Investigating Individual Specific Effects in Omics Datasets

Studentship start date: Sep/Oct 2016
Stipend: £14,296 (RCUK rate 2016/17)

New omics technologies have yielded vast data sets on the degree of molecular variation in individuals with a wide range of inherited and acquired disorders. Analysis of these datasets under the classic case-control paradigm has revealed key differences between those with and without disease, but many of the claims made of these technologies remain unfulfilled. Limitations in the analysis of datasets play their part. There is also considerable symptomatic and molecular variation within any group of patients that the case-control paradigm oversimplifies. Recently, we have found evidence of a previously unaccounted for source of variability in microarray data from myotonic dystrophy type 1 (DM1) patients. Severity of DM1 is linked to the length of a trinucleotide DNA repeat – the greater the number of repeats, the greater the severity. Regression modelling of gene expression using repeat length as a predictor revealed that each patient’s expression values tended to be either consistently and statistically significantly above or below predicted values across all genes. Accounting for these Individual Specific Effects has the potential to dramatically increase the power and reproducibility of all omic studies.

This project aims to develop analytical methods that account for Individual Specific Effects in the analysis of omic datasets and investigate their significance across a range of studies and disorders. The student will develop these methods using Generalized Linear Models and Bayesian analyses and apply them to in-house and publicly available data sets.

This project will provide the student with training in a wide range of statistical analysis tools, applied to an important biomedical research problem. It will also give the student the opportunity to work at the boundary between two disciplines.

The student will be based in the Institute of Cardiovascular and Medical Sciences at the University of Glasgow with the primary supervisor, Dr McClure, and will also be supervised by Prof Monckton (Institute of Molecular, Cell and Systems Biology) and Dr Rogers (School of Computing Science). Glasgow is a vibrant and forward-looking city, situated close to some of Europe’s most beautiful countryside.

Application
You can apply here: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/
Within the application, at the programme of study search field option, please select ‘MRC DTP in Precision Medicine’.

Please note that, in step 6 within the online application process, you are asked to detail supervisor/project title information. Please ensure that you clearly detail this information from the information provided within this abstract advert. Within the research area text box area, you can also add further details if necessary.

Please ensure that all of the following supporting documents are uploaded at point of application:
• CV/Resume
• Degree certificate (if you have graduated prior to 1 July 2016)
• Language test (if relevant)
• Passport
• Personal statement
• Reference 1 (should be from an academic who has a knowledge of your academic ability from your most recent study/programme)
• Reference 2 (should be from an academic who has a knowledge of your academic ability)
• Transcript

This is a joint programme between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

For more information visit: http://www.gla.ac.uk/colleges/mvls/graduateschool/precisionmedicine/