Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2022 Sep 29;17(9):e0274879. doi: 10.1371/journal.pone.0274879

A transcriptome-wide association study of uterine fibroids to identify potential genetic markers and toxic chemicals

Gayeon Kim 1,#, Gyuyeon Jang 1,#, Jaeseung Song 1, Daeun Kim 1, Sora Lee 1, Jong Wha J Joo 2, Wonhee Jang 1,*
Editor: Dylan Glubb3
PMCID: PMC9521910  PMID: 36174000

Abstract

Uterine fibroid is one of the most prevalent benign tumors in women, with high socioeconomic costs. Although genome-wide association studies (GWAS) have identified several loci associated with uterine fibroid risks, they could not successfully interpret the biological effects of genomic variants at the gene expression levels. To prioritize uterine fibroid susceptibility genes that are biologically interpretable, we conducted a transcriptome-wide association study (TWAS) by integrating GWAS data of uterine fibroid and expression quantitative loci data. We identified nine significant TWAS genes including two novel genes, RP11-282O18.3 and KBTBD7, which may be causal genes for uterine fibroid. We conducted functional enrichment network analyses using the TWAS results to investigate the biological pathways in which the overall TWAS genes were involved. The results demonstrated the immune system process to be a key pathway in uterine fibroid pathogenesis. Finally, we carried out chemical–gene interaction analyses using the TWAS results and the comparative toxicogenomics database to determine the potential risk chemicals for uterine fibroid. We identified five toxic chemicals that were significantly associated with uterine fibroid TWAS genes, suggesting that they may be implicated in the pathogenesis of uterine fibroid. In this study, we performed an integrative analysis covering the broad application of bioinformatics approaches. Our study may provide a deeper understanding of uterine fibroid etiologies and informative notifications about potential risk chemicals for uterine fibroid.

Introduction

Uterine fibroids (UFs) or uterine leiomyoma are the most common benign tumors in women, and >40% of Caucasian women will be diagnosed with UF at least once in their lifetime [1]. Thirty percent of UF patients have severe symptoms such as uterine bleeding, pelvic pain, and infertility [2]. Reportedly, UF can transform into malignant tumors in some patients [3, 4]. The high prevalence of UF has a serious impact on annual healthcare costs all over the world; however, the pathogenesis of UF has not been completely understood [2].

Previous studies reported several genetic risk factors affecting UF [5–9]. A Finnish twin cohort study identified the strong heritability of UF, estimating that monozygotic twins had twice the incidence rate of UF compared with dizygotic twins [5]. Chromosomal abnormalities such as trisomy 12 and rearrangements of chromosomes 12, 13, and/or X are involved in the growth of UF [6]. The overexpression of High mobility group AT-hook 2, located in 12q 14–15, is related to the development of UF with or without chromosomal rearrangements [7]. Germline mutations in fumarate hydratase, a tumor suppressor gene, stimulate benign and malignant tumor development of UF [8, 9]. Deletion of collagen type Ⅳ alpha 5 and 6 chain mapped to chromosome X is also known for its association with UF [10].

Several pre-existing conditions and environmental substances such as sex steroids, obesity, hypertension, and endocrine-disrupting chemicals (EDCs) are reported to stimulate the pathogenesis and growth of UF [11–14]. Steroid hormones are involved in the proliferation and differentiation of uterine cells; in particular, estrogen and progesterone are regarded as agonists of fibroid growth [15, 16]. A previous study reported that obese women have a high prevalence of UF and body mass index levels have a strong positive correlation with fibroid growth [17]. Faerstein et al. identified that hypertension patients have high risk of UF pathogenesis [18]. Exposure to EDCs can also increase the risk of UF onset [19]. Despite the biological effects of EDCs, there have been very few studies on how these chemicals regulate biological responses to affect the pathogenesis of UF.

Recent genome-wide association studies (GWAS) identified several risk loci attributed to the development of UF. Nakamura’s group proposed 10q24.33, 11p15.5, and 22q13.1 regions as risk loci from their Japanese cohort, and Zhang et al. reported 2q32.2 and 1q42.2 regions as risk loci using African-American and European-American populations [20, 21]. A GWAS meta-analysis on UF for European ancestry conducted by Gallagher’s group reported eight risk loci: 2p23.2, 4q22.3, 6p21.31, 7q31.2, 10p11.22, 11p14.1, 12q15, and 12q24.31 [22]. Even though GWAS discovers risk loci via the association between single nucleotide polymorphism (SNP) and the disease, it is hard to determine what genetic effects are derived from the expression levels of risk variants. Transcriptome-wide association study (TWAS) is a useful solution to overcome such limitations by integrating GWAS with expression quantitative trait loci (eQTLs). TWAS utilizes pre-computed predictive models of gene expression trained by reference eQTL data to impute gene expression from large-scale genotype data [23]; it prioritizes putative causal genes where the cis-genetic component is associated with the disease [24].

Herein, we conducted a TWAS by using the meta-analyzed GWAS summary statistics data of UF with eQTL weight panels derived from large-scale consortium data and reference linkage disequilibrium (LD) matrix [22, 23, 25–27]. Conditional and joint analyses were performed to demonstrate the expressional independence of and associations in the TWAS and genes, and then carried out gene set enrichment analysis (GSEA) to explore biological functions. Finally, we conducted a chemical–gene interaction analysis using the comparative toxicogenomics database (CTD) to identify toxic chemicals associated with the expression of significant UF TWAS genes. We believe that our results may provide new insights into the pathogenesis of UF and useful information on how toxic chemicals affect the development of UF.

Methods

Data collection

The GWAS summary statistics data (GCST009158) were retrieved from the GWAS Catalog (https://www.ebi.ac.uk/gwas/studies/GCST009158). The GCST009158 dataset consists of 20,406 UF patients and 223,918 control subjects of European ancestry and is currently the largest publicly available UF GWAS data. Eight tissue-specific eQTL reference panels related to UF and reference LD data from the 1000 Genomes Project were retrieved from the functional summary-based imputation (FUSION) webpage (http://gusevlab.org/projects/fusion/) [24, 27]. Six tissue panels from Genotype-Tissue Expression version 7 (GTEx v7) including three female reproductive organs (ovary, uterus, and vagina), two tissue panels related to the regulation of ovarian hormones (hypothalamus and pituitary), and whole blood panel were selected as reference panels [23]. To uncover as many associations as possible, two different blood panels from individual studies representing whole blood (Young Finns Study, YFS) and peripheral blood (Netherlands Twin Register, NTR) were also included following previous studies [24, 25, 28].

Transcriptome-wide association study

A TWAS was performed using the FUSION tool with default settings [24]. The GWAS summary statistics data file was converted into a sumstats-formatted file prior to transcriptomic imputation (TI) using the LD-score regression (LDSC), and the results from major histocompatibility complex regions were excluded to prevent inflating the association statistics [29]. TI was conducted using eight tissue-specific eQTL reference panels and the LD reference data from the European population with FUSION. To obtain statistically robust signatures, Bonferroni-corrected thresholds were used as significance thresholds to identify transcriptome-wide significant associations (P < 0.05/sum of SNP–gene pairs across the tissue panels (26,279) = ~1.90 × 10−6).

The GWAS summary statistics data were analyzed by the functional mapping and annotation (FUMA) for comparison with the TWAS of FUSION [30]. Two gene-prioritizing tests of FUMA—SNP2GENE process and multi-marker analysis of genomic annotation (MAGMA)—were conducted as post-GWAS annotation analyses. The SNP2GENE process maps SNPs to neighboring genes based on the physical position, eQTL associations, and chromatin interaction information [30]. MAGMA, which is one of the most widely used post-GWAS annotations, prioritizes genes associated with SNPs based on a multiple regression model [31]. Moreover, we performed colocalization tests with the COLOC R package to determine whether the gene expression signals were colocalized with the GWAS signals [32]. COLOC was conducted with FUSION software, and five posterior probabilities (PP0–4) were calculated corresponding to five different hypotheses (H0–4). H0: no association; H1: functional association but no GWAS association; H2: GWAS association but no functional association; H3: association with gene expression and GWAS signals but each is independent; H4: gene expression and GWAS association are colocalized. Genes satisfying the threshold of PP3+PP4 > 0.8 and PP4/PP3 > 2 were prioritized in the colocalization tests, following previous studies [28, 33].

Conditional and joint analyses

Conditional and joint analyses were conducted to identify independent TWAS genes in a specific locus harboring multiple TWAS associations after conditioning on the expression of TWAS genes using the FUSION.post_process.R code provided by FUSION. TWAS associations that were statistically significant after Bonferroni-correction were subjected to the conditional and joint analyses. Jointly significant genes in a locus were regarded as the robust genetic signatures for UF.

Functional network analysis of TWAS results

To interpret the systemic biological roles of TWAS genes, GSEA was conducted with the Metascape, a web-based gene list annotation tool [34]. In order to analyze the broad genetic signatures of UF, GSEA was conducted with marginally significant TWAS associations (P < 0.05) instead of the Bonferroni-adjusted threshold (P < 1.90 × 10−6). For eight tissues, individual lists of genes positively associated with UF risks (TWAS Z-score > 0) were analyzed using reference gene sets provided by the Metascape tool to annotate its biological function. The identical process was applied to genes negatively associated with UF risks (TWAS Z-score < 0). These analyses were conducted with default settings for the Metascape and the results were retrieved in Cytoscape file format for further analyses.

To detect representative pathways that may play crucial roles in the pathogenesis of UF, functional enrichment network analysis was conducted on the enriched biological pathways identified by the Metascape (P < 0.01). The functional enrichment networks comprising the pathways identified by the GSEA with TWAS genes were visualized using Cytoscape (v. 3.8.2) [35]. Duplicated enriched pathways were removed before the construction of functional enrichment networks. Among the networks, sub-network clusters consisting of closely interconnected pathways were identified by molecular complex detection (MCODE) [36].

Chemical–gene interaction analysis

To identify the chemical risk factors of UF, chemical–gene interaction analysis was conducted by CTD using the significant TWAS genes (P < 1.90 × 10−6). CTD provides curated information on chemical–gene/protein interactions, chemical–disease relationships, and gene–disease relationships from peer-reviewed scientific literature [37]. The analysis was performed by setting the organism as Homo sapiens. To obtain chemicals that may increase the risk of UF onset, significant TWAS genes with positive or negative Z-scores were respectively provided as input gene sets into the CTD. The chemicals were estimated to increase the expression levels of significant TWAS genes with positive Z-scores (TWAS Z-score > 0) or to decrease those with negative Z-scores (TWAS Z-score < 0). In short, chemicals expected to be involved in the pathogenesis of UF, were selected as potentially toxic chemicals for UF.

Results

Prioritization of susceptibility genes for UF using TWAS

To identify risk genes significantly associated with the pathogenesis of UF, we conducted a TWAS using currently the largest GWAS summary statistics dataset (GCST009158; total: 244,324; number of UF patients: 20,406, number of controls: 223,918) of European UF and eight eQTL tissue panels with the FUSION tool. S1 Table lists all 26,279 TWAS associations. The result showed 10 significant TWAS associations between the predicted expression of eQTL panels and UF, identifying nine genes as risk genes for UF after Bonferroni-correction (P < 1.90 × 10−6) (Figs 1 and S1). The nine significant TWAS genes were secretoglobin family 1C member 1 (LOC653486), proteasome 26S subunit, non-ATPase 13 (PSMD13), RP11-282O18.3, M-phase phosphoprotein 9 (MPHOSPH9), strawberry notch homolog 1 (SBNO1), ADP ribosylation factor like GTPase 6 interacting protein 4 (ARL6IP4), SET domain containing (lysine methyltransferase) 8 (SETD8), kelch repeat and bric-a-brac, tramtrack, and broad-complex domain containing 7 (KBTBD7), and mitochondrial ribosomal protein S31 (MRPS31) (Table 1). Among these nine significant genes, eight genes were detected in one of the three different blood panels (GTEx whole blood, NTR, or YFS), and only RP11-282O18.3 showed a significant association in the uterus panel. While significant GWAS signals were observed on most chromosomes, the significant TWAS genes were only detected in chromosomes 11, 12, and 13 (S2A Fig). These results may be based on the cytogenetic rearrangement of the specific loci (12q15, 12q24, and 13q) that are characteristics of UF [38–40].

Fig 1. A Manhattan plot of the TWAS results for UF.

Fig 1

Open in a new tab

Each dot corresponds to the gene of which predicted expression is associated with UF. The X-axis denotes the chromosome numbers of the genes and the Y-axis denotes −log10(TWAS P-values). The yellow line indicates a Bonferroni significant threshold (P < 1.90 × 10−6). Nine significant TWAS genes are represented as red dots with gene labels.

Table 1. A list of significant TWAS genes associated with UF (P < 1.90 × 10−6).

Gene Tissue Chromosome LeadGWAS rsID TWAS.Z (FUSION) TWAS.P (FUSION) SNP2GENE MAGMA
LOC653486 NTR blood 11 rs532483 5.1836 2.18E-07 No No
PSMD13 NTR blood 6.6731 2.50E-11 Yes No
RP11-282O18.3* Uterus 12 rs641760 5.347 8.94E-08 No No
MPHOSPH9 Whole blood -5.5 3.80E-08 Yes Yes
YFS blood -4.9871 6.13E-08
SBNO1 YFS blood 5.416 6.10E-08 Yes No
ARL6IP4 Whole blood 5.4736 4.41E-08 Yes No
SETD8 Whole blood -4.8328 1.35E-06 Yes No
KBTBD7* Whole blood 13 rs4943810 -5.4073 6.40E-08 No No
MRPS31 YFS blood rs7986407 -5.6906 1.27E-08 Yes No
Open in a new tab

The significant TWAS genes from the FUSION that were neither reported in SNP2GENE nor MAGMA are highlighted in bold. The LeadGWAS rsID indicates the lead SNPs of the locus where each significant TWAS gene is located, and they were calculated by FUSION.

*Genes that have not been identified in previous UF-related studies

Then, we performed additional post-GWAS annotation analyses using the SNP2GENE and the MAGMA, which are position-based gene-mapping methods, to confirm the robustness of our TWAS results and validate the novel association from the FUSION. We investigated whether the nine susceptibility genes identified by the FUSION overlapped with those identified by the SNP2GENE and/or the MAGMA (S2B Fig and S2 Table). Among the nine TWAS genes from the FUSION, six genes (PSMD13, MPHOSPH9, SBNO1, ARL6IP4, SETD8, and MRPS31) overlapped with genes from the SNP2GENE, while MPHOSPH9 also overlapped with the genes from the MAGMA. Three genes (LOC653486, RP11-282O18.3, and KBTBD7) were only detected by the FUSION, not from other gene-prioritizing tests (S3 Fig), which suggests that these three genes cannot be detected by conventional methods such as MAGMA or SNP2GENE. Among the three genes only detected by the FUSION, RP11-282O18.3 and KBTBD7 were reported for the first time as susceptibility genes for UF, to the best of our knowledge. Colocalization tests were performed to confirm the robustness of the possible causal relationship between the significant TWAS signals and UF. PPs for each TWAS signal associated with UF were calculated by COLOC. The COLOC results showed that six out of the nine significant TWAS genes (PSMD13, MPHOSPH9, SBNO1, ARL6IP4, SETD8, and MRPS31) were replicated in colocalization analyses (PP3+PP4 > 0.8 and PP4/PP3 > 2) (S3 Table and S4 Fig). Together, we identified a total of nine significant TWAS genes including two novel genes—RP11-282O18.3 and KBTBD7—and confirmed the robustness of our results.

Assessing independence of TWAS signals through conditional and joint analysis

Conditional and joint analyses were applied to genomic regions at chromosomes 11, 12, and 13—as listed in Table 1—to determine whether the expressions of the multiple associated genes in the regions were regulated by the same causal variants. The analyses were carried out for each tissue separately, and the results at the same locus are displayed together in a single plot for better visibility. In the region harboring rs532483 at chromosome 11, GWAS signals showed significant drops after being conditioned on the predicted expression levels of LOC653486 and PSMD13 from the NTR blood panel (Fig 2A). Both LOC653486 and PSMD13 were observed as independently significant TWAS genes. In the genomic region within 1 Mb of rs641760, five TWAS genes—RP11-282O18.3, MPHOSPH9, SBNO1, ARL6IP4, and SETD8—were observed from GTEx uterus, GTEx whole blood, and YFS blood panels. Among these five genes, the GWAS signals were significantly decreased when conditioned on the predicted expression level of MPHOSPH9 from the GTEx whole blood panel, which indicates that MPHOSPH9 was a jointly significant gene and responsible for the most signals at the locus (Fig 2B). The other four genes—RP11-282O18.3, SBNO1, ARL6IP4, and SETD8—were identified as marginally significant genes that were no longer significant after conditioning on the predicted expression level of MPHOSPH9 (conditioned P-value of RP11-282O18.3, 0.23; SBNO1, 0.16; ARL6IP4, 0.18; and SETD8, 0.20).

Fig 2. Regional association plots showing conditional and joint analyses results.

Fig 2

Open in a new tab

(A) The regional association plot of chromosome 11. (B) The regional association plot of chromosome 12. (C) The regional association plot of chromosome 13. The middle part of each figure represents all genes located in the region. The green bars indicate jointly significant genes that are responsible for most GWAS signals in the region, the yellow bars represent TWAS genes that are no longer significant after accounting for conditionally independent genes, and the gray bars indicate genes that were neither jointly significant nor marginally significant in that region. The colored dots next to the jointly or marginally significant genes indicate tissue panels where the genes were detected, as summarized in Table 1. The lowest part of each panel is a Manhattan plot of the GWAS signals. Black dots represent the GWAS P-values of SNPs before conditioning tests, and blue dots represent the GWAS P-value of SNPs after removing the effects of jointly significant genes.

In the chromosome 13q14.11 region where rs4943810 and rs7986407 were located, the significant GWAS signals at the locus became no longer significant when conditioned on the predicted expression level of KBTBD7 from GTEx whole blood panel and MRPS31 from the YFS blood panel, respectively (Fig 2C). The result indicated that both genes are responsible for the effect size of the GWAS locus where they are located. One of the two novel genes, KBTBD7, was a jointly significant TWAS gene, and its expected expression level accounted for most GWAS signals at the locus where KBTBD7 was observed. Together, we identified that LOC653486, PSMD13, MPHOSPH9, KBTBD7, and MRPS31 of the nine TWAS genes, including a novel gene (KBTBD7), were independently significant genes after being conditioned on their predicted expression levels, which suggests that we successfully detected robust TWAS genes for UF.

Functional annotation of TWAS genes for UF

In order to explore the biological functions of TWAS genes, we performed GSEA with UF TWAS genes by applying a soft threshold (TWAS P-value < 0.05) using the Metascape annotation tool. A total of 139 pathways were enriched with the 242 positively associated TWAS genes (TWAS Z-score > 0), while 138 biological pathways were enriched with the 235 negatively associated TWAS genes (TWAS Z-score < 0) (S4 and S5 Tables).

To detect representative pathways among the enriched biological pathways, we constructed functional enrichment networks consisting of positively or negatively associated TWAS genes. A functional enrichment network consisting of 139 positively associated pathways was clustered into 14 sub-networks by the MCODE. The connectivity score of each sub-network was calculated (score: 3.00–10.00; median: 7.57) and we defined four clusters that had the top 25% scores as major clusters of the enrichment network (Fig 3A). The four major clusters were categorized into three parental pathways: immune system process, metabolic process, and localization. Next, a total of 15 sub-networks were obtained from the enrichment network of 138 negatively associated pathways and the connectivity scores of sub-networks were calculated (score: 3.33–11.82; median 8.12) by the MCODE. Four clusters with the top 25% scores were defined as major clusters and were classified into four parental pathways: cell cycle, immune system process, mitochondrial gene expression, and cellular component organization or biogenesis (Fig 3B).

Fig 3. Functional annotation networks consisting of biological pathways enriched with TWAS genes for UF.

Fig 3

Open in a new tab

(A) Four major clusters grouped into three parental biological pathways were enriched with positively associated TWAS genes (TWAS P-value < 0.05 and Z-score > 0). (B) Four major clusters of biological pathways were enriched with negatively associated TWAS genes (TWAS P-value < 0.05 and Z-score < 0). The major clusters are the sub-networks in the top 25% for connectivity score as calculated by the MCODE in each functional annotation network. Each node represented by a pie chart indicates an enriched biological pathway, and the sector size is proportional to the number of genes that originate from each tissue panel. The node size corresponds to the number of panels where TWAS genes were enriched.

Identification of toxic chemicals associated with UF risk

To identify toxic chemicals such as EDCs that may contribute to the pathogenesis of UF, we performed a chemical–gene interaction analysis using CTD to evaluate the relationship between chemicals and genes. Only six of the nine significant TWAS genes—namely PSMD13, SBNO1, and ARL6IP4 that were positively associated with TWAS genes, and MPHOSPH9, KBTBD7, and MRPS31 that were negatively associated—were available to search to elucidate their chemical–gene interactions in the CTD. The result showed that a total of 67 chemicals correlated with the six TWAS genes (S6 Table). Among the 67 chemicals, 28 were estimated to increase the expression levels of positively associated TWAS genes (S7 Table), and 33 were estimated to decrease the expression of negatively associated TWAS genes (S8 Table). Notably, the remaining six chemicals were estimated to increase the expression levels of positively associated TWAS genes while also decreasing those of negatively associated TWAS genes. However, we removed valproic acid from the six chemicals because it may either increase or decrease the expression of negatively associated TWAS genes (Table 2). In short, these 66 chemicals may contribute to worsening UF symptoms and/or progression, although the results should be validated by experimental procedures. Among them, aflatoxin B1 and 7,8-dihydro-7,8-dihydroxy benzo(a)pyrene 9,10-oxide (BPDE) are known to act as EDCs [41, 42].

Table 2. A list of five toxic chemicals discovered based on CTD as potentially toxic chemicals for UF.

Chemical ID Chemical name DrugBank ID PubChem ID TWAS genes (Z-scores > 0) TWAS genes (Z-scores < 0)
D019327 Copper Sulfate DB06778 24462 PSMD13, SBNO1 KBTBD7, MRPS31
D016572 Cyclosporine DB00091 5284373 PSMD13 KBTBD7
D004317 Doxorubicin DB00997 31703 PSMD13 MPHOSPH9
D016604 Aflatoxin B1 - 186907 SBNO1 MPHOSPH9, KBTBD7
D015123 7,8-Dihydro-7,8-dihydroxybenzo(a)pyrene 9,10-oxide (BPDE) - 53788654 ARL6IP4 MRPS31
Open in a new tab

The chemicals reported as EDCs are highlighted in bold.

Discussion

UFs are common benign uterine tumors in women, while their pathological mechanism remains underexplored. Despite previous GWAS contributions to detecting genetic variations associated with UF, only limited insights were provided to explicate the genuine effects of genetic risk variants. Here, we integrated the largest UF GWAS data with eight tissue-specific eQTL panels for TI to overcome the limitations of GWAS. Our TWAS successfully identified nine putative causal genes for UF, including two novel genes affected by GWAS SNPs (Fig 1 and Table 1). A previous study that performed a TWAS using Summary-PrediXcan (S-PrediXcan) software with UF GWAS data from 227,329 samples identified leucine zipper protein 1 from the vagina on chromosome 1 and oligonucleotide/oligosaccharide binding fold containing 1 on chromosome 10 from the esophagus as potential causal genes for UF [43]. Even though S-PrediXcan reportedly shows results consistent with those of FUSION, our study differs from the previous TWAS in a few ways [43, 44]. The previous study utilized S-PrediXcan as the TI method with every tissue-specific panel of GTEx v7 excluding male-specific tissues. We performed TI using FUSION with UF-related tissue panels including two blood panels from NTR and YFS that were not analyzed in the previous publication, because our main focus was potentially strong tissue-specific regulatory effects on the pathogenesis of UF. The reduction in the multiple-testing burden resulting from the use of fewer tissue panels may have contributed to identifying novel putative causal genes for UF that were previously undetected [43].

The fact that the results from several studies were in line with our TWAS results supports the robustness of our TWAS genes. Homologs of LOC653486 and PSMD13 were implicated in UF risk loci by a previous GWAS study by Nakamura et al. [20]. Seven significant TWAS genes from our study—LOC653486, PSMD13, MPHOSPH9, SBNO1, ARL6IP4, SETD8, and MRPS31—had been previously identified as residing in genetic regions associated with UF [45]. These seven genes were also found to be related to immune response, tumorigenesis, or metabolic diseases. One study showed that LOC653486 is significantly associated with nasal polyposis and asthma, which are chronic inflammatory diseases [46]. Another revealed that PSMD13 is associated with the number of platelets, which are mediators in the immune and inflammatory response [47]. Two adjacent genes MPHOSPH9 and SBNO1, located in 12q24, were reported as having susceptible associations with type 2 diabetes [48, 49]. A study on the shared risk of schizophrenia and cardiometabolic diseases including obesity, body mass index, and type 2 diabetes suggested that MPHOSPH9, ARL6IP4, and SETD8 are pleiotropic risk genes [50]. SETD8, a subtype of lysine demethylase, was also studied to examine how its dysregulation is involved in the progression of various biological processes including tumorigenesis [51]. It was found that MRPS31 encodes ribosomal protein Imogen 38, which is a suggested target for autoimmune attack in type 1 diabetes [52]. In addition, we identified two novel susceptibility genes for UF, RP11-282O18.3 and KBTBD7 (Fig 2B and 2C). RP11-282O18.3 and KBTBD7 have been mentioned as being connected with immunological features and various diseases. RP11-282O18.3, a long non-coding RNA that likely affects non-allergic asthma, has been shown to be involved in the network comprising genes relevant to the estradiol phenotype of polycystic ovary syndrome (PCOS) [53]. Wise et al. described that PCOS patients had a higher UF incidence than healthy controls and suggested that PCOS and UF are positively correlated with each other [54]. Studies have revealed that KBTBD7 encodes a transcriptional activator forming a complex with cullin 3 to regulate the degradation of neurofibromin involved in the Ras/extracellular signal-regulated kinase pathway, playing crucial roles in the development of various malignant tumors in the event of dysfunction [55–57]. KBTBD7 has been shown to increase the transcription of activator protein-1 (AP-1) and serum response element (SRE) [55]. Increased transcriptions of AP-1 and SRE have been found to positively regulate the mitogen-activated protein kinase signaling pathway inducing inflammatory responses [55, 58]. Previous studies reported that microRNA-21 (miR-21), which is also observed in humans, induces Kbtbd7 mRNA degradation and inhibits the translation of Kbtbd7 in mice [59, 60]. Additionally, miR-21 was more expressed in UF samples than in normal myometrium samples [61]. Since our datasets do not contain data on microRNAs, we cannot be certain whether miR-21 played a role in KBTBD7 and UF pathogenesis at this point. However, we cannot rule out the possibility that miR-21 plays a major role in our findings that KBTBD7 is a novel susceptibility gene for UF. It may be worthwhile to perform an in silico and experimentation study on human UF patients and healthy control subjects side-by-side to prove our hypothesis. Together, we suggest that our study successfully identified robust novel TWAS genes that are putatively causal for the pathogenesis of UF.

In the functional annotation of TWAS genes, both positively and negatively associated TWAS genes of UF were involved in the immune system process (Fig 3). The clusters of the immune system process showed high connectivity scores from both enrichment networks composed of positively and negatively associated pathways, both scoring 10.00. TWAS genes from seven out of the eight tissue panels, all except the uterus panel, were enriched in the immune system process. Given this result, we believe that the immune system process pathway participates in the underlying pathogenesis of UF in multiple tissue levels. We identified that positively associated UF TWAS genes were enriched in the metabolic process, which may support the robustness of our functional analysis since metabolic syndromes are well-known risk factors for UF [62]. In addition, mitochondria participate in the central metabolic pathway, which explains the high connectivity score of the mitochondrial gene expression cluster (score: 10.00) in the enrichment networks of negatively associated pathways as well as the metabolic process cluster [63]. The metabolic process pathway was mainly enriched in the blood tissue panels, whereas the mitochondrial gene expression pathway was mostly detected in the uterus panel. It has been reported that the differential expression of mitochondrial progesterone receptors is associated with UF, since progesterone may affect the growth of UF by altering mitochondrial activity [64]. It is possible that the genetic variants of UF have tissue-specific effects on metabolic processes and mitochondrial gene expression pathways. Some of the other TWAS genes positively associated with UF were found to be enriched in pathways belonging to the parental pathway ‘localization’. A previous study reported that exposure to nonylphenol and di(2-ethylhexyl) phthalate modified the localization and colocalization patterns of uterine estrogen receptors and progesterone receptors, resulting in changes in the proliferation patterns of endometrial tissues [65]. We believe that the localization of cellular molecules, especially steroid receptors, may affect cell proliferation and induce the formation of UF. The localization cluster was only enriched in YFS blood panels, suggesting that their interference with the localization of intracellular material in the blood cells could be related to UF. The cell cycle cluster showed the highest connectivity score (score: 11.82) among the four major clusters enriched with negatively associated TWAS genes. Since the loss of cell cycle regulation is a critical characteristic of tumor progression, our GSEA results also explain why some UF patients develop tumors [66]. Finally, we identified the cellular component organization or biogenesis cluster, which has rarely been referenced in UF pathogenesis. Taken together, we found the immune system process, metabolic process, mitochondrial gene expression, localization, cell cycle, and cellular component organization as the key pathways that may be related to the pathophysiology of UF.

Using the CTD, we identified five toxic chemicals associated with our TWAS genes expected to be involved in the pathogenesis of UF (Table 2). The five chemicals comprising two drugs (cyclosporine and doxorubicin) and three chemicals (copper sulfate, aflatoxin B1, and BPDE), which enhance the expression levels of both positively and negatively associated TWAS genes, were previously implicated in female reproductive diseases such as endometrial cancer, ovarian cancer, and PCOS. Among these potential chemical hazards, copper sulfate had associations with four TWAS genes, implicating its detrimental effects on UF. Copper has been found to play an important role in tumor growth by promoting tumor angiogenesis and stimulating cell proliferation [67, 68]. A previous study showed that the detected level of serum copper was higher in women with hysteromyoma than in healthy controls and suggested that copper is interrelated with UF, known as a hysteromyoma disease [69]. The two drugs—cyclosporine and doxorubicin—have been indirectly associated with UFs in terms of female-specific diseases. Cyclosporine, used as an immunosuppressant, reportedly promotes tumor angiogenesis and causes fibroadenoma, which is positively associated with the pathogenesis of UF [70–72]. Doxorubicin is a treatment for uterine sarcoma but has cardiotoxicity that accelerates the risk of cardiovascular disease in some female breast cancer patients [73–75]. Cardiovascular risk factors are more prevalent in UF patients than in controls and it was suggested that there are common risk factors between cardiovascular disease and UF such as BMI and hypertension [76]. Thus, the use of these two drugs may indirectly increase the risk of UF. The other two chemicals—aflatoxin B1 and BPDE, which have been described as human carcinogens in previous studies—are regarded as potentially toxic chemicals and EDCs [41, 42, 77–80]. Aflatoxin B1 was reported to induce uterine damage in mice and estrogen synthesis by changing physiological aromatase functions, causing endocrine disruptors in the placenta [41, 77]. BPDE was revealed to be a benzo(a)pyrene metabolite that causes toxicity in various organs [78]. Exposure to benzo(a)pyrene reportedly affected the occurrence of infertility and ovarian cancer and increased the prevalence of UF in a female genital tract study [78, 81, 82]. Benzo(a)pyrene has also been shown to be a xenoestrogen that may affect the growth of UF by mimicking the effects of estrogen and acting as an estrogen receptor agonist in rat uterine leiomyoma cells [83–85]. BPDE treated in mice ovaries is also known to affect the suppression of steroidogenic enzymes and induce ovarian disorders [86]. Our data suggest that exposure to one or more of these chemicals may contribute to the occurrence of UF. Aflatoxin B1, known as a secondary fungal metabolite, is widely found in foods such as rotten nuts or dried fruits [87]. High concentrations of benzo(a)pyrene are found in heat-treated foods such as charcoal-grilled meat [88]. Based on our results, women with certain genetic backgrounds of UF combination with chronic dietary exposure to aflatoxin B1 and/or BPDE may show a higher risk of UF; although the actual effects of the exposure should be validated through experimental studies, it is also true that exposure to aflatoxin B1 and BPDE should always be avoided because they are toxic. Overall, our results suggest that these five toxic chemicals may increase the development of UF and their intake needs to be carefully monitored due to their various side effects.

Although this study contributed to comprehending the genetic and chemical risks associated with UF, several limitations remain to be addressed. Our TWAS was conducted on autosomes because the FUSION only implements sumstats-file formatting of GWAS data on autosomes, even though rearrangements of the X chromosome were reportedly implicated in UF [38]. Since complicated biological phenomena at the X chromosome such as mosaic inactivation may play a crucial role in UF pathogenesis, further studies on sex chromosomes are also warranted to investigate their overall genetic influences on UF etiologies when the technology becomes available. The GTEx panels contain a significant proportion of samples from women aged 50–70 years who are post-menopausal, whereas the majority of cases of UFs occur in pre-menopausal women. As the tissue panels used in our study did not consist only of age groups with a high UF risk, age-related statistical specificity was reduced. In addition, since the panels used in this study were single-tissue eQTL panels, the statistical power may have been insufficient to detect all true associations. This lack of resources may have contributed to the discrepancy between the number of significant loci in our study and previous UF GWASs. Although it is difficult to address this issue immediately due to a lack of resources, increasing the sample size of UF-related tissue panels or publication of robust multi-content panels may allow the detection of more genotype–gene expression associations for UFs. Even though we identified significant TWAS genes as potential causal genes of UF and confirmed the robustness of this result through the colocalization tests, three genes including our novel findings, RP11-282O18.3 and KBTBD7, were not replicated in the colocalization results. Therefore, the actual effects of our significant TWAS genes on UF risk should be validated through experimental studies. We detected five toxic chemicals that may increase the pathogenesis of UF; however, their effects under physiological conditions should be validated since the results were obtained using in silico analyses. Despite these limitations, we believe that this study successfully identified TWAS genes associated with UF risks and potentially toxic chemicals expected to influence TWAS genes, which suggests that our results may contribute to a deeper understanding of UF etiologies and provide informative notifications of potentially risky chemicals associated with UF.

Supporting information

S1 Fig. Circos plots showing how marginally significant TWAS gene lists (P < 0.05) for each of the eight eQTL panels overlap.

On the outside, the arc represents the eight eQTL panels. On the inside, the dark orange arc represents genes shared by several panels and the light orange arc represents genes unique to those panels. The purple lines link the genes shared by several panels. (A) The plot shows the shared genes between positively associated TWAS genes (TWAS Z-score > 0). (B) The plot shows shared genes between negatively associated TWAS genes (TWAS Z-score < 0).

(TIF)

Click here for additional data file. (2MB, tif)
S2 Fig. Manhattan plots of the UF GWAS summary statistics data analyzed and visualized by FUMA.

(A) A Manhattan plot of the input GWAS summary statistics. (B) A Manhattan plot of the MAGMA results. Significant prioritized genes associated with SNPs are visualized with gene symbols. The dashed red line indicates a Bonferroni significant threshold (P < 1.90 × 10−6).

(TIF)

Click here for additional data file. (943.3KB, tif)
S3 Fig. A Venn diagram showing the overlap between UF-associated genes discovered by TWAS analysis, SNP2GENE process, and MAGMA.

The number of genes only identified in the FUSION is highlighted in bold.

(TIF)

Click here for additional data file. (556.3KB, tif)
S4 Fig. A ternary plot of colocalization test results.

PP0–4 indicate PPs of five hypotheses (H0–4). The gray dots are the genes that were not significant in either TWAS or the colocalization tests. The red and blue dots indicate the significantly associated genes in TWAS and the colocalization tests, respectively. The genes that were prioritized in both TWAS and the colocalization tests are represented as purple dots.

(TIFF)

Click here for additional data file. (108.8KB, tiff)
S1 Table. Every TWAS association from the eight tissue panels.

(XLSX)

Click here for additional data file. (5MB, xlsx)
S2 Table. UF-associated genes discovered by the FUMA-SNP2GENE process.

(XLSX)

Click here for additional data file. (81KB, xlsx)
S3 Table. Colocalization test results of COLOC-prioritized and significant TWAS genes.

(XLSX)

Click here for additional data file. (11.4KB, xlsx)
S4 Table. Gene sets enriched with positively associated TWAS genes provided by the Metascape.

(XLSX)

Click here for additional data file. (221.3KB, xlsx)
S5 Table. Gene sets enriched with negatively associated TWAS genes provided by the Metascape.

(XLSX)

Click here for additional data file. (221.1KB, xlsx)
S6 Table. Chemicals correlated with the significant TWAS genes of UF.

(XLSX)

Click here for additional data file. (76.6KB, xlsx)
S7 Table. Chemicals that increase the expression levels of significant TWAS genes positively associated with UF.

(XLSX)

Click here for additional data file. (74.7KB, xlsx)
S8 Table. Chemicals that decrease the expression levels of significant TWAS genes negatively associated with UF.

(XLSX)

Click here for additional data file. (73.6KB, xlsx)

Acknowledgments

The authors appreciate the researchers who deposited their data in public database.

Data Availability

All results are available as Supporting Information files, and the source files can be obtained from the TWAS/FUSION webpage (http://gusevlab.org/projects/fusion/) which is freely accessible.

Funding Statement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1A2C1008804). This work was supported by the Dongguk University Research Fund of 2020. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Ahrendt HJ, Tylkoski H, Rabe T, Szczes A, Friedrich C, Roehl FW, et al. Prevalence of uterine myomas in women in Germany: data of an epidemiological study. Arch Gynecol Obstet. 2016;293(6):1243–53. doi: 10.1007/s00404-015-3930-8 [DOI] [PubMed] [Google Scholar]
  • 2.Giuliani E, As-Sanie S, Marsh EE. Epidemiology and management of uterine fibroids. Int J Gynaecol Obstet. 2020;149(1):3–9. doi: 10.1002/ijgo.13102 [DOI] [PubMed] [Google Scholar]
  • 3.Al Ansari AA, Al Hail FA, Abboud E. Malignant transformation of uterine leiomyoma. Qatar Med J. 2012;2012(2):71–4. doi: 10.5339/qmj.2012.2.16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Parker NA, Dakhil CSR, Dakhil SR, Lalich D. Metastasis of Benign Leiomyomas Outside the Uterus. Kans J Med. 2018;11(2):1–11. [PMC free article] [PubMed] [Google Scholar]
  • 5.Luoto R, Kaprio J, Rutanen EM, Taipale P, Perola M, Koskenvuo M. Heritability and risk factors of uterine fibroids—the Finnish Twin Cohort study. Maturitas. 2000;37(1):15–26. doi: 10.1016/s0378-5122(00)00160-2 [DOI] [PubMed] [Google Scholar]
  • 6.Hodge JC, Morton CC. Genetic heterogeneity among uterine leiomyomata: insights into malignant progression. Hum Mol Genet. 2007;16 Spec No 1:R7–13. doi: 10.1093/hmg/ddm043 [DOI] [PubMed] [Google Scholar]
  • 7.Klemke M, Meyer A, Nezhad MH, Bartnitzke S, Drieschner N, Frantzen C, et al. Overexpression of HMGA2 in uterine leiomyomas points to its general role for the pathogenesis of the disease. Genes Chromosomes Cancer. 2009;48(2):171–8. doi: 10.1002/gcc.20627 [DOI] [PubMed] [Google Scholar]
  • 8.Lehtonen R, Kiuru M, Vanharanta S, Sjoberg J, Aaltonen LM, Aittomaki K, et al. Biallelic inactivation of fumarate hydratase (FH) occurs in nonsyndromic uterine leiomyomas but is rare in other tumors. Am J Pathol. 2004;164(1):17–22. doi: 10.1016/S0002-9440(10)63091-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Tomlinson IP, Alam NA, Rowan AJ, Barclay E, Jaeger EE, Kelsell D, et al. Germline mutations in FH predispose to dominantly inherited uterine fibroids, skin leiomyomata and papillary renal cell cancer. Nat Genet. 2002;30(4):406–10. doi: 10.1038/ng849 [DOI] [PubMed] [Google Scholar]
  • 10.Mehine M, Kaasinen E, Heinonen HR, Makinen N, Kampjarvi K, Sarvilinna N, et al. Integrated data analysis reveals uterine leiomyoma subtypes with distinct driver pathways and biomarkers. Proc Natl Acad Sci U S A. 2016;113(5):1315–20. doi: 10.1073/pnas.1518752113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Marsh EE, Bulun SE. Steroid hormones and leiomyomas. Obstet Gynecol Clin North Am. 2006;33(1):59–67. doi: 10.1016/j.ogc.2005.12.001 [DOI] [PubMed] [Google Scholar]
  • 12.Sato F, Nishi M, Kudo R, Miyake H. Body fat distribution and uterine leiomyomas. J Epidemiol. 1998;8(3):176–80. doi: 10.2188/jea.8.176 [DOI] [PubMed] [Google Scholar]
  • 13.Boynton-Jarrett R, Rich-Edwards J, Malspeis S, Missmer SA, Wright R. A prospective study of hypertension and risk of uterine leiomyomata. Am J Epidemiol. 2005;161(7):628–38. doi: 10.1093/aje/kwi072 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Shen Y, Xu Q, Xu J, Ren ML, Cai YL. Environmental exposure and risk of uterine leiomyoma: an epidemiologic survey. Eur Rev Med Pharmacol Sci. 2013;17(23):3249–56. [PubMed] [Google Scholar]
  • 15.Borahay MA, Asoglu MR, Mas A, Adam S, Kilic GS, Al-Hendy A. Estrogen Receptors and Signaling in Fibroids: Role in Pathobiology and Therapeutic Implications. Reprod Sci. 2017;24(9):1235–44. doi: 10.1177/1933719116678686 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.McWilliams MM, Chennathukuzhi VM. Recent Advances in Uterine Fibroid Etiology. Semin Reprod Med. 2017;35(2):181–9. doi: 10.1055/s-0037-1599090 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Sun K, Xie Y, Zhao N, Li Z. A case-control study of the relationship between visceral fat and development of uterine fibroids. Exp Ther Med. 2019;18(1):404–10. doi: 10.3892/etm.2019.7575 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Faerstein E, Szklo M, Rosenshein NB. Risk factors for uterine leiomyoma: a practice-based case-control study. II. Atherogenic risk factors and potential sources of uterine irritation. Am J Epidemiol. 2001;153(1):11–9. doi: 10.1093/aje/153.1.11 [DOI] [PubMed] [Google Scholar]
  • 19.Katz TA, Yang Q, Trevino LS, Walker CL, Al-Hendy A. Endocrine-disrupting chemicals and uterine fibroids. Fertil Steril. 2016;106(4):967–77. doi: 10.1016/j.fertnstert.2016.08.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Cha PC, Takahashi A, Hosono N, Low SK, Kamatani N, Kubo M, et al. A genome-wide association study identifies three loci associated with susceptibility to uterine fibroids. Nat Genet. 2011;43(5):447–50. doi: 10.1038/ng.805 [DOI] [PubMed] [Google Scholar]
  • 21.Zhang K, Wiener H, Aissani B. Admixture mapping of genetic variants for uterine fibroids. J Hum Genet. 2015;60(9):533–8. doi: 10.1038/jhg.2015.60 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Gallagher CS, Makinen N, Harris HR, Rahmioglu N, Uimari O, Cook JP, et al. Genome-wide association and epidemiological analyses reveal common genetic origins between uterine leiomyomata and endometriosis. Nat Commun. 2019;10(1):4857. doi: 10.1038/s41467-019-12536-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Consortium GT, Laboratory DA, Coordinating Center -Analysis Working G, Statistical Methods groups-Analysis Working G, Enhancing Gg, Fund NIHC, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204–13. doi: 10.1038/nature24277 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48(3):245–52. doi: 10.1038/ng.3506 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Wright FA, Sullivan PF, Brooks AI, Zou F, Sun W, Xia K, et al. Heritability and genomics of gene expression in peripheral blood. Nat Genet. 2014;46(5):430–7. doi: 10.1038/ng.2951 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Raitakari OT, Juonala M, Ronnemaa T, Keltikangas-Jarvinen L, Rasanen L, Pietikainen M, et al. Cohort profile: the cardiovascular risk in Young Finns Study. Int J Epidemiol. 2008;37(6):1220–6. doi: 10.1093/ije/dym225 [DOI] [PubMed] [Google Scholar]
  • 27.Genomes Project C, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. doi: 10.1038/nature15393 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Song J, Kim D, Lee S, Jung J, Joo JWJ, Jang W. Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning. Commun Biol. 2022;5(1):615. doi: 10.1038/s42003-022-03564-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Lokki ML, Paakkanen R. The complexity and diversity of major histocompatibility complex challenge disease association studies. HLA. 2019;93(1):3–15. doi: 10.1111/tan.13429 [DOI] [PubMed] [Google Scholar]
  • 30.Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826. doi: 10.1038/s41467-017-01261-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11(4):e1004219. doi: 10.1371/journal.pcbi.1004219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383. doi: 10.1371/journal.pgen.1004383 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Li YI, Wong G, Humphrey J, Raj T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat Commun. 2019;10(1):994. doi: 10.1038/s41467-019-08912-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. doi: 10.1101/gr.1239303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2. doi: 10.1186/1471-2105-4-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Davis AP, Wiegers TC, Wiegers J, Grondin CJ, Johnson RJ, Sciaky D, et al. CTD Anatomy: analyzing chemical-induced phenotypes and exposures from an anatomical perspective, with implications for environmental health studies. Curr Res Toxicol. 2021;2:128–39. doi: 10.1016/j.crtox.2021.03.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Medikare V, Kandukuri LR, Ananthapur V, Deenadayal M, Nallari P. The genetic bases of uterine fibroids; a review. J Reprod Infertil. 2011;12(3):181–91. [PMC free article] [PubMed] [Google Scholar]
  • 39.Pandis N, Bardi G, Sfikas K, Panayotopoulos N, Tserkezoglou A, Fotiou S. Complex chromosome rearrangements involving 12q14 in two uterine leiomyomas. Cancer Genet Cytogenet. 1990;49(1):51–6. doi: 10.1016/0165-4608(90)90163-5 [DOI] [PubMed] [Google Scholar]
  • 40.Nibert M, Heim S. Uterine leiomyoma cytogenetics. Genes Chromosomes Cancer. 1990;2(1):3–13. doi: 10.1002/gcc.2870020103 [DOI] [PubMed] [Google Scholar]
  • 41.Storvik M, Huuskonen P, Kyllonen T, Lehtonen S, El-Nezami H, Auriola S, et al. Aflatoxin B1—a potential endocrine disruptor—up-regulates CYP19A1 in JEG-3 cells. Toxicol Lett. 2011;202(3):161–7. doi: 10.1016/j.toxlet.2011.01.028 [DOI] [PubMed] [Google Scholar]
  • 42.Yang W, Zhang G, Jiang F, Zeng Y, Zou P, An H, et al. BPDE and B[a]P induce mitochondrial compromise by ROS-mediated suppression of the SIRT1/TERT/PGC-1alpha pathway in spermatogenic cells both in vitro and in vivo. Toxicol Appl Pharmacol. 2019;376:17–37. [DOI] [PubMed] [Google Scholar]
  • 43.Edwards TL, Giri A, Hellwege JN, Hartmann KE, Stewart EA, Jeff JM, et al. A Trans-Ethnic Genome-Wide Association Study of Uterine Fibroids. Front Genet. 2019;10:511. doi: 10.3389/fgene.2019.00511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE, Torres JM, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9(1):1825. doi: 10.1038/s41467-018-03621-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Kho PF, Mortlock S, Endometrial Cancer Association C, International Endometriosis Genetics C, Rogers PAW, Nyholt DR, et al. Genetic analyses of gynecological disease identify genetic relationships between uterine fibroids and endometrial cancer, and a novel endometrial cancer genetic risk region at the WNT4 1p36.12 locus. Hum Genet. 2021;140(9):1353–65. doi: 10.1007/s00439-021-02312-0 [DOI] [PubMed] [Google Scholar]
  • 46.Ozdas S, Izbirak A, Ozdas T, Ozcan KM, Erbek SS, Koseoglu S, et al. Single-Nucleotide Polymorphisms on the RYD5 Gene in Nasal Polyposis. DNA Cell Biol. 2015;34(10):633–42. doi: 10.1089/dna.2015.2897 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Shameer K, Denny JC, Ding K, Jouni H, Crosslin DR, de Andrade M, et al. A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Hum Genet. 2014;133(1):95–109. doi: 10.1007/s00439-013-1355-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Matsuba R, Imamura M, Tanaka Y, Iwata M, Hirose H, Kaku K, et al. Replication Study in a Japanese Population of Six Susceptibility Loci for Type 2 Diabetes Originally Identified by a Transethnic Meta-Analysis of Genome-Wide Association Studies. PLoS One. 2016;11(4):e0154093. doi: 10.1371/journal.pone.0154093 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Mahajan A, Wessel J, Willems SM, Zhao W, Robertson NR, Chu AY, et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet. 2018;50(4):559–71. doi: 10.1038/s41588-018-0084-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Liu H, Sun Y, Zhang X, Li S, Hu D, Xiao L, et al. Integrated Analysis of Summary Statistics to Identify Pleiotropic Genes and Pathways for the Comorbidity of Schizophrenia and Cardiometabolic Disease. Front Psychiatry. 2020;11:256. doi: 10.3389/fpsyt.2020.00256 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Hamamoto R, Saloura V, Nakamura Y. Critical roles of non-histone protein lysine methylation in human tumorigenesis. Nat Rev Cancer. 2015;15(2):110–24. doi: 10.1038/nrc3884 [DOI] [PubMed] [Google Scholar]
  • 52.Arden SD, Roep BO, Neophytou PI, Usac EF, Duinkerken G, de Vries RR, et al. Imogen 38: a novel 38-kD islet mitochondrial autoantigen recognized by T cells from a newly diagnosed type 1 diabetic patient. J Clin Invest. 1996;97(2):551–61. doi: 10.1172/JCI118448 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Jiao J, Shi B, Wang T, Fang Y, Cao T, Zhou Y, et al. Characterization of long non-coding RNA and messenger RNA profiles in follicular fluid from mature and immature ovarian follicles of healthy women and women with polycystic ovary syndrome. Hum Reprod. 2018;33(9):1735–48. doi: 10.1093/humrep/dey255 [DOI] [PubMed] [Google Scholar]
  • 54.Wise LA, Palmer JR, Stewart EA, Rosenberg L. Polycystic ovary syndrome and risk of uterine leiomyomata. Fertil Steril. 2007;87(5):1108–15. doi: 10.1016/j.fertnstert.2006.11.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Hu J, Yuan W, Tang M, Wang Y, Fan X, Mo X, et al. KBTBD7, a novel human BTB-kelch protein, activates transcriptional activities of SRE and AP-1. BMB Rep. 2010;43(1):17–22. doi: 10.5483/bmbrep.2010.43.1.017 [DOI] [PubMed] [Google Scholar]
  • 56.Guo YJ, Pan WW, Liu SB, Shen ZF, Xu Y, Hu LL. ERK/MAPK signalling pathway and tumorigenesis. Exp Ther Med. 2020;19(3):1997–2007. doi: 10.3892/etm.2020.8454 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Hollstein PE, Cichowski K. Identifying the Ubiquitin Ligase complex that regulates the NF1 tumor suppressor and Ras. Cancer Discov. 2013;3(8):880–93. doi: 10.1158/2159-8290.CD-13-0146 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Kim HK. Role of ERK/MAPK signalling pathway in anti-inflammatory effects of Ecklonia cava in activated human mast cell line-1 cells. Asian Pac J Trop Med. 2014;7(9):703–8. [Google Scholar]
  • 59.Yang L, Wang B, Zhou Q, Wang Y, Liu X, Liu Z, et al. MicroRNA-21 prevents excessive inflammation and cardiac dysfunction after myocardial infarction through targeting KBTBD7. Cell Death Dis. 2018;9(7):769. doi: 10.1038/s41419-018-0805-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Song J, Wu Q, Jiang J, Sun D, Wang F, Xin B, et al. Berberine reduces inflammation of human dental pulp fibroblast via miR-21/KBTBD7 axis. Arch Oral Biol. 2020;110:104630. doi: 10.1016/j.archoralbio.2019.104630 [DOI] [PubMed] [Google Scholar]
  • 61.Marsh EE, Lin Z, Yin P, Milad M, Chakravarti D, Bulun SE. Differential expression of microRNA species in human uterine leiomyoma versus normal myometrium. Fertil Steril. 2008;89(6):1771–6. doi: 10.1016/j.fertnstert.2007.05.074 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Tak YJ, Lee SY, Park SK, Kim YJ, Lee JG, Jeong DW, et al. Association between uterine leiomyoma and metabolic syndrome in parous premenopausal women: A case-control study. Medicine (Baltimore). 2016;95(46):e5325. doi: 10.1097/MD.0000000000005325 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Annesley SJ, Fisher PR. Mitochondria in Health and Disease. Cells. 2019;8(7). doi: 10.3390/cells8070680 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Feng Q, Crochet JR, Dai Q, Leppert PC, Price TM. Expression of a mitochondrial progesterone receptor (PR-M) in leiomyomata and association with increased mitochondrial membrane potential. J Clin Endocrinol Metab. 2014;99(3):E390–9. doi: 10.1210/jc.2013-2008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Kim J, Cha S, Lee MY, Hwang YJ, Yang E, Choi D, et al. Chronic and Low Dose Exposure to Nonlyphenol or Di(2-Ethylhexyl) Phthalate Alters Cell Proliferation and the Localization of Steroid Hormone Receptors in Uterine Endometria in Mice. Dev Reprod. 2019;23(3):263–75. doi: 10.12717/DR.2019.23.3.263 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Pucci B, Kasten M, Giordano A. Cell cycle and apoptosis. Neoplasia. 2000;2(4):291–9. doi: 10.1038/sj.neo.7900101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Lowndes SA, Harris AL. The role of copper in tumour angiogenesis. J Mammary Gland Biol Neoplasia. 2005;10(4):299–310. doi: 10.1007/s10911-006-9003-7 [DOI] [PubMed] [Google Scholar]
  • 68.McAuslan BR, Reilly W. Endothelial cell phagokinesis in response to specific metal ions. Exp Cell Res. 1980;130(1):147–57. doi: 10.1016/0014-4827(80)90051-8 [DOI] [PubMed] [Google Scholar]
  • 69.He Q, Ma RH, Tang Y. [Determination of trace element Cu, Zn, Mg, Cr in serum of women with barrenness and hysteromyoma disease]. Guang Pu Xue Yu Guang Pu Fen Xi. 2002;22(4):685–6. [PubMed] [Google Scholar]
  • 70.Baildam AD, Higgins RM, Hurley E, Furlong A, Walls J, Venning MC, et al. Cyclosporin A and multiple fibroadenomas of the breast. Br J Surg. 1996;83(12):1755–7. doi: 10.1002/bjs.1800831229 [DOI] [PubMed] [Google Scholar]
  • 71.Spinos N, Terzis G, Crysanthopoulou A, Adonakis G, Markou KB, Vervita V, et al. Increased frequency of thyroid nodules and breast fibroadenomas in women with uterine fibroids. Thyroid. 2007;17(12):1257–9. doi: 10.1089/thy.2006.0330 [DOI] [PubMed] [Google Scholar]
  • 72.Zhou AY, Ryeom S. Cyclosporin A promotes tumor angiogenesis in a calcineurin-independent manner by increasing mitochondrial reactive oxygen species. Mol Cancer Res. 2014;12(11):1663–76. doi: 10.1158/1541-7786.MCR-14-0136 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Zhao L, Zhang B. Doxorubicin induces cardiotoxicity through upregulation of death receptors mediated apoptosis in cardiomyocytes. Sci Rep. 2017;7:44735. doi: 10.1038/srep44735 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Burridge PW, Li YF, Matsa E, Wu H, Ong SG, Sharma A, et al. Human induced pluripotent stem cell-derived cardiomyocytes recapitulate the predilection of breast cancer patients to doxorubicin-induced cardiotoxicity. Nat Med. 2016;22(5):547–56. doi: 10.1038/nm.4087 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Clark RA, Marin TS, Berry NM, Atherton JJ, Foote JW, Koczwara B. Cardiotoxicity and cardiovascular disease risk assessment for patients receiving breast cancer treatment. Cardiooncology. 2017;3:6. doi: 10.1186/s40959-017-0025-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Laughlin-Tommaso SK, Fuchs EL, Wellons MF, Lewis CE, Calderon-Margalit R, Stewart EA, et al. Uterine Fibroids and the Risk of Cardiovascular Disease in the Coronary Artery Risk Development in Young Adult Women’s Study. J Womens Health (Larchmt). 2019;28(1):46–52. doi: 10.1089/jwh.2018.7122 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Li Y, Huang P, Gao F, Xiao X, Li L. Selenium ameliorates aflatoxin B1-induced uterine injury in female mice and necrosis of human endometrial microvascular endothelial cells. J Appl Toxicol. 2021;41(5):799–810. doi: 10.1002/jat.4120 [DOI] [PubMed] [Google Scholar]
  • 78.Rekhadevi PV, Diggs DL, Huderson AC, Harris KL, Archibong AE, Ramesh A. Metabolism of the environmental toxicant benzo(a)pyrene by subcellular fractions of human ovary. Hum Exp Toxicol. 2014;33(2):196–202. doi: 10.1177/0960327113489050 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Liang Z, Lippman SM, Kawabe A, Shimada Y, Xu XC. Identification of benzo(a)pyrene diol epoxide-binding DNA fragments using DNA immunoprecipitation technique. Cancer Res. 2003;63(7):1470–4. [PubMed] [Google Scholar]
  • 80.Marchese S, Polo A, Ariano A, Velotto S, Costantini S, Severino L. Aflatoxin B1 and M1: Biological Properties and Their Involvement in Cancer Development. Toxins (Basel). 2018;10(6). doi: 10.3390/toxins10060214 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Zepiridis LI, Grimbizis GF, Tarlatzis BC. Infertility and uterine fibroids. Best Pract Res Clin Obstet Gynaecol. 2016;34:66–73. doi: 10.1016/j.bpobgyn.2015.12.001 [DOI] [PubMed] [Google Scholar]
  • 82.Tseng JJ, Huang CC, Chiang HY, Chen YH, Lin CH. Prior uterine myoma and risk of ovarian cancer: a population-based case-control study. J Gynecol Oncol. 2019;30(5):e72. doi: 10.3802/jgo.2019.30.e72 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Tsai KS, Yang RS, Liu SH. Benzo[a]pyrene regulates osteoblast proliferation through an estrogen receptor-related cyclooxygenase-2 pathway. Chem Res Toxicol. 2004;17(5):679–84. doi: 10.1021/tx0499517 [DOI] [PubMed] [Google Scholar]
  • 84.Hodges LC, Hunter DS, Bergerson JS, Fuchs-Young R, Walker CL. An in vivo/in vitro model to assess endocrine disrupting activity of xenoestrogens in uterine leiomyoma. Ann N Y Acad Sci. 2001;948:100–11. doi: 10.1111/j.1749-6632.2001.tb03991.x [DOI] [PubMed] [Google Scholar]
  • 85.Hodges LC, Bergerson JS, Hunter DS, Walker CL. Estrogenic effects of organochlorine pesticides on uterine leiomyoma cells in vitro. Toxicol Sci. 2000;54(2):355–64. doi: 10.1093/toxsci/54.2.355 [DOI] [PubMed] [Google Scholar]
  • 86.Xu H, Mu X, Ding Y, Tan Q, Liu X, He J, et al. Melatonin alleviates benzo(a)pyrene-induced ovarian corpus luteum dysfunction by suppressing excessive oxidative stress and apoptosis. Ecotoxicol Environ Saf. 2021;207:111561. doi: 10.1016/j.ecoenv.2020.111561 [DOI] [PubMed] [Google Scholar]
  • 87.Ostadrahimi A, Ashrafnejad F, Kazemi A, Sargheini N, Mahdavi R, Farshchian M, et al. Aflatoxin in raw and salt-roasted nuts (pistachios, peanuts and walnuts) sold in markets of tabriz, iran. Jundishapur J Microbiol. 2014;7(1):e8674. doi: 10.5812/jjm.8674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Chen YH, Xia EQ, Xu XR, Li S, Ling WH, Wu S, et al. Evaluation of benzo[a]pyrene in food from China by high-performance liquid chromatography-fluorescence detection. Int J Environ Res Public Health. 2012;9(11):4159–69. doi: 10.3390/ijerph9114159 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Dylan Glubb

9 Jun 2022

PONE-D-22-12765A transcriptome-wide association study of uterine fibroids to identify potential genetic markers and toxic chemicalsPLOS ONE

Dear Dr. Jang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Several important issues were raised by Reviewer 2 and the authors should address all their comments to improve the quality of the study. Particular attention should be paid to comment #9: LD contamination is a major issue for TWAS and leads to spurious associations. Colocalisation analysis should be performed using the corresponding eQTL data to prioritise for causal TWAS associations.

Please submit your revised manuscript by Jul 24 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Dylan Glubb

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. 

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

3. Thank you for stating the following in the Acknowledgments Section of your manuscript: 

"This work was supported by the Dongguk University Research Fund of 2020 and 2021 (S-2021-G0001-00094)."

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. 

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: 

"The author(s) received no specific funding for this work."

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors compile together publicly available GWAS summary statistics with other publicly available sources of gene expression data. The study is driven by material derived from public databases and readily available in silico data-analysis methods. Overall, their choice of methods appear reasonable, and the presentation is adequate. The material has several limitations, some of which are already mentioned in the discussion.

Minor comments:

i) Discuss the limitations of the material used: first, the age distribution of the tissue material does not appear suitable, simply because GTEx tissues originate predominately from 50-70 year-old individuals (postmenopausal individuals), while uterine fibroids typically occur premenopausal. Second, discuss limitations of statistical power (see also my next comment).

ii) Discuss the discrepancy between the large number of significant GWAS loci and small number of significant TWAS loci. The former is more than 20 loci (according to Supplementary Fig S4 and many previous UF GWAS studies), while the latter, your novel results, originate from just three distinct loci. - Is it the lack of power in the GTEx material which explains that majority of the GWAS hits do not show TWAS?

Reviewer #2: The manuscript by Kim et al describes transcriptome-wide analyses of uterine fibroid risk with extensions to chemical target identification. The work is interesting and leverages many publicly available resources. The results are interesting, however, the organization needs some adjustment. Mainly there are several paragraphs which would be better housed in the discussion, which would then likely need to be shortened considerably. There are also some issues of clarity throughout.

Specific Comments:

1. Lines 48-58: The 2nd paragraph of the introduction mentions genetic risk factors, but then proceeds to predominantly discuss cytogenetic changes observed in fibroid tumors, which are not necessarily risk factors as they can only be observed once a tumor has formed. It may be better to separate the heritability concept from the cytogenetic alterations and instead place it with the germline genetic analyses which are actually evaluating disease risk.

2. Lines 69-72: The GWAS paragraph includes mention of one GWAS (out of at least 9 that have been performed) and one admixture mapping study, neither of which contain the results which were used in the present manuscript.

3. Line 81: The Gallagher et al paper that produced the GWAS results investigated in this paper but which is not cited directly is not the most recent GWAS.

4. Line 115: it is unclear whether the 26,316 represents the total number of unique genes across all 8 tissues, or the sum of genes predicted in each tissue (the technical number of actual tests)

5. Are the joint and conditional tests of TWAS loci performed within FUSION or was there another software/analysis package used for these steps?

6. Line 152: What does CTD stand for?

7. The chemical-gene interaction analysis methods need a bit of rewording to make a couple of items clear. Line 155: chemicals that may induce development of fibroids. Lines 159-160: “the expression caused by the UF” – the gene expressions you have predicted are based on risk OF developing a fibroid, and the tissues are not all from uterus, let alone from the fibroid itself, so this needs to be reworded to be more accurate.

8. The results should indicate that only one of the nine genes was significant in uterus, and that all of the rest were significant in only blood (what is the rationale for including three different whole blood tissue sets? The results do not seem consistent between the three).

9. It doesn’t appear that any colocalization methods were used to eliminate the potential for LD contamination driving the observed associations in TWAS. This should be considered for robustness.

10. The paragraph beginning on line 203 should be moved to discussion, it is not presentation of results.

11. How does the conditional analysis handle the differing tissues from which the expression is associated? How were the mentioned SNPs (lines 231, 234, 257) selected? Lead SNPs in the region from FUMA, or known eQTLs for the genes of interest?

12. Numbers should be included for the numbers of genes included in the up- and down-regulated subsets (lines 273 and 274)

13. A large portion of the paragraph beginning on line 299 should be moved to discussion.

14. The statement on line 346 is overstated in the absence of experimental or observational data.

15. The paragraphs beginning on line 352 and line 360 seem more like discussion.

16. The tissues from which the significant results from the previous study’s TWAS came from should be noted.

17. A 7% sample size increase at the scale of 200 thousand participants seems like a stretch to claim increased power for detection. It is notable that the 5 of the 10 significant results came from tissue sources not analyzed in the previous publication, and likely that the differing software and reduced multiple-testing burden in your paper also contributed to the differences in findings.

18. The sentence on lines 426-427 doesn’t make sense.

19. The paragraph beginning on line 436 has 10 lines about a chemical you excluded from results, this seems unnecessary to interpretation of the current results.

20. The sentence on lines 464-465 recommending that women with certain genetic profiles should avoid dietary chemical exposures is a complete overstatement. At MOST this should be presented as a relationship which should be evaluated in model organism experimental or human observational studies well before guidelines should be presented to patients, who most likely do not know what the risk “genetic backgrounds of UF” are, let alone whether they carry such profiles.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2022 Sep 29;17(9):e0274879. doi: 10.1371/journal.pone.0274879.r002

Author response to Decision Letter 0

5 Aug 2022

We appreciate all of the helpful comments about our work. Detailed responses and cover letters are provided in the attached file.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (324.8KB, docx)

Decision Letter 1

Dylan Glubb

7 Sep 2022

A transcriptome-wide association study of uterine fibroids to identify potential genetic markers and toxic chemicals

PONE-D-22-12765R1

Dear Dr. Jang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Dylan Glubb

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The manuscript is provisionally accepted but the authors need to revise the following sentence (as per Reviewer 2's comments): "Based on our results, women with certain genetic backgrounds of UF due to chronic dietary exposure to aflatoxin B1 and/or BPDE may show a higher risk of UF;". As the reviewer states "the genetic backgrounds of UF are likely not due to the exposure, but rather I believe the authors are trying to state that those genetic backgrounds in combination with the exposure may increase risk" and thus the sentence should be revised accordingly.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: The authors have adequately addressed all of my comments. My one very minor remaining comment is related to the revised sentence in the discussion "Based on our results, women with certain genetic

backgrounds of UF due to chronic dietary exposure to aflatoxin B1 and/or BPDE may show a

higher risk of UF;". The genetic backgrounds of UF are likely not due to the exposure, but rather I believe the authors are trying to state that those genetic backgrounds in combination with the exposure may increase risk

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

Acceptance letter

Dylan Glubb

20 Sep 2022

PONE-D-22-12765R1

A transcriptome-wide association study of uterine fibroids to identify potential genetic markers and toxic chemicals

Dear Dr. Jang:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Dylan Glubb

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Circos plots showing how marginally significant TWAS gene lists (P < 0.05) for each of the eight eQTL panels overlap.

    On the outside, the arc represents the eight eQTL panels. On the inside, the dark orange arc represents genes shared by several panels and the light orange arc represents genes unique to those panels. The purple lines link the genes shared by several panels. (A) The plot shows the shared genes between positively associated TWAS genes (TWAS Z-score > 0). (B) The plot shows shared genes between negatively associated TWAS genes (TWAS Z-score < 0).

    (TIF)

    Click here for additional data file. (2MB, tif)
    S2 Fig. Manhattan plots of the UF GWAS summary statistics data analyzed and visualized by FUMA.

    (A) A Manhattan plot of the input GWAS summary statistics. (B) A Manhattan plot of the MAGMA results. Significant prioritized genes associated with SNPs are visualized with gene symbols. The dashed red line indicates a Bonferroni significant threshold (P < 1.90 × 10−6).

    (TIF)

    Click here for additional data file. (943.3KB, tif)
    S3 Fig. A Venn diagram showing the overlap between UF-associated genes discovered by TWAS analysis, SNP2GENE process, and MAGMA.

    The number of genes only identified in the FUSION is highlighted in bold.

    (TIF)

    Click here for additional data file. (556.3KB, tif)
    S4 Fig. A ternary plot of colocalization test results.

    PP0–4 indicate PPs of five hypotheses (H0–4). The gray dots are the genes that were not significant in either TWAS or the colocalization tests. The red and blue dots indicate the significantly associated genes in TWAS and the colocalization tests, respectively. The genes that were prioritized in both TWAS and the colocalization tests are represented as purple dots.

    (TIFF)

    Click here for additional data file. (108.8KB, tiff)
    S1 Table. Every TWAS association from the eight tissue panels.

    (XLSX)

    Click here for additional data file. (5MB, xlsx)
    S2 Table. UF-associated genes discovered by the FUMA-SNP2GENE process.

    (XLSX)

    Click here for additional data file. (81KB, xlsx)
    S3 Table. Colocalization test results of COLOC-prioritized and significant TWAS genes.

    (XLSX)

    Click here for additional data file. (11.4KB, xlsx)
    S4 Table. Gene sets enriched with positively associated TWAS genes provided by the Metascape.

    (XLSX)

    Click here for additional data file. (221.3KB, xlsx)
    S5 Table. Gene sets enriched with negatively associated TWAS genes provided by the Metascape.

    (XLSX)

    Click here for additional data file. (221.1KB, xlsx)
    S6 Table. Chemicals correlated with the significant TWAS genes of UF.

    (XLSX)

    Click here for additional data file. (76.6KB, xlsx)
    S7 Table. Chemicals that increase the expression levels of significant TWAS genes positively associated with UF.

    (XLSX)

    Click here for additional data file. (74.7KB, xlsx)
    S8 Table. Chemicals that decrease the expression levels of significant TWAS genes negatively associated with UF.

    (XLSX)

    Click here for additional data file. (73.6KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (324.8KB, docx)

    Data Availability Statement

    All results are available as Supporting Information files, and the source files can be obtained from the TWAS/FUSION webpage (http://gusevlab.org/projects/fusion/) which is freely accessible.

    Articles from PLoS ONE are provided here courtesy of PLOS

    RESOURCES