Exploring the relationship of genetics, proteins, metabolites and other biomarkers with clinical endpoints
Approved Research ID: 95801
Approval date: December 14th 2022
Blood-based biomarkers including metabolites and proteins may be important in disease. We wish to use conventional and cutting-edge analyses to better understand the role that these biomarkers have in disease. We also want to better understand the relationship of genetic variants combined into a score exhibit with biomarkers and disease.
To conduct observational analyses between metabolites, proteins, conventional biomarkers, risk factors and diseases To conduct GWAS of protein and metabolite traits together with clinical biochemistry traits to identify associated single nucleotide polymorphisms To conduct genetic epidemiological analyses (e.g. Mendelian randomization, colocalisation) of proteins and metabolites as exposures onto diseases in UKB and summary GWAS data from large consortia (e.g. CARDIoGRAMplusC4D, DIAMANTE) To conduct genetic epidemiological analysis of proteins, metabolites and biomarkers as outcomes to explore whether complex biomarkers (e.g. adiposity) and diseases (e.g. type 2 diabetes) causally influences the proteome and metabolome To examine the relationship of polygenic scores, biomarkers, quantitative traits and disease risk.
To understand the transferability, generalizability, and utility of these risk prediction models developed in one large cohort into a different cohort (i.e., 23andMe and UKBB). Understanding this generalizability is a critical step in the broader potential for these risk models to estimate disease risk or traits. Different modeling methods will be evaluated in their ability to demonstrate superior transferability.