Researchers discover 195 genetic risk factors that drive women's reproductive diseases

Researchers discover 195 genetic risk factors that drive women's reproductive diseases

This groundbreaking study shows how your DNA can shape the future of reproductive health - and what this means for millions of women worldwide, from uncovering hidden genetic risks to developing predictive tools.

In a study recently published in the journalNatural medicineResearchers 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 suffer 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, affecting fertility, pregnancy outcomes and overall well-being.

Diseases such as polycystic ovary syndrome (PCOS), endometriosis and intrahepatic cholestasis of pregnancy (ICP) are associated with genetic and environmental factors. Despite advances, many underlying genetic risk factors remain unidentified.

Some genetic variants linked to reproductive conditions are also linked to other health problems such as breast cancer, showing how interconnected these risks can be.

Studies have shown that genetic variations influence susceptibility to these disorders, but existing research has focused primarily on common variants and ignored rare or population-specific variants. The new study shows the importance of analyzing genetic data from isolated populations such as Finland and Estonia, where unique population-related variants may appearChek2AndMyh11have been identified – variants that are much rarer in other European populations. Additionally, genetic correlations between various reproductive health disorders are not well understood.

Understanding these genetic predispositions can aid risk assessment, early diagnosis and personalized treatment strategies.

Given the complexity of these conditions, further investigation is essential for refining genetic risk prediction models and identifying new therapeutic targets. The ability to predict reproductive health risks through genetics could revolutionize how women manage their health worldwide.

About the study

Genetic data were analyzed from large biobank cohorts, including the Estonian Biobank (ESTBB) and Finngen, which included almost 300,000 women. Diagnosis codes from the International Classification of Diseases, Tenth Revision (ICD-10) were used to define cases and controls for 42 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-Winberg equilibrium, and imputation quality scores.

The researchers identified 83 genetic loci that had never been linked to female reproductive health, expanding the understanding of these disorders.

The lead single nucleotide polymorphisms (SNPs) were identified, and genomic risk loci were annotated using the FUMA functional mapping and annotation (FUMA) platform.

Genetic correlations were estimated using linkage disequilibrium assessment (LDSC) regression (LDSC), and polygenicity and detection were assessed using Mixer software (Polygenicity and Discovery Analysis Tool).

To assess pleiotropy, loci associated with multiple reproductive diseases were mapped and candidate genes were prioritized using the Open Targets Genetics Portal. Furthermore, a polygenic risk score (PRS) for ICP was developed and validated in both the Estonian biobank and an independent Norwegian cohort (Hunt study), confirming the robustness of the results.

Associations between PRS and other phenotypes were examined using phenomenon-wide association studies (PHEAs). All analyzes were adjusted for population stratification and potential confounders.

Study results

A total of 195 genome-wide significant loci were identified in 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), WNT family tumor 1 (Wnt1) and folliculogenesis (Pax8), Wnt family tumor 1 (Wnt1) and Folliculogenesis (Pax8), Wilms tumor 1 (Wt1) and folliculogenesis (Pax8), are recorded as important contributors to female reproductive health. Additionally, novel ovarian cyst loci such as PDE4D, ID4 and NR0B1 have been identified, providing new insights into folliculogenesis and potential drug targets.

Many of the identified genetic risks are related to hormone signaling and the development of reproductive organs, explaining why so many conditions affect fertility and pregnancy.

Genetic correlation analysis revealed significant associations between various reproductive disorders. Notably, strong correlations have been observed between uterine fibroids and excessive menstruation and between cervical dysplasia and cervicitis. Interestingly, the study also reported a negative genetic correlation between PCOS and preterm birth, contradicting epidemiological studies and highlighting the need for further investigation.

These results suggest that overlapping genetic pathways contribute to these conditions. Polygenicity analysis revealed that reproductive health disorders have a high degree of genetic complexity, with many small effect variants contributing to disease susceptibility. Estimates of heritability varied widely, from 1% to 21%, with higher estimates for metabolic diseases such as ICP (12–30%) and PCOS (10–21%).

PRS for ICP showed a significant association with disease risk. Women in the highest decile of the PRS had an ICP prevalence of 6.1% 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 × 10⁻³).

The model with PRS improved risk prediction and achieved an area under the curve (AUC) of 0.66. Importantly, validation in the hunting study increased the association with an odds ratio of 1.7 (95% CI: 1.3–2.1, p = 2.8 × 10⁻⁹) per standard deviation, and an AUC of 0.71, highlighting the potential clinical benefit.

Aside from ICP, PHEAs identified cholelithiasis as a phenotype significantly associated with the ICP-PRS, supporting a common 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, neck dysplasia and infertility, which appeared across genetic links.

These results have far-reaching implications. Understanding genetic predisposition can help individuals make informed reproductive health decisions, assist clinicians in early diagnosis, and guide public health policy to better address reproductive disorders on a global scale. Personalized risk assessment could transform women's healthcare by shifting from reactive to proactive interventions. Furthermore, 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

In summary, the results underscore the polygenic nature of these conditions and collectively highlight 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 diverse reproductive diseases and pave the way for targeted therapeutic strategies. Nevertheless, the study acknowledges limitations such as reliance on ICD-10 codes and the need for further replication in non-European populations.

Integrating genetic data with clinical and environmental factors is essential to translating these findings into improved health strategies for women worldwide. By leveraging genetic insights, health systems can better allocate resources, design preventive interventions, and ultimately improve the quality of life for millions of women worldwide.

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