Using genetic data to strengthen causal inference in observational research

Jean Baptiste Pingault; Paul F. O’Reilly; Tabea Schoeler; George B. Ploubidis; Frühling Rijsdijk; Frank Dudbridge

doi:10.1038/s41576-018-0020-3

Using genetic data to strengthen causal inference in observational research

Jean Baptiste Pingault^*, Paul F. O’Reilly, Tabea Schoeler, George B. Ploubidis, Frühling Rijsdijk, Frank Dudbridge

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

308 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	566-580
Number of pages	15
Journal	NATURE REVIEWS GENETICS
Volume	19
Issue number	9
Early online date	5 Jun 2018
DOIs	https://doi.org/10.1038/s41576-018-0020-3
Publication status	Published - 1 Sept 2018

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AU - Dudbridge, Frank

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Using genetic data to strengthen causal inference in observational research

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