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Bayesian statistical approaches to identification of shared genetic signals

Project Description

A number of genes have been found associated with certain clinical outcome of interest 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, GIANT. In particular, we will examine the performance of statistical colocalizaiton ([1],[2],[3]) and fine-mapping ([4],[5]). Despite the different underlying assumptions between fine-mapping and colocalization, we see similarities of the two approaches. Since each of the methods has their own strengths and limitations, we aim to develop a Bayesian approach, taking forward strengths of both, to address shared genetic signals. Examination and validation of our method 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 (maximum of 150 words)
As the primary supervisor, Hui Guo will lead the project and provide student with statistical training as well as advice on transferable skills. Hui Guo and Carlo Berzuini will supervise the student on the development of statistical model, programming and data analysis, and also provide assistance to make sure that the project runs smoothly. Magnus Rattray will advice the student on Bayesian theories and methodology. 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.

Funding Notes

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area / subject. Candidates with experience in statistics, statistical genetics or with an interest in statistical genetics are encouraged to apply.

This project has a Band 1 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

Informal enquiries may be made directly to the primary supervisor.


[1] Guo H, Fortune MD et al. (2015) Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases. Human Molecular Genetics. 24(12):3305-13.
[2] Fortune MD, Guo H et al. (2015) Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nature Genetics. 47:839-46.
[3] Giambartolomei C, Vukcevic D et al. (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genetics. 10:e1004383.
[4] Hormozdiari F, Van de Bunt M et al. (2016) Colocalization of GWAS and eQTL signals detects target genes. The American Journal of Human Genetics. 99:1245-60.
[5] Donovan J, Martin R. (2018) Bayesian fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants and refined their contribution to familial relative risk. Nature (in press).

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