1. research questions
1.1 What genetic variants and biomarkers are associated with inflammatory myopathies (IM) in the UK population?
1.2 How do comorbidities (e.g., cancer, interstitial lung disease) influence IM progression and outcomes?
1.3 Can multi-omics data improve early diagnosis and personalized treatment strategies for IM?
2. objectives
2.1 Identify IM-related genetic loci through genome-wide association studies (GWAS).
2.2 Characterize serum biomarkers (e.g., CK, autoantibodies) linked to disease subtypes.
2.3 Develop predictive models integrating genetic, clinical, and biomarker data to stratify disease risk and therapeutic response.
3. scientific rationale
Inflammatory myopathies (IM), including polymyositis and dermatomyositis, are rare autoimmune disorders causing progressive muscle weakness and systemic complications. Despite their severity, IM pathogenesis remains poorly understood, with limited diagnostic biomarkers and no curative therapies. Existing studies are hindered by small sample sizes and lack of multi-omics integration. UK Biobank’s large-scale genetic, proteomic, and longitudinal health data (n ! 500,000) offers a unique opportunity to address these gaps. By analyzing GWAS data, serum biomarkers, and linked electronic health records (e.g., HES, cancer registries), this study aims to uncover novel genetic drivers, clarify comorbidity relationships, and generate translatable tools for early intervention. Findings may advance precision medicine in autoimmune diseases, aligning with UK Biobank’s mission to improve public health through data-driven research.