Developing functional genomics methods to detect clinically-relevant disease endotypes

Background

In this cross-disciplinary project, you will build on an extensive program of work to create and apply analytic methods that translate disease-associated genetic variants into meaningful biological understanding. We will apply these methods to a very broad range of disease processes and other phenotypes, with a primary focus on poorly-understood life-threatening processes such as influenza and sepsis.

Building on previous work in which we demonstrated that many biological pathways can be detected from expression patterns in high-resolution transcriptomic data[1], we have shown that GWAS hits for a given disease tend to be near promoter/enhancer elements with similar expression profiles, which enables us to find more hits, fine map probable causative SNPs, and implicate cell types in pathogenesis. Surprisingly, we also observe clear groupings in the promoter/enhancer elements associated with some diseases[2]. This discovery may indicate two distinct mechanisms underlying each disease. Alternatively, it may indicate the existence of two distinct endotypes[3] of each condition. It is likely that this discovery will have direct therapeutic relevance - that is, that patients with a preponderance of genetic variants in one group will respond differently to specific therapies.

More information is available at http://baillielab.net/coexpression

Aims

The student will develop computational and statistical tools to produce network-based analyses of shared features between different genetic elements, incorporating data from carefully-selected functional genomics and genome-wide association studies. These methods will be applied to detect and validate these observations in GWAS data from completed studies including the IIBDGC data for inflammatory bowel disease (access already agreed) and other disease phenotypes, together with GWAS data from recent analyses of the UK biobank genotyping data. This will form several distinct stages which will overlap in time during the course of the PhD:

1. Detection of pathways. Optimisation of coexpression methodology for high-performance computing and incorporation of data from different sources including GTEx, Roadmap Epigenetics and ENCODE.

Training outcomes: process optimisation, paralellisation, SQL database construction and usage, handling large files

2. Development of methodology for evaluation of patient subgroups. We will test for this firstly using recently-developed software[4] to detect clinical heterogeneity within GWAS results, and secondly by constructing a regression model in order to test for conditional independence of genes belonging to each of the putative subgroups

Training outcomes: statistics, quantitative genetics, regression modelling

3. Application to existing and ongoing GWAS studies. Re-analyses of published and ongoing GWAS studies, including UK biobank, will be performed to detect distinct biological pathways underlying clinical phenotypes. Candidates will be chosen for further validation, in large population studies or clinical trials where genotyping data are available. Biological validation of gene-gene interactions in specific cell types will be performed in genome-editing experiments performed by wet-lab scientists in the Baillie lab (myeloid cells, endothelial cells) and others (hepatocytes, epithelial cells, iPSC-derived primary cells).

Training outcomes: collaboration, hypothesis testing, academic writing

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

All applications should be made via the University of Edinburgh, irrespective of project location:

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you are encouraged to contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

http://www.ed.ac.uk/usher/precision-medicine