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Causation and causal inference for genetic effects

(2012) HUMAN GENETICS. 131(10). p.1665-1676
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Bioinformatics: from nucleotids to networks (N2N)
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.
Keywords
MARGINAL STRUCTURAL MODELS, EPIDEMIOLOGY, SUFFICIENT CAUSE INTERACTIONS, STATISTICAL INTERACTIONS, MEDIATION ANALYSIS, MENDELIAN RANDOMIZATION, ASSOCIATION, EPISTASIS, STRATIFICATION, INDEPENDENCE

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Citation

Please use this url to cite or link to this publication:

Chicago
Vansteelandt, Stijn, and Christoph Lange. 2012. “Causation and Causal Inference for Genetic Effects.” Human Genetics 131 (10): 1665–1676.
APA
Vansteelandt, S., & Lange, C. (2012). Causation and causal inference for genetic effects. HUMAN GENETICS, 131(10), 1665–1676.
Vancouver
1.
Vansteelandt S, Lange C. Causation and causal inference for genetic effects. HUMAN GENETICS. 2012;131(10):1665–76.
MLA
Vansteelandt, Stijn, and Christoph Lange. “Causation and Causal Inference for Genetic Effects.” HUMAN GENETICS 131.10 (2012): 1665–1676. Print.
@article{3167740,
  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.},
  author       = {Vansteelandt, Stijn and Lange, Christoph},
  issn         = {0340-6717},
  journal      = {HUMAN GENETICS},
  keyword      = {MARGINAL STRUCTURAL MODELS,EPIDEMIOLOGY,SUFFICIENT CAUSE INTERACTIONS,STATISTICAL INTERACTIONS,MEDIATION ANALYSIS,MENDELIAN RANDOMIZATION,ASSOCIATION,EPISTASIS,STRATIFICATION,INDEPENDENCE},
  language     = {eng},
  number       = {10},
  pages        = {1665--1676},
  title        = {Causation and causal inference for genetic effects},
  url          = {http://dx.doi.org/10.1007/s00439-012-1208-9},
  volume       = {131},
  year         = {2012},
}

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