{"paper_id":"139302f2-f41f-481e-9584-86bd5fa59eaf","body_text":"1 \nEvolution of the genetic architecture of uterine disorders 1 \nEulalie Liorzou1, Hanna Julienne2, Maëlle Daunesse1, Guillaume Laval3, Hugues Aschard2,4, Camille 2 \nBerthelot1,# 3 \n 4 \n1 Institut Pasteur, Université Paris Cité, CNRS UMR 3525, INSERM U1351, Comparative Functional 5 \nGenomics group, F-75015 Paris, France. 6 \n2 Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France 7 \n3 Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, F-75015 8 \nParis, France. 9 \n4 Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA 10 \n# Corresponding author: camille.berthelot@pasteur.fr  11 \n 12 \nAbstract 13 \nUterine disorders and menstrual abnormalities are prevalent reproductive conditions with significant 14 \nclinical consequences. Recent genome-wide association studies have identified hundreds of variants 15 \ncontributing to different uterine disorders, while epidemiological evidence suggests that these disorders 16 \nco-occur more frequently than expected, implying shared genetic mechanisms. However, the specific 17 \ngenetic variants and biological pathways underlying this shared architecture remain poorly 18 \ncharacterized. Here, w e conducted a uterus -centric, multi-trait genome -wide association analys is 19 \nacross ten uterine disorders in a European cohort to elucidate this shared genetic architecture. Further, 20 \nwe embedded this architecture in a functional and population genomics framework to investigate its 21 \nplausible biological mechanisms and  its evolutionary history  in humans. We confirm strong positive 22 \ngenetic correlations between major uterine disorders  and identify 31 independent susceptibility loci 23 \njointly affecting the genetic risk of multiple uterine pathologies, substantiating an intertwined biological 24 \nbasis. Populational analyses demonstrated that several of these susceptibility variants exhibit 25 \npronounced allele frequency differentiation across global populations  suggestive of recent polygenic 26 \nselection, including variants with well-supported regulatory functions at the ESR1-CCDC170, WNT4, 27 \nSFR1, FOXO1, ITPR1, DMRT1 and CDKN2B loci. Notably, we show that most derived alleles acquired 28 \nduring recent human evolution increase risk across multiple uterine disorders and may evolve under 29 \nantagonistic selection.  These findings provide functional annotation and population-level prioritization 30 \nof genetic variants influencing multiple uterine disorders . They also  highlight how past evolutionary 31 \nhistories may contribute to population differences in uterine disease prevalence and pathogenesis.  32 \n 33 \n  34 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 2 \nIntroduction 1 \nMenstrual abnormalities are important indicators of reproductive pathological conditions  such as 2 \nendometriosis, endometrial cancers or recurrent miscarriages (Gao et al. 1987; Oehler et Rees 2003; 3 \nFraser et al. 2011; Bourdon et al. 2021) . Despite their clinical significance, the genetic architecture 4 \nunderlying abnormal menstrual bleeding risk and its association with other uterine disorders remain 5 \npoorly understood. Over the past 15 years, large-scale genome-wide association studies (GWAS) have 6 \nidentified hundreds of genetic variants associated with different uterine disorders (X. Wang et al. 2022; 7 \nBuyukcelebi et al. 2024; Rahmioglu et al. 2023; Thibord et al. 2025) . For instance, more than 80 loci 8 \nhave been shown to contribute to the polygenic architecture of endometriosis risk (Rahmioglu et al. 9 \n2023; Guare et al. 2025; Koller et al. 2025) . In parallel, epidemiological studies have reported that 10 \nseveral uterine disorders co-occur more frequently than expected (Soliman et al. 2016; Choi et al. 2017; 11 \nYe et al. 2022; Fiore et al. 2025; La Vecchia et al. 2025) , suggesting that they involve overlapping 12 \nbiological pathways, possibly due to common environmental causes or  to shared genetic origins. In 13 \nsupport of the latter, the genetic architecture of endometriosis is correlated to that of uterine fibroids 14 \n(Rafnar et al. 2018a; Gallagher et al. 2019), abnormal menstrual bleeding (Rahmioglu et al. 2023), and 15 \nendometrial cancers (Painter et al. 2018) . Further, this shared genetic architecture across uterine 16 \ndisorders may differ between populations due to their past demographic histories and specific selective 17 \npressures. Substantial evidence supports that several uterine disorders show different prevalences 18 \namong women of African, Asian and European ancestries (Stewart et al. 2017a; Sinharoy et al. 2023; 19 \nYamamoto et al. 2017; Kyama et al. 2007; Mecha et al. 2022). For example, uterine fibroids are more 20 \nfrequent and appear earlier in life in women of African ancestry  (Stewart et al. 2017b; Giuliani et al. 21 \n2020), suggesting population-level differences in genetic susceptibility that may have shaped the risk 22 \nof uterine diseases across populations . Altogether, genetic and epidemiological reports highlight a 23 \nshared genetic basis of uterine disorders, although the genetic variants and biological functions involved 24 \nremain unclear. 25 \nIn this study, we combine multi-trait GWAS analyses, population genetics and functional genomics to 26 \ninvestigate the genetic foundations of shared genetic risk across 10 uterine disorders,  and their 27 \nevolutionary history in the recent human past . Using data from European cohorts, we confirm the 28 \npresence of strong positive genetic correlations between major uterine disorders, consistent with an 29 \nintertwined genetic architecture. We identify susceptibility loci showing pronounced allele frequency 30 \ndifferences across global populations, including variants with putative regulatory effects at the ESR1-31 \nCCDC170, WNT4, SFR1, FOXO1, ITPR1, DMRT1 and CDKN2B loci. Several of these loci exhibit 32 \nsignatures of recent polygenic selection, suggesting that evolutionary pressures have shaped 33 \ncontemporary patterns of risk and protection in human populations. Altogether, our findings provide a 34 \nfunctional and population-level prioritization of genetic variants involved in multiple uterine disorders 35 \nand illuminate how evolutionary processes may contribute to population differences in uterine disease 36 \npathogenesis. 37 \n 38 \n 39 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 3 \nResults 1 \nMapping the shared genetic architecture of uterine diseases in European populations  2 \nTo study the shared genetic architecture of uterine pathologies, we first assessed the estimated genetic 3 \ncorrelation captured by GWAS on 10 frequent uterine disorders. We leveraged the FinnGen biobank 4 \n(Kurki et al. 2023) , a Finnish cohort for medical GWAS that has collected phenotyp ic information for 5 \ncommon gynaecological diseases with high numbers of cases and appropriately designed controls for 6 \nsex specific conditions (i.e. excluding males in this case) . We included all traits related to menstrual 7 \nsymptoms and uterine diseases  with more than 1,500 documented cases,  excluding pregnancy 8 \ncomplications (Methods), resulting in the inclusion of 1 0 GWAS summary statistics (Fig. 1A, Table 9 \nS1). Using LD score regression (Bulik-Sullivan et al. 2015), we identified significant genetic correlations 10 \nbetween 16 pairs of uterine disorders after FDR correction, with correlation coefficients ranking from 11 \n0.28 (leiomyoma vs. inflammatory uterine diseases, p-val = 0.024 with BH correction for multiple testing) 12 \nto 0.94 (excessive frequent and irregular menstruation vs. abnormal uterine bleedings, p-val = 2.10-49) 13 \n(Methods, Fig. 1B, Table S2).  All disorders except “uterine polyps” and “other menopausal symptoms” 14 \nshared significant genetic correlations with at least one other trait.  These genetic correlations confirm 15 \npreviously reported genetic correlations between endometriosis, leiomyoma and abundant menses in 16 \nJapanese (Masuda et al. 2020) and European cohorts (Rahmioglu et al. 2023; McGrath et al. 2023) , 17 \nwhile others are novel (e.g. endometriosis and post-menopausal bleeding), revealing that the genetic 18 \nburden of common uterine dysfunctions is intricately linked across conditions.  19 \nGenetic correlation is a single summary measure that captures the overlap in shared genetic 20 \ndeterminants across pairs of  traits averaged over the entire genome , and it cannot identify specific 21 \ngenetic loci contributing to the shared genetic risk of uterine diseases. To further identify specific genetic 22 \nfactors shared between common uterine disorders, we analysed those 10 traits using joint analysis of 23 \nsummary statistics (JASS) (Julienne et al. 2020, 2021) , a statistical software to conduct multi-trait 24 \nGWAS association. JASS identified a total of 2,670 variants associated with uterine traits reaching 25 \ngenome-wide significance (p < 5.10-8), corresponding to 31 independent genomic loci (Fig. 1C, Fig. S1, 26 \nTable S3). Of these 2,670 pleiotropic SNPs associated with uterine disorders (uSNPs), 93 were not 27 \ngenome-wide significant in any individual uterine disorders GWAS, while 2,577 were significant in  at 28 \nleast one single-trait GWAS prior to joint analysis (Fig. 1D, Fig. S 2). Further, 2,587 SNPs that are 29 \nsignificantly associated with at least one individual trait did not reach genome-wide significance in the 30 \njoint test, suggesting that they contribute less to the shared architecture of uterine disorders. uSNPs 31 \nprioritized by JASS typically exhibited larger effect sizes (b) across uterine diseases compared to SNPs 32 \nthat were only significant in univariate analys es (Fig. 1E). Additionally, uSNPs prioritized with JASS 33 \nshowed overall stronger positive correlations of effect sizes across most uterine diseases compared to 34 \nSNPs that were only significant in univariate analyses (Fig. 1F). Interestingly, jointly prioritized variants 35 \nshowed a strong negative correlation of effect sizes between endometrial cancer and other pathologies, 36 \nand in particular endometriosis, leiomyoma and menses abnormalities (average r = -0.26; Fig. 1F) 37 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 4 \nsuggesting potential antagonistic effect of some variants for the risk of endometrial cancer and other 1 \nuterine diseases.   2 \nTo replicate this analysis, we used matching uterine traits from the UKBiobank on European ancestry 3 \ncohorts (Karczewski et al. 2025) (Methods, Table S4). While the UK Biobank contained fewer cases 4 \nthan FinnGen for most uterine disorders, we found strong directional consistency in the effects of jointly 5 \nprioritized SNPs for matching traits (Table S5). Across uterine diseases, 60% to 83% of uSNPs with a 6 \nsignificant association in FinnGen demonstrate a consistent direction of effect  in UKBiobank GWAS, 7 \nwhich is more than expected by chance (binomial test, p <  0.001 for all comparisons ), except for 8 \nspontaneous abortion and post-menopausal bleeding for which only 43% and 48% of uSNPs have 9 \nreplicated directions of effect, respectively. 583 out of 2,670 uSNPs identified in FinnGen were also 10 \nsignificant after joint analysis using UK Biobank uterine disorders GWAS (Table S6). These results 11 \nconfirm that we adequately captured SNPs with pleiotropic effects on commo n uterine diseases in 12 \nEuropean populations. 13 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 5 \n 1 \nFigure 1: Multi-trait analysis of uterine disorder GWAS. (A) numbers of cases and controls (in thousands) for 2 \neach of the 10 uterine disorder traits from the Finn Gen database included in this study (B). Genetic correlations 3 \nbetween traits estimated by LDscore (* p <0.05, ** p <0.01, *** p <0.001). (C). Manhattan plot of the genome-wide 4 \nsignificant uSNPs after multi -trait analysis across all 10 uterine disorders (p-val < 5.e -08; significant loci are 5 \nhighlighted in orange). (D). Quadrant plot representing the -log10 p-value of each uSNP after the multi-trait analysis 6 \nagainst the lowest p-value across single-trait GWAS. “Sig.” means significant with a p-val < 5.e-08 threshold. The 7 \nx and y axis are cropped to a maximum -log10 p-value of 20 for readability (uncropped quadrant plot in Fig. S2). 8 \n(E). Absolute effect sizes (b) of uSNPs significant after multi-trait analysis (uSNPs, orange) and of SNPs significant 9 \nin single-trait GWAS that do not reach significance in the multi-trait analysis (grey).  (F). Correlation of effect sizes 10 \nfor uSNPs (top) and SNPs only significant in single GWAS (bottom) across uterine disorders (Pearson correlation). 11 \nColour code represents significant correlations after BH correction for multiple testing; non-significant correlations 12 \nare shown in grey.  13 \n**\n*\n*********\n*\n************\n**\n**\n*********\n******\n***\n******\n*****\n******\n***\n********\n*********\nEndometrial cancer (malignant)\nUterinepolypsLeiomyomaUterine inflammato\nryd i s e a s e s\nSpontaneous abo\nrtion\nOther menopausal symptomsPost menopausal\nbleeding\nEndometriosis\nAbnormal uterinebleeding\nExcessiveI r r e g u l a rm e n s t\nruations\nEndometrial cancer (malignant)UterinepolypsLeiomyomaUterine inflammatoryd i s e a s e sSpontaneous abortionOther menopausal symptomsPost menopausalbleedingEndometriosisAbnormal uterinebleedingExcessiveI r r e g u l a rm e n s truations\n10.50-0.5-1\nGenetic correlation(LDSC estimation)\nB\n2577/2670\n2587\n93/2670\nE\nsig. after JASS\nnovel sig. SNP after JASS\nsig. in GWAS before JASS\nnon significant\nSNP classification\nF\nOther menopausal symptomsEndometrial cancer (malignant)Spontaneous abortionPost menopausalbleedingUterine inflammatoryd i s e a s e sPolypsLeiomyomaAbnormal uterinebleedingEndometriosisExcessiveI r r e g u l a rm e n s truations\nEndometrial cancer (malignant)LeiomyomaEndometriosisExcessiveI r r e g u l a rm e n s truationsAbnormal uterinebleedingPolypsPost menopausalbleedingOther menopausal symptomsSpontaneous abortionUterine inflammatoryd i s e a s e s\n−1−0.500.51\nCorrelation of effect sizeSNP sig. with JASSn.sD\nA Curation ofGWAS on uterinedisorders(FinnGendatabase)\nBleedingsymptoms\n● Abnormal uterinebleeding\n● Excessive irregularmenstruations\n● Post menopausalbleeding\nEndometrialdisorders\n● Endometriosis\n● Uterine polyps\n● Endometrialcancer (malignant)\nMyometrialdisorder\n● Leiomyoma\nOtheruterine disorders\n● Spontaneous abortion\n● Uterineinflammatorydiseases\n● Othermenopausal symptoms\n0 20\nNumber of individuals\n(thousands)\nCases Controls\n0 100\nC\n0.00.10.2Effect size (absolute value)\nAbnormal uterinebleeding\nOther menopausalsymptoms\nEndometriosis\nPolyps\nEndometrialcancer(malignant)\nLeiomyoma\nExcessive Irregularmenstruations\nPost menopausalbleedingSpontaneous abortion\nUterine inflammatorydiseases\nJASS UNIVARIATEonly\n***\n***\n***\n***\n***\n***\n***\n***\n***\n***\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 6 \n 1 \nPleiotropic uterine-associated SNPs are enriched near genes involved in female reproduction, 2 \ndevelopment and senescence 3 \nWe next investigate d how pleiotropic uSNPs associated with uterine disorders  may affect gene 4 \nregulation in the uterus. Almost 98% of uSNPs are non-coding, with 60% (n=1597) falling in introns, 2% 5 \nin UTRs and 36% (n=962) in intergenic regions (Fig. 2A, Table S7), as expected for GWAS significant 6 \nSNPs.  uSNPs are enriched in gene promoters compared to SNPs that are significant in single-trait 7 \nGWAS only (proportion test; p-val = 4.10-4; Fig. 2B), and are  also enriched in promoter -enhancer 8 \ncontacts captured by Hi-C in endometrial stromal cells (Sakabe et al. 2020) (p-val = 8.10-6; Fig. 2B). 9 \nWe speculate that uSNPs have more pleiotropic and larger effects on uterine traits in part because they 10 \nmore frequently fall within gene regulatory regions, and in particular gene promoters.  11 \nTo further explore the functions of genes that may be regulated by uSNPs, we combined functional and 12 \nlocation-based information to associate uSNPs with putative target genes (Methods, Fig. S3,  Table 13 \nS7). In total, uSNPs associate with 227 genes, including well-described loci involved in uterine diseases, 14 \nendometriosis and uterine fibroids such as ESR1, WNT4, WT1 or FSHB (Gallagher et al. 2019; Pavlicev 15 \net al. 2022; Rahmioglu et al. 2023; Kim et al. 2024; Sheu et al. 2024b). These genes are over-expressed 16 \nin fallopian tubes, uterus, ovary, cervix and vagina based on GTEX transcriptomes (GTEx Consortium 17 \n2017), and under-expressed in several other organs such as brain, blood, muscle or pancreas (Fig. 2C; 18 \nMethods). This result highlights that uSNPs are enriched around genes with specialized expression in 19 \nfemale reproductive organs.  20 \nGene set enrichment analysis confirms that the putative targets of uSNPs are strongly enriched in terms 21 \nrelated to reproductive and developmental processes, involving genes such as  WNT4, WT1, SOX15, 22 \nNEURL1, FBXO5, and CFAP58 (Fig. 2D-E). The strongest enrichment was found for genes associated 23 \nwith replicative senescence (38-fold enrichment; Fig. 2D, Table S 8), driven by canonical regulators 24 \nsuch as TERT, TP53, CDKN1A, ATM and CHEK2 (Fig. 2E). Additionally, we find enrichment in terms 25 \nrelated to ubiquitylation (e.g. BTRC, CDKN2A, FBXO5) and arsenic response (e.g. GSTO1, GSTO2, 26 \nSLC38A2). While those three processes are not specific to uterine or reproductive physiology, they 27 \nrepresent conserved cellular networks that may indirectly influence reproductive function. This suggests 28 \nthat pleiotropic uterine SNPs influence both canonical reproductive pathways and broader cellular 29 \nprocesses, highlighting their potential role in linking systemic biological networks with female 30 \nreproductive functions. 31 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 7 \n 1 \nFigure 2: Functional characterisation of uterine-associated SNPs (uSNPs). (A) Distribution of uSNPs across 2 \ncoding and non-coding elements of the genome based on ENCODE annotations. (B) Overlap of uSNPs and single-3 \ntrait GWAS SNPs (univariate) with functional genomic elements from different databases and from endometrial 4 \nregulatory profiles (Methods). Absolute numbers of SNPs overlapping the elements are represented (chi-2 test; n.s 5 \np > 0.05, *** p  < 0.001, BH-corrected for multiple testing). (C) Enrichment of uSNPs target genes (uGenes) in 6 \norgan-specific expression profiles from GTEx. Significant enrichments are represented in colour (turquoise for 7 \ndown-regulated genes, yellow for up -regulated genes, BH-corrected for multiple testing). (D) Gene ontology 8 \nenrichment analysis using uSNPs target genes. Hierarchical clustering was computed using similarities between 9 \nGO terms (estimated with Jaccard index). € Network plot of target genes contributing to  the 5 first GO enriched 10 \nterms. Dot size represents the number of target genes associated to each GO term. (F) Gene expression heatmap 11 \nof uSNPs target genes across endometrial cell types, in log2CPM scaled to the maximum by row. Only genes with 12 \ncell-type specific expression are represented (full expression heatmap in Fig. S4).  13 \n 14 \nTo investigate how uSNPs might influence uterine cell functions, we re -analysed endometrial single-15 \nnuclei transcriptomes of healthy donors from the HECA atlas (Marečková et al. 2023), (Fig. S4A). This 16 \nB\nEndometrial ATAC peaks\nCTCF\nEnhancer like structure\nEpithelial HiC maps\nREMAP peaks all\nStromal HiC maps\nREMAP peaks uterus\nAllele specific binding\neQTL\nPromoter\n0 500 1000 1500\nnumber of SNPs ***nsnsns***\nns\nns\nnsnsns\nuSNP\nsingle-trait SNP C\nAdipose TissueAdrenal Gland\nBladder\nBlood\nBlood Vessel\nBrain\nBreast\nCervix Uteri\nColon\nEsophagus\nFallopian Tube\nHeart\nKidney\nLiver\nLung\nMuscle\nNerve\nOvary\nPancreas\nPituitary\nProstateSalivary Gland\nSkin\nSmall Intestine\nSpleen\nStomach\nTestis\nThyroid\nUterus\nVagina\n042 86-log10 (P-value)\nEnrichment of uGenesinup-regulated DEG\n846 02-log10 (P-value)\nEnrichment of uGenes indown-regulated DEGA\nED\nnumber of genes5.07.510.0\n12.5\np.adjust\n0.010.020.030.04\nUbiquitilation\nDevelopmental processes involvedin reproduction\nDNA damage and senescence\nResponse to arsenic\nDevelopmental morphogenesis\nDevelopmental processes\nreplicatives e n e s c e n c ecellular senescence\ncellular response to gammaradiation\ntelomere maintenance via telomeraseRNA−templated DNA biosynthetic process\nsexd i fferentiationmale sexd i fferentiation\ngonad development\nfemale gonad development\ndevelopment of primarysexual characteristics\nregulation of ubiquitin protein ligase activity\nnegativer e g u l a t i o no fu b i q u i t i np r o t e i nl i g a s ea c t i v i t ynegativer e g u l a t i o no fu b i q u i t i n − p r o t e i ntransferase activity\nregulation of ubiquitin−protein transferase activity\nSCF−dependent proteasomal ubiquitin−dependent protein catabolic process\ngland morphogenesis\nresponse to arsenic−containing substance\nresponse to starvation\nsynapse pruning\nsalivaryg l a n ddevelopment\ncellular response to arsenic−containing substance\npatterns p e c i fi c a t i o np r o c e s s\ncellular process involved in reproduction inmulticellular organism\nregulation of gonad developmentgermc e l ldevelopment\noocyte developmentoocyte differentiation\nregulation ofnuclear division\nF\nCoding\n(2%)\nIntronic\n(60%)\nSplicing\n(n=1)\n5'UTR\n(0.5%)\nIntergenic\n>1kb of genes\n(36%)\nDownstream gene\n(<1kb), (1%)\nUpstream gene\n(<1kb), (1%)\nncRNA exon\n(1%)\nexonic\n(1%)\n3'UTR\n(2%)\nNon-Coding\n(98%)\nCiliatedGlandular SecretoryLuminalpreGlandularProliferativeEndothelialLymphaticLymphoidMyeloidDecidualPerivascularNon-decidualProliferative\nEpithelialStromal\nCDKN2BCDKN1AITPRIPIFITM1CALHM2NEURL1SORCS1FGF11SHBGC22orf31SORCS3SOX15\nSULT1E1IFNEALOXE3TERT\nMYCT1VIPLDLRAD2ARHGEF15KDRSULT1B1\nCCDC181COL17A1NKD2C2orf50CFAP58DNAH2\nASGR2DEPDC7F5CD68C1QCC1QBC1QA\nSLC25A35ALPLUSP44CFAP43TNFSF13CDC42−AS1TMEM102NTN4TSPAN12PKP3EXPH5GPR160PRRG4MIR31HG\nDOCK8CCDC73SLC38A1STN1ALOX12SLC35F2CD109MPPED2GSTO2KANK1ATP1B1LINC00339CCDC170DPCDC11orf65FBXO5CHEK2CEP72CDKN2AHPS6WRAP53\n0\n0.2\n0.4\n0.6\n0.8\n1\nGene expression(rowmax oflog2CPM counts)\nreplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c ereplicatives e n e s c e n c e\nsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiationsexd i fferentiation\nregulation of ubiquitin protein ligase activityregulation of ubiquitin protein ligase activityregulation of ubiquitin protein ligase activityregulation of ubiquitin protein ligase activityregulation of ubiquitin protein ligase activityregulation of ubiquitin 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ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic processSCF−dependent proteasomal ubiquitin−dependent protein catabolic process\ncellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescencecellular senescenceATMATMATMATMATMATMATMATMATMATMATMATMATMATMATMATMATM\nTP53TP53TP53TP53TP53TP53TP53TP53TP53TP53TP53TP53TP53TP53TP53TP53TP53\nCHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2CHEK2\nTERTTERTTERTTERTTERTTERTTERTTERTTERTTERTTERTTERTTERTTERTTERTTERTTERTCDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1ACDKN1A\nCDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2ACDKN2A\nWNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4WNT4FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8FGF8\nCYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1CYP17A1\nFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHBFSHB\nWT1WT1WT1WT1WT1WT1WT1WT1WT1WT1WT1WT1WT1WT1WT1WT1WT1\nSOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15SOX15\nKDRKDRKDRKDRKDRKDRKDRKDRKDRKDRKDRKDRKDRKDRKDRKDRKDR\nESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1ESR1\nDMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT1DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT3DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2DMRT2\nBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRCBTRC\nUSP44USP44USP44USP44USP44USP44USP44USP44USP44USP44USP44USP44USP44USP44USP44USP44USP44\nFBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5FBXO5\nFBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXW4FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15FBXL15\nFBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3FBXO3\nCUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5CUL5\nCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2BCDKN2B\nNumber of genes46810\n12\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 8 \nshowed that 74% of uSNP target genes are expressed in at least one uterine cell type (Fig. S4B). Many 1 \nof these genes, including well-documented loci such as ESR1, WT1, and WNT4, are broadly expressed 2 \nacross multiple uterine cell types and likely have ubiquitous effects across the reproductive system. 3 \nMore interestingly, several target genes displayed cell -type–specific expression ( Fig. 2F ). For 4 \nexample, CDKN2B, CDKN1A, ITPRIP and IFITM1 were preferentially expressed in decidual stromal 5 \ncells, while CCDC181, COL17A1, CFAP58 and DNAH2 were strongly expressed in ciliated epithelial 6 \ncells. This observation suggests that uSNPs may also capture pleiotropic effects resulting from altered 7 \nfunctions in restricted cell populations. 8 \nAs expected for pleiotropic loci, most uSNP target genes were also expressed in tissues beyond the 9 \nuterus. Among the few targets not detected in healthy uterus, several showed highly tissue -specific 10 \nexpression in the testis, pituitary or brain (e.g., FSH, INA, CYP17A1) (Fig. S5). These patterns suggest 11 \nthat some uSNPs may exert pleiotropic effects on uterine traits via systemic endocrine or 12 \nneuroendocrine pathways, or that certain genes may become aberrantly expressed in the uterus under 13 \npathological conditions. 14 \n 15 \nSubstructure of uSNPs with risk-increasing or protective effects on uterine disorders 16 \nWe next investigated how the shared genetic architecture of uterine diseases is structured into groups 17 \nof variants with similar effects on multiple disorders. To obtain a set of largely independent variants, we 18 \nreduced the full set of 2,670 uSNPs to 235 lead uSNPs in pseudo -independent linkage disequilibrium 19 \nblocks (R2 < 0.5), and identified the ancestral and derived alleles for each lead uSNP (Methods). Then, 20 \nwe performed unsupervised K-means clustering on effect sizes (b) of lead uSNP derived alleles across 21 \ntraits (Methods). This analysis identified an optimal clustering with two main  clusters representing 22 \nuSNPs for which the derived allele has  an overall protective effect (negative effect size) across most 23 \nuterine disorders (cluster 1), or has an overall risk-increasing effect (cluster 2; positive effect size) (Fig. 24 \n3A, Table S9). Risk-increasing cluster 2 contained twice as many lead uSNPs as protective cluster 1, 25 \nsuggesting that recently-acquired alleles are more frequently deleterious in terms of uterine disorder 26 \nrisk.  27 \nTo further explore how protective and risk -increasing loci associate with  different disorders , we 28 \ncomputed pairwise correlations of normalized effect sizes between disorders for each cluster (Fig. 3B). 29 \nVariants in cluster 1 had correlated and protective effects on endometriosis and different menses and 30 \nmenopause disorders, but negative correlation s and deleterious  effects with other traits such as 31 \nendometriosis and leiomyoma – suggesting that these variants tend to be protective for either disease 32 \nbut not both. Variants in cluster 2 had overall correlated risk-increasing effects on most disorders, with 33 \nthe notable exception of malignant endometrial cancer and leiomyoma , which were negatively 34 \ncorrelated with endometriosis. Specifically, we identified a n antagonistic pleiotropic  relationship 35 \nbetween endometriosis and malignant endometrial cancer , where derived alleles at  lead uSNPs in 36 \ncluster 2 typically increased risk for endometriosis but had overall protective effects on endometrial 37 \ncancer, in particular at the ESR1 and GREB1 loci (Fig. 3A-B). By focusing on a subset of largely 38 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 9 \nindependent SNPs that associate with multiple disorders, this analysis breaks down the genome-wide 1 \ngenetic correlations identified in Fig. 1B into more nuanced components with correlated or antagonistic 2 \npleiotropic effects between disorders.   3 \nThis analysis highlighted a locus of particular interest on chromosome 10 (10.q25.1), containing a 4 \nremarkably high concentration of  pleiotropic uSNPs of large effect sizes on multiple disorders , 5 \nespecially leiomyoma, excessive irregular menstruation  and polyps (Fig. 3C ). This locus is rich in  6 \nputative non-coding regulatory elements active in the endometrium (accessible in single-cell ATAC-seq 7 \ndata from human endometrium (Vrljicak et al. 2023), and corresponding to promoter-enhancer contacts 8 \nin decidual stromal cells assessed by promoter-capture Hi-C (Sakabe et al. 2020)). rs4387287*C and 9 \nrs111447985*C, which overlap with CTCF binding sites and are predicted to enhance the binding of the 10 \nBRD4 transcription factor, respectively, are notable in this locus. Both SNPs are situated within the 11 \npromoter region of the STN1 gene, a chromatin region that is accessible across various endometrial 12 \ncell types (Fig. 3C, Table S7). Remarkably, these SNPs coincide with 116 Hi-C contacts in decidualized 13 \nendometrial stromal cells, establishing connections notably with the promoters of SFR1, CFAP43, and 14 \nITPRIP, suggesting robust regulatory activity of this chromatin region across endometrial cell types. In 15 \nhealthy endometrium, the STN1 gene demonstrates consistent expression across different endometrial 16 \ncell types, with particularly elevated levels in endometrial glandular cells and immune cells ( Fig. 2E). 17 \nSTN1 is known to play a crucial role in cancer progression, acting as an upstream regulator of the 18 \nepithelial-to-mesenchymal transition (Nguyen et al. 2023; Dong et al. 2025). Notably, previous GWAS 19 \nstudies have associated this locus with the risk of endometriosis (Sheu et al. 2024a), and our findings 20 \nsuggest that variants in this locus may have pleiotropic effects on other uterine disorders, such as 21 \nuterine leiomyomas. While the functional roles of these candidate uSNPs in endometrial cells await 22 \nexperimental validation, they have the potential to disrupt the regulatory landscape of STN1 expression, 23 \nas well as those of SFR1, CFAP43, and ITPRIP.  24 \n  25 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 10 \n 1 \n 2 \nFigure 3:  Substructure of uSNPs effect sizes across uterine disorders. (A) Heatmap of effect sizes for derived 3 \nalleles of pseudo-independent uSNPs across uterine traits. Cluster 1 and cluster 2 were determined by k-means 4 \nclustering on uSNPs effect sizes. For each cluster, uSNPs are ordered by genomic position and labelled by their 5 \nnearest gene name. (B) Correlations of uSNPs effect sizes across uterine traits for cluster 1 (top) and cluster 2 6 \n(bottom). Only significant correlations are represented (BH-corrected for multiple testing). (C) Representation of 7 \nthe SFR1 locus showing normalised effect sizes of derived alleles and functional annotations of uSNPs.   8 \n 9 \nEvidence of polygenic selection at uterine disease risk loci in European populations 10 \nBecause genetic variants affecting reproductive functions may be particularly accessible to natural 11 \nselection due to their direct effect on fitness, we next investigated whether the shared genetic 12 \narchitecture of uterine disease  risk displays evidence of polygenic selection in  human 13 \npopulations(Pritchard et al. 2010; Pritchard et Di Rienzo 2010; Stephan et John 2020). We hypothesized 14 \nthat uSNPs may be under stronger evolutionary pressures than background SNPs because they may 15 \nhave pleiotropic effects on gene expression  and therefore be more likely to evolve under selection 16 \n(Pavlicev et Wagner 2012; Hämälä et al. 2020) . We compared the fixation index ( Fst) distributions 17 \nbetween the 191 lead uSNPs and 1,000 sets of control SNPs match ed for recombination rate, minor 18 \nallele frequency (MAF) and local gene density, in populations from the  1000 Genomes Project (The 19 \n1000 Genomes Project Consortium et al. 2015) (Methods). Fst measures allelic differentiation between 20 \npairs of populations and is increased for variants that experienced past positive selection (Holsinger et 21 \nWeir 2009; Stephan 2016). The Fst distribution of lead uSNPs is shifted towards higher Fst values in the 22 \n2.q11.2−LYG1\n17.p13.1−MYH10\n16.p13.11−MYH11\n9.p24.3−KANK1\n5.p15.33−TERT\n2.p25.1−GREB1\n1.p36.12−WNT4\n7.q31.31−TSPAN12\n11.q22.3−ATM\n4.q12−KDR\n4.q13.3−SULT1E1\n6.q25.1−ESR1\n9.p21.3−CDKN2B\n10.q25.1−SFR1\n11.p13−WT1\n13.q14.11−FOXO1\n17.p13.1−TP53\n22.q12.1−TTC28\n22.q13.1−TNRC6B\n3.p26.1−ITPR1\n6.q13−CD109\nEndometriosis\nLeiomyoma\nAbnormal uterine bleeding\nExcessiveI r r e g u l ar menstruations\nSpontaneous abortion\nPost menopausal bleeding\nOther.menopausal symptoms\nUterine inflammatoryd i s e a s e s\nPolyps\nEndometrial cancer malignant\n11.q22.3−ATM\n12.q22−VEZT\n11.p14.1−FSHB\n11.p15.5−BET1L\n5.p15.33−TERT\n4.q12−KDR\n2.p14−MEIS1\n2.p14−ETAA1\n3.q26.2−MYNN\n1.q24.2−ATP1B1\n1.p36.12−WNT4\n2.p25.1−GREB1\n10.q25.1−SFR1\n9.p24.3−KANK1\n11.p13−WT1\n9.p21.3−CDKN2B\n6.q25.2−ESR1\n17.p13.1−TP53\n13.q14.11−FOXO1\n22.q12.1−TTC28\nEffect-size\n−0.5 0 0.5 1\nCluster 2Cluster 1\nB\nA\nC\nLeiomyoma\nAbnormal uterine\nbleeding\nExcessiveI r r e g u l a r\nMenstruation\nSpontaneous\nabortion\nPost menopausal\nbleeding\nOther menopausal\nsymptoms\nUterine inflammatory\ndiseases\nPolyps\nEndometrial cancer\nmalignant.\nEndometriosis\nCorrelation of effect-size\n0.25 0.50 0.75 1 Positive\nNegative\nEndometriosis\nLeiomyoma\nAbnormal.uterine.bleeding\nExcessive.Irregular.menstruations\nSpontaneous.abortion\nPost.menopausal.bleeding\nOther.menopausal.symptoms\nUterine.inflammatory.diseases\nPolyps\nEndometrial.cancer..malignant.\nIn promoter-enhancer contact\nof decidual stromal cells\nAllele specific binding\nAccessible in endometrial cell types\nEffect-size\n−0.5 0 0.5 1\nSTN1 SLK COL17A1 SFR1 CFAP43\nnon-significant\nuSNPs\npHiC dStromal\nCTCF\nrs4387287\nrs111447985\nrs73331554rs74154737\n0\n20\n40\n60\n103900000 104000000 104100000 104200000\nlogp\nLeiomyoma\nAbnormal uterine\nbleeding\nExcessiveI r r e g u l a r\nMenstruation\nSpontaneous\nabortion\nPost menopausal\nbleeding\nOther menopausal\nsymptoms\nUterine inflammatory\ndiseases\nPolyps\nEndometrial cancer\nmalignant.\nEndometriosis\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 11 \nreference European population (CEU) compared to the reference East Asian (CHB) and African (YRI) 1 \npopulations (Fig. 4A; CEU vs. CHB, p-val = 0.024, CEU vs. YRI, p-val = 0.0009; Wilcoxon test with BH 2 \ncorrection for multiple testing). In contrast, we detected no significant difference in Fst values between 3 \nYRI and CHB populations. We next compared the integrated haplotype scores (iHS) of lead uSNPs and 4 \ncontrols in each  population, which measures the local strength of selection based on  haplotype 5 \nhomozygosity to detect more recent selection events (Voight et al. 2006a). uSNPs have overall higher 6 \nabsolute iHS than matched controls  in Europeans (CEU), although the signal does not reach 7 \nsignificance after multiple testing correction (Fig. 4B; p-val = 0.087; Wilcoxon test with BH correction 8 \nfor multiple testing). These results suggest that uterine disease risk loci captured from European cohorts 9 \nhave likely been influenced by past selective pressures resulting in polygenic allelic differentiation 10 \nbetween human populations.  11 \n 12 \nrs12035094CDC42rs9315762LINC00332rs9371246SYNE1rs1408459SYNE1rs1343987 RP11−415K20.1rs9383580CCDC170rs2623964SYNE1rs1971256CCDC170rs10815717DMRT1rs1392916CD109rs16922717KANK1rs11191835SH3PXD2Ars2779747 RP11−408N14.1rs12638862TERCrs1052001SFR1rs2473290LINC00339rs3820282WNT4rs1265005SFR1rs13028479GREB1rs7571803GREB1rs2488002OBFC1rs60622800MIR4457\n−0.50−0.250.000.250.500.75\nrs5752763TTC28rs12213214VIPrs2207548 RP1−65P5.1rs13218956SYNE1\nsignedFST\nCHBhigher DAF inYRI\nrs12499134SULT1D1Prs9383889RMND1rs2982556ESR1rs2779747RP11−408N14.1rs10835891RP1−65P5.1rs12213214VIPrs12250162SLKrs1392916CD109rs961605RP4−562D20.2rs60622800MIR4457rs9397414CCDC170rs1052001SFR1rs7571803GREB1rs13028479GREB1rs2488002OBFC1rs1971256CCDC170rs1265005SFR1\n−0.50−0.250.000.250.500.75\nrs5752763TTC28rs6503048TP53\nsignedFST\nCEUhigher DAF inYRI\n−0.50−0.250.000.250.500.75\nrs3804984ITPR1rs2347792C22orf31rs1095900 RP11−408N14.1rs35194179SYNE1rs10976019KANK1rs11795218KANK1rs12173791CCDC170rs931423SYNE1rs11191835SH3PXD2Ars12035094CDC42\nrs2324596RP11−172E9.2rs9566498LINC00332rs16922717KANK1rs11791986KANK1:RP11rs851983ESR1rs57295696KANK1rs607987FSHBrs9397414CCDC170rs2982556ESR1rs9383580CCDC170rs7854629CDKN2B−AS1rs10835893WT1 CEUhigher DAF in\nsignedFST\nCHB\nCEU CHB YRI\n0\n1\n2\n3|iHS|\np=0.087p=0.54 p=0.54\nControlsControlsControlsuSNPsuSNPsuSNPs\nB\n0.0\nControlsControlsControlsuSNPsuSNPsuSNPs\n0.1\n0.2\n0.3\n0.4\n0.5FST\np=0.024\np=0.0009\np=0.78\nCEUvs.YRICEUvs.CHB CHBvs.YRIA\nLead SNPs not\nin outliers\ntop5_CEU\ntop5_CHB\ntop5_YRI\nFST outliers\nC D E\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 12 \nFigure 4:  Evolutionary statistics of uSNPs in reference European, African and East-Asian populations. (A-1 \nB) Distributions of F st (A) and iHS values (B) at pseudo-independent lead uSNPs compared to 1,000 sets of 2 \nmatched control SNPs (Wilcoxon rank sum test, BH-corrected for multiple testing). (C-D-E) Signed Fst values of 3 \nthe top pseudo-independent lead uSNPs for all three population comparisons: (C) CEU (Northern European from 4 \nUtah) vs. CHB (Chinese Han from Beijing); (D) CEU vs. YRI (Nigerian Yoruba from Ibadan); (E) YRI vs. CHB. 5 \nuSNPs in the top  5% genome-wide F st values are represented in colour; signed F st scores point towards the 6 \npopulation where the derived allele frequency is the highest.  7 \n 8 \nTo highlight specific uSNPs of interest that might have experienced stronger events of selection, w e 9 \nnext focused on uSNPs with outlier Fst values (top 5%), which exhibit very strong allelic differentiation 10 \nbetween populations and are prime functional candidate loci, similarly to previous approaches (Quach 11 \net al. 2016). We find that 50 uSNPs out of 191 uSNPs tested are Fst outliers in at least one comparison 12 \n(23%, p-val = 0.0004, binomial test, Table S10, Methods). Moreover, uSNPs are over-represented in 13 \nFst outliers in both the CEU vs. CHB and CHB vs. YRI comparisons (Fig. 4C-E; CEU vs. CHB, p-val < 14 \n2.2.10-16; CEU vs. YRI, p-val = 0.12; CHB vs. YRI, p-val = 0.01; empirical test with BH correction for 15 \nmultiple testing, Methods). We compared the GWAS effect sizes of uSNPs with outlier Fst values to 16 \nnon-outlier uSNPs and found no significant differences between groups (Fig. S7).  17 \nAmongst uSNPs with high Fst values, we prioritized a list of candidate variants with putative functional 18 \nimpact on uterine cell types as described in Fig. 2. This analysis highlights 17 uSNPs with both evidence 19 \nof allelic differentiation across human populations and functional importance for uterine cell functions, 20 \nspanning the ESR1, WNT4, SFR1, FOXO1, KANK1 and CDKN2B loci (Table 1). As CDKN2B, ITPR1 21 \nand WNT4 are mostly expressed in  endometrial stromal cells, and DMRT1 and CCDC170-ESR1 in 22 \nepithelial cells, we speculate that variants at these loci may mostly affect these cell types. Interestingly, 23 \nvariants at the SFR1 locus, and presented in Fig. 3C, also show evidence of allelic differentiation across 24 \npopulations and display the highest Fst score of all tested uSNPs (Fig. 4D). Additionally, rs3820282 at 25 \nthe WNT4 locus was recently highlighted as a pleiotropic variant with beneficial roles in gestation length 26 \nand pre -term birth while increasing the risk of endometriosis, breast cancer and ovarian cancer 27 \n(Pavlicev et al. 2022). Functional validation in human cell lines and a transgenic mouse model confirmed 28 \nthat rs3820282 increases the binding of ESR1 and induces an over-expression of WNT4 in endometrial 29 \nstromal cells (Pavlicev et al. 2022).  30 \nAltogether, our results support that a sizeable fraction of  uSNPs display evolutionary signature s of 31 \npositive selection in European populations, and further prioritize candidate SNPs within these genomic 32 \nregions with joint evolutionary and functional signatures. 33 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 13 \n 1 \nTable 1: Prioritized susceptibility loci harbouring candidate uSNPs with signatures of strong allelic 2 \ndifferentiation and with regulatory potential in endometrial cells. The full table of functional and evolutionary 3 \nannotation of those variants is available in Table S10. pELS: proximal enhancer -like structure; dELS: distant 4 \nenhancer-like structure; Epi: epithelial; Str: stromal; Im: immune; Ed: endothelial.  5 \n 6 \nThe ESR1-CCDC170 locus displays strong evidence of positive selection in European 7 \npopulations 8 \nAmong genetic loci with evidence of recent selective pressures in human populations, the genomic 9 \nregion including both the CCDC170 and ESR1 genes is of particular interest as it carries a high number 10 \nuterine-relevant SNPs with outlier Fst values (Table 1, Fig. 5A). Notably, rs1971256 displays the 11 \nstrongest Fst value of this locus (CEU  vs. YRI, Fst =0.607; CHB vs. YRI, Fst = 0.331) and the highest 12 \nscore of pathogenicity (CADD  = 9.988, Table 1 , Methods ). Additionally, this locus exhibits well-13 \nsupported evidence of selective sweeps in the last 100,000 years across all European populations and 14 \nin one African population, which constitute rare events of strong positive selection (Laval et al. 2021) 15 \n(Fig. 5C). Indeed, we observe that the rs1971256 derived allele (T) has become the major allele in most 16 \nhuman populations, except those of African descent  (Fig. 5D). This variant was previously prioritised 17 \nas increasing the risk of endometriosis as well as endometrioid and clear cell ovarian cancers (Sapkota 18 \net al. 2017a; Mortlock et al. 2022) , but its functional impact on endometrial cell types  has not been 19 \ninvestigated. In this study, we found that the derived allele of rs1971256 has a pleiotropic protective 20 \nrole across multiple uterine traits, notably endometriosis, abnormal menstrual bleeding, excessive and 21 \nfrequent menstruation and inflammatory uterine diseases (Fig. 5B). This variant is in the promoter of 22 \nCCDC170, and additionally displays 22 significant Hi-C contacts with the ESR1 promoter and introns 23 \nidentified in decidual stromal cells (Fig. 5C). Additionally, this region is in accessible chromatin across 24 \nseveral endometrial cell types and is a predicted CTCF binding site by ENCODE (Dunham et al. 2012) 25 \n(Fig. 5C). In healthy endometrium, CCDC170 is particularly expressed in proliferating endometrial 26 \nepithelial cells, pre -glandular cells and ciliated cells , and ESR1 is expressed across most cell types 27 \nduring the proliferative phase (Fig. 5E). Other uSNPs with outlier Fst value in this locus are accessible 28 \nCEUvs CHB CEUvs YRI CHBvs YRI EpiS tr Im Ed\nrs2473290 C T 0.119 0.212 0.474 4.146\nCDC42 ,LINC00\n339;CDC42-\nAS1;LINC01635\n1 XXX\nCDC42-\nAS1;CDC4\n2;LINC003\n39;LINC01\n635\nrs3820282 C T 0.171 0.195 0.489 20.3 WNT4 X X pELS\nrs12250162 C C 0.012 0.324 0.252 1.105 SFR1,COL17A1 1 COL17A1\nrs1265005 A A 0.052 0.637 0.546 7.47 SFR1 X\n13.q14.11 rs9315762 C T 0.056 0.143 0.288 3.009 FOXO1 X\n3.p26.1 rs3804984 T T 0.387 0.028 0.241 1.756 ITPR1 X X\nrs1971256 C C 0.115 0.607 0.331 9.988\nCCDC170,\nESR1 22 X XXX pELS,CTCF\nrs9383580 A G 0.356 0.013 0.325 2.802 ESR1, 1 X\nrs851983 A G 0.259 0.115 0.039 4.955 ESR1, 1 X X dELS,CTCF\nrs9371246 G G 0.105 0.068 0.301 1.746 C6orf211 1 XXX X\nrs1408459 T C 0.087 0.096 0.313 4.512 SYNE1 X dELS,CTCF\nrs11795218 G A 0.278 0.198 0.009 9.516 KANK1 X\nrs11791986 A G 0.242 0.231 -0.005 6.901 DMRT1 X XX dELS\nrs10976019 A G 0.287 0.044 0.133 9.458 DMRT1 X XX dELS\nrs10815717 G A 0.189 0.048 0.369 20.3 DMRT1 X X dELS\nrs7854629 A G 0.359 0.114 0.118 0.598 CDKN2B 1\nrs2779747 G T 0.025 0.282 0.42 0.854 CDKN2B 1 dELS\n9.p21.3\nTarget genes eQTL\n1.p36.12\n10.q25.1\n6.q25.1\n9.p24.3\nsnATAC ENCODE\nanno tation\nPCHi-C\nstromal\nREMAP\nuteruscytoband ID AncestralM inor allele FST CADD\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 14 \nin endometrial epithelial cells preferentially ( Table 1), which additionally suggests that this genomic 1 \nlocus might contribute to uterine disease pathogenesis by affecting endometrial epithelial cells. Future 2 \nfunctional studies may reveal how these genetic variants that have reached higher frequencies across 3 \nmany human populations affect the physiology of endometrial cells and protect against endometriosis 4 \nor abnormal uterine bleeding.  5 \n 6 \nFigure 5: Evolutionary and functional annotation of the CCDC170-ESR1 locus. A. Regional plot highlighting 7 \nsignificant uSNPs with outlier Fst values in the CEU population (in blue) and with functional evidence of regulatory 8 \nactivity in endometrial cells (circled in red). B. Heatmap of the effect sizes of the derived allele at rs1971256 across 9 \nuterine disorders. C. Annotation of selective sweeps at the CCDC170-ESR1 locus from Laval et al. 2021 across 10 \n1000 Genomes populations, overlaid with open chromatin peaks identified across endometrial cell types, CTCF 11 \nbinding sites from ENCODE and promoter Hi -C contacts captured in decidual stromal cells. D. Global map of  12 \nrs1971256 allele frequencies across populations included in the 1000 Genomes Project. E. Average gene 13 \nexpression of CCDC170 and ESR1 across endometrial cell types using single-nuclei RNA-seq from healthy human 14 \nendometrium. 15 \nEndometrial cancer (malignant)\nPolyps\nUterine inflammatoryd i s e a s e s\nOther menopausal symptoms\nPost menopausalbleeding\nSpontaneous abortion\nLeiomyoma\nExcessive Irregular menstruations\nAbnormal uterinebleeding\nEndometriosis\n−0.2 −0.1 0.0 0.1\nEffect size\nrs1971256-e f f e c ts i z eo ft h ed e r i v e da l l e l e( T )\nprotectiverisk\nRefseq GenesRMND1ARMT1CCDC170ESR1CEUFINGRBsweep inEuropeanpopulations\nsweep inAfricanpopulations\nsweep inEast Asianpopulations\nIBSTSIYRIESNGRWLWKMSLCDXCHBCHSJPTKHVRefseq GenesRMND1ARMT1CCDC170ESR1ProgenitorsGlandsLuminalCiliatedStromalEndothelialImmuneCTCFpHiC map indecidual cells\nCCDC170ESR1StromalEpithelial_preGlandularEpithelial_CiliatedEndothelialStromal_perivascularLymphaticEpithelial_GlandularSecretoryImmune_LymphoidEpithelial_LuminalEpithelial_ProliferativeStromal_ProliferativeImmune_MyeloidStromal_decidual\nPercent Expressed255075\nAverage Expression\n−1\n0\n1\nE\nnoyes\n0\n10\n20\n30\n40\n151400000151500000151600000151700000chr6\nlogp\nRegulatory potentialin uterine cells\nFstoutlier in CEUnoyes\nrs1971256\nA\nC\nB\nAncestral AFDerived AF\nAncestral allele :CDerivedallele :Trs1971256\nD\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 15 \nDiscussion 1 \nIn t his study , we  explored how  combining uterus-centric GWAS summary statistics can illuminate 2 \nshared genetic and cellular  mechanisms of uterine dysfunction . We integrated the polygenic 3 \narchitecture of ten uterine disorders  – most of them associated with menstrual symptoms  – and 4 \nconfirmed extensive, genome-wide genetic correlations across these disorders. Further, we prioritized 5 \n31 major susceptibility loci with pleiotropic effects  contributing to this shared  genetic burden and 6 \npotentially involved in  the pathogenesis of multiple uterine disorders based on functional and 7 \nevolutionary evidence. Compared to previous work investigating genetic correlations between pairs of 8 \ndiseases (Painter et al. 2018; Rafnar et al. 2018a; Gallagher et al. 2019; Masuda et al. 2020; Rahmioglu 9 \net al. 2023) , this approach integrates evidence across multiple uterine pathologies  into a unified 10 \nstatistical framework to reveal genetic loci impacting core pathways within the uterus . To date, few 11 \nstudies have investigated how organ-centric integration of GWAS statistics can highlight genetic 12 \nvariants of functional significance to organ function and dysfunction  (Sheng et al. 2021; Levin et al. 13 \n2022; J. Song et al. 2024; X.-Y. Wang et al. 2025).    14 \nThe substantial genetic correlations observed among st uterine disorders are consistent with prior 15 \nevidence that gynaecological disease susceptibility frequently involves overlapping biological pathways 16 \nbroadly related to  senescence, hormonal response and reproductive development mechanisms  17 \n(Sapkota et al. 2017b; Rafnar et al. 2018b; Bulun et al. 2019; Cardoso et al. 2020; Bulun et al. 2025) . 18 \nFurther, our results show that most genetic variants identified by our approach have congruent effects 19 \nand either decrease or increase risk across diseases, consistent a fundamental aetiology common to 20 \nuterine disorders. One exception was endometrial cancer, which was typically anticorrelated with other 21 \nuterine disorders, capturing antagonistic genetic effects.  A major hurdle to understand disease 22 \nmechanisms underlying this shared genetic risk is that most GWAS variants are likely non-functional or 23 \ntheir functional potential is rarely investigated in detail. Here, to support biological interpretation, we 24 \nintegrated a broad array of  functional genomics resources to clarify how these variants may impact 25 \ngene expression across tissues and specifically in the uterine endometrium . Our results reveal that 26 \npleiotropic variants affect loci harbouring genes whose expression is highly biased towards  female 27 \nreproductive tissues. Further, most of these genes are highly expressed in endometrial cells, confirming 28 \nthat this multi-trait analysis strategy captures variants within loci of direct interest for  uterine biology. 29 \nOur results suggest that at least a subset of shared susceptibility loci act by disrupting gene regulation 30 \nin different  uterine cell types . Although functional annotation alone cannot establish causality, the 31 \nconvergence of genetic association s, regulatory context  in uterine cell s, and uterine expression of 32 \nnearby genes strengthens the plausibility that these loci contribute mechanistically to uterine disease 33 \npathogenesis, rather than reflecting indirect systemic effects. However, experimental validation will be 34 \nrequired to resolve causal variants and target genes and confirm the functional consequences of these 35 \nvariants. 36 \nTo understand how mutations that arose recently in human history contribute to uterine disorder risk, 37 \nwe investigated the directionality and effect sizes of derived alleles at pleiotropic loci. To our knowledge, 38 \nthis is a novel approach as most studies define reference and alternative alleles arbitrarily and therefore 39 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 16 \nrely on absolute effect sizes only, ignoring directionality of effects. Considering the risk contributions of 1 \nancestral and derived alleles revealed that recently acquired alleles were almost twice more likely to 2 \nincrease disease risk. Overall, our findings support that most recent mutations in the human genome 3 \nthat impact uterine functions  are deleterious. The relatively high frequencies at which some of these 4 \nderived alleles are present in human populations suggests that either this deleterious effect is too weak 5 \nto be purged by natural selection, or these variants may have been influenced by antagonistic selection 6 \nacting on other genetically correlated traits. 7 \nTo further investigate how these pleiotropic variants have been influenced by past selective pressures, 8 \nwe considered different metrics of natural  selection based on allelic frequenc ies and haplotype 9 \nhomozygosity in populational data (Weir et Cockerham 1984; Voight et al. 2006b; Pritchard et al. 2010). 10 \nOur results support that pleiotropic variants acting on uterine disorder risk  present signatures of 11 \nselection in European populations. This signal was stronger when using the Fst score compared to the 12 \niHS score, which detects independent events of rapid selection (Voight et al. 2006b), suggesting that 13 \nallelic differentiation at these loci  more likely reflects polygenic selection. We however note that  14 \nalternative explanations such as demographic history, background selection, or genetic drift cannot be 15 \nentirely excluded. Moreover, the modest number of loci identified in our study (31 loci represented by 16 \n235 pseudo-independent variants) limits statistical power relative to large -scale studies of polygenic 17 \nadaptation in traits such as height or metabolic phenotypes (Berg et al. 2019; Choin et al. 2021; Kun et 18 \nal. 2023). Nonetheless, evidence of consistent allelic differentiation across multiple loci suggests that 19 \nuterine disorder genetic risk has been shaped by recent population history, and that the risk architecture 20 \ncaptured in European populations may not reflect that of other populations, highlighting the importance 21 \nof expanding GWAS studies to population samples of diverse genetic ancestries. 22 \nOur observations overall suggest that, despite being on average  deleterious, pleiotropic variants  23 \nimpacting uterine disease risk tend to be selected by polygenic adaptation processes. This ties in with 24 \ntheories of antagonistic selection at pleiotropic loci, where pleiotropic variants are adaptative for early-25 \nlife traits and reproductive success but become deleterious in later life stages, particularly by increasing 26 \ndisease burden (Rose 1982; Pavlicev et Wagner 2012). This interpretation is supported by observations 27 \nthat some uterine disorders are genetically correlated with  traits related to fertility, such as age at 28 \nmenarche, or to pregnancy (Rahmioglu et al. 2023; Benonisdottir et al. 2024; Venkatesh et al. 2025; 29 \nPujol Gualdo et al. 2025). Previous work has reported that variants affecting reproductive traits are often 30 \nlocated near genes involved in cellular aging and lifespan regulation (Rodríguez et al. 2017a; D. Wu et 31 \nal. 2022), which we also confirm here with an enrichment of senescen ce-related genes close to the 32 \ngenetic variants prioritized by our approach. Human genetic studies increasingly document genetic 33 \ncorrelations between reproductive traits and later-life disease risk, although the molecular mechanisms 34 \nunderlying these relationships remain poorly characterized  (D. Wu et al. 2022; Chen et Zhang 2020; 35 \nRodríguez et al. 2017b).  36 \nOur study comes with a number of limitations. First, we did not evaluate associations between prioritized 37 \nvariants and other reproductive phenotypes such as age at menarche, age at first birth  or gestational 38 \nduration, as those traits are not included in the Finngen biobank. However, several of these traits have 39 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 17 \npreviously shown evidence of selection favouring earlier reproductive maturation (Lahdenperä et al. 1 \n2004; W. Song et al. 2021; Long et Zhang 2023; Xiang et al. 2024) . Future work integrating multi-trait 2 \nGWAS across reproductive life -history traits may clarify whether recently acquired alleles increasing 3 \nuterine disorder risk are also associated with reproductive advantages. Another limitation of this work 4 \nis that GWAS data used in this study were derived from individuals of European ancestry, limiting the 5 \ngeneralizability of both pleiotropic associations and population genetic analyses.  Additionally, 6 \ndiagnostic heterogeneity across uterine disorders in biobank-based phenotyping likely includes cases 7 \nmisclassified as controls, which likely reduces power to detect significant effects for some traits. Indeed, 8 \nwhile the Finngen biobank includes more uterine disorder cases compared to other biobanks (Kurki et 9 \nal. 2022; Constantinescu et al. 2022), the proportions of cases vs. controls remain lower to the estimated 10 \nprevalence for several uterine disorders in European populations. For instance, uterine fibroids have 11 \nan estimated prevalence of ~70% in women after 50 years  (Stewart et al. 2017b), but only represent 12 \n17% of patients in the Finngen cohort.  13 \nIn summary, our study explores the shared genetic architecture of uterine disorders and identifies 14 \n31susceptibility loci with pleiotropic effects, several of which show marked population differentiation and 15 \nregulatory activity in uterine tissues. These findings pave to the way  to explore common biological 16 \npathways underlying multiple gynaecological conditions and suggest that some components of uterine 17 \ndisease risk are shaped by population-specific genetic histories. Expanding multi-trait and evolutionary 18 \nanalyses to larger number of traits related to female reproductive functions and to cohorts from diverse 19 \nancestry, will be critical for clarifying the generality and biological significance of these genetic variants. 20 \n 21 \nMaterial and Methods 22 \nSelection of GWAS on uterine disorders in the Finngen database 23 \nTo explore the genetic architecture of uterine disorders, we leveraged available summary statistics from 24 \nFinnGen, a hospital cohort with well -defined phenotypes uterine disorders, including  sex-specific 25 \ndesigns for male/female -specific traits, and which include s higher case counts for uterine disorders 26 \ncompared to other publicly accessible cohorts such as the UKBiobank (See Table S1 and Table S4). 27 \nFinnGen database (r.7) was used to select GWAS summary statistics related to uterine traits and 28 \ndiseases using “uterine” , “endometrium”, “menstruation”, “fertility”, “menarche”, “menopause”, 29 \n“leimyoma” and “bleeding” as keywords. We did not include traits and diseases related to pregnancy 30 \ncomplications as the focus of our study concerns uterine functions outside of pregnancy. Phenotypes 31 \nwith fewer than 1,500 cases were filtered out. Endometriosis included a number of subcategories, and 32 \nwe only retained the broad phenotype with the largest number of cases (“endometriosis”), resulting in 33 \n10 final uterine phenotypes: endometriosis (ICD10-N80), malignant endometrial cancer (ICD10-C54), 34 \nleiomyoma (ICD10-D25),, abnormal uterine bleeding  (ICD10-N93), inflammatory uterine disease  35 \n(ICD10-N71), post-menopausal bleeding (ICD10-N95.0), other menopausal disorder  (ICD10-N95.8), 36 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 18 \nexcessive-frequent-irregular menstruation  (ICD10-N92),, uterine polyps  (ICD10-N84.0), and 1 \nspontaneous abortion (ICD10-O03) (Table S1). We retained “spontaneous abortion” as a proxy for 2 \nuterine receptivity, as failure in effective endometrial preparation to implantation largely contributes to 3 \nmiscarriage in humans (Lucas et al. 2016; Muter et al. 2021), although embryo defects also contribute. 4 \nAdditionally, we also retained post-menopausal bleeding and other menopausal disorders, which can 5 \nbe symptoms of post-menopausal uterine diseases, notably endometrial cancer or uterine leiomyomas 6 \n(Makker et al. 2021; Munro 2019).   7 \nSelection of GWAS on uterine phenotypes in Pan-UK Biobank database 8 \nFor replication purposes, we selected uterine-associated phenotypes in the Pan-UK Biobank matching 9 \nthose selected from Finngen and using the same inclusion criteria (See Table S4). Summary statistics 10 \nperformed on individuals of European ancestry were downloaded from the Pan -UK Biobank website, 11 \nresulting in the inclusion of 8 uterine phenotypes: malignant endometrial neoplasm  (ICD10-C54), 12 \nleiomyomas (ICD10-D25), endometriosis (ICD10-N80), uterine polyps  (ICD10-N84.0),  irregular 13 \nmenstrual cycle bleedings , excessive menstrual cycle bleeding, post-menopausal bleeding, and 14 \nmiscarriage. Inflammatory uterine diseases and other menopausal disorders of the uterus were not 15 \nphenotyped in the Pan-UK Biobank.  16 \nSummary statistics curation and pre-processing  17 \nGWAS summary statistics were harmonized using the JASS pre -processing pipeline (Julienne et al. 18 \n2020). This pipeline consists in (i) harmonizing heterogeneous formats of summary statistics, (ii) 19 \naligning the effect allele of each study to the effect allele of the reference panel, (iii) removing strand -20 \nambiguous variants and variants with low sample sizes, (iv) computing trait heritability, and genetic and 21 \nresidual covariances across traits using the LDscore regression. For FinnGen summary statistics, we 22 \nused a Finnish reference panel and LDscore data derived from GnomAD v2 23 \n(https://gnomad.broadinstitute.org/downloads#v2-linkage-disequilibrium). For  Pan-UK Biobank 24 \nsummary statistics, we used the European reference panel and LDscore data based on the 1000G 25 \ndatabase provided as part of the JASS pipeline. We used positional SNP identifier s (defined as the 26 \ncombination of chr:pos:ref:alt) to map all summary statistic SNPs to the reference panel. For both 27 \ndatasets, no imputation step was performed. The summary information for each summary statistics 28 \nused as input for the JASS preprocessing pipeline can be found in Table S1 and Table S4. We report 29 \nthe genetic correlation and the significance of the genetic correlation computed by LDscore (B. K. Bulik-30 \nSullivan et al. 2015; B. Bulik -Sullivan et al. 2015)  and corrected for false discovery rate using  the 31 \nBenjamini-Hochberg correction (Table S2).  32 \nJoint analysis of uterine trait summary statistics with JASS 33 \nMulti-trait analysis of uterine traits summary statistics was conducted using the omnibus test 34 \nimplemented in the JASS suite (Julienne et al. 2020) .  JASS output summary statistic s were further 35 \nprocessed by associating each SNP to their RSID using the initial summary statistics from FinnGen. 36 \nFor the 18,524 SNPs without an RSID in initial summary statistics, we used ANNOVAR (hg38) SNPs 37 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 19 \nRSID and retrieved RSIDs for an additional 4,680 SNPs. The remaining 14,044 SNPs without official 1 \nRSIDs in Annovar were named using their positional identifier.  2 \nFor each SNP, we reported the p -value of the joint test and the smallest p -value across univariate 3 \nGWASs. The ability of JASS to capture variants with pleiotropic effect s across uterine traits was 4 \ninvestigated (1) for each uterine trait independently, by comparing the effect sizes of uSNPs vs single-5 \ntrait SNPs; and (2) across uterine traits, by computing pairwise uSNPs effect size correlations. We also 6 \ncompared effect size magnitudes of uSNPs and single-trait SNPs for each uterine trait independently 7 \nusing Wilcoxon rank sum tests, FDR-corrected using the Benjamini-Hochberg correction.  8 \nReplication analysis using GWAS from the Pan-UK Biobank  9 \nFor each trait, we compared the directionality of allele effects between effects estimated in the Finngen 10 \ncohorts and in the Pan-UK Biobank cohorts. For all uSNPs identified with the Finngen dataset and also 11 \npresent in Pan-UK Biobank summary statistics, we tested whether directions of effects were consistent 12 \nafter matching effect alleles between both biobanks. Using a directional binomial test, we tested whether 13 \nthe proportion of uSNPs with replicated effect directions was greater than expected by chance (Table 14 \nS5). After grouping uSNPs into loci, we additionally tested if the number of loci with replicated effect 15 \ndirections was greater than expected by chance using the same approach (Table S5). 16 \nSelection of independent and lead SNPs with FUMA 17 \nFUMA (v1.5.2) was used to define 241 lead SNPs with a linkage disequilibrium  correlation r2 < 0.5, 18 \nwhich is the default threshold used by PLINK (Purcell et al. 2007). Lead SNPs within 500 kb of each 19 \nother were merged as a single genomic locus, resulting in the identification of 31 genomic risk loci. To 20 \ncalculate r2, MAF (minor allele frequency) and conduct LD analyses, European population genetic data 21 \nfrom the 1000G Project phase 3 (2015) was used as the reference panel. 22 \nFunctional annotation of candidate SNPs 23 \nThe ANNOVAR (v2017-07-17) (K. Wang et al. 2010) annotation available from the FUMA interface was 24 \nused to associate SNPs to functional elements in the genome. 61  uSNPs were not in ANNOVAR. 25 \nFunctional annotation of those 61 SNPs was performed based on intersection with protein-coding gene 26 \ncoordinates. SNPs not overlapping protein-coding gene annotations were classified as intergenic. We 27 \nalso identified SNPs overlapping protein-coding promoters, defined as regions spanning 5 kb upstream 28 \nand 1 kb downstream of protein-coding TSSs from Ensembl (v109, (Cunningham et al. 2022)).  29 \nTo annotate the regulatory potential of uSNPs, we combined genomic annotations including histone 30 \nmodifications and chromatin accessibility across relevant human tissues. We downloaded ENCODE 31 \n(v3 2021, hg38 (Dunham et al. 2012)) annotations of 1,063,878 cis-regulatory elements across 1,518 32 \ncell types and tissues. Using bedtools intersect ( -wao), we annotated uSNP mapping to predicted 33 \npromoters and enhancers (proximal or distal) or CTCF sites in any human tissue or cell type. We used 34 \nReMap 2022 to identify uSNPs overlapping transcription factor binding peaks (Hammal et al. 2022). We 35 \ndownloaded non-redundant peaks for all 1,210 TF datasets present in ReMap across all cell types and 36 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 20 \ntissues. The annotation of single SNPs was performed with bedtools intersect and a minimum overlap 1 \nof 1bp. Variants overlapping peaks in uterine cell lines (myofibroblasts, endometrial stromal cells, 2 \ndecidualized stromal cells) , healthy uterine tissues (uterus, endometrium, myometrial) or diseased 3 \nuterine samples (leiomyomas, primary endometrial cancer) were annotated as `REMAP peak uterus`, 4 \nin contrast to uSNPs overlapping any TF peak in REMAP (` REMAP peak all`). We also identified uSNPs 5 \nannotated as eQTLs by exact matching in at least one tissue in  the GTEx (v10) database. Because 6 \nGTEx contains only a small number of uterine eQTLs, we elected to retain all eQTLs as potentially 7 \nrelevant.  Finally, we used the Ananastra database (Abramov et al. 2021) to annotate significant uSNPs 8 \npotentially disrupting a transcription factor binding site, using the European linkage disequilibrium map 9 \nand a FDR of 0.05.  10 \nGenomic 3D contacts in endometrial stromal and epithelial cells 11 \nPromoter capture Hi-C (PCHi-C) data on endometrial decidualised stromal cells derived from a full-term 12 \nplacenta was retrieved from (Sakabe et al. 2020). Bed files were converted to hg38 coordinates using 13 \nthe UCSC Liftover tool. uSNP coordinates were intersected with either PCHi-C baits or significant 14 \ncontacts.  15 \nHi-C datasets on endometrial epithelial organoids from two separate donors were retrieved from (Hewitt 16 \net al. 2022). We removed contacts that overlapped ENCODE blacklisted regions using `pairToBed -17 \n-type neither`. We then selected consensus contacts found in both donors, resulting in a list of 18 \n3,873 endometrial epithelial 3D genomic contacts. Those contacts were intersected with the coordinates 19 \nof human protein -coding promoters, defined as a -1kb/+5kb window around gene TSSs, with 20 \n(pairToBed --type either).  21 \nuSNPs overlapping PCHi-C or Hi-C contacts in stromal and/or epithelial cells were associated to  22 \nputative target genes in the corresponding Hi-C contact, i.e. one or two genes depending on the type 23 \nof contact (CRE-promoter, or promoter-promoter), resulting in a list of 246 and 61 uSNPs in endometrial 24 \nstromal and epithelial contacts respectively.  25 \nReanalysis of single-nuclei RNA and ATAC-seq of healthy endometrial cells 26 \nSingle-nuclei RNA-seq data of endometrial tissue from the HECA atlas (Marečková et al. 2023) was re-27 \nanalysed using Seurat (v5). The HECA atlas was reduced to samples obtained from healthy donors 28 \nwithout hormonal treatment . The resulting dataset was re -clustered and cluster annotation was 29 \nhomogenised based on the endometrial cell type annotation available in HECA. Nuclei from the uterine 30 \ncervix (Epithelial MUC5B: n=1000; and Stromal HOXA13: n= 1471)  were removed from the resulting 31 \natlas to only consider cells of endometrial origin. The resulting annotation of endometrial cell populations 32 \nis presented in Figure S4 . Pseudobulk transcriptome s were generated  for each endometrial cell 33 \npopulation using aggregate.Matrix, and scaled to the raw library size of each pseudobulk using edgeR 34 \ncalcNormFactors. Resulting pseudobulks were normalised to log2CPM (counts per million) and used to 35 \nmap the expression of uSNPs target genes across endometrial cell types.  36 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 21 \nSingle-nuclei ATAC-seq data from healthy human endometrium samples was retrieved from Vrljicak et 1 \nal. (2023). Data was reanalysed using the same parameters as the original analysis to filter low quality 2 \nnuclei and call ATAC-seq peaks (nCount_peaks > 3000; nCount_peaks < 30000; nucleosome signal > 3 \n4; TSS enrichment > 3).  To infer cell type s, we combined the original cell lineage annotation from 4 \nVrljicak et al.  with snRNA-seq annotation prediction s using the HECA atlas (after cell type 5 \nharmonisation, see above). To perform this step, gene scores were predicted for snATAC-seq nuclei 6 \nusing GeneActivity() from the Signac package (Stuart et al. 2021) . These gene score s were then 7 \ncorrelated with gene expression levels in the  snRNA-seq data to transfer cell annotation s using 8 \nFindTransferAnchors and TransferData with default parameters. The resulting annotation is shown in 9 \nFigure S6. Peak calling was performed by cell type using Signac with default parameters. uSNPs were 10 \nthen overlapped with accessible peaks in each cell type. 11 \nTissue-specific enrichment of uSNP target genes with FUMA 12 \nPutative uSNP target genes were passed to the FUMA GENE2FUNC interface to compute expression 13 \nenrichment across 30 tissues from GTEx. Differentially expressed genes (DEG) sets in FUMA are 14 \ndefined using a two-sided t-test per tissue against all other tissues, with a Bonferroni corrected p-value 15 \n<0.05 and a minimal absolute fold-change cut-off. The fold-enrichment in the present study represents 16 \nthe hypergeometric enrichment of uSNP target genes in tissue-specific DEGs.  17 \nGene Ontology analysis 18 \nGene Ontology (GO) enrichment analysis was performed with the clusterProfiler package (v4.16) (T. 19 \nWu et al. 2021)  in R by comparing the 227 uSNP target genes to the full transcriptome. Gene sets from 20 \nthe \"Biological processes\" category were used for gene set enrichment analysis. Enrichment was 21 \nconsidered significant for p < 0.05 after Benjamini-Hochberg correction for multiple testing. Significant 22 \nbiological processes were clustered using the Ward distance method to group higher-order terms based 23 \non shared genes. The comprehensive results of the GO analysis are presented in Table S8. 24 \nAnalysis of uSNP derived allele effect sizes  25 \nThe 1000 Genomes Phase 3 vcf files (Auton et al. 2015) were used as input to retrieve the ancestral 26 \nand derived alleles for every SNP , determined using the six primate s (human, chimpanzee, gorilla, 27 \norangutan, macaque, marmoset) EPO alignments from Ensembl . We restricted the list of 235 lead 28 \nuSNPs to 211 bi-allelic lead uSNPs, for which an ancestral and a derived allele can be identified. If the 29 \nalternate allele in the reference dataset corresponded to the ancestral allele, we switched effect size 30 \ndirection to reflect the impact of the derived allele on the trait . We then performed unsupervised K -31 \nmeans clustering on derived allele effect sizes across traits. The optimal number of clusters was 32 \nidentified using the average silhouette width criterion. To explore how derived alleles contribute to risk 33 \nincrease or decrease across  different uterine disorders, effect sizes of the derived allele s were 34 \nnormalized across traits by computing the ratio of the effect size (β) to its standard error (SE_β). We 35 \nthen correlated derived allele effect size s in each cluster across uterine disorders using Spearman’s 36 \nrank correlation. Correlation p-values were adjusted using BH correction.  37 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 22 \nConstruction of control SNP datasets 1 \nVcf files from 1000 Genomes Phase 3 were used as reference for all evolutionary analys es. SNPs in 2 \nthe 1000 Genome Phase 3 panels were further annotated with their minor allele frequencies for all 1000 3 \nGenomes populations, local gene densit ies, GERP score s and recombination rate s. Minor allele 4 \nfrequencies (MAF) were computed using vcftools (--freq) and filtered to keep only dimorphic sites for 5 \neach population independently. Local gene densities were calculated by binning the genome into 1 Mb 6 \nblocks to generate pseudo-independent regions of the genome, and counting all annotated TSS within 7 \nthe 1 Mb region, including lncRNAs and pseudogenes. GERP scores were obtained from Ensembl, 8 \ncalculated from the 91-mammal whole genome alignments.  Recombination rate s from  the 1000 9 \nGenomes panels were downloaded from UCSC 10 \n(http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recomb1000GAvg.bw).  11 \nFor each lead uSNP present in  the 1000 Genomes panels, control SNPs were defined as SNPs 12 \nmatched for MAF, recombination rate and gene density. To control for recombination rate and gene 13 \ndensity, SNPs were ranked and divided into 20 bins of equal size. Control SNPs meet the following 14 \ncriteria: (1) with a MAF within 0.025 of their corresponding lead uSNP based on allele frequencies in 15 \nthe CEU population; (2) within the same recombination rate bin; (3) within the same gene density bin; 16 \nand (4) excluding SNPs in LD (r2 > 0.2) with their corresponding lead uSNP. For each lead uSNP, the 17 \nresulting list of matching control SNPs was sampled 1000 times at random with replacement to create 18 \n1000 control sets per lead uSNP. Controls SNPs across lead uSNPs were further combined to form 19 \n1000 sets of control SNPs  and used to compare FST distributions. Out of 241 lead uSNPs,  50 either 20 \nwere not present in 1000 Genomes panels or could not be assigned appropriate matching control sets, 21 \nand were excluded from the comparison.  22 \nFST score comparisons 23 \nFST scores (Weir et Cockerham 1984) were computed from the 1000 Genomes Phase 3 vcf files using 24 \nthe software selink (https://github.com/h-e-g/selink). Pairwise FST scores were computed between three 25 \n1000 Genomes populations of European, East Asian and African ancestry (respectively CEU, CHB and 26 \nYRI). To test for polygenic selection signatures, we compared the distributions of FST scores between 27 \nlead uSNPs and matched control sets (see above) using Wilcoxon rank sum tests. P-values were BH-28 \ncorrected for multiple testing across population pairs (CEU vs. CHB, CEU vs. YRI and CHB vs. YRI).  29 \nFor each pair of populations, a directional signed FST score (positive or negative) defined as previously 30 \nproposed in (Quach et al. 2016), to orient whether the derived allele is more or less frequent with respect 31 \nto a reference population. Directional FST scores were used to identify lead uSNPs with outlier FST scores 32 \nand under putative selection in either European , East-Asian or African populations. Outlier FST SNPs 33 \nwere defined as the top 5% genome -wide FST signals for each population comparison. To test for 34 \nenrichment in outlier FST SNPs, we compared the fraction of lead uSNPs in the top 5% FST values to 35 \nthe fraction of controls across all 1000 control sets and derived an empirical p-value. Resulting p-values 36 \nwere BH-corrected for multiple testing across population pairs (CEU vs. CHB, CEU vs. YRI and CHB 37 \nvs. YRI). We also tested for an overall enrichment of lead uSNPs in the top 5% FST signals using a two-38 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted March 25, 2026. ; https://doi.org/10.64898/2026.03.24.713926doi: bioRxiv preprint \n\n \n 23 \nsided binomial test (probability of finding a uSNPs in the top 5% in at least one FST comparison = 1-1 \n0.953).  2 \niHS score comparisons 3 \niHS scores were obtained from Johnson et Voight (2018). We compared distributions of absolute iHS 4 \nscores between lead uSNPs and matched control sets using Wilcoxon rank sum tests. Out of 241 lead 5 \nuSNPs, only 163 lead uSNPs had an available iHS value in at least one population.  6 \nCode availability 7 \nAll original code produced during this project is accessible at https://gitlab.pasteur.fr/euliorzo/popgen. 8 \nJASS is available at https://statistical-genetics.pages.pasteur.fr/jass/index.html. 9 \nLicences 10 \nFigures were produced with BioRender under licence to Institut Pasteur. 11 \nAcknowledgments 12 \nWe thank Jan Brosens, Pavle Vrljicak and Mireia Taus -Nebot (University of Warwick) for sharing 13 \nanalyzed snATAC-seq datasets from their previous study. 14 \nFunding 15 \nThis project was supported by Institut Pasteur (G5 package), Centre National de la Recherche 16 \nScientifique (CNRS UMR 3525), Institut National de la Santé et de la Recherche Médicale (INSERM 17 \nUA12), and the Inception program (Investissement d’Avenir grant ANR -16-CONV-0005). EL is 18 \nsupported by a PhD fellowship from Université Paris Cité  and a grant from the Fondation pour la 19 \nRecherche Médicale (grant agreement FDT202504020295).  20 \nAuthors contributions 21 \nEL and CB conceived and designed the project and analys es. EL performed all analyse s, with 22 \ncontributions from HJ and HA for multi-trait GWAS analyses, MD for functional genomics analyses, and 23 \nGL for evolutionary analyses. EL designed all figures. EL and CB wrote the manuscript with input from 24 \nall authors. All authors approved the manuscript. 25 \nCompeting interests 26 \nThe authors declare no competing interests. 27 \n 28 \n  29 \n.CC-BY 4.0 International licenseperpetuity. 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