Exploring the genetic space of spontaneous POI/EM based on an integrated analysis of common and rare monogenic variants
Approved Research ID: 74808
Approval date: October 26th 2021
1-2% of women experience interruption of their menstruation prior to the age of 40, while a substantial 8-10% of women undergo early menopause before the age of 45. Considering that natural fertility ceases about 10 years before menopause and the common trend of delaying childbearing to older ages, developing reliable predictors of individual Age at Natural Menopause (ANM) is becoming very important in the general context of women's health.
The most used predictor of age at menopause variation is currently Anti-Müllerian hormone (AMH) concentration, but AMH-based forecasts have several limitations: they have low accuracy, in particular with increasing age of the woman, and they cannot predict extremely anticipated menopause. Genetics, on the other side, can play an important role in this context: recent studies have shown that both common and rare genetic variants contribute to extreme variation in age at menopause.
The aim of the present project is to leverage the large-scale UK Biobank genomic data to discover novel rare highly penetrant rare variants and to devise newer generation genomic predictors for ANM variation. A key question that we would like to address is how monogenic and polygenic risk interact: can disease risk from a monogenic variant that causes major disruption to a specific pathway to be meaningfully modified by polygenic risk factors that involve small perturbations to a wide range of cellular pathways? To what extend monogenic and polygenic genomic variations can synergistically enhance the prediction performance toward the clinical extremes of ANM? Also, we would like to test how genetic factors related to ANM influence other parameters defining the general woman's health and reproductive behavior. Apart from the emotional and psychological burden, women with clinical lower extremes of ANM are at increased risk for several chronic diseases, comorbidities, for which genetic architecture has been already investigated by genome-wide association study (such as cardiovascular morbidity, metabolic diseases and cognitive impairment). Here, we also aim to systematically assess the genetic overlap between women with early menopause and several associated chronic diseases and to develop early genetic diagnostic predictors for women at higher risk of comorbidities.
The present research project has an estimated duration of 24 months. Our finding will lead to new enhanced diagnostic approaches for early menopause risk prediction, way beyond traditional age at menopause forecasts. In conclusion, the present research has the potential to significantly help improve reproductive and women's health.