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Causation and causal inference for genetic effects

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Abstract

Over the past three decades, substantial developments have been made on how to infer the causal effect of an exposure on an outcome, using data from observational studies, with the randomized experiment as the golden standard. These developments have reshaped the paradigm of how to build statistical models, how to adjust for confounding, how to assess direct effects, mediated effects and interactions, and even how to analyze data from randomized experiments. The congruence of random transmission of alleles during meiosis and the randomization in controlled experiments/trials, suggests that genetic studies may lend themselves naturally to a causal analysis. In this contribution, we will reflect on this and motivate, through illustrative examples, where insights from the causal inference literature may help to understand and correct for typical biases in genetic effect estimates.

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Acknowledgments

S. Vansteelandt acknowledges support from Ghent University (Multidisciplinary Research Partnership ‘Bioinformatics: from nucleotides to networks’), IAP research network grant nr. P06/03 from the Belgian government (Belgian Science Policy) and FWO research project G.0111.12.

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Authors and Affiliations

  1. Department of Applied Mathematics and Computer Science, Ghent University Krijgslaan, 281 S9, 9000, Ghent, Belgium

    Stijn Vansteelandt

  2. Department of Biostatistics, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA, 02115, USA

    Christoph Lange

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  1. Stijn Vansteelandt
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  2. Christoph Lange
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Correspondence to Stijn Vansteelandt.

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Vansteelandt, S., Lange, C. Causation and causal inference for genetic effects. Hum Genet 131, 1665–1676 (2012). https://doi.org/10.1007/s00439-012-1208-9

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