3.2 Mendelian randomization analysis
To make sure the causal relationship in MR analysis is reasonable, the
instrumental variable (IV) assumptions must meet the following three
conditions: (1) It must closely relate to BW. (2) It must not be
relevant to other confounding factors. (3) It only affects cancers
through BW. Before the MR analysis, we calculated the power value and F
statistic of each cancer GWAS studies we chose to test whether the IV
was strong enough to explain the exposure under the existing sample size50. (power>80%, F>100) It
is based on simulations and specific parameters for two-stage least
squares (2SLS) MR analyses to make sure that the degree of deviation in
estimating causal correlation was within an acceptable range. The main
statistical test we used to estimate BW for different cancers is a
random-effects inverse-variance weighted (IVW) meta-analysis of the Wald
ratio for individual SNPs. Besides, we also applied other methods
including the weighted median, weighted mode and MR-Egger regression
methods to test the third assumption. Then we analyzed the accuracy of
MR results in three aspects. First, we conducted a heterogeneity test to
identify the differences between each IVs. Furthermore, the intercept of
MR-Egger and MR-PRESSO were used to check the gene pleiotropy ensuring
the feasibility of the second assumption. The MR-PRESSO was a recently
published method for testing gene-level pleiotropy which could assess it
more accurately 51. At last, we employed a
leave-one-out sensitivity analysis to assess the sensitivity of each IVs
to MR results. Several palindromic SNPs were moved to decreased the bias
of our MR analysis. (Table S5) Our MR analysis was conducted using the
package “Two Sample MR (version 0.5.5)”.