Evidence of a causal effect of genetic tendency to gain muscle mass on uterine leiomyomata

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Evidence of a causal effect of genetic tendency to gain muscle mass on uterine leiomyomata © The Author(s) 2023 Published version Sliz, Eeva; Tyrmi, Jaakko S.; Rahmioglu, Nilufer; Zondervan, Krina T.; Becker, Christian M.; FinnGen; Uimari, Outi; Kettunen, Johannes Sliz, Eeva, Tyrmi, Jaakko S., Rahmioglu, Nilufer, Zondervan, Krina T., Becker, Christian M., FinnGen, Uimari, Outi, Kettunen, Johannes. (2023). Evidence of a causal effect of genetic tendency to gain muscle mass on uterine leiomyomata. Nature Communications, 14, Article 542. https://doi.org/10.1038/s41467-023-35974-7 2023 Article https://doi.org/10.1038/s41467-023-35974-7 Evidence of a causal effect of genetic tendency to gain muscle mass on uterine leiomyomata Eeva Sliz 1,2 , Jaakko S. Tyrmi 1,2 , Nilufer Rahmioglu 3,4 , Krina T. Zondervan 3,4 , Christian M. Becker 3 , FinnGen*, Outi Uimari 5,6,7,72 & Johannes Kettunen 1,2,72 Uterine leiomyomata (UL) are the most common tumours of the female genital tract and the primary cause of surgical removal of the uterus. Genetic factors contribute to UL susceptibility. To add understanding to the heritable genetic risk factors, we conduct a genome-wide association study (GWAS) of UL in up to 426,558 European women from FinnGen and a previous UL meta-GWAS. In addition to the 50 known UL loci, we identify 22 loci that have not been associated with UL in prior studies. UL-associated loci harbour genes enriched for development, growth, and cellular senescence. Of particular interest are the smooth muscle cell differentiation and proliferation-regulating genes functioning on the myocardin-cyclin dependent kinase inhibitor 1 A pathway. Our results further suggest that genetic predisposition to increased fat-free mass may be causally related to higher UL risk, underscoring the involvement of altered muscle tissue biology in UL pathophysiology. Overall, our findings add to the understanding of the genetic pathways underlying UL, which may aid in developing novel therapeutics. Uterine leiomyomata (UL) are the most common benign tumours of the female genital tract, with an estimated lifetime incidence of up to 70%1and the primary cause of hysterectomy. Female sex hormones stimulate UL growth and, thus, UL are almost exclusively found in females of reproductive age. UL are present in single or multiple numbers, with sizes ranging from millimetres to 20 cm or more in diameter2, and they are composed mostly of smooth muscle cells (SMC) and fibroblasts with a profound component of extracellular matrix (ECM). In 25–50% of women with ULs, the enlarged and deformed uterus causes symptoms that reduce the quality of life, such as heavy or prolonged menstrual bleeding resulting in anaemia, reduced fertility and pregnancy complications3. Until recently, the focus in the genetics of UL has been on somatic rearrangements, and key driver variations, for example, in MED12 and HMGA2 have been reported4. Familial aggregation, the disparity in prevalence between different ethnic groups, and high heritability estimates obtained in twin studies (h2up to 69%) suggest, however, that heritable genetic factors modulate UL risk5–8. To date, 11 GWASs on UL have been conducted in populations of European, Japanese, and African ancestries9–19. In a recent UL meta-GWAS, the SNP-based heritability of UL was estimated to be 2.8%12, suggesting that there may be other genetic variants contributing to UL susceptibility that are yet to be discovered. Significant GWAS findings provide opportunities for testing causal inferences between UL and traits associated with UL. For instance, Received: 19 November 2021 Accepted: 10 January 2023 Check for updates 1 Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland. 2 Biocenter Oulu, Oulu, Finland. 3 Oxford Endometriosis CaRe Centre, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, UK. 4 Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. 5 Department of Obstetrics and Gynecology, Oulu University Hospital, Oulu, Finland. 6 PEDEGO Research Unit, University of Oulu and Oulu University Hospital, Oulu, Finland. 7 Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland. 72 These authors contributed equally: Outi Uimari, Johannes Kettunen. *A list of authors and their affiliations appears at the end of the paper. e-mail: eeva.sliz@oulu.fi Nature Communications | (2023) 14:542 1 1234567890():,; 1234567890():,; the causal relationship between UL and excessive menstrual bleeding has been demonstrated using the Mendelian randomisation method12. The causal inferences between UL and metabolic risk factors, such as blood lipid levels or body mass index (BMI), however, have not been extensively studied even if previous cross-sectional studies indicate that those are associated with UL risk20. In this work, we conducted two sets of meta-analyses with data from FinnGen and a previously published UL meta-GWAS12 in order to add understanding to the UL-related heritable genetic risk factors. We further utilised the GWAS results to estimate genetic correlations and causal relationships between UL and metabolic and anthropometric traits. Our findings provide a different perspective on UL pathobiology and suggest an involvement of fat-free mass rather than fat mass in the underlying causal pathway. Results 22 uterine leiomyomata-associated loci that have not been described in prior studies ‘META-1’comprised data from FinnGen and the previous UL metaGWAS12 with up to 53,534 cases and 373,024 female controls, and the analysis was restricted to publicly available 10,000 variants from the previous study12.‘META-2’was conducted with data from up to 38,466 cases and 329,437 controls from FinnGen and the genome-wide summary statistics from the same study by ref. 12. excluding 23andMe data due to the data usage policy. The study setting is illustrated in Fig. 1. In META-1, we identified 63 genomic regions located more than 1 Mb apart with at least one variant associating with UL at p<5×10 −8 (Fig. 2, Table S1, and Supplementary Data 1, 2); of these, 16 had not been reported in association with UL in prior UL GWASs (Table 1)while the remaining 47 were in the proximity of known UL risk loci. In META2, we identified 61 genomic regions, out of which six had not been associated with UL risk in prior GWASs or in META-1 (Fig. 2,Table1, Table S2, and Supplementary Data 3). However, the association at 10q24.32-10q25.1 likely spans a region larger than the ±1 Mb locus definition overlapping with a previously reported UL association near STN1 subunit of CST complex (STN1) and STE20-like kinase (SLK)12. This expanded association signal appears to be driven by variants enriched in the Finnish population (Finnish enrichment 46x-198x calculated as a ratio of the Finnish allele frequency and the non-Finnishnon-Estonian European allele frequency; Table 1). Regional association plotsofthelocithathavenotbeenassociatedwithULriskinprior studies are presented in Figs. S1–S18, and the regional plot of the large signal on chr10 is presented in Fig. S19. Genomic inflation factor of 1.105 suggested minor inflation in the test statistics that was most notably accounted for by a polygenic signal, with the intercept being close to one21 (1.0066; Fig. S20). There was very little or no heterogeneity between the results obtained in FinnGen and the previous study12 (Table 1andFig.S21).WeestimatedLDscore(LDSC) regression-derived SNP-based heritability to be 0.105 (standard error [SE] = 0.011) on the liability scale, which corresponds to an ~7.7 percentage point increase compared with the LDSC-based estimate obtained in the previous study12. The SNP-based heritability estimate obtained additionally using SumHer22 was 0.034 (SE = 0.003). Characterisation of the genome-wide results of META-2 suggested that the key UL-associated variants were mostly intronic (Fig. 3aand Supplementary Data 1). We also found enrichment in variants located on 3′untranslated regions, 5′untranslated regions, and upstream sequences, whereas the proportions of intergenic and non-coding RNA variants were lower than expected by chance (Fig. 3a). In the conditional association tests conducted using genome-wide results from META-2, we identified secondary signals in altogether 14 loci (Table S2). Multiple signals were detected in some of the loci, including the well-known UL-risk locus on chr13 near ‘forkhead box O1’ (FOXO1), in which we observed four secondary signals in addition to the original association. The lead variants of these secondary signals were either intronic (rs7986407, rs9548898 and rs6563799) or intergenic (rs9576914). Of the UL risk loci that had not been identified in prior studies, we detected a secondary signal in chr2 near ‘myocardininduced smooth muscle cell lncRNA, an inducer of differentiation’ (MYOSLID) where an intronic variant (rs7584910) reached genomewide significance (p=4.75×10 −8) after conditioning the association to the original lead variant (Table S2). The results of fine-mapping on 146 association signals, including all UL associations in META-1 and META-2 as well as the independent associations observed in the conditional tests, suggested that 6 signals (near Meis homeobox 1 [MEIS1], inositol 1,4,5-trisphosphate receptor type 1 [ITPR1], spectrin repeat containing nuclear envelope protein 1 [SYNE1], forkhead box O1 [FOXO1], tumour protein p53 [TP53]and minichromosome maintenance 8 homologous recombination repair factor [MCM8]) had a single variant in the 99% credible set concordantly in both META-1 and META-2 (Tables S3, S4). Of these, the missense variant rs16991615 in MCM8,the3′UTR variant rs78378222 in TP53, and the intron variant rs117245733 in LINC00598 near FOXO1 have been reported previously12. The remaining variants, i.e. rs17631680 near MEIS1, rs3804984 near ITPR1 and rs58415480 near SYNE1 are intergenic or intronic variants with no strong evidence of regulatory consequences according to RegulomeDB23 and, thus, the association-driving mechanism remains unclear. In addition, 6 secondary signals were found to have a single variant in the 99% credible set (Table S5), all of which were intronic/intergenic. Description of the key loci Previous GWAS findings have indicated that genetic factors altering pathways involved in oestrogen signalling, Wnt signalling, transforming growth factor (TGF)-βsignalling, and cell cycle progression are associated with UL risk10–19. The loci identified in this study further underscore the involvement of pathways regulating SMC proliferation in the modulation of UL risk. Many of these pathways are interrelated: for example, both oestrogen and progesterone increase the secretion of Wnt ligands from myometrial or leiomyoma SMC, which promotes cell proliferation and tumorigenesis via activation of β-catenin24. Steroid hormones also influence the production of ECM via signalling through the TGF-βfamily of ligands and receptors that are highly expressed in multiple fibrotic conditions and contribute to the fibrotic phenotype seen in UL25.Weidentified multiple loci with potential candidate genes functioning in one or more of these pathways, and, in thefollowing,wedescribesomeofourkeyfindings with a focus on loci involved in the regulation of SMC proliferation. Acentralfinding is an association at 17p12 harbouring myocardin (MYOCD; Tables S1,S2). Myocardin is a transcription factor expressed in smooth muscle tissues, including most prominently arteries and colon, but also the uterus (Fig. S22), and it is required for SMC differentiation26. Theexpressionofmyocardinhasbeenshowntobedownregulatedin UL tissue compared with normal myometrium27. Also, it has been proposed that the loss of myocardin function may be a key factor in driving SMC proliferation in UL27; however, prior to our findings, only one study9has reported GWAS association implicating myocardin. The lead variants near MYOCD are intergenic variants with no strong evidence of altered regulatory consequences (Table S6), and, thus, a possible association-driving mechanism remains inconclusive. We identified another myocardin-related UL risk association at 2q33.3 near MYOSLID, a transcriptional target of myocardin28 (Fig. S3)—this locus has not been reported in prior studies. The association lead variant (rs10804157) is a regulatory variant (Table S7) altering the binding of multiple transcription factors (Table S8), including Fos proto-oncogene (FOS) that has been shown to be downregulated in UL29.Toaddyetanother example of a myocardin-related UL risk locus, a well-established association at 22q13.110,12,15,16 locates near ‘myocardin-related transcription factor A’(MRTFA;alsoknownasMKL1), a gene interacting with myocardin. Article https://doi.org/10.1038/s41467-023-35974-7 Nature Communications | (2023) 14:542 2 Others have suggested that loss of myocardin function may account for the differentiation defects of human leiomyosarcoma cells during malignant transformation30: downregulation of myocardin resulted in lower expression of cyclin-dependent kinase inhibitor 1 A (CDKN1A; also known as p21), a mediator of cell cycle G1 phase arrest, which facilitated cell cycle progression. The lead variants of the UL association at 6p21.29near CDKN1A locate in an intergenic region with possible regulatory consequences (Table S9). Previous evidence suggests that CDKN1A is among the genes, the expression of which correlates with UL size31. The UL association at 20q13.319harbours RNA binding motif protein 38 (RBM38; Fig. S14) that binds to and regulates the stability of CDKN1A transcripts32. In this locus, the UL riskincreasing rs13039273-C is associated with lower RBM38 expression in the ovary (p=8.7×10 −6; Fig. S23; nominal significance in the uterus, p= 2.2 × 10−3). Interestingly, oestrogen receptor (ER)αhas been shown to inhibit the expression of myocardin27, suggesting that the ability of myocardin-CDKN1A-signalling to inhibit cell cycle progression may be impaired in tissues enriched with ERα. Taking together our findings and previous evidence, it seems highly probable that downregulation of myocardin-CDKN1A signalling increases the risk of UL. Enrichment for genes regulating development, growth, and cellular senescence In a gene-based association test, we identified 97 genes associated with UL risk (Fig. 3b and Supplementary Data 1) that were enriched for 50 curated gene sets and/or Gene Ontology (GO) terms (Fig. 3cand Table S10). These included multiple terms related to developmental processes such as, most notably, gonad development (‘regulation of male gonad development’, false discovery rate (FDR)-corrected pvalue (p FDR )=1.78×10 −7;‘regulation of gonad development’,p FDR =0.0017; ‘positive regulation of gonad development’,p FDR = 0.0065; ‘negative regulation of gonad development’,p FDR =0.042) but also others, including the development of kidney, respiratory system, biomineral tissue, and adrenal gland (‘kidney mesenchyme development’, Meta-analyses META-1 (limited to top 10,000 variants) - 53,534 cases; 373,024 controls META-2 (genome-wide) - 38,466 cases; 329,437 controls Data sets FinnGen - Data Freeze 5 (18,060 cases; 105,519 controls) Gallagher: top 10,000 variants - WGHS - NFBC1966 - QIMR - UKBB - 23andMe Gallagher: genome-wide data - WGHS - NFBC1966 - QIMR - UKBB (3,375 cases; 9,465 controls) (363 cases; 5,000 controls) (1,484 cases; 3,701 controls) (15,184 cases; 205,752 controls) (15,068 cases; 43,587 controls) (3,375 cases; 9,465 controls) (363 cases; 5,000 controls) (1,484 cases; 3,701 controls) (15,184 cases; 205,752 controls) Downstream analyses Functional annotations, gene-based associations, enrichment analyses: FUMA Gene expression: Coloc, SMR Causal inferences: TwoSampleMR Genetic correlations: LDSC Fine-mapping: SuSiE Conditional tests: GCTA Fig. 1 | Study setting. The flow diagram illustrates the data usage and analytical steps of our study. We conducted a GWAS of uterine leiomyomata (UL) in 18,060 cases and 150,519 female controls from the FinnGen project. Subsequently, we meta-analysed the FinnGen-based results with summary statistics from a previous UL meta-GWAS12. META-1 included 53,534 cases and 373,024 female controls and was restricted to the top 10,000 variants from the previous study12. META-2 was conducted genome-widely in 38,466 cases and 329,437 female controls, excluding 23andMe data due to the data usage policy. Downstream analyses assessing functional annotations, gene-based associations, pathway enrichment, conditional association tests, fine-mapping, and genetic correlations were conducted using genome-wide summary statistics from META-2. In addition, fine-mapping was also conducted for the results of META-1. In Mendelian randomisation analyses evaluating causal inferences between UL and other, mostly UKBB-based traits, we extracted instruments for UL from the FinnGen-based summary statistics to avoid possible bias from overlapping UKBB samples. 0 15 30 60 90 1234567891011121314151617181920212223 −log10(p−value) 15 30 60 90 Chromosome META-1META-2 Fig. 2 | A combined Manhattan plot of uterine leiomyomata (UL) associations in two sets of meta-analyses. We conducted UL GWAS in FinnGen and, subsequently, two sets of meta-analyses with data from a previously published UL metaanalysis:12 META-1 (top) was limited to the top 10,000 most significant variants from the previous study12, and included up to 53,534 UL cases and 373,024 female controls whereas META-2 (bottom) was conducted genome-widely in 38,466 UL cases and 329,437 female controls. The purple colour denotes UL risk loci identified in META-1 that have not been described in prior studies, and the pink color indicates loci identified in META-2 that were not associated previously with UL risk in prior GWASs or META-1. Black and grey colours indicate odd and even chromosome numbers, respectively. The red dashed lines correspond to the threshold for genome-wide significance (p<5×10 −8). Article https://doi.org/10.1038/s41467-023-35974-7 Nature Communications | (2023) 14:542 3 Table1|Overviewof22locithathavenotbeenreported in association with UL in prior studies Locus Chr:Pos (hg38) Nearest gene(s) Candidate gene(s) rsID EA EAF OR (95% CI) Pvalue HetPVal INFO FinnGen FIN enr. META-1 1q43 1:241860596 EXO1 EXO1, FH rs4149909 G 0.03 1.13 (1.08-1.18) 1.16E-08 0.265 0.996 1.03 1q44 1:244151650 ZBTB18, C1orf100 ZBTB18, AKT3 rs2183478 G 0.18 1.07 (1.05-1.09) 1.75E-11 0.774 0.969 1.57 2q33.3 2:207258660 MYOSLID, KLF7 MYOSLID rs10804157 C 0.44 1.04 (1.03-1.05) 1.04E-08 0.143 0.994 0.87 3q27.2 3:185807411 IGF2BP2 IGF2BP2 rs13060777 G 0.26 1.05 (1.04-1.07) 1.14E-11 0.595 0.999 1.10 4q23 4:99031559 METAP1 EIF4E, ADH5 rs1037475 G 0.57 1.04 (1.03-1.05) 8.38E-09 0.707 1.000 0.95 5q31.1 5:133099880 HSPA4 HSPA4 rs4367292 T 0.27 0.96 (0.94-0.97) 2.49E-08 0.882 0.997 1.15 6q21 6:109054915 SESN1 SESN1 rs11153158 C 0.13 0.93 (0.92–0.95) 1.05E-10 0.243 0.996 1.19 7p14.3 7:33008785 FKBP9, NT5C3A NT5C3A, BBS9 rs4723230 T 0.80 1.05 (1.03-1.07) 4.68E-08 0.946 0.998 1.03 7q31.31 7:121132432 CPED1 WNT16 rs12706314 A 0.53 1.04 (1.03-1.06) 2.69E-10 0.777 0.998 0.89 7q32.3 7:130935964 LINC-PINT LINC-PINT rs35908158 C 0.08 1.08 (1.05-1.10) 1.60E-08 0.883 0.998 1.41 10p12.31 10:21517903 SKIDA1 DNAJC1 rs946711 C 0.33 1.05 (1.03-1.06) 2.96E-10 0.460 0.995 0.96 10q23.31 10:88331783 RNLS RNLS rs1426619 T 0.45 1.04 (1.03-1.06) 4.92E-09 0.653 0.998 1.09 11q23.2 11:112703765 ENSG00000285769 ENSG00000285769 rs10891420 C 0.42 1.05 (1.03-1.06) 1.38E-10 0.019 0.995 1.16 20q13.31 20:57441016 CTCFL RBM38, BMP7 rs13039273 C 0.46 1.04 (1.03-1.06) 3.08E-09 0.874 0.995 1.22 21q22.12 21:35072824 RUNX1 RUNX1 rs2834747 G 0.30 0.96 (0.94-0.97) 1.44E-08 0.711 0.997 1.17 22q12.3 22:36287509 MYH9, APOL1 MYH9 rs9610482 T 0.19 1.06 (1.04-1.08) 7.89E-11 0.354 0.994 0.94 META-2 10q22.3 10:76884502 KCNMA1 KCNMA1 rs2082415 T 0.52f1.05 (1.03-1.06) 2.80E-08 0.460 0.949 1.11 10q24.32a10:101726828 FGF8 SLK rs189195982 T 0.03f1.26 (1.17-1.36) 2.78E-10 0.285 0.994 198.19 10q24.32a10:102788270 WBP1L SLK rs75731980 T 0.06f1.23 (1.17-1.30) 2.42E-16 0.801 0.992 46.07 10q25.1a10:105587387 SORCS3 SLK rs17119191 T 0.97f0.76 (0.70-0.82) 5.81E-13 0.314 0.977 49.89 10q25.1a10:106822067 SORCS1 SLK rs1336619 T 0.03f1.25 (1.17-1.34) 3.48E-10 0.127 0.999 7.47 12q15 12:68692314 NUP107 MDM2 rs142808358 T 0.03f0.87 (0.83-0.91) 5.45E-09 0.958 0.987 0.87 ‘Nearest gene(s)’reports the gene closest to the association lead variant. ‘Candidate gene(s)’indicates the biologically most relevant gene within a 1 Mb window around the association lead variant. Chr chromosome, Pos position (build 38), EA effect allele, EAF effect allele frequency, OR odds ratio, CI confidence interval, Ppvalue, HetPVal p value for heterogeneity, INFO FinnGen imputation info in FinnGen, FIN enr Finnish enrichment (calculated as FIN AF/NFEE AFintheGenomeAggregationDatabase(gnomAD),whereFINAFistheFinnishallele frequency and NFEE AF is the non-Finnish-non-Estonian European allele frequency). aThe locus spans a genomic region larger than ±1 Mb. fFinnGen-based effect allele frequency (allele frequencies were not available for the genome-wide summary statistics of the previous study12). The table reports distinct loci (more than 1 Mb apart) that contain at least one variant identified to be associated with UL at p<5×10 −8and that have not been reported in association with UL in prior studies. META-1 is a meta-analysis of 53,534 UL cases and 373,024 female controls from FinnGen limited to the top 10,000 variants of a previously published meta-GWAS of UL12, and META-2 is a meta-analysis of 38,466 UL cases and 329,473 female controls from FinnGen and the genome-wide results of the same meta-GWAS12 excluding 23andMe data. All significant loci are listed in Tables S1, S2. Article https://doi.org/10.1038/s41467-023-35974-7 Nature Communications | (2023) 14:542 4 p FDR =0.0063;‘metanephric mesenchyme development’,p FDR = 0.0065; ‘cell proliferation involved in metanephros development’,p FDR =0.028; ‘respiratory system development’,p FDR = 0.0063; ‘diaphragm development’,p FDR = 0.0097; ‘regulation of biomineral tissue development’, p FDR =0.031;‘adrenal gland development’,p FDR =0.049).UL-associated genes were also enriched for the regulation of cell cycle and senescence (‘regulation of cell cycle’,p FDR =0.028;‘positive regulation of cell cycle’, p FDR =0.034;‘cell cycle’,p FDR = 0.048; ‘cellular senescence, p FDR =0.031; ‘stress-induced premature senescence’,p FDR =0.049).Enrichmentfora curated gene set ‘RUNX3 regulates CDKN1A transcription’(p FDR =0.049) provided further evidence that CDKN1A-related signalling may play a key role in UL. In addition, four of the terms, namely ‘positive regulation of hearth growth’(p FDR = 0.0052), ‘positive regulation of organ growth’ (p FDR =0.028), ‘regulation of hearth growth’(p FDR =0.041), and ‘organ growth’(p FDR = 0.049) indicated enrichment for genes that function in processes activating growth rate and increasing the size or mass of organs and heart in particular. Gene expression colocalization and mediation effects Expectedly, the strongest positive relationships between the expression of UL-associated genes and disease-gene associations were seen in the uterus and cervix (Fig. S24). We found evidence of the colocalization of UL signals with gene expression of 16 genes in one or more of the four studied tissues (posterior probability (PP) for a shared variant ≥0.8; Fig. 4a and Supplementary Data 1, 4). At 16q12.1, a wellknown UL risk locus, the UL association signal colocalized with the expression of HEATR3 in all studied tissues (PP cultured fibroblasts =0.92; PP skeletal muscle =0.90;PP uterus =0.93;PP whole blood =0.95;Fig.4b–e). Of the loci that had not been described in association with UL in prior studies, the association signal at 5q31.1 colocalized with the expression of heat shock protein family A (Hsp70) member 4 (HSPA4)incultured fibroblasts (PP = 0.98) and skeletal muscle (PP = 0.93; Fig. 4f, g). Previous studies have shown HSPA4 to associate with ERαand thus to play a role in oestrogen signalling33 as well as to enhance the angiogenesis ability of vessel endothelial cells in placenta accreta, a condition where the placenta grows too deeply in the uterine wall34. Both oestrogen signalling35 and angiogenic growth factor dysregulation36 are also involved in UL, which makes HSPA4 a highly plausible candidate to drive the UL association at 5q31.1. We further tested if the UL-risk associations are mediated by gene expression in the significant loci. The results of the mediation tests were mostly inconclusive, and we found no genome-wide significant mediation effects that would have passed the test for heterogeneity in dependent instruments (HEIDI; p HEIDI ≥0.05) (Supplementary Data 5−8). In our study, the previously reported result suggesting that the expression of HEATR3 mediates UL risk association at 16q12.111 reached genome-wide significance in whole blood (p=1.49×10 −9), skeletal muscle (p=4.78×10 −9), and transformed fibroblasts (p=3.21×10 −8) but none of the mediation effects −log10(P) Ngenes <15 15-100 100-200 >200 b) 10 8 6 4 Proportion 0.5 0.4 0.3 0.2 0.1 0.0 intronic intergenic ncRNA intronic UTR3 upstream downstream ncRNA exonic exonic UTR5 splicing ncRNA splicing -log10(P) 30 25 20 15 10 5 0 Chromosome 123 45678 9 10 11 12 13 14 15 16 18 20 22 X a) c) SYNE1 GREB1 WNT4 CDC42 DNM3 CELA3A MYNN THRB EIF5B REV1 BRE TERT SULT1E1 CLPTM1L FIP1L1 DMRT1 RIC8A KANK1 TNFSF13 HEATR3 TP53 SESN1 RAP2C SLC7A3 CPED1 IGF2BP2 RNLS WT1 ODF3 OBFC1 KDELC2 TNRC6B SLC38A2 DLEU1 RBPMS ING5 DLG3 SNX12 TEX11 MST4 FRMD 7 ZNF257 SOX15 EIF4A1 NLGN2 LRRIQ4 SLC4A7 SULT1B1 PDLIM5 CD109 CASC10 MLLT10BETL1 FOXO1 CD44 NPAT CNEP1R1 ELF1 MRPS31 PAPD5 TNFSF12 -log2(E) 0.64 -0.62 p=1.13e-259 p p = 2. 05 e - 1 7 0 p =1 . 01 e - 39 p =1. 39 e - 6 p =0 . 038 p = 0.70 1 p = 0 .4 83 p =0 . 36 2 p =0.00 7 p = 1 p = 1 Fig. 3 | Variant summary and gene set-based results using genome-wide summary statistics from META-2. a The proportions of ‘independent genome-wide significant variants’and ‘variants in LD with independent significant variants’having corresponding functional annotation. Bars are coloured according to −log 2 (enrichment) relative to all variants in the reference panel. Pvalues are obtained using Fisher’s exact test (two-sided). bA Manhattan plot of the genebased test computed by MAGMA51. The input variants were mapped to 19,920 protein-coding genes and, thus, significance was considered at p<2.51×10 −6(0.05/ 19,920). Purple and pink colours indicate odd and even chromosome numbers, respectively. Thirty-seven gene symbols are omitted. cMAGMA51 gene-set enrichment analysis was performed for curated gene sets and GO terms available at MsigDB52. The plot shows the results for significantly enriched pathways (p FDR < 0.05). All data plotted in Fig. 3a–c were produced using FUMA49. Article https://doi.org/10.1038/s41467-023-35974-7 Nature Communications | (2023) 14:542 5 passed the HEIDI test (respective pvalues: p HEIDI.whole.blood = 2.71 × 10−27,p HEIDI.skeletal.muscle =2.51×10 −25,p HEIDI.transformed.fibroblasts = 5.71 × 10−10). Genetic correlations with metabolic and anthropometric traits We used LDSC software21 to evaluate the genetic correlations (r g )of UL with 20 metabolic and anthropometric traits (Fig. 5a, Table S11, a) 0369 12 10 20 UL-GWAS -log10(P) HE A E E TR 3 e Q T L cultur L e d fibroblas t s - l o g 10 ( P ) b) HSPA4 eQTL cultured fibroblasts -log10(P) 10 8 6 4 UL-GWAS -log10(P) 012345 0369 12 UL-GWAS -log10(P) HE A E E TR 3 e Q T L skelet L a l muscle -lo g 10 ( P ) 40 30 20 10 HEATR3 eQTL uterus -log10(P) UL-GWAS -log10(P) 12.5 10.0 7.5 5.0 36912 UL-GWAS -log10(P) HEATR3 eQTL whole blood -log10(P) 80 60 40 20 0369 12 HSPA4 eQTL skeletal muscle -log10(P) 25 20 15 10 5 024 UL-GWAS -log10(P) Cultured fibroblasts Muscle - skeletal Uterus Whole blood ACTRT3 BABAM2 BET1L C11orf65 CD44 FGFR4 HEATR3 HSPA4 NEK10 PIAS1 RAP2C REV1 SESN1 SLC2A4RG SLC38A2 TP53 PP4 0.2 0.4 0.6 0.8 c) d) e) f) g) PP4: 0.92 PP4: 0.90 PP4: 0.93 PP4: 0.95 PP4: 0.98 PP4: 0.93 r2 0.8 0.6 0.4 0.2 r2 0.8 0.6 0.4 0.2 r2 0.8 0.6 0.4 0.2 r2 0.8 0.6 0.4 0.2 r2 0.8 0.6 0.4 0.2 r2 0.8 0.6 0.4 0.2 Fig. 4 | Colocalizations between UL-GWAS signals and eQTL signals. We estimated approximate Bayes factor colocalizations of UL association signals from META-2 and gene expression in GTEx v862 (cultured fibroblasts, skeletal muscle, uterus, and whole blood) using coloc.abf function from the coloc R library54. Altogether 92 genes, including the genes closest to the association lead variant at each UL-associated locus and biologically plausible candidate genes, when different from the closest genes, were included in the analysis (Table S2). The figure illustrates aall genes, the expression of which colocalizes with UL signal (posterior probability for a single causal variant [PP4] >0.8) in at least one of the studied tissues, as well as colocalization signals for HEATR3 in bcultured fibroblasts, cskeletal muscle, duterus, and ewhole blood, and for HSPA4 in fcultured fibroblasts, and gskeletal muscle. Article https://doi.org/10.1038/s41467-023-35974-7 Nature Communications | (2023) 14:542 6 and Supplementary Data 1). In line with previous observational studies reporting associations between cardiometabolic risk factors and UL risk20,37, we found UL to show a positive genetic correlation with serum triglyceride level (r g =0.161, p FDR = 1.10 × 10−7), waist circumference (r g =0.101, p FDR =1.23×10 −4), diastolic blood pressure (r g =0.098, p FDR = 2.76 × 10−4), waist-to-hip ratio (r g =0.095, p FDR = 0.020), body mass index (BMI; r g =0.091,p FDR =7.63×10 −4), systolic blood pressure (r g =0.061, p FDR = 0.020), whole-body fat mass (r g =0.057, p FDR = 0.026), and hip circumference (r g = 0.051, p FDR = 0.048), and negative genetic correlation with concentrations of high-density lipoprotein cholesterol (HDL-C; r g =−0.139, p FDR = 1.48 × 10−7) and apolipoprotein A-I (ApoA-I; r g =−0.110, p FDR = 1.13 × 10−4). Somewhat unexpectedly, we found UL to be closely genetically correlated with basal metabolic rate (r g =0.084, p FDR = 0.002), whole-body water mass (r g =0.083, p FDR = 0.002), and whole-body fat-free mass (r g =0.083,p FDR = 0.003). Compatible with these findings, UL showed a negative genetic correlation with the impedance of whole-body (r g =−0.130, p FDR = 1.04 × 10−5) (i.e. a bioelectrical measure used for estimating body composition; higher muscle mass leads to lower impedance). Compared with whole-body fat mass, the genetic correlations of UL with these anthropometric traits indicating good physical health (i.e. basal metabolic rate, water mass, and fat-free mass) tended to be more robust in terms of both larger r g values and smaller pvalues. Causal evidence underscores the involvement of altered muscle tissue biology To further evaluate the causal relations between UL and the same 20 metabolic and anthropometric traits, we applied bi-directional twosample Mendelian randomisation. Regarding circulating lipids, we found higher HDL-C to be causally associated with a lower risk of UL (inverse variance-weighted [IVW] method-based odds ratio [OR] = 0.89 [0.82, 0.97], p FDR = 0.037; Fig. 5b, Table S12, and Supplementary Data 1). There was no evidence of a causal relationship between UL and blood triglyceride level (Fig. 5b) even if, among the studied traits, triglycerides showed the most robust genetic correlation with UL in terms of both r g and pvalue (Fig. 5a). Likewise, atherogenic cholesterol measures, total-C and low-density lipoprotein (LDL)-C, and apolipoprotein B (ApoB) concentration were not causally related to UL risk (Fig. 5b). We found multiple causal associations between anthropometric traits and UL risk (Fig. 5b). Of the traits commonly linked with compromised health, waist circumference (OR = 1.19 [1.05,1.35], p FDR = 0.033) and BMI (OR = 1.13 [1.03–1.24], p FDR =0.037) were causally associated with UL risk. Compared with these, the causal associations between UL and traits implying good physical health were somewhat more robust (basal metabolic rate: OR = 1.24 (1.08, 1.43], p FDR = 0.020; whole-body water mass: OR = 1.22 [1.06, 1.40], p FDR = 0.033; whole-body fat-free mass: OR = 1.24 [1.08, 1.42], p FDR = 0.020; impedance of whole body: OR = 0.79 [0.69, 0.91], Fig. 5 | Genetic correlations and causal relationships between uterine leiomyomata and metabolic and anthropometric traits. We estimated agenetic correlations (r g ) between uterine leiomyomata (UL) and 20 metabolic and anthropometric traits using UL-GWAS data from META-2 (n= 367,903) and summary statistics for other traits as provided by the MRC Integrative Epidemiology Unit (IEU) GWAS database (nranges from 33,231 to 757,601; the trait-specific sample sizes are provided in Table S11). The analysis softwarewasLDSC21.Todissect the causal relationships, we performed bi-directional two-sample Mendelian randomisation (MR) implemented in the TwoSampleMR R library60,63;theplots(b,c) show the causal estimates obtained using the inverse variance-weighted (IVW) method. We further estimated dthe multivariable effects of whole-body fat-free mass, whole-body fat mass, and estradiol level on UL risk using the same TwoSampleMR R library60,63. In sensitivity analyses, we derived causal estimates using eMR Egger (as implemented in TwoSampleMR), foutlier-corrected MR-PRESSO43, and gMRMix44 methods for the traits showing a significant IVW-based causal effect on UL. For all MR analyses, genetic instruments for UL were extracted from the GWAS completed in FinnGen (n= 123,579) and for other, mostly UKBB-based, traits fromtheMRCIEUGWASdatabase(nranges from 33,231 to 757,601; Table S11) except for estradiol, for which the instruments were extracted from a study by ref. 61.(n= 206,927). In all Mendelian randomisation analyses, LD pruning was completed using a European population reference, the threshold of r2=0.001,and a clumping window of 10 kb. False discovery rate (FDR)-corrected64 pvalues <0.05 were considered significant in primary analyses (a–c). Multivariable MR and sensitivity analyses (d–g) were considered exploratory, and no multiple testing correction was applied. The error bars represent the corresponding 95% confidence intervals (CI). Numerical details are provided in Tables S12–S15, and scatter plots and the results of the leave-one-out analyses are shown in Figs. S25, 26 and S28–35, respectively. Article https://doi.org/10.1038/s41467-023-35974-7 Nature Communications | (2023) 14:542 7 p FDR = 0.020). When considering the null causal effect of whole-body fat mass on UL risk (p FDR = 0.712), it seems apparent that the causal effect of BMI on UL arises from the increased lean body mass rather than fat mass. Taken together, it seems that obesity-related cardiometabolic risk factors may not play a causal role in the pathophysiology of UL even if those are associated with UL risk on a population level20,37.Ourfindings are in line with a previous report suggesting obesity to be causal for uterine endometrial cancer, but not for the other four studied gynaecologic diseases, including UL38. Of note, the causal relationship between UL and diastolic blood pressure remained inconclusive as the causal estimate was significant in both directions (Fig. 5b, c and Supplementary Data 1). UL are considered oestrogen-dependent, and UL have higher ERα expression compared with normal uterine myometria35.ERsare expressed in a variety of tissues, including all musculoskeletal tissues39. In females, muscle mass and strength are closely coupled with oestrogen status: girls begin to gain muscle mass after the onset of puberty40,whereasinolderage,duringperimenopausalandpostmenopausal periods, muscle strength declines considerably41.Ifoestrogen enhances muscle growth42, the observed causal relationship between fat-free mass and UL risk could arise secondary to high oestrogen contributing to muscle growth. Therefore, we further tested the multivariable effects of whole-body fat-free mass, whole-body fat mass, and estradiol on UL risk. The results of the multivariable model (Fig. 5d and Table S13) indicate that, among the three traits, only whole-body fat-free mass has a nominally significant causal effect on UL risk (p= 0.018) and, thus, support the original findings. We note that the results of Mendelian randomisation should be interpreted with caution: although we did not observe horizontal pleiotropy (Table S12), the causal estimates were typically heterogenic (Table S12; scatter plots in Figs. S25, 26). Funnel plots did not suggest major asymmetry indicative of directional pleiotropy; however, minor asymmetry due to outliers was present for some exposures (Fig. S27). We further obtained outlier-corrected estimates using MR-PRESSO43 (Table S14) and an outlier-robust MRMix method44 (Table S15). The results were highly matching to the original findings (Fig. 5f, g), thus providing assurance of the validity of the evidence obtained in the primary analyses. Also, in the leave-one-out sensitivity analyses (Figs. S28–35), all causal estimates were consistently positive (higher fat-free mass was causally associated with a higher risk of UL; Fig. S34) or negative (higher impedance was causally associated with a lower risk of UL; Fig. S32) suggesting that there is no single variant driving the causal associations. Strengths and limitations Compared with the previous UL GWASs, our study had a larger sample size, which facilitated discoveries of multiple association signals at loci that had not been described in prior studies and also confirmed a high number of previously reported UL risk loci. Importantly, careful manual curation of the biological function of the genes in the ULassociated loci was highly beneficial in providing an understanding of UL-related biology. Due to the limitations in data availability, we needed to conduct two distinct meta-analyses to maximise the sample size in META-1 (including 23andMe but limited to the top 10,000 variants from the previous study12) and to obtain genome-wide results in META2 (including genome-wide data from the previous study12 but excluding 23andMe). Multiple analyses conducted downstream of the GWAS provided further insights into the key genetic pathways. We found only minimal evidence suggesting that the UL risk associations would be mediated by gene expression; it must be acknowledged, however, that the currently available gene expression data is limited in terms of the number of relevant tissue samples (the number of samples with genotype data is only 129 for the uterus in GTEx Analysis Release V8) and the low statistical power may interfere the discovery of significant effects. Regarding the multivariable Mendelian randomisation, the genetic instruments for estradiol are weaker than the instruments for body composition measures, which may contribute to poor statistical power to detect a causal effect—it would be beneficial to reassess the multivariable effects once a larger estradiol GWAS, preferably conducted in females, will be available potentially providing stronger instruments for MR. Given that our work only includes computational approaches, further functional studies would be warranted to provide molecular evidence for our findings. Finally, the replication of our findings in other non-European ethnicities would be of high value. Discussion The numerous UL risk loci identified in the present study provide valuable insights into the architecture of heritable genetic risk factors in UL. Multiple aspects of our study, including the results of genebased enrichment analyses and LDSC regression-derived genetic correlations, indicate altered muscle tissue biology in UL. Most notably, Mendelian randomisation-based evidence suggesting a causal relationship between genetic tendencyto accumulate fat-free mass and UL risk provides an alternative perspective on UL-related pathophysiology. When considering the oestrogen-dependency of UL, it remains possible that the oestrogen-rich environment, due to sexual maturity, may trigger excess SMC growth resulting in UL in women who are genetically inclined to build up muscle. Currently, the only essentially curative treatment for UL is hysterectomy, which underscores the high demand for the development of alternative effective therapies2. The herein presented results provide several potential targets for translational research to develop pharmacologic interventions for UL. Therapies targeted at myocardinCDKN1A signalling or, considering the causal evidence, other factors regulating muscle growth may hold the greatest potential. Methods Our research complies with all relevant ethical regulations. FinnGen participants provided written informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, older research cohorts, collected before the start of FinnGen (in August 2017), were collected based on study-specific written informed consents and later transferred to the Finnish biobanks after approval by Fimea, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (Helsingin ja Uudenmaan Sairaanhoitopiiri, HUS) approved the FinnGen study protocol Nr HUS/990/2017. The FinnGen study is approved by Finnish Institute for Health and Welfare (Terveyden ja hyvinvoinnin laitos, THL), approval number THL/2031/6.02.00/2017, amendments THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/ 2222/6.02.00/2018, THL/283/6.02.00/2019, THL/1721/5.05.00/2019, Digital and population data service agency VRK43431/2017−3, VRK/ 6909/2018-3, VRK/4415/2019-3 the Social Insurance Institution (Kansaneläkelaitos, KELA) KELA 58/522/2017, KELA 131/522/2018, KELA 70/ 522/2019, KELA 98/522/2019, and Statistics Finland TK-53-1041-17. The Biobank Access Decisions for FinnGen samples and data utilised in FinnGen Data Freeze 5 include THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26, Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, Auria Biobank AB17-5154, Biobank Borealis of Northern Finland_2017_1013, Biobank of Eastern Finland 1186/ 2018, Finnish Clinical Biobank Tampere MH0004, Central Finland Biobank 1-2017 and Terveystalo Biobank STB 2018001. Study populations FinnGen (www.finngen.fi/en) is a public-private partnership project launched in 2017 with an aim to improve human health through genetic research. The project utilises genome information from a Article https://doi.org/10.1038/s41467-023-35974-7 Nature Communications | (2023) 14:542 8