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PhD STUDENTSHIPS, MRC BIOSTATISTICS UNIT, CAMBRIDGE The BSU is an internationally recognised research unit specialising in statistical modelling with application to medical, biological or public health sciences. Details of the work carried out in the Unit appear on the website www.mrc-bsu.cam.ac.uk. The Unit has at least 2 MRC PhD studentships to start from October 2006. Awards cover Cambridge University fees and a stipend for a period of 3 years. Awards are subject to strict eligibility criteria. In addition, applicants must have or expect to get a first or high 2.1 honours degree in mathematics, statistics or a related discipline. A masters degree is highly desirable but not essential. Applications should be returned to Dr. Linda Sharples at the address below or by e-mail by 28th February 2006. Interviews will take place in the week beginning 13th March. Examples of some of the projects available for PhD study are given below. Other projects will also be considered. Title: Analysis of genome wide association studies Supervisor: Dr. Frank Dudbridge With the completion of the human genome sequencing project, many large scale projects are now underway to unravel the genetic basis of common diseases. To analyse these studies effectively, new methods are required to exploit the complexity of the underlying genetics while managing the genomewide scale of the data. This project will explore methods for finding associations from multiple hypothesis tests, for integrating Bioinformatics knowledge with genetic epidemiology, and for efficient analysis of multiple-cohort studies. The work will be motivated by data from the European Bloodomics consortium, which is identifying genetic risk factors in coronary artery disease. Title: Benchmarking of longitudinal healthy active life expectancy methods Supervisor: Dr. Fiona Matthews Longer life expectancy is no longer the goal for most developed nations. The aim is to live a healthy and longer life. Over recent years methods have been developed for measuring healthy active life expectancy using longitudinal data. To date these methods have been specific to the study type that underpinned the research. There is now a consensus among HALE researchers that these disparate methodologies need to be compared using similar data sources with known properties. These discussions have been led by Professor Carol Jagger who will co-supervise the project. Professor Jagger co-editor of the book? Determining Health Expectancies?, a 10 year compendium of the work of REVES (the International Network on Health Expectancy and the Disability Process) and responsible for the methodology section. The aim of this project would be to collaborate with the worldwide group of HALE researchers to compare each of the methodologies in turn. A detailed investigation of each methodology together with the assumptions and limitations will be undertaken. Further scope for methodological development of the current techniqies will be evaluated. A toolkit for new researchers will be developed and evaluated using data sources from the UK and the US. Agreement is already in place with collaborators within the UK and abroad to assist with data and programs for this project. Title: Longitudinal studies of chronic diseases with applications to psoriatic arthritis Supervisor: Prof. Vern Farewell This PhD project will make use of the unique data resource derived from a 25 year study of over 600 patients with psoriatic arthritis. Building on previous work, the aim will be to characterise patterns of disease that may also inform treatment decisions. A particular focus will be on the modelling of joint destruction at the individual joint level and its relationship to inflammation patterns in the same and neighbouring joints. Initial approaches will likely involve the use of random effects models to reflect the complicated correlated outcome data of interest. The appropriate definition and incorporation of explanatory variables at both the joint and individual level will be required. There will also be the possibility of incorporating genetic information into the model building process. Title: Studies of genetic association and integrative genomics Supervisor: Dr. Carlo Berzuini We are developing a general framework for studies of genetic association based on unrelated cases and controls together with nuclear families ascertained via an affected proband. The method we have in mind involves a sequence of two analysis stages. In the first stage, all non-founder individuals, which belong to a family without being among its founders, are removed. The resulting - simplified - dataset is analyzed via standard packages to generate a set of simulated (complete and phase-solved) datasets with corresponding ``posterior weights". The second stage of the analysis involves re-incorporating the removed individuals, solving each family's chromosome transmission pattern (allowing for genotyping error and recombination) and updating the posterior weights to reflect the newly incorporated information. The resulting (weighted) collection of imputed datasets is used for relative risk estimation and or testing, for example via suitable Mantel-Haenszel type tests, or via multiple weighted logistic regression. We aim at enhancing and generalizing the above method, by incorporating new ideas of haplotype clustering, as well as by incorporating knowledge from bioinformatic databases. We shall apply the methods to our available DNA database generated from an isolated population with high prevalence of Multiple Sclerosis in Sardinia, Italy. This project will be jointly supervised by Luisa Bernardinelli. A second project concerns the development of statistical methods for integrative genomics. We want to develop methods for a combined analysis of data representing different levels of organization of the genome, the proteome and the metabolome. We want to apply these methods to data generated via advanced experimental platforms within our European Project collaboration. In particular, our available data will include micro-array gene expression levels, genome-wide genotypes of individuals affected by various clinical phenotypes (such as cardiovascular disease), data from cell function assays, array CGH data, DNA methylation data, small metabolite concentration profile data, and so on. One idea will be to consider a list of "candidate" genes with elevated functional annotation with respect to a specific clinical phenotype, such as cardiovascular disease. Then we shall apply different methods of multiple test correction (Bonferroni, false discovery rate, (permutations) to find statistically significant associations of single nucleotide polymorphisms (SNPs) along the genome with expression variation in genes belonging to the above candidate list. More specifically, we shall aim at identifying proximal (cis-) influences of regulatory SNPs on the expression of particular genes involved in key molecular pathways of the platelet cells. Another investigation will involve application of Bayesian graphical modelling methods to combined genotype and platelet functional assay data, the aim being to perform simultaneous clustering of individuals into functional behaviour classes and of genes or SNPs which are associated with these classes. -- _________________________________________________________________________ Dr. Linda D. Sharples MRC Biostatistics Unit and R&D Unit, Papworth NHS Foundation Trust Cambridge, UK Tel: 01223 330389 and 01480 364445 Fax: 01223 330388 and 01480 831450 [log in to unmask] [log in to unmask] __________________________________________________________________________