on cross-disciplinary global disability research.
To join the mailing list for this seminar series please e-mail your details to Ellie Cole.
Members of the department contribute to IGH public meetings which bring
together expertise on global health issues from all faculties within
Contact: Sarah Ball, Tel: (internal x82 72 2352)
'5pm' External Speakers Seminar Series
19 November 2012; Galton Theatre, 1-19 Torrington Place; 17.00-18.00
"Mendelian randomization: the next 10 years"
George Davey Smith
Professor of Clinical Epidemiology, University of Bristol
Director of the Children of the 90s cohort study
Director of the MRC Centre for Causal Analyses in Translational Epidemiology
Biography: George Davey Smith has been professor of clinical epidemiology at the University of Bristol since 1994. He is currently Scientific Director of the Avon Longitudinal Study of Parents and Children (ALSPAC) and Director of the MRC Centre for Causal Analyses in Translational Epidemiology (CAiTE). He previously held appointments at the University of Cardiff, MRC Epidemiology Unit, South Wales, University College London, LSHTM and Glasgow University.
Abstract: The use of genetic variants as instruments for environmentally modifiable risk factors, sometimes called "Mendelian randomization", is now a widely used method in epidemiology. This seminar, which will assume at least a basic understanding of Mendelian Randomization, will speculate about ways Mendelian randomization may develop over the next ten years to incorporate whole-genome data, to further test the assumptions of the approach, to address potential mediation by epigenetic and other "omic" processes and ultimately to move towards hypothesis-free causality.
Suggested reading before the seminar for those with interested in the topic but without previously encountering the approach: Davey Smith G. Use of genetic markers and gene-diet interactions for interrogating population-level causal influences of diet on health. Genes & Nutrition 2011;6:27–43. Davey Smith G. Random Allocation in Observational Data: How Small But Robust Effects Could Facilitate Hypothesis-free Causal Inference. Epidemiology 2011; 22:460-463.
Page last modified on 07 jan 13 16:55