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.
 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.
 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.
 Giambartolomei C, Vukcevic D et al. (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genetics. 10:e1004383.
 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.
 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).