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Uncovering therapeutic targets for Pre-eclampsia and pregnancy hypertension via multi-tissue data integration

BMC Pregnancy and Childbirth volume 25, Article number: 479 (2025) Cite this article

Abstract

Background

Pre-eclampsia (PE) and pregnancy hypertension (PH) are common and serious complications during pregnancy, which can lead to maternal and fetal death in severe cases. Therefore, further research on the potential therapeutic targets of PE and PH is of great significance for developing new treatment strategies.

Methods

This study used the summary data-based Mendelian randomization (SMR) method to analyze expression quantitative trait loci (eQTL) data from blood, aorta, and uterus with Genome-wide association studies (GWAS) data on PE and PH, exploring potential genetic loci involved in PE and PH. Since proteinuria is a clinical manifestation of PE, we also analyzed genes related to the kidney and PE. The HEIDI test was used for heterogeneity testing, and results were adjusted using FDR. The cis-eQTL data were obtained from the blood summary-level data of the eQTLGen Consortium and the aorta and uterus data from the V8 release of the GTEx eQTL summary data. The GWAS data for PE and PH were obtained from the FinnGen Documentation of R10 release. This study utilized the STROBE-MR checklist for reporting Mendelian Randomization (MR) studies.

Results

This study identified several potential therapeutic targets by integrating eQTL data from blood, uterus, and aorta with GWAS data for PE and PH, as well as kidney eQTL data with GWAS data for PE. Additionally, the study discovered some genes with common roles in PE and PH, offering new insights into the shared pathological mechanisms of these two conditions. These findings not only provide new clues to the pathogenesis of PE and PH but also offer crucial foundational data for the development of future therapeutic strategies.

Conclusion

This study revealed multiple potential therapeutic targets for PE and PH, providing new insights for basic experimental research and clinical treatment to mitigate the severe consequences of PE and PH.

Clinical trial number

Not applicable.

Peer Review reports

Introduction

PE is a common pregnancy complication, mainly characterized by blood pressure rising to 140/90 mmHg or above after 20 weeks of gestation, usually accompanied by proteinuria or other organ dysfunction [1,2,3]. PH is defined as hypertension first appearing after 20 weeks of gestation, without accompanying proteinuria or other organ dysfunction. According to the ACOG (American College of Obstetricians and Gynecologists) report No. 222, PE affects 2–8% of pregnancies worldwide, making it one of the most severe pregnancy complications and a leading cause of maternal and perinatal mortality globally [1, 4, 5]. Numerous studies have shown that PE and PH can lead to severe sequelae, such as persistent hypertension, kidney failure, and cardiovascular diseases in the mother, and brain and lung developmental disorders, growth restriction in the fetus [6,7,8]. Magnesium sulfate is the first-choice drug for the prevention and treatment of PE, and termination of pregnancy is the ultimate measure for managing PE and PH, especially when severe symptoms occur after 34 weeks of gestation [1, 9]. Finding effective treatment methods to help pregnant women achieve healthy pregnancies is an urgent problem that needs to be addressed.

The exact causes of PE and PH are not yet fully understood, but studies suggest that vascular lesions, placental dysfunction, immune factors, genetic factors, and nutritional factors are important causes [10, 11]. GWAS are powerful tools for identifying risk loci associated with the occurrence of PE and PH [12]. However, the complex structure of the genome means that the most relevant variants may not be causal [13]. SMR analysis is a powerful tool for causal inference, combining GWAS and eQTL data. It can reveal the causal relationship between genetic variations and disease phenotypes [14,15,16]. By using genetic variations as instrumental variables, SMR overcomes confounding biases commonly found in traditional observational studies, thereby providing reliable evidence for the causal relationship between genes and diseases. In recent years, several genetic variants have been closely associated with the risk of PE and PH, potentially mediating the development of pregnancy-related hypertensive disorders through pathways such as vascular function, immune response, and placental function [17,18,19]. In previous studies, we explored the causal relationship between the gut microbiota and pregnancy loss using MR methods [20].

In this study, through SMR analysis, combining GWAS and eQTL databases, we uncovered the causal relationship between genetic variations associated with pregnancy hypertension and biological phenotypes. The analysis not only provides new insights into the pathophysiological mechanisms of PE and PH, but also reveals key gene regulatory factors, offering potential therapeutic targets for early clinical screening and intervention.

Materials and methods

Data sources

The study methodology adheres to the STROBE-MR checklist [21], with further details available in Supplementary File 2.

Since blood and arteries may reflect metabolic characteristics related to PE and PH, and the uterus is closely related to PE and PH, the cis-eQTL data were partly obtained from the eQTLGen Consortium’s blood summary-level data [22], which includes 37 datasets with a total of 31,684 participants. The data can be downloaded from https://eqtlgen.org/. On the other hand, data were obtained from the V8 release of the GTEx eQTL summarized data for Artery Aorta, uterus, and Kidney Cortex [23], which include 387, 129, and 73 participants, respectively. The data can be downloaded from https://yanglab.westlake.edu.cn/software/smr/#eQTLsummarydata. GWAS data were obtained from the FinnGen Documentation of R10 release [24], with 7,377 cases and 211,957 controls for PE, and 16,417 cases and 213,893 controls for PH. The data can be downloaded from https://finngen.gitbook.io/documentation/data-download. The summary data used in the article are all from public data sources. Each eQTL and GWAS mentioned in the article has received ethical approval from their respective institutions. For specific information, see Table 1.

Table 1 Details of the eQTL and GWASs included in the summary data-based Mendelian randomization
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SMR analysis

This study used summary statistics from eQTL and GWAS to test the association between gene expression and PE and PH through SMR analysis. Gene expression was used as the exposure factor, and PE and PH were the outcome factors. The SNPs () of cis-eQTL were used as instrumental variables (IVs), which must meet the three assumptions of MR: (1) IV is closely related to the gene; (2) IV is independent of any confounders; (3) IV affects PE and PH only through gene expression and not through other pathways [25]. The analysis was performed using Rstudio (version 4.3.2) and SMR software (version 1.3.1), with default settings applied during the analysis, Only SNPs with a minor allele frequency (MAF) greater than 0.01 are included. For cis-eQTL data, a p-value threshold (p < 5.00e-08) was applied. In addition, we excluded SNPs with an LD (linkage disequilibrium) r² value greater than 0.90 or less than 0.05 to ensure that the SNPs used represent genetic variation without introducing confounding factors [26]. Genes with significant loci were selected using P_SMR less than 0.05, and the HEIDI test was used to evaluate the heterogeneity of the results (p_HEIDI > 0.05). To correct for multiple comparisons in the SMR results, we further applied false discovery rate (FDR) correction with a threshold of FDR < 0.05. Workflow of the study is shown in Fig. 1.

Fig. 1
figure 1

Workflow of the study. A series of analyses were conducted to identify potential pathogenic genes associated with the development of Pre-eclampsia (PE) and Pregnancy Hypertension (PH). Cis-eQTL data for Blood were obtained from the eQTLGen consortium, while cis-eQTL data for Artery Aorta, Uterus, and Kidney Cortex were extracted from the GTEx database as exposure datasets. GWAS summary data for PE and PH were obtained from the FinnGen R10 database as outcome datasets, including cases with PE or PH and healthy controls. SMR, HEIDI, and FDR analyses were performed to integrate cis-eQTLs from Blood, Artery Aorta, Uterus, and Kidney Cortex with GWAS data for PE, and cis-eQTLs from Blood, Artery Aorta, and Uterus with GWAS data for PH. Genes satisfying the criteria of SMR p < 0.05, HEIDI p > 0.05, and FDR p < 0.05 were considered potential pathogenic genes. GO and KEGG enrichment analyses were conducted to further explore the biological functions and pathways associated with potential pathogenic genes in PE and PH

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GO and KEGG analysis

To identify potential biological functions and pathways, we conducted an analysis using Rstudio (version 4.3.2), setting the gene ontology database to org.Hs.eg.db. Enrichment analysis was performed using the clusterProfiler package and the enrichGO function, and the results were visualized to display the biological processes, molecular functions, cellular component categories, and pathways significantly associated with differentially expressed genes [27].

Results

Gene screening

Through SMR analysis and filtering by P_SMR, P_FDR, and P_HEIDI, we found that 14,075 probes in the eQTLGen blood data were associated with PE (Supplementary Table S1), with 7 genes showing pleiotropic causal relationships with PE. In the GTEx_V8 uterus, aorta, and kidney cortex data, there were 1,416, 6,408, and 514 probes associated with PE, respectively (Supplementary Tables S2-S4), with one gene in the kidney cortex showing a significant pleiotropic causal relationship with PE. Specifically, in blood, ENSG00000234608 (marked MAPKAPK5-AS1, p_SMR = 1.03E-06), ENSG00000198270 (marked TMEM116, p_SMR = 1.61E-06), ENSG00000226469 (marked ADAM1B, p_SMR = 2.65E-06), ENSG00000111252 (marked SH2B3, p_SMR = 7.46E-06), ENSG00000111271 (marked ACAD10, p_SMR = 2.23E-05), ENSG00000132781 (marked MUTYH, p_SMR = 3.02E-05), and ENSG00000204536 (marked CCHCR1, p_SMR = 3.35E-05) showed significant pleiotropic associations with PE. In the kidney cortex, ENSG00000138675 (marked FGF5, p_SMR = 1.95E-05) showed a significant pleiotropic association with PE (Table 2). High expression of SH2B3, MUTYH, CCHCR1, and FGF5 is associated with an increased risk of PE. High expression of MAPKAPK5-AS1, TMEM116, ADAM1B, and ACAD10 is associated with a decreased risk of PE.

Table 2 A gene probe associated with significant Pleiotropy in Pre-eclampsia
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Through a series of screenings, we found that 15,679 probes in the eQTLGen blood data were associated with PH (Supplementary Table S5), with 19 genes showing significant pleiotropic causal relationships. In the GTEx_V8 uterus and aorta data, there were 1,416 and 6,408 probes associated with PH, respectively (Supplementary Tables S6-S7), with 1 and 7 genes showing significant pleiotropic causal relationships with PH, respectively.

Specifically, in blood, the top 7 genes significantly associated with PH with pleiotropic effects ranked by p_SMR value are ENSG00000226469 (marked ADAM1B, p_SMR = 3.81E-07), ENSG00000198324 (marked FAM109A, p_SMR = 6.57E-07), ENSG00000270018 (marked RP3-462E2.5, p_SMR = 1.08E-06), ENSG00000152518 (marked ZFP36L2, p_SMR = 1.23E-05), ENSG00000257595 (marked RP3-473L9.4, p_SMR = 1.41E-05), ENSG00000103653 (marked CSK, p_SMR = 1.70E-05), and ENSG00000172922 (marked RNASEH2C, p_SMR = 2.46E-05). In the uterus, ENSG00000111788 (marked RP11-22B23.1, p_SMR = 1.97E-05) is significantly associated with PH with pleiotropic effects. In the arteries, ENSG00000165895 (marked ARHGAP42, p_SMR = 2.09E-07), ENSG00000076003 (marked MCM6, p_SMR = 7.26E-06), ENSG00000172922 (marked RNASEH2C, p_SMR = 5.78E-05), ENSG00000172543 (marked CTSW, p_SMR = 6.43E-05), ENSG00000089022 (marked MAPKAPK5, p_SMR = 6.82E-05), ENSG00000182472 (marked CAPN12, p_SMR = 7.14E-05), and ENSG00000164308 (marked ERAP2, p_SMR = 1.23E-04) are significantly associated with PH with pleiotropic effects (Table 3).

High expression of RP3-462E2.5, CSK, MAPKAPK5, and ERAP2 is associated with an increased risk of PH. High expression of ADAM1B, FAM109A, ZFP36L2, RP3-473L9.4, RNASEH2C, RP11-22B23.1, ARHGAP42, MCM6, RNASEH2C, CTSW, and CAPN12 is associated with a decreased risk of PH.

Table 3 Gene probe associated with significant Pleiotropy of pregnancy hypertension
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Single gene analysis

We performed single-gene analyses on MAPKAPK5-AS1, FGF5, ADAM1B, RP11-22B23.1, and ARHGAP42, which ranked first in p_SMR values in various tissues for PE and PH. To comprehensively consider the results of GWAS and eQTL, we used Locus plots to gain a deeper understanding of how specific genetic variants might influence PE and PH by affecting gene expression. When an SNP shows a high association in GWAS and the same position strongly affects a gene’s expression in eQTL analysis, it can be inferred that this genetic variant may influence the disease manifestation by regulating the gene’s expression, as shown in Fig. 2, the top layer presents the GWAS results, where gray dots represent the -log10(P value) of SNPs associated with the disease. The diamonds indicate probes that pass the HEIDI test, highlighting the strength of SNP-disease associations. The middle layer shows the eQTL analysis, illustrating the SNPs’ impact on gene expression. The bottom layer labels the genes within the chromosome region and their transcription directions. Because gene expression is influenced by multiple cis-eQTLs, scatter plots of single genes can intuitively show the positive and negative regulatory relationships between genes and diseases. We compared the effect sizes of these eQTLs with those in GWAS. This effect relationship is depicted by the orange dashed trend line in the figure, as shown in Fig. 3, the x-axis represents the effect values of SNPs in eQTL genes, and the y-axis represents the effect values of SNPs in the disease. Blue circles represent SNPs, while red triangles indicate topSNPs, which show the most significant associations. The results indicate that as the expression of MAPKAPK5-AS1, ADAM1B, RP11-22B23.1, and ARHGAP42 increases (eQTL effect value increases), the corresponding disease risk decreases. Conversely, as the expression of FGF5 increases (eQTL effect value increases), the corresponding disease risk increases. These findings provide important evidence for further exploration of the functions of individual genes in PE and PH and their potential as therapeutic targets.

Fig. 2
figure 2

Locus plots. A) MAPKAPK5-AS1 in Pre-eclampsia; B) FGF5 in Pre-eclampsia; C) ADAM1B in pregnancy hypertension; D) RP11-22B23.1 in pregnancy hypertension; E) ARHGAP42 in pregnancy hypertension

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Fig. 3
figure 3

Scatter plots. A) MAPKAPK5-AS1 in Pre-eclampsia; B) FGF5 in Pre-eclampsia; C) ADAM1B in pregnancy hypertension; D) RP11-22B23.1 in pregnancy hypertension; E) ARHGAP42 in pregnancy hypertension

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GO and KEGG enrichment analysis

Through GO and KEGG enrichment analysis, it was found that the significant genes related to PE are associated with 62 biological processes, 1 cellular component, 20 molecular functions, and 3 pathways. The highest-ranked p-values are for regulation of protein kinase B signaling, centriole, oxidized DNA binding, and Base excision repair (Fig. 4A-B). The genes related to PH are associated with 113 biological processes, 5 cellular components, 26 molecular functions, and 1 pathway. The highest-ranked p-values are for ERK1 and ERK2 cascade, endoplasmic reticulum lumen, protein phosphatase binding, and DNA replication (Fig. 4C-D). Specific GO and KEGG information can be found in Supplementary Tables S8-S11.

Fig. 4
figure 4

GO and KEGG enrichment analysis. A) Bar plot of GO enrichment analysis for genes related to Pre-eclampsia, with the x-axis representing the -log10(p-value) of gene enrichment and the y-axis representing biological terms. B) Cnetplot of KEGG enrichment analysis for genes related to Pre-eclampsia, with red dots on the left representing gene names, lines of different colors representing pathway names, and the size of the circles on the right representing the number of genes enriched in the pathway. C) GO enrichment analysis for genes related to pregnancy hypertension. D) Cnetplot of KEGG enrichment analysis for genes related to pregnancy hypertension

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Discussion

In this study, our SMR analysis integrated GWAS and eQTL databases to explore potential therapeutic targets for PE and PH. A previous GWAS study identified risk genetic loci associated with PE and PH through meta-analysis [12]. The SMR analysis used in this study better inferred the causal relationships between exposure and disease, exploring potential therapeutic targets for PE and PH in blood and related tissues. Through rigorous statistical analysis, we not only confirmed the significant pleiotropic associations of four previously reported genetic risk loci genes (FGF5, SH2B3, MAPKAPK5, ACAD10) [28,29,30,31], but also identified the potential targeted therapeutic roles of many other related genes. We identified 8 potential targets related to PE and 27 potential targets related to PH. These targets highlight the roles of vascular disease, placental dysfunction, kidney dysfunction, and genetic factors in PE and PH. These findings have important clinical implications for predicting and treating PE and PH.

For example, clinical studies on FGF5 have shown that its high expression is positively correlated with the incidence of hypertension. Based on this finding, we propose that downregulating the expression of this gene may help reduce the risk of elevated blood pressure, thereby providing new insights for the treatment of pregnancy-related hypertensive disorders [32]. Future research could leverage these findings to develop personalized therapeutic strategies for hypertensive disorders during pregnancy and explore the potential of FGF5 as a target for gene therapy. This highlights the necessity of translational research to bring these discoveries closer to clinical application.

PE and PH usually involve vascular abnormalities, such as increased vasoconstriction and dysfunction of endothelial growth factors [33, 34]. Interestingly, our study found several genes associated with these vascular abnormalities. High expression of Grb10 may promote vasodilation and angiogenesis by enhancing VEGF signaling, thereby helping to lower blood pressure [35]. ARHGAP42, as a key regulator of endothelial growth, can inhibit the RhoA signaling pathway when highly expressed, limiting vasoconstriction and thereby lowering blood pressure [36,37,38]. Additionally, downregulation of MCM6 expression may lead to restricted proliferation of coronary endothelial cells, affecting vascular function and leading to abnormal blood pressure [39]. These findings further support the view that abnormal blood pressure in PE and PH is closely related to vascular abnormalities.

Placental dysfunction and estrogen metabolism disorders are key factors in the development of PE and PH [10, 40]. Our study found that the expression of CSK in the blood is positively correlated with PH. Specifically, high expression of CSK inhibits the activity of Src family kinases, leading to insufficient remodeling of uterine spiral arteries and placental hypoperfusion, thereby exacerbating placental dysfunction [41,42,43]. Additionally, the expression of HSD17B1 enhances estrogen metabolism, promoting placental growth and development, and maintaining blood pressure balance [44, 45]. Therefore, the abnormal expression of these two genes, through their effects on placental function and estrogen metabolism, further supports the important roles of placental dysfunction and estrogen metabolism disorders in PE and PH.

It is well known that proteinuria is another important characteristic of PE [46, 47]. Interestingly, we found that high expression of FGF5 in kidney tissue is closely associated with PE. FGF5 is a fibroblast growth factor involved in biological processes such as tissue repair, embryonic development, and cell growth [48, 49]. A comprehensive multi-omics study indicated that high expression of FGF5 leads to CKD (chronic kidney disease), increasing the risk of proteinuria [50]. A GWAS meta-analysis found that FGF5-related variants damage eGFR (estimated Glomerular Filtration Rate), leading to protein leakage into urine [51]. A GWAS meta-analysis provided evidence that FGF5 variants significantly increase the risk of PE [29, 52, 53]. Furthermore, FGF5 is an important risk genetic locus in hypertension [54,55,56]. Overall, FGF5 is a very important potential therapeutic target, and its regulation may help prevent and treat PE. Additionally, ADAM1B is a zinc-containing metalloprotease and a member of the ADAM protein family. Abnormal expression of ADAM affects systemic inflammation and endothelial cell damage, as well as abnormal protein release in urine, leading to PE and PH [57]. Future research needs to further elucidate the specific mechanisms of FGF5 and ADAM1B in the development of PE and evaluate the potential efficacy and safety of strategies targeting FGF5 and ADAM1B in the clinical treatment of PE.

In this study, we identified several genes significantly associated with PE and PH that have not yet been reported in this field. However, these genes have shown close associations in hypertension studies. For example, an exome-wide association study (GWAS) indicated that ACAD10 is an important gene influencing genetic susceptibility to hypertension [31]. Additionally, TMEM116 is also considered another genetic susceptibility gene affecting hypertension, and its role may be related to autoimmune mechanisms [58]. A basic experimental study suggested that TOR1B may cause elevated blood pressure by enhancing the activity of the sympathetic nervous system [59].

This study has several significant advantages. First, SMR analysis uses the principle of MR and employs SNPs as IVs, which can more effectively infer causal relationships. Secondly, SMR can reveal causal relationships between gene expression and diseases, which is highly valuable for identifying new therapeutic targets and understanding disease pathology. Lastly, SMR utilizes publicly available summary statistics data, enabling efficient integration of data from multiple studies and conducting large-scale analyses, thereby enhancing statistical power and the reliability of results. This study has some unavoidable limitations that warrant further discussion. First, the databases used in this study are predominantly derived from European populations, which may limit the generalizability of the findings to other ethnic groups and regional backgrounds. Future studies should expand to include more diverse populations, encompassing different ethnicities and cultural contexts, to validate the broad applicability of the key gene associations. Second, the GWAS database utilized does not provide stratified data based on variables such as age and gender, which may limit the exploration of specific associations within different subgroups. Additionally, the cis-eQTL sites employed in this study directly regulate the expression of neighboring genes and often exhibit high correlation, which could pose challenges to the independence of the results. Finally, this study primarily relies on statistical methods to reveal the associations between genes and diseases, lacking external experimental validation and in-depth investigations into the specific mechanisms of the identified genes. Future research should incorporate larger sample sizes and more diverse datasets to address these limitations and employ functional experiments, such as gene knockdown or overexpression studies, to verify the biological roles of key genes. These improvements would not only enhance the reliability of the findings but also provide a solid foundation for the clinical translation of these genes into practical applications.

Conclusion

In summary, our SMR analysis identified 8 potential therapeutic targets for PE and 27 potential therapeutic targets for PH. These potential targets are closely related to vascular, placental, renal, and genetic factors. Of course, more functional studies are needed in the future to explore the physiological mechanisms of pregnancy-related hypertensive disorders.

Data availability

Data supporting the findings of this study are publicly available in the following repositories: The eQTL data were obtained from the eQTLGen Consortium and are available at the following URL: [eQTLGen Consortium](https://eqtlgen.org/). The GTEx V8 release data can be accessed via: [GTEx V8 Data Release](https://yanglab.westlake.edu.cn/software/smr/#eQTLsummarydata). The GWAS data for pre-eclampsia and pregnancy hypertension were sourced from the FinnGen R10 release, available at: [FinnGen R10 Data Release](https://finngen.gitbook.io/documentation/data-download).

Abbreviations

PE:

Pre-eclampsia

PH:

Pregnancy Hypertension

SMR:

summary data-based Mendelian randomization

eQTL:

expression quantitative trait loci

GWAS:

Genome-wide association studies

IV:

instrumental variable

SNP:

Single-Nucleotide Polymorphism

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Acknowledgements

We would like to thank the eQTLGen Consortium, GTEx Consortium and the FinnGen study group for providing the summary-level data that were essential to our research. Their public databases allowed us to perform the analysis presented in this manuscript. Additionally, we express our gratitude to all those who contributed to the data collection and made this study possible.

Funding

This research was supported by the Natural Science Foundation of Shandong Province (No. ZR202103050666) and Provincial Postgraduate Innovation Special Funds of Jiangxi University of Chinese Medicine (YC2023-S771).

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Author notes

  1. Hang Yao, Jiahao Chen, and Yu Wang contributed equally to this work and shared the first authorship.

Authors and Affiliations

  1. School of Traditional Chinese Medicine, Binzhou Medical University, Yantai, China

    Hang Yao, Peiyu Tang, Mingpeng Liang & Qingling Jiang

  2. School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China

    Jiahao Chen

  3. Graduate School of Jiangxi University of Traditional Chinese Medicine, Nanchang, China

    Yu Wang & Yuxin Li

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  1. Hang Yao
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Contributions

HY: Data curation, Formal analysis, Methodology, Software, Writing – original draft. JHC: Methodology, Software, Writing – review & editing. YW: Software, Writing – review & editing. YXL: Writing – review & editing. PYT: Software, Writing. MPL: Software, Writing. QLJ: Funding acquisition, Supervision, Validation, Writing – review & editing. Corresponding author: Qingling Jiang, Email: [jangqingling@163.com]All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Corresponding author

Correspondence to Qingling Jiang.

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Ethics approval and consent to participate

In this summary data-based Mendelian randomization study, all data were extracted from three publicly available databases, including the eQTLGen Consortium (https://www.eqtlgen.org/), the GTEx Consortium (https://www.gtexportal.org/home/aboutAdultGtex), and the FinnGen Documentation of R10 release study (https://finngen.gitbook.io/documentation), all three of which had previously obtained ethical approval and informed consent. Therefore, no further ethical approval was required. The studies were conducted in accordance with local legislation and institutional requirements.

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The authors declare no competing interests.

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Yao, H., Chen, J., Wang, Y. et al. Uncovering therapeutic targets for Pre-eclampsia and pregnancy hypertension via multi-tissue data integration. BMC Pregnancy Childbirth 25, 479 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12884-025-07608-x

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BMC Pregnancy and Childbirth

ISSN: 1471-2393

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