Shared genetic architecture across fibrotic diseases

Shared genetic architecture across fibrotic diseases | 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 Shared genetic architecture across fibrotic diseases Johan Bundgaard, Søren Rand, Mette Bentsen, Ole Pedersen, Erik Sørensen, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8156241/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Fibrotic diseases show common pathophysiological features irrespective of their anatomical locations. By mapping the genomic landscape behind selected fibrotic diseases, we aim to investigate the genome-wide and locus-wide genetic overlap across fibrotic diseases. We conducted genome-wide meta-analyses using five genetic cohorts (Copenhagen Hospital Biobank (CHB), the Danish Blood Donor Study (DBDS), UK Biobank (UKB), FinnGen, the Million Veteran Program (MVP)) across 17 fibrotic traits comprising nine prototypical fibrotic diseases (e.g. carpal tunnel syndrome and idiopathic pulmonary fibrosis), four organ-diseases with known fibrotic components (e.g. heart failure and chronic kidney disease), and four imaging-derived fibrotic phenotypes. Global genetic correlations across fibrotic traits were estimated using linkage disequilibrium score regression (LDSC) and Locus-wide genetic overlaps were evaluated using colocalization analyses. Across 17 fibrotic traits with case sample sizes ranging from 4,559 for Peyronie’s disease to 126,358 for chronic kidney disease, we identified 645 genome-wide significant associations, of which 136 had not been reported previously for the respective trait. Using genetic correlation and hierarchical clustering, we found that fibrotic diseases clustered mainly into organ and non-organ specific clusters. The strongest correlations were between carpal tunnel syndrome and trigger finger ( r g = 0.60, P = 1.1 × 10 −63 ) and between chronic kidney disease and heart failure ( r g = 0.51, P = 9.5 × 10 −87 ). We identified 64 loci that colocalized across traits, of which 12 overlapped with three or more diseases. Many of the colocalizing genes belonged to gene families with established roles in fibrosis, including WNT signaling ( WNT7B , TCF7L2 , and WNT2 ), extracellular matrix ( COL11A1, MMP14 and P4HA2 ), fibroblast growth factors ( FGFR2, FGF21 ), and inflammation ( IRF5, STAT3 , and TNFAIP3 ). Our findings identified novel genetic variants and provide strong evidence for a shared genetic predisposition across fibrotic traits, converging on key biological pathways including extracellular matrix remodeling, immune regulation, and developmental signaling. Health sciences/Diseases/Rheumatic diseases/Connective tissue diseases Biological sciences/Genetics/Population genetics Health sciences/Diseases/Immunological disorders/Inflammatory diseases Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Fibrosis is a complex pathological process characterized by the accumulation of excess extracellular matrix proteins. These changes can progressively disrupt normal tissue structure and function with the potential to affect virtually any anatomical site. 1 This aberrant remodeling underlies a diverse spectrum of fibrotic diseases, including Dupuytren’s contracture, Peyronie’s disease, and idiopathic pulmonary fibrosis (IPF). Fibrosis is also a characteristic feature of end-stage disease across several organ systems, including in heart failure (HF), liver cirrhosis, and chronic kidney disease (CKD) with varying degrees of fibrosis. 2 , 3 Fibrotic diseases collectively affect one in five individuals, they are estimated to contribute to nearly one-third of deaths worldwide, and have a very limited number of marketed pharmacological therapies. 4 – 9 Genome-wide association studies (GWAS) for fibrotic traits have delineated hundreds of susceptibility loci. 10 – 15 While fibrotic manifestations are traditionally viewed as anatomically confined and genetically specific, emerging genetic and epidemiologic evidence suggests the presence of shared genetic risk. 12 , 16 – 21 Genetic correlations have been reported between Dupuytren´s contracture and Peyronie´s disease 22 and between trigger finger and carpal tunnel syndrome. 12 In the latter pair, a colocalized signal at DIRC3 (a regulator of the fibrosis-implicated gene IGFBP5 ) has been highlighted. Observational data further substantiates these findings with frequent co-occurrences reported, for example, between Dupuytren´s contracture and plantar fibromatosis. 23 The presence of histologically similar fibrotic changes across distinct anatomical locations underscores the likelihood of common molecular and pathophysiological pathways underlying fibrogenesis. 24 These data raise two fundamental questions. First, is the genetic predisposition to fibrotic disease primary site-specific, or does it reflect a systemic susceptibility to fibrosis? Second, if there is shared genetic architecture underlying fibrotic traits, which genes and molecular pathways converge in the pathophysiology? Integrating genetic data across anatomically distinct conditions, including prototypical fibrotic diseases as well as end-stage organ failure characterized by fibrotic components, may identify susceptibility loci of shared fibrotic pathophysiology. In the present study, we address these questions by performing comprehensive GWAS-analyses of 17 fibrotic diseases to systematically evaluate the global and locus-wide genetic overlap across fibrotic diseases. METHODS Trait selection and study cohorts We included 17 fibrotic traits, comprising nine prototypical fibrotic diseases, four organ-diseases with a fibrotic component, and four imaging-derived fibrotic traits ( Supplementary Table 1 ). Prototypical fibrotic diseases were selected based on a comprehensive literature review guided by the following criteria: (i) prior evidence supporting the condition as a prototypical fibrotic disease, (ii) availability of robust and standardized diagnostic definitions, and (iii) sufficient prevalence to enable adequately powered GWAS. Based on these criteria, we included carpal tunnel syndrome, Dupuytren’s contracture, trigger finger, Peyronie’s disease, phimosis, plantar fibromatosis, hypertrophic skin, liver cirrhosis, and IPF. In addition, we included conditions with a known fibrotic component, though not exclusively defined by fibrosis, such as HF, CKD, inflammatory bowel disease (IBD), and systemic sclerosis (SSc). Finally, we included imaging-derived fibrotic traits obtained from magnetic resonance imaging (MRI) T1 mapping of the liver, kidneys, myocardium, and pancreas, available for 43,881 individuals in the UK Biobank (UKB). 15 Genotyping, quality control and meta-analyses We performed GWASs of 11 diseases (excluding the imaging-derived phenotypes, carpal tunnel syndrome and HF) using individual-level data from the Copenhagen Hospital Biobank (CHB), 25 the Danish Blood Donor Study (DBDS), 26 and the UKB. 27 Cases were defined by International Classification of Diseases (ICD)-10 diagnostic codes, and controls comprised individuals without such registration. Detailed information on cohort descriptions, ICD-10 codes, quality control, and datasets contributing to each meta-analysis are provided in Supplementary Table 1 . Each GWAS conducted in CHB, DBDS and UKB were subsequently meta-analyzed with, when available, European ancestry GWAS summary statistics from FinnGen freeze 12 28 and the Million Veteran Program (MVP) 29 and the largest non-overlapping GWAS for each disease. 10 , 11 , 14 , 30 – 32 Prior to meta-analyses, standard quality control (QC) filters were applied: Variants were excluded if imputation quality score (INFO) was 5, a standard error > 10, or a minor allele frequency (MAF) < 0.001. Meta-analyses were performed for 11 of the 17 traits for European ancestry using the fixed-effects inverse-variance-weighted method implemented in METAL. 33 Only variants present in at least two studies were included in the meta-analyses. Risk locus definition For each trait, independent genetic associations were identified using PLINK 34 employing clumping with a ± 500 kb window and a linkage disequilibrium (LD) threshold of r 2 < 0.2. Genome-wide significance was defined as P < 5 × 10 − 8 . Within each clump, the variant with the lowest P-value was defined as the lead variant, and the corresponding genomic locus per trait was defined as ± 500 kb around the lead variant. To classify lead variants as novel or known, we queried NHGRI-EBI GWAS Catalogue 35 and reviewed the most recent trait-specific GWAS publications. We compared each lead variant with those previously reported and considered a lead variant as known if the lead variant was in high LD ( r 2 > 0.7) with a previously reported variant in a ± 500kb window around the lead variant. Heritability, genetic inflation, and correlation We used Linkage Disequilibrium Score Regression (LDSC) to estimate genomic inflation (lambda, LDSC-intercept, and standard error), SNP-based heritability on the liability scale, and quantify pairwise genetic correlations across fibrotic diseases. 36 Analyses were restricted to high-quality, well-imputed variants included in the HapMap3 CEU reference panel. Precomputed LD scores based on European-ancestry populations from the 1000 Genomes Project were used as the reference. Genomic inflation was defined as an LDSC intercept > 1.10 and corrected for by multiplying GWAS standard errors by the square root of the intercept. 36 , 37 Pairwise genetic correlations were clustered using agglomerative hierarchical clustering and heat-map visualization to identify groups of traits with shared genetic architecture using Ward´s method. Cross-trait colocalization To test whether fibrotic traits shared causal variants, we performed pairwise cross-trait colocalization analyses using the R coloc package. 38 We defined loci as ± 500 kb windows around each lead variant. We allowed for colocalization analyses between pairs, in the case where one locus was genome-wide significant for one trait and was still strongly associated with a second trait below genome-wide significance ( P 0.7 in at least one pair in a locus was considered as evidence of colocalization. Loci meeting this threshold were visually inspected using colocalization plots. Gene prioritization To prioritize genes underlying fibrotic risk loci, we integrated four complementary lines of evidence. First, we annotated each lead variant to its nearest gene based on genomic proximity. Second, we mapped lead variants or variants in high LD ( r 2 > 0.7) that were predicted to affect the coding of a protein using Variant Effect Predictor. 39 Third, we evaluated the effects of lead variants on gene expression using expression quantitative trait locus (eQTL) data from tissues profiled in GTEx v8. 40 Trait-eQTL colocalization analyses were performed, with evidence for colocalization defined as genes with PP 4 ≥ 0.70. Fourth, we utilized findings from the Open Targets Variant-to-Gene (V2G) algorithm, which provides weighted scores based on multiple layers of evidence (e.g. QTL, interaction with promoter capture Hi-C and in silico functional prediction), provided by Open Targets Genetics ( https://genetics.opentargets.org/ ) 41 and used the gene assigned the highest score. Genes supported by at least two lines of evidence and the highest overall support (i.e. the greatest number of evidence lines) were deemed putative effector genes. Tissue and pathway enrichment Tissue-specific expression enrichment was conducted using MAGMA, 42 as per fibrosis trait-specific full summary statistics in 49 GTEx tissues using European-ancestry populations from the 1000 Genomes Project as reference. 37 Statistical significance was evaluated with average tissue correction. Gene-based pathway enrichment was performed using MAGMA from MsigDB and assessed by clustering. RESULTS Genome-wide meta-analyses We performed GWAS meta-analyses for 11 fibrotic traits using data from CHB, DBDS, UKB, FinnGen, MVP, and other publicly available studies with non-overlapping samples. A summary of the 11 meta-analyses is provided in Fig. 1 , including case counts and specific studies contributing to each analysis. Case sample sizes ranged from 4,559 for Peyronie’s disease to 126,358 for CKD. The largest relative increases in sample size compared to previous studies were observed for Dupuytren´s contracture (177%, 11,320 to 31,369) and liver cirrhosis (300%, 15,225 to 45,696). Additionally, plantar fibromatosis represents a trait for which no prior GWAS has been conducted. SNP-based heritability estimates ranged from 3% (hypertrophic skin) to 22% (Peyronie´s disease). Across the 17 fibrotic traits, 645 genome-wide significant risk variants met the conventional genome-wide significance ( P < 5 × 10 − 8 ) of which nine variants ( Supplementary Table 1 ) were identical across traits and 50 variant-pairs were in LD. Of these 645 genome-wide significant variants, 136 (21%) were novel for their given fibrotic trait (Supplementary Table 2) . Using a Bonferroni-corrected threshold ( P < 4.5 × 10 − 9 , 5 × 10 − 8 /11 traits), we identified 494 risk variants. The number of genome-wide significant loci by fibrotic traits is summarized in Fig. 2 . Traits with the most genome-wide significant risk variants were IBD, followed by Dupuytren´s contracture, CKD and carpal tunnel syndrome. The largest number of novel genome-wide significant loci was discovered for Dupuytren´s contracture, trigger finger, and IBD ( Supplementary Table 3 ). The largest effect estimate was observed for an intergenic variant, rs148475840, for Peyronie´s disease (β = 1.58, MAF = 0.2%, P = 9.1 × 10 − 9 ), and the lowest P-value was seen for an intron variant rs28971325, associated with Dupuytren´s contracture (β = 0.87, MAF = 23%, P = 1.0 × 10 − 847 ). Prioritization of candidate genes To identify candidate causal genes at each of the 645 genome-wide significant loci, we applied four complementary strategies: (1) selecting the nearest gene by genomic proximity, (2) identifying coding variants that were either lead variants or in high LD (r² > 0.7) with one, (3) assessing gene expression effects using colocalization with GTEx v8 eQTLs, and (4) using the Open Targets Variant-to-Gene (V2G) score. Genes were prioritized if they were supported by two or more lines of evidence. In total, we identified 515 genes with ≥ 2 lines of evidence, of which 30 were supported by four lines of evidence, including COL11A1 , (which encodes type XI collagen), 43 LOXL1 (with known roles in cross-linking of collagen and elastin), 44 and KIF15 (known for cross-linking and sliding along microtubules) 45 ( Supplementary Table 8 ). Of the 645 genome-wide significant risk variants, we identified 29 unique protein-altering variants, including one predicted loss-of-function variant, rs2286323 (p.Met189Thr) in MTERF4 (CADD 23.4), which associated with carpal tunnel disease 11 (Supplementary Tables 7 and 10). Using gene expression data, we found evidence for colocalization at 452 loci with at least one tissue (Supplementary Table 9) . For example, the gene COL11A1 colocalized between carpal tunnel syndrome and trigger finger, and with gene expression of COL11A1 in skeletal muscle tissue (PP4 = 0.89). The locus harbouring R3HCC1L (R3H domain and coiled-coil containing 1 like) colocalized between carpal tunnel syndrome and CKD, and with evidence of gene expression in multiple tissues including adipose, lung, and whole blood with concordant direction of effect across tissues. Several studies indicate that transcription factors are key mediators of fibrosis as activation of myofibroblasts relies on transcriptional reprogramming. 46 – 49 Among prioritized genes, we identified many transcription factors, including NKX family genes (e.g. NKX2-3 , NKX2-5 and NKX3-1 ) as regulators of organ development, IRF-family genes (e.g. IRF4 , IRF5 and IRF7 for hypertrophic skin and SSc) involved in the regulation of interferons, SMAD family genes (e.g. SMAD3 and SMAD6 for carpal tunnel syndrome and IBD) as core transcription factors in TGF-β signalling, and Zinc Finger family genes. To gain insights into the biological mechanisms of fibrosis, we performed gene set enrichment analyses. Our findings aligned with established fibrosis pathology, highlighting various developmental pathways such as cell population proliferation (gene ratio = 0.26, P = 3.41 x 10 − 5 ), epithelium development (gene ratio = 0.23, P = 1.27 x 10 − 7 ), as well as epithelial to mesenchymal transition (gene ratio = 0.08, P = 4.54 x 10 − 4 ), and response to TGF-β (gene ratio = 0.09, P = 4.25 x 10 − 4 ) for non-organ fibrotic traits. For the organ fibrotic traits, enriched pathways included both profibrotic processes such as cell population proliferation (gene ratio = 0.23, P = 9.14 x 10 − 8 ), and response to stress (gene ratio = 0.19, P = 9.82 x 10 − 6 ), as well as broader biological pathways including regulation of the immune system (gene ratio = 0.23, P = 8.59 x 10 –10 ), response to oxygen containing compound (gene ratio = 0.22, P = 8.04 x 10 − 7 ), and response to lipids (gene ratio = 0.51, P = 2.11 x 10 − 7 ) ( Supplementary Figs. 15 and 16 ). Cell type signature analyses showed enrichment of endothelial cells and mesenchymal cells, consistent with endothelial-mesenchymal transition (EndMT), and of fibroblast and fibro-adipogenic progenitor subsets. These findings highlight the shared common pathophysiologic importance of vascular and stromal cell types across fibrotic diseases ( Supplementary Figs. 17 and 18 ). Trait correlations and tissue enrichments We conducted pair-wise genetic correlation analyses across fibrotic traits and applied hierarchical clustering to the resulting correlation matrix to identify patterns of shared genetic architecture. We observed three distinct clusters of fibrotic diseases: organ fibrosis, non-organ fibrosis, and imaging-derived fibrosis ( Fig. 2 ) . Within the non-organ fibrosis traits, the strongest correlation was observed between carpal tunnel syndrome and trigger finger ( r g = 0.60, P = 1.1 × 10 − 63 ) and between plantar fibromatosis and trigger finger ( r g = 0.42, P = 1.1 × 10 − 6 ). Among organ fibrosis traits, the strongest correlation was between CKD and HF ( r g = 0.51, P = 9.5 × 10 − 87 ) and between HF and cirrhosis ( r g = 0.40, P = 1.4 × 10 − 21 ). As previously reported, imaging-derived fibrosis traits exhibited modest genetic correlations with one another. 15 Interestingly, the genetic correlation between imaging-derived renal fibrosis and CKD was weak ( r g = 0.10, P = 1.0). Likewise, imaging-derived liver fibrosis showed a weaker genetic correlation with liver cirrhosis (rg = 0.33, P = 5.1 × 10 − 5 ) than with HF (rg = 0.37, P = 2.4 × 10 − 7 ) and CKD (rg = 0.37, P = 1.5 × 10 − 6 ), despite liver cirrhosis being the end-stage manifestation of metabolic-associated steatotic liver disease (MASLD). Through MAGMA tissue enrichment analysis, we found that organ fibrotic diseases were primarily enriched in their respective tissues of disease, e.g. ileum for IBD ( P = 1.0 × 10 –16 ), as well as kidney medulla ( P = 2.9 × 10 − 3 ) and cortex ( P = 3.3 × 10 − 3 ) for CKD, and liver for liver cirrhosis ( P = 2.2 × 10 − 4 ). On the other hand, non-organ fibrotic diseases were enriched in more wide-spread tissues and cells, such as fibroblasts and arteries ( Supplementary Fig. 12 ). For instance, Dupuytren’s contracture signals were significantly enriched in fibroblasts ( P = 5.4 × 10 − 7 ) and arterial vascular compartments (artery tibial, P = 4.9 × 10 − 6 for carpal tunnel syndrome and artery aorta, P = 7.8 × 10 − 6 for Dupuytren´s contracture). Cross-trait colocalization We performed pairwise colocalization analyses across 17 fibrotic traits. Overall, we found evidence for shared causal variants for 104 cross-trait pairs (PP 4 > 0.7, Supplementary Table 5 ), spanning 64 loci. Among these, 12 loci colocalized with three or more fibrotic disease traits. Carpal tunnel syndrome had the highest number of cross-trait colocalized loci (Fig. 3 ). The disease pairs with the most shared loci were carpal tunnel syndrome and trigger finger ( n = 7, Fig. 3 ), followed by CKD and HF ( n = 6, Fig. 3 ). Given that non-organ fibrotic traits appear to reflect core fibrotic pathophysiological mechanisms, we next focused on cross-trait colocalizations that included at least one non-organ fibrotic disease. For example, the SLC39A8 locus colocalized with Dupuytren´s contracture, imaging-derived fibrosis traits (liver, pancreas, renal), HF, IBD, and liver cirrhosis ( Supplementary Table 5 ). Another example of cross-trait colocalization is at the SH2B3 locus, which overlapped with six diseases, spanning both organ and non-organ fibrotic traits (phimosis, IPF, CKD, IBD, HF, and cirrhosis). Autoimmune disease is a well-known risk driver for cardiovascular disease. 50 We identified a shared locus between SSc and HF at the gene GSDMB locus (PP 4 = 0.98). Since non-organ fibrotic traits such as Dupuytren’s contracture and carpal tunnel syndrome are far more prevalent than organ-specific fibrotic diseases like IPF, their GWASs provide greater statistical power. Leveraging these high-prevalence traits can therefore expose core fibrotic loci shared across the fibrosis spectrum that would otherwise remain undetected. For instance, at the TULP1 locus, we observed a strong colocalization between Dupuytren’s contracture ( P = 9.0 x 10 − 14 ) and IPF ( P = 4.3 x 10 − 8 , PP 4 = 0.99), suggesting a shared causal variant despite differences in clinical manifestation and prevalence. Given the well-established role of the WNT gene family in wound healing and fibrogenesis, 17,51 two loci stood out for their cross-phenotype signals. At the WNT2 locus, we found evidence for colocalization between Peyronie´s disease and Dupuytren´s contracture (PP 4 = 0.91), and at the WNT7B locus, we found evidence for colocalization between Peyronie’s disease, Dupuytren’s contracture, and plantar fibromatosis ( Supplementary Table 5 ). Across colocalized loci, 85/104 (82%) pairs had concordant direction of effect. For instance, at the SLC 39A8 locus, we found that the lead variant shared the same direction of effect across all colocalized traits, whereas the top variant at the AKR1C1/2 locus (rs138746166) associated with decreased risk of Peyronie´s disease, but increased risk of carpal tunnel syndrome. These findings support the presence of shared genetic signals influencing multiple fibrotic phenotypes, with the majority acting in the same risk direction. DISCUSSION Fibrosis contributes to a wide spectrum of chronic diseases across organ systems and remains a major driver of morbidity. In this genome-wide meta-analyses leveraging multiple genetic biobanks, we assembled the most extensive cross-trait genetic study of fibrosis to date, spanning 17 fibrotic diseases or imaging-traits. Among 645 genome-wide significant variant associations, we identified more than 100 novel associations between genetic risk variants and fibrotic diseases. Many of these discoveries came from GWAS of non-organ fibrotic diseases such as Dupuytren’s contracture, trigger finger, and Peyronie’s disease, highlighting the polygenicity of these traits. We observed that global genetic correlations of fibrotic traits tended to cluster within either non-organ or organ-specific groups. Moreover, we observed multiple shared loci across diseases, suggesting that core fibrotic mechanisms may act systemically rather than being confined to a single organ. Anchoring our cross-trait analyses in non-organ fibrotic traits offers a window into fundamental fibrotic biology that might be overlooked when focusing on diseases where fibrosis constitutes only part of the disease pathology, such as renal and cardiac end-stage organ failures. Individual fibrotic diseases have traditionally been regarded as distinct, organ-specific disease entities. However, our results reveal a substantial shared genetic architecture, linking apparently diverse fibrosis phenotypes. Pairwise genetic correlation analyses clustered traits into two main groups: a cluster of non-organ diseases (e.g. Dupuytren’s, trigger finger, carpal tunnel syndrome, plantar fibromatosis) and a second cluster of organ fibroses (e.g. HF, CKD, cirrhosis, IPF). Imaging-based fibrosis formed a smaller third cluster with only modest mutual correlations and limited correlations to their respective organ diseases. Sub-clustering into organ and non-organ fibrosis may suggest partly distinct genetic underpinnings. Beyond genome-wide correlations, we found evidence of shared genetic architecture on a locus-wide level, revealing specific genomic regions that contribute to multiple fibrotic disease. In total, we identified over 50 loci where association signals were shared between at least two fibrotic traits. Among these, carpal tunnel syndrome showed the highest number of shared loci with other traits and the most common colocalized trait-pair was carpal tunnel syndrome and trigger finger, which is a pair that has been highlighted previously. 12 For example, one of the highly pleiotropic risk loci was near SLC39A8 , which encodes the metal ion transporter ZIP8 . 52 The locus was shared across Dupuytren´s contracture, several imaging-derived fibrosis traits, HF, IBD, and liver cirrhosis. SLC39A8 has previously been implicated in a range of diseases 53 , including organ fibrosis and Crohn´s disease. 15,54 It is ubiquitously expressed and plays a critical role in transporting manganese, zinc and iron into cells. 55 Interestingly, zinc is a key cofactor for matrix metalloproteinases, which are central to ECM remodelling in fibrosis 56 and ZIP8 overexpression in mouse fibroblasts has been shown to reorganize filament actin and affect cell-cell adhesion and SLC39A8-null mouse embryos have marked ECM accumulation. 52,57 ZIP8 has been suggested as a drug-candidate for inflammatory arthritis. 58 The locus for TULP1 was shared between Dupuytren’s contracture and IPF and is a member of the tubby-like gene family pathway with known involvement in cytoskeletal protein transport and diseases such as retinal degeneration. 59,60 Apart from being associated to fibrosis in both palmar fascia and the lungs in our study it has been associated with progression of hepatitis C infection to liver fibrosis. 61 We also noted that genes within the Wnt-family were shared across fibrotic diseases, including WNT2 , WNT7B , and TCF7L2 (a primary transcriptional effector of the Wnt-pathway). 62 For instance, the WNT2 locus was shared between Peyronie’s disease and Dupuytren’s contracture, the WNT7B locus between Peyronie´s disease, Dupuytren’s contracture and plantar fibromatosis, and the TCF7L2 locus between liver cirrhosis and CKD. These findings reinforce Wnt signaling as a core driver of fibrosis across tissues. 13 17 By leveraging findings from MRI-based imaging analyses of interstitial fibrosis by Nauffal et al 15 , we found that the CAMK2D locus was shared between myocardial fibrosis and HF. CAMK2D encodes a kinase known to mediate cardiac hypertrophy and fibrosis in mice and could be a potential drug target. 63,64 Taken together, these shared loci findings provide evidence that genetic variation at specific loci may confer predisposition to fibrosis that can manifest across anatomical sites. At present, only few antifibrotic agents are approved with pirfenidone and nintedanib mainly for IPF and resmetirom for liver fibrosis in NASH. Pirfenidone and nintedanib reduce the rate of lung function decline but do not reverse disease,62,63 underscoring the need for reversing agents for IPF, while resmetirom has demonstrated improvement in liver fibrosis.64 Notably, nerandomilast, a selective PDE4B inhibitor, has in phase III trials shown efficacy in preserving lung function in IPF and is now being explored in broader fibrosing lung diseases. 68 Encouragingly, a new wave of therapies that reflect the shared biology of fibrosis across organs is emerging with targets across canonical fibrotic pathways such as TGF‑β, CTGF, Wnt, JAK-STAT, and PDGF, as well as extracellular matrix (ECM) effectors (e.g., LOX, MMP7, HSP47) and immune-metabolic regulators (e.g., PPAR, oxidative stress, interleukins, and RAAS). 69 Several genes from our analyses overlap with active drug programs, highlighting the role of genetic evidence for clinical success 70 and opportunities for drug repurposing. For example, pegozafermin, a FGF21 analogue has recently been shown to improve liver fibrosis in a phase 2b non-alcoholic steatohepatitis (NASH, now termed MASH) trial. 71 We found that the FGF21 association was shared between cirrhosis and CKD, suggesting additional potential benefit to resolve fibrosis in CKD. We also identified a variant in PDGFB , encoding platelet derived growth factor subunit B, which associated with IBD, and is currently targeted by treprostinil in a phase 3 IPF trial (NCT04905693), indicative of a potential for repurposing. 72 In Dupuytren´s contracture, we identified a variant in kinase insert domain receptor ( KDR/VEGFR2 ), targeted by the drugs sorafenib and axitinib, and found to reduce cardiac fibrosis in mice, suggesting repurposing potential for HF. 73 Likewise, we identified a variant in FGFR2 , which encodes fibroblast growth factor receptor 2, that associated with Dupuytren’s contracture. This target is inhibited by futibatinib in cholangiocarcinoma 74 , and is under investigation for other oncology indications. Targeting central regulators of fibrosis may broaden therapeutic reach. For example, rentosertib, an AI-discovered TNIK inhibitor that modulates Wnt/β-catenin signaling pathway, has been shown to have antifibrotic effects in lung, liver, and skin models. Causal inference implicates TNIK as functionally linked to genes identified in our study, including FLT1/VEGFR1 (trigger finger), KDR/VEGFR2 (Dupuytren’s contracture), SMAD-family (carpal tunnel syndrome), and NF-κB (SSc). 75,76 Together, these findings emphasize genetic backing for the development of diagnostics and therapies in fibrotic diseases. Despite the strengths of our study, including large sample sizes and a cross-trait design, there are several limitations that warrant consideration. First, although we included multiple biobanks and publicly available GWAS data, our analysis was restricted to individuals of European descent limiting applicability to diverse ancestries. As global research efforts expand, replicating and extending these findings in more diverse populations is important to capture population specific variants and ensure broader generalizability. Second, our fibrotic phenotypes were defined using either diagnostic codes from electronic health records or publicly available imaging-derived metrics, which can introduce some misclassification or heterogeneity. For example, using ICD codes for cirrhosis or HF may capture different etiologies, and MRI-based fibrosis measures (such as T1 mapping for liver fibrosis) can be affected by technical factors (e.g. iron deposition) that can introduce noise. 15,77 This could attenuate genetic associations or make the phenotypes less precise than biopsy-confirmed fibrosis, however large-scale cohorts with biopsy confirmed fibrosis are not available. Third, while we uncovered statistical co-localizations, proving the functional connection between a variant and fibrotic mechanism requires experimental follow-up. Many of the implicated genes have unknown roles in fibrosis, warranting in vitro and in vivo studies to elucidate their biological roles and downstream effects. In conclusion, our cross-trait genetic analysis emphasizes both shared (tissue-agnostic) and distinct (tissue-specific) genetic factors that influence fibrotic diseases. These findings support the concept of fibrosis as a partially systemic process, rather than a collection of isolated conditions. Furthermore, the identification of genetic associations of organ failures such as heart failure and chronic kidney disease with fibrotic components, which are also associated to core fibrotic diseases, enriches our understanding of fibrotic biology and may facilitate the development of anti-fibrotic therapeutics and the broadening of their clinical applications across multiple fibrotic diseases. Declarations Data availability Meta-analyses across 11 fibrotic diseases are available at the GWAS Catalog (https://www.ebi.ac.uk/gwas/) (GCST X). Data from internal organ fibrosis from UK Biobank MRI study is publically available through original publication (https://www.nature.com/articles/s41591-024-03010-w). Data from CTS GWAS is publically available at https://www.decode.com/summarydata/. GWAS summary statistics utilized from FinnGen biobank and Million Veterans Program are publicly accessible following registration at respective websites (FinnGen: https://www.finngen.fi/en/genetic_data, and MVP: https://www.mvp.va.gov/pwa/discover-mvp-data). GTEx v.8 eQTL data used in this study are available in the GTEx Portal (https://gtexportal.org/home/datasets). Code availability The following software and packages were used for data analysis: PLINK 2.0 (https://www.cog-genomics.org/plink/2.0/), METAL v.2011-03-25 (http://csg.sph.umich.edu/abecasis/Metal/download/), LDSC v.1.0.1 (https://github.com/bulik/ldsc), REGENIE v.2.0.1 (https://rgcgithub.github.io/regenie/), Coloc, v5.2.3 (https://cran.r-project.org/web/packages/coloc/index.html) R v.4.1.2 (https://www.r-project.org/). Funding This work was supported by Copenhagen University Hospital, Rigshospitalet (to JSB). Acknowledgements We would like to acknowledge the participants and investigators of the UK Biobank, FinnGen, Million Veterans Affair, Copenhagen Hospital Biobank, deCODE study, and the Danish Blood Donor study. We want to acknowledge the participants and investigators of the FinnGen study. Copenhagen Hospital Biobank (CHB) is supported by the Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark and by grants from Novo Nordisk Foundation (NNF23OC0082015, NNF17OC0027594) and Rigshospitalet Research Council (Framework grant). The Danish Blood Donor Study (DBDS) is funded by an annual grant from Bio- and Genome Bank Denmark. The initiation of DBDS was supported by the Danish Administrative Regions (02/2611) and the Danish Council for Independent Research (09–069412). Additionally, the DBDS is funded by the Novo Nordisk Foundation (NNF23OC0082015, NNF17OC0027864, and NNF17OC0027594). We would like to acknowledge Bjarne Kuno Møller(Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark) for his comments on the findings of the manuscript. Conflicts of interest LD has sponsored research agreements with C2i Genomics, Veracyte, Natera, AstraZeneca, Photocure, and Ferring and serves in an advisory/consulting role for Ferring, MSD, Cystotech, and UroGen. LD has received speaker honoraria from AstraZeneca, Pfizer, and Roche, as well as travel support from MSD. All others report no conflicts of interest. References Lurje, I., Gaisa, N. T., Weiskirchen, R. & Tacke, F. Mechanisms of organ fibrosis: Emerging concepts and implications for novel treatment strategies. Molecular Aspects of Medicine vol. 92 Preprint at https://doi.org/10.1016/j.mam.2023.101191 (2023). Wynn, T. A. & Ramalingam, T. R. Mechanisms of fibrosis: Therapeutic translation for fibrotic disease. Nature Medicine vol. 18 Preprint at https://doi.org/10.1038/nm.2807 (2012). 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Nat Biotechnol 43 , (2025). Seidelin, A.-S., Nordestgaard, B. G., Tybjærg-Hansen, A. & Stender, S. Does SLC39A8 Ala391Thr Confer Risk of Chronic Liver Disease? Antioxid Redox Signal 41 , 591–596 (2024). Additional Declarations Yes there is potential Competing Interest. LD has sponsored research agreements with C2i Genomics, Veracyte, Natera, AstraZeneca, Photocure, and Ferring and serves in an advisory/consulting role for Ferring, MSD, Cystotech, and UroGen. LD has received speaker honoraria from AstraZeneca, Pfizer, and Roche, as well as travel support from MSD. All others report no conflicts of interest. Supplementary Files Fibrogeneticssupplementaryfinal.xlsx Supplementary tables SupplementaryMaterialsfinal2.docx Supplementary Material Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Stage 2 was the meta-analyses of available cohorts with genetic information. Stage 3 was colocalization analyses across associations found in stage 2. Following studies were included in the various phenotype GWAS; Peyronie’s disease (PD - CHB, DBDS, MVP), Phimosis (Phi - CHB, DBDS, FinnGen, MVP), Hypertrophic skin (Skin - CHB, DBDS, FinnGen, MVP), Carpal tunnel syndrome (CTS - Skuladottir et al), Trigger finger (TF - CHB, DBDS, FinnGen, Patel et al), Plantar fibromatosis (PF - CHB, DBDS, FinnGen), Dupuytren’s contracture (DC - CHB, DBDS, Riesmeijer et al, FinnGen, MVP), Idiopathic pulmonary fibrosis (IPF - CHB, DBDS, Partanen et al, MVP), Cirrhosis (Cirr - Ghouse et al, MVP), Pancreas fibrosis (PCF - Nauffal et al), Liver fibrosis (LF - Nauffal et al), Myocardial fibrosis (MF - Nauffal et al), Renal fibrosis (RF - Nauffal et al). Other abbreviations: CHB = Copenhagen Hospital Biobank, DBDS = The Danish Blood Donor Study, MVP = Million Veterans Program.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8156241/v1/cb588b8bbcf26147f7c14c17.png"},{"id":99317277,"identity":"d05a3ced-1158-496b-bb66-e906594167ca","added_by":"auto","created_at":"2025-12-31 16:29:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":215970,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic correlation clustering across fibrotic diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e Heatmap showing genetic correlations (one asterisk (*) denotes \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 and two (**) denotes Bonferroni corrected (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.03, 0.05/17) between fibrotic diseases. Cirrhosis, Dupuytren´s contracture, Idiopathic pulmonary fibrosis, Peyronie´s disease, phimosis, plantar fibromatosis, skin fibrosis, and trigger finger were derived from meta-analyses, whereas liver, myocardial, pancreas, and renal fibrosis are from publication by Nauffal et al. Red or blue color indicate a higher or lower genetic correlation, respectively. \u003cstrong\u003eb)\u003c/strong\u003e Barplot showing number of known and novel loci per trait.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8156241/v1/73a7d0c51f6200b0d001c729.png"},{"id":99191581,"identity":"7478262b-c3b5-49a9-a847-fed56eb8d003","added_by":"auto","created_at":"2025-12-30 00:56:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eColocalizations across fibrotic traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea) Upset plot illustrating number of colocalizations across 17 fibrotic diseases grouped by prototypical fibrotic disease, organ diseases with fibrotic component, and imaging-derived fibrotic traits. b) Examples of trait-trait colocalization plots for organ/non-organ traits.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8156241/v1/24a60ae0a530f2ed0770c198.png"},{"id":99323489,"identity":"bb5d5359-2c51-415b-8e7e-333748afda97","added_by":"auto","created_at":"2025-12-31 16:45:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1693463,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8156241/v1/3de4da9a-ae36-4428-97c7-39895d6bbbef.pdf"},{"id":99191582,"identity":"37b3a93e-8901-40c3-9ec5-bd36b186c594","added_by":"auto","created_at":"2025-12-30 00:56:12","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3003860,"visible":true,"origin":"","legend":"Supplementary tables","description":"","filename":"Fibrogeneticssupplementaryfinal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8156241/v1/de8d158734a0953165143e02.xlsx"},{"id":99191584,"identity":"6d6f58c0-05b0-40d2-bc8d-af680791acd6","added_by":"auto","created_at":"2025-12-30 00:56:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29642293,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"SupplementaryMaterialsfinal2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8156241/v1/d4e80fd7bc340fbd62401f10.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nLD has sponsored research agreements with C2i Genomics, Veracyte, Natera, AstraZeneca, Photocure, and Ferring and serves in an advisory/consulting role for Ferring, MSD, Cystotech, and UroGen. LD has received speaker honoraria from AstraZeneca, Pfizer, and Roche, as well as travel support from MSD.\r\nAll others report no conflicts of interest.","formattedTitle":"Shared genetic architecture across fibrotic diseases","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eFibrosis is a complex pathological process characterized by the accumulation of excess extracellular matrix proteins. These changes can progressively disrupt normal tissue structure and function with the potential to affect virtually any anatomical site.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e This aberrant remodeling underlies a diverse spectrum of fibrotic diseases, including Dupuytren\u0026rsquo;s contracture, Peyronie\u0026rsquo;s disease, and idiopathic pulmonary fibrosis (IPF). Fibrosis is also a characteristic feature of end-stage disease across several organ systems, including in heart failure (HF), liver cirrhosis, and chronic kidney disease (CKD) with varying degrees of fibrosis.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Fibrotic diseases collectively affect one in five individuals, they are estimated to contribute to nearly one-third of deaths worldwide, and have a very limited number of marketed pharmacological therapies.\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) for fibrotic traits have delineated hundreds of susceptibility loci.\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e While fibrotic manifestations are traditionally viewed as anatomically confined and genetically specific, emerging genetic and epidemiologic evidence suggests the presence of shared genetic risk.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Genetic correlations have been reported between Dupuytren\u0026acute;s contracture and Peyronie\u0026acute;s disease\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and between trigger finger and carpal tunnel syndrome.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e In the latter pair, a colocalized signal at \u003cem\u003eDIRC3\u003c/em\u003e (a regulator of the fibrosis-implicated gene \u003cem\u003eIGFBP5\u003c/em\u003e) has been highlighted. Observational data further substantiates these findings with frequent co-occurrences reported, for example, between Dupuytren\u0026acute;s contracture and plantar fibromatosis.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e The presence of histologically similar fibrotic changes across distinct anatomical locations underscores the likelihood of common molecular and pathophysiological pathways underlying fibrogenesis.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThese data raise two fundamental questions. First, is the genetic predisposition to fibrotic disease primary site-specific, or does it reflect a systemic susceptibility to fibrosis? Second, if there is shared genetic architecture underlying fibrotic traits, which genes and molecular pathways converge in the pathophysiology? Integrating genetic data across anatomically distinct conditions, including prototypical fibrotic diseases as well as end-stage organ failure characterized by fibrotic components, may identify susceptibility loci of shared fibrotic pathophysiology. In the present study, we address these questions by performing comprehensive GWAS-analyses of 17 fibrotic diseases to systematically evaluate the global and locus-wide genetic overlap across fibrotic diseases.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTrait selection and study cohorts\u003c/h2\u003e \u003cp\u003eWe included 17 fibrotic traits, comprising nine prototypical fibrotic diseases, four organ-diseases with a fibrotic component, and four imaging-derived fibrotic traits (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Prototypical fibrotic diseases were selected based on a comprehensive literature review guided by the following criteria: (i) prior evidence supporting the condition as a prototypical fibrotic disease, (ii) availability of robust and standardized diagnostic definitions, and (iii) sufficient prevalence to enable adequately powered GWAS. Based on these criteria, we included carpal tunnel syndrome, Dupuytren\u0026rsquo;s contracture, trigger finger, Peyronie\u0026rsquo;s disease, phimosis, plantar fibromatosis, hypertrophic skin, liver cirrhosis, and IPF. In addition, we included conditions with a known fibrotic component, though not exclusively defined by fibrosis, such as HF, CKD, inflammatory bowel disease (IBD), and systemic sclerosis (SSc). Finally, we included imaging-derived fibrotic traits obtained from magnetic resonance imaging (MRI) T1 mapping of the liver, kidneys, myocardium, and pancreas, available for 43,881 individuals in the UK Biobank (UKB).\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenotyping, quality control and meta-analyses\u003c/h3\u003e\n\u003cp\u003eWe performed GWASs of 11 diseases (excluding the imaging-derived phenotypes, carpal tunnel syndrome and HF) using individual-level data from the Copenhagen Hospital Biobank (CHB),\u003csup\u003e25\u003c/sup\u003e the Danish Blood Donor Study (DBDS),\u003csup\u003e26\u003c/sup\u003e and the UKB.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Cases were defined by International Classification of Diseases (ICD)-10 diagnostic codes, and controls comprised individuals without such registration. Detailed information on cohort descriptions, ICD-10 codes, quality control, and datasets contributing to each meta-analysis are provided in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. Each GWAS conducted in CHB, DBDS and UKB were subsequently meta-analyzed with, when available, European ancestry GWAS summary statistics from FinnGen freeze 12\u003csup\u003e28\u003c/sup\u003e and the Million Veteran Program (MVP)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and the largest non-overlapping GWAS for each disease.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Prior to meta-analyses, standard quality control (QC) filters were applied: Variants were excluded if imputation quality score (INFO) was \u0026lt;\u0026thinsp;0.7, an absolute β\u0026thinsp;\u0026gt;\u0026thinsp;5, a standard error\u0026thinsp;\u0026gt;\u0026thinsp;10, or a minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Meta-analyses were performed for 11 of the 17 traits for European ancestry using the fixed-effects inverse-variance-weighted method implemented in METAL.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Only variants present in at least two studies were included in the meta-analyses.\u003c/p\u003e\n\u003ch3\u003eRisk locus definition\u003c/h3\u003e\n\u003cp\u003eFor each trait, independent genetic associations were identified using PLINK\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e employing clumping with a\u0026thinsp;\u0026plusmn;\u0026thinsp;500 kb window and a linkage disequilibrium (LD) threshold of \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.2. Genome-wide significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e. Within each clump, the variant with the lowest P-value was defined as the lead variant, and the corresponding genomic locus per trait was defined as \u0026plusmn;\u0026thinsp;500 kb around the lead variant. To classify lead variants as novel or known, we queried NHGRI-EBI GWAS Catalogue\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and reviewed the most recent trait-specific GWAS publications. We compared each lead variant with those previously reported and considered a lead variant as known if the lead variant was in high LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7) with a previously reported variant in a\u0026thinsp;\u0026plusmn;\u0026thinsp;500kb window around the lead variant.\u003c/p\u003e\n\u003ch3\u003eHeritability, genetic inflation, and correlation\u003c/h3\u003e\n\u003cp\u003eWe used Linkage Disequilibrium Score Regression (LDSC) to estimate genomic inflation (lambda, LDSC-intercept, and standard error), SNP-based heritability on the liability scale, and quantify pairwise genetic correlations across fibrotic diseases.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Analyses were restricted to high-quality, well-imputed variants included in the HapMap3 CEU reference panel. Precomputed LD scores based on European-ancestry populations from the 1000 Genomes Project were used as the reference. Genomic inflation was defined as an LDSC intercept\u0026thinsp;\u0026gt;\u0026thinsp;1.10 and corrected for by multiplying GWAS standard errors by the square root of the intercept.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Pairwise genetic correlations were clustered using agglomerative hierarchical clustering and heat-map visualization to identify groups of traits with shared genetic architecture using Ward\u0026acute;s method.\u003c/p\u003e\n\u003ch3\u003eCross-trait colocalization\u003c/h3\u003e\n\u003cp\u003eTo test whether fibrotic traits shared causal variants, we performed pairwise cross-trait colocalization analyses using the R coloc package.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e We defined loci as \u0026plusmn;\u0026thinsp;500 kb windows around each lead variant. We allowed for colocalization analyses between pairs, in the case where one locus was genome-wide significant for one trait and was still strongly associated with a second trait below genome-wide significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). A posterior probability of a common causal variant (PP\u003csub\u003e4\u003c/sub\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;0.7 in at least one pair in a locus was considered as evidence of colocalization. Loci meeting this threshold were visually inspected using colocalization plots.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene prioritization\u003c/h2\u003e \u003cp\u003eTo prioritize genes underlying fibrotic risk loci, we integrated four complementary lines of evidence. First, we annotated each lead variant to its nearest gene based on genomic proximity. Second, we mapped lead variants or variants in high LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7) that were predicted to affect the coding of a protein using Variant Effect Predictor.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Third, we evaluated the effects of lead variants on gene expression using expression quantitative trait locus (eQTL) data from tissues profiled in GTEx v8.\u003csup\u003e40\u003c/sup\u003e Trait-eQTL colocalization analyses were performed, with evidence for colocalization defined as genes with PP\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.70. Fourth, we utilized findings from the Open Targets Variant-to-Gene (V2G) algorithm, which provides weighted scores based on multiple layers of evidence (e.g. QTL, interaction with promoter capture Hi-C and in silico functional prediction), provided by Open Targets Genetics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genetics.opentargets.org/\u003c/span\u003e\u003cspan address=\"https://genetics.opentargets.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e41\u003c/sup\u003e and used the gene assigned the highest score. Genes supported by at least two lines of evidence and the highest overall support (i.e. the greatest number of evidence lines) were deemed putative effector genes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTissue and pathway enrichment\u003c/h3\u003e\n\u003cp\u003eTissue-specific expression enrichment was conducted using MAGMA,\u003csup\u003e42\u003c/sup\u003e as per fibrosis trait-specific full summary statistics in 49 GTEx tissues using European-ancestry populations from the 1000 Genomes Project as reference.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Statistical significance was evaluated with average tissue correction. Gene-based pathway enrichment was performed using MAGMA from MsigDB and assessed by clustering.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide meta-analyses\u003c/h2\u003e \u003cp\u003eWe performed GWAS meta-analyses for 11 fibrotic traits using data from CHB, DBDS, UKB, FinnGen, MVP, and other publicly available studies with non-overlapping samples. A summary of the 11 meta-analyses is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, including case counts and specific studies contributing to each analysis. Case sample sizes ranged from 4,559 for Peyronie\u0026rsquo;s disease to 126,358 for CKD. The largest relative increases in sample size compared to previous studies were observed for Dupuytren\u0026acute;s contracture (177%, 11,320 to 31,369) and liver cirrhosis (300%, 15,225 to 45,696). Additionally, plantar fibromatosis represents a trait for which no prior GWAS has been conducted. SNP-based heritability estimates ranged from 3% (hypertrophic skin) to 22% (Peyronie\u0026acute;s disease). Across the 17 fibrotic traits, 645 genome-wide significant risk variants met the conventional genome-wide significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) of which nine variants (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e) were identical across traits and 50 variant-pairs were in LD. Of these 645 genome-wide significant variants, 136 (21%) were novel for their given fibrotic trait \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Using a Bonferroni-corrected threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;4.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e, 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e/11 traits), we identified 494 risk variants. The number of genome-wide significant loci by fibrotic traits is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Traits with the most genome-wide significant risk variants were IBD, followed by Dupuytren\u0026acute;s contracture, CKD and carpal tunnel syndrome. The largest number of novel genome-wide significant loci was discovered for Dupuytren\u0026acute;s contracture, trigger finger, and IBD (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). The largest effect estimate was observed for an intergenic variant, rs148475840, for Peyronie\u0026acute;s disease (β\u0026thinsp;=\u0026thinsp;1.58, MAF\u0026thinsp;=\u0026thinsp;0.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e), and the lowest P-value was seen for an intron variant rs28971325, associated with Dupuytren\u0026acute;s contracture (β\u0026thinsp;=\u0026thinsp;0.87, MAF\u0026thinsp;=\u0026thinsp;23%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;847\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrioritization of candidate genes\u003c/h2\u003e \u003cp\u003eTo identify candidate causal genes at each of the 645 genome-wide significant loci, we applied four complementary strategies: (1) selecting the nearest gene by genomic proximity, (2) identifying coding variants that were either lead variants or in high LD (r\u0026sup2; \u0026gt; 0.7) with one, (3) assessing gene expression effects using colocalization with GTEx v8 eQTLs, and (4) using the Open Targets Variant-to-Gene (V2G) score. Genes were prioritized if they were supported by two or more lines of evidence. In total, we identified 515 genes with \u0026ge;\u0026thinsp;2 lines of evidence, of which 30 were supported by four lines of evidence, including \u003cem\u003eCOL11A1\u003c/em\u003e, (which encodes type XI collagen),\u003csup\u003e43\u003c/sup\u003e \u003cem\u003eLOXL1\u003c/em\u003e (with known roles in cross-linking of collagen and elastin),\u003csup\u003e44\u003c/sup\u003e and \u003cem\u003eKIF15\u003c/em\u003e (known for cross-linking and sliding along microtubules)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eOf the 645 genome-wide significant risk variants, we identified 29 unique protein-altering variants, including one predicted loss-of-function variant, rs2286323 (p.Met189Thr) in \u003cem\u003eMTERF4\u003c/em\u003e (CADD 23.4), which associated with carpal tunnel disease\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e \u003cb\u003e(Supplementary Tables\u0026nbsp;7 and 10).\u003c/b\u003e Using gene expression data, we found evidence for colocalization at 452 loci with at least one tissue \u003cb\u003e(Supplementary Table\u0026nbsp;9)\u003c/b\u003e. For example, the gene \u003cem\u003eCOL11A1\u003c/em\u003e colocalized between carpal tunnel syndrome and trigger finger, and with gene expression of \u003cem\u003eCOL11A1\u003c/em\u003e in skeletal muscle tissue (PP4\u0026thinsp;=\u0026thinsp;0.89). The locus harbouring \u003cem\u003eR3HCC1L\u003c/em\u003e (R3H domain and coiled-coil containing 1 like) colocalized between carpal tunnel syndrome and CKD, and with evidence of gene expression in multiple tissues including adipose, lung, and whole blood with concordant direction of effect across tissues.\u003c/p\u003e \u003cp\u003eSeveral studies indicate that transcription factors are key mediators of fibrosis as activation of myofibroblasts relies on transcriptional reprogramming.\u003csup\u003e\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e Among prioritized genes, we identified many transcription factors, including NKX family genes (e.g. \u003cem\u003eNKX2-3\u003c/em\u003e, \u003cem\u003eNKX2-5\u003c/em\u003e and \u003cem\u003eNKX3-1\u003c/em\u003e) as regulators of organ development, IRF-family genes (e.g. \u003cem\u003eIRF4\u003c/em\u003e, \u003cem\u003eIRF5\u003c/em\u003e and \u003cem\u003eIRF7\u003c/em\u003e for hypertrophic skin and SSc) involved in the regulation of interferons, SMAD family genes (e.g. \u003cem\u003eSMAD3\u003c/em\u003e and \u003cem\u003eSMAD6\u003c/em\u003e for carpal tunnel syndrome and IBD) as core transcription factors in TGF-β signalling, and Zinc Finger family genes.\u003c/p\u003e \u003cp\u003eTo gain insights into the biological mechanisms of fibrosis, we performed gene set enrichment analyses. Our findings aligned with established fibrosis pathology, highlighting various developmental pathways such as cell population proliferation (gene ratio\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.41 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), epithelium development (gene ratio\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e), as well as epithelial to mesenchymal transition (gene ratio\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.54 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and response to TGF-β (gene ratio\u0026thinsp;=\u0026thinsp;0.09, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.25 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) for non-organ fibrotic traits. For the organ fibrotic traits, enriched pathways included both profibrotic processes such as cell population proliferation (gene ratio\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.14 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), and response to stress (gene ratio\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.82 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), as well as broader biological pathways including regulation of the immune system (gene ratio\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.59 x 10\u003csup\u003e\u0026ndash;10\u003c/sup\u003e), response to oxygen containing compound (gene ratio\u0026thinsp;=\u0026thinsp;0.22, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.04 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e), and response to lipids (gene ratio\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.11 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) (\u003cb\u003eSupplementary Figs.\u0026nbsp;15 and 16\u003c/b\u003e). Cell type signature analyses showed enrichment of endothelial cells and mesenchymal cells, consistent with endothelial-mesenchymal transition (EndMT), and of fibroblast and fibro-adipogenic progenitor subsets. These findings highlight the shared common pathophysiologic importance of vascular and stromal cell types across fibrotic diseases (\u003cb\u003eSupplementary Figs.\u0026nbsp;17 and 18\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTrait correlations and tissue enrichments\u003c/h2\u003e \u003cp\u003eWe conducted pair-wise genetic correlation analyses across fibrotic traits and applied hierarchical clustering to the resulting correlation matrix to identify patterns of shared genetic architecture. We observed three distinct clusters of fibrotic diseases: organ fibrosis, non-organ fibrosis, and imaging-derived fibrosis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Within the non-organ fibrosis traits, the strongest correlation was observed between carpal tunnel syndrome and trigger finger (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e = 0.60, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;63\u003c/sup\u003e) and between plantar fibromatosis and trigger finger (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e = 0.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). Among organ fibrosis traits, the strongest correlation was between CKD and HF (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e = 0.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;87\u003c/sup\u003e) and between HF and cirrhosis (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e = 0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;21\u003c/sup\u003e). As previously reported, imaging-derived fibrosis traits exhibited modest genetic correlations with one another.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Interestingly, the genetic correlation between imaging-derived renal fibrosis and CKD was weak (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e = 0.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0). Likewise, imaging-derived liver fibrosis showed a weaker genetic correlation with liver cirrhosis (rg\u0026thinsp;=\u0026thinsp;0.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) than with HF (rg\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) and CKD (rg\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), despite liver cirrhosis being the end-stage manifestation of metabolic-associated steatotic liver disease (MASLD).\u003c/p\u003e \u003cp\u003eThrough MAGMA tissue enrichment analysis, we found that organ fibrotic diseases were primarily enriched in their respective tissues of disease, e.g. ileum for IBD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0 \u0026times; 10\u003csup\u003e\u0026ndash;16\u003c/sup\u003e), as well as kidney medulla (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and cortex (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) for CKD, and liver for liver cirrhosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). On the other hand, non-organ fibrotic diseases were enriched in more wide-spread tissues and cells, such as fibroblasts and arteries (\u003cb\u003eSupplementary Fig.\u0026nbsp;12\u003c/b\u003e). For instance, Dupuytren\u0026rsquo;s contracture signals were significantly enriched in fibroblasts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) and arterial vascular compartments (artery tibial, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e for carpal tunnel syndrome and artery aorta, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.8 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e for Dupuytren\u0026acute;s contracture).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCross-trait colocalization\u003c/h2\u003e \u003cp\u003eWe performed pairwise colocalization analyses across 17 fibrotic traits. Overall, we found evidence for shared causal variants for 104 cross-trait pairs (PP\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7, \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e), spanning 64 loci. Among these, 12 loci colocalized with three or more fibrotic disease traits. Carpal tunnel syndrome had the highest number of cross-trait colocalized loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The disease pairs with the most shared loci were carpal tunnel syndrome and trigger finger (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), followed by CKD and HF (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Given that non-organ fibrotic traits appear to reflect core fibrotic pathophysiological mechanisms, we next focused on cross-trait colocalizations that included at least one non-organ fibrotic disease. For example, the \u003cem\u003eSLC39A8\u003c/em\u003e locus colocalized with Dupuytren\u0026acute;s contracture, imaging-derived fibrosis traits (liver, pancreas, renal), HF, IBD, and liver cirrhosis (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). Another example of cross-trait colocalization is at the \u003cem\u003eSH2B3\u003c/em\u003e locus, which overlapped with six diseases, spanning both organ and non-organ fibrotic traits (phimosis, IPF, CKD, IBD, HF, and cirrhosis). Autoimmune disease is a well-known risk driver for cardiovascular disease.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e We identified a shared locus between SSc and HF at the gene \u003cem\u003eGSDMB\u003c/em\u003e locus (PP\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.98). Since non-organ fibrotic traits such as Dupuytren\u0026rsquo;s contracture and carpal tunnel syndrome are far more prevalent than organ-specific fibrotic diseases like IPF, their GWASs provide greater statistical power. Leveraging these high-prevalence traits can therefore expose core fibrotic loci shared across the fibrosis spectrum that would otherwise remain undetected. For instance, at the \u003cem\u003eTULP1\u003c/em\u003e locus, we observed a strong colocalization between Dupuytren\u0026rsquo;s contracture (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e) and IPF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.3 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, PP\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.99), suggesting a shared causal variant despite differences in clinical manifestation and prevalence. Given the well-established role of the WNT gene family in wound healing and fibrogenesis,\u003csup\u003e17,51\u003c/sup\u003e two loci stood out for their cross-phenotype signals. At the \u003cem\u003eWNT2\u003c/em\u003e locus, we found evidence for colocalization between Peyronie\u0026acute;s disease and Dupuytren\u0026acute;s contracture (PP\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.91), and at the \u003cem\u003eWNT7B\u003c/em\u003e locus, we found evidence for colocalization between Peyronie\u0026rsquo;s disease, Dupuytren\u0026rsquo;s contracture, and plantar fibromatosis (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). Across colocalized loci, 85/104 (82%) pairs had concordant direction of effect. For instance, at the \u003cem\u003eSLC\u003c/em\u003e39A8 locus, we found that the lead variant shared the same direction of effect across all colocalized traits, whereas the top variant at the AKR1C1/2 locus (rs138746166) associated with decreased risk of Peyronie\u0026acute;s disease, but increased risk of carpal tunnel syndrome. These findings support the presence of shared genetic signals influencing multiple fibrotic phenotypes, with the majority acting in the same risk direction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eFibrosis contributes to a wide spectrum of chronic diseases across organ systems and remains a major driver of morbidity. In this genome-wide meta-analyses leveraging multiple genetic biobanks, we assembled the most extensive cross-trait genetic study of fibrosis to date, spanning 17 fibrotic diseases or imaging-traits. Among 645 genome-wide significant variant associations, we identified more than 100 novel associations between genetic risk variants and fibrotic diseases. Many of these discoveries came from GWAS of non-organ fibrotic diseases such as Dupuytren’s contracture, trigger finger, and Peyronie’s disease, highlighting the polygenicity of these traits. We observed that global genetic correlations of fibrotic traits tended to cluster within either non-organ or organ-specific groups. Moreover, we observed multiple shared loci across diseases, suggesting that core fibrotic mechanisms may act systemically rather than being confined to a single organ. \u0026nbsp;Anchoring our cross-trait analyses in non-organ fibrotic traits offers a window into fundamental fibrotic biology that might be overlooked when focusing on diseases where fibrosis constitutes only part of the disease pathology, such as renal and cardiac end-stage organ failures.\u003c/p\u003e\n\u003cp\u003eIndividual fibrotic diseases have traditionally been regarded as distinct, organ-specific disease entities. However, our results reveal a substantial shared genetic architecture, linking apparently diverse fibrosis phenotypes. Pairwise genetic correlation analyses clustered traits into two main groups: a cluster of non-organ diseases (e.g. Dupuytren’s, trigger finger, carpal tunnel syndrome, plantar fibromatosis) and a second cluster of organ fibroses (e.g. HF, CKD, cirrhosis, IPF). Imaging-based fibrosis formed a smaller third cluster with only modest mutual correlations and limited correlations to their respective organ diseases. Sub-clustering into organ and non-organ fibrosis may suggest partly distinct genetic underpinnings.\u003c/p\u003e\n\u003cp\u003eBeyond genome-wide correlations, we found evidence of shared genetic architecture on a locus-wide level, revealing specific genomic regions that contribute to multiple fibrotic disease. In total, we identified over 50 loci where association signals were shared between at least two fibrotic traits. Among these, carpal tunnel syndrome showed the highest number of shared loci with other traits and the most common colocalized trait-pair was carpal tunnel syndrome and trigger finger, which is a pair that has been highlighted previously.\u003csup\u003e12\u003c/sup\u003e For example, one of the highly pleiotropic risk loci was near \u003cem\u003eSLC39A8\u003c/em\u003e, which encodes the metal ion transporter \u003cem\u003eZIP8\u003c/em\u003e.\u003csup\u003e52\u003c/sup\u003e The locus was shared across Dupuytren´s contracture, several imaging-derived fibrosis traits, HF, IBD, and liver cirrhosis. \u003cem\u003eSLC39A8\u003c/em\u003e has previously been implicated in a range of diseases\u003csup\u003e53\u003c/sup\u003e, including organ fibrosis and Crohn´s disease.\u003csup\u003e15,54\u003c/sup\u003e It is ubiquitously expressed and plays a critical role in transporting manganese, zinc and iron into cells.\u003csup\u003e55\u003c/sup\u003e Interestingly, zinc is a key cofactor for matrix metalloproteinases, which are central to ECM remodelling in fibrosis\u003csup\u003e56\u003c/sup\u003e and ZIP8 overexpression in mouse fibroblasts has been shown to reorganize filament actin and affect cell-cell adhesion and SLC39A8-null mouse embryos have marked ECM accumulation.\u003csup\u003e52,57\u003c/sup\u003e ZIP8 has been suggested as a drug-candidate for inflammatory arthritis.\u003csup\u003e58\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe locus for \u003cem\u003eTULP1\u003c/em\u003e was shared between Dupuytren’s contracture and IPF and is a member of the tubby-like gene family pathway with known involvement in cytoskeletal protein transport and diseases such as retinal degeneration.\u003csup\u003e59,60\u003c/sup\u003e Apart from being associated to fibrosis in both palmar fascia and the lungs in our study it has been associated with progression of hepatitis C infection to liver fibrosis.\u003csup\u003e61\u003c/sup\u003e We also noted that genes within the Wnt-family were shared across fibrotic diseases, including \u003cem\u003eWNT2\u003c/em\u003e, \u003cem\u003eWNT7B\u003c/em\u003e, and \u003cem\u003eTCF7L2\u003c/em\u003e (a primary transcriptional effector of the Wnt-pathway).\u003csup\u003e62\u003c/sup\u003e For instance, the \u003cem\u003eWNT2\u0026nbsp;\u003c/em\u003elocus was shared between Peyronie’s disease and Dupuytren’s contracture, the \u003cem\u003eWNT7B\u003c/em\u003e locus between Peyronie´s disease, Dupuytren’s contracture and plantar fibromatosis, and the \u003cem\u003eTCF7L2\u003c/em\u003e locus between liver cirrhosis and CKD. These findings reinforce Wnt signaling as a core driver of fibrosis across tissues.\u003csup\u003e13 17\u003c/sup\u003e By leveraging findings from MRI-based imaging analyses of interstitial fibrosis by Nauffal et al\u003csup\u003e15\u003c/sup\u003e, we found that the \u003cem\u003eCAMK2D\u003c/em\u003e locus was shared between myocardial fibrosis and HF. \u003cem\u003eCAMK2D\u003c/em\u003e encodes a kinase known to mediate cardiac hypertrophy and fibrosis in mice and could be a potential drug target.\u003csup\u003e63,64\u003c/sup\u003e Taken together, these shared loci findings provide evidence that genetic variation at specific loci may confer predisposition to fibrosis that can manifest across anatomical sites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt present, only few antifibrotic agents are approved with pirfenidone and nintedanib mainly for IPF and resmetirom for liver fibrosis in NASH. Pirfenidone and nintedanib reduce the rate of lung function decline but do not reverse disease,62,63 underscoring the need for reversing agents for IPF, while resmetirom has demonstrated improvement in liver fibrosis.64 Notably, nerandomilast, a selective PDE4B inhibitor, has in phase III trials shown efficacy in preserving lung function in IPF and is now being explored in broader fibrosing lung diseases.\u003csup\u003e68\u003c/sup\u003e Encouragingly, a new wave of therapies that reflect the shared biology of fibrosis across organs is emerging with targets across canonical fibrotic pathways such as TGF‑β, CTGF, Wnt, JAK-STAT, and PDGF, as well as extracellular matrix (ECM) effectors (e.g., LOX, MMP7, HSP47) and immune-metabolic regulators (e.g., PPAR, oxidative stress, interleukins, and RAAS).\u003csup\u003e69\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSeveral genes from our analyses overlap with active drug programs, highlighting the role of genetic evidence for clinical success\u003csup\u003e70\u003c/sup\u003e and opportunities for drug repurposing. For example, pegozafermin, a FGF21 analogue has recently been shown to improve liver fibrosis in a phase 2b non-alcoholic steatohepatitis (NASH, now termed MASH) trial.\u003csup\u003e71\u003c/sup\u003eWe found that the \u003cem\u003eFGF21\u003c/em\u003e association was shared\u0026nbsp;between cirrhosis and CKD, suggesting additional potential benefit to resolve fibrosis in CKD. We also identified a variant in\u0026nbsp;\u003cem\u003ePDGFB\u003c/em\u003e, encoding\u0026nbsp;platelet derived growth factor subunit B, which associated with\u0026nbsp;\u0026nbsp;IBD, and is currently targeted by\u0026nbsp;treprostinil in a phase 3 IPF trial (NCT04905693), indicative of a potential for repurposing.\u003csup\u003e72\u003c/sup\u003e In Dupuytren´s contracture, we identified a variant in kinase insert domain receptor (\u003cem\u003eKDR/VEGFR2\u003c/em\u003e), targeted by the drugs sorafenib and axitinib, and found to reduce cardiac fibrosis in mice, suggesting repurposing potential for HF.\u003csup\u003e73\u003c/sup\u003e Likewise, we identified a variant in\u003cem\u003e\u0026nbsp;FGFR2\u003c/em\u003e, which encodes fibroblast growth factor receptor 2, that associated with Dupuytren’s contracture. This target is inhibited by futibatinib in cholangiocarcinoma\u003csup\u003e74\u003c/sup\u003e, and is under investigation for other oncology indications. Targeting central regulators of fibrosis may broaden therapeutic reach. For example, rentosertib, an AI-discovered TNIK inhibitor that modulates Wnt/β-catenin signaling pathway, has been shown to have antifibrotic effects in lung, liver, and skin models. Causal inference implicates TNIK as functionally linked to genes identified in our study, including FLT1/VEGFR1 (trigger finger), KDR/VEGFR2 (Dupuytren’s contracture), SMAD-family (carpal tunnel syndrome), and NF-κB (SSc).\u003csup\u003e75,76\u003c/sup\u003e Together, these findings emphasize genetic backing for the development of diagnostics and therapies in fibrotic diseases.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Despite the strengths of our study, including large sample sizes and a cross-trait design, there are several limitations that warrant consideration. First, although we included multiple biobanks and publicly available GWAS data, our analysis was restricted to individuals of European descent limiting applicability to diverse ancestries. As global research efforts expand, replicating and extending these findings in more diverse populations is important to capture population specific variants and ensure broader generalizability. Second, our fibrotic phenotypes were defined using either diagnostic codes from electronic health records or publicly available imaging-derived metrics, which can introduce some misclassification or heterogeneity. For example, using ICD codes for cirrhosis or HF may capture different etiologies, and MRI-based fibrosis measures (such as T1 mapping for liver fibrosis) can be affected by technical factors (e.g. iron deposition) that can introduce noise.\u003csup\u003e15,77\u003c/sup\u003e This could attenuate genetic associations or make the phenotypes less precise than biopsy-confirmed fibrosis, however large-scale cohorts with biopsy confirmed fibrosis are not available. Third, while we uncovered statistical co-localizations, proving the functional connection between a variant and fibrotic mechanism requires experimental follow-up. Many of the implicated genes have unknown roles in fibrosis, warranting \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003eand \u003cem\u003ein vivo\u003c/em\u003e studies to elucidate their biological roles and downstream effects.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our cross-trait genetic analysis emphasizes both shared (tissue-agnostic) and distinct (tissue-specific) genetic factors that influence fibrotic diseases. These findings support the concept of fibrosis as a partially systemic process, rather than a collection of isolated conditions. Furthermore, the identification of genetic associations of organ failures such as heart failure and chronic kidney disease with fibrotic components, which are also associated to core fibrotic diseases, enriches our understanding of fibrotic biology and may facilitate the development of anti-fibrotic therapeutics and the broadening of their clinical applications across multiple fibrotic diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeta-analyses across 11 fibrotic diseases are available at the GWAS Catalog (https://www.ebi.ac.uk/gwas/) (GCST X). Data from internal organ fibrosis from UK Biobank MRI study is publically available through original publication (https://www.nature.com/articles/s41591-024-03010-w). Data from CTS GWAS is publically available at https://www.decode.com/summarydata/. GWAS summary statistics utilized from FinnGen biobank and Million Veterans Program are publicly accessible following registration at respective websites (FinnGen: https://www.finngen.fi/en/genetic_data, and MVP: https://www.mvp.va.gov/pwa/discover-mvp-data). GTEx v.8 eQTL data used in this study are available in the GTEx Portal (https://gtexportal.org/home/datasets).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following software and packages were used for data analysis: \u003c/p\u003e\n\u003cp\u003ePLINK 2.0 (https://www.cog-genomics.org/plink/2.0/),\u003c/p\u003e\n\u003cp\u003eMETAL v.2011-03-25 (http://csg.sph.umich.edu/abecasis/Metal/download/),\u003c/p\u003e\n\u003cp\u003eLDSC v.1.0.1 (https://github.com/bulik/ldsc),\u003c/p\u003e\n\u003cp\u003eREGENIE v.2.0.1 (https://rgcgithub.github.io/regenie/),\u003c/p\u003e\n\u003cp\u003eColoc, v5.2.3 (https://cran.r-project.org/web/packages/coloc/index.html)\u003c/p\u003e\n\u003cp\u003eR v.4.1.2 (https://www.r-project.org/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Copenhagen University Hospital, Rigshospitalet (to JSB).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the participants and investigators of the UK Biobank, FinnGen, Million Veterans Affair, Copenhagen Hospital Biobank, deCODE study, and the Danish Blood Donor study. We want to acknowledge the participants and investigators of the FinnGen study. Copenhagen Hospital Biobank (CHB) is supported by the Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark and by grants from Novo Nordisk Foundation (NNF23OC0082015, NNF17OC0027594) and Rigshospitalet Research Council (Framework grant). The Danish Blood Donor Study (DBDS) is funded by an annual grant from Bio- and Genome Bank Denmark. The initiation of DBDS was supported by the Danish Administrative Regions (02/2611) and the Danish Council for Independent Research (09–069412). Additionally, the DBDS is funded by the Novo Nordisk Foundation (NNF23OC0082015, NNF17OC0027864, and NNF17OC0027594).\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Bjarne Kuno Møller(Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark) for his comments on the findings of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLD has sponsored research agreements with C2i Genomics, Veracyte, Natera, AstraZeneca, Photocure, and Ferring and serves in an advisory/consulting role for Ferring, MSD, Cystotech, and UroGen. LD has received speaker honoraria from AstraZeneca, Pfizer, and Roche, as well as travel support from MSD.\u003c/p\u003e\n\u003cp\u003eAll others report no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLurje, I., Gaisa, N. T., Weiskirchen, R. \u0026amp; Tacke, F. 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G., Tybj\u0026aelig;rg-Hansen, A. \u0026amp; Stender, S. Does SLC39A8 Ala391Thr Confer Risk of Chronic Liver Disease? \u003cem\u003eAntioxid Redox Signal\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 591\u0026ndash;596 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8156241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8156241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFibrotic diseases show common pathophysiological features irrespective of their anatomical locations. By mapping the genomic landscape behind selected fibrotic diseases, we aim to investigate the genome-wide and locus-wide genetic overlap across fibrotic diseases.\u003cstrong\u003e \u003c/strong\u003eWe conducted genome-wide meta-analyses using five genetic cohorts (Copenhagen Hospital Biobank (CHB), the Danish Blood Donor Study (DBDS), UK Biobank (UKB), FinnGen, the Million Veteran Program (MVP)) across 17 fibrotic traits comprising nine prototypical fibrotic diseases (e.g. carpal tunnel syndrome and idiopathic pulmonary fibrosis), four organ-diseases with known fibrotic components (e.g. heart failure and chronic kidney disease), and four imaging-derived fibrotic phenotypes. Global genetic correlations across fibrotic traits were estimated using linkage disequilibrium score regression (LDSC) and Locus-wide genetic overlaps were evaluated using colocalization analyses. Across 17 fibrotic traits with case sample sizes ranging from 4,559 for Peyronie’s disease to 126,358 for chronic kidney disease, we identified 645 genome-wide significant associations, of which 136 had not been reported previously for the respective trait. Using genetic correlation and hierarchical clustering, we found that fibrotic diseases clustered mainly into organ and non-organ specific clusters. The strongest correlations were between carpal tunnel syndrome and trigger finger (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e = 0.60,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 1.1 × 10\u003csup\u003e−63\u003c/sup\u003e) and between chronic kidney disease and heart failure (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e = 0.51,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 9.5 × 10\u003csup\u003e−87\u003c/sup\u003e). We identified 64 loci that colocalized across traits, of which 12 overlapped with three or more diseases. Many of the colocalizing genes belonged to gene families with established roles in fibrosis, including WNT signaling (\u003cem\u003eWNT7B\u003c/em\u003e, \u003cem\u003eTCF7L2\u003c/em\u003e, and \u003cem\u003eWNT2\u003c/em\u003e), extracellular matrix (\u003cem\u003eCOL11A1, MMP14\u003c/em\u003e and \u003cem\u003eP4HA2\u003c/em\u003e), fibroblast growth factors (\u003cem\u003eFGFR2, FGF21\u003c/em\u003e), and inflammation (\u003cem\u003eIRF5, STAT3\u003c/em\u003e, and \u003cem\u003eTNFAIP3\u003c/em\u003e).\u003cstrong\u003e \u003c/strong\u003eOur findings identified novel genetic variants and provide strong evidence for a shared genetic predisposition across fibrotic traits, converging on key biological pathways including extracellular matrix remodeling, immune regulation, and developmental signaling.\u003c/p\u003e","manuscriptTitle":"Shared genetic architecture across fibrotic diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 00:56:07","doi":"10.21203/rs.3.rs-8156241/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5ee72579-8dc4-4d7d-9bbe-a9c513b31216","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59208218,"name":"Health sciences/Diseases/Rheumatic diseases/Connective tissue diseases"},{"id":59208219,"name":"Biological sciences/Genetics/Population genetics"},{"id":59208220,"name":"Health sciences/Diseases/Immunological disorders/Inflammatory diseases"}],"tags":[],"updatedAt":"2025-12-30T00:56:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 00:56:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8156241","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8156241","identity":"rs-8156241","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}