Precision Medicine DTP - Harnessing genome characterisation to uncover disease mechanisms

Precision medicine aims to understand how our genetics, environment and lifestyle can help determine the best approaches to prevent or treat disease for each one of us. One of the strategies used is to study the genetic control of specific diseases or disease-related phenotypes (such as blood pressure or cholesterol levels). Based on experience, knowledge gained from these studies will inform on new treatments or help us better classify patients into subtypes of a given disease using their genetic data and inform on the best treatment options. Recent and current advances in genomics and phenomics (proteomics, gene expression, metabolomics, etc. at whole organism, tissue or single cell level) have meant that we have now uncovered tens of thousands of genetic loci (SNPs) that modify disease-related phenotypes1. For some of these loci, we understand the mechanisms by which they affect disease. This is mainly the case for associated loci located in genes, which represent a very low proportion of the uncovered associations. However, for the vast majority of these associations, we still don’t understand how they affect the observed variation in disease susceptibility.
Together with the large numbers of genetic associations uncovered, there is a wealth of data characterising genome regions2,3, these are referred to as annotations and many are publicly available in databases, others we have derived from in-house datasets. For example, there are annotations based on function (e.g. predicted regulatory elements, receptors of particular ligands, known or predicted drug targets) or conservation. We can use these annotations to better understand the mechanisms by which the yet uncharacterised genetic associations affect disease-related phenotypes and use this information to improve disease prevention and treatment. We can do this at a very large scale, using tens of thousands of genetic associations and hundreds of annotations.
In Kindt et al. (2013)4, we assessed enrichment and depletion of genetic variants associated with phenotypic variation in 54 annotation classes such as genic regions, regulatory features, measures of conservation, and patterns of histone modifications. We showed that SNPs associated with all of the enriched annotations, that included chromatin states and prior knowledge of the existence of a local expression QTL (eQTL), were 8 times more likely to be trait-associated than variants annotated with none of them.
Since we published this work, the amount of discovered genetic associations has grown exponentially, and so has the quality and wealth of the genomic and epigenomic annotations available.
Aims

We propose to use bioinformatics and statistical analyses, including multivariate logistic regression and enrichment tests, to further characterise the genetic variants that affect disease. We will do that for individual annotations and combinations of them to disentangle the possible mechanisms underlying variation. We can find mechanisms that underlie variation across phenotypic categories, including several omics, and those that are different across categories of disease-related phenotypes (such as protein or lipid levels and DNA methylation) and disease (such as diabetes or different types of cancer). We will follow-up at trait and region level the most promising of these findings, using colocalisation and causality analyses, to determine mechanism of causation and control of disease variation.

Training outcomes

This project will equip the student with a broad range of quantitative skills, including the ability to integrate information from different data sources and perform statistical and bioinformatic analyses of large datasets, using R and other programming languages in high performance computer clusters.

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. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

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 must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine