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


Project Description

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.

Funding Notes

Multiple sources of scholarship funding are potentially available, including university, research council (EPSRC) and research group. Some are open to international students, some to EU and some UK only.

Applicants should have a good first degree in mathematics, statistics or another discipline with substantial numerical component. Applicants with degrees in other subjects (e.g., biology) should have the equivalent of A-level/Higher mathematics, and experience using statistical methods; such candidates should discuss their qualifications with the Postgraduate Officer. A masters-level degree is an advantage.

Further details of the application procedure, including contact details for the Postgraduate Officer, are available at View Website

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

How good is research at University of St Andrews in Mathematical Sciences?

FTE Category A staff submitted: 30.60

Research output data provided by the Research Excellence Framework (REF)

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