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Background <p>Polycystic ovary syndrome (PCOS) is increasingly recognized as a metabolic disorder, with dysregulated lipid metabolism emerging as a key factor in its pathogenesis. However, the precise mechanisms underlying this relationship remain unclear. We employed a summary data-based Mendelian randomization (SMR) approach to investigate the potential causal relationships between lipid metabolism-related genes and PCOS. First, we integrated PCOS genome-wide association study (GWAS) data from the Finngen_R11_E4_PCOS cohort with three layers of blood-based molecular quantitative trait loci (QTLs): methylation QTLs (mQTLs), expression QTLs (eQTLs), and protein QTLs (pQTLs). To ensure the robustness of the SMR signals, we performed the heterogeneity in dependent instruments (HEIDI) test to detect potential horizontal pleiotropy and conducted colocalization analysis to identify shared potential causal genetic variants. Findings were validated in two independent cohorts: Phenocode_265.4 and Felix. We then integrated mQTL and eQTL data to further dissect methylation-mediated gene expression regulation. Finally, we examined the expression profiles of key candidate genes using publicly available transcriptomic datasets from the Gene Expression Omnibus (GEO) database.</p> Results <p>SMR analysis in the discovery cohort identified 132 mQTLs (corresponding to 70 genes), 16 eQTLs, and 7 pQTLs associated with PCOS. Among these, 71 mQTLs (corresponding to 39 genes), 11 eQTLs, and 4 pQTLs were supported by strong colocalization evidence. Further validation in the Phenocode_265.4 and Felix cohorts confirmed the association of 13 mQTLs (corresponding to 7 genes) and 2 eQTLs with PCOS. Specifically, the associations of <i>ACOX2</i> (cg22012981), <i>GLIPR1</i> (cg01554451), and <i>PIK3R3</i> (cg27584146) with PCOS were further supported by subsequent analyses. mQTL‑eQTL SMR analysis confirmed the negative regulatory effects of these methylation sites on the expression of their corresponding genes. Finally, transcriptomic data revealed upregulation of <i>GLIPR1</i> and <i>PIK3R3</i> in tissues from patients with PCOS.</p> Conclusion <p>This study shows the association of lipid metabolism-related genes, particularly <i>ACOX2</i>, <i>PIK3R3</i>, and <i>GLIPR1</i>, with PCOS pathogenesis. These findings highlight them as candidates for further functional studies and potential future therapeutic development.</p>

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Unraveling the role of lipid metabolism in polycystic ovary syndrome through multi-omics Mendelian randomization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Ronghuan He,
  • Xiaoyu Tu,
  • Bei Liu,
  • Zeyi Jiang,
  • Jin Chen,
  • Jingyi Li

摘要

Background

Polycystic ovary syndrome (PCOS) is increasingly recognized as a metabolic disorder, with dysregulated lipid metabolism emerging as a key factor in its pathogenesis. However, the precise mechanisms underlying this relationship remain unclear. We employed a summary data-based Mendelian randomization (SMR) approach to investigate the potential causal relationships between lipid metabolism-related genes and PCOS. First, we integrated PCOS genome-wide association study (GWAS) data from the Finngen_R11_E4_PCOS cohort with three layers of blood-based molecular quantitative trait loci (QTLs): methylation QTLs (mQTLs), expression QTLs (eQTLs), and protein QTLs (pQTLs). To ensure the robustness of the SMR signals, we performed the heterogeneity in dependent instruments (HEIDI) test to detect potential horizontal pleiotropy and conducted colocalization analysis to identify shared potential causal genetic variants. Findings were validated in two independent cohorts: Phenocode_265.4 and Felix. We then integrated mQTL and eQTL data to further dissect methylation-mediated gene expression regulation. Finally, we examined the expression profiles of key candidate genes using publicly available transcriptomic datasets from the Gene Expression Omnibus (GEO) database.

Results

SMR analysis in the discovery cohort identified 132 mQTLs (corresponding to 70 genes), 16 eQTLs, and 7 pQTLs associated with PCOS. Among these, 71 mQTLs (corresponding to 39 genes), 11 eQTLs, and 4 pQTLs were supported by strong colocalization evidence. Further validation in the Phenocode_265.4 and Felix cohorts confirmed the association of 13 mQTLs (corresponding to 7 genes) and 2 eQTLs with PCOS. Specifically, the associations of ACOX2 (cg22012981), GLIPR1 (cg01554451), and PIK3R3 (cg27584146) with PCOS were further supported by subsequent analyses. mQTL‑eQTL SMR analysis confirmed the negative regulatory effects of these methylation sites on the expression of their corresponding genes. Finally, transcriptomic data revealed upregulation of GLIPR1 and PIK3R3 in tissues from patients with PCOS.

Conclusion

This study shows the association of lipid metabolism-related genes, particularly ACOX2, PIK3R3, and GLIPR1, with PCOS pathogenesis. These findings highlight them as candidates for further functional studies and potential future therapeutic development.