Precision Medicine DTP: Leveraging proteomics to discover new biology and therapeutic targets

Proteins circulating in blood are derived from multiple organs and cell types, and therefore provide a “snapshot” of different processes occurring in these cells. Plasma proteins are often used as diagnostic or prognostic biomarkers and given that they can be directly perturbed using conventional small molecules or biologics, they are often targets of medicines1.
However, a prerequisite for successful drug development is efficacy, which is predicated on the drug target not only being associated with the disease, but also playing a causal role in it. One approach to clarifying causation is through Mendelian randomisation (MR), which has successfully reproduced the outcome of randomised controlled trials (RCT) for pharmacological targets such as PCSK9, LpPLA2 and NPC1L1, and is increasingly becoming a standard tool for triaging pharmacological treatment targets2. In contrast to more complex phenotypes, MR using cis-pQTL carries the advantage of being potentially more specific for a single protein, which should limit bias from horizontal pleiotropy that can violate MR assumptions2.

Recent technological developments have enabled hundreds to thousands of circulating proteins to be measured simultaneously in large studies, which has paved the way for studies of their genetic regulation using genome-wide association studies (GWAS)3. In addition, historic data has shown that potential drug targets with direct genetic support were two times more likely to be approved than those without the genetic support4.
The combination of well-powered GWAS for discovery of protein quantitative trait loci (pQTL), integration with biological pathways and drug targets followed by assessment of causality using an MR framework provides the opportunity to evaluate the likelihood of target-mediated effects (including both therapeutic efficacy and on-target adverse outcomes) of ongoing drug development.

Aims
(a) Identify rare variants influencing plasma protein levels using whole genome sequence (WGS) data in ORCADES and similar data from collaborators.
(b) Identify trans-pQTL using large scale genome-wide association meta-analyses in SCALLOP
(c) Apply state-of-the-art downstream analysis methods to disentangle the biology, assess drug repurposing and make causal inferences

The project will have three main elements.

First the sequence-based analysis using data from both ORCADES and a number of collaborators from the SCALLOP consortium, who have WGS or exome data for further cohorts. There are many important unanswered questions in whether rare pQTL exist, the strength of their effects, the degree to which they are located in the same genes as more common pQTL, the relative importance of cis vs trans effects and how best they can be used in MR and other downstream analyses. We belong to a nascent working group within SCALLOP which will co-ordinate WGS-based proteomics activities. Traditional single point GWAS will be supplemented with gene-based and other aggregation tests which assess the burden of rare variants across genomic segments.

The second element is focussed on traditional GWAS but in very large numbers. We are presently leading a number of such analyses with N>20,000, which are revealing many more trans-pQTL and in turn open up a new window onto pathway biology. This project will extend this work to hundreds of new proteins and for the first time apply multivariate approaches to discover further loci.

The third element includes the post-GWAS analyses, for example integrating pQTL with mRNA expression, DNA methylation, curated protein-protein interaction networks, biological pathways (such as KEGG and similar), to reveal examples of molecular processes involved in the regulation of circulating proteins. Using Mendelian randomisation and mining of drug databases, we shall highlight protein-outcome associations predictive of potential successful drug development.

Training outcomes
• Integration of ‘omics’ and population-scale genetics datasets.
• Bioinformatics characterisation of genetic variants.
• Ability to use a wide variety of statistical genetic and bioinformatics tools, e.g. GWAS, SMR-HEIDI, coloc, SOHO, MULTIABEL, phenoscanner, STRING, Mendelian Randomisation, SMMAT, SKAT-O, pathway analyses, programming in R and similar

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