Development and validation of enhanced prediction models based on polygenic analysis and clinical factors for chronic diseases
Approved Research ID: 92668
Approval date: August 25th 2022
Chronic diseases have become one of the leading causes of mortality in the world and brought a huge burden of disease. Therefore, early risk assessment and prevention of chronic diseases are important goals of health care. The causes of chronic diseases are complex and multifactorial. Genes, environment and lifestyle habits play important roles in the occurrence and development of chronic diseases. However, the added value of poly-genic risk score on top of clinical risk factors or validation prediction models of many chronic diseases is not examined and the clinical utility of polygenic risk score in risk prediction remains unclear. This study proposes to establish a prospective cohort of natural populations by requesting the demographic information, education, lifestyle, health and medical history, physical measurements, death register, cancer register, biochemical and haematological assays, follow-up data and genomics data from the UK Biobank.
We plan to construct PRS models and integrated models (including polygenic risk score and no-genetic risk factors) by incorporating the large genome-wide association results for major chronic diseases in UKBIOBANK cohort. Then we will apply the models in a large population-based prospective cohort in China to assess how the polygenic risk affect lifetime trajectories of chronic diseases risk and whether the polygenic score could refine risk stratification beyond the clinical risk prediction.
Our aim is to develop and validate enhanced prediction models based on polygenic analysis and clinical factors to refine risk stratification for chronic diseases. We plan to evaluate the potential of the polygenic risk score to improve risk prediction for chronic diseases. Further, we plan to evaluate the relative contribution of non-genetic and genetic factors to the chronic diseases, and gene-environment interactions among the samples.
We expect to develop enhanced prediction models (including polygenic risk score and no-genetic risk factors) to identify high-risk individuals for targeted intervention (like adjusting life style and dietary habit), guiding the precise prevention and management of chronic diseases in order to reduce the incidence of some major chronic diseases and reduce the diseases burden of family and society.