A whole exome analysis of chronic disease and their risk factors

background

Understanding the consequences of altering each protein-coding gene in the genome on chronic disease and established risk factors can provide key insights into aetiology and may propose future strategies for prevention. 

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The selected individual will acquire computational skills completing statistical analyses of whole exome sequence and prospective cohort data. This will include developing skills in Python/R and in bash using high performance computing. In completing this project, the individual will work with a team of collaborators to advance our understanding of the causes common chronic disease including cancers, neurodegenerative and cardiovascular diseases. This includes processing data newly available on 50 thousand whole exomes within the Million Women Study and complementary data available within the UK Biobank. By the end of this DPhil, individuals will be a competent genomic epidemiologist.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The individual will receive computational training and will be expected to present results at national/international conferences/meetings. 

PROSPECTIVE  STUDENT

The ideal candidate will have a Master's degree in a relevant area (e.g. statistics/epidemiology/computer sciences/bioinformatics) but most importantly, the individual must have a resilience to solving complex problems and the ability to embrace the unknown and find working solutions.