sc-eQTL unveil Immunogenetic Architecture of Polycystic Ovary Syndrome

sc-eQTL unveil Immunogenetic Architecture of Polycystic Ovary Syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article sc-eQTL unveil Immunogenetic Architecture of Polycystic Ovary Syndrome Xiaoqian Xu, Yuzhou Bao, Qi Zhang, Hao Wang, Lixia Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6942288/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Polycystic ovary syndrome (PCOS) is a complex endocrine disorder characterized by metabolic, inflammatory, and reproductive dysfunction. We integrated genome-wide association study (GWAS) data, and single-cell expression quantitative trait loci (sc-eQTL) across various immune cells to elucidate the genetic and immune mechanisms underlying PCOS at the cellular level. Finally, we identified 20 19 risk loci for PCOS, prioritizing 16 candidate causal genes, approximately 30% of these genes are immune-related. Pathway analysis revealed 15 PCOS-associated signaling pathways, spanning hormonal regulation and immune function, notably T cell-mediated responses. Single-cell RNA sequencing analysis implicated NK cells, CD8 T cells, and CD4 T cells play a crucial role in PCOS pathogenesis, with the highest trait-relevant scores. sc-eQTL colocalization analysis identified IRF1 and MAPRE1 as causal genes with regulatory effects in specific T cell subsets and NK cells, which were obscured by bulk eQTL analysis. Moreover, MAPRE1 showing a discordant effect in NK cells, suggesting immune dysregulation. Our findings underscore the pivotal roles of metabolic and immune-related genes in PCOS and the importance of immune cell-specific mechanisms, enhancing our understanding of its pathophysiology and potential therapeutic targets. Health sciences/Endocrinology/Endocrine system and metabolic diseases Biological sciences/Immunology/Gene regulation in immune cells/Immunogenetics Figures Figure 1 Figure 2 Figure 3 Introduction Polycystic Ovary Syndrome (PCOS) is a globally significant gynecological endocrine disorder, affecting approximately 10% of women of reproductive age(Stener-Victorin et al. 2024 ). It is characterized by a range of clinical manifestations, including infertility and metabolic complications(Singh et al. 2023 ). Infertility in women with PCOS primarily stems from ovulatory dysfunction, resulting from associated hormonal imbalances. Additionally, metabolic complications such as insulin resistance, obesity, dyslipidemia, and an elevated risk for type 2 diabetes frequently co-occur(Su et al. 2025 ). Genetic factors play a significant role in the development of PCOS, with certain types showing a familial pattern and being linked to specific genome locis(Abbott et al. 2002 , Franks et al. 2006 , Chen et al. 2011 ). The current pathological model of PCOS has largely focused on hyperandrogenism and insulin resistance. Hyperandrogenism leads to symptoms such as hirsutism, acne, and androgenic alopecia, while insulin resistance contributes to the metabolic complications(Rosenfield and Ehrmann 2016 , An et al. 2025 ). Despite the extensive research on these aspects, there is still a need for a more comprehensive understanding of the underlying mechanisms of PCOS. PCOS appears to be a highly heritable condition with genetic factors accounting for ~ 70–80% of the variation in susceptibility to the condition(Hardy and Norman 2019 ). Recent genome-wide association studies (GWAS) have significantly advanced the identification of genetic factors associated with PCOS. Thus far, several risk loci have been uncovered, providing key insights into the genetic architecture of the condition(Chen et al. 2011 , Lee et al. 2015 ). However, there are still limitations in our understanding of the mechanisms through which these genetic variants contribute to PCOS. One of the main bottlenecks is that the traditional metabolic pathways have limited explanatory power. For example, although insulin resistance is a common feature of PCOS, the genetic variants identified by GWAS do not fully account for the variation in insulin resistance among women with the condition(Dhar and Bhattacharjee 2024 , Ghalehzan et al. 2025 ). This suggests that there may be other factors, such as immune - related pathways, that are involved in the pathogenesis of PCOS. In recent years, there has been emerging evidence suggesting that immune dysregulation may play a role in PCOS. For instance, chronic inflammation has been observed in women with PCOS, with elevated levels of inflammatory cytokines such as interleukin − 6 (IL − 6) and tumor necrosis factor - alpha (TNF - alpha) found in their serum(Sharma et al. 2023 ). In addition, the presence of autoantibodies has also been reported in some studies(Xia et al. 2023 ). However, despite these findings, there is still a lack of systematic genetic evidence to support the involvement of the immune system in PCOS. Most of the previous studies have focused on a limited number of candidate genes or pathways, and there has been no comprehensive analysis of the genetic basis of immune - related abnormalities in PCOS. Moreover, the cell-type resolution of the immune system in PCOS is still unclear, and it is not known which specific immune cell types are involved in the pathogenesis of the condition. Single-cell expression quantitative trait loci (sc-eQTL) analysis allows for the dissection of genetic effects on gene expression at an unprecedented cellular resolution(Ding et al. 2024 ). The integration of sc-eQTLs with genome-wide association studies (GWAS) has revealed novel relationships between gene expression and disease risk for autoimmune diseases(Perez et al. 2022 , Aquino et al. 2023 ). sc-eQTL show the potential to uncover critical cell types and cell type-specific susceptibility genes and regulatory mechanisms that could be pivotal in the development and treatment of PCOS. In this study, we integrate large-scale GWAS data with functional annotation, pathway enrichment, and single-cell analyses to systematically dissect the genetic architecture of PCOS. We pinpoint 19 independent risk loci and 16 risk genes, revealing key signaling pathways and immune cell types, specifically NK cells and T cells. Notably, colocalization analysis using sc-eQTL data uncovered two susceptibility genes, IRF1 and MAPRE1, which were overlooked by traditional methods, underscoring the power of single-cell resolution. Our findings not only reaffirm the centrality of metabolic and hormonal disruptions in PCOS but also unveil novel immune contributions, such as T cell dysfunction and NK cell activity. These insights offer new perspectives on potential therapeutic targets and deepen our mechanistic understanding of this complex disorder. Materials and Methods Data collection and processing We reused published GWAS summary statistics for PCOS from a large-scale meta-analysis(Day et al. 2018 ), comprising 10,074 PCOS cases and 103,164 European-ancestry controls, available through the GWAS Catalog. Genomic coordinates were converted from human reference genome build hg19 to hg38 using the UCSC LiftOver tool. Harmonization and imputation of GWAS summary statistics were performed following the standardized pipeline from the HakyImLab framework ( https://github.com/hakyimlab/summary-gwas-imputation/ ). This process utilized the 1000 Genomes Project Phase 3 European population data (release 20130502) as the genotype reference panel. sc-eQTL datasets across immune celltypes were downloaded from onok1k(Yazar et al. 2022 ). Bulk eQTL summary data of ovary, lymphocytes, and visceral adipose were downloaded from GTEx Project (v8)(Consortium 2020 ). Identification of GWAS risk loci and risk gene Prioritization Plink (v1.9)(Purcell et al. 2007 ) Clumping procedure was used to identify independent genome-wide risk loci of PCOS (P < 5e-9, clump-r2 = 0.3, clump-kb = 250). For each identified locus, fine-mapping was performed using SusieX(Yuan et al. 2024 ), with the variant exhibiting the lowest p-value designated as the lead SNP. This approach defined candidate risk genes as the protein-coding gene(s) located nearest to each lead SNP within a ± 500 kb window. We employed this established positional strategy as a pragmatic first step for gene prioritization, acknowledging its widespread use in initial GWAS follow-up analyses to generate tractable candidate gene lists. Pathway enrichment of GWAS To identify biological pathways significantly associated with PCOS susceptibility, we performed gene-set enrichment analysis using Multi-marker Analysis of Genomic Annotation (MAGMA v1.10) (MAGMA v1.10)(de Leeuw et al. 2015 ) to identify biological pathways significantly associated with PCOS. We implemented a two-stage analytical approach with MAGMA: (1). Gene-level association: We aggregated SNP-level association signals into gene-level associations using MAGMA's principal component-based multi-SNP model. This model accounts for linkage disequilibrium (LD) between SNPs, utilizing LD reference data from Phase 3 of the 1,000 European Genomes Project. Gene boundaries were defined according to the NCBI RefSeq database (build GRCh38/hg38), extending 10 kb upstream of the transcription start site (TSS) to 10 kb downstream of the transcription end site (TES) to capture putative regulatory elements. (2). Competitive gene-set analysis: We tested for enrichment of association signals within biological pathways using a competitive gene-set analysis framework. Pathways were sourced from the Molecular Signatures Database (MSigDB v2024.1.Hs). Significance of pathway enrichment was assessed after stringent correction for multiple testing. Colocalization Analysis To identify putative causal genes whose expression is influenced by variants conferring PCOS susceptibility, we performed colocalization analysis using the xQTLbiolinks R package (v1.7.3) (Ding et al. 2023 ). This Bayesian framework evaluates shared genetic architecture by computing posterior probabilities for five competing hypotheses: PPH0: No association with either trait; PPH1: Association with trait 1 (PCOS GWAS) only; PPH2: Association with trait 2 (QTL) only; PPH3: Association with both traits, driven by two distinct causal variants (LD); PPH4: Association with both traits, driven by a single shared causal variant (colocalization). We tested for colocalization between PCOS GWAS summary statistics and: (1). Single-cell eQTLs (sc-eQTLs): Derived from diverse immune cell types in the OneK1K dataset(Yazar et al. 2022 ). (2). Bulk tissue eQTLs: From three PCOS-relevant tissues in the GTEx portal(Consortium 2020 ): ovary, lymphocytes, and visceral adipose. Genomic regions exhibiting posterior probability PPH4 > 0.75 were considered strong evidence of colocalization. Trait-Relevant Score (TRS) calculation The TRS is a key metric used to quantify the association between specific cell types and traits of interest. Higher TRS values indicate a stronger association of a cell type with the trait. TRS calculations were performed using scPagwas v1.0 (Ma et al. 2023 ), a computational framework that integrates GWAS summary statistics with scRNA-seq data to infer trait-associated gene activity patterns across cell types. Given the large size of the OneK1K dataset (approximately 1 million cells), a random subset comprising 30% (approximately 300,000 cells) was selected for subsequent scPagwas analysis to manage computational demands while maintaining representativeness. The analysis was conducted as follows: (1). Cell types were annotated following the methodology described in the original OneK1K publication(Yazar et al. 2022 ), utilizing canonical marker genes and unsupervised clustering. (2) The normalized gene-by-cell matrix was transformed into a pathway activity score (PAS)-by-cell matrix. This PAS matrix is based on the first principal component (PC1) of gene expression within each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway(Kanehisa and Goto 2000 ). (3). A trait-relevant score (TRS) of each cell is computed by averaging the expression level of the trait-relevant genes and subtracting the random control cell score via the cell-scoring method used in Seurat. Results Identification and functional classification of PCOS risk genes Leveraging a published large-scale genome-wide association study (GWAS) encompassing 113,238 individuals(Day et al. 2018 ), we identified 19 independent risk loci for polycystic ovary syndrome (PCOS) at genome-wide significance (P < 5e10-8) using PLINK clumping procedures (P-value < 5e-8, clump-r2 = 0.3, clump-kb = 250, Supplementary Table S1 ). Chromosomal positional mapping prioritized 16 candidate causal genes proximal to lead SNPs of each loci ( Fig. 1 A )(Supplementary Table S2 ) , including DENND1A and THADA with known metabolic functions(McAllister et al. 2014 , Tian et al. 2020 , Alarcon-Granados et al. 2022 ). PCOS pathogenesis is characterized by complex interactions among metabolic dysregulation (e.g., insulin resistance, hyperandrogenemia), chronic inflammation (e.g., elevated TNF-α and IL-6), and direct ovarian dysfunction (e.g., follicular arrest)(Stener-Victorin et al. 2024 ). Based on known biological functions derived from functional annotation database (GeneCards), we categorized these 16 candidate genes into three distinct groups: i) Metabolic-related, including FSHB, TOX3, ERBB4, THADA, DENND1A (31.25%, 5/16), ii) Immune-related, including ZBTB16, ARL14EP, IKZF4, IRF1, PLGRKT (31.25%, 5/16), iii) Other, including YAP1, MPPED2, ERBB3, KRR1, MAPRE1, AOPEP (37.5%, 6/16) ( Fig. 1 B )(Supplementary Table S2 ) . Among the metabolic-related genes, DENND1A is implicated in insulin signaling and has been associated with insulin resistance(Dallel et al. 2018 ). THADA , involved in thyroid hormone signaling, is linked to type 2 diabetes and may influence PCOS through regulation of insulin sensitivity and glucose metabolism(Alarcon-Granados et al. 2022 ). FSHB encodes the beta subunit of follicle-stimulating hormone (FSH), which is essential for ovarian follicle development and ovulation(Li et al. 2020 ); variants in FSHB may disrupt FSH levels or function, contributing to ovulatory dysfunction in PCOS. To assess the relative contributions of these genes to PCOS pathogenesis, we performed fine-mapping analysis using Susie method(Yuan et al. 2024 ) to establish 95% credible sets for each locus, identifying the most probable causal variants (Supplementary Table S3 ) . We then assessed the distribution of effect sizes (beta coefficients from the GWAS) of SNPs within these credible sets across the three gene functional categories. This analysis revealed significant differences (P < 0.001, Wilcoxon rank-sum test), with SNPs near metabolic-related genes exhibiting larger effect sizes compared to those near immune-related genes, and the smallest effect sizes observed for genes in the "other" category ( Figs. 1 C ) . These findings indicate that metabolic-related genes exert the strongest genetic influence on PCOS risk. However, immune-related genes also play a critical role, likely through pathways that interact with or amplify metabolic dysregulation. For instance, chronic inflammation, characterized by elevated levels of C-reactive protein (CRP), TNF-α, and IL-6, may exacerbate insulin resistance and contribute to ovarian dysfunction in PCOS(Aboeldalyl et al. 2021 ). Identification of Key Signaling Pathways and Immune Cell Types in PCOS We applied Multi-marker Analysis of Genomic Annotation (MAGMA) method(de Leeuw et al. 2015 ) to PCOS GWAS data, identifying 15 biological processes significantly enriched for PCOS-associated variants (p-value < 0.01) ( Fig. 2 A)( Supplementary Table S4 ). These pathways demonstrate significant enrichment in both endocrine regulatory mechanisms and immune system functions. Several exhibited associations with hormonal regulation, including "cAMP-PKA signal transduction," "cellular response to insulin stimulus," "cellular response to peptide hormone stimulus," "response to peptide hormone," and "steroid hormone biosynthetic process." These findings align with the established role of endocrine dysregulation in PCOS, particularly insulin resistance and hormonal imbalances, which are central to the condition. Additionally, our analysis uncovered biological processes linked to immune cell function, such as "CD4-positive alpha-beta T cell proliferation," "lymphocyte anergy," "negative regulation of alpha-beta T cell proliferation," "negative regulation of CD4-positive alpha-beta T cell proliferation," "negative regulation of T cell migration," "positive regulation of T cell tolerance induction," and "T cell tolerance induction." This cluster of immune-related processes suggests that T cell-mediated immune responses contribute significantly to PCOS development and progression. To pinpoint immune cell types relevant to PCOS, we analyzed single-cell RNA sequencing data from the OneK1K cohort(Yazar et al. 2022 ) using scPagwas(Ma et al. 2023 ). Trait-relevant scores (TRS) were calculated for seven major immune cell populations with a Seurat-based cell-scoring approach [4], which integrates normalized expression of PCOS-associated genes and corrects for technical variation through random control subtraction. NK cells showed the highest median TRS (0.25), followed by CD8 T cells (0.23) and CD4 T cells (0.22). Other cell types exhibited lower scores: B cells (0.09), monocytes (0.05), dendritic cells (0.07), and plasma cells (0.12) ( Fig. 2 B ) . These results indicate that NK cells, CD8 T cells, and CD4 T cells may play prominent roles in PCOS pathogenesis. Supporting these findings, prior studies have noted a disrupted CD4+/CD8 + T cell ratio in PCOS, contributing to immune dysregulation and chronic inflammation(Gao et al. 2023 ). Additionally, women with PCOS exhibit an elevated percentage of NK cells(He et al. 2020 ) ( Fig. 2 C-D ) . In summary, our results reinforce the critical role of hormonal imbalances in PCOS while highlighting the significant involvement of immune system components, particularly T cells and NK cells, in the disease process. Sc-eQTL uncovers immune-relevant susceptibility genes of PCOS overlooked by traditional eQTL To identify putative causal genes associated with PCOS susceptibility, we implemented a colocalization analysis framework that integrates GWAS signals for PCOS with single-cell expression quantitative trait loci (sc-eQTL) data derived from the OneK1K dataset across diverse immune cell types. Our analysis pinpointed two genes, IRF1 (interferon regulatory factor 1) and MAPRE1 (microtubule-associated protein RP/EB family member 1), exhibiting robust colocalization signals (posterior probability of hypothesis 4, PPH4 > 0.75) across multiple immune cell types ( Fig. 3 A )(Supplementary Table S5) . Specifically, IRF1 showed significant colocalization in several T cell subsets, including CD4 + effector memory T cells (PPH4 = 0.879), CD4 + naive T cells (PPH4 = 0.903), and CD8 + naive T cells (PPH4 = 0.781) ( Fig. 3 B ) , while MAPRE1 showed colocalization in CD4 + naive T cells (PPH4 = 0.945), CD8 + S100B + T cells (PPH4 = 0.954), and NK cells (PPH4 = 0.840) ( Fig. 3 C ) . These findings highlight the involvement of specific immune cell populations in the genetic regulation of PCOS susceptibility. The functional roles of IRF1 and MAPRE1 provide insights into their potential contributions to PCOS. IRF1 is known to regulate interferon signaling and cell cycle progression, IRF1 is implicated in autoimmune and inflammatory pathways, which may underline aspects of PCOS pathogenesis(Feng et al. 2021 , Yan et al. 2024 ), MAPRE1 is essential for microtubule dynamics and immune cell migration, processes that could influence the metabolic dysregulation frequently observed in PCOS(Su and Qi 2001 , Trepat et al. 2012 , Wang et al. 2023 ). A notable observation emerged for MAPRE1 in NK cells, where we detected a discordant direction of effect between the sc-eQTL and GWAS signals ( Fig. 3 D ) . Specifically, the genetic variant associated with increased PCOS risk correlates with reduced MAPRE1 expression in NK cells. For example, diminished MAPRE1 expression could impair microtubule dynamics(Su and Qi 2001 , Chen et al. 2014 ), potentially hindering NK cell migration to ovarian tissues or their capacity to modulate local immune responses. Such alterations might exacerbate PCOS symptoms. In addition, we conducted colocalization analysis using bulk eQTL data from three relevant tissues—ovary, lymphocytes, and visceral adipose—sourced from the GTEx portal(Consortium 2020 ). In contrast to the single-cell results, no significant colocalization was detected for either IRF1 or MAPRE1 in these bulk tissues (PPH4 < 0.5) ( Fig. 3 E ) . This finding underscores the critical advantage of single-cell resolution in genetic regulatory studies, enabling the detection of cell-type-specific regulatory mechanisms that are obscured in aggregated tissue analyses. These results emphasize the necessity of leveraging single-cell approaches to unravel immune-mediated pathways in complex disorders such as PCOS. Discussion Although GWAS has identified several risk loci for PCOS, the functional roles of these loci and their interplay across metabolic and immune domains in pathogenesis have yet to be fully clarified. Furthermore, traditional eQTL analyses using bulk tissue data may fail to capture critical cellular regulatory mechanisms, particularly in immune cells increasingly implicated in PCOS. Our integrative genomic and single-cell analysis provides novel insights into the genetic and immune mechanisms underlying PCOS. A major strength of this study is the integration of GWAS and sc-eQTL data, providing a comprehensive view of genetic and immune mechanisms at a cellular level. The use of single-cell resolution allowed us to detect context-dependent gene regulatory mechanisms that bulk analyses overlooked, enhancing our understanding of PCOS pathophysiology. This study has several limitations. First, while colocalization analyses identified causal genes, functional validation in immune cell subtypes is required to confirm their roles. Second, the GWAS cohort predominantly included European ancestry populations, limiting generalizability. Third, the interplay between metabolic and immune pathways warrants further exploration—for example, whether insulin resistance directly modulates T cell function or vice versa. Future studies should explore the interactions between metabolic and immune pathways, potentially using multi-omics approaches to map these networks comprehensively. Moreover, developing targeted therapies that modulate immune dysregulation, such as reducing chronic inflammation or enhancing NK cell function, could complement existing treatments focused on insulin sensitivity and hormonal balance. In conclusion, our study advances the understanding of PCOS by highlighting the pivotal roles of immune-related genes and specific immune cell types, particularly NK cells, CD8 T cells, and CD4 T cells. The identification of IRF1 and MAPRE1 in immune cell types as susceptibility genes represents a significant advance in our understanding of the genetic architecture of PCOS. These findings not only enhance our comprehension of PCOS pathophysiology but also suggest novel therapeutic targets for this complex and prevalent disorder Declarations Author contributions statement Xiaoqian Xu: Conceptualization, Data Curation, Methodology, Formal Analysis, Visualization, Writing-Original Draft. Yuzhou Bao: Data Processing, Software, Methodology, Investigation, Writing-Review & Editing. Qi Zhang: Resources, Software, Validation, Visualization, Writing-Review & Editing. Hao Wang: Conceptualization, Supervision, Project Administration, Writing-Review & Editing, Funding Acquisition. Lixia Wang: Conceptualization, Supervision, Writing-Review & Editing, Funding Acquisition. Acknowledgments We acknowledge all members of the Li lab for constructive discussions and help. We also thank Qin Wang at Shenzhen Bay Laboratory supercomputing center for high-computing support. Funding This work was supported by Shenzhen Nanshan District Technology R&D and Creative Design Project Sub-funding(No. NS2023130) Conflict of interest The authors declare no competing financial interests. Data availability The GWAS summary statistics used in this study are available from the GWAS Catalog under accession number GCST90044903 (https://www.ebi.ac.uk/gwas/studies/GCST90044903). Single-cell eQTL datasets for immune cell types were obtained from the OneK1K project (https://onek1k.org/). Bulk eQTL data across tissues were sourced from the Genotype-Tissue Expression (GTEx) Portal (https://gtexportal.org/home/downloads/adult-gtex/qtl). Code availability The custom source codes to perform the data analysis relevant to this study are available under the MIT license at the GitHub repository: https://github.com/earthminator86/PCOS. References Abbott, D.H., et al., 2002. Developmental origin of polycystic ovary syndrome - a hypothesis. 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Supplementary Files SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor assigned by journal 08 Oct, 2025 Editor invited by journal 08 Jul, 2025 Submission checks completed at journal 05 Jul, 2025 First submitted to journal 05 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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 Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6942288","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531712600,"identity":"960988fc-4dda-421f-816e-fac1ca41e5f9","order_by":0,"name":"Xiaoqian Xu","email":"","orcid":"","institution":"Shenzhen University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqian","middleName":"","lastName":"Xu","suffix":""},{"id":531712603,"identity":"9712c103-6d54-454c-b59e-e11092c28c0b","order_by":1,"name":"Yuzhou Bao","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Yuzhou","middleName":"","lastName":"Bao","suffix":""},{"id":531712605,"identity":"d1166f2e-28ec-4914-8b12-2f843ef5a7bd","order_by":2,"name":"Qi Zhang","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Zhang","suffix":""},{"id":531712608,"identity":"be27c1b9-ae48-4558-a676-00191d2ff2da","order_by":3,"name":"Hao Wang","email":"","orcid":"","institution":"Maternal Fetal Medicine Institute, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""},{"id":531712610,"identity":"ee41a4fe-a9a5-4c4a-a289-0694e9e0cbff","order_by":4,"name":"Lixia Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie2QvWrDMBRGFQLKItpVJSF+BQWDyWBIHiAPcQ3FWZySMWsJyIuha6c+Q6fO1wiSxaGrIR3crUMGd9PQodJcLHcsVGe4g/iO7g8hHs9fpRGEkdEeETSf0lGO/QpYhR0SfC/i8IpV8AvFFr4Ky4amyRNfCmdaHE/qA7Zqcv3IBAJTa8nNF3r30q1Ud+kcRMr4WW4R5mojx/c4KKpzpxJhFgkQMSNv6tl22cgJwnAgHcrrxSqcBTWYwahaUw7CrdRZ2Nguor61Sgq9yqK+RMTuMqsOgEkRz6Q5cuna5eYhC9v2Sy2mx7361JoHQZ6Xjd51KwbKfzyhK28Ytj0Bj8fj+e98A0z2YTSlyDg9AAAAAElFTkSuQmCC","orcid":"","institution":"Shenzhen University General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-21 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17:23:38","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78209,"visible":true,"origin":"","legend":"","description":"","filename":"50f0d3421fec48ceb26776ec91f08fa01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-6942288/v1/113370c1fdef1f5d78461d81.xml"},{"id":94038981,"identity":"8705dcb5-b4a6-4007-93de-e30d3f9208e3","added_by":"auto","created_at":"2025-10-21 17:39:38","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86387,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6942288/v1/bb57f5b5a3c190071ba4f8a2.html"},{"id":94038536,"identity":"1b798476-ec12-4ead-8dd7-fc22b2e692f1","added_by":"auto","created_at":"2025-10-21 17:23:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":828068,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of PCOS risk genes. (A) Manhattan plot illustrating GWAS signals. The x-axis represents chromosomes, and the y-axis denotes the -log10 (P-value). Risk genes corresponding to lead SNPs are labeled. (B) Pie chart depicting the classification of risk genes. Different gene catalogs are represented by distinct colors. (C) Comparison of the GWAS effect sizes of SNPs within credible sets. The y-axis indicates the effect size, while the x-axis represents the gene catalogs.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6942288/v1/d0f7d423267b03a1bd719696.png"},{"id":94038315,"identity":"662b8739-9fb0-41dc-a2ef-94140f199a45","added_by":"auto","created_at":"2025-10-21 17:15:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1034435,"visible":true,"origin":"","legend":"\u003cp\u003eKey Signaling Pathways and Immune Cell Types in PCOS. (A) Barplot of Pathway Enrichment Analysis for GWAS Signals. The x-axis represents the -log₁₀(P-value), and the y-axis lists pathway names. Bars are colored by the number of genes involved in each pathway. (B) UMAP Visualization of OneK1K Immune Cells. Cells are colored by cell type. (C)UMAP Visualization of OneK1K Immune Cells Colored by scPagwas TRS Scores. (D). Boxplot of scPagwas TRS Scores Across Immune Cell Types. The x-axis represents cell types, and the y-axis displays TRS scores.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6942288/v1/516ce84a4260851dad5a6241.png"},{"id":94038318,"identity":"57b79b6f-6331-48f0-9457-0d179e260d01","added_by":"auto","created_at":"2025-10-21 17:15:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1730393,"visible":true,"origin":"","legend":"\u003cp\u003esc-eQTL revealed susceptibility genes in PCOS. (A) Heatmap illustrating PPH4 values derived from colocalization results of risk genes across different cell types. The x-axis represents various cell types, the y-axis denotes gene names. LocusZoomplot showing the significant loci associated with PCOS for the gene \u003cem\u003eIRF1\u003c/em\u003e (B) and \u003cem\u003eMAPRE1\u003c/em\u003e (C). (D) disconcordant direction of effect between PCOS GWAS signal and sc-eQTL signal of gene \u003cem\u003eMAPRE1\u003c/em\u003e in NK cells. (E) Comparison of PPH4 values derived from colocaziation anaysis using sc-eQTL (purple bar) and eQTL (yellow bar) for the genes \u003cem\u003eIRF1\u003c/em\u003e and \u003cem\u003eMAPRE1\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6942288/v1/1392bed3fbc1c16279208377.png"},{"id":100069989,"identity":"b87313db-8fdf-43c1-a72c-b41aed38643b","added_by":"auto","created_at":"2026-01-12 16:15:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4117822,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6942288/v1/c47351a9-3b6a-4b98-afcf-d744f953c502.pdf"},{"id":94038665,"identity":"1d77e90b-1821-4cad-ae28-1424ebdefadd","added_by":"auto","created_at":"2025-10-21 17:31:37","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28119,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6942288/v1/a7170d39ecbc4e46c543b5b8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"sc-eQTL unveil Immunogenetic Architecture of Polycystic Ovary Syndrome","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolycystic Ovary Syndrome (PCOS) is a globally significant gynecological endocrine disorder, affecting approximately 10% of women of reproductive age(Stener-Victorin et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is characterized by a range of clinical manifestations, including infertility and metabolic complications(Singh et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Infertility in women with PCOS primarily stems from ovulatory dysfunction, resulting from associated hormonal imbalances. Additionally, metabolic complications such as insulin resistance, obesity, dyslipidemia, and an elevated risk for type 2 diabetes frequently co-occur(Su et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Genetic factors play a significant role in the development of PCOS, with certain types showing a familial pattern and being linked to specific genome locis(Abbott et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Franks et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The current pathological model of PCOS has largely focused on hyperandrogenism and insulin resistance. Hyperandrogenism leads to symptoms such as hirsutism, acne, and androgenic alopecia, while insulin resistance contributes to the metabolic complications(Rosenfield and Ehrmann \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, An et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite the extensive research on these aspects, there is still a need for a more comprehensive understanding of the underlying mechanisms of PCOS.\u003c/p\u003e\u003cp\u003ePCOS appears to be a highly heritable condition with genetic factors accounting for ~\u0026thinsp;70\u0026ndash;80% of the variation in susceptibility to the condition(Hardy and Norman \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent genome-wide association studies (GWAS) have significantly advanced the identification of genetic factors associated with PCOS. Thus far, several risk loci have been uncovered, providing key insights into the genetic architecture of the condition(Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Lee et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, there are still limitations in our understanding of the mechanisms through which these genetic variants contribute to PCOS. One of the main bottlenecks is that the traditional metabolic pathways have limited explanatory power. For example, although insulin resistance is a common feature of PCOS, the genetic variants identified by GWAS do not fully account for the variation in insulin resistance among women with the condition(Dhar and Bhattacharjee \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Ghalehzan et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This suggests that there may be other factors, such as immune - related pathways, that are involved in the pathogenesis of PCOS.\u003c/p\u003e\u003cp\u003eIn recent years, there has been emerging evidence suggesting that immune dysregulation may play a role in PCOS. For instance, chronic inflammation has been observed in women with PCOS, with elevated levels of inflammatory cytokines such as interleukin \u0026minus;\u0026thinsp;6 (IL \u0026minus;\u0026thinsp;6) and tumor necrosis factor - alpha (TNF - alpha) found in their serum(Sharma et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, the presence of autoantibodies has also been reported in some studies(Xia et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, despite these findings, there is still a lack of systematic genetic evidence to support the involvement of the immune system in PCOS. Most of the previous studies have focused on a limited number of candidate genes or pathways, and there has been no comprehensive analysis of the genetic basis of immune - related abnormalities in PCOS. Moreover, the cell-type resolution of the immune system in PCOS is still unclear, and it is not known which specific immune cell types are involved in the pathogenesis of the condition.\u003c/p\u003e\u003cp\u003eSingle-cell expression quantitative trait loci (sc-eQTL) analysis allows for the dissection of genetic effects on gene expression at an unprecedented cellular resolution(Ding et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The integration of sc-eQTLs with genome-wide association studies (GWAS) has revealed novel relationships between gene expression and disease risk for autoimmune diseases(Perez et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Aquino et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). sc-eQTL show the potential to uncover critical cell types and cell type-specific susceptibility genes and regulatory mechanisms that could be pivotal in the development and treatment of PCOS.\u003c/p\u003e\u003cp\u003eIn this study, we integrate large-scale GWAS data with functional annotation, pathway enrichment, and single-cell analyses to systematically dissect the genetic architecture of PCOS. We pinpoint 19 independent risk loci and 16 risk genes, revealing key signaling pathways and immune cell types, specifically NK cells and T cells. Notably, colocalization analysis using sc-eQTL data uncovered two susceptibility genes, IRF1 and MAPRE1, which were overlooked by traditional methods, underscoring the power of single-cell resolution. Our findings not only reaffirm the centrality of metabolic and hormonal disruptions in PCOS but also unveil novel immune contributions, such as T cell dysfunction and NK cell activity. These insights offer new perspectives on potential therapeutic targets and deepen our mechanistic understanding of this complex disorder.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eData collection and processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe reused published GWAS summary statistics for PCOS from a large-scale meta-analysis(Day et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), comprising 10,074 PCOS cases and 103,164 European-ancestry controls, available through the GWAS Catalog. Genomic coordinates were converted from human reference genome build hg19 to hg38 using the UCSC LiftOver tool. Harmonization and imputation of GWAS summary statistics were performed following the standardized pipeline from the HakyImLab framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/hakyimlab/summary-gwas-imputation/\u003c/span\u003e\u003cspan address=\"https://github.com/hakyimlab/summary-gwas-imputation/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This process utilized the 1000 Genomes Project Phase 3 European population data (release 20130502) as the genotype reference panel. sc-eQTL datasets across immune celltypes were downloaded from onok1k(Yazar et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bulk eQTL summary data of ovary, lymphocytes, and visceral adipose were downloaded from GTEx Project (v8)(Consortium \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of GWAS risk loci and risk gene Prioritization\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePlink (v1.9)(Purcell et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) Clumping procedure was used to identify independent genome-wide risk loci of PCOS (P\u0026thinsp;\u0026lt;\u0026thinsp;5e-9, clump-r2\u0026thinsp;=\u0026thinsp;0.3, clump-kb\u0026thinsp;=\u0026thinsp;250). For each identified locus, fine-mapping was performed using SusieX(Yuan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with the variant exhibiting the lowest p-value designated as the lead SNP. This approach defined candidate risk genes as the protein-coding gene(s) located nearest to each lead SNP within a\u0026thinsp;\u0026plusmn;\u0026thinsp;500 kb window. We employed this established positional strategy as a pragmatic first step for gene prioritization, acknowledging its widespread use in initial GWAS follow-up analyses to generate tractable candidate gene lists.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePathway enrichment of GWAS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify biological pathways significantly associated with PCOS susceptibility, we performed gene-set enrichment analysis using Multi-marker Analysis of Genomic Annotation (MAGMA v1.10) (MAGMA v1.10)(de Leeuw et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) to identify biological pathways significantly associated with PCOS. We implemented a two-stage analytical approach with MAGMA: (1). Gene-level association: We aggregated SNP-level association signals into gene-level associations using MAGMA's principal component-based multi-SNP model. This model accounts for linkage disequilibrium (LD) between SNPs, utilizing LD reference data from Phase 3 of the 1,000 European Genomes Project. Gene boundaries were defined according to the NCBI RefSeq database (build GRCh38/hg38), extending 10 kb upstream of the transcription start site (TSS) to 10 kb downstream of the transcription end site (TES) to capture putative regulatory elements. (2). Competitive gene-set analysis: We tested for enrichment of association signals within biological pathways using a competitive gene-set analysis framework. Pathways were sourced from the Molecular Signatures Database (MSigDB v2024.1.Hs). Significance of pathway enrichment was assessed after stringent correction for multiple testing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eColocalization Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify putative causal genes whose expression is influenced by variants conferring PCOS susceptibility, we performed colocalization analysis using the xQTLbiolinks R package (v1.7.3) (Ding et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This Bayesian framework evaluates shared genetic architecture by computing posterior probabilities for five competing hypotheses: PPH0: No association with either trait; PPH1: Association with trait 1 (PCOS GWAS) only; PPH2: Association with trait 2 (QTL) only; PPH3: Association with both traits, driven by two distinct causal variants (LD); PPH4: Association with both traits, driven by a single shared causal variant (colocalization). We tested for colocalization between PCOS GWAS summary statistics and: (1). Single-cell eQTLs (sc-eQTLs): Derived from diverse immune cell types in the OneK1K dataset(Yazar et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). (2). Bulk tissue eQTLs: From three PCOS-relevant tissues in the GTEx portal(Consortium \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e): ovary, lymphocytes, and visceral adipose. Genomic regions exhibiting posterior probability PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.75 were considered strong evidence of colocalization.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrait-Relevant Score (TRS) calculation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe TRS is a key metric used to quantify the association between specific cell types and traits of interest. Higher TRS values indicate a stronger association of a cell type with the trait. TRS calculations were performed using scPagwas v1.0 (Ma et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), a computational framework that integrates GWAS summary statistics with scRNA-seq data to infer trait-associated gene activity patterns across cell types. Given the large size of the OneK1K dataset (approximately 1\u0026nbsp;million cells), a random subset comprising 30% (approximately 300,000 cells) was selected for subsequent scPagwas analysis to manage computational demands while maintaining representativeness. The analysis was conducted as follows: (1). Cell types were annotated following the methodology described in the original OneK1K publication(Yazar et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), utilizing canonical marker genes and unsupervised clustering. (2) The normalized gene-by-cell matrix was transformed into a pathway activity score (PAS)-by-cell matrix. This PAS matrix is based on the first principal component (PC1) of gene expression within each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway(Kanehisa and Goto \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). (3). A trait-relevant score (TRS) of each cell is computed by averaging the expression level of the trait-relevant genes and subtracting the random control cell score via the cell-scoring method used in Seurat.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eIdentification and functional classification of PCOS risk genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLeveraging a published large-scale genome-wide association study (GWAS) encompassing 113,238 individuals(Day et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we identified 19 independent risk loci for polycystic ovary syndrome (PCOS) at genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5e10-8) using PLINK clumping procedures (P-value\u0026thinsp;\u0026lt;\u0026thinsp;5e-8, clump-r2\u0026thinsp;=\u0026thinsp;0.3, clump-kb\u0026thinsp;=\u0026thinsp;250, \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Chromosomal positional mapping prioritized 16 candidate causal genes proximal to lead SNPs of each loci \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)(Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e, including \u003cem\u003eDENND1A\u003c/em\u003e and \u003cem\u003eTHADA\u003c/em\u003e with known metabolic functions(McAllister et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Tian et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Alarcon-Granados et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePCOS pathogenesis is characterized by complex interactions among metabolic dysregulation (e.g., insulin resistance, hyperandrogenemia), chronic inflammation (e.g., elevated TNF-α and IL-6), and direct ovarian dysfunction (e.g., follicular arrest)(Stener-Victorin et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Based on known biological functions derived from functional annotation database (GeneCards), we categorized these 16 candidate genes into three distinct groups: i) Metabolic-related, including \u003cem\u003eFSHB, TOX3, ERBB4, THADA, DENND1A\u003c/em\u003e (31.25%, 5/16), ii) Immune-related, including \u003cem\u003eZBTB16, ARL14EP, IKZF4, IRF1, PLGRKT\u003c/em\u003e (31.25%, 5/16), iii) Other, including \u003cem\u003eYAP1, MPPED2, ERBB3, KRR1, MAPRE1, AOPEP\u003c/em\u003e (37.5%, 6/16) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)(Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e. Among the metabolic-related genes, \u003cem\u003eDENND1A\u003c/em\u003e is implicated in insulin signaling and has been associated with insulin resistance(Dallel et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eTHADA\u003c/em\u003e, involved in thyroid hormone signaling, is linked to type 2 diabetes and may influence PCOS through regulation of insulin sensitivity and glucose metabolism(Alarcon-Granados et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). FSHB encodes the beta subunit of follicle-stimulating hormone (FSH), which is essential for ovarian follicle development and ovulation(Li et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); variants in FSHB may disrupt FSH levels or function, contributing to ovulatory dysfunction in PCOS.\u003c/p\u003e\u003cp\u003eTo assess the relative contributions of these genes to PCOS pathogenesis, we performed fine-mapping analysis using Susie method(Yuan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to establish 95% credible sets for each locus, identifying the most probable causal variants \u003cb\u003e(Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e)\u003c/b\u003e. We then assessed the distribution of effect sizes (beta coefficients from the GWAS) of SNPs within these credible sets across the three gene functional categories. This analysis revealed significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Wilcoxon rank-sum test), with SNPs near metabolic-related genes exhibiting larger effect sizes compared to those near immune-related genes, and the smallest effect sizes observed for genes in the \"other\" category \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. These findings indicate that metabolic-related genes exert the strongest genetic influence on PCOS risk. However, immune-related genes also play a critical role, likely through pathways that interact with or amplify metabolic dysregulation. For instance, chronic inflammation, characterized by elevated levels of C-reactive protein (CRP), TNF-α, and IL-6, may exacerbate insulin resistance and contribute to ovarian dysfunction in PCOS(Aboeldalyl et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of Key Signaling Pathways and Immune Cell Types in PCOS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe applied Multi-marker Analysis of Genomic Annotation (MAGMA) method(de Leeuw et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) to PCOS GWAS data, identifying 15 biological processes significantly enriched for PCOS-associated variants (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003cb\u003e(\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA)(\u003cb\u003eSupplementary Table S4\u003c/b\u003e). These pathways demonstrate significant enrichment in both endocrine regulatory mechanisms and immune system functions. Several exhibited associations with hormonal regulation, including \"cAMP-PKA signal transduction,\" \"cellular response to insulin stimulus,\" \"cellular response to peptide hormone stimulus,\" \"response to peptide hormone,\" and \"steroid hormone biosynthetic process.\" These findings align with the established role of endocrine dysregulation in PCOS, particularly insulin resistance and hormonal imbalances, which are central to the condition. Additionally, our analysis uncovered biological processes linked to immune cell function, such as \"CD4-positive alpha-beta T cell proliferation,\" \"lymphocyte anergy,\" \"negative regulation of alpha-beta T cell proliferation,\" \"negative regulation of CD4-positive alpha-beta T cell proliferation,\" \"negative regulation of T cell migration,\" \"positive regulation of T cell tolerance induction,\" and \"T cell tolerance induction.\" This cluster of immune-related processes suggests that T cell-mediated immune responses contribute significantly to PCOS development and progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo pinpoint immune cell types relevant to PCOS, we analyzed single-cell RNA sequencing data from the OneK1K cohort(Yazar et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) using scPagwas(Ma et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Trait-relevant scores (TRS) were calculated for seven major immune cell populations with a Seurat-based cell-scoring approach [4], which integrates normalized expression of PCOS-associated genes and corrects for technical variation through random control subtraction. NK cells showed the highest median TRS (0.25), followed by CD8 T cells (0.23) and CD4 T cells (0.22). Other cell types exhibited lower scores: B cells (0.09), monocytes (0.05), dendritic cells (0.07), and plasma cells (0.12) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. These results indicate that NK cells, CD8 T cells, and CD4 T cells may play prominent roles in PCOS pathogenesis. Supporting these findings, prior studies have noted a disrupted CD4+/CD8\u0026thinsp;+\u0026thinsp;T cell ratio in PCOS, contributing to immune dysregulation and chronic inflammation(Gao et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, women with PCOS exhibit an elevated percentage of NK cells(He et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eIn summary, our results reinforce the critical role of hormonal imbalances in PCOS while highlighting the significant involvement of immune system components, particularly T cells and NK cells, in the disease process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSc-eQTL uncovers immune-relevant susceptibility genes of PCOS overlooked by traditional eQTL\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify putative causal genes associated with PCOS susceptibility, we implemented a colocalization analysis framework that integrates GWAS signals for PCOS with single-cell expression quantitative trait loci (sc-eQTL) data derived from the OneK1K dataset across diverse immune cell types. Our analysis pinpointed two genes, \u003cem\u003eIRF1\u003c/em\u003e (interferon regulatory factor 1) and \u003cem\u003eMAPRE1\u003c/em\u003e (microtubule-associated protein RP/EB family member 1), exhibiting robust colocalization signals (posterior probability of hypothesis 4, PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.75) across multiple immune cell types \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)(Supplementary Table S5)\u003c/b\u003e. Specifically, \u003cem\u003eIRF1\u003c/em\u003e showed significant colocalization in several T cell subsets, including CD4\u0026thinsp;+\u0026thinsp;effector memory T cells (PPH4\u0026thinsp;=\u0026thinsp;0.879), CD4\u0026thinsp;+\u0026thinsp;naive T cells (PPH4\u0026thinsp;=\u0026thinsp;0.903), and CD8\u0026thinsp;+\u0026thinsp;naive T cells (PPH4\u0026thinsp;=\u0026thinsp;0.781) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, while \u003cem\u003eMAPRE1\u003c/em\u003e showed colocalization in CD4\u0026thinsp;+\u0026thinsp;naive T cells (PPH4\u0026thinsp;=\u0026thinsp;0.945), CD8\u0026thinsp;+\u0026thinsp;S100B\u0026thinsp;+\u0026thinsp;T cells (PPH4\u0026thinsp;=\u0026thinsp;0.954), and NK cells (PPH4\u0026thinsp;=\u0026thinsp;0.840) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. These findings highlight the involvement of specific immune cell populations in the genetic regulation of PCOS susceptibility. The functional roles of \u003cem\u003eIRF1\u003c/em\u003e and \u003cem\u003eMAPRE1\u003c/em\u003e provide insights into their potential contributions to PCOS. \u003cem\u003eIRF1\u003c/em\u003e is known to regulate interferon signaling and cell cycle progression, \u003cem\u003eIRF1\u003c/em\u003e is implicated in autoimmune and inflammatory pathways, which may underline aspects of PCOS pathogenesis(Feng et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Yan et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), \u003cem\u003eMAPRE1\u003c/em\u003e is essential for microtubule dynamics and immune cell migration, processes that could influence the metabolic dysregulation frequently observed in PCOS(Su and Qi \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Trepat et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA notable observation emerged for \u003cem\u003eMAPRE1\u003c/em\u003e in NK cells, where we detected a discordant direction of effect between the sc-eQTL and GWAS signals \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Specifically, the genetic variant associated with increased PCOS risk correlates with reduced \u003cem\u003eMAPRE1\u003c/em\u003e expression in NK cells. For example, diminished \u003cem\u003eMAPRE1\u003c/em\u003e expression could impair microtubule dynamics(Su and Qi \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), potentially hindering NK cell migration to ovarian tissues or their capacity to modulate local immune responses. Such alterations might exacerbate PCOS symptoms.\u003c/p\u003e\u003cp\u003eIn addition, we conducted colocalization analysis using bulk eQTL data from three relevant tissues\u0026mdash;ovary, lymphocytes, and visceral adipose\u0026mdash;sourced from the GTEx portal(Consortium \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast to the single-cell results, no significant colocalization was detected for either \u003cem\u003eIRF1\u003c/em\u003e or \u003cem\u003eMAPRE1\u003c/em\u003e in these bulk tissues (PPH4\u0026thinsp;\u0026lt;\u0026thinsp;0.5) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. This finding underscores the critical advantage of single-cell resolution in genetic regulatory studies, enabling the detection of cell-type-specific regulatory mechanisms that are obscured in aggregated tissue analyses. These results emphasize the necessity of leveraging single-cell approaches to unravel immune-mediated pathways in complex disorders such as PCOS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough GWAS has identified several risk loci for PCOS, the functional roles of these loci and their interplay across metabolic and immune domains in pathogenesis have yet to be fully clarified. Furthermore, traditional eQTL analyses using bulk tissue data may fail to capture critical cellular regulatory mechanisms, particularly in immune cells increasingly implicated in PCOS. Our integrative genomic and single-cell analysis provides novel insights into the genetic and immune mechanisms underlying PCOS. A major strength of this study is the integration of GWAS and sc-eQTL data, providing a comprehensive view of genetic and immune mechanisms at a cellular level. The use of single-cell resolution allowed us to detect context-dependent gene regulatory mechanisms that bulk analyses overlooked, enhancing our understanding of PCOS pathophysiology.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, while colocalization analyses identified causal genes, functional validation in immune cell subtypes is required to confirm their roles. Second, the GWAS cohort predominantly included European ancestry populations, limiting generalizability. Third, the interplay between metabolic and immune pathways warrants further exploration—for example, whether insulin resistance directly modulates T cell function or vice versa. Future studies should explore the interactions between metabolic and immune pathways, potentially using multi-omics approaches to map these networks comprehensively. Moreover, developing targeted therapies that modulate immune dysregulation, such as reducing chronic inflammation or enhancing NK cell function, could complement existing treatments focused on insulin sensitivity and hormonal balance.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study advances the understanding of PCOS by highlighting the pivotal roles of immune-related genes and specific immune cell types, particularly NK cells, CD8 T cells, and CD4 T cells. The identification of \u003cem\u003eIRF1\u003c/em\u003e and \u003cem\u003eMAPRE1\u003c/em\u003e in immune cell types as susceptibility genes represents a significant advance in our understanding of the genetic architecture of PCOS. These findings not only enhance our comprehension of PCOS pathophysiology but also suggest novel therapeutic targets for this complex and prevalent disorder\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaoqian Xu: Conceptualization, Data Curation, Methodology, Formal Analysis, Visualization, Writing-Original Draft.\u003c/p\u003e\n\u003cp\u003eYuzhou Bao: Data Processing, Software, Methodology, Investigation, Writing-Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eQi Zhang: Resources, Software, Validation, Visualization, Writing-Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eHao Wang: Conceptualization, Supervision, Project Administration, Writing-Review \u0026amp; Editing, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003eLixia Wang: Conceptualization, Supervision, Writing-Review \u0026amp; Editing, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge all members of the Li lab for constructive discussions and help. We also thank Qin Wang at Shenzhen Bay Laboratory supercomputing center for high-computing support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Shenzhen Nanshan District Technology R\u0026amp;D and Creative Design Project Sub-funding(No. NS2023130)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS summary statistics used in this study are available from the GWAS Catalog under accession number GCST90044903 (https://www.ebi.ac.uk/gwas/studies/GCST90044903). Single-cell eQTL datasets for immune cell types were obtained from the OneK1K project (https://onek1k.org/). Bulk eQTL data across tissues were sourced from the Genotype-Tissue Expression (GTEx) Portal (https://gtexportal.org/home/downloads/adult-gtex/qtl).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe custom source codes to perform the data analysis relevant to this study are available under the MIT license at the GitHub repository: https://github.com/earthminator86/PCOS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbott, D.H., et al., 2002. Developmental origin of polycystic ovary syndrome - a hypothesis. J Endocrinol\u003cem\u003e. \u003c/em\u003e174, 1-5\u003c/li\u003e\n\u003cli\u003eAboeldalyl, S., et al., 2021. The role of chronic inflammation in polycystic ovarian syndrome-a systematic review and meta-analysis. Int J Mol Sci\u003cem\u003e. \u003c/em\u003e22, \u003c/li\u003e\n\u003cli\u003eAlarcon-Granados, M.C., et al., 2022. 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Nat Genet\u003cem\u003e. \u003c/em\u003e56, 1841-1850\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6942288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6942288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePolycystic ovary syndrome (PCOS) is a complex endocrine disorder characterized by metabolic, inflammatory, and reproductive dysfunction. We integrated genome-wide association study (GWAS) data, and single-cell expression quantitative trait loci (sc-eQTL) across various immune cells to elucidate the genetic and immune mechanisms underlying PCOS at the cellular level. Finally, we identified \u003cdel\u003e20 \u003c/del\u003e19 risk loci for PCOS, prioritizing 16 candidate causal genes, approximately 30% of these genes are immune-related. Pathway analysis revealed 15 PCOS-associated signaling pathways, spanning hormonal regulation and immune function, notably T cell-mediated responses. Single-cell RNA sequencing analysis implicated NK cells, CD8 T cells, and CD4 T cells play a crucial role in PCOS pathogenesis, with the highest trait-relevant scores. sc-eQTL colocalization analysis identified \u003cem\u003eIRF1\u003c/em\u003e and \u003cem\u003eMAPRE1\u003c/em\u003e as causal genes with regulatory effects in specific T cell subsets and NK cells, which were obscured by bulk eQTL analysis. Moreover, \u003cem\u003eMAPRE1\u003c/em\u003e showing a discordant effect in NK cells, suggesting immune dysregulation. Our findings underscore the pivotal roles of metabolic and immune-related genes in PCOS and the importance of immune cell-specific mechanisms, enhancing our understanding of its pathophysiology and potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"sc-eQTL unveil Immunogenetic Architecture of Polycystic Ovary Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 17:15:32","doi":"10.21203/rs.3.rs-6942288/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-12T09:36:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T10:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235629501474532537110106705539141616656","date":"2025-10-14T09:41:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T20:19:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T20:16:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-08T09:24:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-05T04:42:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-05T04:40:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7169c2f-1dc3-448b-93fc-83ddbae78cb0","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56528961,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases"},{"id":56528962,"name":"Biological sciences/Immunology/Gene regulation in immune cells/Immunogenetics"}],"tags":[],"updatedAt":"2026-01-12T16:09:19+00:00","versionOfRecord":{"articleIdentity":"rs-6942288","link":"https://doi.org/10.1038/s41598-025-34726-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-06 15:58:35","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-10-21 17:15:32","video":"","vorDoi":"10.1038/s41598-025-34726-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-34726-5","workflowStages":[]},"version":"v1","identity":"rs-6942288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6942288","identity":"rs-6942288","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}