Genetic risk factors for endometriosis identified and validated

A breakthrough in understanding the biology of endometriosis 

Endometriosis affects around 10% of women of reproductive age, yet its underlying biology has remained only partially understood. 

Previous studies have used GWAS and polygenic risk scores, which look for individual effects of genetic factors on overall disease risk and fail to capture the complexity underlying chronic disease like endometriosis where many genes and lifestyle factors come together to increase disease risk.

In this presentation from the European Endometriosis Congress 2026, Krystyna Taylor (VP Product Strategy, PrecisionLife) presents research using PrecisionLife's combinatorial analysis platform, which is uniquely able to capture the non-linear effects of genetic interactions, making it better served to understand complex chronic diseases.

Using large datasets including UK Biobank and All of Us, the research identifies and validates novel genetic risk factors for endometriosis, providing a robust and reproducible view of the biology driving the condition.

 Key findings: first validated genetic drivers and novel biological insight 

The study identified over 1,700 genetic signatures associated with endometriosis, many of which were reproducible in an independent multi-ancestry cohort.

These findings establish:

• Validated genetic risk factors for endometriosis, reproducible across populations
• High-frequency genetic signatures mapping to 98 genes, providing a robust core set of disease drivers
• 75 novel genes linked to endometriosis, opening entirely new avenues for research and therapeutic development
• Biological pathways directly tied to disease mechanisms, including cell adhesion, proliferation, angiogenesis, fibrosis, and neuropathic pain

Together, these results provide the most comprehensive and reproducible understanding of the genetic architecture of endometriosis to date.

From uncertainty to actionable insight

A lack of validated biological drivers has long limited progress in endometriosis - contributing to delayed diagnosis, inconsistent treatment, and slow therapeutic innovation.

By establishing reproducible, biologically grounded genetic risk factors, this research creates a foundation for:

• Earlier and more confident identification of patients at risk
• Mechanism-based patient stratification, enabling more targeted care
• Discovery of new therapeutic targets, particularly among newly identified genes
• Improved clinical trial design, using validated biomarkers to select patients more likely to respond

For healthcare systems, this offers a pathway to reduce diagnostic delays and improve outcomes. For pharma and biotech, it provides validated targets and biomarkers to support more efficient and successful drug development.

Going beyond traditional genetic approaches

While GWAS and polygenic risk studies have identified important associations, they explain only a small proportion of disease risk. This work further demonstrates that combinatorial analysis can uncover and validate the interacting genetic drivers of complex disease, revealing critical biology that single-variant approaches miss.

By identifying and validating novel genetic drivers of endometriosis, this research marks a pivotal step towards mechanism-driven diagnosis, treatment, and therapeutic development in women’s health. 

This represents a step-change in how we understand and study diseases like endometriosis and a foundation for precision medicine in conditions affecting women's health.

Find out more

Read our full published study to learn more about our findings and their potential impact in improving women's health.