Development and application of methodology for “polygenic risk” prediction in pharmacogenetic genome-wide association studies

The proposal is focused around development of “polygenic risk scores” for clinical outcomes in pharmacogenetic association studies. These approaches have been successfully applied in genome-wide association studies (GWAS) of complex human traits, but have predominantly focused on binary outcomes (presence/absence of disease) and quantitative measures (such as anthropometrics and lipid profiles). However, in pharmacogenetic studies, the outcome of interest is often more complex, such as categorical “sub-phenotypes” (e.g. severity of adverse drug reaction) and “time to event” data (e.g. survival time after clinical intervention). For rare clinical outcomes, such as severe drug-induced hypersensitivity, we also expect a major contribution of rare genetic variants of large effect, which are not widely incorporated in polygenic risk scores for complex traits.

The primary aim of this project is to develop and apply methodology for polygenic risk scores for complex pharmacogenetic outcomes (including categorical and time to event data).
Scientific objectivesIn order to achieve these aims, the primary objectives of this project are: (i) to adapt methodology previously proposed for polygenic risk scores in the context of binary and quantitative outcomes to complex clinical outcomes in pharmacogenetic studies (including categorical and time to event data); (ii) develop novel methodology to build polygenic risk scores on the basis of gene-based analyses of rare variants; (iii) evaluate utility of incorporating prior biological information on genes/variants associated with related outcomes into polygenic risk scores; and (iv) apply these approaches to a pharmacogenetic GWAS undertaken at the University of Liverpool.
Person specification: The successful candidate is likely to hold a 1st or 2:1 degree in a relevant discipline (statistics or mathematics). A Masters degree in Statistics would be desirable. Some experience of working with genetic data would be desirable but not essential. Experience of coding in a statistical package (e.g. R, SAS, stata, Winbugs) is desirable.
Training and supportThe supervisors will provide continuous support to the student, lending their expertise and extensive experience of developing and applying statistical methodology to the analysis of genetic data. The student will also receive training on analysing genetic datasets by attending training workshops (one at University of Liverpool and one at Sanger Institute, Cambridge), and on working with rare variants by attending a training course on this topic (University of Liverpool). The student will also have the opportunity attend training workshops on working with time-to-event data, if required. More general training, such as training on scientific writing and on presentation skills will also be provided through the University of Liverpool’s Doctoral College.

Links: https://www.liverpool.ac.uk/translational-medicine/staff/andrea-jorgensen/) and Statistical and Pharmacogenetics Research Group (www.liverpool.ac.uk/translational-medicine/research/statistical-genetics/about/

Applicants should send a CV, academic transcripts, a letter of motivation and two names of referees who can send letters of recommendation to Nyree Collinson [Email Address Removed].