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@ARTICLE{Dai:137578,
      author       = {J. Y. Dai and U. Peters and X. Wang and J. Kocarnik and J.
                      Chang-Claude$^*$ and M. L. Slattery and A. Chan and M.
                      Lemire and S. I. Berndt and G. Casey and M. Song and M. A.
                      Jenkins and H. Brenner$^*$ and A. P. Thrift and E. White and
                      L. Hsu},
      title        = {{D}iagnostics of {P}leiotropy in {M}endelian
                      {R}andomization {S}tudies: {G}lobal and {I}ndividual {T}ests
                      for {D}irect {E}ffects.},
      journal      = {American journal of epidemiology},
      volume       = {187},
      issn         = {1476-6256},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2018-01458},
      pages        = {2672-2680},
      year         = {2018},
      abstract     = {Diagnosing pleiotropy is critical for assessing the
                      validity of Mendelian randomization (MR) analyses. The
                      popular MR-Egger method evaluates whether there is evidence
                      of bias-generating pleiotropy among a set of candidate
                      genetic instrumental variables. In this article, we propose
                      GLIDE, GLobal and Individual tests for Direct Effects, a
                      statistical method to systematically evaluate pleiotropy
                      among the set of genetic variants, e.g., single nucleotide
                      polymorphisms (SNPs), used for MR. As a global test,
                      simulation experiments suggest that GLIDE is nearly
                      uniformly more powerful than the MR-Egger method. As a
                      sensitivity analysis, GLIDE is capable of detecting outliers
                      in individual variant-level pleiotropy, in order to obtain a
                      refined set of genetic instrumental variables. We used GLIDE
                      to analyze both body-mass index and height for risk of
                      colorectal cancer in the Genetics and Epidemiology of
                      Colorectal Cancer Consortium. Among the body mass index
                      associated SNPs and the height associated SNPs, several
                      individual variants showed evidence of pleiotropy. Removal
                      of these potentially pleiotropic SNPs resulted in
                      attenuation of respective estimates of the causal effects.
                      In summary, the proposed GLIDE method is useful for
                      sensitivity analyses and improves the validity of MR.},
      cin          = {C070 / G110 / C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)G110-20160331 /
                      I:(DE-He78)C020-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30188971},
      doi          = {10.1093/aje/kwy177},
      url          = {https://inrepo02.dkfz.de/record/137578},
}