cem\u003eAgeing Res Rev\u003c/em\u003e 2018;47:123-132. doi: 10.1016/j.arr.2018.07.005\u003c/li\u003e\n\u003cli\u003eBulik-Sullivan BK, Loh PR, Finucane HK, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. \u003cem\u003eNat Genet\u003c/em\u003e 2015;47:291-5. doi: 10.1038/ng.3211\u003c/li\u003e\n\u003cli\u003eLyon MS, Andrews SJ, Elsworth B, Gaunt TR, Hemani G, Marcora E. The variant call format provides efficient and robust storage of GWAS summary statistics. \u003cem\u003eGenome Biol\u003c/em\u003e 2021;22:32. doi: 10.1186/s13059-020-02248-0\u003c/li\u003e\n\u003cli\u003eNing Z, Pawitan Y, Shen X. High-definition likelihood inference of genetic correlations across human complex traits. \u003cem\u003eNat Genet\u003c/em\u003e 2020;52:859-864. doi: 10.1038/s41588-020-0653-y\u003c/li\u003e\n\u003cli\u003eWerme J, van der Sluis S, Posthuma D, de Leeuw CA. An integrated framework for local genetic correlation analysis. \u003cem\u003eNat Genet\u003c/em\u003e 2022;54:274-282. doi: 10.1038/s41588-022-01017-y\u003c/li\u003e\n\u003cli\u003eZhang Y, Lu Q, Ye Y, et al. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. \u003cem\u003eGenome Biol\u003c/em\u003e 2021;22:262. doi: 10.1186/s13059-021-02478-w\u003c/li\u003e\n\u003cli\u003eZhu X, Feng T, Tayo BO, et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. \u003cem\u003eAm J Hum Genet\u003c/em\u003e 2015;96:21-36. doi: 10.1016/j.ajhg.2014.11.011\u003c/li\u003e\n\u003cli\u003eSoskic B, Cano-Gamez E, Smyth DJ, et al. Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation. \u003cem\u003eNat Genet\u003c/em\u003e 2022;54:817-826. doi: 10.1038/s41588-022-01066-3.\u003c/li\u003e\n\u003cli\u003eSey NYA, Pratt BM, Won H. Annotating genetic variants to target genes using H-MAGMA. \u003cem\u003eNat Protoc\u003c/em\u003e 2023;18:22-35. doi: 10.1038/s41596-022-00745-z\u003c/li\u003e\n\u003cli\u003eOliva M, Mu\u0026ntilde;oz-Aguirre M, Kim-Hellmuth S, et al. The impact of sex on gene expression across human tissues. \u003cem\u003eScience\u003c/em\u003e 2020;369:eaba3066. doi: 10.1126/science.aba3066\u003c/li\u003e\n\u003cli\u003eZhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. \u003cem\u003eNat Genet\u003c/em\u003e 2016;48:481-7. doi: 10.1038/ng.3538\u003c/li\u003e\n\u003cli\u003eGene Ontology Consortium. Gene Ontology Consortium: going forward. \u003cem\u003eNucleic Acids Res \u003c/em\u003e2015;43:D1049-56.\u003c/li\u003e\n\u003cli\u003eKanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. \u003cem\u003eNucleic Acids Res \u003c/em\u003e2000;28:27-30. doi: 10.1093/nar/28.1.27\u003c/li\u003e\n\u003cli\u003eKuleshov MV, Jones MR, Rouillard AD, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. \u003cem\u003eNucleic Acids Res\u003c/em\u003e 2016;44:W90-7. doi: 10.1093/nar/gkw377\u003c/li\u003e\n\u003cli\u003eLiu L, Yan R, Guo P, et al. Conditional transcriptome-wide association study for fine-mapping candidate causal genes. \u003cem\u003eNat Genet\u003c/em\u003e 2024;56:348-356. doi: 10.1038/s41588-023-01645-y\u003c/li\u003e\n\u003cli\u003eMancuso N, Freund MK, Johnson R, et al. Probabilistic fine-mapping of transcriptome-wide association studies. \u003cem\u003eNat Genet\u003c/em\u003e 2019;51:675-682. doi: 10.1038/s41588-019-0367-1\u003c/li\u003e\n\u003cli\u003eParrish WR, Byers BA, Su D, et al. Intra-articular therapy with recombinant human GDF5 arrests disease progression and stimulates cartilage repair in the rat medial meniscus transection (MMT) model of osteoarthritis. \u003cem\u003eOsteoarthritis Cartilage\u003c/em\u003e 2017;25:554-560. doi: 10.1016/j.joca.2016.11.002\u003c/li\u003e\n\u003cli\u003eGeorg I, D\u0026iacute;az-Barreiro A, Morell M, Pey AL, Alarc\u0026oacute;n-Riquelme ME. BANK1 interacts with TRAF6 and MyD88 in innate immune signaling in B cells. \u003cem\u003eCell Mol Immunol\u003c/em\u003e 2020;17:954-965. doi: 10.1038/s41423-019-0254-9\u003c/li\u003e\n\u003cli\u003eHoque KM, Dixon EE, Lewis RM, et al. The ABCG2 Q141K hyperuricemia and gout associated variant illuminates the physiology of human urate excretion. \u003cem\u003eNat Commun\u003c/em\u003e 2020;11:2767. doi: 10.1038/s41467-020-16525-w\u003c/li\u003e\n\u003cli\u003eBae S, Kim K, Kang K, et al. RANKL-responsive epigenetic mechanism reprograms macrophages into bone-resorbing osteoclasts. \u003cem\u003eCell Mol Immunol\u003c/em\u003e 2023;20:94-109. doi: 10.1038/s41423-022-00959-x.\u003c/li\u003e\n\u003cli\u003eLi Y, Liu Z, Yan H, et al. Polygonatum sibiricum polysaccharide ameliorates skeletal muscle aging and mitochondrial dysfunction via PI3K/Akt/mTOR signaling pathway. \u003cem\u003ePhytomedicine\u003c/em\u003e 2025;136:156316. doi: 10.1016/j.phymed.2024.156316\u003c/li\u003e\n\u003cli\u003eCano-Garc\u0026iacute;a L, Manrique-Arija S, Dom\u0026iacute;nguez-Quesada C, et al. Sarcopenia and Nutrition in Elderly Rheumatoid Arthritis Patients: A Cross-Sectional Study to Determine Prevalence and Risk Factors. \u003cem\u003eNutrients\u003c/em\u003e 2023;15:2440. doi: 10.3390/nu15112440\u003c/li\u003e\n\u003cli\u003eLi TH, Chang YS, Liu CW, et al. The prevalence and risk factors of sarcopenia in rheumatoid arthritis patients: A systematic review and meta-regression analysis. \u003cem\u003eSemin Arthritis Rheum\u003c/em\u003e 2021;51:236-245. doi: 10.1016/j.semarthrit.2020.10.002\u003c/li\u003e\n\u003cli\u003eLiao CD, Chen HC, Huang MH, Liou TH, Lin CL, Huang SW. Comparative Efficacy of Intra-Articular Injection, Physical Therapy, and Combined Treatments on Pain, Function, and Sarcopenia Indices in Knee Osteoarthritis: A Network Meta-Analysis of Randomized Controlled Trials. \u003cem\u003eInt J Mol Sci\u003c/em\u003e 2023;24:6078. doi: 10.3390/ijms24076078\u003c/li\u003e\n\u003cli\u003eHsu WH, Wang SY, Chao YM, Chang KV, Han DS, Lin YL. Novel metabolic and lipidomic biomarkers of sarcopenia. \u003cem\u003eJ Cachexia Sarcopenia Muscle\u003c/em\u003e 2024;15:2175-2186. doi: 10.1002/jcsm.13567\u003c/li\u003e\n\u003cli\u003eNielsen RL, Monfeuga T, Kitchen RR, et al. Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning. \u003cem\u003eNat Commun\u003c/em\u003e 2024 ;15:2817. doi: 10.1038/s41467-024-46663-4\u003c/li\u003e\n\u003cli\u003eHatazawa Y, Ono Y, Hirose Y, et al Reduced Dnmt3a increases Gdf5 expression with suppressed satellite cell differentiation and impaired skeletal muscle regeneration. \u003cem\u003eFASEB J\u003c/em\u003e 2018;32:1452-1467. doi: 10.1096/fj.201700573R\u003c/li\u003e\n\u003cli\u003eJones G, Trajanoska K, Santanasto AJ, et al. Genome-wide meta-analysis of muscle weakness identifies 15 susceptibility loci in older men and women. \u003cem\u003eNat Commun\u003c/em\u003e 2021;12:654. doi: 10.1038/s41467-021-20918-w\u003c/li\u003e\n\u003cli\u003eG\u0026eacute;nin E, Coustet B, Allanore Y, et al. Epistatic interaction between BANK1 and BLK in rheumatoid arthritis: results from a large trans-ethnic meta-analysis. \u003cem\u003ePLoS One\u003c/em\u003e 2013;8:e61044. doi: 10.1371/journal.pone.0061044\u003c/li\u003e\n\u003cli\u003eDoyle MJ, Zhou S, Tanaka KK, et al. Abcg2 labels multiple cell types in skeletal muscle and participates in muscle regeneration. \u003cem\u003eJ Cell Biol\u003c/em\u003e 2011;195:147-63. doi: 10.1083/jcb.201103159\u003c/li\u003e\n\u003cli\u003eTang G, Du Y, Guan H, et al. Butyrate ameliorates skeletal muscle atrophy in diabetic nephropathy by enhancing gut barrier function and FFA2-mediated PI3K/Akt/mTOR signals. \u003cem\u003eBr J Pharmacol\u003c/em\u003e 2022;179:159-178. doi: 10.1111/bph.15693\u003c/li\u003e\n\u003cli\u003eCui J, Shibata Y, Zhu T, Zhou J, Zhang J. Osteocytes in bone aging: Advances, challenges, and future perspectives. \u003cem\u003eAgeing Res Rev\u003c/em\u003e 2022;77:101608. doi: 10.1016/j.arr.2022.101608 \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":"clinical-and-experimental-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clem","sideBox":"Learn more about [Clinical and Experimental Medicine](https://www.springer.com/journal/10238)","snPcode":"10238","submissionUrl":"https://submission.nature.com/new-submission/10238/3","title":"Clinical and Experimental Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sarcopenia, Arthritis, Genetic architecture, Phenotypic loci","lastPublishedDoi":"10.21203/rs.3.rs-7589916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7589916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSarcopenia and arthritis, characterized by age-related progressive loss of skeletal muscle mass and function, profoundly impact the well-being of older adults. Our study endeavors to explore the unclear genetic structure between them. Using advanced statistical genetic approaches and genome-wide association study (GWAS) summary statistics, we explored the shared genetic basis among multiple manifestations of sarcopenia and four distinct arthritic conditions: osteoarthritis, rheumatoid arthritis, psoriatic arthritis, and gouty arthritis. A local analysis method for variant annotation was applied to approximately 2,495 genomic regions of equal size and partial independence, determining binary local genetic correlations among these regions. Cross-phenotype association GWAS studies have revealed many genetic variations associated with complex traits. Transcriptome-wide association studies were conducted using weights from various human tissues to identify risk loci. We functionally annotated genomic multi-markers and fine-mapping colocalization by conducting the whole-genome unified testing of molecular characteristics. Significant correlations between sarcopenia and four types of arthritis were detected through comprehensive and local genetic correlation analyses. At the genomic level, we identified 19 unique bivariate regions, including chr3q27.3, chr5q35.3, and chr12q13.2-q13.3, involving multiple human cell lines such as KBM7, GM12878, and IMR90. Gene enrichment analyses revealed that the selected loci primarily signaled through elementary pathways, including central nervous system neuron axonogenesis, glutamatergic synapse, and beta-catenin binding. Specifically, GDF5 and DNAJC27 were prioritized as the most probable candidate genes via precision transcriptomics. Our study has identified pleiotropic genomic regions linking sarcopenia and arthritis, providing effective strategies for the treatment of diseases.\u003c/p\u003e","manuscriptTitle":"Multi-omics and molecular testing: A new insight into the genetic mechanisms of sarcopenia and arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 17:19:23","doi":"10.21203/rs.3.rs-7589916/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-18T07:25:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T03:54:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T16:43:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127423194816706102954968418387822803599","date":"2025-10-02T13:49:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200207269987268487209722583495895137538","date":"2025-10-02T06:52:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102098076628577373719878901020133856786","date":"2025-09-29T19:05:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-29T18:50:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T09:47:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T09:46:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical and Experimental Medicine","date":"2025-09-11T08:54:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-and-experimental-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clem","sideBox":"Learn more about [Clinical and Experimental Medicine](https://www.springer.com/journal/10238)","snPcode":"10238","submissionUrl":"https://submission.nature.com/new-submission/10238/3","title":"Clinical and Experimental Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ae191c41-c0f5-42fd-a10d-ea5c1104f363","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:49:24+00:00","versionOfRecord":{"articleIdentity":"rs-7589916","link":"https://doi.org/10.1007/s10238-025-01985-5","journal":{"identity":"clinical-and-experimental-medicine","isVorOnly":false,"title":"Clinical and Experimental Medicine"},"publishedOn":"2026-01-14 16:31:04","publishedOnDateReadable":"January 14th, 2026"},"versionCreatedAt":"2025-10-10 17:19:23","video":"","vorDoi":"10.1007/s10238-025-01985-5","vorDoiUrl":"https://doi.org/10.1007/s10238-025-01985-5","workflowStages":[]},"version":"v1","identity":"rs-7589916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7589916","identity":"rs-7589916","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}