Objectively measuring physical activity and its health consequences
Low physical activity is associated with an increased risk of cardiovascular disease. However previous studies are largely based on self-reported data that are imprecise and prone to measurement error. Therefore uncertainty exists on: 1) the amount and type of physical activity people should engage in to prevent disease onset; and 2) how much of this variance can be explained by genomics. In response, large cohorts such as UK Biobank now aim to provide new insights on population physical activity through collecting blood samples and distributing wrist-worn accelerometers to participants.
However, flaws exist with traditional epidemiological methods to gain new insights into the physical activity phenotype from these large complex biobanks. For example parametric statistics are used to describe non-normal time series data, and adjustments are not made for sleep time. Furthermore, time spent in different activities (sleep, sedentary behaviour, moderate intensity activity) are all co-dependent which creates issues of collinearity and spurious correlations. Therefore, new methods are needed to assess objectively measured physical activity patterns, their genetic variants and combined association with cardiovascular disease.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
This project will involve:
- Developing methods to robustly describe physical activity characteristics from rich time-series wearable device data
- Traditional epidemiological investigations into the association between objectively measured physical activity patterns and subsequent risk of cardiovascular disease
- Performing genome-wide association studies to detect the pleiotropic effects of genetic variants associated with both physical activity characteristics and disease outcomes
Analysing the gene-environment relationships between objectively measured physical activity 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.
Our training programme will help students develop state-of-the-art skills in large scale health data analysis. The required statistical and bioinformatic approaches for this project are well established, with access to expertise in genomics (Lindgren) and wearable sensors (Doherty) and epidemiological analysis (Bennett). The group has access to some of the largest physical activity & genetic datasets in the world such as UK Biobank and China Kadoorie Biobank.
This project will also benefit from extensive collaborative links with biomedical engineers and epidemiologists, meaning the student will be well placed for future research positions in the health sciences.
A BSc, or ideally MSc, in a discipline with a substantive computational component.