@article{TEXTUAL,
      recid = {5945},
      author = {Wang, Jingshu and Zhao, Qingyuan and Bowden, Jack and  Hemani, Gibran and Davey Smith, George and Small, Dylan S.  and Zhang, Nancy R.},
      title = {Causal inference for heritable phenotypic risk factors  using heterogeneous genetic instruments},
      journal = {PLOS Genetics},
      address = {2021-06-22},
      number = {TEXTUAL},
      abstract = {<p>Over a decade of genome-wide association studies (GWAS)  have led to the finding of extreme polygenicity of complex  traits. The phenomenon that “all genes affect every complex  trait” complicates Mendelian Randomization (MR) studies,  where natural genetic variations are used as instruments to  infer the causal effect of heritable risk factors. We  reexamine the assumptions of existing MR methods and show  how they need to be clarified to allow for pervasive  horizontal pleiotropy and heterogeneous effect sizes. We  propose a comprehensive framework GRAPPLE to analyze the  causal effect of target risk factors with heterogeneous  genetic instruments and identify possible pleiotropic  patterns from data. By using GWAS summary statistics,  GRAPPLE can efficiently use both strong and weak genetic  instruments, detect the existence of multiple pleiotropic  pathways, determine the causal direction and perform  multivariable MR to adjust for confounding risk factors.  With GRAPPLE, we analyze the effect of blood lipids, body  mass index, and systolic blood pressure on 25 disease  outcomes, gaining new information on their causal  relationships and potential pleiotropic pathways  involved.</p>},
      url = {http://knowledge.uchicago.edu/record/5945},
}