Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption

@inproceedings{Bowden2018ImprovingTA,
  title={Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption},
  author={Jack Bowden and Fabiola M. Del Greco and Cosetta Minelli and Qingyuan and Zhao and Deborah A Lawlor and Nuala A Sheehan and John F.H. Thompson and George D. Smith},
  booktitle={International journal of epidemiology},
  year={2018}
}
Background Two-sample summary-data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated. Methods Causal… CONTINUE READING
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