@article{8688729,
  abstract     = {{The increasing availability of genetic cohort data has led to many genome-wide association studies (GWAS) successfully identifying genetic associations with an ever-expanding list of phenotypic traits. Association, however, does not imply causation, and therefore methods have been developed to study the issue of causality. Under additional assumptions, Mendelian randomization (MR) studies have proved popular in identifying causal effects between two phenotypes, often using GWAS summary statistics. Given the widespread use of these methods, it is more important than ever to understand, and communicate, the causal assumptions upon which they are based, so that methods are transparent, and findings are clinically relevant. Causal graphs can be used to represent causal assumptions graphically and provide insights into the limitations associated with different analysis methods. Here we review GWAS and MR from a causal perspective, to build up intuition for causal diagrams in genetic problems. We also examine issues of confounding by ancestry and comment on approaches for dealing with such confounding, as well as discussing approaches for dealing with selection biases arising from study design.}},
  author       = {{Hines, Oliver and Diaz-Ordaz, Karla and Vansteelandt, Stijn and Jamshidi, Yalda}},
  issn         = {{1094-8341}},
  journal      = {{PHYSIOLOGICAL GENOMICS}},
  keywords     = {{Genetics,Physiology,causal graphs,GWAS,Mendelian randomisation,MENDELIAN RANDOMIZATION,RISK,INSTRUMENTS,FRAMEWORK,INFERENCE,VARIANTS,DESIGN,BIAS,SEX}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{369--378}},
  title        = {{Causal graphs for the analysis of genetic cohort data}},
  url          = {{http://dx.doi.org/10.1152/physiolgenomics.00115.2019}},
  volume       = {{52}},
  year         = {{2020}},
}

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