Assessing causal relationships in genomics: From Bradford-Hill criteria to complex gene-environment interactions and directed acyclic graphs
1 Department of Statistics, London School of Economics, Houghton Street, London, UK
2 Department of Epidemiology and Public Health, Imperial College, London, UK
3 Department of Social and Environmental Research, London School of Hygiene and Tropical Medicine, UK
4 Institut Municipal d'Investigació Mèdica, and School of Medicine, Universitat Autònoma de Barcelona, Catalonia, Spain
5 National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, USA
6 Department of Epidemiology and Public Health, Imperial College, London, UK
7 HuGeF, Via Nizza 52, 10126 Torino, Italy
Emerging Themes in Epidemiology 2011, 8:5 doi:10.1186/1742-7622-8-5Published: 9 June 2011
Observational studies of human health and disease (basic, clinical and epidemiological) are vulnerable to methodological problems -such as selection bias and confounding- that make causal inferences problematic. Gene-disease associations are no exception, as they are commonly investigated using observational designs. A rich body of knowledge exists in medicine and epidemiology on the assessment of causal relationships involving personal and environmental causes of disease; it includes seminal causal criteria developed by Austin Bradford Hill and more recently applied directed acyclic graphs (DAGs). However, such knowledge has seldom been applied to assess causal relationships in clinical genetics and genomics, even in studies aimed at making inferences relevant for human health. Conversely, incorporating genetic causal knowledge into clinical and epidemiological causal reasoning is still a largely unexplored area.
As the contribution of genetics to the understanding of disease aetiology becomes more important, causal assessment of genetic and genomic evidence becomes fundamental. The method we develop in this paper provides a simple and rigorous first step towards this goal. The present paper is an example of integrative research, i.e., research that integrates knowledge, data, methods, techniques, and reasoning from multiple disciplines, approaches and levels of analysis to generate knowledge that no discipline alone may achieve.