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Approved Research

Integration of traditional risk factors and biomarkers from UK Biobank and other omics data to diagnose, predict, understand, and inform treatment of complex traits or diseases.

Principal Investigator: Professor Chang-Qing Sun
Approved Research ID: 71051
Approval date: February 7th 2022

Lay summary

Aims: In this proposal, we have three main aims: First, we will develop novel approach to improve disease prediction, by combing traditional clinical variables and -omics data (genome, proteome, transcriptome, and metabolome) from UK Biobank and other omics datasets. Second, we will integrate genomic data from UK Biobank and other omics data to further understand the biological mechanism of human complex diseases. Third, we consider using a systematic biology approach (holistic approach) which combines genomic data with other omics data to identify some potential drug candidates for human diseases, which will help us accelerate the discovery of new treatments.

Scientific rationale: Despite traditional observational studies and genome-wide association studies have discovered multiple traditional clinical variables and genetic variation for complex diseases. It is still a big challenge using genomic data or traditional clinical variables only to predict individuals' risk of developing diseases. In addition, the biological mechanisms and interaction between different omics or diseases remained unknown. Furthermore, it is an urgent problem to select high-risk groups and identify new potential disease treatment drugs. Therefore, novel powerful and robust statistical methods (methods that are with good performance and are not unduly affected by outliers) are still pressing need for prediction, diagnosis and identifying new treatment for complex diseases.

Project duration: The project period will be maximally 36 months.

Public health impact: This proposal will develop novel approach to accurately predict the risk of individuals developing complex diseases, identify the causal biological mechanism underlying complex diseases, and provide novel insight into the treatment for complex diseases. The findings will help us understand the biological process of complex diseases and make drug discovery for disease process more efficient.