This research project aims to leverage the extensive datasets available through the UK Biobank to deepen our understanding of disease mechanisms and treatment responses in Oncology, Neurology/Immunology, and Fertility. Our primary research questions include:
Oncology: What are the relationships between polygenic risk scores, environmental factors and cancer outcomes? We aim to investigate how integrating polygenic risk scores with lifestyle factors such as smoking and BMI can enhance risk stratification for rare cancer screening. Additionally, we will explore the causal roles of circulating biomarkers (e.g., CRP, lipids) in cancer risk and progression through Mendelian randomization, which may lead to the nomination of novel drug targets.
Neurology and Immunology: How do environmental exposures and genetic factors interact to influence the onset and progression of neurodegenerative and autoimmune diseases? We will analyze brain MRI data alongside cognitive assessments and genetic markers to identify prognostic features for conditions like multiple sclerosis. Furthermore, we will construct autoimmune disease cohorts to uncover endotypes and stratification markers based on HLA variation and immune-related loci.
Fertility: What genetic and phenotypic factors drive reproductive health and treatment responses? We will link reproductive traits and diagnoses (e.g., PCOS, endometriosis) with hormonal profiles to identify key drivers of fertility. Our analysis will also include the impact of modifiable factors such as BMI and smoking on fertility outcomes, aiming to develop predictors of treatment responders.
By utilizing phenotypic, laboratory, imaging and genomic data, we seek to identify novel biomarkers and therapeutic targets that can inform our current pipeline. Our interdisciplinary approach will employ advanced statistical methods and machine learning, to enhance patient stratification, optimize treatments and improve clinical and safety outcomes.