Email Address: email@example.com
Other Research Theme Collaborations: SGX
BackgroundStephen Burgess completed his BA and MMath (Part III) in Mathematics from the University of Cambridge. He studied for a PhD in the MRC Biostatistics Unit, Cambridge, from 2008-11 working on methods for Mendelian randomization analysis. He joined the Cardiovascular Epidemiology Unit in the Department of Public Health and Primary Care of the University of Cambridge in September 2011. In January 2013, Stephen was awarded a Wellcome Trust entry-level fellowship (Sir Henry Wellcome Post-doctoral Fellowship) to continue theoretical and applied work in the field of Mendelian randomization. In January 2017, he moved to the MRC Biostatistics Unit on a Wellcome Trust/Royal Society intermediate fellowship (Sir Henry Dale Fellowship), and was appointed a Programme Leader Track position to establish a research group in the MRC Biostatistics Unit in April 2017. He retains a position in the Cardiovascular Epidemiology Unit, with a 30% time commitment.
Research InterestsStephen's main area of research is causal inference and specifically methods for Mendelian randomization: the use of genetic variants to understand whether putative risk factors are causally related to specific disease outcomes (target validation). He is always open to requests for collaboration, either on theoretical or applied Mendelian randomization projects.
Mendelian randomization bookThe book “Mendelian Randomization: Methods for Using Genetic Variants in Causal Estimation”, by Stephen Burgess and Simon G Thompson was published by Chapman and Hall in 2015. More details are available at the book website: www.mendelianrandomization.com. A two-day course which roughly follows the content of the book is run annually in the late autumn.
Recent PublicationsA full list of citations can be found at https://scholar.google.com/citations?user=TAE0DykAAAAJ&hl=en. Key recent publications:
- J.M.B. Rees, A.M. Wood, S. Burgess. Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy. Statist Med 2017 (to appear).
- S. Burgess, V. Zuber, E. Valdes-Marquez, B.B. Sun, J.C. Hopewell. Mendelian randomization with fine-mapped genetic data: choosing from large numbers of correlated instrumental variables. Genet Epidemiol 2017 (to appear).
- S. Burgess, G. Davey Smith. Mendelian randomization implicates HDL-cholesterol associated mechanisms in aetiology of age-related macular degeneration. Ophthalmology 2017; 124(8):1165-1174.
- O.O. Yavorska, S. Burgess. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol 2017. Available online at http://dx.doi.org/10.1093/ije/dyx034.
- S. Burgess, J. Bowden, T. Fall, E. Ingelsson, S.G. Thompson. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology 2017; 28(1):30-42.
- L.A. Lotta, R.A. Scott, S.J. Sharp, S. Burgess et al. Genetic predisposition to an impaired metabolism of the branched chain amino acids and risk of type 2 diabetes: A Mendelian randomisation analysis. PLOS Medicine 2016; 13(11):e1002179.
- R.N. Eppinga, Y. Hagemeijer, S. Burgess et al. Identification of genomic loci associated with resting heart rate and shared genetic predictors with all-cause mortality. Nat Genet 2016; 48:1557-1563.
- L.A. Lotta, S.J. Sharp, S. Burgess et al. Association between LDL-cholesterol lowering genetic variants and risk of type 2 diabetes. JAMA 2016; 16(13):1383-1391.
- S. Burgess, F. Dudbridge, S.G. Thompson. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Statist Med 2016; 35(11):1880-1906.
- S. Burgess, A.S. Butterworth, J.R. Thompson. Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors. J Clin Epidemiol 2016; 69:208-216.
- C. Zhang, J.A. Doherty, S. Burgess et al. Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study. Hum Mol Genet 2015; 24(18):5356-5366.
- J. Murray, S. Burgess, L. Zuccolo, M. Hickman, R. Gray, S.J. Lewis. Moderate alcohol drinking in pregnancy increases risk for children’s persistent conduct problems: Causal effects in a Mendelian randomisation study. J Child Psychol Psychiatry 2016; 57(5):575-584.
- J. Bowden, G. Davey Smith, P.C. Haycock, S. Burgess. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 2016; 40(4):304-314.