We elucidate the aetiology of human immune-mediated diseases, to improve diagnosis and treatment. We use integrative statistical analysis of genomic data and develop statistical methods to improve inference, or adapt to new biological techniques. Overall, our research can be described in four broad themes:
1) Genetic association discovery and fine mapping. This focuses on increasing power through optimal study design, borrowing information between related diseases using the conditional False Discovery Rate, and distinguishing the variant(s) driving the association from their correlated neighbours.
2) Comparative genetic studies of immune-mediated diseases. We use co-localisation to integrate over the fine mapped posterior distributions of causal variants of different diseases to determine which diseases share which genetic risk factors
3) Integrative analysis of genetic and other omic data. Co-localisation methods to enable us to understand disease mechanisms by mapping disease associated genetic variants to the genes they regulate. We also develop techniques to integrate genetic association data with maps of promoter-enhancer chromatin contacts in the cell nucleus.
4) Dissection of heterogeneity within diseases. Human diseases have been classified according to clinical presentation, but there is increasing evidence that clinical symptoms may not align to aetiological cause. This is motivating a transition towards alternative aetiological, molecular, or treatment-responsive, classifications of diseases and their subtypes. Our comparative studies of diseases can inform treatment decisions using stratified medicine approaches.
Burren OS, García AR, Javierre B-M, Rainbow DB, Cairns J, Cooper NJ, Lambourne JJ, Schofield E, Dopico XC, Ferreira RC, Coulson R, Burden F, Rowlston SP, Downes K, Wingett SW, Frontini M, Ouwehand WH, Fraser P, Spivakov M, Todd JA, Wicker LS, Cutler AJ, Wallace C. Chromosome contacts in activated T cells identify autoimmune disease candidate genes.
Genome Biology 2017. DOI: 10.1186/s13059-017-1285-0
Liley J, Todd JA, Wallace C. A method for identifying genetic heterogeneity within phenotypically-defined disease subgroups. Nature Genetics 2017. DOI: 10.1038/ng.3751
Liley J, Wallace C. A pleiotropy-informed Bayesian false discovery rate adapted to a shared control design finds new disease associations from GWAS summary statistics. PLoS Genetics 2015. DOI: 10.1371/journal.pgen.1004926
Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, Sewitz S, Cairns J, Wingett SW, Várnai C, Thiecke MJ, Burden F, Farrow S, Cutler AJ, Rehnström K, Downes K, Grassi L, Kostadima M, Freire-Pritchett P, Wang F; BLUEPRINT Consortium, Stunnenberg HG, Todd JA, Zerbino DR, Stegle O, Ouwehand WH, Frontini M, Wallace C, Spivakov M, Fraser P. Lineage-specific genome architecture links disease variants to target genes. Cell 2016. DOI: 10.1016/j.cell.2016.09.037
Fortune MD, Guo H, Burren O, Schofield E, Walker NM, Ban M, Sawcer SJ, Bowes J, Worthington J, Barton A, Eyre S, Todd JA, Wallace C. Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nature Genetics 2015. DOI: 10.1038/ng.3330