Genetic regulation of traits and disorders affecting women's reproductive health
Principal Investigator: Dr Sally Mortlock
Approved Research ID: 54861
Approval date: April 6th 2020
Female reproductive disorders represent a significant burden on women's health and welfare, society and the economy. Our group are part of world leading research using genetics and genomics to find factors explaining variation in reproductive traits and increasing risk for reproductive diseases. Risk factors for complex diseases such as reproductive disorders can span genetics, lifestyle, behavioural, occupational and environmental exposures as well as health conditions. Identification of risk factors for disease allow us not only to better understand disease aetiology but can also present opportunities to intervene with disease progression or prevent disease onset. Randomised control trials (RCTs), whilst popular in medical research as a method to test causality, can be costly, time-consuming, unethical or impractical. Genetic variants offer an innovative solution to test casual relationships between risk factors and disease. Genetic variants offer an innovative solution to test casual relationships between risk factors. Over three years we aim to use the large comprehensive set of genetic and phenotypic information in UK Biobank alongside our own datasets and other large datasets to investigate the relationship between reproductive traits and between potential risk factors and reproductive disorders and to investigate shared and disease specific genetic risk factors. Genetic overlap between disorders and traits can indicate shared underlying genetic risk factors and disease pathways that may be targets for treatment and prevention of reproductive disorders. As we move into the era of personalised medicine, genetic testing and clinical genomics are powerful tools to inform both future and current health decisions to prevent, manage and treat disease. Genome-wide polygenic risk scores (PRS) are a way in which variation at multiple genetic variants across the genome is used to predict risk of disease. We aim to develop a risk score, using a combination of genetic and non-genetic risk factors, for reproductive disorders, focusing on endometriosis, to enable risk stratification. Our work will provide insights into the relationship between female reproductive disorders, in particular endometriosis, and associated risk factors. In the future, this may help identify novel biological pathways and therapeutic targets for improving prevention and disease management and help our understanding of the molecular aetiology of disease risk. Improving the accuracy of risk prediction models and identification of individuals at greater risk of disease in a population can enable more effective healthcare management, targeted screening, pre-emptive therapies and lifestyle changes and reduce unnecessary disease burden.