Uncovering the genetic drivers of female infertility in endometriosis and women's health related conditions

Advancing understanding of infertility through mechanism-level genetics 

Female infertility affects an estimated 10–15% of couples globally, with complex and often poorly understood causes. For many women, conditions such as endometriosis, adenomyosis, uterine fibroids, and polycystic ovary syndrome (PCOS) contribute to challenges in conception, yet clinical pathways to diagnosis and treatment remain inconsistent and frequently delayed.

In this presentation from the European Endometriosis Congress 2026, Krystyna Taylor (VP Product Strategy, PrecisionLife) shares new research applying PrecisionLife’s AI-led combinatorial analytics platform to uncover the genetic mechanisms underlying these conditions and their link to infertility.

Drawing on large-scale datasets including UK Biobank and All of Us, this research moves beyond traditional single-variant genetic studies to identify combinations of genetic variants associated with disease risk and patient subtypes.

Key findings: distinct genetic subgroups and reproducible biological signal 

The analysis identified hundreds of genetic variant combinations associated with endometriosis, many of which were reproducible across independent cohorts. These signals mapped to genes with known biological relevance to reproductive health, as well as novel targets not previously linked to the disease.

Importantly, the research revealed:

• Distinct patient subgroups defined by shared genetic mechanisms, highlighting the heterogeneity of endometriosis and related conditions
• Genetic signatures associated with fertility status, suggesting a biological basis for differences in clinical outcomes
• Cross-condition insights spanning endometriosis, adenomyosis, fibroids, and PCOS, reflecting the interconnected nature of female reproductive health

These findings begin to translate complex genomic data into clinically meaningful stratification, with the potential to better understand why some women experience infertility while others with similar diagnoses do not.

Earlier insight and more personalized care 

By identifying the underlying biological mechanisms driving disease and infertility risk, this research opens the door to:

• Earlier identification of women at higher risk of infertility, enabling more proactive care pathways
• More precise referral and triage, reducing time to appropriate specialist support
• Stratified approaches to fertility treatment, improving outcomes and reducing unnecessary interventions
• New targets for therapeutic development, supporting innovation in women’s health

For healthcare systems, this represents an opportunity to improve outcomes while reducing the cost and inefficiency associated with late diagnosis and ineffective treatment cycles. For pharma and biotech, it provides a route to more targeted drug development and better-designed clinical trials in women’s health.

Mechanism-driven women’s health  

This work reflects a broader shift from symptom-based classification to mechanism-driven understanding of disease, particularly in complex, chronic conditions like endometriosis.

While further validation in larger and more diverse populations is ongoing, these findings demonstrate how combinatorial analytics can uncover the biological complexity underlying female infertility and translate it into actionable insight.

Find out more

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