Genome-wide association studies of continuous activity tracker data
Principal Investigator: Professor Cathryn Lewis
Approved Research ID: 13427
Approval date: January 1st 2016
The aims of this research are to a) identify genetic variants associated with physical activity, b) establish the total genetic contribution to activity and the genetic overlap between physical activity and cardio-respiratory fitness, and c) create a genetic risk score by summing the information across all activity-associated genetic variants (UK Biobank) to predict blood metabolite levels (Metabolomics GWAS Server ? publicly-available data). We would like to quantify the extent to which physical activity and fitness are under the influence of genetic variation using whole-genome data, and create a genetic risk map between activity and human blood metabolites. Understanding the genetic links between activity, fitness, and blood metabolites will provide insight into the benefits of activity, and may inform drug prescription and reveal new drug (and exercise) targets. To move the concept of personalised medicine into clinical practice we need to link all an individual's information, including activity levels and genetics. Clinicians and individuals will benefit from being better able to navigate the sea of health-related information; individuals will be better equipped to make healthy choices and play an active role in their healthcare, especially the elderly and chronically ill. This research will test for association between summary phenotype data on tracking and fitness and genetic variants in genome-wide association studies (GWAS). Techniques that consider all the evidence from GWAS results simultaneously (e.g. polygenic risk scoring, SNP-heritability methods), making use of data from the UK Biobank project and publicly-available GWAS data for blood metabolites (Shin et al. Nature Genetics, 2014) will also be applied. All analyses will be performed using freely-available, powerful, and up-to-date software. The subset of UK Biobank participants with availability of (1) activity tracker and cardio-respiratory fitness data, and (2) genotype data.