Chris Wallace is a Wellcome Trust Senior Research Fellow at the University of Cambridge and joined the MRC Biostatistics Unit as an Honorary Programme Leader in February 2016. Prior to this, she held fellowships from the Wellcome Trust and the British Heart Foundation at other institutes in Cambridge and Queen Mary University of London, where she worked on various aspects of statistical genomics. Her focus is on the twin goals of using genomic analysis to make meaningful contributions to the understanding of human autoimmune disease and the development of statistical methodology to enable these contributions.
Biography
I am a statistician who works with biological datasets to understand the mechanisms underlying human immune-mediated diseases and identify possible treatments.
During the GWAS era, we, and others, identified many genetic polymorphisms that alter risk of human immune mediated diseases such as type 1 diabetes and rheumatoid arthritis. I now follow up this research in three directions:
1. jointly analysing multiple immune-mediated diseases/traits to borrow information between them and understand shared and distinct components of risk
2. identifying the cell specific mechanisms through which these variants affect disease risk
3. understanding how the immune system is dysregulated in disease, and how this may be modulated
Statistically, my current work is focused on variable selection, Bayesian model averaging and empirical Bayes false discovery rates, and matrix factorisation, applied to integrated analysis of multiple related genetic and genomic datasets.
I am funded by the Wellcome Trust as a Senior Research Fellow and PI in the Department of Medicine (University of Cambridge) and hold an honorary Programme Leader position at the MRC Biostatistics Unit. I am a member of the MRC funded stratified medicine programme CLUSTER, working with partners at GOSH, QMUL, Manchester, and Liverpool on childhood arthritis.
Research
Chris Wallace’s research programme has three complementary aims. First, to develop the statistical tools needed to identify genetic associations with disease, then robustly and empirically link each genetic association with a gene, cell type, stimulatory condition and ultimately a biological pathway. Specifically, this requires methods for:
- horizontal integration of different layers of omics data
- mapping the variants which regulate gene expression
- “fine mapping” causal genetic variants from amongst associated variants in genetic association data, typically using sparse variable selection
- “fine mapping” regulatory contacts in 3D maps of folded DNA from technologies such as promoter capture Hi-C
While the methods are broadly applicable across a range of common complex diseases, Chris’s second aim is to use the methods to understand the causes of autoimmune diseases, the links between different diseases, and identify pharmaceutical targets and opportunities for pharmaceutical re-purposing. Finally, a third strand of research aims to build upon what we have learnt about mechanisms underlying to disease to make stronger inference about the relationship of omics biomarkers and treatment outcomes in childhood arthritis, in order to aid treatment decisions.
For further details of my group’s research, including papers and code, see chr1swallace.github.io.
Publications
Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases Asimit J, Rainbow D, Fortune M, Grinberg N, Wicker L, Wallace C. Nat. Commun. 2019
Fine mapping chromatin contacts in capture Hi-C data. Eijsbouts C, Burren O, Newcombe P, Wallace C. BMC genomics 2019.
Genetically distinct clinical subsets, and associations with asthma and eosinophil abundance, within Eosinophilic Granulomatosis with Polyangiitis Lyons P, Peters J, Alberici F, Liley J, Coulson R, Astle W, Baldini C, Bonatti F, Cid M, Elding H, Emmi G, Epplen J, Guillevin L, Jayne D, Jiang T, Gunnarsson I, Lamprecht P, Leslie S, Little M, Martorana D, Moosig F, Neumann T, Ohlsson S, Quickert S, Ramirez G, Rewerska B, Schett G, Sinico R, Szczeklik W, Tesar V, Vukcevic D, Terrier B, Watts R, Vaglio A, Holle J, Wallace C, Smith K. bioRxiv 2018.
simGWAS: a fast method for simulation of large scale case-control GWAS summary statistics Fortune M, Wallace C. Bioinformatics 2018.
Chromosome contacts in activated T cells identify autoimmune disease candidate genes Burren O, Rubio García A, Javierre B, Rainbow D, Cairns J, Cooper N, Lambourne J, Schofield E, Castro Dopico X, Ferreira R, Coulson R, Burden F, Rowlston S, Downes K, Wingett S, Frontini M, Ouwehand W, Fraser P, Spivakov M, Todd J, Wicker L, Cutler A, Wallace C. Genome Biology 2017
Lineage-specific genome architecture links disease variants to target genes Javierre B, Burren O, Wilder S, Kreuzhuber R, Hill S, Sewitz S, Cairns J, Wingett S, Várnai C, Thiecke M, Burden F, Farrow S, Cutler A, Rehnstrom K, Downes K, Grassi L, Kostadima M, Freire-Pritchett P, Wang F, The BLUEPRINT Consortium , Stunnenberg H, Todd J, Zerbino D, Stegle O, Ouwehand W, *Frontini M , *Wallace C, *Spivakov M , *Fraser P . Cell 2016.
Wallace C. Statistical testing of shared genetic control for potentially related traits. Genet Epidemiol. 2013 Dec;37(8):802-13.
Pekalski ML, Ferreira RC, Coulson RM, Cutler AJ, Guo H, Smyth DJ, Downes K, Dendrou CA, Castro Dopico X, Esposito L, Coleman G, Stevens HE, Nutland S, Walker NM, Guy C, Dunger DB,Wallace C, Tree TI, Todd JA, Wicker LS. Postthymic expansion in human CD4 naive T cells defined by expression of functional high-affinity IL-2 receptors. J Immunol. 2013 Mar 15;190(6):2554-66.
Yang X, Todd JA, Clayton D, Wallace C. Extra-binomial variation approach for analysis of pooled DNA sequencing data. Bioinformatics. 2012 Nov 15;28(22):2898-904.
Wallace C, Rotival M, Cooper JD, Rice CM, Yang JH, McNeill M, Smyth DJ, Niblett D, Cambien F; Cardiogenics Consortium, Tiret L, Todd JA, Clayton DG, Blankenberg S. Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes. Hum Mol Genet. 2012 Jun 15;21(12):2815-24.
Davison LJ, Wallace C, Cooper JD, Cope NF, Wilson NK, Smyth DJ, Howson JM, Saleh N, Al-Jeffery A, Angus KL, Stevens HE, Nutland S, Duley S, Coulson RM, Walker NM, Burren OS, Rice CM, Cambien F, Zeller T, Munzel T, Lackner K, Blankenberg S; Cardiogenics Consortium, Fraser P, Gottgens B, Todd JA, Attwood T, Belz S, Braund P, Cambien F, Cooper J, Crisp-Hihn A, Diemert P, Deloukas P, Foad N, Erdmann J, Goodall AH, Gracey J, Gray E, Gwilliams R, Heimerl S, Hengstenberg C, Jolley J, Krishnan U, Lloyd-Jones H, Lugauer I, Lundmark P, Maouche S, Moore JS, Muir D, Murray E, Nelson CP, Neudert J, Niblett D, O'Leary K, Ouwehand WH, Pollard H, Rankin A, Rice CM, Sager H, Samani NJ, Sambrook J, Schmitz G, Scholz M, Schroeder L, Schunkert H, Syvannen AC, Tennstedt S, Wallace C. Long-range DNA looping and gene expression analyses identify DEXI as an autoimmune disease candidate gene. Hum Mol Genet. 2012 Jan 15;21(2):322-33.
Wallace C, Smyth DJ, Maisuria-Armer M, Walker NM, Todd JA, Clayton DG. The imprinted DLK1-MEG3 gene region on chromosome 14q32.2 alters susceptibility to type 1 diabetes. Nat Genet. 2010 Jan;42(1):68-71.
Heinig M, Petretto E, Wallace C, Bottolo L, Rotival M, Lu H, Li Y, Sarwar R, Langley SR, Bauerfeind A, Hummel O, Lee YA, Paskas S, Rintisch C, Saar K, Cooper J, Buchan R, Gray EE, Cyster JG, Consortium C, Erdmann J, Hengstenberg C, Maouche S, Ouwehand WH, Rice CM, Samani NJ, Schunkert H, Goodall AH, Schulz H, Roider HG, Vingron M, Blankenberg S, Münzel T, Zeller T, Szymczak S, Ziegler A, Tiret L, Smyth DJ, Pravenec M, Aitman TJ, Cambien F, Clayton D, Todd JA, Hubner N, and Cook SA. A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Nature, 2010 467:460-464
Downes K, Pekalski M, Angus KL, Hardy M, Nutland S, Smyth DJ, Walker NM, Wallace C, and Todd JA. Reduced expression of IFIH1 is protective for type 1 diabetes. PLoS One, 2010 5.