Proteins are attractive drug targets, however, a prerequisite for successful drug development is efficacy, which is predicated on the drug target playing a causal role in disease1. A leading approach to clarifying causation is through Mendelian randomisation (MR), which has successfully reproduced the outcome of randomised controlled trials and is increasingly becoming a standard tool in drug development2.
Recent technological developments have enabled thousands of circulating proteins to be measured in large studies3,4, allowing the application of genome-wide association studies (GWAS) for detection of protein quantitative trait loci (pQTL).
Here we shall utilise both the rich proteomic datasets available in Edinburgh and those of the SCALLOP consortium (https://www.olink.com/scallop/
), to which we belong. There is strong complementarity from the power derived from large numbers (SCALLOP) and the breadth of genomic and ancillary data available in Edinburgh datasets such as the ORCADES study (with n=1360 WGS).
This project will expand our knowledge by (a) being among the first to analyse proteomes versus whole genomes and exomes, and (b) analysing hundreds of proteins at scale, which we know reveals in particular a large harvest of trans-pQTL and thus information on protein networks and pathways.
(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 and make causal inferences
The project will have three main elements.
First the sequence-based analysis using data from ORCADES and collaborators. There are many important unanswered questions regarding rare pQTL: 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 GWAS in very large numbers. We presently lead a number of such analyses with N>20,000, which open up a new window onto pathways. 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 post-GWAS analyses, for example integrating pQTL with mRNA expression, DNA methylation, curated protein-protein interaction networks and text-mining, to reveal examples of molecular processes involved in the regulation of circulating proteins. Using MR and mining of drug databases, we shall highlight protein-outcome associations predictive of potential successful drug development.
Integration of big ‘omics’ data and population-scale genetics datasets.
Ability to use a wide variety of statistical genetic and bioinformatics tools, e.g. GWAS, SMR-HEIDI, SOJO, MULTIABEL, phenoscanner, SKAT-O, MONSTER, network analysis, and MR; coding pipelines in R and unix.
Publications in high impact journals
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
1. Fitzgerald, K. et al. A Highly Durable RNAi Therapeutic Inhibitor of PCSK9. N Engl J Med 376, 41-51 (2017).
2. Holmes, M.V., Ala-Korpela, M. & Smith, G.D. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat Rev Cardiol 14, 577-590 (2017).
3. Folkersen, L. et al. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet 13, e1006706 (2017).
4. Sun, B.B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73-79 (2018).