Evidence for both a gene-exposure and a gene-outcome association suggests a causal effect, provided that the MR assumptions are met. To combine estimates from individual genetic variants we applied Inverse-Variance Weighted (IVW) linear regression67. In addition, 4 sensitivity analyses more robust to horizontal pleiotropy were applied, each relying on distinct assumptions regarding instrument validity: Weighted Median68, MR-Egger SIMEX27, Weighted Mode69, and Generalized Summary-data based Mendelian Randomization (GSMR)64. These sensitivity analyses rely on orthogonal assumptions, making their inclusion important for triangulation. The Weighted Median approach provides a consistent estimate of the causal effect even when up to 50% of the weight comes from invalid instruments68. MR-Egger regression applies Egger’s test to MR instruments that consist of multiple genetic variants27,28. MR-Egger provides a consistent estimate of the causal effect, provided that the strength of the genetic instrument (the association between SNPs and exposure) does not correlate with the effect the instrument has on the outcome (i.e. the InSIDE assumption: Instrument Strength Independent of Direct Effect). This is a weaker assumption than the assumption of no pleiotropy. MR-Egger may, however, be biased when
