Details of Grant 

EPSRC Reference:	GR/M14197/01
Title:	INFINITE DIMENSIONAL STOCHASTIC MODELS AND INFERENCE FOR POPULATION GENETICS
Principal Investigator:	Donnelly, Professor P
Other Investigators:	Laws, Dr C Laws, Dr C Etheridge, Professor A
Researcher Co-Investigators:
Project Partners:	University of Wisconsin Madison
Department:	Statistics
Organisation:	University of Oxford
Scheme:	Standard Research (Pre-FEC)
Starts:	01 October 1998	Ends:	30 September 2001	Value (£):	132,055
EPSRC Research Topic Classifications:	Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:
Summary on Grant Application Form
Recent advances in molecular biology have led to an explosion in the availability of DNA sequence data documenting genetic variability at particular loci within populations. While such data is high dimensional, it is highly positively correlated, and represents only a snapshot, at a single timepoint, of a complicated underlying process. Correct qualitative interpretation of such data requires an understanding of realistic (hence sophisticated) stochastic models of these processes. The proposed research will study detailed properties of available models and develop tools and results for a new class of measure-valued processes which incorporate rather general interactions between individuals. Efficient inference requires exact or approximate likelihood methods which are highly non-trivial in this context. Two approaches (MCMC and importance sampling) have recently been introduced. These are not practicable for typical data sets under anything but the simplest (and hence at least realistic) models. The research will develop a wider class of importance sampling and MCMC methods, utilising specific results in the stochastic modelling part to improve efficiency substantially, and generalise the method to the models developed here. Specific applications would include inference under selection, and likelihoods and predictive probabilities for linkage disequilibrium around disease mutations and anonymous markers.
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.ox.ac.uk