This proposal aims to apply UK Biobank data to investigate the causal genetic and molecular factors underlying human disease using novel statistical methods. Our focus is on moving beyond associations to uncovering biological factors, such as genetic variants and proteins, that play a causal role in disease onset and progression. Existing genome-wide association studies (GWAS) have identified many genetic loci associated with complex diseases, yet many of these associations do not establish causality and often leave the underlying mechanisms unclear. Reliable identification of disease-causing genes and molecular factors is essential for advancing both understanding and treatment of human disease. To tackle this challenge, we have developed a statistical causal method for discovering disease-causing genes using RNA sequencing data. In previous work, we demonstrated the feasibility of this approach on relatively small-scale RNA sequencing datasets. While encouraging, these studies were limited in scale and diversity, making it essential to test and refine our methodology in a large population cohort. UK Biobank provides an unparalleled opportunity for this, offering comprehensive genetic and biomarker data linked with detailed health records across half a million participants. Using UK Biobank data, we aim to develop and apply new statistical causal methods to identify disease-causing genetic and molecular factors across a wide range of diseases, and to evaluate the performance, robustness, and scalability of these methods at scale. Our goal is for the proposed methods and findings to contribute to both methodological innovation and biological discovery.
