Using genetic data to strengthen causal inference in observational research
Cette étude passe en revue les différentes méthodes et outils exploitant des données génétiques, fait le point sur leurs limites et leur usage en recherche observationnelle pour analyser les relations causales des maladies
Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology — including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining — has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.
Nature Reviews Genetics , résumé, 2018