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

Multi-omics and molecular testing: A new insight into the genetic mechanisms of primary biliary cholestasis and sarcopenia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multi-omics and molecular testing: A new insight into the genetic mechanisms of primary biliary cholestasis and sarcopenia Zhonghai Wang, Meiling Yu, Han Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7113071/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Primary biliary cholangitis (PBC) has the potential to impact skeletal muscles through the muscle-liver axis, subsequently leading to sarcopenia. Our study aims to explore the complex and unclear genetic structures between them. We investigated the common genetic basis of PBC and sarcopenia using advanced statistical genetics methods and genome - wide association summary analysis. We employed global and local genetic correlation to achieve valuable insights into potential shared biological mechanisms. We identified risk single nucleotide polymorphisms (SNPs) and functionally annotated genomic multi-markers by conducting the whole-genome unified testing of molecular characteristics. Finally, the fine-mapping analysis was prioritized to emphasize the significant causal genes in every region. Our study has revealed noteworthy genomic associations, suggesting the intricate genetic interplay between PBC and sarcopenia. At the genomic level, we identified 17 unique bivariate regions among 88 trait pairs, including chr3q27.1 and chr2q32.2-q32.3. The analysis of GWAS pleiotropy across traits has discovered 82,136 SNPs. Among these, bivariate locus correlation analysis has identified 136 pleiotropic sites. Bayesian colocation has pinpointed 18 causal variants. Functional enrichment analysis highlighted putative pleiotropic genomic regions, including brain and spleen. Our study has identified pleiotropic genomic regions linking PBC and sarcopenia, providing effective strategies for the treatment of diseases. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Gastroenterology Biological sciences/Genetics Primary biliary cholangitis Sarcopenia Genome-wide association studies Pleiotropic genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As an autoimmune liver disease, primary biliary cholangitis (PBC) is featured by a gradual, non-suppurative inflammatory destruction of the small bile ducts within the liver. With the progression of the disease, it can contribute to biliary obstruction and cholestasis, ultimately developing into liver cirrhosis [ 1 ]. The serological marker of PBC is an anti-mitochondrial antibody, which is an autoantibody highly related to this disease. The disease might present without symptoms and progress at a relatively slow pace [ 1 ]. However, in the advanced stage, PBC may present with conspicuous symptoms accompanied by rapid progression of liver disease. Fatigue and skin itching are among the clinical manifestations of PBC, which may also be correlated with autoimmune diseases and accompanied by conditions such as osteopenia, hypercholesterolemia, and sarcopenia [ 2 ]. Sarcopenia is a widespread musculoskeletal disorder that progresses gradually and represents a prevalent skeletal muscle disorder in elderly individuals, affecting 10–27% of those aged 60 and above [ 3 ]. In 2019, the European Working Group on Sarcopenia in Older Adults revised the definition of sarcopenia (EWGSOP2) suggested taking into account three fundamental metrics for diagnosing sarcopenia: decreased muscle mass, insufficient muscle strength, and compromised physical functioning [ 4 ]. Epidemiological research revealed that individuals with chronic liver disorders faced an increasing risk of developing osteoporosis as well as sarcopenia. Retrospective analysis indicated that sarcopenia was frequently observed among PBC patients, with a prevalence of 25.9% [ 5 ]. Although recent relevant studies have been conducted, the precise connection between PBC and sarcopenia is still not thoroughly understood [ 6 ]. Therefore, improving the long-term quality of life for patients with chronic liver disease accompanied by sarcopenia is a significant challenge. With the advancement of genome sequencing technology, establishing the correlation between traits and genetics has been prioritized to be a practical approach to handling the constraints inherent in observational studies, randomized controlled trials, and other conventional research paradigms. Genetic correlation (rg) is utilized to explore the genetic overlap. Theoretically, it can be investigated across the entire genome and represents the average of shared genetic effects among all causal loci within the genome [ 7 ]. Global and local genetic correlation analyses were first applied to identify the features associated with PBC and sarcopenia-related traits [ 8 ]. Subsequently, we employed a cross-trait genome-wide association study (GWAS) meta-analysis at the single nucleotide polymorphism (SNP) level to recognize pleiotropic genetic variations or loci. Consequently, our study delves into the common drug targets and pathogenic pathways between PBC and sarcopenia. Methods Data resource The data of PBC originated from 7 populations recently registered in the IEU OpenGWAS database ( https://gwas.mrcieu.ac.uk/ ), containing 5 European groups and 2 East Asian groups, encompassing 5,004,018 SNPs [ 9 ]. Based on the EWGSOP2 definition [ 4 ], the data comprised a broad spectrum of statistics regarding sarcopenia from various metrics, such as the usual walking pace (UWP), appendicular lean mass (ALM), hand grip strength for both left (HGSL) and right (HGSR), leg fat percentage for left (LFPL) and right (LFPR), as well as arm fat percentage for left (AFPL) and right (AFPR) (Table 1 ). To guarantee the dependability of our research results, we selected SNPs that demonstrated a notable genome-wide association with traits ( P 0.001 and kb < 10,000). Furthermore, it was important to discard palindromic SNPs, where the effective and the other alleles were complementary. Figure 1 illustrates the design of the experiment. Table 1 Overview of traits included in our study. Phenotype ID Sample SNP-based heritability Case,n Control,n Total,N SNPs Consortium Global h 2 SNP Mean χ² PBC ebi-a-GCST003129 8,021 16,489 24,510 5,004,018 NA 0.122 1.53 UWP ukb-b−4711 NA NA 459,915 9,851,867 MRC-IEU 0.118 1.69 ALM ebi-a-GCST90000026 NA NA 205,513 18,164,071 NA 0.352 1.08 HGSL ukb-b−7478 NA NA 461,026 9,851,867 MRC-IEU 0.132 1.71 HGSR ukb-b−10215 NA NA 461,089 9,851,867 MRC-IEU 0.135 1.72 LFPL ukb-b−18377 NA NA 454,826 9,851,867 MRC-IEU 0.194 2.93 LFPR ukb-b−20531 NA NA 454,826 9,851,867 MRC-IEU 0.194 2.93 AFPL ukb-b−20188 NA NA 454,724 9,851,867 MRC-IEU 0.208 3.07 AFPR ukb-b−12854 NA NA 454,789 9,851,867 MRC-IEU 0.204 3.04 Abbreviations: SNPs, single nucleotide polymorphisms; PBC, Primary Biliary Cholangitis; 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 Research We conducted comprehensive genetic correlation analyses via linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) to evaluate the shared polygenic architecture among traits [ 7 , 10 ]. These two methodologies can integrate data from European ancestry samples within the 1000G EUR Project for calculating linkage disequilibrium (LD) scores, serving as a standard reference. Simultaneously, rigorous quality control measures were implemented on the SNPs throughout this procedure. Additionally, we retained only those SNPs with a minor allele frequency (MAF) exceeding 0.01 [ 11 ]. Local Variation Association Analysis (LAVA) was enforced to explore the local genetic correlations between pairs of genes [ 8 ]. Given the complexity of the genome's structure, LAVA can effectively examine the genetic sequence variations in diverse regions and accurately approximate the genetic correlations within smaller genome segments. Thus, compared to the global genetic correlation analyses, LAVA provides a more comprehensive insight into the genetic overlap between traits. This method analyzes the 2495 independent LD blocks, utilizing 1000G EUR dataset. To ensure the stability of the results, the Benjamini-Hochberg method was established to adjust the rate of false discoveries (FDR) ( P < 0.05). SUPER GeNetic cOVariance Analyzer (SUPERGNOVA) utilizes a random-effects model for analyzing local genetic correlations and supplies a more accurate estimate of the similarity between pairs of traits compared to the fixed-effects model applied in LAVA [ 12 ]. Cross-trait GWAS Meta-analysis Multi-trait analysis of GWAS (MTAG) presents an efficient way to detect genetic risk variations across traits, which employs a random-effects meta-analysis method to compute summary statistics at the SNP level, effectively leveraging the genetic structure of shared traits to enhance detection capabilities [ 17 ]. The Cross-Phenotype Association (CPASSOC) was employed for cross-trait association analysis to recognize causal pleiotropic SNPs among multiple traits. We analyzed the traits jointly through statistical heterogeneity (SHet) to confirm the association of at least one genetic variant with a trait. Integrating MTAG and CPASSOC, the P -value of SNP was set less than 5 x 10 − 8 in both methods, and it met a criterion of less than 5 x 10 − 3 for the single-trait association between PBC and sarcopenia [ 14 ]. Unified Test for Molecular Signatures The Unified Test for Molecular Signatures (UTMOST) has the potential to determine the associations between genes and traits, which takes into account the combined impacts of SNPs within LD regions and integrating the Genotype - Tissue Expression (GTEx) data of organisms [ 10 ]. Subsequently, we validated the results of UTMOST by three methods: Multi-marker Analysis of Genomic Annotation (MAGMA), Functional Summarisation Attribution (FUSION), and Fine-mapping of Causal Gene Sets (FOCUS). We conducted SNP genetic enrichment analyses on 54 different tissues using MAGMA with GTEx.V8 data [ 15 ]. To guarantee the robustness of these associations, we implemented a Bonferroni correction to account for multiple tests. For pleiotropic genes, we established a significant threshold ( P < 0.05/the total number of genes). Integrating the tissues detected by MAGMA, we enforced a cross-tissue transcriptome-wide association study (TWAS) analysis by Functional Summarisation Attribution (FUSION) to compute the genetic elements across diverse tissues [ 16 ]. FUSION generates predictive models of functional and molecular phenotypic traits, which utilizes global genomic studies to summarize statistical predictions. Adjusting the significance level, the Bonferroni-corrected TWAS thresholds were set ( P < 0.05/the total number of transcripts). As a probabilistic fine-mapping method, FOCUS integrates the correlation signals by simulating the association between TWAS signals [ 17 ]. Genes with posteriori probability of causality (PIP) values of 0.9 or above were considered potential causal candidates. Correlation Analysis of Bivariate Loci Using the Summary data-based Mendelian randomization (SMR) method, we have detected genes expressed across various tissues pinpointed by MAGMA [ 18 ]. In our research for gene expression proxies, we chose expression quantitative trait loci (eQTLs) from various tissues acquired from the GTEx Consortium website. SMR was conducted to evaluate the influence of these genes on traits, centering on cis-eQTL and trans-eQTL with an MAF greater than 1% and a remarkable threshold of P < 5 x 10 − 8 . To assess the robustness of our SMR findings, we performed the dependent instrumental heterogeneity (HEIDI) test, serving to highlight significant differences among diverse datasets. For this study, the P-value of HEIDI was greater than 0.05 and an FDR < 0.05 were considered significant. We performed a colocalization analysis to evaluate the likelihood of shared causal variants between PBC and sarcopenia [ 19 ]. This process involved removing SNPs with significant genome-wide associations ( P < 5 x 10 − 8 ), eliminating duplicates and missing values, and extracting SNPs that appeared in both PBC and sarcopenia groups. Then, we assessed five potential scenarios to determine if the two traits could be collectively affected by a single genetic variant: PPH0, no correlation with either trait; PPH1, linked to PBC but not the sarcopenia-related traits; PPH2, associated with the sarcopenia-related traits but not PBC; PPH3, connected to both traits with different causal variants; PPH4, related to both traits, sharing a causal variant. Based on the criterion of PPH3 + PPH4 > 0.8, we established the evidence for colocalized genes. Functional Enrichment Analysis To investigate the functional characteristics and biological connections of pre-selected potential drug genes, we comprehensively applied Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and Gene Set Enrichment Analysis (GSEA). Within the GO framework, we were dedicated to three essential dimensions: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC), aiming to elucidate the activity patterns, biological roles, and cellular localization of these genes [ 20 ]. KEGG provided a widespread understanding of gene-related metabolic pathways and signaling networks, revealing the underlying pathogenesis [ 21 ]. GSEA comprehensively analyzed the complete gene sequence list, effectively detecting the biological functions of gene sets for gene complementation [ 22 ]. While implementing GSEA, we conducted 10,000 alignments while setting the minimum and maximum gene set sizes to 10 and 200, respectively. Furthermore, to accurately identify significantly enriched gene groups, a variety of criteria were combined, including a normalized enrichment score (NES) with an absolute value greater than 1 (|NES| >1) and an FDR less than 0.25. Results Genome-wide Genetic Correlations and Overlap In the analysis of genetic correlations, a threshold of unadjusted P -value less than 0.05 was programmed as the criterion for selecting relevant traits. LDSC and HDL were performed to detect PBC and eight pairs of traits linked to sarcopenia. These traits were subsequently characterized and estimated, revealing eminent genetic correlations (Table 2 ). After applying Bonferroni correction, the LAVA analysis identified 88 specific genetic regions associated with PBC and sarcopenia (Table 2 , Supplementary Table S1 ). Among these, we have selected 17 significant bivariate regions. The region of chr3q27.1 has exhibited the strongest genetic link between PSC and ALM, including genes KLHL24, MAP6D1, PARL, ABCC5, and AP2M1. In addition, the region of chr2q32.2-q32.3 was recognized to present essential aggregation of PBC with LFPL, LFPR, HGSR, AFPL, and AFPR. This region encompassed several less explored gene regions, including C2orf88, HIBCH, TMEM194B, NAB1, and the loci of the STAT family. SUPERGNOVA analysis confirmed the genetic association between PBC and most cases of sarcopenia at the chr2q32.2-q32.3 region. The results of genetic correlation analysis using different genetic techniques were consistent, strengthening the robustness of the genetic correlations between genes. Table 2 Globe and local genetic correlation analysis Traits LDSC HDL LAVA rg P-value SE rg P-value SE chr start end N-SNPs P-value UWP 0.176 5.51E-12 0.026 0.209 1.60E−08 0.012 2 105120579 106546524 645 8.15E-04 ALM 0.164 3.01E−02 0.022 0.091 3.08E−02 0.023 3 183203156 184524268 564 2.09E-04 LFPL 0.129 1.44E−08 0.023 0.037 4.02E−02 0.019 2 191051955 193033982 620 2.14E-02 LFPR 0.129 1.26E−08 0.024 0.091 9.69E−03 0.020 2 191051955 193033982 620 2.98E-02 HGSL 0.091 9.00E−04 0.028 0.229 6.65E−12 0.032 3 183203156 184524268 564 6.79E-03 HGSR 0.103 2.00E−04 0.028 0.233 3.98E−12 0.031 2 191051955 193033982 620 1.93E-02 AFPL 0.132 5.33E−05 0.016 0.227 1.53E−12 0.012 2 191051955 193033982 620 1.92E-02 AFPR 0.132 2.50E−08 0.018 0.227 2.43E−12 0.014 2 191051955 193033982 620 9.04E-04 Abbreviations: LDSC, linkage disequilibrium score regression; HDL, high-definition likelihood; LAVA, local variation association analysis; rg, genetic correlation; SNPs, single nucleotide polymorphisms; SE, standard error; chr, chromosome; 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). Cross-trait GWAS Meta-analysis After deleting SNPs with linkage disequilibrium, we combined MTAG and CPASSOC to collaboratively identify 82,136 shared phenotypic risk SNPs for PBC and sarcopenia, which has passed the strict threshold ( P < 5 x 10 − 8 ). The most significant shared SNP rs2272593, mapped at NFKBIL1, was discovered in PBC along with four sarcopenia-related traits, including LFPL, LFPR, AFPL, and AFPR. The most crucial shared SNP by HGSL and HGSR with PBC was rs9275219, located at HLA-DQB1, which has been proven strongly linked to the risk of PBC and sarcopenia. The most prominent SNP, rs644045, was localized to SNORD48 in ALM. In addition, the loci of UWP did not pass the MTAG analysis test but exhibited the most notable SNP rs7774434 ( P SHet = 1.03 x 10 − 60 ), situated on HLA-DQA1, through CPASSOC analysis (Supplementary Tables S2-8). Unified Test for Molecular Signatures MAGMA showed that most sarcopenia-related traits existed significantly enriched gene sets in the brain cerebellum region. Specifically, HGSL ( P = 3.37 x 10 − 2 ), LFPL ( P = 2.26 x 10 − 10 ), AFPR ( P = 1.91 x 10 − 11 ), and AFPL ( P = 2.98 x 10 − 12 ) were mainly concentrated in the cerebellar region. UWP ( P = 4.86 x 10 − 8 ) and LFPL ( P = 1.99 x 10 − 10 ) were converged harmoniously in the cerebellar hemisphere. Additionally, ALM ( P = 7.98 x 10 − 18 ) and HGSR ( P = 1.35 x 10 − 2 ) remarkably thrived in the uterus and muscle-skeletal tissues, respectively. The spleen demonstrated the most prominent accumulated PBC ( P = 2.28 x 10 − 14 ), with additional tissues like the ileum, lymphocytes, whole blood, and lung tissue also exceeding the significance threshold. MAGMA analysis performed a more profound examination of the gene-level outcomes obtained from CPASSOC, revealing 9,377 loci that successfully passed the test (Supplementary Tables S9-17). Using the tissues that passed the MAGMA detection, the FUSION approach has detected 8,696 genes that reached the significance threshold ( P < 0.05) and a further 1,800 genes that met the genome-wide significance criteria ( P < 5.75 x 10 − 6 ). AFP demonstrated the highest number of tissue-specific genes, with 380 genes identified in AFPL and 367 genes in AFPR, and both groups shared significant genes, including PTRHD1, DNAJC27, ADCY3, CENPO, NPIPB6, ATP2A1, and SH2B1. In the brain cerebellar tissues, LFP contained a significant number of genes. To be precise, LFPL included 329 genes, whereas LFPR comprised 330 genes, and the shared significant genes like PTRHD1, DNAJC27, ADCY3, CENPO, RBM6, and NCOA1 were also of considerable interest (Fig. 2 a). In the spleen tissues, we have observed 26 significant genes for PBC, including IL12RB2, IRF5, SYNGR1, DENND1B, and IKZF3, all of which were strongly associated with the underlying mechanism of PBC (Fig. 2 b, Supplementary Tables S18-26). Furthermore, FOCUS has confirmed critical loci such as PTRHD1, DNAJC27, and CENPO (with a PIP > 0.9) between PBC and sarcopenia. Correlation Analysis of Bivariate Loci The SMR and HEIDI tests confirmed that genes with co-expression at eQTL levels, serving as probes, were selected to investigate whether DNA methylation regulates the progress. After our investigation, we discovered that in PBC, specific probes such as ENSG00000241106, ENSG00000228789, ENSG00000204531, ENSG00000204520, and ENSG00000272221 located on chromosome 6, along with probe ENSG00000236935 on chromosome 11, negatively regulate the expression of PBC. This regulatory mechanism ultimately reduced the risk of developing PBC. The probe ENSG00000164989, located at CCDC171, demonstrated significant levels in multiple sarcopenia-related traits, with the most significant levels exhibited in LFPR, AFPL, and AFPR (Fig. 2 c). Through colocalization analysis, we confirmed the results of shared causal loci identified by cross-trait GWAS meta-analysis (Fig. 3 ). PBC and sarcopenia were colocated at multiple common pleiotropic loci, containing rs539515 (mapped at ASTN1, PPH4 = 0.946), rs12747058 (mapped at TGFB2, PPH4 = 0.942), rs6744646(mapped at ACP1, PPH4 = 0.924), rs6735681 (mapped at MYCNOS, PPH4 = 0.999), rs13078908 (mapped at LINC00620, PPH4 = 0.912), and rs13079464 (mapped at LINC00620, PPH4 = 0.995) (Supplementary Tables S27-32). Functional Enrichment Analysis We selected pleiotropic genes that passed the MAGMA test for biological analysis of functional enrichment. The gene-based GO analysis suggested that through BP, CC, and MF, three major pathways enriched in PBC and sarcopenia have been identified, respectively: chondrocyte differentiation (GO:0002062, P = 3.52 x 10 − 11 ), glutamatergic synapse (GO:0098978, P = 1.71 x 10 − 9 ), phosphatase binding (GO:0019902, P = 3.62 x 10 − 8 ) (Fig. 4 a, Supplementary Tables S33-35). KEGG analysis showed that the relevant genes exhibited a high transduction phenomenon in the cGMP-PKG signaling pathway (Fig. 4 b). The GSEA results indicated that 4,465 genomes were up-regulated, contributing to the enrichment of 47 gene sets. Moreover, three groups demonstrated significant enrichment, including ultraviolet response downregulated gene sets (UV Response DN), transforming growth factor (TGF) beta signaling, and interferon-gamma response (Figs. 4 c-f, Supplementary Table S36). The highly expressed UV Response DN genes were accumulated in pathways such as the cGMP-PKG receptor-based signaling and the Phospholipase D signaling pathway. Specifically, the cGMP receptor was involved in bile metabolism and crucial for developing liver cirrhosis. The outcomes exhibited by the above functional enrichment analysis present high consistency. Discussion This is the first study investigating the genetic overlap correlation and shared risk loci between PBC and sarcopenia by performing a multi-trait analysis utilizing a large-scale GWAS database. First, we analyzed the genetic correlation between PBC and sarcopenia-related phenotypes, revealing 8 significant symptoms associated with sarcopenia. Furthermore, we accurately recognized 17 eminent loci with bivariate correlations among 88 semi-independent sites through local genetic analysis. Second, we performed an across-trait GWAS meta-analysis to communally identify 82,136 phenotypic risks through rigorous testing. We also detected the presence of top SNP loci between multiple trait pairs. Third, we employed gene expression data from the human spleen and brain tissues to functionally annotate essential loci and mapped the input gene expression to the loci within significant bivariate loci. Finally, functional enrichment analyses have exhibited that the shared risk genes demonstrated a high level of transactivation in the cGMP-PKG signaling pathway. Musculoskeletal diseases related to PBC are receiving increasing attention. PBC involves a range of metabolic and nutritional disorders, including decreased vitamin D absorption, disrupted bile acid metabolism, and changes in the gut microbiota. Furthermore, chronic liver inflammation, as a trademark of PBC, can contribute to progressive muscle atrophy and accelerate the development of sarcopenia [ 23 ]. Our study has further confirmed and supplemented the deficiencies of some observational studies. Our comprehensive genetic correlation study discovered a significant and robust association between PBC and sarcopenia. Additionally, the evident binary genetic correlation between PBC and sarcopenia was identified in specific gene regions, such as local areas like chr3q27.1 and chr2q32.2-q32.3. An MR research was performed to investigate the potential relationship between chronic liver diseases and musculoskeletal conditions. The findings supported that cholangitis could elevate the risk of osteoporosis and osteoarthritis, which may contribute to sarcopenia [ 6 ]. Despite the unclear specific mechanism of concurrent sarcopenia in patients with PBC, several causal elements may contribute to its development, including the liver-muscle axis, hyperammonemia, and endotoxemia [ 24 ]. As a critical center for ammonia metabolism, the liver undertakes ammonia's metabolic transformation. When muscles perform amino acid transamination, ammonia is generated, and this portion of the produced ammonia is transported to the liver for subsequent processing. It is worth mentioning that hyperammonemia, an important element in liver-gut axis imbalance, can potentially initiate mitochondrial malfunction and activate the myostatin signaling pathway, thereby intensifying sarcopenia [ 25 ]. Through the analysis of GWAS meta-analysis across traits and the correlation analysis of bivariate loci, it was verified that PBC and sarcopenia have shared significant sites such as IL12RB2, RBM6, HLA-DQB1, and less frequently reported genes such as NFKBIL1, ZBTB38, PTRHD1, and UQCC1. It has been confirmed that an important correlation exists between the variations in the HLA, IL12A, and IL12RB2 genes and PBC via one analysis of the genetic loci related to PBC risk in a population of DNA samples [ 26 ]. The susceptibility of PBC to the IL12A and IL12RB2 gene loci may regulate their protein products, interleukin-12 p35, and interleukin-12 receptor beta-2, which may be achieved by facilitating the Th1-type immune response, activating self-antigen presentation, and collaborating with inflammatory pathways [ 27 ]. The colocalization analysis further confirmed the detection results of molecular integration analysis and cross-trait GWAS meta-analysis. In the animal models of PBC, an increase in TGFB2 expression may be linked to inflammatory reactions and participate in the fibrotic process of cholestatic liver disease. Furthermore, a strong positive correlation has been observed between the expression of TGFB2 and CD45 in PBC patients, both present in similar areas of the affected liver tissue [ 28 ]. MAGMA analysis identified 9,337 shared gene loci and revealed that sarcopenia and PBC are enriched in brain and spleen tissues, respectively. Then, functional enrichment analysis disclosed potential shared biological mechanisms, involving pathways such as Hippo, JAK/STAT, TGF - β/Smad, NF - κB, and cGMP - PKG signaling pathways. A single-cell analysis has demonstrated that the overall expression of GPBAR1 is elevated in the liver of PBC patients [ 29 ]. Furthermore, BAR501, a selective GPBAR1 agonist, counteracts the down-regulation of lipopolysaccharide-induced GPBAR1 expression in a manner dependent on NF-κB pathway [ 30 ]. In a mouse model of PBC, blocking the JAK-STAT pathway diminishes hepatic inflammation and eliminates liver-resident Th1-like cells. This pathway is a potential drug target for PBC therapy. This study has several limitations. Firstly, our study focused on individuals with European heritage to minimize the effects of ancestral diversity. Future research should widen the scope to contain individuals of various ancestries to ensure broader applicability and validate the results across diverse racial populations. Secondly, the current study has not considered potential confounding factors, such as lifestyle, environmental impacts, and socioeconomic status, which could influence the observation of genetic correlations. Ultimately, to minimize the linkage disequilibrium in the MHC region, we excluded it from the analysis, which could contribute to missing some important outcomes. Conclusion This research reinforces the shared genetic relationship between PBC and sarcopenia by analyzing global and local genetic correlation overlap. Through the unified test of the molecular characteristics, complex interactions between tissues and genes have been detected, encompassing diverse biological pathways. The findings provide a substantial foundation for advancing research and treatment strategies related to liver diseases and aging. Abbreviations PBC, Primary Biliary Cholangitis EWGSOP2, European Working Group on Sarcopenia in Older Adults revised the definition of sarcopenia rg, Genetic Correlation GWAS, Genome-wide Association Studies SNP, Single Nucleotide Polymorphism 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) LSDC, Linkage Disequilibrium Score Regression HDL, High-definition Likelihood LD, Linkage Disequilibrium MHC, Major Histocompatibility Complex MAF, Minor Allele Frequency LAVA, Local Variation Association Analysis FDR, False Discovery Rate SUPERGNOVA, SUPER GeNetic cOVariance Analyzer MTAG, Multi-trait Analysis of GWAS CPASSOC, Cross-Phenotype Association SHet, Statistical Heterogeneity UTMOST, Unified Test for Molecular Signatures GTEx.V8, Genotype - Tissue Expression (Version 8) MAGMA, Multi-marker Analysis of Genomic Annotation FUSION, Functional Summarisation Attribution FOCUS, Fine-mapping of Causal Gene Sets TWAS, Transcriptome-wide Association Study PIP, Posteriori Probability of Causality SMR, Summary Data-based Mendelian Randomization eQTLs, Expression Quantitative Trait Loci GO, Gene Ontology KEGG, Kyoto Encyclopedia of Genes and Genomes GSEA, Gene Set Enrichment Analysis BP, Biological Processes MF, Molecular Functions CC, Cellular Components NES, Normalized Enrichment Score UV Response DN, Ultraviolet Response Downregulated Gene TGF, Transforming Growth Factor 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. We would like to thank the Kanehisa laboratories for granting us the permission to use the KEGG pathway database and related imagery in this study under an Open - Access license. Additional Information Conflicts of interest The authors declare no conflicts of interest. Funding None. Authors’ contributions Zhonghai Wang: designed the study, interpreted the results, produced figures 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. Ethics approval and consent to participate The data we used were obtained from published studies approved by the corresponding ethics committee. Data availability statement 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/josefin-werme/LAVA/tree/main/support_data. 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) References Levy, C., Manns, M. & Hirschfield, G. New Treatment Paradigms in Primary Biliary Cholangitis. Clin. Gastroenterol. Hepatol. 21 (8), 2076–2087. 10.1016/j.cgh.2023.02.005 (2023). Zheng, L. et al. 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LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47 (3), 291–295. 10.1038/ng.3211 (2015). Werme, J., van der Sluis, S., Posthuma, D. & de Leeuw, C. A. An integrated framework for local genetic correlation analysis. Nat. Genet. 54 (3), 274–282. 10.1038/s41588-022-01017-y (2022). Cordell, H. J. et al. An international genome-wide meta-analysis of primary biliary cholangitis: Novel risk loci and candidate drugs. J. Hepatol. 75 (3), 572–581. 10.1016/j.jhep.2021.04.055 (2021). Ning, Z., Pawitan, Y. & Shen, X. High-definition likelihood inference of genetic correlations across human complex traits. Nat. Genet. 52 (8), 859–864. 10.1038/s41588-020-0653-y (2020). Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47 (D1), D1005–D1012. 10.1093/nar/gky1120 (2019). Zhang, Y. et al. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biol. 22 (1), 262. 10.1186/s13059-021-02478-w (2021). Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50 (2), 229–237. 10.1038/s41588-017-0009-4 (2018). Zhu, Z., Hasegawa, K., Camargo, C. A. Jr & Liang, L. Investigating asthma heterogeneity through shared and distinct genetics: Insights from genome-wide cross-trait analysis. J. Allergy Clin. Immunol. 147 (3), 796–807. 10.1016/j.jaci.2020.07.004 (2021). Sey, N. Y. A. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat. Neurosci. 23 (4), 583–593. 10.1038/s41593-020-0603-0 (2020). Liu, L. et al. Conditional transcriptome-wide association study for fine-mapping candidate causal genes. Nat. Genet. 56 (2), 348–356. 10.1038/s41588-023-01645-y (2024). Mancuso, N. et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat. Genet. 51 (4), 675–682. 10.1038/s41588-019-0367-1 (2019). Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48 (5), 481–487. 10.1038/ng.3538 (2016). Soskic, B. et al. Immune disease risk variants regulate gene expression dynamics during CD4 + T cell activation. Nat. Genet. 54 (6), 817–826. 10.1038/s41588-022-01066-3 (2022). Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. ;43(Database issue):D1049-56. (2015). 10.1093/nar/gku1179 Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53 (D1), D672–D677. 10.1093/nar/gkae909 (2025). Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44 (W1), W90–W97. 10.1093/nar/gkw377 (2016). Terziroli Beretta-Piccoli, B. et al. The challenges of primary biliary cholangitis: What is new and what needs to be done. J. Autoimmun. 105 , 102328. 10.1016/j.jaut.2019.102328 (2019). Dasarathy, S. & Merli, M. Sarcopenia from mechanism to diagnosis and treatment in liver disease. J. Hepatol. 65 (6), 1232–1244. 10.1016/j.jhep.2016.07.040 (2016). Jindal, A., Jagdish, R. K. & Sarcopenia Ammonia metabolism and hepatic encephalopathy. Clin. Mol. Hepatol. 25 (3), 270–279. 10.3350/cmh.2019.0015 (2019). Hirschfield, G. M. et al. Primary biliary cirrhosis associated with HLA, IL12A, and IL12RB2 variants. N Engl. J. Med. 360 (24), 2544–2555. 10.1056/NEJMoa0810440 (2009). Goriely, S., Neurath, M. F. & Goldman, M. How microorganisms tip the balance between interleukin-12 family members. Nat. Rev. Immunol. 8 (1), 81–86. 10.1038/nri2225 (2008). Dropmann, A. et al. TGF-β2 silencing to target biliary-derived liver diseases. Gut 69 (9), 1677–1690. 10.1136/gutjnl-2019-319091 (2020). Di Giorgio, C. et al. Liver GPBAR1 Associates With Immune Dysfunction in Primary Sclerosing Cholangitis and Its Activation Attenuates Cholestasis in Abcb4-/- Mice. Liver Int. 45 (2), e16235. 10.1111/liv.16235 (2025). Gui, W. et al. Colitis ameliorates cholestatic liver disease via suppression of bile acid synthesis. Nat. Commun. 14 (1), 3304. 10.1038/s41467-023-38840-8 (2023). Additional & Information. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Editor invited by journal 21 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 17 Jul, 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7113071","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496844803,"identity":"7b2887dc-be35-4927-bac6-b71a4f85f912","order_by":0,"name":"Zhonghai Wang","email":"","orcid":"","institution":"Chengdu Third People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhonghai","middleName":"","lastName":"Wang","suffix":""},{"id":496844804,"identity":"48f38379-9bf1-41e6-97a2-4f0445afad53","order_by":1,"name":"Meiling Yu","email":"","orcid":"","institution":"North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Meiling","middleName":"","lastName":"Yu","suffix":""},{"id":496844805,"identity":"d92de0bd-b72f-4477-91d4-d9f2aa3d561a","order_by":2,"name":"Han Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYJACZiCWsz/e//EBkCHDwMBGnBZjhjMHjA0YGAx4iNaS2HAjwUyCKC0Gx88efl1QcyexsSEhreJj2x8efva2BIYfFdtwazmTl2Y949gz42aGA8duzmwz4JHsOXaAsefMbZxazA7kmBnzsB2WbWNsbLvNC9RicCO9gZmxDY+W82+AWv4dZuxhZmYrJk7LjRzjx7xthxVnsLGxMUO0pB3Aq8X+xhszZt6+w8YGPDzMkjPOGYP8knAQn18k+3OMP/N8OyxnIP+G8cOHMjk5YIgZPvhRgVsLELBJYAgdwKceCJg/EFAwCkbBKBgFIx0AAMUwV1RT5fanAAAAAElFTkSuQmCC","orcid":"","institution":"Chengdu Third People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Han","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-13 11:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7113071/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7113071/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-19697-x","type":"published","date":"2025-10-14T15:58:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88527964,"identity":"bb5602a8-e54c-483f-ba40-1066df1152b0","added_by":"auto","created_at":"2025-08-07 10:49:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196583,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of our study design.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7113071/v1/757cf4789d84c67d60b1ea12.jpg"},{"id":88526294,"identity":"68ee5e21-7eee-4367-bced-0f53292ce981","added_by":"auto","created_at":"2025-08-07 10:33:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":225147,"visible":true,"origin":"","legend":"\u003cp\u003eThe FUSION method detected the genes that reached the significance threshold. (a) Venn diagram displays the genes involved in the overlapping sections of various sarcopenia-related traits. (b) The Manhattan plot shows the genes associated with PBC that passed the FUSION test. The red line indicates the results that meet the whole - genome significance standard (p \u0026lt; 5.75 x 10-6) after removing the probes without specific gene names. The blue line reveals FUSION method identified a total of 8,696 genes that reached the significance threshold (p \u0026lt; 0.05). (c) Summary data-based analysis shows shared susceptible probes between PBC and eight sarcopenia-related traits. FUSION, Functional Summarisation Attribution.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7113071/v1/cd3741a10f2cd257f6e19464.png"},{"id":88526295,"identity":"6284d928-75ac-40a6-83f2-0cb26dee2273","added_by":"auto","created_at":"2025-08-07 10:33:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":712149,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian colocalization analysis. Schematic plot shows SNPs between PBC and sarcopenia-related traits with their associated genes in the 1 MB range. (a) depicts the rs533543 locus associated with AFPR; (b) presents the rs12724708 locus linked to HGSR; (c) reveals the rs6744646 locus connected with LFPR; (d) illustrates the rs6735681 locus related to ALM; (e) demonstrates the rs13079464 locus correlated with HGSR; (f) manifests the rs13079464 locus tied to LFPL.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7113071/v1/08325422b63bbf53d162e7da.png"},{"id":88529032,"identity":"1fb52740-d513-433d-bf85-8a87f720ef27","added_by":"auto","created_at":"2025-08-07 10:57:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1756313,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of the shared biological mechanisms across various regulatory pathways among multi-potential patterns. (a) The gene network diagram displays the most significant signal transduction pathways and core genes from GO analysis. (b) The mulberry plot shows the main signal transduction pathways and essential genes revealed by KEGG analysis.\u003c/p\u003e\n\u003cp\u003eKEGG pathway database (Kanehisa M, et al., 2025; Release 98.0). https://www.kegg.jp/kegg/pathway.html. Reproduced with permission from Kanehisa laboratories under an Open Access license. (c) shows the expression discrepancies based on KEGG enrichment among three different pathways obtained by GSEA; (d)(e)(f) demonstrate the specific transduction pathways and differentially expressed genes of three different pathways. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis; UV Response DN, Ultraviolet Response Downregulated Gene; TGF, Transforming Growth Factor.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7113071/v1/8431541f784546e45be98d8f.png"},{"id":93956106,"identity":"c3c7f641-fd94-41a5-8f8c-d5c405c564ce","added_by":"auto","created_at":"2025-10-20 16:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3404740,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7113071/v1/9df69866-0281-4744-aef7-3fc9bce2d708.pdf"},{"id":88527624,"identity":"4e48936f-2afe-45ff-90e5-2d9e4c20dc6a","added_by":"auto","created_at":"2025-08-07 10:41:22","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7053437,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7113071/v1/d133237ada1fd00d73d563b3.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics and molecular testing: A new insight into the genetic mechanisms of primary biliary cholestasis and sarcopenia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs an autoimmune liver disease, primary biliary cholangitis (PBC) is featured by a gradual, non-suppurative inflammatory destruction of the small bile ducts within the liver. With the progression of the disease, it can contribute to biliary obstruction and cholestasis, ultimately developing into liver cirrhosis [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The serological marker of PBC is an anti-mitochondrial antibody, which is an autoantibody highly related to this disease. The disease might present without symptoms and progress at a relatively slow pace [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, in the advanced stage, PBC may present with conspicuous symptoms accompanied by rapid progression of liver disease. Fatigue and skin itching are among the clinical manifestations of PBC, which may also be correlated with autoimmune diseases and accompanied by conditions such as osteopenia, hypercholesterolemia, and sarcopenia [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSarcopenia is a widespread musculoskeletal disorder that progresses gradually and represents a prevalent skeletal muscle disorder in elderly individuals, affecting 10\u0026ndash;27% of those aged 60 and above [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. In 2019, the European Working Group on Sarcopenia in Older Adults revised the definition of sarcopenia (EWGSOP2) suggested taking into account three fundamental metrics for diagnosing sarcopenia: decreased muscle mass, insufficient muscle strength, and compromised physical functioning [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\n\u003cp style=\"display: inline !important;\"\u003eEpidemiological research revealed that individuals with chronic liver disorders faced an increasing risk of developing osteoporosis as well as sarcopenia. Retrospective analysis indicated that sarcopenia was frequently observed among PBC patients, with a prevalence of 25.9% [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although recent relevant studies have been conducted, the precise connection between PBC and sarcopenia is still not thoroughly understood [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, improving the long-term quality of life for patients with chronic liver disease accompanied by sarcopenia is a significant challenge. With the advancement of genome sequencing technology, establishing the correlation between traits and genetics has been prioritized to be a practical approach to handling the constraints inherent in observational studies, randomized controlled trials, and other conventional research paradigms. Genetic correlation (rg) is utilized to explore the genetic overlap. Theoretically, it can be investigated across the entire genome and represents the average of shared genetic effects among all causal loci within the genome [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Global and local genetic correlation analyses were first applied to identify the features associated with PBC and sarcopenia-related traits [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Subsequently, we employed a cross-trait genome-wide association study (GWAS) meta-analysis at the single nucleotide polymorphism (SNP) level to recognize pleiotropic genetic variations or loci. Consequently, our study delves into the common drug targets and pathogenic pathways between PBC and sarcopenia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData resource\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of PBC originated from 7 populations recently registered in the IEU OpenGWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003c/span\u003e), containing 5 European groups and 2 East Asian groups, encompassing 5,004,018 SNPs [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Based on the EWGSOP2 definition [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e], the data comprised a broad spectrum of statistics regarding sarcopenia from various metrics, such as the usual walking pace (UWP), appendicular lean mass (ALM), hand grip strength for both left (HGSL) and right (HGSR), leg fat percentage for left (LFPL) and right (LFPR), as well as arm fat percentage for left (AFPL) and right (AFPR) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). To guarantee the dependability of our research results, we selected SNPs that demonstrated a notable genome-wide association with traits (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) and eradicated SNPs with high linkage disequilibrium (r2\u0026thinsp;\u0026gt;\u0026thinsp;0.001 and kb\u0026thinsp;\u0026lt;\u0026thinsp;10,000). Furthermore, it was important to discard palindromic SNPs, where the effective and the other alleles were complementary. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the design of the experiment.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverview of traits included in our study.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePhenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSNP-based heritability\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCase,n\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl,n\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal,N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSNPs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConsortium\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGlobal h\u003csup\u003e2\u003c/sup\u003e SNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eebi-a-GCST003129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,004,018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUWP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b\u0026minus;4711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e459,915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eebi-a-GCST90000026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205,513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18,164,071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHGSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b\u0026minus;7478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e461,026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHGSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b\u0026minus;10215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e461,089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLFPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b\u0026minus;18377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e454,826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLFPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b\u0026minus;20531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e454,826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b\u0026minus;20188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e454,724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b\u0026minus;12854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e454,789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eAbbreviations: SNPs, single nucleotide polymorphisms; PBC, Primary Biliary Cholangitis; 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\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic Correlation Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted comprehensive genetic correlation analyses via linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) to evaluate the shared polygenic architecture among traits [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. These two methodologies can integrate data from European ancestry samples within the 1000G EUR Project for calculating linkage disequilibrium (LD) scores, serving as a standard reference. Simultaneously, rigorous quality control measures were implemented on the SNPs throughout this procedure. Additionally, we retained only those SNPs with a minor allele frequency (MAF) exceeding 0.01 [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eLocal Variation Association Analysis (LAVA) was enforced to explore the local genetic correlations between pairs of genes [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Given the complexity of the genome\u0026apos;s structure, LAVA can effectively examine the genetic sequence variations in diverse regions and accurately approximate the genetic correlations within smaller genome segments. Thus, compared to the global genetic correlation analyses, LAVA provides a more comprehensive insight into the genetic overlap between traits. This method analyzes the 2495 independent LD blocks, utilizing 1000G EUR dataset. To ensure the stability of the results, the Benjamini-Hochberg method was established to adjust the rate of false discoveries (FDR) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). SUPER GeNetic cOVariance Analyzer (SUPERGNOVA) utilizes a random-effects model for analyzing local genetic correlations and supplies a more accurate estimate of the similarity between pairs of traits compared to the fixed-effects model applied in LAVA [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-trait GWAS Meta-analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMulti-trait analysis of GWAS (MTAG) presents an efficient way to detect genetic risk variations across traits, which employs a random-effects meta-analysis method to compute summary statistics at the SNP level, effectively leveraging the genetic structure of shared traits to enhance detection capabilities [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Cross-Phenotype Association (CPASSOC) was employed for cross-trait association analysis to recognize causal pleiotropic SNPs among multiple traits. We analyzed the traits jointly through statistical heterogeneity (SHet) to confirm the association of at least one genetic variant with a trait. Integrating MTAG and CPASSOC, the \u003cem\u003eP\u003c/em\u003e-value of SNP was set less than 5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e in both methods, and it met a criterion of less than 5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for the single-trait association between PBC and sarcopenia [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnified Test for Molecular Signatures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Unified Test for Molecular Signatures (UTMOST) has the potential to determine the associations between genes and traits, which takes into account the combined impacts of SNPs within LD regions and integrating the Genotype - Tissue Expression (GTEx) data of organisms [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Subsequently, we validated the results of UTMOST by three methods: Multi-marker Analysis of Genomic Annotation (MAGMA), Functional Summarisation Attribution (FUSION), and Fine-mapping of Causal Gene Sets (FOCUS).\u003c/p\u003e\n\u003cp\u003eWe conducted SNP genetic enrichment analyses on 54 different tissues using MAGMA with GTEx.V8 data [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. To guarantee the robustness of these associations, we implemented a Bonferroni correction to account for multiple tests. For pleiotropic genes, we established a significant threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05/the total number of genes). Integrating the tissues detected by MAGMA, we enforced a cross-tissue transcriptome-wide association study (TWAS) analysis by Functional Summarisation Attribution (FUSION) to compute the genetic elements across diverse tissues [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. FUSION generates predictive models of functional and molecular phenotypic traits, which utilizes global genomic studies to summarize statistical predictions. Adjusting the significance level, the Bonferroni-corrected TWAS thresholds were set (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05/the total number of transcripts). As a probabilistic fine-mapping method, FOCUS integrates the correlation signals by simulating the association between TWAS signals [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Genes with posteriori probability of causality (PIP) values of 0.9 or above were considered potential causal candidates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis of Bivariate Loci\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the Summary data-based Mendelian randomization (SMR) method, we have detected genes expressed across various tissues pinpointed by MAGMA [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our research for gene expression proxies, we chose expression quantitative trait loci (eQTLs) from various tissues acquired from the GTEx Consortium website. SMR was conducted to evaluate the influence of these genes on traits, centering on cis-eQTL and trans-eQTL with an MAF greater than 1% and a remarkable threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e. To assess the robustness of our SMR findings, we performed the dependent instrumental heterogeneity (HEIDI) test, serving to highlight significant differences among diverse datasets. For this study, the P-value of HEIDI was greater than 0.05 and an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e\n\u003cp\u003eWe performed a colocalization analysis to evaluate the likelihood of shared causal variants between PBC and sarcopenia [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. This process involved removing SNPs with significant genome-wide associations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), eliminating duplicates and missing values, and extracting SNPs that appeared in both PBC and sarcopenia groups. Then, we assessed five potential scenarios to determine if the two traits could be collectively affected by a single genetic variant: PPH0, no correlation with either trait; PPH1, linked to PBC but not the sarcopenia-related traits; PPH2, associated with the sarcopenia-related traits but not PBC; PPH3, connected to both traits with different causal variants; PPH4, related to both traits, sharing a causal variant. Based on the criterion of PPH3\u0026thinsp;+\u0026thinsp;PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.8, we established the evidence for colocalized genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the functional characteristics and biological connections of pre-selected potential drug genes, we comprehensively applied Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and Gene Set Enrichment Analysis (GSEA). Within the GO framework, we were dedicated to three essential dimensions: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC), aiming to elucidate the activity patterns, biological roles, and cellular localization of these genes [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. KEGG provided a widespread understanding of gene-related metabolic pathways and signaling networks, revealing the underlying pathogenesis [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. GSEA comprehensively analyzed the complete gene sequence list, effectively detecting the biological functions of gene sets for gene complementation [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. While implementing GSEA, we conducted 10,000 alignments while setting the minimum and maximum gene set sizes to 10 and 200, respectively. Furthermore, to accurately identify significantly enriched gene groups, a variety of criteria were combined, including a normalized enrichment score (NES) with an absolute value greater than 1 (|NES| \u0026gt;1) and an FDR less than 0.25.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eGenome-wide Genetic Correlations and Overlap\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the analysis of genetic correlations, a threshold of unadjusted \u003cem\u003eP\u003c/em\u003e-value less than 0.05 was programmed as the criterion for selecting relevant traits. LDSC and HDL were performed to detect PBC and eight pairs of traits linked to sarcopenia. These traits were subsequently characterized and estimated, revealing eminent genetic correlations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After applying Bonferroni correction, the LAVA analysis identified 88 specific genetic regions associated with PBC and sarcopenia (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Among these, we have selected 17 significant bivariate regions. The region of chr3q27.1 has exhibited the strongest genetic link between PSC and ALM, including genes KLHL24, MAP6D1, PARL, ABCC5, and AP2M1. In addition, the region of chr2q32.2-q32.3 was recognized to present essential aggregation of PBC with LFPL, LFPR, HGSR, AFPL, and AFPR. This region encompassed several less explored gene regions, including C2orf88, HIBCH, TMEM194B, NAB1, and the loci of the STAT family. SUPERGNOVA analysis confirmed the genetic association between PBC and most cases of sarcopenia at the chr2q32.2-q32.3 region. The results of genetic correlation analysis using different genetic techniques were consistent, strengthening the robustness of the genetic correlations between genes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGlobe and local genetic correlation analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eLDSC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHDL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e\u003cp\u003eLAVA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003erg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003echr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003estart\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eN-SNPs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eP-value\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\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.51E-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.60E\u0026minus;08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e105120579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e106546524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8.15E-04\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\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.01E\u0026minus;02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.08E\u0026minus;02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e183203156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e184524268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.09E-04\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\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.44E\u0026minus;08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.02E\u0026minus;02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e191051955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e193033982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.14E-02\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\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.26E\u0026minus;08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.69E\u0026minus;03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e191051955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e193033982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.98E-02\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\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.00E\u0026minus;04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.65E\u0026minus;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e183203156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e184524268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.79E-03\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\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00E\u0026minus;04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.98E\u0026minus;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e191051955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e193033982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.93E-02\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\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.33E\u0026minus;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.53E\u0026minus;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e191051955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e193033982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.92E-02\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\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.50E\u0026minus;08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.43E\u0026minus;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e191051955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e193033982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e9.04E-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviations: LDSC, linkage disequilibrium score regression; HDL, high-definition likelihood; LAVA, local variation association analysis; rg, genetic correlation; SNPs, single nucleotide polymorphisms; SE, standard error; chr, chromosome; 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\u003cp\u003e\u003cb\u003eCross-trait GWAS Meta-analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter deleting SNPs with linkage disequilibrium, we combined MTAG and CPASSOC to collaboratively identify 82,136 shared phenotypic risk SNPs for PBC and sarcopenia, which has passed the strict threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). The most significant shared SNP rs2272593, mapped at NFKBIL1, was discovered in PBC along with four sarcopenia-related traits, including LFPL, LFPR, AFPL, and AFPR. The most crucial shared SNP by HGSL and HGSR with PBC was rs9275219, located at HLA-DQB1, which has been proven strongly linked to the risk of PBC and sarcopenia. The most prominent SNP, rs644045, was localized to SNORD48 in ALM. In addition, the loci of UWP did not pass the MTAG analysis test but exhibited the most notable SNP rs7774434 (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eSHet\u003c/em\u003e\u003c/sub\u003e = 1.03 x 10\u003csup\u003e\u0026minus;\u0026thinsp;60\u003c/sup\u003e), situated on HLA-DQA1, through CPASSOC analysis (Supplementary Tables S2-8).\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnified Test for Molecular Signatures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMAGMA showed that most sarcopenia-related traits existed significantly enriched gene sets in the brain cerebellum region. Specifically, HGSL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.37 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), LFPL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.26 x 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e), AFPR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.91 x 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e), and AFPL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.98 x 10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e) were mainly concentrated in the cerebellar region. UWP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.86 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) and LFPL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.99 x 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e) were converged harmoniously in the cerebellar hemisphere. Additionally, ALM (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.98 x 10\u003csup\u003e\u0026minus;\u0026thinsp;18\u003c/sup\u003e) and HGSR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.35 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) remarkably thrived in the uterus and muscle-skeletal tissues, respectively. The spleen demonstrated the most prominent accumulated PBC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.28 x 10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e), with additional tissues like the ileum, lymphocytes, whole blood, and lung tissue also exceeding the significance threshold. MAGMA analysis performed a more profound examination of the gene-level outcomes obtained from CPASSOC, revealing 9,377 loci that successfully passed the test (Supplementary Tables S9-17).\u003c/p\u003e\u003cp\u003eUsing the tissues that passed the MAGMA detection, the FUSION approach has detected 8,696 genes that reached the significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a further 1,800 genes that met the genome-wide significance criteria (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5.75 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). AFP demonstrated the highest number of tissue-specific genes, with 380 genes identified in AFPL and 367 genes in AFPR, and both groups shared significant genes, including PTRHD1, DNAJC27, ADCY3, CENPO, NPIPB6, ATP2A1, and SH2B1. In the brain cerebellar tissues, LFP contained a significant number of genes. To be precise, LFPL included 329 genes, whereas LFPR comprised 330 genes, and the shared significant genes like PTRHD1, DNAJC27, ADCY3, CENPO, RBM6, and NCOA1 were also of considerable interest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In the spleen tissues, we have observed 26 significant genes for PBC, including IL12RB2, IRF5, SYNGR1, DENND1B, and IKZF3, all of which were strongly associated with the underlying mechanism of PBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Tables S18-26). Furthermore, FOCUS has confirmed critical loci such as PTRHD1, DNAJC27, and CENPO (with a PIP\u0026thinsp;\u0026gt;\u0026thinsp;0.9) between PBC and sarcopenia.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation Analysis of Bivariate Loci\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe SMR and HEIDI tests confirmed that genes with co-expression at eQTL levels, serving as probes, were selected to investigate whether DNA methylation regulates the progress. After our investigation, we discovered that in PBC, specific probes such as ENSG00000241106, ENSG00000228789, ENSG00000204531, ENSG00000204520, and ENSG00000272221 located on chromosome 6, along with probe ENSG00000236935 on chromosome 11, negatively regulate the expression of PBC. This regulatory mechanism ultimately reduced the risk of developing PBC. The probe ENSG00000164989, located at CCDC171, demonstrated significant levels in multiple sarcopenia-related traits, with the most significant levels exhibited in LFPR, AFPL, and AFPR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Through colocalization analysis, we confirmed the results of shared causal loci identified by cross-trait GWAS meta-analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PBC and sarcopenia were colocated at multiple common pleiotropic loci, containing rs539515 (mapped at ASTN1, PPH4\u0026thinsp;=\u0026thinsp;0.946), rs12747058 (mapped at TGFB2, PPH4\u0026thinsp;=\u0026thinsp;0.942), rs6744646(mapped at ACP1, PPH4\u0026thinsp;=\u0026thinsp;0.924), rs6735681 (mapped at MYCNOS, PPH4\u0026thinsp;=\u0026thinsp;0.999), rs13078908 (mapped at LINC00620, PPH4\u0026thinsp;=\u0026thinsp;0.912), and rs13079464 (mapped at LINC00620, PPH4\u0026thinsp;=\u0026thinsp;0.995) (Supplementary Tables S27-32).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional Enrichment Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe selected pleiotropic genes that passed the MAGMA test for biological analysis of functional enrichment. The gene-based GO analysis suggested that through BP, CC, and MF, three major pathways enriched in PBC and sarcopenia have been identified, respectively: chondrocyte differentiation (GO:0002062, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.52 x 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e), glutamatergic synapse (GO:0098978, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.71 x 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e), phosphatase binding (GO:0019902, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.62 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Supplementary Tables S33-35). KEGG analysis showed that the relevant genes exhibited a high transduction phenomenon in the cGMP-PKG signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The GSEA results indicated that 4,465 genomes were up-regulated, contributing to the enrichment of 47 gene sets. Moreover, three groups demonstrated significant enrichment, including ultraviolet response downregulated gene sets (UV Response DN), transforming growth factor (TGF) beta signaling, and interferon-gamma response (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec-f, Supplementary Table S36). The highly expressed UV Response DN genes were accumulated in pathways such as the cGMP-PKG receptor-based signaling and the Phospholipase D signaling pathway. Specifically, the cGMP receptor was involved in bile metabolism and crucial for developing liver cirrhosis. The outcomes exhibited by the above functional enrichment analysis present high consistency.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study investigating the genetic overlap correlation and shared risk loci between PBC and sarcopenia by performing a multi-trait analysis utilizing a large-scale GWAS database. First, we analyzed the genetic correlation between PBC and sarcopenia-related phenotypes, revealing 8 significant symptoms associated with sarcopenia. Furthermore, we accurately recognized 17 eminent loci with bivariate correlations among 88 semi-independent sites through local genetic analysis. Second, we performed an across-trait GWAS meta-analysis to communally identify 82,136 phenotypic risks through rigorous testing. We also detected the presence of top SNP loci between multiple trait pairs. Third, we employed gene expression data from the human spleen and brain tissues to functionally annotate essential loci and mapped the input gene expression to the loci within significant bivariate loci. Finally, functional enrichment analyses have exhibited that the shared risk genes demonstrated a high level of transactivation in the cGMP-PKG signaling pathway.\u003c/p\u003e\u003cp\u003eMusculoskeletal diseases related to PBC are receiving increasing attention. PBC involves a range of metabolic and nutritional disorders, including decreased vitamin D absorption, disrupted bile acid metabolism, and changes in the gut microbiota. Furthermore, chronic liver inflammation, as a trademark of PBC, can contribute to progressive muscle atrophy and accelerate the development of sarcopenia [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our study has further confirmed and supplemented the deficiencies of some observational studies. Our comprehensive genetic correlation study discovered a significant and robust association between PBC and sarcopenia.\u003c/p\u003e\u003cp\u003eAdditionally, the evident binary genetic correlation between PBC and sarcopenia was identified in specific gene regions, such as local areas like chr3q27.1 and chr2q32.2-q32.3. An MR research was performed to investigate the potential relationship between chronic liver diseases and musculoskeletal conditions. The findings supported that cholangitis could elevate the risk of osteoporosis and osteoarthritis, which may contribute to sarcopenia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite the unclear specific mechanism of concurrent sarcopenia in patients with PBC, several causal elements may contribute to its development, including the liver-muscle axis, hyperammonemia, and endotoxemia [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. As a critical center for ammonia metabolism, the liver undertakes ammonia's metabolic transformation. When muscles perform amino acid transamination, ammonia is generated, and this portion of the produced ammonia is transported to the liver for subsequent processing. It is worth mentioning that hyperammonemia, an important element in liver-gut axis imbalance, can potentially initiate mitochondrial malfunction and activate the myostatin signaling pathway, thereby intensifying sarcopenia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThrough the analysis of GWAS meta-analysis across traits and the correlation analysis of bivariate loci, it was verified that PBC and sarcopenia have shared significant sites such as IL12RB2, RBM6, HLA-DQB1, and less frequently reported genes such as NFKBIL1, ZBTB38, PTRHD1, and UQCC1. It has been confirmed that an important correlation exists between the variations in the HLA, IL12A, and IL12RB2 genes and PBC via one analysis of the genetic loci related to PBC risk in a population of DNA samples [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The susceptibility of PBC to the IL12A and IL12RB2 gene loci may regulate their protein products, interleukin-12 p35, and interleukin-12 receptor beta-2, which may be achieved by facilitating the Th1-type immune response, activating self-antigen presentation, and collaborating with inflammatory pathways [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The colocalization analysis further confirmed the detection results of molecular integration analysis and cross-trait GWAS meta-analysis. In the animal models of PBC, an increase in TGFB2 expression may be linked to inflammatory reactions and participate in the fibrotic process of cholestatic liver disease. Furthermore, a strong positive correlation has been observed between the expression of TGFB2 and CD45 in PBC patients, both present in similar areas of the affected liver tissue [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMAGMA analysis identified 9,337 shared gene loci and revealed that sarcopenia and PBC are enriched in brain and spleen tissues, respectively. Then, functional enrichment analysis disclosed potential shared biological mechanisms, involving pathways such as Hippo, JAK/STAT, TGF - β/Smad, NF - κB, and cGMP - PKG signaling pathways. A single-cell analysis has demonstrated that the overall expression of GPBAR1 is elevated in the liver of PBC patients [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, BAR501, a selective GPBAR1 agonist, counteracts the down-regulation of lipopolysaccharide-induced GPBAR1 expression in a manner dependent on NF-κB pathway [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In a mouse model of PBC, blocking the JAK-STAT pathway diminishes hepatic inflammation and eliminates liver-resident Th1-like cells. This pathway is a potential drug target for PBC therapy.\u003c/p\u003e\u003cp\u003eThis study has several limitations. Firstly, our study focused on individuals with European heritage to minimize the effects of ancestral diversity. Future research should widen the scope to contain individuals of various ancestries to ensure broader applicability and validate the results across diverse racial populations. Secondly, the current study has not considered potential confounding factors, such as lifestyle, environmental impacts, and socioeconomic status, which could influence the observation of genetic correlations. Ultimately, to minimize the linkage disequilibrium in the MHC region, we excluded it from the analysis, which could contribute to missing some important outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research reinforces the shared genetic relationship between PBC and sarcopenia by analyzing global and local genetic correlation overlap. Through the unified test of the molecular characteristics, complex interactions between tissues and genes have been detected, encompassing diverse biological pathways. The findings provide a substantial foundation for advancing research and treatment strategies related to liver diseases and aging.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePBC, Primary Biliary Cholangitis\u003c/p\u003e\n\u003cp\u003eEWGSOP2, European Working Group on Sarcopenia in Older Adults revised the definition of sarcopenia\u003c/p\u003e\n\u003cp\u003erg, Genetic Correlation\u003c/p\u003e\n\u003cp\u003eGWAS, Genome-wide Association Studies\u003c/p\u003e\n\u003cp\u003eSNP, Single Nucleotide Polymorphism\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\u003eLSDC, Linkage Disequilibrium Score Regression\u003c/p\u003e\n\u003cp\u003eHDL, High-definition Likelihood\u003c/p\u003e\n\u003cp\u003eLD, Linkage Disequilibrium\u003c/p\u003e\n\u003cp\u003eMHC, Major Histocompatibility Complex\u003c/p\u003e\n\u003cp\u003eMAF, Minor Allele Frequency\u003c/p\u003e\n\u003cp\u003eLAVA, Local Variation Association Analysis\u003c/p\u003e\n\u003cp\u003eFDR, False Discovery Rate\u003c/p\u003e\n\u003cp\u003eSUPERGNOVA, SUPER GeNetic cOVariance Analyzer\u003c/p\u003e\n\u003cp\u003eMTAG, Multi-trait Analysis of GWAS\u003c/p\u003e\n\u003cp\u003eCPASSOC, Cross-Phenotype Association\u003c/p\u003e\n\u003cp\u003eSHet, Statistical Heterogeneity\u003c/p\u003e\n\u003cp\u003eUTMOST, Unified Test for Molecular Signatures\u003c/p\u003e\n\u003cp\u003eGTEx.V8, Genotype - Tissue Expression (Version 8)\u003c/p\u003e\n\u003cp\u003eMAGMA, Multi-marker Analysis of Genomic Annotation\u003c/p\u003e\n\u003cp\u003eFUSION, Functional Summarisation Attribution\u003c/p\u003e\n\u003cp\u003eFOCUS, Fine-mapping of Causal Gene Sets\u003c/p\u003e\n\u003cp\u003eTWAS, Transcriptome-wide Association Study\u003c/p\u003e\n\u003cp\u003ePIP, Posteriori Probability of Causality\u003c/p\u003e\n\u003cp\u003eSMR, Summary Data-based Mendelian Randomization\u003c/p\u003e\n\u003cp\u003eeQTLs, Expression Quantitative Trait Loci\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\u003eGSEA, Gene Set Enrichment Analysis\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\u003eNES, Normalized Enrichment Score\u003c/p\u003e\n\u003cp\u003eUV Response DN, Ultraviolet Response Downregulated Gene\u003c/p\u003e\n\u003cp\u003eTGF, Transforming Growth Factor\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. We would like to thank the Kanehisa laboratories for granting us the permission to use the KEGG pathway database and related imagery in this study under an Open - Access license.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\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\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\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, produced figures 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\u003eEthics 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\u003eData availability statement\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/josefin-werme/LAVA/tree/main/support_data. 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"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLevy, C., Manns, M. \u0026amp; Hirschfield, G. New Treatment Paradigms in Primary Biliary Cholangitis. \u003cem\u003eClin. Gastroenterol. Hepatol.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (8), 2076\u0026ndash;2087. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cgh.2023.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.cgh.2023.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng, L. et al. 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Commun.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (1), 3304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-023-38840-8\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-38840-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdditional \u0026amp; Information.\u003c/span\u003e\u003c/li\u003e\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Primary biliary cholangitis, Sarcopenia, Genome-wide association studies, Pleiotropic genes","lastPublishedDoi":"10.21203/rs.3.rs-7113071/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7113071/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrimary biliary cholangitis (PBC) has the potential to impact skeletal muscles through the muscle-liver axis, subsequently leading to sarcopenia. Our study aims to explore the complex and unclear genetic structures between them. We investigated the common genetic basis of PBC and sarcopenia using advanced statistical genetics methods and genome - wide association summary analysis. We employed global and local genetic correlation to achieve valuable insights into potential shared biological mechanisms. We identified risk single nucleotide polymorphisms (SNPs) and functionally annotated genomic multi-markers by conducting the whole-genome unified testing of molecular characteristics. Finally, the fine-mapping analysis was prioritized to emphasize the significant causal genes in every region. Our study has revealed noteworthy genomic associations, suggesting the intricate genetic interplay between PBC and sarcopenia. At the genomic level, we identified 17 unique bivariate regions among 88 trait pairs, including chr3q27.1 and chr2q32.2-q32.3. The analysis of GWAS pleiotropy across traits has discovered 82,136 SNPs. Among these, bivariate locus correlation analysis has identified 136 pleiotropic sites. Bayesian colocation has pinpointed 18 causal variants. Functional enrichment analysis highlighted putative pleiotropic genomic regions, including brain and spleen. Our study has identified pleiotropic genomic regions linking PBC and sarcopenia, 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 primary biliary cholestasis and sarcopenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 10:33:17","doi":"10.21203/rs.3.rs-7113071/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-18T09:57:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T04:23:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T01:42:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242156231134800330917313089208310922589","date":"2025-08-07T18:13:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173353649785826772839011184880236659165","date":"2025-08-07T00:34:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T07:04:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T07:02:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-21T05:46:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T18:19:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-17T10:39:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae191c41-c0f5-42fd-a10d-ea5c1104f363","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52776145,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":52776146,"name":"Health sciences/Diseases"},{"id":52776147,"name":"Health sciences/Gastroenterology"},{"id":52776148,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2025-10-20T16:05:22+00:00","versionOfRecord":{"articleIdentity":"rs-7113071","link":"https://doi.org/10.1038/s41598-025-19697-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-14 15:58:39","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-08-07 10:33:17","video":"","vorDoi":"10.1038/s41598-025-19697-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-19697-x","workflowStages":[]},"version":"v1","identity":"rs-7113071","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7113071","identity":"rs-7113071","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}