This project aims to understand more about Cardiovascular Diseases (CVDs) and their connections to other health conditions. Using advanced deep learning technology, we’ll investigate the relationships between CVDs and various traits, such as hypertension, diabetes, and obesity. Our goal is to prioritize these connections and identify potential genetic causes. Additionally, we’ll use a method called Mendelian randomization to figure out if these traits might actually cause CVDs. By comparing our deep learning approach with traditional methods, we hope to discover new biomarkers and crucial genes related to CVDs (including disease risk and prognosis) in the UK Biobank data.
Coronary heart disease is a complex condition influenced by genes, environment, and lifestyle. Previous studies have identified many genetic factors linked to CVDs, but we aim to go further. Our new deep learning method, DSpaLaRefiner, allows us to analyze the data more effectively, identifying patterns and relationships between different traits. Unlike traditional methods, our approach does not rely on external data, giving us a unique advantage in discovering new insights. By understanding the genetic correlations and disease susceptibilities and prognosis, we hope to find more accurate ways to predict, prevent, and manage CVDs.
We will use the GATK software for quality control and Plink1.9, ‘survival’ R package, and custom R codes for genome-wide association analysis on the UK Biobank data. DSpaLaRefiner, our in-house deep learning algorithm, will play a crucial role in identifying disease-associated variants and understanding the relationships between different traits. Mendelian randomization analysis will help us investigate if there are causal links between CVDs and other health conditions. This comprehensive approach aims to uncover new insights into CVDs and their connections, potentially revolutionizing our understanding of this complex disease.
The duration of this project is expected to be 3 years; we will consider extending the research duration based on the progress or discoveries made during the project.