Integrative Multi-omics Analysis for Prioritization of Candidate Genes in Polycystic Ovary Syndrome

The genetic architecture of polycystic ovary syndrome (PCOS) has not been fully elucidated. Translating genome-wide association study (GWAS) loci into functional effector genes represents a key challenge for elucidating disease mechanisms and advancing targeted therapeutic strategies. In this study, the FinnGen R12 dataset was utilized in conjunction with the Genotype-Tissue Expression Project (GTEx) v8 eQTL dataset to conduct cross-tissue transcriptome-wide association studies (TWAS). External validation was performed in two independent datasets, comprising a PCOS meta-analysis of Rotterdam- and NIH-criteria cohorts and UK Biobank summary statistics. To prioritize high-confidence candidate genes, we integrated a cross-tissue (UTMOST) TWAS and tissue-specific (FUSION) TWAS with a gene-based association test (MAGMA). Furthermore, causal inference analyses including Mendelian randomization (MR), colocalization analysis, summary data-based MR, and the heterogeneity in dependent instrument (HEIDI) test were carried out on candidate genes. GeneMANIA, gene-chemical-disease analysis, and phenome-wide association study (PheWAS) were further performed on candidate genes. Finally, we explored expression profiles of core candidate genes using transcriptomic datasets from the Gene Expression Omnibus (GEO) database. We prioritized 15 candidate genes potentially associated with PCOS. External analyses across independent datasets showed limited consistency, which may reflect differences in statistical power and cohort heterogeneity. MCM6 was prioritized by all three approaches, whereas the remaining 14 genes were supported by two approaches. Among the 15 candidate genes, six genes were demonstrated to have causal associations with PCOS risk. GeneMANIA network analysis further revealed that these candidate genes were involved in key biological functions, including DNA replication and regulation, DNA repair and recombination, protein-DNA complex, cell cycle regulation, nucleic acid-enzyme activities, DNA structure and chromosomal regions, reproductive system development, and sex differentiation. Gene-chemical-disease analysis suggested that the prioritized genes may mediate crosstalk between PCOS genetic risk and environmental/metabolic factors. PheWAS analysis highlighted the pleiotropic roles of PCOS candidate genes, which contributed to both PCOS susceptibility and a spectrum of PCOS-related traits including metabolic and cardiovascular phenotypes. Finally, transcriptomic data showed downregulated MCM6 expression in granulosa cells of PCOS patients. Our study established a multipronged pipeline for PCOS gene prioritization and provided novel insights into the genetic architecture of PCOS.