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Investigating modes of action of genetic risk variants through integrated analysis of multiple high-dimensional “omics” data.


   School of Mathematics and Statistics

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  Prof Andy Lynch, Dr Michail Papathomas  Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

Genome-wide association studies have identified thousands of genomic loci that are associated with higher risk of a trait (often a disease such as breast cancer). While these associations may have been identified, the mechanisms through which they act have tended not to be elucidated. This despite the growing number of diverse data sets potentially available for the purpose (see for example The American Journal of Human Genetics 103, 637–653 for discussion).

This project will look to develop a flexible modelling framework to incorporate many potential sources of evidence in suggesting and evaluating mechanisms of action. This could potentially build upon work of Papathomas et al. (2012) and related efforts in producing a flexible Bayesian approach for the analysis if GWAS data. The evaluation of potential mechanisms may then feed back into the detection and prioritization of association loci through, e.g., specification of prior probabilities.

For more information, please see the School's Postgraduate Research page, and in particular the information about Statistics PhD opportunities.


Funding Notes

Full funding (fees, plus stipend of approx. £15,840) is available for well-qualified students; we encourage applications as soon as possible to maximize your chances of being funded. UK, EU and other overseas students are all encouraged to apply. New PhD students would typically start in September 2022, but this is flexible. More information is available School's Postgraduate Research web page -- please see the link at the bottom of the project description.

References

Papathomas, M., Molitor, J., Hoggart, C., Hastie, D. and Richardson, S. 2012. Exploring data from genetic association studies using Bayesian variable selection and the Dirichlet process: application to searching for gene-gene patterns. Genetic Epidemiology 36, 663-674

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