Using genetic data to strengthen causal inference in observational research

• Published in: Nature reviews. Genetics Vol. 19; no. 9; pp. 566 - 580
• Main Authors: Pingault, Jean-Baptiste, O’Reilly, Paul F., Schoeler, Tabea, Ploubidis, George B., Rijsdijk, Frühling, Dudbridge, Frank
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.09.2018; Nature Publishing Group
• Subjects: 631/114/2415; 631/208/1515; 631/208/205; 631/208/212; 692/700/478/174; Agriculture; Alcohol; Animal Genetics and Genomics; Biomedical and Life Sciences; Biomedicine; Cancer Research; Cardiovascular disease; Causal inference; Causality; Computer applications; Data mining; Data Mining - methods; Data processing; Databases, Genetic; Environmental toxicology; Epidemiologic methods; Epidemiology; Gene Function; Genetic aspects; Genetic research; Genetics; Genomes; Genotype; Genotyping Techniques; Human Genetics; Humans; Lung cancer; Marijuana; Medical research; Methods; Observational studies; Pregnancy; Public health; Review Article; Risk factors; Risk factors (Health); Smoking; Social sciences; Statistics; Variables; Vitamin E
• ISSN: 1471-0056; 1471-0064
• DOI: 10.1038/s41576-018-0020-3

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. Various types of observational studies can provide statistical associations between factors, such as between an environmental exposure and a disease state. This Review discusses the various genetics-focused statistical methodologies that can move beyond mere associations to identify (or refute) various mechanisms of causality, with implications for responsibly managing risk factors in health care and the behavioural and social sciences.