Adverse risks of obesity

Background

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 (type 2 diabetes mellitus, hyperlipidemia, liver steatosis, cardiovascular disease, subfertility traits and certain types of cancer etc.).

This studentship aims to dissect the environmental, lifestyle and genetic underpinnings as well as the relationships between an array of obesity traits and their comorbidities through epidemiological investigations and genome-wide association analyses. The full spectrum of obesity traits and their underlying causes and links to adverse outcomes is not well characterised. There are currently very few efficient treatment strategies for obesity. The identification of risk factors would offer an increased understanding for obesity traits that are key to improve the prevention, diagnosis and treatment of diseases.

Research Experience, Research Methods and Training

This project will use data from UK Biobank and the Genetic Investigation of ANthropometric Traits (GIANT) consortium and include:

• Developing an array of phenotypes from simple tape measures to state of the art whole body image scans (using MRI)
• Identifying demographic, disease, family history, environmental and lifestyle factors associated with increased risk of obesity;
• Performing genome-wide association studies to detect novel genetic variants associated with obesity traits, particularly focused on the strong differences between men and women;
• Using “omics” data (GTEx, ENCODE, PPI, etc.) to move from genetic association to function in associated regions

Analysing the gene-environment relationships between obesity traits and adverse health outcomes using emerging methods such as partitioning heritability, LD Score regression, and Mendelian Randomization

Field Work, Secondments, Industry Placements and Training

This project will offer a comprehensive training programme in bioinformatics and genomic science in a new exciting research institute with state of the art facilities. We are committed to training and mentoring students to success.

The required statistical and bioinformatic approaches for this project are well established. Celia Lindgren is an expert in the field of genomic research in obesity traits and has access to some of the largest GWAS datasets available worldwide (e.g. UK Biobank, and the GiANT and CardioMetabolic consortia), and Aiden Doherty has expertise in the extraction of lifestyle health behaviours from wearable sensor data e.g. objectively measured physical activity.

This project benefits from extensive collaborative links both within Oxford, nationally and internationally, which means that the student will be well placed to further develop their understanding of the related biology. 

Potential candidate

A BSc, or ideally MSc, in a discipline with a substantive computational component.