Baillie lab, Roslin Institute, University of Edinburgh
We are a cross-disciplinary translational research group focused on using genomics and transcriptomics to better understand and treat critical illness. The fundamental problem that we focus on is the lack of treatments to stop people dying from severe infections - a syndrome known as sepsis. We believe that a functional genomics approach can lead us to biological processes that might be amenable to treatment. Much of our work focuses on specific infections, such as influenza, or critical illness caused by non-infectious injuries, such as pancreatitis.
We develop and apply computational tools, and use in vitro and in vivo models to generate and test hypotheses. We are based in the Roslin Institute, University of Edinburgh and Intensive Care Unit, Royal Infirmary Edinburgh.
Read a summary of our approach in this perspective article:Baillie, J. K. Science 344, 807–808 (May 23, 2014).
Our new post-GWAS analysis method (network density analysis; NDA) reveals new biological features of numerous disease states and traits. It works by examining a coexpression network of transcription start sites (discovered in FANTOM5). We find that transcripts containing GWAS hits for a given trait tend to fall into more dense groupings in the coexpression network than randomly-selected transcripts.
NDA demonstrates 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, for some diseases, the underlying variants fall into distinct functional groups, suggesting either dual mechanisms of disease, or distinct disease endotypes.Baillie JK et al. “Shared Activity Patterns Arising at Genetic Susceptibility Loci Reveal Underlying Genomic and Cellular Architecture of Human Disease.” PLOS Computational Biology 14, no. 3 (March 1, 2018): e1005934. PMC5849332.
Throughout the history of medicine, progress has been made by recognizing patterns of disease, or syndromes. When new technologies are invented, they can reveal observable characteristics that have close relationships to disease trajectories, outcomes, and most importantly of all, response to therapy. Genomics and transcriptomics have these properties, but raise an additional challenge: the scale of the data is such that detecting relevant signals is a substantial computational and mathematical challenge.Russell, C. D. & Baillie, J. K. Current Opinion in Systems Biology 2, 139–145 (Apr. 2017).
The lack of specific hypotheses in traditional genome-wide association studies has a disadvantage - it greatly limits the statistical power, ensuring that only the strongest signals can be detected, even with very large numbers of subjects.
Gene set hypothesis testing (GSHT) enables investigators to take a biological hypothesis and test it using GWAS data. For example, in our paper, we tested the hypothesis that failure to downregulate inflammatory signals during macrophage maturation is a key mechanism in the pathogenesis of Crohn's disease. We defined search criteria to find inflammatory genes that are downregulated during macrophage differentiation, and then empirically tested the hypothesis that these genes are likely to be associated with Crohn's disease.Baillie, J. K. et al. PLOS Genetics 13, e1006641 (Mar. 6, 2017).
In the FANTOM5 consortium we showed that, with a sufficiently rich expression dataset, it is possible to infer functional relationships between genes (and other transcribed elements in the genome, such as enhancers and lncRNAs) by grouping them according to similarities in expression. We proved this using known pathways, but the real value of this finding is that it enables us to discover relationships between genomic regions about which almost nothing is already known.Forrest*, Kawaji*, Rehli*, Baillie*, et al. Nature 507, 462–470 (Mar. 27, 2014).
We are very grateful to recieve funding from the following sources: Wellcome Trust, BBSRC, Intensive Care Society, MRC, NIH.