Bayesian statistical approaches to identification of shared genetic signals

Project description:
A number of genes have been found associated with health conditions of interest (e.g. autoimmune disease) from multiple studies. Identification of shared causal genes from these studies is crucial to understand the aetiology of certain diseases and the underlying causal pathways.

To date, several statistical methods have been developed in the research field. However, there is a lack of comprehensive review of these approaches in the literature to guide researchers in health data science. In this project, we will be investigating state-of-the-art methods by using summary statistics from large-scale association studies, e.g. UK Biobank. In particular, we will examine the performance of statistical colocalization ([1] - [5]). Despite the different underlying assumptions between these approaches, we see similarities of them. Each of the methods has their own strengths and limitations. In addition, we believe that using summary statistics only to investigate shared aetiology is prone to bias due to coarsened data information. we aim to develop a Bayesian approach which outperforms existing methods by making use of individual-level data. Prior to applying our method to real studies, examination and validation will be carried out via a number of simulations. We will also develop statistical software for applications of our method in the Comprehensive R Archive Network (CRAN) and make it publicly accessible.

Training/techniques to be provided:
As the primary supervisor, Hui Guo will lead the project and provide student with statistical training as well as advice on transferable skills. To make sure the project runs smoothly, Hui Guo and Carlo Berzuini will supervise the student on the development of statistical model, programming and data analysis. Moreover, student will benefit from regular research group meetings and support from the postdoc. With the research training support grant, the student will be able to attend and/or present their research output at the national and/or international conferences, e.g. the Royal Statistical Society Meetings, European Mathematical Genetics Meeting and International Genetic Epidemiology Society Meeting.

Entry requirements:
Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a Statistics, Computational Statistics, Statistical Genetics or related area / subject. Candidates with experience in statistical programming and with an interest in Bayesian statistics are encouraged to apply.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk