1/1
2 files

Using genetic data to strengthen causal inference in observational research.

journal contribution
posted on 10.08.2018, 11:28 by Jean-Baptiste Pingault, Paul F. O'Reilly, Tabea Schoeler, George B. Ploubidis, Frühling Rijsdijk, Frank Dudbridge
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.

Funding

The authors thank S. Gage and J. M. Vink for cannabis initiation summary statistics and syntax and J. Rees for multivariable Mendelian randomization (MR) syntax. J.-B.P. is a fellow of MQ: Transforming Mental Health (MQ16IP16) and affiliated with the Centre for Research in Epidemiology and Population Health (CESP), French National Institute for Health and Medical Research (INSERM), Université de Paris-Sud, Université de Versailles-Saint Quentin, and Université Paris-Saclay, Paris, France. P.F.O. receives funding from the UK Medical Research Council (MR/N015746/1) and the Wellcome Trust (109863/Z/15/Z). This report represents independent research (partly) funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King’s College London.

History

Citation

Nature Reviews Genetics, 2018

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences

Version

AM (Accepted Manuscript)

Published in

Nature Reviews Genetics

Publisher

Nature Publishing Group

issn

1471-0056

eissn

1471-0064

Copyright date

2018

Available date

05/12/2018

Publisher version

https://www.nature.com/articles/s41576-018-0020-3

Notes

Supplementary information is available for this paper at https://doi.org/10.1038/s41576-018-0020-3;The file associated with this record is under embargo until 6 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.

Language

en

Usage metrics

Categories

Keywords

Exports