Integrative genomic analyses reveal regional and cellular convergence between uterine fibroids and endometriosis

Integrative genomic analyses reveal regional and cellular convergence between uterine fibroids and endometriosis | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative genomic analyses reveal regional and cellular convergence between uterine fibroids and endometriosis Vijayachitra Modhukur, Prakash Lingasamy, Naisarg Patel, Andres Salumets This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-11036637/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Uterine fibroids and endometriosis are common oestrogen-responsive gynaecological disorders with partially overlapping inherited susceptibility. The extent to which this overlap is concentrated in specific genomic regions, genes, and reproductive cell types remains incompletely resolved. Methods We integrated publicly available FinnGen Release 9 genome-wide association summary statistics for uterine fibroids (31,661 cases and 179,209 controls) and endometriosis (15,088 cases and 107,564 controls). Genome-wide and local genetic sharing were evaluated using LD Score Regression and LAVA. Gene-level associations were assessed using MAGMA, while published single-cell RNA-sequencing datasets and single-cell-informed stratified LD Score Regression provided cellular context. Selected shared regions were further examined using Bayesian SuSiE fine-mapping. Results The disorders showed a moderate positive genome-wide genetic correlation (rg = 0.511, SE = 0.069, P = 1.37 × 10⁻¹³). Among 92 genomic blocks with valid primary bivariate estimates, 16 remained significant after false-discovery-rate correction, and all had positive local genetic-correlation estimates. MAGMA identified 147 genes associated with uterine fibroids and 38 with endometriosis after Bonferroni correction; ten were significant in both analyses, and seven of these lay within an FDR-significant LAVA region. Single-cell contextualisation and stratified LD Score Regression supported stromal involvement in both disorders, while fine-mapping indicated allelic complexity at selected shared regions. Conclusions Uterine fibroids and endometriosis share a moderate component of common-variant liability, but this overlap is regionally heterogeneous and accompanied by substantial disorder-specific gene-level architecture. Convergence across local genetic correlation, gene-level association and stromal enrichment prioritises reproductive and hormone-responsive regions for independent replication and functional evaluation. uterine fibroids endometriosis genetic correlation LAVA MAGMA fine-mapping single-cell RNA sequencing stromal cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Uterine fibroids (uterine leiomyoma) and endometriosis are common oestrogen-responsive gynaecological disorders that substantially affect reproductive health and quality of life. Fibroids are benign myometrial smooth-muscle tumours characterised by excessive extracellular-matrix deposition and associated with heavy menstrual bleeding, pelvic pressure, infertility and hysterectomy [ 1 – 4 ]. Endometriosis is defined by endometrial-like tissue outside the uterus and is associated with pelvic pain, dysmenorrhoea, dyspareunia and infertility [ 5 – 7 ]. Although their anatomical and cellular manifestations differ, both disorders are heritable, polygenic and influenced by ovarian steroid-hormone signalling. The two disorders frequently co-occur, and cross-trait genetic studies support partial sharing of inherited susceptibility [ 8 , 9 ]. A previous analysis reported shared loci and a moderate genome-wide genetic correlation between uterine leiomyomata and endometriosis (rg = 0.39, SE = 0.05, P = 9.77 × 10⁻¹³) [ 9 ]. Broader analyses of female reproductive disorders have also identified pleiotropic loci containing genes such as WNT4, GREB1, ESR1 and SYNE1 [ 10 , 11 ]. Genome-wide correlation, however, averages genetic sharing across the genome and cannot establish whether overlap is concentrated within particular regions or coexists with substantial disorder-specific architecture. Genome-wide association studies have expanded the genetic maps of both conditions. Fibroid-associated loci implicate reproductive-tract development, smooth-muscle proliferation, extracellular-matrix biology, genome stability and hormone-responsive transcription [ 9 , 12 , 13 , 15 , 17 , 18 ]. Endometriosis-associated loci implicate reproductive development, ovarian hormone signalling, inflammation and pain-related biology [ 14 , 16 , 19 ]. Assigning regional signals to genes, variants and relevant cellular contexts nevertheless remains difficult because association signals may contain multiple correlated variants and may span more than one plausible gene. Integrating complementary summary-statistic methods can provide greater resolution than any single analysis. Related studies have combined cross-trait association, local or gene-level architecture, cell-type annotation and post-GWAS prioritisation across female reproductive and autoimmune traits [10,11,18,19,34]. Local genetic correlation can identify genomic regions contributing disproportionately to shared liability, while gene-level analysis aggregates association across mapped variants. Fine-mapping evaluates allelic complexity within prioritised regions, and single-cell-informed heritability enrichment and expression mapping provide cellular context without establishing causal cell types or mechanisms. Concordance across these layers can therefore prioritise regions and biological contexts while preserving the distinct inferential scope of each method. In this study, we used publicly available FinnGen Release 9 genome-wide association summary statistics and public reference datasets to characterise the shared and distinct common-variant architecture of uterine fibroids and endometriosis. We evaluated genome-wide and local genetic correlation, compared gene-level association profiles, examined convergence between locally correlated regions and shared genes, assessed reproductive cell-type enrichment and expression context, and fine-mapped selected shared regions. We aimed to determine where the two disorders share inherited susceptibility and where their genetic architectures remain distinct. Materials and methods Study design and analytical overview We conducted a comparative genetic-epidemiology study utilizing publicly available FinnGen Release 9 genome-wide association study summary statistics for uterine fibroids and endometriosis. This was complemented by public linkage-disequilibrium reference data, annotation resources, and published single-cell datasets (Table 1 ) [ 20 ]. The analytical framework included genome-wide genetic correlation, LAVA local genetic correlation, MAGMA gene-level association, exploratory pathway annotation, single-cell expression contextualization, single-cell-informed stratified LD Score Regression, SuSiE fine-mapping (Fig. 1 ). No individual-level or controlled-access data were analyzed. Analyses incorporated all variants meeting the prespecified harmonization and method-specific quality-control criteria; no prospective sample-size calculation was performed. Table 1 Public data sources used in this study. All genome-wide association summary statistics, reference panels and single-cell datasets are publicly available; no individual-level or controlled-access data were analysed. Dataset Purpose in this study N (cases / controls) Source / reference FinnGen R9 — CD2_BENIGN_LEIOMYOMA_UTERI Uterine fibroid GWAS summary statistics: genome-wide and local genetic correlation, MAGMA gene-level association, fine-mapping 31,661 / 179,209 FinnGen Release 9 [ 20 ]; https://r9.finngen.fi FinnGen R9 — N14_ENDOMETRIOSIS Endometriosis GWAS summary statistics: same analyses as above 15,088 / 107,564 FinnGen Release 9 [ 20 ]; https://r9.finngen.fi 1000 Genomes Phase 3 (European) Linkage-disequilibrium reference panel for LDSC, LAVA, MAGMA and fine-mapping 503 individuals 1000 Genomes Project Consortium [ 24 ] GSE162122 Published single-cell RNA-seq of fibroid and myometrial tissue: cell-type contextualisation of shared MAGMA genes and S-LDSC marker-gene annotations not re-analysed at sample level; processed annotations used Goad et al. [ 27 ]; NCBI GEO GSE203191 Published single-cell RNA-seq of endometriosis tissue: same purpose as above not re-analysed at sample level; processed annotations used Shih et al. [ 28 ]; NCBI GEO FinnGen Release 9 GWAS summary statistics and harmonisation FinnGen Release 9 GWAS summary statistics were obtained for CD2_BENIGN_LEIOMYOMA_UTERI and N14_ENDOMETRIOSIS [ 20 ]. Endpoint metadata listed 31,661 fibroid cases and 179,209 controls, and 15,088 endometriosis cases and 107,564 controls. The summary-statistic files retained in the project archive did not contain per-variant sample-size columns. For LDSC munging, the analysis scripts assigned constant total-N values of 218,728 for fibroids and 239,335 for endometriosis. Because these script-supplied values differ from the endpoint-catalogue case-control totals, particularly for endometriosis, they are reported as analysis parameters rather than verified cohort denominators. Endpoint denominators and analysis-specific sample-size parameters are therefore reported separately and are not treated as interchangeable. Sample overlap between the two FinnGen endpoints was addressed in LAVA using the LDSC-derived sampling-correlation matrix described below. Summary statistics were harmonised by rsID, allele compatibility and strand checks; unresolved palindromic variants were excluded. Genome build conversion or liftOver was not the primary harmonisation step. Coordinates were used after rsID and allele harmonisation for locus windows, linkage-disequilibrium matching, gene mapping and fine-mapping. NCBI Build 37 gene annotations and 1000 Genomes Phase 3 European reference panels supported gene-level and linkage-disequilibrium-based analyses. Because the fibroid and endometriosis GWAS were both derived from FinnGen R9, the primary LAVA analysis used a sampling-correlation matrix obtained by standardising the full LDSC intercept covariance matrix. The count-derived and archived no-overlap implementations were retained as sensitivities. Endpoint, ancestry, harmonisation and source details are reported above and in Supplementary Table S1 . Genome-wide genetic correlation Genome-wide genetic correlation between uterine fibroids and endometriosis was estimated using LDSC, implemented through the GenomicSEM R package [ 21 – 23 ]. FinnGen R9 GWAS summary statistics were munged using standard LDSC procedures, restricted to HapMap3 SNPs and filtered using an imputation-quality threshold of 0.90. Allele quality control, minor-allele-frequency filtering and LDSC high-chi-square outlier filtering were applied. The MHC region was not explicitly removed as a separate preprocessing step. Summary statistics were analysed using 1000 Genomes Phase 3 European HapMap3 LD scores as the reference panel [ 24 ]. Genetic correlation was reported as rg with standard error and P value, and LDSC intercepts were inspected to evaluate potential inflation, residual confounding or sample-overlap effects. Full LDSC parameters and results are provided in Supplementary Table S1 . Local genetic correlation Local genetic correlation was estimated with LAVA v0.1.5 across 2,495 predefined, approximately independent linkage-disequilibrium-based genomic blocks [ 25 ]. These LD-informed partitions vary in physical size across the genome. Univariate local heritability was estimated for each disorder, and bivariate testing was restricted to blocks meeting the LAVA local-heritability eligibility and convergence requirements for both traits. The primary overlap-aware sampling-correlation matrix was derived from the LDSC intercept covariance matrix: diagonal intercepts of 1.0967 and 1.1274 and a cross-trait intercept of 0.1917 were standardised with cov2cor, yielding an off-diagonal sampling correlation of 0.17240. The primary analysis produced 150 bivariate output rows, of which 92 yielded valid bivariate P values after quality-control and boundary handling; Benjamini-Hochberg FDR correction was applied across these 92 valid tests. Count-derived overlap and no-overlap specifications were retained as sensitivity analyses. An earlier unit-diagonal matrix using the unstandardised cross-trait value of 0.1917 was retained only as implementation provenance and was not used for inference. Chromosome X blocks were excluded because sex-chromosome linkage disequilibrium and modelling assumptions differ from the autosomes, and targeted lead-locus summaries were excluded from genome-wide inference and result numbering. Complete primary results, sensitivity comparisons and implementation provenance are provided in Supplementary Table S1 . In the count-derived sensitivity analysis, the archived value of 59,264 produced an implied sampling correlation of 0.2590 but could not be verified as the number of participants shared between endpoints. This specification was therefore interpreted only as a sensitivity analysis. No alternative LAVA run using a UK Biobank linkage-disequilibrium reference was available; reference-panel dependence remains a limitation. Gene-level association and pathway enrichment Gene-level association analysis was performed using MAGMA v1.10 with the SNP-wise mean model, 1000 Genomes Phase 3 European linkage-disequilibrium reference data and NCBI Build 37 gene boundaries with a 0 kb window [ 26 ]. We tested 18,301 genes for fibroids and 18,300 genes for endometriosis. Genome-wide gene-level significance was defined using a Bonferroni threshold of P < 2.5 × 10⁻⁶. Significant genes were classified as significant in both trait-specific analyses, significant only in the fibroid analysis or significant only in the endometriosis analysis according to cross-condition significance. Full gene-level results and classification fields are provided in Supplementary Table S2 . Pathway and gene-set annotation was retained as an exploratory prioritisation layer based on curated biological categories and Reactome/gene-set terms for prioritised genes. Curated categories included oestrogen signalling, WNT/development, extracellular matrix, TGF-beta, immune/inflammatory, pain/neuroimmune, cell-cycle/proliferative and vascular/endothelial pathways. Source terms, identifiers, intersections and false-discovery-rate values are detailed in Supplementary Table S3. These results were not treated as definitive pathway causality because regional linkage disequilibrium, gene density, database composition and incomplete archived pathway provenance can influence enrichment. Single-cell RNA-seq contextualisation and cell-type heritability enrichment Publicly available single-cell RNA-seq resources from fibroid, myometrial, endometrial and endometriosis tissues were used to contextualise prioritised genes, including GSE162122 for fibroid/myometrium and GSE203191 for endometriosis [ 27 , 28 ]. Analyses used processed expression matrices, metadata or annotated cell-type summaries. Published annotations and ranked markers mapped genes significant in both trait-specific MAGMA analyses and genes significant only in one trait-specific analysis to smooth-muscle, stromal/fibroblast, epithelial, endothelial, immune and lesion-associated compartments. These analyses provided cellular context rather than evidence of causal gene action; Cell-type heritability enrichment was evaluated with stratified LD Score Regression (S-LDSC) using marker-gene-derived annotations [ 29 , 30 ]. For each major cell class, the top 500 marker genes were mapped to gene bodies with ± 100 kb flanks to create binary annotations. Each annotation was evaluated separately for fibroids and endometriosis against baseline model v1.2 and baseline-LD model v2.2. Alternative marker definitions provided sensitivity analyses. Additional expression context is shown in Supplementary Figure S1 , and complete single-cell and S-LDSC results are provided in Supplementary Table S5. The published datasets were not re-clustered or reannotated. Cell-context summaries used the original cell labels, normalised expression values and ranked-marker evidence. For S-LDSC, seven primary cell classes were evaluated, and false-discovery-rate correction was applied across these seven annotations separately within each disorder. Alternative top-100, fold-change-based and canonical stromal-marker annotations were treated as sensitivity analyses. These analyses provide cellular prioritisation and do not identify a causal cell type. Fine-mapping Sum of Single Effects fine-mapping was performed with susieR [ 32 ] separately for uterine fibroids and endometriosis at ESR1/SYNE1, WNT4, GREB1, WT1 and DNM3. Each analysis used a ± 500 kb window centred on the locus lead variant and local linkage disequilibrium from 1000 Genomes Project Phase 3 European samples. The model allowed up to ten effects per locus and defined credible sets at 95% coverage. Effective sample size was calculated as 4/(1/Ncases + 1/Ncontrols), using 31,661 cases and 179,209 controls for fibroids and 15,088 cases and 107,564 controls for endometriosis. Posterior inclusion probabilities, credible sets and lead variants are reported in Supplementary Figure S2 and Supplementary Table S4. Ethics, reporting, and reproducibility This study used publicly available, de-identified summary statistics and published molecular datasets. Original data collection was approved by the relevant ethics committees and participants provided informed consent, as described by FinnGen and the primary dataset publications. No new participant recruitment, individual-level data access or additional ethics approval was required for this secondary analysis. Reporting was guided by STREGA principles for genetic-association studies [ 31 ]. The analyses were not preregistered. Pathway annotation, single-cell contextualisation and fine-mapping were interpreted as prioritisation analyses and not as evidence of causal genes, variants or cell types. Analysis parameters, sensitivity analyses and provenance are documented in the Methods and supplementary tables. AI-assisted tools were used during manuscript preparation for language editing, formatting checks and consistency review. They were not used to generate primary data, perform unsupervised analyses or make independent scientific conclusions. All analyses, interpretations and revisions were reviewed and approved by the authors, who take responsibility for the final content. No direct participant identifiers were entered into these tools. Results Genome-wide and local genetic correlation support shared but incomplete genetic architecture LD Score Regression (LDSC) estimated a moderate positive genome-wide genetic correlation between uterine fibroids and endometriosis (rg = 0.511, SE = 0.069, P = 1.37 × 10⁻¹³; Supplementary Table S1 ), supporting shared but incomplete common-variant genetic architecture. Local Analysis of [co]Variant Association (LAVA) was performed across 2,495 LD-based genomic blocks using an overlap-aware sampling-correlation matrix derived from the full LDSC intercept covariance matrix as the primary analysis. The endometriosis and uterine-fibroid univariate intercepts (1.0967 and 1.1274) and the cross-trait intercept (0.1917) were standardised to an off-diagonal sampling correlation of 0.17240. The run produced 150 bivariate output rows, of which 92 yielded valid P values after LAVA quality-control and boundary handling. After Benjamini–Hochberg FDR correction across these valid tests, 16 blocks were significant at FDR < 0.05. All 16 significant blocks had positive local genetic-correlation estimates, supporting region-specific genetic sharing between the two disorders (Fig. 2 A–C). Several local-rg estimates approached the boundary of the parameter space and confidence intervals were broad for some blocks; therefore, the LAVA results were interpreted as evidence of regional genetic sharing rather than as a precise ranking of local-rg magnitude. MAGMA identifies shared oestrogen-responsive and developmental genes alongside condition-specific signatures Multi-marker Analysis of GenoMic Annotation (MAGMA) gene-level analysis identified 147 Bonferroni-significant genes for uterine fibroids and 38 significant genes for endometriosis (Fig. 3 A, B; Supplementary Table S2 ). A significant MAGMA gene denotes aggregate gene-level association after correction across the tested gene universe and does not by itself establish the causal gene within a locus. Figure 3 C compares the two gene-level profiles and identifies ten genes significant in both disorders: SYNE1, GREB1, WNT4, ESR1, EFR3B, VIP, LDLRAD2, DNM3, PLEKHM3 and ARL14EP. Seven of these ten shared MAGMA genes—LDLRAD2, WNT4, GREB1, ESR1, SYNE1, VIP and ARL14EP—mapped within at least one of the 16 FDR-significant primary LAVA blocks, providing convergent regional support across local-correlation and gene-level analyses. This overlap supports prioritisation but does not establish the causal gene within each region. The remaining 137 genes were significant only in the fibroid analysis and 28 only in the endometriosis analysis, indicating substantial trait-specific gene-level architecture. Full MAGMA gene-level association results are provided in Supplementary Table S2 . Cell-type heritability enrichment provided complementary tissue context. Stratified LD Score Regression (S-LDSC) using single-cell-derived marker-gene annotations showed significant stromal enrichment for both fibroids and endometriosis (Fig. 4 A; Supplementary Table S5). Fibroid signals additionally showed epithelial and endothelial enrichment, whereas endometriosis showed predominantly stromal enrichment. Endothelial expression provided complementary single-cell context but was not a significant endometriosis S-LDSC enrichment. These results indicate shared stromal context for inherited risk, while supporting broader cellular heterogeneity for fibroids. Exploratory pathway and gene-set annotation highlighted hormone/reproductive signalling, WNT/developmental signalling, extracellular-matrix/fibrosis, cell-adhesion/migration, immune/inflammatory, epigenetic-regulation, cell-cycle/proliferative and PI3K/mTOR/metabolic categories (Fig. 4 B,C; Supplementary Table S3). Figure 4 B shows fold-enrichment-style summaries, whereas Fig. 4 C shows the corresponding statistical-prioritisation values. Because these curated pathway analyses were exploratory and the retained outputs do not represent a complete confirmatory disease-specific MAGMA pathway-regression universe, they were used to provide biological context rather than to establish disease-specific pathway causality. Single-cell expression and heritability enrichment converge on stromal context To determine whether genes prioritised in both trait-specific MAGMA analyses were expressed in biologically relevant reproductive-tissue compartments, we performed single-cell RNA-seq contextualisation using public uterine fibroid/myometrial and endometriosis datasets [ 27 , 28 ]. Single-cell contextualisation mapped shared MAGMA genes to disease-relevant reproductive-tissue compartments (Supplementary Figure S1 ; Supplementary Table S5). In endometriosis data, GREB1, WNT4 and ESR1 were prominent in stromal compartments, whereas DNM3 mapped to endothelial context. In fibroid/myometrial data, ESR1 and GREB1 showed smooth muscle/stromal expression context, WNT4 mapped to fibroblast/stromal compartments and DNM3 again showed endothelial context. These expression maps provide cellular context for prioritised loci but do not establish causal gene action or cell-type-specific heritability. Cell-type heritability enrichment analysis using stratified LD Score Regression and single-cell-derived marker-gene annotations identified stromal enrichment in both uterine fibroids and endometriosis [ 29 , 30 ]. S-LDSC provided an independent heritability-enrichment test using single-cell-derived marker-gene annotations. Stromal enrichment was significant for both fibroids (z = 2.49, enrichment = 3.52-fold, FDR = 0.006) and endometriosis (z = 2.53, enrichment = 3.42-fold, FDR = 0.035; Fig. 4 A; Supplementary Table S5). Immune-cell and smooth-muscle annotations were not significantly enriched, indicating that common-variant heritability captured by GWAS was not primarily driven by immune-cell marker sets or differentiated smooth-muscle marker annotations. This does not exclude inflammatory endometriosis biology or the smooth-muscle histology of fibroids; rather, it suggests that inherited risk is more strongly captured by stromal, mesenchymal or hormone-responsive regulatory programmes [ 6 , 16 ]. Fibroid epithelial and endothelial annotations met the within-disease FDR threshold, but coefficient z-scores were modest, and these signals were not treated as definitive causal cell-type enrichments. Alternative marker definitions and complete single-cell and S-LDSC results are provided in Supplementary Figure S1 and Supplementary Table S5. Fine-mapping supports allelic complexity at shared loci Sum of Single Effects (SuSiE) fine-mapping of ESR1/SYNE1, WNT4, GREB1, WT1 and DNM3 supported allelic complexity at several shared regions, particularly ESR1/SYNE1 (Supplementary Figure S2 ; Supplementary Table S4). These patterns are compatible with multiple correlated variants, partially overlapping credible sets or locus-specific heterogeneity, and caution against assigning a single causal variant or gene from regional association strength alone. Together with the LAVA and MAGMA results, fine-mapping supports shared regional architecture at reproductive and hormone-responsive loci while indicating that the shared signal is not explained by a simple one-variant, two-disease model. Integrated genetic evidence supports partial convergence with disorder-specific architecture Across the genetic analyses, uterine fibroids and endometriosis showed partial but non-uniform convergence. A moderate genome-wide genetic correlation was resolved into 16 FDR-significant local-correlation regions, while MAGMA identified ten shared significant genes and substantially larger disorder-specific gene sets. Seven shared MAGMA genes mapped within a significant LAVA region. Concordant stromal enrichment provided cellular context for the shared component, whereas SuSiE fine-mapping indicated that selected regions could not be reduced to a simple one-variant, two-disorder model. These analyses prioritise regions and cellular contexts for follow-up but do not establish causal variants, genes or cell types. Discussion This study aimed to characterise genetic relationship between uterine fibroids and endometriosis across genome-wide, regional, gene-level and cellular scales using integrative framework. The disorders showed a moderate genome-wide genetic correlation, while overlap-aware local analysis identified 16 FDR-significant regions among 92 blocks with valid bivariate estimates. MAGMA identified ten genes significant in both trait-specific analyses, seven of which mapped within an FDR-significant LAVA region, alongside 137 fibroid-specific and 28 endometriosis-specific genes. Public single-cell expression data and single-cell-informed stratified LD Score Regression further supported stromal context in both disorders. Collectively, these findings indicate partial genetic convergence rather than a unified genetic architecture. The genome-wide result is consistent with previous evidence for shared common-variant liability [ 9 , 16 ], but the local analyses show that this relationship is not distributed uniformly across the genome. All 16 regions meeting the primary FDR threshold had positive local genetic-correlation estimates. The count-derived overlap sensitivity retained 10 of these regions, whereas the no-overlap analysis produced larger changes in block eligibility and locus-specific estimates. The overall evidence therefore supports regional genetic sharing, while the status of individual blocks remains sensitive to assumptions about sample overlap and the linkage-disequilibrium reference. Genome-wide and local estimates should consequently be interpreted as complementary measures of common-variant sharing rather than evidence that the disorders are genetically interchangeable. Gene-level analyses refined this regional pattern. Ten genes were significant in both trait-specific MAGMA analyses: SYNE1 , GREB1 , WNT4 , ESR1 , EFR3B , VIP , LDLRAD2 , DNM3 , PLEKHM3 and ARL14EP . Seven of these genes also lay within an FDR-significant LAVA region, providing convergent statistical prioritisation across local-correlation and gene-level analyses. Several of the shared signals occur in established reproductive-developmental or hormone-responsive regions, including ESR1 / SYNE1 , WNT4 and GREB1 [ 9 , 14 – 19 ]. Nevertheless, MAGMA association and regional convergence do not identify a causal gene. The much larger sets of genes significant in only one trait-specific analysis further indicate that shared susceptibility is accompanied by substantial disorder-specific gene-level architecture. The cellular analyses provide biological context for this shared component. Stromal marker-gene annotations were enriched for common-variant heritability in both disorders, and expression mapping placed several shared MAGMA-prioritised genes in stromal, fibroblast, smooth-muscle-associated, endothelial or hormone-responsive reproductive-tissue compartments [ 18 , 22 , 27 – 30 ]. The concordant stromal signal is compatible with shared mesenchymal and tissue-remodelling programmes. It does not establish a common causal cell of origin, and the absence of significant enrichment for some immune-cell or differentiated smooth-muscle annotations should not be interpreted as evidence against the established inflammatory biology of endometriosis or smooth-muscle histology of fibroids. Heritability enrichment depends on marker definitions, annotation construction and the cellular states represented in the source datasets. Fine-mapping of selected regions, including ESR1/SYNE1, WNT4, GREB1, WT1 and DNM3, indicated allelic complexity rather than a simple one-variant, two-disorder model. Multiple correlated variants or partially overlapping credible sets can produce concordant regional evidence while leaving the causal variant and effector gene unresolved. LAVA, MAGMA and SuSiE address different questions and should not be treated as interchangeable confirmation. Their combined value lies in identifying regions where several forms of statistical evidence converge and where targeted functional investigation would be most informative [ 21 , 22 , 25 , 26 , 31 , 33 ]. The study has two principal strengths. First, it integrates complementary genetic analyses within a focused cross-disorder framework, including an overlap-aware primary LAVA specification and sensitivity analyses. Second, the analysis can be reproduced using public summary statistics, reference panels, annotation resources and published single-cell datasets. Several limitations remain. Both disease GWAS were derived from FinnGen, so shared participants or controls may induce correlated estimation errors despite overlap-aware modelling in LAVA. The predominantly Finnish and European-ancestry evidence limits generalisability to other populations. Use of a 1000 Genomes European linkage-disequilibrium reference may imperfectly represent Finnish haplotype structure and particularly affects locus-level and fine-mapping inference. LDSC and LAVA assess common-variant architecture; MAGMA does not establish causal genes; fine-mapping depends on the accuracy of the linkage-disequilibrium reference; and the cellular analyses provide annotation-based context rather than functional validation. Independent replication, ancestry-diverse analyses and experimental evaluation of prioritised variants, genes and cell states are required before mechanistic or clinical conclusions can be drawn. Conclusions Uterine fibroids and endometriosis exhibit a shared but complex genetic architecture, characterized by moderate genome-wide common-variant correlation, region-specific genetic convergence, and substantial disorder-specific genomic contributions. Integrative analysis across local genetic correlation, gene-level association, single-cell contextualisation, and cell-type heritability enrichment identifies stromal and hormone-responsive regulatory landscapes as important components of shared susceptibility. However, the observed convergence does not imply a unified genetic mechanism, as multiple loci retain distinct disorder-specific architectures and require functional interpretation. Collectively, this multi-layer genomic framework provides a reproducible strategy for prioritising candidate regions, genes, and cellular contexts for experimental validation and advances the understanding of molecular relationships between two major oestrogen-responsive reproductive disorders. Abbreviations FDR false discovery rate GWAS genome-wide association study LAVA Local Analysis of [co]Variant Association LD linkage disequilibrium LDSC LD Score Regression MAGMA Multi-marker Analysis of GenoMic Annotation scRNA-seq single-cell RNA sequencing S-LDSC stratified LD Score Regression SuSiE Sum of Single Effects. Declarations Ethics approval and consent to participate This study used publicly available summary statistics and published molecular datasets. The original studies obtained ethics approval and informed consent as described in their primary publications. No individual-level participant data were accessed, and no additional ethics approval was required for this secondary analysis. Consent for publication Not applicable. Clinical trial number Not applicable. Availability of data and materials FinnGen Release 9 genome-wide association summary statistics are publicly available through the FinnGen data portal ( https://finngen.gitbook.io/documentation/r9/data-download ). The 1000 Genomes Project reference data and the published single-cell datasets used for contextualisation are publicly available from their cited repositories. This study generated no new individual-level data. Derived summary results underlying the principal analyses are described in the supplementary information. Analysis code and project documentation are available at https://github.com/vijayachitrabio/fibroids-endometriosis-genomics. Competing interests The authors declare that they have no competing interests. Funding The present study was funded by the European Commission through Horizon Europe (NESTOR, grant 101120075); Novo Nordisk Foundation (NNF24OC0092384); Swedish Research Council (2024-02530); Estonian Ministry of Education and Research through the Centre of Excellence for Personalised Medicine (TK214). The funders had no role in study design, data collection, data analysis, interpretation, manuscript preparation or the decision to submit for publication. Authors' contributions VM: Conceptualisation, Methodology, Formal analysis, Investigation, Data curation, Software, Visualisation, Validation, Writing – original draft, Writing – review and editing, Supervision and Project administration. PL: Conceptualisation, Methodology, Investigation, Interpretation of biological findings, Visualisation, Writing – original draft and Writing – review and editing. NP: Investigation, Data curation, Validation, Interpretation of results and Writing – review and editing. AS: Conceptualisation, Resources, Supervision, Funding acquisition, Interpretation of results and Writing – review and editing. All authors read and approved the final manuscript. Acknowledgements The authors thank the participants and investigators of FinnGen and the investigators who made the single-cell datasets used in this study publicly available. References Baird DD, Dunson DB, Hill MC, Cousins D, Schectman JM (2003) High cumulative incidence of uterine leiomyoma in black and white women: ultrasound evidence. Am J Obstet Gynecol 188:100–107 Bulun SE (2013) Uterine fibroids. N Engl J Med 369:1344–1355 Stewart EA, Cookson CL, Gandolfo RA, Schulze-Rath R (2017) Epidemiology of uterine fibroids: a systematic review. BJOG 124:1501–1512 Laughlin-Tommaso SK, Stewart EA (2018) Moving Toward Individualized Medicine for Uterine Leiomyomas. 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Hum Genet 132:943–953 Sapkota Y, Steinthorsdottir V, Morris AP, Fassbender A, Rahmioglu N, De Vivo I, Buring JE, Zhang F, Edwards TL, Jones S et al (2017) Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism. Nat Commun 8:15539 Valimaki N, Kuisma H, Pasanen A, Heikinheimo O, Sjoberg J, Butzow R, Sarvilinna N, Heinonen HR, Tolvanen J, Bramante S et al (2018) Genetic predisposition to uterine leiomyoma is determined by loci for genitourinary development and genome stability. Elife. ;7 Rahmioglu N, Mortlock S, Ghiasi M, Moller PL, Stefansdottir L, Galarneau G, Turman C, Danning R, Law MH, Sapkota Y et al (2023) The genetic basis of endometriosis and comorbidity with other pain and inflammatory conditions. Nat Genet 55:423–436 Kim J, Williams A, Noh H, Jasper EA, Jones SH, Jaworski JA, Shuey MM, Ruiz-Narvaez EA, Wise LA, Palmer JR et al (2025) Genome-wide meta-analysis identifies novel risk loci for uterine fibroids within and across multiple ancestry groups. Nat Commun 16:2273 Buyukcelebi K, Duval AJ, Abdula F, Elkafas H, Seker-Polat F, Adli M (2024) Integrating leiomyoma genetics, epigenomics, and single-cell transcriptomics reveals causal genetic variants, genes, and cell types. Nat Commun 15:1169 Koller D, He J, Lokhammer S, Aranda S, Qiu D, Davtian D, Chen Q, Xu Z, Mao Z, Friligkou E et al (2026) Multi-ancestry genome-wide association and integrated multi-omics analyses of endometriosis and its clinical manifestations. Nat Genet 58:1051–1061 Kurki MI, Karjalainen J, Palta P, Sipila TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA et al (2023) FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613:508–518 Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric, Genomics C, Patterson N, Daly MJ, Price AL, Neale BM (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47:291–295 Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, ReproGen C, Psychiatric Genomics C, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case, Control C, Duncan L et al (2015) An atlas of genetic correlations across human diseases and traits. Nat Genet 47:1236–1241 Grotzinger AD, Rhemtulla M, de Vlaming R, Ritchie SJ, Mallard TT, Hill WD, Ip HF, Marioni RE, McIntosh AM, Deary IJ et al (2019) Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav 3:513–525 1000 Genomes Project Consortium (2015) A global reference for human genetic variation. Nature 526:68–74 Werme J, van der Sluis S, Posthuma D, de Leeuw CA (2022) An integrated framework for local genetic correlation analysis. Nat Genet 54:274–282 de Leeuw CA, Mooij JM, Heskes T, Posthuma D (2015) MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 11:e1004219 Goad J, Rudolph J, Zandigohar M, Tae M, Dai Y, Wei JJ, Bulun SE, Chakravarti D, Rajkovic A (2022) Single-cell sequencing reveals novel cellular heterogeneity in uterine leiomyomas. Hum Reprod 37:2334–2349 Shih AJ, Adelson RP, Vashistha H, Khalili H, Nayyar A, Puran R, Herrera R, Chatterjee PK, Lee AT, Truskinovsky AM et al (2022) Single-cell analysis of menstrual endometrial tissues defines phenotypes associated with endometriosis. BMC Med 20:315 Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, Anttila V, Xu H, Zang C, Farh K et al (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47:1228–1235 Finucane HK, Reshef YA, Anttila V, Slowikowski K, Gusev A, Byrnes A, Gazal S, Loh PR, Lareau C, Shoresh N et al (2018) Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat Genet 50:621–629 Little J, Higgins JP, Ioannidis JP, Moher D, Gagnon F, von Elm E, Khoury MJ, Cohen B, Davey-Smith G, Grimshaw J et al (2009) STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement. PLoS Med 6:e22 Wang G, Sarkar A, Carbonetto P, Stephens M (2020) A simple new approach to variable selection in regression, with application to genetic fine mapping. J R Stat Soc Ser B Stat Methodol 82:1273–1300 Modhukur V, Khatun M, Patel N, Sajitha Lulu S, Lingasamy P, Salumets A (2026) Integrative post-GWAS analysis prioritizes immune regulatory pathways and candidate effector signals in systemic lupus erythematosus. J Transl Autoimmun 13:100399. 10.1016/j.jtauto.2026.100399 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFiguresFIG.docx SupplementaryTablesS1toS5FIG.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Sep, 2026 Reviewers agreed at journal 25 Sep, 2026 Reviewers agreed at journal 24 Sep, 2026 Reviewers invited by journal 18 Sep, 2026 Editor assigned by journal 17 Sep, 2026 Submission checks completed at journal 17 Sep, 2026 First submitted to journal 14 Sep, 2026 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. 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Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-11036637","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":724892550,"identity":"a6641244-2bfa-4ed7-aa12-e0028fbd9b3b","order_by":0,"name":"Vijayachitra Modhukur","email":"data:image/png;base64,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","orcid":"","institution":"Celvia CC AS","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Vijayachitra","middleName":"","lastName":"Modhukur","suffix":""},{"id":724892551,"identity":"906a49fc-1126-4c3a-bd7c-aa287bb9905d","order_by":1,"name":"Prakash Lingasamy","email":"","orcid":"","institution":"Celvia CC AS","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Prakash","middleName":"","lastName":"Lingasamy","suffix":""},{"id":724892552,"identity":"d2eb565e-74d5-414b-b650-20562eef5479","order_by":2,"name":"Naisarg Patel","email":"","orcid":"","institution":"Celvia CC AS","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Naisarg","middleName":"","lastName":"Patel","suffix":""},{"id":724892553,"identity":"74b5878b-093b-4013-9ea1-8c3755941130","order_by":3,"name":"Andres Salumets","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andres","middleName":"","lastName":"Salumets","suffix":""}],"badges":[],"createdAt":"2026-09-14 13:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-11036637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-11036637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":121520785,"identity":"86129a20-b5fd-422a-8c7d-5b7335defc4d","added_by":"auto","created_at":"2026-09-29 12:58:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":194255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIntegrated framework for investigating shared and distinct genetic architecture of uterine fibroids and endometriosis\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Publicly available FinnGen Release 9 genome-wide association summary statistics for uterine fibroids and endometriosis were integrated with the 1000 Genomes European linkage-disequilibrium reference and published single-cell datasets. Analyses included LD Score Regression, LAVA local genetic correlation, MAGMA gene-level association, SuSiE fine-mapping, and single-cell-informed stratified LD Score Regression. The evidence supported moderate genome-wide genetic sharing, 16 FDR-significant local-correlation regions, ten shared MAGMA-significant genes, substantial\u003c/em\u003e \u003cem\u003edisorder-specific gene-level architecture, and stromal heritability enrichment in both disorders. Arrows represent analytical flow only and do not imply causality.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-11036637/v1/b81cfdf877d86e7a26945ff5.png"},{"id":121458415,"identity":"cc300cfb-471a-4331-bd06-9d2d6e172acd","added_by":"auto","created_at":"2026-09-29 00:50:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150050,"visible":true,"origin":"","legend":"\u003cp\u003eFormal LAVA local genetic-correlation analysis. (A) Local genetic-correlation estimates and confidence intervals for the 92 genomic blocks yielding valid bivariate P values under the primary LDSC-standardised overlap-aware specification, ordered by chromosome. (B) The same 92 blocks ranked by local genetic correlation; the dashed line indicates the median estimate. (C) Local genetic correlation versus -log10(P). Blue filled points denote the 16 blocks meeting the Benjamini–Hochberg FDR \u0026lt; 0.05 threshold, whereas open points denote blocks that were not FDR-significant. The dashed horizontal line in Panel C indicates a conservative Bonferroni reference based on all 2,495 predefined blocks and was not used to determine the FDR-significant set. Count-derived overlap and archived no-overlap sensitivity results are provided in Supplementary Table S1.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-11036637/v1/5bff37fb5a5ff0864e543437.png"},{"id":121521234,"identity":"718d917e-e2a6-4970-bf4a-6a7116086f2b","added_by":"auto","created_at":"2026-09-29 12:58:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":274167,"visible":true,"origin":"","legend":"\u003cp\u003eGene-level architecture of uterine fibroids and endometriosis. (A,B) MAGMA gene-level association results for uterine fibroids and endometriosis, showing Bonferroni-significant genes ranked by -log10(P). Colours distinguish genes significant in both trait-specific analyses from disorder-specific signals. (C) Cross-disorder comparison of MAGMA gene-level results, showing genes significant in both disorders and genes significant only in the fibroid or endometriosis analysis. Gene-level significance identifies aggregate association across mapped variants and does not by itself establish a causal gene. Full results are provided in Supplementary Table S2.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-11036637/v1/22bab725a8656d7497133c95.png"},{"id":121458418,"identity":"4936d356-760d-4f13-8f4c-63e257302560","added_by":"auto","created_at":"2026-09-29 00:50:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":211578,"visible":true,"origin":"","legend":"\u003cp\u003eCell-type heritability enrichment and exploratory pathway annotation. (A) Cell-type heritability enrichment estimated using stratified LD Score Regression with single-cell-derived marker-gene annotations; asterisks denote FDR \u0026lt; 0.05. (B) Fold-enrichment-style summaries for curated pathway and gene-set categories. (C) Corresponding -log10(P) values. The pathway panels are exploratory annotations based on curated categories and are not presented as an independently confirmatory pathway-regression analysis. Cell-type estimates are provided in Supplementary Table S5, and pathway annotations are provided in Supplementary Table S3.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-11036637/v1/791b37b5160e72c83e26b307.png"},{"id":121621909,"identity":"2e085336-8200-4cf6-8211-7e920ec9275a","added_by":"auto","created_at":"2026-09-30 09:25:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1074703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-11036637/v1/9889253c-fa24-4f29-b243-7ff34f621764.pdf"},{"id":121458413,"identity":"0f047714-1bc1-49f9-be9c-085ed025532b","added_by":"auto","created_at":"2026-09-29 00:50:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":927075,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresFIG.docx","url":"https://assets-eu.researchsquare.com/files/rs-11036637/v1/804e778f5a3dcc72d99fc6b7.docx"},{"id":121458414,"identity":"5e342814-bbda-4564-bd11-656bcf07d0f6","added_by":"auto","created_at":"2026-09-29 00:50:38","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3979032,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS1toS5FIG.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-11036637/v1/a98f5f41a7fb33ce9e873383.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative genomic analyses reveal regional and cellular convergence between uterine fibroids and endometriosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUterine fibroids (uterine leiomyoma) and endometriosis are common oestrogen-responsive gynaecological disorders that substantially affect reproductive health and quality of life. Fibroids are benign myometrial smooth-muscle tumours characterised by excessive extracellular-matrix deposition and associated with heavy menstrual bleeding, pelvic pressure, infertility and hysterectomy [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Endometriosis is defined by endometrial-like tissue outside the uterus and is associated with pelvic pain, dysmenorrhoea, dyspareunia and infertility [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although their anatomical and cellular manifestations differ, both disorders are heritable, polygenic and influenced by ovarian steroid-hormone signalling.\u003c/p\u003e \u003cp\u003eThe two disorders frequently co-occur, and cross-trait genetic studies support partial sharing of inherited susceptibility [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A previous analysis reported shared loci and a moderate genome-wide genetic correlation between uterine leiomyomata and endometriosis (rg\u0026thinsp;=\u0026thinsp;0.39, SE\u0026thinsp;=\u0026thinsp;0.05, P\u0026thinsp;=\u0026thinsp;9.77 \u0026times; 10⁻\u0026sup1;\u0026sup3;) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Broader analyses of female reproductive disorders have also identified pleiotropic loci containing genes such as WNT4, GREB1, ESR1 and SYNE1 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Genome-wide correlation, however, averages genetic sharing across the genome and cannot establish whether overlap is concentrated within particular regions or coexists with substantial disorder-specific architecture.\u003c/p\u003e \u003cp\u003eGenome-wide association studies have expanded the genetic maps of both conditions. Fibroid-associated loci implicate reproductive-tract development, smooth-muscle proliferation, extracellular-matrix biology, genome stability and hormone-responsive transcription [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Endometriosis-associated loci implicate reproductive development, ovarian hormone signalling, inflammation and pain-related biology [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Assigning regional signals to genes, variants and relevant cellular contexts nevertheless remains difficult because association signals may contain multiple correlated variants and may span more than one plausible gene.\u003c/p\u003e \u003cp\u003eIntegrating complementary summary-statistic methods can provide greater resolution than any single analysis. Related studies have combined cross-trait association, local or gene-level architecture, cell-type annotation and post-GWAS prioritisation across female reproductive and autoimmune traits [10,11,18,19,34]. Local genetic correlation can identify genomic regions contributing disproportionately to shared liability, while gene-level analysis aggregates association across mapped variants. Fine-mapping evaluates allelic complexity within prioritised regions, and single-cell-informed heritability enrichment and expression mapping provide cellular context without establishing causal cell types or mechanisms. Concordance across these layers can therefore prioritise regions and biological contexts while preserving the distinct inferential scope of each method.\u003c/p\u003e \u003cp\u003eIn this study, we used publicly available FinnGen Release 9 genome-wide association summary statistics and public reference datasets to characterise the shared and distinct common-variant architecture of uterine fibroids and endometriosis. We evaluated genome-wide and local genetic correlation, compared gene-level association profiles, examined convergence between locally correlated regions and shared genes, assessed reproductive cell-type enrichment and expression context, and fine-mapped selected shared regions. We aimed to determine where the two disorders share inherited susceptibility and where their genetic architectures remain distinct.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and analytical overview\u003c/h2\u003e \u003cp\u003eWe conducted a comparative genetic-epidemiology study utilizing publicly available FinnGen Release 9 genome-wide association study summary statistics for uterine fibroids and endometriosis. This was complemented by public linkage-disequilibrium reference data, annotation resources, and published single-cell datasets (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The analytical framework included genome-wide genetic correlation, LAVA local genetic correlation, MAGMA gene-level association, exploratory pathway annotation, single-cell expression contextualization, single-cell-informed stratified LD Score Regression, SuSiE fine-mapping (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No individual-level or controlled-access data were analyzed. Analyses incorporated all variants meeting the prespecified harmonization and method-specific quality-control criteria; no prospective sample-size calculation was performed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePublic data sources used in this study. All genome-wide association summary statistics, reference panels and single-cell datasets are publicly available; no individual-level or controlled-access data were analysed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurpose in this study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (cases / controls)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource / reference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinnGen R9 \u0026mdash; CD2_BENIGN_LEIOMYOMA_UTERI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUterine fibroid GWAS summary statistics: genome-wide and local genetic correlation, MAGMA gene-level association, fine-mapping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,661 / 179,209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFinnGen Release 9 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r9.finngen.fi\u003c/span\u003e\u003cspan address=\"https://r9.finngen.fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinnGen R9 \u0026mdash; N14_ENDOMETRIOSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndometriosis GWAS summary statistics: same analyses as above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,088 / 107,564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFinnGen Release 9 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r9.finngen.fi\u003c/span\u003e\u003cspan address=\"https://r9.finngen.fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000 Genomes Phase 3 (European)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinkage-disequilibrium reference panel for LDSC, LAVA, MAGMA and fine-mapping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e503 individuals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1000 Genomes Project Consortium [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE162122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublished single-cell RNA-seq of fibroid and myometrial tissue: cell-type contextualisation of shared MAGMA genes and S-LDSC marker-gene annotations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enot re-analysed at sample level; processed annotations used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGoad et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; NCBI GEO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE203191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublished single-cell RNA-seq of endometriosis tissue: same purpose as above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enot re-analysed at sample level; processed annotations used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShih et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; NCBI GEO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFinnGen Release 9 GWAS summary statistics and harmonisation\u003c/h3\u003e\n\u003cp\u003eFinnGen Release 9 GWAS summary statistics were obtained for CD2_BENIGN_LEIOMYOMA_UTERI and N14_ENDOMETRIOSIS [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Endpoint metadata listed 31,661 fibroid cases and 179,209 controls, and 15,088 endometriosis cases and 107,564 controls. The summary-statistic files retained in the project archive did not contain per-variant sample-size columns. For LDSC munging, the analysis scripts assigned constant total-N values of 218,728 for fibroids and 239,335 for endometriosis. Because these script-supplied values differ from the endpoint-catalogue case-control totals, particularly for endometriosis, they are reported as analysis parameters rather than verified cohort denominators. Endpoint denominators and analysis-specific sample-size parameters are therefore reported separately and are not treated as interchangeable. Sample overlap between the two FinnGen endpoints was addressed in LAVA using the LDSC-derived sampling-correlation matrix described below.\u003c/p\u003e \u003cp\u003eSummary statistics were harmonised by rsID, allele compatibility and strand checks; unresolved palindromic variants were excluded. Genome build conversion or liftOver was not the primary harmonisation step. Coordinates were used after rsID and allele harmonisation for locus windows, linkage-disequilibrium matching, gene mapping and fine-mapping. NCBI Build 37 gene annotations and 1000 Genomes Phase 3 European reference panels supported gene-level and linkage-disequilibrium-based analyses. Because the fibroid and endometriosis GWAS were both derived from FinnGen R9, the primary LAVA analysis used a sampling-correlation matrix obtained by standardising the full LDSC intercept covariance matrix. The count-derived and archived no-overlap implementations were retained as sensitivities. Endpoint, ancestry, harmonisation and source details are reported above and in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eGenome-wide genetic correlation\u003c/h3\u003e\n\u003cp\u003eGenome-wide genetic correlation between uterine fibroids and endometriosis was estimated using LDSC, implemented through the GenomicSEM R package [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. FinnGen R9 GWAS summary statistics were munged using standard LDSC procedures, restricted to HapMap3 SNPs and filtered using an imputation-quality threshold of 0.90. Allele quality control, minor-allele-frequency filtering and LDSC high-chi-square outlier filtering were applied. The MHC region was not explicitly removed as a separate preprocessing step. Summary statistics were analysed using 1000 Genomes Phase 3 European HapMap3 LD scores as the reference panel [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Genetic correlation was reported as rg with standard error and P value, and LDSC intercepts were inspected to evaluate potential inflation, residual confounding or sample-overlap effects. Full LDSC parameters and results are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eLocal genetic correlation\u003c/h3\u003e\n\u003cp\u003eLocal genetic correlation was estimated with LAVA v0.1.5 across 2,495 predefined, approximately independent linkage-disequilibrium-based genomic blocks [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These LD-informed partitions vary in physical size across the genome. Univariate local heritability was estimated for each disorder, and bivariate testing was restricted to blocks meeting the LAVA local-heritability eligibility and convergence requirements for both traits. The primary overlap-aware sampling-correlation matrix was derived from the LDSC intercept covariance matrix: diagonal intercepts of 1.0967 and 1.1274 and a cross-trait intercept of 0.1917 were standardised with cov2cor, yielding an off-diagonal sampling correlation of 0.17240. The primary analysis produced 150 bivariate output rows, of which 92 yielded valid bivariate P values after quality-control and boundary handling; Benjamini-Hochberg FDR correction was applied across these 92 valid tests. Count-derived overlap and no-overlap specifications were retained as sensitivity analyses. An earlier unit-diagonal matrix using the unstandardised cross-trait value of 0.1917 was retained only as implementation provenance and was not used for inference. Chromosome X blocks were excluded because sex-chromosome linkage disequilibrium and modelling assumptions differ from the autosomes, and targeted lead-locus summaries were excluded from genome-wide inference and result numbering. Complete primary results, sensitivity comparisons and implementation provenance are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn the count-derived sensitivity analysis, the archived value of 59,264 produced an implied sampling correlation of 0.2590 but could not be verified as the number of participants shared between endpoints. This specification was therefore interpreted only as a sensitivity analysis. No alternative LAVA run using a UK Biobank linkage-disequilibrium reference was available; reference-panel dependence remains a limitation.\u003c/p\u003e\n\u003ch3\u003eGene-level association and pathway enrichment\u003c/h3\u003e\n\u003cp\u003eGene-level association analysis was performed using MAGMA v1.10 with the SNP-wise mean model, 1000 Genomes Phase 3 European linkage-disequilibrium reference data and NCBI Build 37 gene boundaries with a 0 kb window [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We tested 18,301 genes for fibroids and 18,300 genes for endometriosis. Genome-wide gene-level significance was defined using a Bonferroni threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;2.5 \u0026times; 10⁻⁶. Significant genes were classified as significant in both trait-specific analyses, significant only in the fibroid analysis or significant only in the endometriosis analysis according to cross-condition significance. Full gene-level results and classification fields are provided in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Pathway and gene-set annotation was retained as an exploratory prioritisation layer based on curated biological categories and Reactome/gene-set terms for prioritised genes. Curated categories included oestrogen signalling, WNT/development, extracellular matrix, TGF-beta, immune/inflammatory, pain/neuroimmune, cell-cycle/proliferative and vascular/endothelial pathways. Source terms, identifiers, intersections and false-discovery-rate values are detailed in Supplementary Table S3. These results were not treated as definitive pathway causality because regional linkage disequilibrium, gene density, database composition and incomplete archived pathway provenance can influence enrichment.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell RNA-seq contextualisation and cell-type heritability enrichment\u003c/h2\u003e \u003cp\u003ePublicly available single-cell RNA-seq resources from fibroid, myometrial, endometrial and endometriosis tissues were used to contextualise prioritised genes, including GSE162122 for fibroid/myometrium and GSE203191 for endometriosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Analyses used processed expression matrices, metadata or annotated cell-type summaries. Published annotations and ranked markers mapped genes significant in both trait-specific MAGMA analyses and genes significant only in one trait-specific analysis to smooth-muscle, stromal/fibroblast, epithelial, endothelial, immune and lesion-associated compartments. These analyses provided cellular context rather than evidence of causal gene action; Cell-type heritability enrichment was evaluated with stratified LD Score Regression (S-LDSC) using marker-gene-derived annotations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For each major cell class, the top 500 marker genes were mapped to gene bodies with \u0026plusmn;\u0026thinsp;100 kb flanks to create binary annotations. Each annotation was evaluated separately for fibroids and endometriosis against baseline model v1.2 and baseline-LD model v2.2. Alternative marker definitions provided sensitivity analyses. Additional expression context is shown in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, and complete single-cell and S-LDSC results are provided in Supplementary Table S5.\u003c/p\u003e \u003cp\u003eThe published datasets were not re-clustered or reannotated. Cell-context summaries used the original cell labels, normalised expression values and ranked-marker evidence. For S-LDSC, seven primary cell classes were evaluated, and false-discovery-rate correction was applied across these seven annotations separately within each disorder. Alternative top-100, fold-change-based and canonical stromal-marker annotations were treated as sensitivity analyses. These analyses provide cellular prioritisation and do not identify a causal cell type.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFine-mapping\u003c/h3\u003e\n\u003cp\u003eSum of Single Effects fine-mapping was performed with susieR [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] separately for uterine fibroids and endometriosis at ESR1/SYNE1, WNT4, GREB1, WT1 and DNM3. Each analysis used a\u0026thinsp;\u0026plusmn;\u0026thinsp;500 kb window centred on the locus lead variant and local linkage disequilibrium from 1000 Genomes Project Phase 3 European samples. The model allowed up to ten effects per locus and defined credible sets at 95% coverage. Effective sample size was calculated as 4/(1/Ncases\u0026thinsp;+\u0026thinsp;1/Ncontrols), using 31,661 cases and 179,209 controls for fibroids and 15,088 cases and 107,564 controls for endometriosis. Posterior inclusion probabilities, credible sets and lead variants are reported in Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Supplementary Table S4.\u003c/p\u003e\n\u003ch3\u003eEthics, reporting, and reproducibility\u003c/h3\u003e\n\u003cp\u003eThis study used publicly available, de-identified summary statistics and published molecular datasets. Original data collection was approved by the relevant ethics committees and participants provided informed consent, as described by FinnGen and the primary dataset publications. No new participant recruitment, individual-level data access or additional ethics approval was required for this secondary analysis.\u003c/p\u003e \u003cp\u003eReporting was guided by STREGA principles for genetic-association studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The analyses were not preregistered. Pathway annotation, single-cell contextualisation and fine-mapping were interpreted as prioritisation analyses and not as evidence of causal genes, variants or cell types. Analysis parameters, sensitivity analyses and provenance are documented in the Methods and supplementary tables.\u003c/p\u003e \u003cp\u003eAI-assisted tools were used during manuscript preparation for language editing, formatting checks and consistency review. They were not used to generate primary data, perform unsupervised analyses or make independent scientific conclusions. All analyses, interpretations and revisions were reviewed and approved by the authors, who take responsibility for the final content. No direct participant identifiers were entered into these tools.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide and local genetic correlation support shared but incomplete genetic architecture\u003c/h2\u003e \u003cp\u003eLD Score Regression (LDSC) estimated a moderate positive genome-wide genetic correlation between uterine fibroids and endometriosis (rg\u0026thinsp;=\u0026thinsp;0.511, SE\u0026thinsp;=\u0026thinsp;0.069, P\u0026thinsp;=\u0026thinsp;1.37 \u0026times; 10⁻\u0026sup1;\u0026sup3;; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), supporting shared but incomplete common-variant genetic architecture. Local Analysis of [co]Variant Association (LAVA) was performed across 2,495 LD-based genomic blocks using an overlap-aware sampling-correlation matrix derived from the full LDSC intercept covariance matrix as the primary analysis. The endometriosis and uterine-fibroid univariate intercepts (1.0967 and 1.1274) and the cross-trait intercept (0.1917) were standardised to an off-diagonal sampling correlation of 0.17240. The run produced 150 bivariate output rows, of which 92 yielded valid P values after LAVA quality-control and boundary handling. After Benjamini\u0026ndash;Hochberg FDR correction across these valid tests, 16 blocks were significant at FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All 16 significant blocks had positive local genetic-correlation estimates, supporting region-specific genetic sharing between the two disorders (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C). Several local-rg estimates approached the boundary of the parameter space and confidence intervals were broad for some blocks; therefore, the LAVA results were interpreted as evidence of regional genetic sharing rather than as a precise ranking of local-rg magnitude.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMAGMA identifies shared oestrogen-responsive and developmental genes alongside condition-specific signatures\u003c/h2\u003e \u003cp\u003eMulti-marker Analysis of GenoMic Annotation (MAGMA) gene-level analysis identified 147 Bonferroni-significant genes for uterine fibroids and 38 significant genes for endometriosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B; Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). A significant MAGMA gene denotes aggregate gene-level association after correction across the tested gene universe and does not by itself establish the causal gene within a locus. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC compares the two gene-level profiles and identifies ten genes significant in both disorders: SYNE1, GREB1, WNT4, ESR1, EFR3B, VIP, LDLRAD2, DNM3, PLEKHM3 and ARL14EP. Seven of these ten shared MAGMA genes\u0026mdash;LDLRAD2, WNT4, GREB1, ESR1, SYNE1, VIP and ARL14EP\u0026mdash;mapped within at least one of the 16 FDR-significant primary LAVA blocks, providing convergent regional support across local-correlation and gene-level analyses. This overlap supports prioritisation but does not establish the causal gene within each region. The remaining 137 genes were significant only in the fibroid analysis and 28 only in the endometriosis analysis, indicating substantial trait-specific gene-level architecture. Full MAGMA gene-level association results are provided in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCell-type heritability enrichment provided complementary tissue context. Stratified LD Score Regression (S-LDSC) using single-cell-derived marker-gene annotations showed significant stromal enrichment for both fibroids and endometriosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Supplementary Table S5). Fibroid signals additionally showed epithelial and endothelial enrichment, whereas endometriosis showed predominantly stromal enrichment. Endothelial expression provided complementary single-cell context but was not a significant endometriosis S-LDSC enrichment. These results indicate shared stromal context for inherited risk, while supporting broader cellular heterogeneity for fibroids.\u003c/p\u003e \u003cp\u003eExploratory pathway and gene-set annotation highlighted hormone/reproductive signalling, WNT/developmental signalling, extracellular-matrix/fibrosis, cell-adhesion/migration, immune/inflammatory, epigenetic-regulation, cell-cycle/proliferative and PI3K/mTOR/metabolic categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB,C; Supplementary Table S3). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB shows fold-enrichment-style summaries, whereas Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC shows the corresponding statistical-prioritisation values. Because these curated pathway analyses were exploratory and the retained outputs do not represent a complete confirmatory disease-specific MAGMA pathway-regression universe, they were used to provide biological context rather than to establish disease-specific pathway causality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell expression and heritability enrichment converge on stromal context\u003c/h2\u003e \u003cp\u003eTo determine whether genes prioritised in both trait-specific MAGMA analyses were expressed in biologically relevant reproductive-tissue compartments, we performed single-cell RNA-seq contextualisation using public uterine fibroid/myometrial and endometriosis datasets [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Single-cell contextualisation mapped shared MAGMA genes to disease-relevant reproductive-tissue compartments (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table S5). In endometriosis data, GREB1, WNT4 and ESR1 were prominent in stromal compartments, whereas DNM3 mapped to endothelial context. In fibroid/myometrial data, ESR1 and GREB1 showed smooth muscle/stromal expression context, WNT4 mapped to fibroblast/stromal compartments and DNM3 again showed endothelial context. These expression maps provide cellular context for prioritised loci but do not establish causal gene action or cell-type-specific heritability.\u003c/p\u003e \u003cp\u003eCell-type heritability enrichment analysis using stratified LD Score Regression and single-cell-derived marker-gene annotations identified stromal enrichment in both uterine fibroids and endometriosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. S-LDSC provided an independent heritability-enrichment test using single-cell-derived marker-gene annotations. Stromal enrichment was significant for both fibroids (z\u0026thinsp;=\u0026thinsp;2.49, enrichment\u0026thinsp;=\u0026thinsp;3.52-fold, FDR\u0026thinsp;=\u0026thinsp;0.006) and endometriosis (z\u0026thinsp;=\u0026thinsp;2.53, enrichment\u0026thinsp;=\u0026thinsp;3.42-fold, FDR\u0026thinsp;=\u0026thinsp;0.035; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Supplementary Table S5). Immune-cell and smooth-muscle annotations were not significantly enriched, indicating that common-variant heritability captured by GWAS was not primarily driven by immune-cell marker sets or differentiated smooth-muscle marker annotations. This does not exclude inflammatory endometriosis biology or the smooth-muscle histology of fibroids; rather, it suggests that inherited risk is more strongly captured by stromal, mesenchymal or hormone-responsive regulatory programmes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Fibroid epithelial and endothelial annotations met the within-disease FDR threshold, but coefficient z-scores were modest, and these signals were not treated as definitive causal cell-type enrichments. Alternative marker definitions and complete single-cell and S-LDSC results are provided in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Supplementary Table S5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFine-mapping supports allelic complexity at shared loci\u003c/h2\u003e \u003cp\u003eSum of Single Effects (SuSiE) fine-mapping of ESR1/SYNE1, WNT4, GREB1, WT1 and DNM3 supported allelic complexity at several shared regions, particularly ESR1/SYNE1 (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Supplementary Table S4). These patterns are compatible with multiple correlated variants, partially overlapping credible sets or locus-specific heterogeneity, and caution against assigning a single causal variant or gene from regional association strength alone. Together with the LAVA and MAGMA results, fine-mapping supports shared regional architecture at reproductive and hormone-responsive loci while indicating that the shared signal is not explained by a simple one-variant, two-disease model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated genetic evidence supports partial convergence with disorder-specific architecture\u003c/h2\u003e \u003cp\u003eAcross the genetic analyses, uterine fibroids and endometriosis showed partial but non-uniform convergence. A moderate genome-wide genetic correlation was resolved into 16 FDR-significant local-correlation regions, while MAGMA identified ten shared significant genes and substantially larger disorder-specific gene sets. Seven shared MAGMA genes mapped within a significant LAVA region. Concordant stromal enrichment provided cellular context for the shared component, whereas SuSiE fine-mapping indicated that selected regions could not be reduced to a simple one-variant, two-disorder model. These analyses prioritise regions and cellular contexts for follow-up but do not establish causal variants, genes or cell types.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to characterise genetic relationship between uterine fibroids and endometriosis across genome-wide, regional, gene-level and cellular scales using integrative framework. The disorders showed a moderate genome-wide genetic correlation, while overlap-aware local analysis identified 16 FDR-significant regions among 92 blocks with valid bivariate estimates. MAGMA identified ten genes significant in both trait-specific analyses, seven of which mapped within an FDR-significant LAVA region, alongside 137 fibroid-specific and 28 endometriosis-specific genes. Public single-cell expression data and single-cell-informed stratified LD Score Regression further supported stromal context in both disorders. Collectively, these findings indicate partial genetic convergence rather than a unified genetic architecture.\u003c/p\u003e \u003cp\u003eThe genome-wide result is consistent with previous evidence for shared common-variant liability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], but the local analyses show that this relationship is not distributed uniformly across the genome. All 16 regions meeting the primary FDR threshold had positive local genetic-correlation estimates. The count-derived overlap sensitivity retained 10 of these regions, whereas the no-overlap analysis produced larger changes in block eligibility and locus-specific estimates. The overall evidence therefore supports regional genetic sharing, while the status of individual blocks remains sensitive to assumptions about sample overlap and the linkage-disequilibrium reference. Genome-wide and local estimates should consequently be interpreted as complementary measures of common-variant sharing rather than evidence that the disorders are genetically interchangeable.\u003c/p\u003e \u003cp\u003eGene-level analyses refined this regional pattern. Ten genes were significant in both trait-specific MAGMA analyses: \u003cem\u003eSYNE1\u003c/em\u003e, \u003cem\u003eGREB1\u003c/em\u003e, \u003cem\u003eWNT4\u003c/em\u003e, \u003cem\u003eESR1\u003c/em\u003e, \u003cem\u003eEFR3B\u003c/em\u003e, \u003cem\u003eVIP\u003c/em\u003e, \u003cem\u003eLDLRAD2\u003c/em\u003e, \u003cem\u003eDNM3\u003c/em\u003e, \u003cem\u003ePLEKHM3\u003c/em\u003e and \u003cem\u003eARL14EP\u003c/em\u003e. Seven of these genes also lay within an FDR-significant LAVA region, providing convergent statistical prioritisation across local-correlation and gene-level analyses. Several of the shared signals occur in established reproductive-developmental or hormone-responsive regions, including \u003cem\u003eESR1\u003c/em\u003e/\u003cem\u003eSYNE1\u003c/em\u003e, \u003cem\u003eWNT4\u003c/em\u003e and \u003cem\u003eGREB1\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nevertheless, MAGMA association and regional convergence do not identify a causal gene. The much larger sets of genes significant in only one trait-specific analysis further indicate that shared susceptibility is accompanied by substantial disorder-specific gene-level architecture.\u003c/p\u003e \u003cp\u003eThe cellular analyses provide biological context for this shared component. Stromal marker-gene annotations were enriched for common-variant heritability in both disorders, and expression mapping placed several shared MAGMA-prioritised genes in stromal, fibroblast, smooth-muscle-associated, endothelial or hormone-responsive reproductive-tissue compartments [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The concordant stromal signal is compatible with shared mesenchymal and tissue-remodelling programmes. It does not establish a common causal cell of origin, and the absence of significant enrichment for some immune-cell or differentiated smooth-muscle annotations should not be interpreted as evidence against the established inflammatory biology of endometriosis or smooth-muscle histology of fibroids. Heritability enrichment depends on marker definitions, annotation construction and the cellular states represented in the source datasets.\u003c/p\u003e \u003cp\u003eFine-mapping of selected regions, including ESR1/SYNE1, WNT4, GREB1, WT1 and DNM3, indicated allelic complexity rather than a simple one-variant, two-disorder model. Multiple correlated variants or partially overlapping credible sets can produce concordant regional evidence while leaving the causal variant and effector gene unresolved. LAVA, MAGMA and SuSiE address different questions and should not be treated as interchangeable confirmation. Their combined value lies in identifying regions where several forms of statistical evidence converge and where targeted functional investigation would be most informative [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study has two principal strengths. First, it integrates complementary genetic analyses within a focused cross-disorder framework, including an overlap-aware primary LAVA specification and sensitivity analyses. Second, the analysis can be reproduced using public summary statistics, reference panels, annotation resources and published single-cell datasets. Several limitations remain. Both disease GWAS were derived from FinnGen, so shared participants or controls may induce correlated estimation errors despite overlap-aware modelling in LAVA. The predominantly Finnish and European-ancestry evidence limits generalisability to other populations. Use of a 1000 Genomes European linkage-disequilibrium reference may imperfectly represent Finnish haplotype structure and particularly affects locus-level and fine-mapping inference. LDSC and LAVA assess common-variant architecture; MAGMA does not establish causal genes; fine-mapping depends on the accuracy of the linkage-disequilibrium reference; and the cellular analyses provide annotation-based context rather than functional validation. Independent replication, ancestry-diverse analyses and experimental evaluation of prioritised variants, genes and cell states are required before mechanistic or clinical conclusions can be drawn.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUterine fibroids and endometriosis exhibit a shared but complex genetic architecture, characterized by moderate genome-wide common-variant correlation, region-specific genetic convergence, and substantial disorder-specific genomic contributions. Integrative analysis across local genetic correlation, gene-level association, single-cell contextualisation, and cell-type heritability enrichment identifies stromal and hormone-responsive regulatory landscapes as important components of shared susceptibility. However, the observed convergence does not imply a unified genetic mechanism, as multiple loci retain distinct disorder-specific architectures and require functional interpretation. Collectively, this multi-layer genomic framework provides a reproducible strategy for prioritising candidate regions, genes, and cellular contexts for experimental validation and advances the understanding of molecular relationships between two major oestrogen-responsive reproductive disorders.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egenome-wide association study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLocal Analysis of [co]Variant Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLD Score Regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAGMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMulti-marker Analysis of GenoMic Annotation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003escRNA-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-cell RNA sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS-LDSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estratified LD Score Regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSuSiE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSum of Single Effects.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available summary statistics and published molecular datasets. The original studies obtained ethics approval and informed consent as described in their primary publications. No individual-level participant data were accessed, and no additional ethics approval was required for this secondary analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinnGen Release 9 genome-wide association summary statistics are publicly available through the FinnGen data portal (\u003cu\u003ehttps://finngen.gitbook.io/documentation/r9/data-download\u003c/u\u003e). The 1000 Genomes Project reference data and the published single-cell datasets used for contextualisation are publicly available from their cited repositories. This study generated no new individual-level data. Derived summary results underlying the principal analyses are described in the supplementary information.\u003c/p\u003e\n\u003cp\u003eAnalysis code and project documentation are available at https://github.com/vijayachitrabio/fibroids-endometriosis-genomics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was funded by the European Commission through Horizon Europe (NESTOR, grant 101120075); Novo Nordisk Foundation (NNF24OC0092384); Swedish Research Council (2024-02530); Estonian Ministry of Education and Research through the Centre of Excellence for Personalised Medicine (TK214). The funders had no role in study design, data collection, data analysis, interpretation, manuscript preparation or the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVM: Conceptualisation, Methodology, Formal analysis, Investigation, Data curation, Software, Visualisation, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing, Supervision and Project administration. PL: Conceptualisation, Methodology, Investigation, Interpretation of biological findings, Visualisation, Writing \u0026ndash; original draft and Writing \u0026ndash; review and editing. NP: Investigation, Data curation, Validation, Interpretation of results and Writing \u0026ndash; review and editing. AS: Conceptualisation, Resources, Supervision, Funding acquisition, Interpretation of results and Writing \u0026ndash; review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the participants and investigators of FinnGen and the investigators who made the single-cell datasets used in this study publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaird DD, Dunson DB, Hill MC, Cousins D, Schectman JM (2003) High cumulative incidence of uterine leiomyoma in black and white women: ultrasound evidence. Am J Obstet Gynecol 188:100\u0026ndash;107\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulun SE (2013) Uterine fibroids. N Engl J Med 369:1344\u0026ndash;1355\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStewart EA, Cookson CL, Gandolfo RA, Schulze-Rath R (2017) Epidemiology of uterine fibroids: a systematic review. 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J Transl Autoimmun 13:100399. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jtauto.2026.100399\u003c/span\u003e\u003cspan address=\"10.1016/j.jtauto.2026.100399\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"uterine fibroids, endometriosis, genetic correlation, LAVA, MAGMA, fine-mapping, single-cell RNA sequencing, stromal cells","lastPublishedDoi":"10.21203/rs.3.rs-11036637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-11036637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUterine fibroids and endometriosis are common oestrogen-responsive gynaecological disorders with partially overlapping inherited susceptibility. The extent to which this overlap is concentrated in specific genomic regions, genes, and reproductive cell types remains incompletely resolved.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe integrated publicly available FinnGen Release 9 genome-wide association summary statistics for uterine fibroids (31,661 cases and 179,209 controls) and endometriosis (15,088 cases and 107,564 controls). Genome-wide and local genetic sharing were evaluated using LD Score Regression and LAVA. Gene-level associations were assessed using MAGMA, while published single-cell RNA-sequencing datasets and single-cell-informed stratified LD Score Regression provided cellular context. Selected shared regions were further examined using Bayesian SuSiE fine-mapping.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe disorders showed a moderate positive genome-wide genetic correlation (rg\u0026thinsp;=\u0026thinsp;0.511, SE\u0026thinsp;=\u0026thinsp;0.069, P\u0026thinsp;=\u0026thinsp;1.37 \u0026times; 10⁻\u0026sup1;\u0026sup3;). Among 92 genomic blocks with valid primary bivariate estimates, 16 remained significant after false-discovery-rate correction, and all had positive local genetic-correlation estimates. MAGMA identified 147 genes associated with uterine fibroids and 38 with endometriosis after Bonferroni correction; ten were significant in both analyses, and seven of these lay within an FDR-significant LAVA region. Single-cell contextualisation and stratified LD Score Regression supported stromal involvement in both disorders, while fine-mapping indicated allelic complexity at selected shared regions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eUterine fibroids and endometriosis share a moderate component of common-variant liability, but this overlap is regionally heterogeneous and accompanied by substantial disorder-specific gene-level architecture. Convergence across local genetic correlation, gene-level association and stromal enrichment prioritises reproductive and hormone-responsive regions for independent replication and functional evaluation.\u003c/p\u003e","manuscriptTitle":"Integrative genomic analyses reveal regional and cellular convergence between uterine fibroids and endometriosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-09-29 00:50:31","doi":"10.21203/rs.3.rs-11036637/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-09-26T01:14:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324821142620555656741604829118790568707","date":"2026-09-26T00:34:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232528600917046687232008968850707077512","date":"2026-09-24T16:15:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-09-18T10:21:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-09-17T13:48:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-09-17T13:47:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Functional \u0026 Integrative Genomics","date":"2026-09-14T13:01:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f70377ba-3aa3-49a6-b5ca-ca841f6dee0e","owner":[],"postedDate":"September 29th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-09-26T01:14:15+00:00","index":48,"fulltext":""},{"type":"reviewerAgreed","content":"324821142620555656741604829118790568707","date":"2026-09-26T00:34:54+00:00","index":47,"fulltext":""},{"type":"reviewerAgreed","content":"232528600917046687232008968850707077512","date":"2026-09-24T16:15:04+00:00","index":44,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-09-18T10:21:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-09-17T13:48:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-09-17T13:47:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Functional \u0026 Integrative Genomics","date":"2026-09-14T13:01:02+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-09-29T00:50:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-09-29 00:50:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-11036637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-11036637","identity":"rs-11036637","version":["v1"]},"buildId":"qQ7_6M8ijIrYJ9CiyUnPg","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}