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Background <p>Ferroptosis has been implicated in the development of ovarian dysfunction; however, the underlying causal genetic mechanisms remain largely unclear. In this study, a multi-omics Mendelian randomization (MR) approach was applied to systematically investigate the causal effects of ferroptosis-related molecular traits on ovarian dysfunction.</p> Methods <p>Summary-level data for blood-based methylation quantitative trait loci (mQTLs), expression QTLs (eQTLs), and protein QTLs (pQTLs) were obtained from large-scale consortia. Genetic associations with ovarian dysfunction were sourced from the FinnGen consortium (discovery) and the GCST90079756 dataset (replication). A two-sample Summary-data-based MR (SMR) analysis, incorporating the HEIDI test (P-HEIDI &gt; 0.01) to filter for pleiotropy, was employed to assess causal relationships. Colocalization analysis (PP.H4 &gt; 0.50) was performed to identify shared causal variants. Key findings were validated using tissue-specific eQTLs from GTEx_Ovary. Subsequently, all significant associations were used to construct a protein-protein interaction (PPI) network and explore enriched biological pathways.</p> Results <p>A total of 1175 ferroptosis-related genes were analyzed. 75 methylation sites, 3 gene expressions, and 4 proteins were identified with ovarian dysfunction through SMR and colocalization analyses. Notably, <i>BMP4</i> was identified in the mQTL and eQTL analyses. Specifically, the methylation at CpG site cg05923197 within <i>BMP4</i> was associated with higher <i>BMP4</i> expression (OR = 1.30, 95% CI = 1.21,1.39); this aligns with the finding that cg05923197 methylation increases the risk of ovarian dysfunction (OR = 1.24, 95% CI = 1.03,1.50). Additionally, tissue-specific validation indicated a positive correlation between HSD17B13 expression in ovarian tissue and ovarian dysfunction risk (OR = 1.20, 95% CI: 1.04-1.38). Integration of all candidate genes into a protein-protein interaction network further underscored the central roles of BMP4 and HSD17B13 and revealed enrichment in key biological pathways, including HIF-1 signaling and apoptosis.</p> Conclusion <p>This multi-omics MR study provides evidence for potential causal associations between ferroptosis-related genes and ovarian dysfunction, with BMP4 and HSD17B13 highlighted as key candidates. These findings improve understanding of its pathogenesis and may guide future therapeutic research.</p>

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Causal effects of ferroptosis-related traits on ovarian dysfunction: insights from integrating genome-wide Mendelian randomization, DNA methylation, gene expression, and proteome

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Qun Zhou,
  • Bangli Song,
  • Huifang Li,
  • Yibo He,
  • Hualiang Zhang,
  • Jing Guo,
  • Minjuan Wu,
  • Wenjun Chen

摘要

Background

Ferroptosis has been implicated in the development of ovarian dysfunction; however, the underlying causal genetic mechanisms remain largely unclear. In this study, a multi-omics Mendelian randomization (MR) approach was applied to systematically investigate the causal effects of ferroptosis-related molecular traits on ovarian dysfunction.

Methods

Summary-level data for blood-based methylation quantitative trait loci (mQTLs), expression QTLs (eQTLs), and protein QTLs (pQTLs) were obtained from large-scale consortia. Genetic associations with ovarian dysfunction were sourced from the FinnGen consortium (discovery) and the GCST90079756 dataset (replication). A two-sample Summary-data-based MR (SMR) analysis, incorporating the HEIDI test (P-HEIDI > 0.01) to filter for pleiotropy, was employed to assess causal relationships. Colocalization analysis (PP.H4 > 0.50) was performed to identify shared causal variants. Key findings were validated using tissue-specific eQTLs from GTEx_Ovary. Subsequently, all significant associations were used to construct a protein-protein interaction (PPI) network and explore enriched biological pathways.

Results

A total of 1175 ferroptosis-related genes were analyzed. 75 methylation sites, 3 gene expressions, and 4 proteins were identified with ovarian dysfunction through SMR and colocalization analyses. Notably, BMP4 was identified in the mQTL and eQTL analyses. Specifically, the methylation at CpG site cg05923197 within BMP4 was associated with higher BMP4 expression (OR = 1.30, 95% CI = 1.21,1.39); this aligns with the finding that cg05923197 methylation increases the risk of ovarian dysfunction (OR = 1.24, 95% CI = 1.03,1.50). Additionally, tissue-specific validation indicated a positive correlation between HSD17B13 expression in ovarian tissue and ovarian dysfunction risk (OR = 1.20, 95% CI: 1.04-1.38). Integration of all candidate genes into a protein-protein interaction network further underscored the central roles of BMP4 and HSD17B13 and revealed enrichment in key biological pathways, including HIF-1 signaling and apoptosis.

Conclusion

This multi-omics MR study provides evidence for potential causal associations between ferroptosis-related genes and ovarian dysfunction, with BMP4 and HSD17B13 highlighted as key candidates. These findings improve understanding of its pathogenesis and may guide future therapeutic research.