Sylvia Richardson is the Director of the MRC Biostatistics Unit and holds a Research Professorship in the University of Cambridge since 2012.
Prior to this Sylvia held the Chair of Biostatistics in the Department of Epidemiology and Biostatistics at Imperial College London since 2000 and was formerly Directeur de Recherches at the French National Institute for Medical Research INSERM, where she held research positions for 20 years. In 2009, Sylvia was awarded the Guy Medal in Silver from the Royal Statistical Society and a Royal Society Wolfson Research Merit award. She is a Fellow of the Institute of Mathematical Statistics and of the International Society for Bayesian Analysis
Sylvia has worked extensively in many areas of biostatistics research and made important contributions to the statistical modelling of complex biomedical data, in particular from a Bayesian perspective. Her work has contributed to progress in epidemiological understanding and has covered spatial modelling and disease mapping, measurement error problems, mixture and clustering models as well as integrative analysis of observational data from different sources. Her recent research has focussed on modelling and analysis of large data problems such as those arising in genomics. She is particularly interested in developing new analytical strategies for integrative and translational genomics, including statistical methodology for risk stratification, discovering disease subtypes, and large scale hierarchical analysis of high dimensional biomedical and multi-omics data.
BSU Research overview
To better understand multifactorial diseases such as cancer, diabetes and cardiovascular diseases, and to ultimately better target treatments to individuals, researchers are using new biotechnologies that measure genetic code at extremely high resolution as well as downstream functional mechanisms essential to the maintenance of human health, and study designs that combine extensive questionnaires, genotyping and biobanks. However, the amount and diversity of information collected render their analysis difficult and statisticians are faced with the challenge of developing efficient dimension reduction approaches that can discover important predictors and patterns among a vast array of possibilities. Our programme proposes to develop a range of improved statistical techniques and algorithms for finding important combinations of features in large genetic and genomics datasets that characterise or predict health outcomes and for carrying out integrative analyses to characterise heterogeneous disease processes. The new methods will be accompanied by the development of freely available software and will be used in a number of collaborative projects to improve understanding of the regulation of genes and immunological response, to study gene-environment interactions and to develop biomarker-based prognostic signatures.
- Mattei F, Liverani S, Guida F, Matrat M, Cenée S, Azizi L, Menvielle G, Sanchez M, Pilorget M, Lapôtre-Ledoux B, Luce D, Richardson S, Stücker I, ICARE Study Group (2016)
Multidimensional analysis of the effect of occupational exposure to organic solvents on lung cancer risk: the ICARE study
Occup Environ Med doi:10.1136/oemed-2015-103177: 1 – 10
- SK Westbury, E Turro, D Greene, WH Ouwehand, S Richardson, AD Mumford & K Freson (2015)
Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disorders
Genome Medicine 7(1): 36
- S Geneletti, AG O'Keeffe, LD Sharples, S Richardson, and G Baio (2015)
Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data.
Statistics in Medicine doi: 10.1002/sim.6486:
- Wallace C, Richardson S, Cutler A J, Wicker L S (2015)
Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping
PLOS Genetics DOI: 10.1371 / journal.pgen.1005272 :
- Vallejos, C. A., Marioni, J. C., & Richardson, S (2015)
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
PLoS Comput Biol 11(6): e1004333. doi:10.1371/journal.pcbi.1004333
- Liverani, S., Hastie, D. I., Azizi, L., Papathomas, M., & Richardson, S (2015)
PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes
Journal of Statistical Software 64(7): 1-30
- Lewin A, Saadi H, Peters JE, Moreno-Moral A, Lee JC, Smith KG, Petretto E, Bottolo L & Richardson S (2015)
MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues
Bioinformatics doi: 10.1093/bioinformatics/btv568:
- Newcombe PJ, Conti DV, Richardson S (2015)
A new and scaleable Bayesian framework for joint re-analysis of marginal SNP effects
Genetic Epidemiology 39(7): 572-572
- J Pettit , R Tomer , K Achim, S Richardson, L Azizi , J Marioni (2014)
Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets. PLoS Computational Biology
PLoS Computational Biology Volume 9 - Issue 10: E1003824
- G Papageorgiou, S Richardson & N Best (2015)
Bayesian nonparametric models for spatially indexed data of mixed type
Journal of the Royal Statistical Society Series B (2014) : First published online: 13 Dec 2014
- PJ Newcombe, H Raza Ali, FM Blows,E Provenzano, PD Pharoah, C Caldas and S Richardson (4 September 2014)
Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival
Stat Methods Med Res 0(0) : 1–23
- D Hastie, S Liverani & S Richardson (2014 )
Sampling from Dirchlet process mixture models with unknown concentration parameter: mixing issues in large data implementations
Statistics and Computing Published online: 3 May 2014:
- L Bottolo, M Chadeau-Hyam, coll. & S Richardson (2013)
GUESS-ing polygenic associations with multiple phenotypes using a GPU-based Evolutionary Stochastic Search algorithm
Journal of Computational Biology Volume 9 - Issue 8 : E1003657
- P Kirk, A Witkover, CRM Bangham, S Richardson, AM Lewin & MPH Stumpf (2013)
Balancing the Robustness and Predictive Performance of Biomarkers
Journal of Computational Biology Volume: 20 Issue 12: : 1-11
- S Geneletti, N Best, MB Toledano, P Elliott & S Richardson (10 July 2013)
Uncovering selection bias in case-control studies using Bayesian post-stratification
Wiley Online Library Volume 32 - Issue 15: 663–674
- M Papathomas, J Molitor; C Hoggart; D Hastie & S Richardson. (September 2012)
Exploring Data From Genetic Association Studies Using Bayesian Variable Selection and the Dirichlet Process: Application to Searching for Gene × Gene Patterns
Genetic Epidemiology Volume 36, Issue 6, : 663–674
- Bottolo, L., Petretto, E., Blankenberg, S., Cambien, F., Cook, S. A., Tiret, L. & Richardson, S. (2011)
Bayesian detection of expression quantitative trait loci hot spots.
Genetics 189: 1449-1459
- Jackson, C., Best, N. & Richardson, S. (2009)
Bayesian graphical models for regression on multiple datasets with different variables.
Biostatistics 10: 335-351
- Molitor, J. T., Papathomas, M., Jerrett, M. & Richardson, S. (2010)
Bayesian Profile Regression with an Application to the National Survey of Children’s Health.
Biostatistics 11: 484-498
- Petretto, E., Bottolo, L., Langley, S. R., Heining, M., McDermott-Roe, C., Sarwar, R., Pravenec, M., Hübner, N., Aitman, T. J., Cook, S. A. & Richardson, S. (2010)
New Insights into the Genetic Control of Gene Expression using a Bayesian Multi-tissue Approach.
PLoS Computational Biology 6: e1000737
- Ratmann, O., Andrieu, C., Wiuf, C. & Richardson, S. (2009)
Model criticism based on likelihood-free inference, with an application to protein network evolution.
Proceedings of the National Academy of Sciences USA 106: 10576-10581