Complex diseases, including cancer, neurodegenerative disorders, and cardiovascular diseases, are among the leading global health challenges. Although GWAS studies have successfully identified numerous genetic markers associated with these diseases, their underlying pathogenic mechanisms remain largely unclear. Recent advances in single-cell and spatial transcriptomics have enabled a deeper exploration of disease mechanisms by revealing cellular heterogeneity, gene expression dynamics, and spatially resolved regulatory networks within the tissue microenvironment. The integration of these technologies offers a more comprehensive and in-depth perspective for precision medicine. However, the integration of highly heterogeneous and large-scale multi-omics datasets remains challenging, particularly across cohorts with varying sample sizes, emphasizing the need for innovative analysis strategies.
To advance our understanding of the genetic architecture of complex diseases and their interactions with environmental factors, we propose to integrate genomic data with multi-omics datasets, including metabolomics, proteomics, environmental exposure, and both single-cell and spatial transcriptomics data. The UK Biobank, with its extensive population-scale dataset and rich genetic, clinical, and environmental information, presents an unparalleled opportunity for such integrative analyses. We aim to leverage UK Biobank data alongside external multi-omics datasets to identify novel disease-associated genetic variants and their corresponding risk factors, validating our findings across independent cohorts. To achieve this, we will develop and refine bioinformatics tools to enhance the efficiency of data integration and analysis, enabling a systematic exploration of genetic risk factors, gene regulatory mechanisms, and environmental influences on disease susceptibility. This project offers valuable insights that will advance precision medicine and personalized treatment strategies.
