Celia Greenwood

Mendelian Randomization in the presence of pleiotropy

In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three assumptions, including that the genetic variants only influence the outcome through phenotype of interest. Pleiotropy, i.e., the situation in which some genetic variants affect more than one phenotype, can invalidate these genetic variants for use as instrumental variables; thus a naive analysis will give biased estimates of the causal relation.

Here, we present new methods (Constrained Instrumental Variable methods [CIV]) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists and when the pleiotropic phenotypes are available. We demonstrate that a smoothed version of CIV performs approximate selection of genetic variants that are valid instruments, and provides unbiased estimates of the causal effects.  CIV performs robustly across different pleiotropic violations of the MR assumptions. We also analyzed the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (Mueller et al., 2005) to disentangle causal relationships of several biomarkers with AD progression.