Harnessing the potential of population biobanks to further our understanding of musculoskeletal disease

The emergence of large-scale population biobanks, such as the UK BioBank (http://www.ukbiobank.ac.uk/), which collect a wealth of genetic, physical and clinical data represent huge potential for understanding the factors that contribute to disease. These resources will play a key role in realising the future ambitions of integrating genetics into healthcare such as the National Genomic Healthcare Strategy.

Combined genetic, clinical and environmental risk factors define an individual’s overall liability to a particular disease. It is therefore essential to consider all these factors when attempting to understand disease risk and the availability of these biobanks allow us to begin to explore disease liability using a multifactorial approach. These datasets will allow us to answer key questions such as understanding disease relationships, identify and explaining causal relationships between correlated traits and helping to understand disease heterogeneity through intermediate phenotypes. However the complexity of these large datasets present a number of analytical challenges and mining the data for clinical benefit represents a key research topic over the coming years. These challenges include the identification of disease specific genetic risk factors, identifying clinical and environmental predictors from large numbers of possible variables, and appropriately combining the genetic, biomarker and epidemiological data.

The proposed PhD project will exploit the wealth of genetic and clinical data from the UK Biobank, and other available population biobanks, to further our understanding of risk to musculoskeletal diseases, such as rheumatoid arthritis, at the Versus Arthritis Centre for Genetics and Genomics based at the University of Manchester.

Entry Requirements:
Applications are invited from UK/EU nationals only. Candidates are expected to hold (or be due to obtain) a minimum upper-second (or equivalent) class undergraduate degree in genetics, bioinformatics, biostatistics or a relevant quantitative subject. A background in genetics is not required for candidates with a strong interest in statistics.

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor.You MUST also submit an online application form - choose PhD Genetics.