From uncovering hidden genetic risks to developing predictive tools, this landmark study reveals how your DNA could shape the future of reproductive health—and what that means for millions of women worldwide.
Study: Atlas of genetic and phenotypic associations across 42 female reproductive health diagnoses. Image Credit: Tartila / Shutterstock
In a recent study published in the journal Nature Medicine, researchers in Estonia and Norway identified genetic risk factors associated with female reproductive health conditions through genome-wide association studies (GWAS) and assessed their clinical significance.
Background
One in ten women worldwide suffers from a reproductive health disorder, yet many of these conditions remain poorly understood. What if the key to unlocking better treatment lies in our genes? Female reproductive health disorders affect millions, impacting fertility, pregnancy outcomes, and overall well-being.
Conditions such as polycystic ovary syndrome (PCOS), endometriosis, and intrahepatic cholestasis of pregnancy (ICP) are linked to genetic and environmental factors. Despite advancements, many underlying genetic risk factors remain unidentified.
Studies have shown that genetic variations influence susceptibility to these disorders, but existing research has focused primarily on common variants, leaving rare or population-specific variants underexplored. The new study highlights the importance of analyzing genetic data from isolated populations such as Finland and Estonia, where unique population-enriched variants like CHEK2 and MYH11 were identified—variants that are much rarer in other European populations. Moreover, genetic correlations between different reproductive health disorders are not well understood.
Understanding these genetic predispositions can aid in risk assessment, early diagnosis, and personalized treatment strategies.
Given the complexity of these conditions, further research is essential to refine genetic risk prediction models and identify novel therapeutic targets. The ability to predict reproductive health risks through genetics could revolutionize how women manage their health globally.
About the study
Genetic data were analyzed from large biobank cohorts, including the Estonian Biobank (EstBB) and FinnGen, comprising nearly 300,000 women. Diagnosis codes from the International Classification of Diseases, Tenth Revision (ICD-10), were used to define cases and controls for 42 female reproductive health phenotypes. Genotyping was performed using high-density genome-wide arrays, followed by imputation with reference panels to increase variant coverage.
GWAS was conducted using an inverse variance-weighted fixed-effects meta-analysis approach. Quality control measures included filtering for call rates, Hardy-Weinberg equilibrium, and imputation quality scores.
Lead single nucleotide polymorphisms (SNPs) were identified, and genomic risk loci were annotated using the Functional Mapping and Annotation (FUMA) platform.
Genetic correlations were estimated using Linkage Disequilibrium Score Regression (LDSC), and polygenicity and discoverability were assessed using MiXeR (polygenicity and discoverability analysis tool) software.
To assess pleiotropy, loci associated with multiple reproductive health conditions were mapped, and candidate genes were prioritized using the Open Targets Genetics portal. Additionally, a polygenic risk score (PRS) for ICP was developed and validated both in the Estonian Biobank and in an independent Norwegian cohort (HUNT study), confirming the robustness of the findings.
Associations between PRS and other phenotypes were explored using a phenome-wide association study (PheWAS). All analyses were adjusted for population stratification and potential confounders.
Study results
A total of 195 genome-wide significant loci were identified across the 42 reproductive health phenotypes. Several previously unidentified and population-enriched variants were detected, highlighting the importance of studying diverse genetic backgrounds.
Among the identified loci, genes involved in hormonal regulation (Follicle-Stimulating Hormone Beta (FSHB), Growth Regulation by Estrogen in Breast Cancer 1 (GREB1)), genital tract development (Wnt Family Member 4 (WNT4), Paired Box Gene 8 (PAX8), Wilms Tumor 1 (WT1)), and folliculogenesis (Checkpoint Kinase 2 (CHEK2)) emerged as key contributors to female reproductive health. Additionally, novel loci such as PDE4D, ID4, and NR0B1 were identified for ovarian cysts, offering new insights into folliculogenesis and potential drug targets.
The genetic correlation analysis revealed significant associations between various reproductive disorders. Notably, strong correlations were observed between uterine fibroids and excessive menstruation, as well as between cervical dysplasia and cervicitis. Interestingly, the study also reported a negative genetic correlation between PCOS and preterm delivery, which contradicts epidemiological studies and highlights the need for further investigation.
These findings suggest that overlapping genetic pathways contribute to these conditions. The polygenicity analysis indicated that reproductive health disorders exhibit a high degree of genetic complexity, with many small-effect variants contributing to disease susceptibility. Heritability estimates varied widely across conditions, from 1% to 21%, with higher estimates for metabolic-related disorders such as ICP (12–30%) and PCOS (10–21%).
The PRS for ICP demonstrated a significant association with disease risk. Women in the highest decile of the PRS had a 6.1% prevalence of ICP compared to 0.9% in the lowest decile. The odds ratio for ICP in the highest PRS decile compared to the lowest was 6.7 (95% confidence interval [CI]: 5.0–9.3, P = 1.9 × 10⁻³³).
The model incorporating PRS improved risk prediction, achieving an area under the curve (AUC) of 0.66. Importantly, validation in the HUNT study confirmed the association, with an odds ratio of 1.7 (95% CI: 1.3–2.1, P = 2.8 × 10⁻⁹) per standard deviation increase in PRS, and an AUC of 0.71, underscoring its potential clinical utility.
Beyond ICP, PheWAS identified cholelithiasis as a phenotype significantly associated with the ICP PRS, supporting a shared genetic basis between these conditions. Additionally, pleiotropic loci were identified, with some genes showing associations across multiple phenotypes, reinforcing the genetic interconnectivity of reproductive disorders. For example, WNT4 was associated with uterine fibroids, endometriosis, pelvic organ prolapse, cervical dysplasia, and infertility, demonstrating cross-condition genetic links.
These findings have far-reaching implications. Understanding genetic predispositions can help individuals make informed reproductive health choices, assist clinicians in early diagnosis, and guide public health policies to better address reproductive disorders on a global scale. Personalized risk assessment could transform women’s healthcare by shifting from reactive to proactive interventions. Moreover, the study highlights potential evolutionary trade-offs in the persistence of genetic risk factors, such as the role of PCOS-associated variants in reproductive aging and balancing selection.
Conclusions
To summarize, the findings underscore the polygenic nature of these conditions and highlight shared genetic factors underlying multiple reproductive disorders. The development of PRS for ICP demonstrates the potential for genetic risk prediction in clinical practice, which could inform personalized monitoring and early interventions.
The identification of pleiotropic loci suggests that common genetic pathways contribute to different reproductive health conditions, paving the way for targeted therapeutic strategies. Nonetheless, the study acknowledges limitations such as reliance on ICD-10 codes and the need for further replication in non-European populations.
The integration of genetic data with clinical and environmental factors will be essential for translating these findings into improved healthcare strategies for women worldwide. By leveraging genetic insights, healthcare systems can better allocate resources, design preventive measures, and ultimately improve the quality of life for millions of women globally.