Adiposity and risk of respiratory diseases in the UK Biobank
Observational studies suggest that excess general and abdominal adiposity are associated with increased risk of chronic respiratory diseases. However, most studies were limited to diagnosed chronic obstructive pulmonary disease, lung cancer, or pneumonia, and the causal relationships between adiposity and such outcomes are largely unknown. Furthermore, different components of body composition or body fat distribution might have very different relevance to risk from different respiratory conditions. The UKB is uniquely positioned to address these questions.
The UK Biobank (UKB) is a cohort of 0.5M participants with extensive phenotypic information from questionnaire and spirometry, biochemistry and imaging, large-scale whole genome genotyping, polygenic risk scores, hospitalisation and mortality registers. This project aims to systematically asses the observational and genetic relevance of various markers of adiposity to multiple respiratory disease within UKB, and to elucidate the causal relationships between genetic signatures of different markers of adiposity and specific respiratory diseases. In-depth analyses will include both diagnosed and un-diagnosed lung impairment, and their severity.
The aims for this project may include:
- Assess the shape and strength of associations of multiple markers of adiposity with lung function-measures and diagnosed respiratory diseases.
- Employ Mendelian randomisation approaches (one- and two-sample methods) to assess causality of associations between adiposity traits and respiratory diseases.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The student will gain experience in non-communicable diseases epidemiological research and analysis of large-scale prospective data. They will develop skills in conducting systematic literature reviews, study design and planning, statistical programming, data analysis, including Mendelian Randomisation and presentation skills. The student will be supported to publish peer-reviewed papers as the lead author during their DPhil.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
Training in advanced statistics, epidemiological methods, programming, and scientific writing will be provided. Attendance at seminars, workshops and courses provided by the Department and University will also be encouraged.
The ideal candidate will have a Masters degree in statistics/epidemiology/public health/biomedical sciences. Proficiency with STATA, R or SAS is essential.