Aims: This project aims 1) to explore deeply the roles of genetic variants, environmental factors, and their complex interplays in the development and progression of across the wide spectrum of rheumatic and musculoskeletal diseases (RMDs), 2) to shed new light on causal pathways through robust causal inference technologies such as Mendelian randomization, and 3) to establish risk prediction models based on the specific and key risk factors using both conventional and machine learning methods.
Scientific rationale: RMDs are the leading cause of disability worldwide and consume a large amount of health and social resources. Although therapeutics for RMDs have evolved remarkably, substantial unmet medical needs still exist, especially in the connective tissue diseases such as osteoarthritis and fibromyalgia, for which effective drug treatments are frequently lacking. As such, identification of risk factors associated with RMDs and subsequent intervention for high-risk individuals remain the most effective measures to reduce the disease burden. The large-scale and high-quality data from the UK Biobank can allow to better investigate the genetic and environmental risk factors as well as their weights and interactions for RMDs.
Project duration: We anticipate the project duration to be 36 months.
Public health impact: By identifying causal risk factors and gene-environment interactions involved in RMDs, this project will extend our understanding of the pathogenesis of RMDs, provide more powerful models for risk assessment, and facilitate the development of prevention and treatment strategies.