Obesity is a heritable and heterogeneous condition that is defined as the accumulation of excess body fat to the extent that it results in long-term adverse health outcomes (psychiatric conditions, osteoarthritis, type 2 diabetes mellitus, hypertension, hyperlipidemia, liver steatosis, cardiovascular disease, subfertility traits and certain types of cancer, amongst others). In this proposal, we propose to dissect the environmental, lifestyle and genetic underpinnings as well as the relationships between an array of obesity traits and their comorbidities in UK Biobank through epidemiological investigations and genome-wide association analyses. Currently ~50% of the population in the UK is overweight with a further 25-30% that are obese and the socioeconomic cost incurred by obesity and related comorbidities are high. The full spectrum of obesity traits and their underlying causes and links to adverse outcomes is not well characterized. There are also few (cost) efficient treatment strategies for obesity. The identification of risk factors an increased understanding for obesity traits thus fits with the central aim of UK Biobank to improve the prevention, diagnosis and treatment of diseases. We will conduct epidemiological analyses to identify demographic, disease, environmental and lifestyle factors associated with increased risk of obesity traits. We will then perform genome-wide association analyses of DNA-genotyping data to identify novel genetic variants that increase the likelihood of obesity traits, after accounting for these ?epidemiological? risk factors. We will also perform analyses to investigate epidemiological and genetic risk factors for obesity trait-related adverse health outcomes and how they are correlated with, and possibly causing, each other. For these analyses, we request access to data generated for all individuals in UK Biobank. We request relevant environmental and lifestyle factors potentially associated with obesity traits (activity, diet, etc.) as well as in depth measures of adiposity (anthropometric measures, MRI data etc.), associated risk factors (lipids, HbA1c, etc.) and co-morbidities (prevalent and incident) inclusive of downstream adverse health outcomes (psychiatric conditions, osteoarthritis, T2D, hypertension, hyperlipidemia, liver steatosis, cardiovascular disease, subfertility traits, certain types of cancer). We also request genetic data for all individuals to evaluate association of genetic variants with obesity traits after accounting for lifestyle factors.
|Return ID||App ID||Description||Archive Date|
|1942||11867||GWAS identifies 14 loci for device-measured physical activity and sleep duration||3 Feb 2020|
|2804||11867||Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry||5 Nov 2020|
|1943||GWAS identifies 14 loci for device-measured physical activity and sleep duration||Doherty A et al.||2018||Nat Commun|
|2805||Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry||Pulit SL et al.||2019||Hum Mol Genet|