{"paper_id":"40687209-6e2a-4aad-847a-889fb449b757","body_text":"ARTICLE | Article in Press\nScientific Reports https://doi.org/10.1038/s41598-026-69642-9\nScientific Reports\nThis Article in Press is shared early to give you faster access to new research. It is citable and carries a permanent DOI. \nThe final edited version will replace it automatically.\nSally Mortlock and Rahul K. Gajbhiye contributed equally to this work.\nGenome-wide association analysis of endometriosis in \nIndian women supports shared genetic susceptibility \nand suggestive population specific signals\nSandhya Anand · Hrishikesh Munshi · Shashank Shekhar · Chaitanya Shembekar ·  \nPushp Lata Sankhwar · Nilajkumar Bagde · Pramathes DasMahapatra · Pranay Phukan · \nBimal John · Milind Shah · Deepali Kale · Ketki Kulkarni · Swati Mahobia · Niraj Mahajan · \nAnil Humane · Nagendra Sardeshpande · Reji Mohan · Muralidhar V. Pai · Kedar Padte · \nAtul Ganatra · Sonal Kumta · Sakshi Gangurde · Nayna Barada · Tabassum Khan · \nShagufta Khan · Geetanjali Sachdeva · Gita D. Mishra · Smita D. Mahale ·  \nGrant W. Montgomery · Sally Mortlock  · Rahul K. Gajbhiye   on behalf of   \non behalf of the ECGRI Consortium\nReceived: 8 April 2026 / Accepted: 28 August 2026\n© The Author(s) 2026\nAbstract\nEndometriosis is an estrogen-dependent, chronic gynaecological disorder affecting approximately 10% of \nwomen of reproductive age and is associated with pelvic pain, infertility, and reduced quality of life. Genetic \nstudies indicate substantial heritability, yet genome-wide association studies (GWAS) have predominantly \nfocused on European populations, with limited evidence from South Asian groups. We conducted a GWAS \nin 2523 participants from a nationwide, multicentre endometriosis case–control study in India. Genetic sus -\nceptibility in Indian women showed both shared features with other populations and evidence of potential \npopulation-specific signals. Twenty-one loci demonstrated suggestive associations ( p < 1 × 10⁻5). The strongest \nsignal mapped to chromosome 13 within a long non-coding RNA and in regulatory elements upstream of \nSHISA2. Colocalization analysis showed shared genetic effects with SHISA2 expression in thyroid tissue and \nmethylation sites in the endometrium. A polygenic risk score derived from European populations demonstrated \ncomparable predictive performance in the Indian cohort. Meta-analysis with a Japanese GWAS replicated two \nknown genome-wide significant associations near WNT4 and CDKN2B-AS1, supporting shared genetic suscep -\ntibility across populations. No variant reached genome-wide significance in the discovery cohort alone. This \nstudy highlights the importance of expanding genetic studies to diverse populations to improve generalisable \ngenetic risk estimates, biological understanding and equitable precision medicine in women’s health.\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nKeywords Endometriosis · Genome-wide association study · South Asian population · Polygenic risk \nscore · Meta-analysis · India\nIntroduction\nEndometriosis is an estrogen-dependent, chronic gynecological disorder characterized by ectopic growth of \nendometrium-like tissue. It significantly impacts a woman’s overall well-being and quality of life, and is associ-\nated with chronic pain, menstrual disturbances, infertility, and psychological distress. 1,2 Endometriosis affects \nabout 10% of women of reproductive age globally; India accounts for an estimated 50 million cases, representing \nnearly 18% of the global burden.3–5 Increasingly, endometriosis is recognised as a condition with impacts beyond \nreproductive health, affecting physical, psychological, and social well-being.6,7\nEndometriosis arises from a complex interplay of hormonal, environmental, and genetic factors. Several theo-\nries have been proposed to explain the disease pathogenesis, including retrograde menstruation, coelomic meta -\nplasia, lymphatics and vascular metastasis, autoimmunity, inflammation, and genetic and epigenetic factors, as \nwell as exposure to a multitude of dietary, environmental, and lifestyle factors.1 Despite decades of research, the \nunderlying causes and associated risk factors remain incompletely understood, contributing to diagnostic delays \nand treatment inadequacies.\nGenetic studies provide strong evidence for a substantial heritable component to the disease, supported by \nfamilial clustering, twin studies, and SNP-based heritability estimates. 8–11 GWAS have identified over 40 loci \nin European and East Asian populations (including Japanese, Taiwanese, and Chinese), implicating pathways \nrelated to hormonal regulation, cell adhesion, inflammation, and pain.12–15 However, these studies explain only a \nsmall proportion of disease variance, and the predominance of European cohorts limits understanding of ances -\ntry-specific or shared genetic architecture.\nDifferences in allele-frequency and linkage disequilibrium (LD) patterns across populations have important \nimplications for the discovery and interpretation of genetic associations. Variants that are rare or absent in Euro-\npean populations may be more common in South Asian groups, while ancestry-specific LD patterns can improve \nfine-mapping resolution and help distinguish causal variants. These differences contribute to reduced replication \nof association signals across populations and limit the transferability of PRS derived from European datasets. 16 \nInclusion of diverse populations is therefore essential to improve locus discovery, refine causal variants, and \nenable equitable risk prediction.\nDespite accounting for a substantial proportion of the global burden of endometriosis, Indian populations \nremain underrepresented in genetic studies. The Endometriosis Clinical and Genetic Research in India (ECGRI) \nstudy was designed to address this gap by investigating the genetic susceptibility in Indian women. 17 In this \nnationwide case–control study, we conducted a genome-wide association analysis of surgically confirmed endo-\nmetriosis in 1288 cases and 1235 ancestry-matched controls. We further performed fine-mapping and functional \nannotation analyses, assessed the replication of previously reported loci, evaluated the transferability of a Euro -\npean-derived polygenic risk score, and conducted a cross-ancestry meta-analysis using Japanese GWAS data. \nThese analyses aim to characterise shared and putative population-specific genetic risk and to improve under -\nstanding of the genetic architecture of endometriosis in an underrepresented population.\nResults\nParticipant characteristics\nA total of 2771 participants (1383 cases and 1388 controls) were recruited. The mean age was significantly higher \nin cases than in controls (32·69 ± 6·77 vs 31·58 ± 6·68; p = 1·37 × 10–5). Participants were recruited across five \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\ngeographic regions with representation from Indo-European, Dravidian and Tibeto-Burman ancestry groups. The \ndistribution of cases and controls was broadly comparable across ancestry groups (Table 1 and Supplementary \nTable S1).\nQuality control and imputation\nFollowing genotyping, quality control retained 2,523 individuals (1288 cases and 1235 controls) and 416,262 \nvariants were retained for analysis. Imputation using the SEAD reference resulted in 9,647,516 variants after \nfiltering, including 8,073,999 bi-allelic SNPs. Concordance between SEAD and TOPMed imputation panels was \nhigh (> 0·97), supporting the robustness of imputation.\nPopulation structure\nPrincipal component analysis of genome-wide genotype data showed that study participants clustered with the \nSouth Asian population in the 1000 Genomes reference panel, with limited spread extending towards the East \nAsian clusters and PCA of our cohort alone showed that clustering by ethnicity was consistent with ancestry. No \nevidence of clustering by genotyping batch was observed (Supplementary Figure S2).\nTable 1 Demographics of the ECGRI study participants.\nCharacteristics Case Control Total\nParticipants (recruited) 1383 1388 2771\nMean Age ± SD* (years) 32·69 ± 6·77 31·58 ± 6·68 NA\nZone Central 391 (28·27) 391 (28·17) 782 (28·22)\nNorth 337 (24·37) 354 (25·5) 691 (24·94)\nEast/North East 297 (21·48) 287 (20·68) 584 (21·08)\nSouth 138 (9·98) 138 (9·94) 276 (9·96)\nWest 220 (15·91) 218 (15·71) 438 (15·81)\nEthnicity Indo-European 1072 (77·51) 1080 (77·81) 2152 (77·66)\nDravidian 165 (11·93) 176 (12·68) 341 (12·31)\nTibeto-Burmese 146 (10·56) 132 (9·51) 278 (10·03)\nEndometriosis stage** I/II 201 (14·64) NA NA\nIII/IV 1172 (85·36) NA NA\nGenotyped participants\nCase Control Total\nParticipants 1288 1235 2523\nMean age ± SD 32·8 ± 6·75 31·7 ± 6·67 NA\nZone Central 364 (28·26) 345 (27·94) 709 (28·1)\nNorth 318 (24·69) 320 (25·91) 638 (25·29)\nEast/North East 268 (20·81) 249 (20·16) 517 (20·49)\nSouth 133 (10·33) 126 (10·2) 259 (10·27)\nWest 205 (15·92) 195 (15·79) 400 (15·85)\nEthnicity Indo-European 995 (77·25) 957 (77·49) 1952 (77·37)\nDravidian 158 (12·27) 160 (12·96) 318 (12·6)\nTibeto-Burmese 135 (10·48) 118 (9·55) 253 (10·03)\nEndometriosis stage*** I/II 185 (14·46) NA NA\nIII/IV 1094 (85·54) NA NA\n*Age missing for 3 cases\n**Stage data missing for 10 cases; proportion calculated as % out of 1373\n***Stage data missing for 9 cases, proportion calculated as % out of 1279\nProportion calculated as % of cases/controls/total respectively\nNA, not applicable.\n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nGenetic associations with endometriosis\nGenome-wide association analysis identified 88 variants associated with endometriosis at a suggestive signifi -\ncance threshold (p < 1 × 1 0–5), corresponding to 21 genomic risk loci (Fig. 1, Supplementary Table S2). No variant \nreached genome-wide significance ( p < 1 × 1 0–8). The test statistic showed minimal inflation (λ = 1·03) and the \nquantile–quantile plots indicated no evidence of systematic bias, though mild deflation of test statistics noted \nwas likely due to small sample size (Supplementary Figure S3). Using the suggestive p-value threshold, FUMA \nidentified 21 genomic risk loci with 24 independent significant lead SNPs (Table 2). All potential 21 risk loci \nwere novel and had not been identified in previous endometriosis GWAS. Analysis with the linear mixed model \nin GCTA gave consistent results, despite the differences in the statistical models and variance assumptions. There \nwas additional deflation of the test statistics with GCTA due to inclusion of the GRM in the model, indicating that \nthe results are robust to population stratification (Supplementary Figure S4).\nReplication of risk loci across different ancestral populations\nOf the 21 genomic risk loci identified, 3 showed allele frequency differences > 0·1 across major Indian ancestry \ngroups, and 16 showed differences across global populations (Supplementary Figure S5; Supplementary Table \nS3—S4). Of the 46 autosomal risk SNPs identified in the European GWAS dataset 13, 38 were present in our \ndataset and proxy SNPs in strong LD (R 2 > 0·75) were identified for the remaining eight using LD proxy. 18 Of \nthese eight variants, rs2040445 was not polymorphic in the South Asian population. A total of seven variants rep-\nlicated with nominal significance (p < 0·05), with similar direction of effect in our dataset (Supplementary Table \nS5). Replicated variants included the variants at the WNT4 and DNM3 loci on Chr 1, the KDR locus on Chr 4, \nthe 7p15·2 locus on Chr 7, the CDKN2B-AS1 locus on Chr 9, the RNLS locus on Chr 10, and the VEZT locus on \nChr 12. Figure 2 shows the differences in the allele frequencies of the 45 risk SNPs, including the proxy SNPs, \nbetween the European and the Indian population. Differences in allele frequency of > 0·1 were observed in 19 out \nof 45 variants (Supplementary Table S6). We replicated one lead SNP from the Taiwanese dataset at WNT4 locus \non Chr 1 with consistent direction of effect and p < 0·05 (Supplementary Table S5). Out of the 65 significantly \nassociated SNPs from the multiancestry meta-anlyses 19, five replicated in our dataset with p < 0.05. One SNP \nFig. 1 Manhattan plot for genome-wide associations for endometriosis in the Indian population. Manhattan plot shows the \ngenome wide association results for endometriosis using 1288 cases and 1235 controls in the ECGRI cohort. The x-axis \nshows the chromosomal position and y-axis shows the -log 10Pvalue. Colours denote alternative chromosomes. The red hori -\nzontal line represents the genome-wide significant threshold (p < 5 × 10–8) and the blue horizontal line represents the nominal \ngenome-wide significance (p < 1 × 1 0–5). The lead variant was rs35236165 on chromosome 13 (p = 8·43 × 10–7).\n \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nTable 2 Summary of the GWAS results for 21 genomic risk loci for endometriosis identified by FUMA.\nChr SNP Position (bp) RA RAF OR LOG(OR)SE P-value Nearest gene/cytoband\n1 rs872595 38,705,915 C 0·26 1·37 0·07 3·46E-06 LINC01343\n1 rs145379000 84,911,878 C 0·94 1·75 0·13 8·67E-06 RPF1, DNASE2B\n1 rs114511502 231,326,592 C 0·96 2·13 0·16 1·18E-06 TRIM67\n1 rs61829563 238,400,886 A 0·91 1·67 0·11 1·63E-06 RP11-136B18·1\n2 rs6710746 10,414,723 G 0·44 1·33 0·06 1·32E-06 RN7SL66P\n2 rs1861768 15,625,186 C 0·50 1·29 0·06 9·86E-06 NBAS\n2 rs2679859 105,927,611 C 0·25 1·35 0·07 5·85E-06 TGFBRAP1\n2 rs7563305 113,673,470 C 0·87 1·49 0·09 8·74E-06 IL37\n3 rs12631821 65,033,653 C 0·85 1·45 0·08 5·99E-06 ADAMTS9\n3 rs1005940 74,221,493 A 0·73 1·37 0·07 3·16E-06 Y-RNA, CNTN3\n5 rs58794955 10,571,893 T 0·23 1·38 0·07 4·69E-06 ANKRD33B\n6 rs9401193 120,284,989 A 0·94 1·72 0·12 9·65E-06 MIR3144, RNU6\n7 rs56991723 73,735,335 G 0·85 1·47 0·09 8·87E-06 CLIP2\n10 rs11017546 132,686,453 T 0·12 1·58 0·09 1·19E-06 MIR378C\n11 rs1599469 98,783,039 A 0·57 1·30 0·06 9·60E-06 RN7SKP53\n12 rs76975203 132,854,297 C 0·95 1·92 0·14 3·12E-06 GALNT9\n13 rs35236165 26,627,563 A 0·41 1·34 0·06 8·43E-07 LINC00415\n13 rs12867495 29,842,523 T 0·05 1·95 0·15 5·50E-06 MTUS2\n15 rs28698234 88,268,228 T 0·94 1·79 0·13 5·87E-06 NTRK3, LINC00052\n16 rs140109794 23,168,249 A 0·07 1·72 0·12 7·37E-06 USP31\n21 rs2838083 43,063,044 A 0·63 1·30 0·06 7·46E-06 LINC00111, SNOU13\nChr, chromosome; SNP, single nucleotide polymorphism; RA, risk allele; RAF, risk allele frequency; OR, odds ratio with respect to the risk \nallele; SE, standard error of log odds ratio. Genomic positions are based on GRCh37 (hg19)\nFig. 2 Allele frequencies of the 45 known endometriosis risk variants. The graph shows the risk allele frequency of the 45 \npreviously established risk variants for endometriosis in the European population (blue) and in the Indian dataset (ECGRI)\n(red). The x-axis shows the lead variants annotated with chromosome and the closest gene, and the y-axis shows the risk \nallele frequency. Dots joined by a grey line represent allele frequencies of the same variant. The variants on the x-axis in red \ncolour represent the proxy SNPs.\n \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\n(rs2779747 at CDKN2B-AS1/DMRTA1 locus, p = 5.56 × 10⁻4) passed the Bonferroni-corrected multiple testing \nthresholds of p < 7.69 × 10–4 (0.05/65) (Supplementary Table S5). These results indicate partial overlap in genetic \nrisk across different populations, highlighting both shared and potentially population specific genetic architecture.\nEvidence for potential causal variants at chr13q12·13/LINC00415\nThe strongest signal was located on chromosome 13 (rs35236165; p = 8·43 × 10–7). Functional annotation analy-\nsis in FUMA showed the lead variant rs35236165 lies within an exon of the long non-coding RNA LINC00415 \nand is in strong LD with an enhancer variant upstream of the gene SHISA2 (Supplementary Figure S6). A 95% \ncredible set of 65 SNPs was identified within 1 Mb region surrounding rs35236165 (Supplementary Table S7). \nThe lead SNP from our GWAS data showed the highest posterior inclusion probability (PIP) score of 0·386 \nindicating moderate causality. Combined with the PIP score, high LD in this region (68% of the SNP pairs had \nr2 > 0·8) suggests a strong but poorly localized association signal.\nPotential target genes\nMapping credible-set variants to epigenetic marks identified 23 variants overlapping enhancer-associated histone \nmarks and one variant (rs3088377) overlapping a promoter-associated histone mark at SHISA2 (Additional Infor-\nmation 1, Supplementary Figure S7), indicating that a considerable fraction of the credible set lies within or close \nto regulatory elements, supporting regulatory mechanisms at this locus.\nColocalisation (COLOC) and summary-data-based Mendelian randomisation (SMR) analyses were used to \nevaluate whether endometriosis GWAS loci and molecular quantitative trait loci (QTLs) were driven by shared \nvariants, providing evidence for potential functional genes and regulatory mechanisms underlying disease risk. \nColocalisation analyses indicated a shared causal signal between endometriosis risk and SHISA2 expression in \nthyroid (PPH4 = 0·64 > PPH3 = 0·26), while blood expression QTL (eQTL) data suggested independent causal \nvariants (PPH3 = 0·73) (Table 3). Strong evidence of colocalisation was also observed between endometriosis \nand DNA methylation at cg19014360 (PPH4 = 0·92) and cg07984168 (PPH4 = 0·91) in endometrium and at \ncg07984168 in blood (PPH4 = 0·94) (Fig. 3). Both CpG sites are located within the non-coding RNA LINC00415, \nwith cg07984168 overlapping a predicted enhancer.\nTable 3 Colocalization of GWAS signals with eQTLs and mQTLs.\nQTL Dataset Gene/ CpG site nsnps PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf\nBlood_eQTL RNF6 1366 2·08E-07 5·97E-07 2·54E-01 7·30E-01 1·64E-02\nBlood_eQTL SHISA2 1366 4·32E-64 1·24E-63 2·55E-01 7·35E-01 9·84E-03\nThyroid_eQTL SHISA2 1284 8·57E-16 2·45E-15 9·26E-02 2·64E-01 6·44E-01\nBlood_mQTL cg07984168 634 5·92E-119 1·64E-118 1·64E-02 4·44E-02 9·39E-01\nBlood_mQTL cg27012920 565 5·50E-153 8·33E-154 2·77E-01 4·12E-02 6·82E-01\nEndometrium_mQTL cg01600817 161 3·37E-07 1·75E-08 3·72E-01 1·87E-02 6·09E-01\nEndometrium_mQTL cg05581851 212 5·51E-15 2·08E-15 3·25E-01 1·22E-01 5·53E-01\nEndometrium_mQTL cg07984168 369 8·99E-58 2·45E-57 2·53E-02 6·80E-02 9·07E-01\nEndometrium_mQTL cg15020125 395 5·10E-51 1·40E-50 3·68E-02 1·00E-01 8·63E-01\nEndometrium_mQTL cg19014360 360 1·07E-19 2·95E-19 2·17E-02 5·88E-02 9·20E-01\nEndometrium_mQTL cg27012920 256 5·56E-19 3·50E-20 3·69E-01 2·27E-02 6·08E-01\nnsnps—number of SNPs in region used in analysis\nPP.H0.abf—posterior probability of hypothesis 0: neither trait has a genetic association in the region\nPP.H1.abf—posterior probability of hypothesis 1: only trait 1 has a genetic association in the region\nPP.H2.abf—posterior probability of hypothesis 2: only trait 2 has a genetic association in the region\nPP.H3.abf—posterior probability of hypothesis 3: both traits are associated, but with different causal variants\n PP.H4.abf—posterior probability of hypothesis 4: both traits are associated and share a single causal variant\n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nFig. 3 Integrated locus plot at \nrs35236165 showing GWAS, eQTL \nand mQTL signals. Overlap of asso-\nciation peaks is observed across (A) \nGWAS SNP plot with variants colored \naccording to LD (r2) with the lead vari-\nant rs35236165, (B) eQTL45 associa-\ntion signal for SHISA2 in thyroid and \nmQTL46,47 association signal for CpG \nsite cg07984168 in blood (C) and \nendometrium (D). The x-axis shows the \ngenomic coordinates in hg38 build and \ny-axis shows the -log10(P) values for \nassociations.\n \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nAlthough no associations reached significance after multiple-testing correction in SMR analyses of gene \nexpression, SHISA2 was among the top signals in thyroid (p_SMR = 3·78 × 10⁻4; p_HEIDI = 0·068). Similarly, \nSMR analysis using DNA methylation QTL (mQTL) data, the association between rs7321426 and rs4770916 \nat this locus and methylation at cg07984168 in LINC00415 was the most significant signal, in endometrium (p_\nSMR = 7·27 × 10–5, p_HEIDI = 5·81 × 10–2) and in blood (p_SMR = 6·26 × 10–5, p_HEIDI = 0·21), respectively \n(Supplementary Table S8). All three variants were part of the credible set.\nGenetic liability is shared across populations\nTo evaluate the extent to which genetic susceptibility identified in European populations is shared in our cohort, \nwe calculated PRSs and tested their association with endometriosis status and disease severity. The mean PRS \nwas higher in cases than controls (p < 2·2 × 10–16) reflected by a shift in the distribution of PRS in cases towards \nthe right in the density plot (Fig. 4a). The AUC of 0·61 was comparable to that estimated in the UK Biobank \n(0·61) and in the Estonian Biobank (0·60) 20. Individuals in the tenth PRS decile had almost 4 times higher risk \nof endometriosis compared to the individuals in the first decile (Fig. 4b). The mean PRS was significantly higher \nin individuals with stage III and stage IV endometriosis compared to individuals with stage I and stage II endo -\nmetriosis (p = 3·99 × 10–4) (Fig. 4c).\nMeta-analyses\nTo increase power, reduce reliance on European LD structure and improve the identification of loci robust across \nancestries, we meta-analysed of our dataset with the Japanese GWAS 21 and identified 31 genome wide signifi -\ncant variants ( p < 1 × 1 0–8) across two loci on chromosomes 1 and 9 (Supplementary Table S9 & Fig. 5). Test \nstatistics showed no inflation (λ = 1·007; Supplementary Figure S8). The chromosome 1 lead SNP (rs7412010, \np = 3·38 × 10–12) was in high LD (r 2 = 0·98) with rs10917151 reported in the European GWAS and the chromo -\nsome 9 lead SNP (rs7041895, p = 1·19 × 10–8) was in high LD (r 2 = 0·91) with rs10122243 reported in the Euro -\npean population. Different lead variants emerged in the Japanese and Indian datasets at both loci, yet all were in \nstrong LD with the known European signals, demonstrating that the meta-analysis signal originates from a com-\nmon association peak across populations rather than one cohort dominating the result.\nFunctional annotation using FUMA identified rs7412010 within the enhancer region upstream of WNT4 and \nCDC42 with WNT4 being the nearest gene. rs7041895 mapped to the lncRNA ENSG00000298449, located \nupstream of the CDKN2A/B locus, with CDKN2B-AS1 as the closest gene.\nFig. 4 Endometriosis Polygenic Risk Scores. (A) Density plot of PRS in 1288 cases and 1235 controls in the Indian popula-\ntion. (B) PRS decile plot showing OR (95% CI, y-axis) of endometriosis, by endometriosis PRS decile (x-axis) compared to \nthe first decile. (C) Box plot of mean PRS in the endometriosis cases in Stage I/II and Stage III/IV.\n \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nColocalization analyses showed both shared and distinct causal variants across the chromosome 1 and chro -\nmosome 9 loci (Supplementary Table S10). At the chromosome 1 locus, endometriosis signals colocalised with \nCDC42 expression in blood (PPH4 = 0·78) and thyroid (PPH4 = 0·61), and were independent (PPH3 = 0·92) in \nendometrium. For WNT4, high posterior support for distinct signals was observed in thyroid and endometrium \n(PPH3 = 0·99). At the chromosome 9 locus, colocalization results supported independent causal variants for \nendometriosis and expression of both CDKN2B and CDKN2A across blood and pituitary tissues (PPH3 ≥ 0·95). \nStrong shared causal signals were also observed between endometriosis and methylation at several CpG sites near \nWNT4, CDC42, in blood and endometrium and CDKN2A in endometrium (PPH4 ≥ 0·95).\nSMR analyses further identified three expression-mediated and four methylation-mediated associations, \nincluding a significant effect of CDC42 expression in blood and multiple methylation sites at the chromosome \n1 locus that remained significant after multiple-testing correction (Supplementary Table S11). Several of these \nmethylation associations were replicated across both endometrial and blood mQTL datasets, located in proximal \nenhancers upstream of WNT4, highlighting consistent regulatory mechanisms at these loci.\nDiscussion\nThis nationwide study provides evidence that genetic susceptibility to endometriosis in Indian women shares key \nfeatures with other populations, while also suggesting potential population-specific signals. Although no variant \nreached genome-wide significance in the discovery cohort alone, we detected 21 loci with suggestive evidence of \nassociation and replicated seven previously reported susceptibility regions. The novel suggestive variant with the \nstrongest evidence of association was located near SHISA2, a strong candidate target gene. A cross-ancestry meta-\nanalysis using a Japanese dataset identified genome-wide significant signals at established loci on chromosomes \n1 (near WNT4) and 9 (near CDKN2B-AS1), reinforcing shared genetic architecture across Asian populations. In \naddition, a European-derived polygenic risk score demonstrated comparable predictive performance in the Indian \ncohort and was associated with disease severity.\nThe absence of genome-wide significant findings in the discovery analysis likely reflects limited statistical \npower compared to large European datasets. Nevertheless, nominal replication of established loci, including \nFig. 5 Manhattan plot for genome wide associations for endometriosis in the Asian meta-analysis. Manhattan plot shows \nthe genome wide association results for endometriosis in 3074 cases and 82,210 controls included in the meta-analysis. The \nx-axis shows the chromosomal position, and y-axis shows the association -log10Pvalue. The red horizontal line represents the \ngenome-wide significant threshold (p < 5 × 10–8) and the blue horizontal line represents the suggestive genome-wide signifi-\ncance (p < 1 × 1 0–5). The lead variant at Chr 1 was rs7412010, (p = 3·38 × 10–12) and at Chr 9 was rs7041895 (p = 1·19 × 10–8).\n \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nregions near WNT4 and CDKN2B-AS1, supports cross-population consistency of key susceptibility signals. Inter-\nestingly, the PRS derived from the European population demonstrated cross-ancestry transferability, with com -\nparable AUCs in Indian and European cohorts. PRS increased progressively with disease severity, reflecting \npatterns observed in European studies 20 and supporting shared genetic architecture across ancestries. However, \nonly seven previously reported risk loci were replicated in our dataset, likely reflecting limited statistical power \nand differences in allele frequencies across ancestries. Similarly, the 21 potential risk loci identified in this study \nhave not been observed in previous GWAS; while some may represent false positives due to limited power, oth-\ners may reflect true ancestry-specific, or previously undetected signals influenced by allele frequency differences \nand cohort characteristics. Notably, the ECGRI study screened controls to exclude individuals with suspected \nendometriosis or a family history of the condition prior to enrolment, reducing the likelihood of misclassification.\nThe lead variant on chromosome 13 rs35236165 was located within an exon of the long non-coding RNA \nLINC00415. This was in high LD with variants in enhancer and promoter regions upstream to SHISA2. SHISA2 \nis reported to negatively regulate WNT signaling and Fibroblast Growth Factor receptor pathways. 22–24 Both \nthese pathways are reported to be biologically relevant to endometrial function and disease.25–28 SHISA2 has also \nbeen previously shown to be nominally upregulated during the mid secretory phase in women with endometriosis \ncompared to controls.29 The allele frequency of rs35236165 was different between the European and South Asian \npopulation, which together with differences in control ascertainment may explain why this locus has not previ -\nously been identified in earlier GWAS studies that were predominantly based on European populations. Using \nCOLOC we identified evidence of shared genetic effects between endometriosis risk at this locus and SHISA2 \nexpression in thyroid derived from eQTL data. We also found strong evidence of a shared causal variant between \nendometriosis risk and methylation at two CpG sites (cg19014360 and cg07984168) within LINC00415 in endo-\nmetrial mQTL datasets, with cg07984168 also showing colocalization in blood and mapping to an enhancer \nregion. Although the SMR analyses integrating GWAS with eQTL and mQTL datasets did not remain significant \nafter correction for multiple testing, SHISA2 and cg07984168 consistently appeared among the top signals in the \nthyroid eQTL dataset and in endometrial and blood mQTL datasets, driven by variants in high LD with the lead \nGWAS variants (r2 > 0·77). Our data suggests SHISA2 as a candidate gene supported by regulatory annotation, \ncolocalisation, and methylation evidence and warrants further detailed investigation as an endometriosis suscep-\ntibility gene.\nClinical studies have previously established association between thyroid dysfunction and endometriosis. 30–32 \nHowever, our study provides the first genetic evidence that may implicate thyroid mediated regulatory mecha -\nnisms in endometriosis susceptibility.\nAlthough South Asian populations are genetically distinct from both European and East Asian groups, we con-\nducted a non-European meta-analysis to minimise the influence of European LD structure and enhance the iden-\ntification and fine-mapping of ancestry-robust loci. Differences in LD patterns between South and East Asians \ncan further improve the resolution of association signals. Our cross ancestry meta-analysis identified 31 genome \nwide significant variants mapping to two loci on Chr 1 and Chr 9, both having been previously reported in Japa-\nnese and European GWAS studies, further strengthening cross-ancestry support for these associations. The lead \nvariant rs7412010 at Chr 1 lies in an enhancer region upstream of WNT4 and CDC42. The lead variant rs7041895 \nat Chr 9 lies within a long non-coding RNA upstream of CDKN2A. Variants within the chromosome 1 locus \nco-localized with eQTL signals for CDC42 in blood and thyroid tissues, with distinct but correlated regulatory \nsignals noted for WNT4 in thyroid and endometrial eQTL datasets. COLOC analysis using endometrium and \nblood derived mQTL datasets provided strong evidence for shared causal variants at methylation sites mapping \nto WNT4 and CDC42, with additional support for CDKN2A in endometrial mQTL dataset. Corroborating these \nfindings, SMR analysis identified statistically significant association for CDC42 in blood eQTL data (driven \nby rs2473290), which showed moderate LD with the lead variant (r 2 > 0·49) across European, East Asian and \nSouth Asian populations. We also observed a suggestive signal for LINC00339 in both blood and endometrium, \ndriven by rs11801382, a variant with high D’ (> 0·9) but low r2 (< 0·1) with the lead SNP, reflecting its low allele \nfrequency, particularly in Asian populations and the use of European eQTL datasets where the variant is more \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\ncommon. In addition, SMR supported shared causal variants (r 2 > 0·8 with lead variant rs7412010) for multiple \nmethylation sites across the WNT4 locus in both endometrial and blood tissues. Both WNT4 and CDKN2A loci are \nwell established endometriosis risk loci in prior GWAS and functional studies, supporting roles in hormonal sig-\nnalling, cellular proliferation, and endometrial tissue regulation. 33 Together, these results reinforce the presence \nof shared, functionally relevant genetic mechanisms across South Asian, East Asian, and European populations, \nand suggest that cross-ancestry risk at these loci may converge on endocrine and regulatory pathways central to \nendometriosis biology.\nThis study has several limitations. The sample size, although substantial for a single-country study, is mod -\nest compared with large international GWAS, limiting the power to detect variants with small effect sizes. Dif -\nferences in allele frequencies and LD structure across ancestries reduce direct comparability with previously \nconducted GWAS studies, and the limited availability of South Asian-specific reference panels necessitated addi-\ntional caution and rigorous quality control. To address these challenges, we used multiple approaches, includ -\ning multiple imputation panels and different GWAS models, to ensure the robustness of our findings. Another \nimportant limitation is the absence of a large, independent South Asian replication cohort for endometriosis, \nwhich restricts our ability to validate the observed association signals in an ancestry-matched population. There-\nfore, the 21 suggestive associations identified in the Indian discovery GWAS should be considered hypothesis-\ngenerating and require independent replication before being regarded as confirmed endometriosis susceptibility \nloci. Although meta-analysis with the Japanese cohort increased power to detect shared ancestral risk loci, it is \nimportant to recognise the inherent limitations arising from differences in study design and phenotype defini -\ntions between cohorts. Variability in case-ascertainment methods, diagnostic criteria, and control selection, or \nsample size, can introduce heterogeneity and may influence the strength or detectability of association signals. \nThus, while the combined analysis provides strong support for cross-ancestry consistency at two established risk \nloci, population-specific effects may still exist but remain difficult to resolve with the current sample sizes and \nheterogeneous clinical datasets.\nDespite these limitations, the study addresses a major gap in genomic research by including a high-burden \nbut underrepresented South Asian population within a well-characterised multicenter clinical cohort. Rigorous \nquality control, comprehensive analytical pipelines, and multiple complementary approaches are employed to \nensure that association signals are robust and biologically interpretable. The integration of a non-European meta-\nanalysis strengthened statistical power and reinforced support for known risk loci across ancestries. Importantly, \nthe potential discovery of a biologically plausible, putative population-specific locus highlights the importance of \nwell-designed studies in diverse populations, revealing mechanisms that may be absent or obscured in larger but \nancestrally homogeneous cohorts.\nIn future, establishing large ancestry-matched cohorts for replication and validation of associations, along \nwith developing more representative reference panels, will be crucial. Detailed functional characterization of the \nnewly identified locus, including experimental validation of its regulatory effects and downstream biological con-\nsequences, would further strengthen these findings. Targeted studies examining the endocrine reproductive inter-\naction in endometriosis may offer deeper insights into thyroid-related regulatory mechanisms in endometriosis.\nIn conclusion, this study reports the first GWAS of endometriosis in an Indian population and highlights the \nimportance of incorporating diverse ancestries in genetic research to fully capture the spectrum of risk fac -\ntors. We identified both shared and putative population-specific risk signals, with evidence of possible involve -\nment of regulatory mechanisms involving WNT signalling, endocrine pathways and epigenetic regulation. The \npopulation specific signals, while requiring independent replication and functional validation, add to the current \nunderstanding of the genetics of endometriosis, also forms a basis for future ancestry-informed and mechanistic \nstudies. Strengthening genomic research infrastructure in South Asia will be crucial to advancing equitable preci-\nsion medicine approaches for endometriosis and improving outcomes for affected women in the region.\n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nMethods\nEthics statement\nThe study was approved by the Institutional Ethics Committee of the Indian Council of Medical Research National \nInstitute for Research on Women’s Health (ICMR-NIRWoH, formerly ICMR-NIRRCH, approval no. D/ICEC/\nSci-87/91/2019), the ethics committees of all participating study sites, and the Health Ministry Screening Com -\nmittee (HMSC), Government of India. The study was conducted in accordance with the Declaration of Helsinki, \nand the National Ethical Guidelines for Biomedical and Health Research Involving Human Participants issued \nby the Indian Council of Medical Research. All participants provided written informed consent prior to enrol -\nment. Genetic data were de-identified before analysis, and data sharing was in accordance with national ethical \nand legal standards.\nStudy design and participants\nThis investigation was conducted as a part of the Endometriosis Clinical and Genetic Research in India (ECGRI) \nstudy, a nationwide, multicenter case–control study. The detailed study design has been published previously. 17 \nParticipants were recruited from multiple public and private tertiary care centres across five geographic regions \nof India, representing Indo-European, Dravidian, and Tibeto-Burmese ancestry groups. Cases and controls were \nenrolled contemporaneously at each site to minimise recruitment bias, and their distribution was broadly compa-\nrable across centres and inferred ancestry clusters.\nWomen aged 18—50 years, with a laparoscopically confirmed endometriosis, were included as cases, with \nhisto-pathological confirmation wherever available. Controls were unrelated women in the same age range \nrecruited from the same sites, without a personal or family history of endometriosis. Clinical data were collected \nusing the modified World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmon-\nisation Project (WERF-EPHect) Patient Questionnaire, and surgical data were recorded using the WERF-EPHect \nStandard Surgical Form. Peripheral blood samples were collected from all participants for genomic analysis.\nDNA extraction and genotyping\nGenomic DNA was extracted from peripheral blood using QIAmp DNA Blood Kit (Qiagen, Hilden, Germany) as \nper the manufacturer’s instructions. DNA concentration and purity were assessed by spectrophotometry, and sam-\nples with yields > 50 ng/µl, and acceptable purity ratios (A260/280: 1·8–2·0; A260/230: 1·8 -2·3) were included.\nGenotyping was performed using the Infinium Global Screening Assay version 3·0 with multi-disease panel \n(Illumina Inc., San Diego, CA, USA). Raw intensity data were processed using GenomeStudio version 2·0 (Illu-\nmina Inc.), and genotype clustering was performed using an Indian population-specific cluster file. 34 The com-\nplete analysis pipeline is summarized in Supplementary Figure S1.\nQuality control and Imputation\nStandard quality control procedures were applied to remove low-quality samples and variants. SNPs with low \ngenotyping quality, minor allele frequency (MAF) < 1%, call rate < 95% or deviation from Hardy–Weinberg equi-\nlibrium (p < 1 × 1 0–6) were excluded. Genome Studio filters of GenTrain Score < 0·6, cluster separation < 0·3, and \nAB ratio < 0·3 were used to remove low-quality SNPs. PLINK (v2) was used to exclude SNPs with MAF < 1% \n(maf 0·01), call rates < 95% (geno 0·05), and deviating from Hardy–Weinberg equilibrium (p < 1 0–6). Individuals \nbeyond second degree relatedness (PI_HAT ≥ 0·18), excessive missingness (mind 0·05), ambiguous sex (F > 0·2), \nand high heterozygosity (Mean F -3SD) were excluded.\n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nPopulation structure was assessed to detect population stratification, ethnicity, and batch effects using principal \ncomponent analysis after merging study genotypes with the 1000 Genomes Project reference panel.\nPre-Imputation quality control was performed using the HRC-1000G-check-bim-v4·3 tool, and SNPs were \naligned to the GenomeAsia100K reference panel, ensuring that only high-quality, properly aligned SNPs were \nused for the imputation step. Genotypes were remapped to hg38 reference genome using LiftOver.\nPhasing, using haplotypes from the South Asian (SAS) population, and imputation were performed using the \nWestlake Imputation Server with a South and East Asian reference Database reference panel (SEAD) that pro -\nvides high-resolution coverage for the Asian population. 35 Genotypes were also phased using EAGLE V2 and \nimputed to the TOPMED reference panel using the TOPMed Imputation Server.36–38 Imputed SNPs were largely \nconsistent between the two imputation approaches; however, the SEAD reference panel enabled the recovery of \na greater number of low-frequency variants and was therefore used for subsequent analyses.\nPost-imputation filtering excluded variants with an imputation quality score (Rsq) < 0·5 and MAF < 0·01. After \nquality control, a total of 9,647,516 SNPs and 2,523 individuals (1288 cases and 1235 controls) were retained \nfor analysis.\nGenome-wide association analysis\nGenome-wide association analysis was performed using logistic regression under an additive genetic model \nusing PLINK version 2·0. Ten principal components generated in PLINK following LD pruning (indep-pairwise \n50 5 0·2) were included in the model as covariates to account for population stratification. The model was also \nadjusted for age, genotyping batch, and homozygosity (F coefficient). A genomic inflation factor (λ) was calcu -\nlated in R (version 4·5·0), and a QQ plot was generated using the ‘qqman’ package to assess potential inflation \nof test statistics. Association analysis was also conducted using GCTA 39 to account for population stratification \nthrough a genetic relationship matrix (GRM).\nFunctional annotation and fine mapping\nFunctional Mapping and Annotation of Genome-Wide Association Studies (FUMA GWAS) platform40 was used \nto identify lead independent variants ( p < 1 × 1 0–5; r2 < 0·1). The 1000 Genome Project Phase 3 SAS population \nwas used as the reference panel. Lead SNPs within 250 kb were merged to construct genomic risk loci, and can-\ndidate genes were mapped. Preliminary functional annotation with eQTL datasets was also performed.\nFine-mapping analysis was performed on the most significant independent risk loci identified by FUMA. A \n500 kb genomic region around the lead SNP was fine-mapped using Sum of Single Effects (SuSiE). 41 Follow-\ning alignment of genotypes, phenotypes, and covariates across samples, SuSiE was applied to GWAS summary \nstatistics aligned to raw genotypes rather than LD reference panels. Assuming a single causal variant per locus \n(L1), 95% credible sets were obtained for each locus. This credible set was further mapped to regulatory sites \nacross tissues using haploreg v4·2 42, phasing with 1000 Genome Phase 1 Asian reference panel. LD thresholds \nwere not applied as the variants were derived from a credible set. The variants showing regulatory marks, includ-\ning promoters and enhancers, were further annotated using ENCODEPROJECT 43 to identify nearby candidate \ncis-regulatory elements (cCRE), their classification, and the closest gene.\nColocalisation and Mendelian randomisation analyses\nCOLOC and SMR analyses were performed using cis-eQTL datasets from tissues relevant to endometriosis, \nincluding endometrium29, blood44, uterus, ovary, thyroid, and pituitary 45 and mQTL dataset from blood 46 and \nendometrium47. GWAS summary statistics within ± 250 kb of the lead variant (rs35236165, chr13) were har -\nmonised with corresponding eQTL and mQTL summary data. COLOC was applied to estimate posterior prob -\nabilities for competing hypotheses. Evidence for colocalisation was defined as a higher probability of a shared \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\ncausal variant (PPH4 > PPH3). SMR analyses were conducted independently for each molecular dataset, with \nHEIDI testing used to differentiate between shared genetic effects and linkage-driven associations. Genes passing \nmultiple-testing correction (pSMR < 0·05 / number of genes tested) and HEIDI filtering (p_HEIDI > 0·01) were \nconsidered to share a common genetic signal.\nReplication and allele frequency analysis\nAllele frequencies of the lead variants were compared between the three ethnic groups in our cohort (Indo-\nEuropean, Dravidian, and Tibeto-Burmese) and across European (EUR), American (AMR), African (AFR), East \nAsian (EAS), and South Asian (SAS) ancestral populations by using population specific allele frequency esti -\nmates from LDpop tool48, which utilizes the 1000 Genome Project Phase 3 reference panel.\nReplication of previously published endometriosis risk variants was also tested. We assessed association of \n49 lead variants identified in the largest European GWAS13, one genome wide significant variant reported in the \nJapanese GWAS12, 16 significant variants from the Taiwanese GWAS14, and 65 significant variants from meta-\nanalysis of a large multi-ancestry GWAS of endometriosis.19.\nPolygenic risk score analysis\nThe PRS for endometriosis derived from a European population20 was applied to our dataset to evaluate the pre-\ndictive utility of the European-derived polygenic scores for endometriosis in the Indian population. SNP weights \ngenerated by McGrath et al20 were used to generate PRSs in our dataset using the ‘score’ function in PLINK. The \ndifference in the mean PRS between cases and controls was tested using a t-test in R. Participants were assigned \nto deciles in order of ascending PRS, and the OR was estimated between each decile and decile 1. To evaluate \nthe predictive accuracy of the PRS AUC was calculated using the pROC package in R. Endometriosis phenotype \nwas classified as per the revised American Society for Reproductive Medicine (r-ASRM) classification 49, with \nStage I, II, III, and IV representing minimum, mild, moderate, and severe disease conditions. To assess if higher \npolygenic burden was associated with more advanced disease stage, we further examined the relation between \nPRS and endometriosis stages. This was done by comparing the PRS distribution across milder stages (I and II) \nagainst advanced stages (III and IV).\nMeta-analysis\nWe performed an Asian meta-analysis for endometriosis combining our GWAS data with a Japanese GWAS \nstudy21 to have a total sample size of 85,284 (3,074 cases and 82,210 controls). The Japanese GWAS summary \nstatistics were publicly available in the GWAS catalogue (GCST90018619). Variants with allele frequency < 1% \nwere filtered out. Summary statistics from both the studies were harmonized to ensure consistent SNP identifiers \nand effect alleles. Meta-analysis was performed in METAL using an inverse-variance weighted fixed-effects \nmodel.50 Genomic control correction was applied to account for any inflation due to population stratification. \nEffective sample sizes were calculated to weight each of the summary statistics as Neff = 4/(1/cases + 1/controls). \nCochran’s Q statistics and I2 were used to assess heterogeneity of effect estimates across both datasets. The meta-\nanalyzed data was used to identify genome-wide significant association signals and further downstream analysis \nwas conducted using FUMA to identify independent signals, lead SNPs and genomic risk loci. Genome-wide sig-\nnificant signals from the meta-analysis were integrated with the eQTL and mQTL data using SMR and COLOC \nto prioritize candidate genes as described above.\nSupplementary Information  The online version contains supplementary material available at https://doi.org/10.1038/\ns41598-026-69642-9.\nAcknowledgements This work was supported by the DBT Wellcome India Alliance Fellowship [Grant no. IA/\n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nCPHI/18/1/503933] awarded to Rahul K Gajbhiye. We acknowledge support from the DHR-ICMR Long Term Fellowship \nAbroad [No. 2024-00250] awarded to Sandhya Anand; Australian National Endometriosis Clinical and Scientific Trials Fel-\nlowship [UNSW-RG223907, awarded to Sally Mortlock. Grant Montgomery was supported by grants from the Australian \nNational Health and Medical Research Council (GNT1177194) and the Novo Nordisk Foundation (NNF24OC0090764). \nGita Mishra was supported by the Australian National Health and Medical Research Council Leadership Fellow (Grant \nNo. APP2009577). We thank Arindam Maitra, BRIC-National Institute of Biomedical Genomics (NIBMG), Kalyani, for \ntechnical support in genotyping and sharing the Indian population-specific cluster files. Data collection was facilitated by \nand conducted in compliance with the World Endometriosis Research Foundation Endometriosis Phenome and Biobanking \nHarmonization Project (WERF EPHect). We acknowledge the contributions of the ECGRI consortium team and ECGRI \nstudy participants.\nAuthor contributions RKG conceptualized the study, designed the study protocol, coordinated with all site investigators of \nthe ECGRI consortium, obtained funding, obtained ethics approvals, and revised the manuscript. GWM and SM contributed \nto the design of the final study protocol, provided domain knowledge expertise in data analysis, and contributed to drafting \nand revision of the manuscript. SA executed the study, trained site teams for sample collection and processing, managed the \nbiorepository, performed DNA extractions and quality controls towards genotyping sample preparation, and oversaw sample \nhandling, coding, and the genotyping workflow. SA and SM contributed to genomic data quality controls, data analysis, data \ninterpretation, and drafted the manuscript. HM supervised data collection and the research team’s training, coordinated with \nthe team at the study sites, and revised the manuscript draft. SS, CS, PLS, NKB, PDM, PP, BJ, MS, DK, KK, SaM, NM, \nAH, NS, RM, MVP, KP, AG, SK, participated in data and sample collection. ShK, SG, NB, and TK performed the DNA \nextraction and quantification for the study. SDM and GS provided technical and administrative support for the study. GDM \nprovided technical support and supervised the study. All authors revised and approved the final version of the manuscript.\nFunding DBT Wellcome India Alliance Clinical and Public Health Fellowship [Grant no. IA/CPHI/18/1/503933, awarded \nto RKG], Australian National Endometriosis Clinical and Scientific Trials Fellowship [UNSW-RG223907, awarded to SM] \nand Department of Health Research-Indian Council of Medical Research Long Term International Fellowship grant [No. \n2024–00250, awarded to SA]. GWM was supported by grants from the Australian National Health and Medical Research \nCouncil (GNT1177194) and the Novo Nordisk Foundation (NNF24OC0090764). G.D.M. is an Australian National Health \nand Medical Research Council Leadership Fellow (Grant No. APP2009577).\nData availability Genome-wide association summary statistics generated in this study have been deposited in the GWAS \nCatalog under accession number GCST90994020. Individual-level genotype and phenotype data are not publicly available \nbecause they contain sensitive human genetic and clinical information and are subject to participant consent, institutional \nethics approval, and national regulatory requirements in India. Requests for access to individual-level data may be submitted \nto the corresponding author (RKG) and will be considered subject to approval by the relevant institutional ethics commit -\ntee and completion of an appropriate data transfer/data use agreement. Processed association results supporting the findings \nof this study are provided in the Supplementary Information and Additional Information files. Summary statistics from the \nJapanese GWAS used in the meta-analysis were obtained from the GWAS Catalog under accession number GCST90018619.\nCode availability Publicly available softwares were used for all the analyses. Appropriate references have been cited in \nmethods.\nDeclarations\nCompeting interests The authors have no competing interests to declare.\nOpen Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Interna-\ntional License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as \nlong as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, \nand indicate if you modified the licensed material. You do not have permission under this licence to share adapted material \nderived from this article or parts of it. The images or other third party material in this article are included in the article’s Cre-\native Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s \nCreative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you \nwill need to obtain permission directly from the copyright holder. To view a copy of this licence, visit  h t t p : / / c r e a t i v e c o m m o \nn s . o r g / l i c e n s e s / b y - n c - n d / 4 . 0 /     .  \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nReferences\n 1. Zondervan, K. T. et al. Endometriosis. Nat. Rev. Dis. Primer 4, 9 (2018).\n 2. As-Sanie, S. et al. Endometriosis: A review. JAMA 334, 64–78 (2025).\n 3. Munshi, H., Piyadigama, I., Senanayake, H. & Gajbhiye, R. K. Endometriosis and adenomyosis research priorities in \nIndia and Sri Lanka: A call for regional collaboration. Lancet Reg. Health Southeast Asia 34, 100547 (2025).\n 4. Feng, J., Zhang, S., Chen, J., Zhu, J. & Yang, J. 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METAL: Fast and efficient meta-analysis of genomewide association scans. \nBioinformatics 26, 2190–2191 (2010).\nPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional \naffiliations.\nAuthors and Affiliations\nSandhya Anand1,2 · Hrishikesh Munshi1 · Shashank Shekhar3 · Chaitanya Shembekar4 · \nPushp Lata Sankhwar5 · Nilajkumar Bagde6 · Pramathes DasMahapatra7 · \nPranay Phukan8 · Bimal John9 · Milind Shah10 · Deepali Kale11 · Ketki Kulkarni11 · \nSwati Mahobia12 · Niraj Mahajan13 · Anil Humane14 · Nagendra Sardeshpande15 · \nReji Mohan16 · Muralidhar V. Pai17 · Kedar Padte18 · Atul Ganatra19 · Sonal Kumta20 · \nSakshi Gangurde1 · Nayna Barada1 · Tabassum Khan1 · Shagufta Khan21 · \nGeetanjali Sachdeva22 · Gita D. Mishra23 · Smita D. Mahale24 · Grant W. Montgomery2,25 · \n\n\nArticle in Press\nScientific Reports\nhttps://doi.org/10.1038/s41598-026-69642-9\nSally Mortlock23  · Rahul K. Gajbhiye1   on behalf of  on behalf of the ECGRI Consortium\n Sally Mortlock\ns.mortlock@imb.uq.edu.au\n Rahul K. Gajbhiye\ngajbhiyer@nirrch.res.in\n1 Clinical Research Laboratory, Indian Council of Medical Research National Institute for Research On Women’s \nHealth (ICMR-NIRWoH, Formerly ICMR-NIRRCH), J. M. Street, Parel, Mumbai 400012, India\n2 Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD 4067, Australia\n3 Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, Jodhpur 342005, India\n4 Omega Hospital, Nagpur 440015, India\n5 Department of Obstetrics and Gynaecology, King George’s Medical University, Lucknow, Uttar Pradesh  \n226003, India\n6 Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, Raipur 492099, India\n7 Spectrum Clinic and Endoscopy Research Institute, Kolkata 700017, India\n8 Department of Obstetrics and Gynaecology, Assam Medical College, Dibrugarh 786002, India\n9 Minimally Invasive Surgery Unit, Credence Hospital, Thiruvananthapuram 695011, India\n10 Naval Maternity Endoscopy & Infertility Center, Solapur 413001, India\n11 Nowrosjee Wadia Maternity Hospital, Mumbai 400012, India\n12 Sai Baba Nursing Home, Raipur 492001, India\n13 Department of Obstetrics and Gynaecology, Topiwala National Medical College and BYL Nair Charitable Hospital, \nMumbai 400008, India\n14 Department of Obstetrics and Gynaecology, Government Medical College, Nagpur 440003, India\n15 Worli Hospital for Women, Mumbai 400030, India\n16 Department of Obstetrics and Gynaecology, Sri Avittam Thirunal Hospital (SAT), Thiruvananthapuram  \n695011, India\n17 Sikkim Manipal Institute of Medical Sciences, Gangtok 737102, India\n18 Dr Kedar’s Maternity & Infertility Hospital, Panaji 403001, India\n19 Progeny IVF Centre & Clinic for Women, Mumbai 400080, India\n20 Fortis Hospital, Mulund, Mumbai 400078, India\n21 Gamete Immunobiology, Indian Council of Medical Research National Institute for Research On Women’s Health \n(ICMR-NIRWoH, Formerly ICMR-NIRRCH), Mumbai 400012, India\n22 Cell Physiology and Pathology, Indian Council of Medical Research National Institute for Research On Women’s \nHealth (ICMR-NIRWoH, Formerly ICMR-NIRRCH), Mumbai 400012, India\n23 Australian Women and Girls’ Health Research (AWaGHR) Centre, School of Public Health, The University of \nQueensland, Herston, Qld 4006, Australia\n24 Indian Council of Medical Research National Institute for Research On Women’s Health (ICMR-NIRWoH, \nFormerly ICMR-NIRRCH), Mumbai 400012, India\n25 Department of Health Science and Technology, Aalborg University, Aalborg, Denmark","source_license":"CC0","license_restricted":false}